diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/args.json b/ckpts/svc/vocalist_l1_contentvec+whisper/args.json deleted file mode 100644 index fec34a60b67cf4f0521e70258aa65c0d6d56d30c..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/args.json +++ /dev/null @@ -1,257 +0,0 @@ -{ - "task_type": "svc", - "dataset": [ - "vocalist_l1", - ], - "exp_name": "vocalist_l1_contentvec+whisper", - "inference": { - "diffusion": { - "scheduler": "pndm", - "scheduler_settings": { - "num_inference_timesteps": 1000, - }, - }, - }, - "model": { - "condition_encoder": { - "content_encoder_dim": 384, - "contentvec_dim": 256, - "f0_max": 1100, - "f0_min": 50, - "input_loudness_dim": 1, - "input_melody_dim": 1, - "merge_mode": "add", - "mert_dim": 256, - "n_bins_loudness": 256, - "n_bins_melody": 256, - "output_content_dim": 384, - "output_loudness_dim": 384, - "output_melody_dim": 384, - "output_singer_dim": 384, - "pitch_max": 1100, - "pitch_min": 50, - "singer_table_size": 512, - "use_conformer_for_content_features": false, - "use_contentvec": true, - "use_log_f0": true, - "use_log_loudness": true, - "use_mert": false, - "use_singer_encoder": true, - "use_spkid": true, - "use_wenet": false, - "use_whisper": true, - "wenet_dim": 512, - "whisper_dim": 1024, - }, - "diffusion": { - "bidilconv": { - "base_channel": 384, - "conditioner_size": 384, - "conv_kernel_size": 3, - "dilation_cycle_length": 4, - "n_res_block": 20, - }, - "model_type": "bidilconv", - "scheduler": "ddpm", - "scheduler_settings": { - "beta_end": 0.02, - "beta_schedule": "linear", - "beta_start": 0.0001, - "num_train_timesteps": 1000, - }, - "step_encoder": { - "activation": "SiLU", - "dim_hidden_layer": 512, - "dim_raw_embedding": 128, - "max_period": 10000, - "num_layer": 2, - }, - "unet2d": { - "down_block_types": [ - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ], - "in_channels": 1, - "mid_block_type": "UNetMidBlock2DCrossAttn", - "only_cross_attention": false, - "out_channels": 1, - "up_block_types": [ - "UpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - ], - }, - }, - }, - "model_type": "DiffWaveNetSVC", - "preprocess": { - "audio_dir": "audios", - "bits": 8, - "content_feature_batch_size": 16, - "contentvec_batch_size": 1, - "contentvec_dir": "contentvec", - "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt", - "contentvec_frameshift": 0.02, - "contentvec_sample_rate": 16000, - "dur_dir": "durs", - "duration_dir": "duration", - "emo2id": "emo2id.json", - "energy_dir": "energys", - "extract_audio": false, - "extract_contentvec_feature": true, - "extract_energy": true, - "extract_label": false, - "extract_mcep": false, - "extract_mel": true, - "extract_mert_feature": false, - "extract_pitch": true, - "extract_uv": true, - "extract_wenet_feature": false, - "extract_whisper_feature": true, - "f0_max": 1100, - "f0_min": 50, - "file_lst": "file.lst", - "fmax": 12000, - "fmin": 0, - "hop_size": 256, - "is_label": true, - "is_mu_law": true, - "lab_dir": "labs", - "label_dir": "labels", - "mcep_dir": "mcep", - "mel_dir": "mels", - "mel_min_max_norm": true, - "mel_min_max_stats_dir": "mel_min_max_stats", - "mert_dir": "mert", - "mert_feature_layer": -1, - "mert_frameshit": 0.01333, - "mert_hop_size": 320, - "mert_model": "m-a-p/MERT-v1-330M", - "min_level_db": -115, - "mu_law_norm": false, - "n_fft": 1024, - "n_mel": 100, - "num_silent_frames": 8, - "num_workers": 8, - "phone_seq_file": "phone_seq_file", - "pin_memory": true, - "pitch_bin": 256, - "pitch_dir": "pitches", - "pitch_extractor": "crepe", // "parselmouth" - "pitch_max": 1100.0, - "pitch_min": 50.0, - "processed_dir": "ckpts/svc/vocalist_l1_contentvec+whisper/data", - "ref_level_db": 20, - "sample_rate": 24000, - "spk2id": "singers.json", - "train_file": "train.json", - "trim_fft_size": 512, - "trim_hop_size": 128, - "trim_silence": false, - "trim_top_db": 30, - "trimmed_wav_dir": "trimmed_wavs", - "use_audio": false, - "use_contentvec": true, - "use_dur": false, - "use_emoid": false, - "use_frame_duration": false, - "use_frame_energy": true, - "use_frame_pitch": true, - "use_lab": false, - "use_label": false, - "use_log_scale_energy": false, - "use_log_scale_pitch": false, - "use_mel": true, - "use_mert": false, - "use_min_max_norm_mel": true, - "use_one_hot": false, - "use_phn_seq": false, - "use_phone_duration": false, - "use_phone_energy": false, - "use_phone_pitch": false, - "use_spkid": true, - "use_uv": true, - "use_wav": false, - "use_wenet": false, - "use_whisper": true, - "utt2emo": "utt2emo", - "utt2spk": "utt2singer", - "uv_dir": "uvs", - "valid_file": "test.json", - "wav_dir": "wavs", - "wenet_batch_size": 1, - "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml", - "wenet_dir": "wenet", - "wenet_downsample_rate": 4, - "wenet_frameshift": 0.01, - "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt", - "wenet_sample_rate": 16000, - "whisper_batch_size": 30, - "whisper_dir": "whisper", - "whisper_downsample_rate": 2, - "whisper_frameshift": 0.01, - "whisper_model": "medium", - "whisper_model_path": "pretrained/whisper/medium.pt", - "whisper_sample_rate": 16000, - "win_size": 1024, - }, - "supported_model_type": [ - "Fastspeech2", - "DiffSVC", - "Transformer", - "EDM", - "CD", - ], - "train": { - "adamw": { - "lr": 0.0004, - }, - "batch_size": 32, - "dataloader": { - "num_worker": 8, - "pin_memory": true, - }, - "ddp": true, - "epochs": 50000, - "gradient_accumulation_step": 1, - "keep_checkpoint_max": 5, - "keep_last": [ - 5, - -1, - ], - "max_epoch": -1, - "max_steps": 1000000, - "multi_speaker_training": false, - "optimizer": "AdamW", - "random_seed": 10086, - "reducelronplateau": { - "factor": 0.8, - "min_lr": 0.0001, - "patience": 10, - }, - "run_eval": [ - false, - true, - ], - "sampler": { - "drop_last": true, - "holistic_shuffle": false, - }, - "save_checkpoint_stride": [ - 3, - 10, - ], - "save_checkpoints_steps": 10000, - "save_summary_steps": 500, - "scheduler": "ReduceLROnPlateau", - "total_training_steps": 50000, - "tracker": [ - "tensorboard", - ], - "valid_interval": 10000, - }, - "use_custom_dataset": true, -} \ No newline at end of file diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/optimizer.bin b/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/optimizer.bin deleted file mode 100644 index 6b5604e3770d0c8de693930332f32ef2e0b16fe0..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/optimizer.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:836af10b834c7aec9209eb19ce43559e6ef1e3a59bd6468e90cadbc9a18749ef -size 249512389 diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/pytorch_model.bin b/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/pytorch_model.bin deleted file mode 100644 index a11911352aa92208e5246cea59e52b3de1f0d704..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/pytorch_model.bin +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d54eed12bef331095fc367f196d07c5061d5cb72dd6fe0e1e4453b997bf1d68d -size 124755137 diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/random_states_0.pkl b/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/random_states_0.pkl deleted file mode 100644 index be96aac1818d9f8fc4dedfcc530ee1e8ea9f78f7..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/random_states_0.pkl +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6798ddffadcd7d5405a77e667c674c474e4fef0cba817fdd300c7c985c1e82fe -size 14599 diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/singers.json b/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/singers.json deleted file mode 100644 index cd56250fa8be439b4ac6d2afe15fed300a69c973..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/singers.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "vocalist_l1_Adele": 0, - "vocalist_l1_Beyonce": 1, - "vocalist_l1_BrunoMars": 2, - "vocalist_l1_JohnMayer": 3, - "vocalist_l1_MichaelJackson": 4, - "vocalist_l1_TaylorSwift": 5, - "vocalist_l1_张学友": 6, - "vocalist_l1_李健": 7, - "vocalist_l1_汪峰": 8, - "vocalist_l1_王菲": 9, - "vocalist_l1_石倚洁": 10, - "vocalist_l1_蔡琴": 11, - "vocalist_l1_那英": 12, - "vocalist_l1_陈奕迅": 13, - "vocalist_l1_陶喆": 14 -} \ No newline at end of file diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.0 b/ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.0 deleted file mode 100644 index df0c6ae73d3d4df3a0c2856e4ddd75bfc4cc520b..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.0 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d7f490fd0c97876e24bfc44413365ded7ff5d22c1c79f0dac0b754f3b32df76f -size 88 diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.1 b/ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.1 deleted file mode 100644 index 4ee06f708e74717bc23b3130ddcb6f82e5cf84ee..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.1 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e01bcf2fa621ba563b70568c18fe0742d0f48cafae83a6e8beb0bb6d1f6d146d -size 77413046 diff --git a/ckpts/svc/vocalist_l1_contentvec+whisper/singers.json b/ckpts/svc/vocalist_l1_contentvec+whisper/singers.json deleted file mode 100644 index cd56250fa8be439b4ac6d2afe15fed300a69c973..0000000000000000000000000000000000000000 --- a/ckpts/svc/vocalist_l1_contentvec+whisper/singers.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "vocalist_l1_Adele": 0, - "vocalist_l1_Beyonce": 1, - "vocalist_l1_BrunoMars": 2, - "vocalist_l1_JohnMayer": 3, - "vocalist_l1_MichaelJackson": 4, - "vocalist_l1_TaylorSwift": 5, - "vocalist_l1_张学友": 6, - "vocalist_l1_李健": 7, - "vocalist_l1_汪峰": 8, - "vocalist_l1_王菲": 9, - "vocalist_l1_石倚洁": 10, - "vocalist_l1_蔡琴": 11, - "vocalist_l1_那英": 12, - "vocalist_l1_陈奕迅": 13, - "vocalist_l1_陶喆": 14 -} \ No newline at end of file diff --git a/config/audioldm.json b/config/audioldm.json deleted file mode 100644 index c0cab48cda2f00ac6c1687da5ab3b6d6a263e317..0000000000000000000000000000000000000000 --- a/config/audioldm.json +++ /dev/null @@ -1,92 +0,0 @@ -{ - "base_config": "config/base.json", - "model_type": "AudioLDM", - "task_type": "tta", - "dataset": [ - "AudioCaps" - ], - "preprocess": { - // feature used for model training - "use_spkid": false, - "use_uv": false, - "use_frame_pitch": false, - "use_phone_pitch": false, - "use_frame_energy": false, - "use_phone_energy": false, - "use_mel": false, - "use_audio": false, - "use_label": false, - "use_one_hot": false, - "cond_mask_prob": 0.1 - }, - // model - "model": { - "audioldm": { - "image_size": 32, - "in_channels": 4, - "out_channels": 4, - "model_channels": 256, - "attention_resolutions": [ - 4, - 2, - 1 - ], - "num_res_blocks": 2, - "channel_mult": [ - 1, - 2, - 4 - ], - "num_heads": 8, - "use_spatial_transformer": true, - "transformer_depth": 1, - "context_dim": 768, - "use_checkpoint": true, - "legacy": false - }, - "autoencoderkl": { - "ch": 128, - "ch_mult": [ - 1, - 1, - 2, - 2, - 4 - ], - "num_res_blocks": 2, - "in_channels": 1, - "z_channels": 4, - "out_ch": 1, - "double_z": true - }, - "noise_scheduler": { - "num_train_timesteps": 1000, - "beta_start": 0.00085, - "beta_end": 0.012, - "beta_schedule": "scaled_linear", - "clip_sample": false, - "steps_offset": 1, - "set_alpha_to_one": false, - "skip_prk_steps": true, - "prediction_type": "epsilon" - } - }, - // train - "train": { - "lronPlateau": { - "factor": 0.9, - "patience": 100, - "min_lr": 4.0e-5, - "verbose": true - }, - "adam": { - "lr": 5.0e-5, - "betas": [ - 0.9, - 0.999 - ], - "weight_decay": 1.0e-2, - "eps": 1.0e-8 - } - } -} \ No newline at end of file diff --git a/config/autoencoderkl.json b/config/autoencoderkl.json deleted file mode 100644 index 72e677770da689af33d4c7bd6f3506927fb24380..0000000000000000000000000000000000000000 --- a/config/autoencoderkl.json +++ /dev/null @@ -1,69 +0,0 @@ -{ - "base_config": "config/base.json", - "model_type": "AutoencoderKL", - "task_type": "tta", - "dataset": [ - "AudioCaps" - ], - "preprocess": { - // feature used for model training - "use_spkid": false, - "use_uv": false, - "use_frame_pitch": false, - "use_phone_pitch": false, - "use_frame_energy": false, - "use_phone_energy": false, - "use_mel": false, - "use_audio": false, - "use_label": false, - "use_one_hot": false - }, - // model - "model": { - "autoencoderkl": { - "ch": 128, - "ch_mult": [ - 1, - 1, - 2, - 2, - 4 - ], - "num_res_blocks": 2, - "in_channels": 1, - "z_channels": 4, - "out_ch": 1, - "double_z": true - }, - "loss": { - "kl_weight": 1e-8, - "disc_weight": 0.5, - "disc_factor": 1.0, - "logvar_init": 0.0, - "min_adapt_d_weight": 0.0, - "max_adapt_d_weight": 10.0, - "disc_start": 50001, - "disc_in_channels": 1, - "disc_num_layers": 3, - "use_actnorm": false - } - }, - // train - "train": { - "lronPlateau": { - "factor": 0.9, - "patience": 100, - "min_lr": 4.0e-5, - "verbose": true - }, - "adam": { - "lr": 4.0e-4, - "betas": [ - 0.9, - 0.999 - ], - "weight_decay": 1.0e-2, - "eps": 1.0e-8 - } - } -} \ No newline at end of file diff --git a/config/base.json b/config/base.json deleted file mode 100644 index 5fa71dfcdde7058eae634007cdf4f034184e753b..0000000000000000000000000000000000000000 --- a/config/base.json +++ /dev/null @@ -1,220 +0,0 @@ -{ - "supported_model_type": [ - "GANVocoder", - "Fastspeech2", - "DiffSVC", - "Transformer", - "EDM", - "CD" - ], - "task_type": "", - "dataset": [], - "use_custom_dataset": false, - "preprocess": { - "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon" - // trim audio silence - "data_augment": false, - "trim_silence": false, - "num_silent_frames": 8, - "trim_fft_size": 512, // fft size used in trimming - "trim_hop_size": 128, // hop size used in trimming - "trim_top_db": 30, // top db used in trimming sensitive to each dataset - // acoustic features - "extract_mel": false, - "mel_extract_mode": "", - "extract_linear_spec": false, - "extract_mcep": false, - "extract_pitch": false, - "extract_acoustic_token": false, - "pitch_remove_outlier": false, - "extract_uv": false, - "pitch_norm": false, - "extract_audio": false, - "extract_label": false, - "pitch_extractor": "parselmouth", // pyin, dio, pyworld, pyreaper, parselmouth, CWT (Continuous Wavelet Transform) - "extract_energy": false, - "energy_remove_outlier": false, - "energy_norm": false, - "energy_extract_mode": "from_mel", - "extract_duration": false, - "extract_amplitude_phase": false, - "mel_min_max_norm": false, - // lingusitic features - "extract_phone": false, - "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", - // content features - "extract_whisper_feature": false, - "extract_contentvec_feature": false, - "extract_mert_feature": false, - "extract_wenet_feature": false, - // Settings for data preprocessing - "n_mel": 80, - "win_size": 480, - "hop_size": 120, - "sample_rate": 24000, - "n_fft": 1024, - "fmin": 0, - "fmax": 12000, - "min_level_db": -115, - "ref_level_db": 20, - "bits": 8, - // Directory names of processed data or extracted features - "processed_dir": "processed_data", - "trimmed_wav_dir": "trimmed_wavs", // directory name of silence trimed wav - "raw_data": "raw_data", - "phone_dir": "phones", - "wav_dir": "wavs", // directory name of processed wav (such as downsampled waveform) - "audio_dir": "audios", - "log_amplitude_dir": "log_amplitudes", - "phase_dir": "phases", - "real_dir": "reals", - "imaginary_dir": "imaginarys", - "label_dir": "labels", - "linear_dir": "linears", - "mel_dir": "mels", // directory name of extraced mel features - "mcep_dir": "mcep", // directory name of extraced mcep features - "dur_dir": "durs", - "symbols_dict": "symbols.dict", - "lab_dir": "labs", // directory name of extraced label features - "wenet_dir": "wenet", // directory name of extraced wenet features - "contentvec_dir": "contentvec", // directory name of extraced wenet features - "pitch_dir": "pitches", // directory name of extraced pitch features - "energy_dir": "energys", // directory name of extracted energy features - "phone_pitch_dir": "phone_pitches", // directory name of extraced pitch features - "phone_energy_dir": "phone_energys", // directory name of extracted energy features - "uv_dir": "uvs", // directory name of extracted unvoiced features - "duration_dir": "duration", // ground-truth duration file - "phone_seq_file": "phone_seq_file", // phoneme sequence file - "file_lst": "file.lst", - "train_file": "train.json", // training set, the json file contains detailed information about the dataset, including dataset name, utterance id, duration of the utterance - "valid_file": "valid.json", // validattion set - "spk2id": "spk2id.json", // used for multi-speaker dataset - "utt2spk": "utt2spk", // used for multi-speaker dataset - "emo2id": "emo2id.json", // used for multi-emotion dataset - "utt2emo": "utt2emo", // used for multi-emotion dataset - // Features used for model training - "use_text": false, - "use_phone": false, - "use_phn_seq": false, - "use_lab": false, - "use_linear": false, - "use_mel": false, - "use_min_max_norm_mel": false, - "use_wav": false, - "use_phone_pitch": false, - "use_log_scale_pitch": false, - "use_phone_energy": false, - "use_phone_duration": false, - "use_log_scale_energy": false, - "use_wenet": false, - "use_dur": false, - "use_spkid": false, // True: use speaker id for multi-speaker dataset - "use_emoid": false, // True: use emotion id for multi-emotion dataset - "use_frame_pitch": false, - "use_uv": false, - "use_frame_energy": false, - "use_frame_duration": false, - "use_audio": false, - "use_label": false, - "use_one_hot": false, - "use_amplitude_phase": false, - "data_augment": false, - "align_mel_duration": false - }, - "train": { - "ddp": true, - "random_seed": 970227, - "batch_size": 16, - "max_steps": 1000000, - // Trackers - "tracker": [ - "tensorboard" - // "wandb", - // "cometml", - // "mlflow", - ], - "max_epoch": -1, - // -1 means no limit - "save_checkpoint_stride": [ - 5, - 20 - ], - // unit is epoch - "keep_last": [ - 3, - -1 - ], - // -1 means infinite, if one number will broadcast - "run_eval": [ - false, - true - ], - // if one number will broadcast - // Fix the random seed - "random_seed": 10086, - // Optimizer - "optimizer": "AdamW", - "adamw": { - "lr": 4.0e-4 - // nn model lr - }, - // LR Scheduler - "scheduler": "ReduceLROnPlateau", - "reducelronplateau": { - "factor": 0.8, - "patience": 10, - // unit is epoch - "min_lr": 1.0e-4 - }, - // Batchsampler - "sampler": { - "holistic_shuffle": true, - "drop_last": true - }, - // Dataloader - "dataloader": { - "num_worker": 32, - "pin_memory": true - }, - "gradient_accumulation_step": 1, - "total_training_steps": 50000, - "save_summary_steps": 500, - "save_checkpoints_steps": 10000, - "valid_interval": 10000, - "keep_checkpoint_max": 5, - "multi_speaker_training": false, // True: train multi-speaker model; False: training single-speaker model; - "max_epoch": -1, - // -1 means no limit - "save_checkpoint_stride": [ - 5, - 20 - ], - // unit is epoch - "keep_last": [ - 3, - -1 - ], - // -1 means infinite, if one number will broadcast - "run_eval": [ - false, - true - ], - // Batchsampler - "sampler": { - "holistic_shuffle": true, - "drop_last": true - }, - // Dataloader - "dataloader": { - "num_worker": 32, - "pin_memory": true - }, - // Trackers - "tracker": [ - "tensorboard" - // "wandb", - // "cometml", - // "mlflow", - ], - }, -} diff --git a/config/comosvc.json b/config/comosvc.json deleted file mode 100644 index 19599e940be8f13e40fe6ac59ff82e720452d10e..0000000000000000000000000000000000000000 --- a/config/comosvc.json +++ /dev/null @@ -1,216 +0,0 @@ -{ - "base_config": "config/base.json", - "model_type": "DiffComoSVC", - "task_type": "svc", - "use_custom_dataset": false, - "preprocess": { - // data augmentations - "use_pitch_shift": false, - "use_formant_shift": false, - "use_time_stretch": false, - "use_equalizer": false, - // acoustic features - "extract_mel": true, - "mel_min_max_norm": true, - "extract_pitch": true, - "pitch_extractor": "parselmouth", - "extract_uv": true, - "extract_energy": true, - // content features - "extract_whisper_feature": false, - "whisper_sample_rate": 16000, - "extract_contentvec_feature": false, - "contentvec_sample_rate": 16000, - "extract_wenet_feature": false, - "wenet_sample_rate": 16000, - "extract_mert_feature": false, - "mert_sample_rate": 16000, - // Default config for whisper - "whisper_frameshift": 0.01, - "whisper_downsample_rate": 2, - // Default config for content vector - "contentvec_frameshift": 0.02, - // Default config for mert - "mert_model": "m-a-p/MERT-v1-330M", - "mert_feature_layer": -1, - "mert_hop_size": 320, - // 24k - "mert_frameshit": 0.01333, - // 10ms - "wenet_frameshift": 0.01, - // wenetspeech is 4, gigaspeech is 6 - "wenet_downsample_rate": 4, - // Default config - "n_mel": 100, - "win_size": 1024, - // todo - "hop_size": 256, - "sample_rate": 24000, - "n_fft": 1024, - // todo - "fmin": 0, - "fmax": 12000, - // todo - "f0_min": 50, - // ~C2 - "f0_max": 1100, - //1100, // ~C6(1100), ~G5(800) - "pitch_bin": 256, - "pitch_max": 1100.0, - "pitch_min": 50.0, - "is_label": true, - "is_mu_law": true, - "bits": 8, - "mel_min_max_stats_dir": "mel_min_max_stats", - "whisper_dir": "whisper", - "contentvec_dir": "contentvec", - "wenet_dir": "wenet", - "mert_dir": "mert", - // Extract content features using dataloader - "pin_memory": true, - "num_workers": 8, - "content_feature_batch_size": 16, - // Features used for model training - "use_mel": true, - "use_min_max_norm_mel": true, - "use_frame_pitch": true, - "use_uv": true, - "use_frame_energy": true, - "use_log_scale_pitch": false, - "use_log_scale_energy": false, - "use_spkid": true, - // Meta file - "train_file": "train.json", - "valid_file": "test.json", - "spk2id": "singers.json", - "utt2spk": "utt2singer" - }, - "model": { - "teacher_model_path": "[Your Teacher Model Path].bin", - "condition_encoder": { - "merge_mode": "add", - "input_melody_dim": 1, - "use_log_f0": true, - "n_bins_melody": 256, - //# Quantization (0 for not quantization) - "output_melody_dim": 384, - "input_loudness_dim": 1, - "use_log_loudness": true, - "n_bins_loudness": 256, - "output_loudness_dim": 384, - "use_whisper": false, - "use_contentvec": false, - "use_wenet": false, - "use_mert": false, - "whisper_dim": 1024, - "contentvec_dim": 256, - "mert_dim": 256, - "wenet_dim": 512, - "content_encoder_dim": 384, - "output_singer_dim": 384, - "singer_table_size": 512, - "output_content_dim": 384, - "use_spkid": true - }, - "comosvc": { - "distill": false, - // conformer encoder - "input_dim": 384, - "output_dim": 100, - "n_heads": 2, - "n_layers": 6, - "filter_channels": 512, - "dropout": 0.1, - // karras diffusion - "P_mean": -1.2, - "P_std": 1.2, - "sigma_data": 0.5, - "sigma_min": 0.002, - "sigma_max": 80, - "rho": 7, - "n_timesteps": 40, - }, - "diffusion": { - // Diffusion steps encoder - "step_encoder": { - "dim_raw_embedding": 128, - "dim_hidden_layer": 512, - "activation": "SiLU", - "num_layer": 2, - "max_period": 10000 - }, - // Diffusion decoder - "model_type": "bidilconv", - // bidilconv, unet2d, TODO: unet1d - "bidilconv": { - "base_channel": 384, - "n_res_block": 20, - "conv_kernel_size": 3, - "dilation_cycle_length": 4, - // specially, 1 means no dilation - "conditioner_size": 100 - } - }, - }, - "train": { - // Basic settings - "fast_steps": 0, - "batch_size": 32, - "gradient_accumulation_step": 1, - "max_epoch": -1, - // -1 means no limit - "save_checkpoint_stride": [ - 10, - 100 - ], - // unit is epoch - "keep_last": [ - 3, - -1 - ], - // -1 means infinite, if one number will broadcast - "run_eval": [ - false, - true - ], - // if one number will broadcast - // Fix the random seed - "random_seed": 10086, - // Batchsampler - "sampler": { - "holistic_shuffle": true, - "drop_last": true - }, - // Dataloader - "dataloader": { - "num_worker": 32, - "pin_memory": true - }, - // Trackers - "tracker": [ - "tensorboard" - // "wandb", - // "cometml", - // "mlflow", - ], - // Optimizer - "optimizer": "AdamW", - "adamw": { - "lr": 4.0e-4 - // nn model lr - }, - // LR Scheduler - "scheduler": "ReduceLROnPlateau", - "reducelronplateau": { - "factor": 0.8, - "patience": 10, - // unit is epoch - "min_lr": 1.0e-4 - } - }, - "inference": { - "comosvc": { - "inference_steps": 40 - } - } -} \ No newline at end of file diff --git a/config/diffusion.json b/config/diffusion.json deleted file mode 100644 index 7ef72f39d3dd578afc35d086a3d19fc892bbefda..0000000000000000000000000000000000000000 --- a/config/diffusion.json +++ /dev/null @@ -1,227 +0,0 @@ -{ - // FIXME: THESE ARE LEGACY - "base_config": "config/base.json", - "model_type": "diffusion", - "task_type": "svc", - "use_custom_dataset": false, - "preprocess": { - // data augmentations - "use_pitch_shift": false, - "use_formant_shift": false, - "use_time_stretch": false, - "use_equalizer": false, - // acoustic features - "extract_mel": true, - "mel_min_max_norm": true, - "extract_pitch": true, - "pitch_extractor": "parselmouth", - "extract_uv": true, - "extract_energy": true, - // content features - "extract_whisper_feature": false, - "whisper_sample_rate": 16000, - "extract_contentvec_feature": false, - "contentvec_sample_rate": 16000, - "extract_wenet_feature": false, - "wenet_sample_rate": 16000, - "extract_mert_feature": false, - "mert_sample_rate": 16000, - // Default config for whisper - "whisper_frameshift": 0.01, - "whisper_downsample_rate": 2, - // Default config for content vector - "contentvec_frameshift": 0.02, - // Default config for mert - "mert_model": "m-a-p/MERT-v1-330M", - "mert_feature_layer": -1, - "mert_hop_size": 320, - // 24k - "mert_frameshit": 0.01333, - // 10ms - "wenet_frameshift": 0.01, - // wenetspeech is 4, gigaspeech is 6 - "wenet_downsample_rate": 4, - // Default config - "n_mel": 100, - "win_size": 1024, - // todo - "hop_size": 256, - "sample_rate": 24000, - "n_fft": 1024, - // todo - "fmin": 0, - "fmax": 12000, - // todo - "f0_min": 50, - // ~C2 - "f0_max": 1100, - //1100, // ~C6(1100), ~G5(800) - "pitch_bin": 256, - "pitch_max": 1100.0, - "pitch_min": 50.0, - "is_label": true, - "is_mu_law": true, - "bits": 8, - "mel_min_max_stats_dir": "mel_min_max_stats", - "whisper_dir": "whisper", - "contentvec_dir": "contentvec", - "wenet_dir": "wenet", - "mert_dir": "mert", - // Extract content features using dataloader - "pin_memory": true, - "num_workers": 8, - "content_feature_batch_size": 16, - // Features used for model training - "use_mel": true, - "use_min_max_norm_mel": true, - "use_frame_pitch": true, - "use_uv": true, - "use_frame_energy": true, - "use_log_scale_pitch": false, - "use_log_scale_energy": false, - "use_spkid": true, - // Meta file - "train_file": "train.json", - "valid_file": "test.json", - "spk2id": "singers.json", - "utt2spk": "utt2singer" - }, - "model": { - "condition_encoder": { - "merge_mode": "add", - "input_melody_dim": 1, - "use_log_f0": true, - "n_bins_melody": 256, - //# Quantization (0 for not quantization) - "output_melody_dim": 384, - "input_loudness_dim": 1, - "use_log_loudness": true, - "n_bins_loudness": 256, - "output_loudness_dim": 384, - "use_whisper": false, - "use_contentvec": false, - "use_wenet": false, - "use_mert": false, - "whisper_dim": 1024, - "contentvec_dim": 256, - "mert_dim": 256, - "wenet_dim": 512, - "content_encoder_dim": 384, - "output_singer_dim": 384, - "singer_table_size": 512, - "output_content_dim": 384, - "use_spkid": true - }, - // FIXME: FOLLOWING ARE NEW!! - "diffusion": { - "scheduler": "ddpm", - "scheduler_settings": { - "num_train_timesteps": 1000, - "beta_start": 1.0e-4, - "beta_end": 0.02, - "beta_schedule": "linear" - }, - // Diffusion steps encoder - "step_encoder": { - "dim_raw_embedding": 128, - "dim_hidden_layer": 512, - "activation": "SiLU", - "num_layer": 2, - "max_period": 10000 - }, - // Diffusion decoder - "model_type": "bidilconv", - // bidilconv, unet2d, TODO: unet1d - "bidilconv": { - "base_channel": 384, - "n_res_block": 20, - "conv_kernel_size": 3, - "dilation_cycle_length": 4, - // specially, 1 means no dilation - "conditioner_size": 384 - }, - "unet2d": { - "in_channels": 1, - "out_channels": 1, - "down_block_types": [ - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D" - ], - "mid_block_type": "UNetMidBlock2DCrossAttn", - "up_block_types": [ - "UpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D" - ], - "only_cross_attention": false - } - } - }, - // FIXME: FOLLOWING ARE NEW!! - "train": { - // Basic settings - "batch_size": 64, - "gradient_accumulation_step": 1, - "max_epoch": -1, - // -1 means no limit - "save_checkpoint_stride": [ - 5, - 20 - ], - // unit is epoch - "keep_last": [ - 3, - -1 - ], - // -1 means infinite, if one number will broadcast - "run_eval": [ - false, - true - ], - // if one number will broadcast - // Fix the random seed - "random_seed": 10086, - // Batchsampler - "sampler": { - "holistic_shuffle": true, - "drop_last": true - }, - // Dataloader - "dataloader": { - "num_worker": 32, - "pin_memory": true - }, - // Trackers - "tracker": [ - "tensorboard" - // "wandb", - // "cometml", - // "mlflow", - ], - // Optimizer - "optimizer": "AdamW", - "adamw": { - "lr": 4.0e-4 - // nn model lr - }, - // LR Scheduler - "scheduler": "ReduceLROnPlateau", - "reducelronplateau": { - "factor": 0.8, - "patience": 10, - // unit is epoch - "min_lr": 1.0e-4 - } - }, - "inference": { - "diffusion": { - "scheduler": "pndm", - "scheduler_settings": { - "num_inference_timesteps": 1000 - } - } - } -} \ No newline at end of file diff --git a/config/fs2.json b/config/fs2.json deleted file mode 100644 index d6f04b593c6f6d8ef67e416b9a3d614d3f963b4f..0000000000000000000000000000000000000000 --- a/config/fs2.json +++ /dev/null @@ -1,117 +0,0 @@ -{ - "base_config": "config/tts.json", - "model_type": "FastSpeech2", - "task_type": "tts", - "dataset": ["LJSpeech"], - "preprocess": { - // acoustic features - "extract_audio": true, - "extract_mel": true, - "mel_extract_mode": "taco", - "mel_min_max_norm": false, - "extract_pitch": true, - "extract_uv": false, - "pitch_extractor": "dio", - "extract_energy": true, - "energy_extract_mode": "from_tacotron_stft", - "extract_duration": true, - "use_phone": true, - "pitch_norm": true, - "energy_norm": true, - "pitch_remove_outlier": true, - "energy_remove_outlier": true, - - // Default config - "n_mel": 80, - "win_size": 1024, // todo - "hop_size": 256, - "sample_rate": 22050, - "n_fft": 1024, // todo - "fmin": 0, - "fmax": 8000, // todo - "raw_data": "raw_data", - "text_cleaners": ["english_cleaners"], - "f0_min": 71, // ~C2 - "f0_max": 800, //1100, // ~C6(1100), ~G5(800) - "pitch_bin": 256, - "pitch_max": 1100.0, - "pitch_min": 50.0, - "is_label": true, - "is_mu_law": true, - "bits": 8, - - "mel_min_max_stats_dir": "mel_min_max_stats", - "whisper_dir": "whisper", - "content_vector_dir": "content_vector", - "wenet_dir": "wenet", - "mert_dir": "mert", - "spk2id":"spk2id.json", - "utt2spk":"utt2spk", - - // Features used for model training - "use_mel": true, - "use_min_max_norm_mel": false, - "use_frame_pitch": false, - "use_frame_energy": false, - "use_phone_pitch": true, - "use_phone_energy": true, - "use_log_scale_pitch": false, - "use_log_scale_energy": false, - "use_spkid": false, - "align_mel_duration": true, - "text_cleaners": ["english_cleaners"] - }, - "model": { - // Settings for transformer - "transformer": { - "encoder_layer": 4, - "encoder_head": 2, - "encoder_hidden": 256, - "decoder_layer": 6, - "decoder_head": 2, - "decoder_hidden": 256, - "conv_filter_size": 1024, - "conv_kernel_size": [9, 1], - "encoder_dropout": 0.2, - "decoder_dropout": 0.2 - }, - - // Settings for variance_predictor - "variance_predictor":{ - "filter_size": 256, - "kernel_size": 3, - "dropout": 0.5 - }, - "variance_embedding":{ - "pitch_quantization": "linear", // support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing - "energy_quantization": "linear", // support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing - "n_bins": 256 - }, - "max_seq_len": 1000 - }, - "train":{ - "batch_size": 16, - "sort_sample": true, - "drop_last": true, - "group_size": 4, - "grad_clip_thresh": 1.0, - "dataloader": { - "num_worker": 8, - "pin_memory": true - }, - "lr_scheduler":{ - "num_warmup": 4000 - }, - // LR Scheduler - "scheduler": "NoamLR", - // Optimizer - "optimizer": "Adam", - "adam": { - "lr": 0.0625, - "betas": [0.9, 0.98], - "eps": 0.000000001, - "weight_decay": 0.0 - }, - } - -} diff --git a/config/transformer.json b/config/transformer.json deleted file mode 100644 index 258bfddb36a21adb91b9e1921a57f738a789a835..0000000000000000000000000000000000000000 --- a/config/transformer.json +++ /dev/null @@ -1,180 +0,0 @@ -{ - "base_config": "config/base.json", - "model_type": "Transformer", - "task_type": "svc", - "use_custom_dataset": false, - "preprocess": { - // data augmentations - "use_pitch_shift": false, - "use_formant_shift": false, - "use_time_stretch": false, - "use_equalizer": false, - // acoustic features - "extract_mel": true, - "mel_min_max_norm": true, - "extract_pitch": true, - "pitch_extractor": "parselmouth", - "extract_uv": true, - "extract_energy": true, - // content features - "extract_whisper_feature": false, - "whisper_sample_rate": 16000, - "extract_contentvec_feature": false, - "contentvec_sample_rate": 16000, - "extract_wenet_feature": false, - "wenet_sample_rate": 16000, - "extract_mert_feature": false, - "mert_sample_rate": 16000, - // Default config for whisper - "whisper_frameshift": 0.01, - "whisper_downsample_rate": 2, - // Default config for content vector - "contentvec_frameshift": 0.02, - // Default config for mert - "mert_model": "m-a-p/MERT-v1-330M", - "mert_feature_layer": -1, - "mert_hop_size": 320, - // 24k - "mert_frameshit": 0.01333, - // 10ms - "wenet_frameshift": 0.01, - // wenetspeech is 4, gigaspeech is 6 - "wenet_downsample_rate": 4, - // Default config - "n_mel": 100, - "win_size": 1024, - // todo - "hop_size": 256, - "sample_rate": 24000, - "n_fft": 1024, - // todo - "fmin": 0, - "fmax": 12000, - // todo - "f0_min": 50, - // ~C2 - "f0_max": 1100, - //1100, // ~C6(1100), ~G5(800) - "pitch_bin": 256, - "pitch_max": 1100.0, - "pitch_min": 50.0, - "is_label": true, - "is_mu_law": true, - "bits": 8, - "mel_min_max_stats_dir": "mel_min_max_stats", - "whisper_dir": "whisper", - "contentvec_dir": "contentvec", - "wenet_dir": "wenet", - "mert_dir": "mert", - // Extract content features using dataloader - "pin_memory": true, - "num_workers": 8, - "content_feature_batch_size": 16, - // Features used for model training - "use_mel": true, - "use_min_max_norm_mel": true, - "use_frame_pitch": true, - "use_uv": true, - "use_frame_energy": true, - "use_log_scale_pitch": false, - "use_log_scale_energy": false, - "use_spkid": true, - // Meta file - "train_file": "train.json", - "valid_file": "test.json", - "spk2id": "singers.json", - "utt2spk": "utt2singer" - }, - "model": { - "condition_encoder": { - "merge_mode": "add", - "input_melody_dim": 1, - "use_log_f0": true, - "n_bins_melody": 256, - //# Quantization (0 for not quantization) - "output_melody_dim": 384, - "input_loudness_dim": 1, - "use_log_loudness": true, - "n_bins_loudness": 256, - "output_loudness_dim": 384, - "use_whisper": false, - "use_contentvec": true, - "use_wenet": false, - "use_mert": false, - "whisper_dim": 1024, - "contentvec_dim": 256, - "mert_dim": 256, - "wenet_dim": 512, - "content_encoder_dim": 384, - "output_singer_dim": 384, - "singer_table_size": 512, - "output_content_dim": 384, - "use_spkid": true - }, - "transformer": { - "type": "conformer", - // 'conformer' or 'transformer' - "input_dim": 384, - "output_dim": 100, - "n_heads": 2, - "n_layers": 6, - "filter_channels": 512, - "dropout": 0.1, - } - }, - "train": { - // Basic settings - "batch_size": 64, - "gradient_accumulation_step": 1, - "max_epoch": -1, - // -1 means no limit - "save_checkpoint_stride": [ - 10, - 100 - ], - // unit is epoch - "keep_last": [ - 3, - -1 - ], - // -1 means infinite, if one number will broadcast - "run_eval": [ - false, - true - ], - // if one number will broadcast - // Fix the random seed - "random_seed": 10086, - // Batchsampler - "sampler": { - "holistic_shuffle": true, - "drop_last": true - }, - // Dataloader - "dataloader": { - "num_worker": 32, - "pin_memory": true - }, - // Trackers - "tracker": [ - "tensorboard" - // "wandb", - // "cometml", - // "mlflow", - ], - // Optimizer - "optimizer": "AdamW", - "adamw": { - "lr": 4.0e-4 - // nn model lr - }, - // LR Scheduler - "scheduler": "ReduceLROnPlateau", - "reducelronplateau": { - "factor": 0.8, - "patience": 10, - // unit is epoch - "min_lr": 1.0e-4 - } - } -} \ No newline at end of file diff --git a/config/tts.json b/config/tts.json deleted file mode 100644 index d46670ebe858f478e3699e49b88e87ec7c0e34d4..0000000000000000000000000000000000000000 --- a/config/tts.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "base_config": "config/base.json", - "supported_model_type": [ - "Fastspeech2", - "VITS", - "VALLE", - ], - "task_type": "tts", - "preprocess": { - "language": "en-us", - // linguistic features - "extract_phone": true, - "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)" - "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", - // Directory names of processed data or extracted features - "phone_dir": "phones", - "use_phone": true, - }, - "model": { - "text_token_num": 512, - } - -} diff --git a/config/valle.json b/config/valle.json deleted file mode 100644 index 4b62d85dd64b577d8ca4ff31d5edf5ce47303a03..0000000000000000000000000000000000000000 --- a/config/valle.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "base_config": "config/tts.json", - "model_type": "VALLE", - "task_type": "tts", - "dataset": [ - "libritts" - ], - "preprocess": { - "extract_phone": true, - "phone_extractor": "espeak", // phoneme extractor: espeak, pypinyin, pypinyin_initials_finals or lexicon - "extract_acoustic_token": true, - "acoustic_token_extractor": "Encodec", // acoustic token extractor: encodec, dac(todo) - "acoustic_token_dir": "acoutic_tokens", - "use_text": false, - "use_phone": true, - "use_acoustic_token": true, - "symbols_dict": "symbols.dict", - "min_duration": 0.5, // the duration lowerbound to filter the audio with duration < min_duration - "max_duration": 14, // the duration uperbound to filter the audio with duration > max_duration. - "sampling_rate": 24000, - }, - "model": { - "text_token_num": 512, - "audio_token_num": 1024, - "decoder_dim": 1024, // embedding dimension of the decoder model - "nhead": 16, // number of attention heads in the decoder layers - "num_decoder_layers": 12, // number of decoder layers - "norm_first": true, // pre or post Normalization. - "add_prenet": false, // whether add PreNet after Inputs - "prefix_mode": 0, // mode for how to prefix VALL-E NAR Decoder, 0: no prefix, 1: 0 to random, 2: random to random, 4: chunk of pre or post utterance - "share_embedding": true, // share the parameters of the output projection layer with the parameters of the acoustic embedding - "nar_scale_factor": 1, // model scale factor which will be assigned different meanings in different models - "prepend_bos": false, // whether prepend to the acoustic tokens -> AR Decoder inputs - "num_quantizers": 8, // numbert of the audio quantization layers - // "scaling_xformers": false, // Apply Reworked Conformer scaling on Transformers - }, - "train": { - "ddp": false, - "train_stage": 1, // 0: train all modules, For VALL_E, support 1: AR Decoder 2: NAR Decoder(s) - "max_epoch": 20, - "optimizer": "ScaledAdam", - "scheduler": "Eden", - "warmup_steps": 200, // number of steps that affects how rapidly the learning rate decreases - "base_lr": 0.05, // base learning rate." - "valid_interval": 1000, - "log_epoch_step": 1000, - "save_checkpoint_stride": [ - 1, - 1 - ] - } -} \ No newline at end of file diff --git a/config/vits.json b/config/vits.json deleted file mode 100644 index d8de83ab4641f5c4efef7752e7017d0733a7ddac..0000000000000000000000000000000000000000 --- a/config/vits.json +++ /dev/null @@ -1,101 +0,0 @@ -{ - "base_config": "config/tts.json", - "model_type": "VITS", - "task_type": "tts", - "preprocess": { - "extract_phone": true, - "extract_mel": true, - "n_mel": 80, - "fmin": 0, - "fmax": null, - "extract_linear_spec": true, - "extract_audio": true, - "use_linear": true, - "use_mel": true, - "use_audio": true, - "use_text": false, - "use_phone": true, - "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", - "n_fft": 1024, - "win_size": 1024, - "hop_size": 256, - "segment_size": 8192, - "text_cleaners": [ - "english_cleaners" - ] - }, - "model": { - "text_token_num": 512, - "inter_channels": 192, - "hidden_channels": 192, - "filter_channels": 768, - "n_heads": 2, - "n_layers": 6, - "kernel_size": 3, - "p_dropout": 0.1, - "resblock": "1", - "resblock_kernel_sizes": [ - 3, - 7, - 11 - ], - "resblock_dilation_sizes": [ - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ] - ], - "upsample_rates": [ - 8, - 8, - 2, - 2 - ], - "upsample_initial_channel": 512, - "upsample_kernel_sizes": [ - 16, - 16, - 4, - 4 - ], - "n_layers_q": 3, - "use_spectral_norm": false, - "n_speakers": 0, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true - "gin_channels": 256, - "use_sdp": true - }, - "train": { - "fp16_run": true, - "learning_rate": 2e-4, - "betas": [ - 0.8, - 0.99 - ], - "eps": 1e-9, - "batch_size": 16, - "lr_decay": 0.999875, - // "segment_size": 8192, - "init_lr_ratio": 1, - "warmup_epochs": 0, - "c_mel": 45, - "c_kl": 1.0, - "AdamW": { - "betas": [ - 0.8, - 0.99 - ], - "eps": 1e-9, - } - } -} \ No newline at end of file diff --git a/config/vocoder.json b/config/vocoder.json deleted file mode 100644 index 1ce29c67f4c1dc6406634b170ca73ef731d4b000..0000000000000000000000000000000000000000 --- a/config/vocoder.json +++ /dev/null @@ -1,84 +0,0 @@ -{ - "base_config": "config/base.json", - "dataset": [ - "LJSpeech", - "LibriTTS", - "opencpop", - "m4singer", - "svcc", - "svcceval", - "pjs", - "opensinger", - "popbutfy", - "nus48e", - "popcs", - "kising", - "csd", - "opera", - "vctk", - "lijian", - "cdmusiceval" - ], - "task_type": "vocoder", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_pitch": false, - "extract_uv": false, - "extract_audio": true, - "extract_label": false, - "extract_one_hot": false, - "extract_amplitude_phase": false, - "pitch_extractor": "parselmouth", - // Settings for data preprocessing - "n_mel": 100, - "win_size": 1024, - "hop_size": 256, - "sample_rate": 24000, - "n_fft": 1024, - "fmin": 0, - "fmax": 12000, - "f0_min": 50, - "f0_max": 1100, - "pitch_bin": 256, - "pitch_max": 1100.0, - "pitch_min": 50.0, - "is_mu_law": false, - "bits": 8, - "cut_mel_frame": 32, - // Directory names of processed data or extracted features - "spk2id": "singers.json", - // Features used for model training - "use_mel": true, - "use_frame_pitch": false, - "use_uv": false, - "use_audio": true, - "use_label": false, - "use_one_hot": false, - "train_file": "train.json", - "valid_file": "test.json" - }, - "train": { - "random_seed": 114514, - "batch_size": 64, - "gradient_accumulation_step": 1, - "max_epoch": 1000000, - "save_checkpoint_stride": [ - 20 - ], - "run_eval": [ - true - ], - "sampler": { - "holistic_shuffle": true, - "drop_last": true - }, - "dataloader": { - "num_worker": 4, - "pin_memory": true - }, - "tracker": [ - "tensorboard" - ], - } -} \ No newline at end of file diff --git a/egs/svc/MultipleContentsSVC/README.md b/egs/svc/MultipleContentsSVC/README.md deleted file mode 100644 index ac999e6253076f79ca59ed05fac168e5679feaea..0000000000000000000000000000000000000000 --- a/egs/svc/MultipleContentsSVC/README.md +++ /dev/null @@ -1,153 +0,0 @@ -# Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion - -[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2310.11160) -[![demo](https://img.shields.io/badge/SVC-Demo-red)](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html) - -
-
- -
-
- -This is the official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). Specially, - -- The muptile content features are from [Whipser](https://github.com/wenet-e2e/wenet) and [ContentVec](https://github.com/auspicious3000/contentvec). -- The acoustic model is based on Bidirectional Non-Causal Dilated CNN (called `DiffWaveNetSVC` in Amphion), which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219). -- The vocoder is [BigVGAN](https://github.com/NVIDIA/BigVGAN) architecture and we fine-tuned it in over 120 hours singing voice data. - -There are four stages in total: - -1. Data preparation -2. Features extraction -3. Training -4. Inference/conversion - -> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: -> ```bash -> cd Amphion -> ``` - -## 1. Data Preparation - -### Dataset Download - -By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md). - -### Configuration - -Specify the dataset paths in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. - -```json - "dataset": [ - "m4singer", - "opencpop", - "opensinger", - "svcc", - "vctk" - ], - "dataset_path": { - // TODO: Fill in your dataset path - "m4singer": "[M4Singer dataset path]", - "opencpop": "[Opencpop dataset path]", - "opensinger": "[OpenSinger dataset path]", - "svcc": "[SVCC dataset path]", - "vctk": "[VCTK dataset path]" - }, -``` - -## 2. Features Extraction - -### Content-based Pretrained Models Download - -By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md). - -### Configuration - -Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`: - -```json - // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc" - "log_dir": "ckpts/svc", - "preprocess": { - // TODO: Fill in the output data path. The default value is "Amphion/data" - "processed_dir": "data", - ... - }, -``` - -### Run - -Run the `run.sh` as the preproces stage (set `--stage 1`). - -```bash -sh egs/svc/MultipleContentsSVC/run.sh --stage 1 -``` - -> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. - -## 3. Training - -### Configuration - -We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. - -```json -"train": { - "batch_size": 32, - ... - "adamw": { - "lr": 2.0e-4 - }, - ... - } -``` - -### Run - -Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`. - -```bash -sh egs/svc/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName] -``` - -> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. - -## 4. Inference/Conversion - -### Pretrained Vocoder Download - -We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`). - -### Run - -For inference/conversion, you need to specify the following configurations when running `run.sh`: - -| Parameters | Description | Example | -| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `--infer_expt_dir` | The experimental directory which contains `checkpoint` | `Amphion/ckpts/svc/[YourExptName]` | -| `--infer_output_dir` | The output directory to save inferred audios. | `Amphion/ckpts/svc/[YourExptName]/result` | -| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir). | The `infer_source_file` could be `Amphion/data/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). | -| `--infer_target_speaker` | The target speaker you want to convert into. You can refer to `Amphion/ckpts/svc/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`. | -| `--infer_key_shift` | How many semitones you want to transpose. | `"autoshfit"` (by default), `3`, `-3`, etc. | - -For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run: - -```bash -sh egs/svc/MultipleContentsSVC/run.sh --stage 3 --gpu "0" \ - --infer_expt_dir Amphion/ckpts/svc/[YourExptName] \ - --infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \ - --infer_source_audio_dir [Your Audios Folder] \ - --infer_target_speaker "opencpop_female1" \ - --infer_key_shift "autoshift" -``` - -## Citations - -```bibtex -@article{zhang2023leveraging, - title={Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion}, - author={Zhang, Xueyao and Gu, Yicheng and Chen, Haopeng and Fang, Zihao and Zou, Lexiao and Xue, Liumeng and Wu, Zhizheng}, - journal={Machine Learning for Audio Worshop, NeurIPS 2023}, - year={2023} -} -``` diff --git a/egs/svc/MultipleContentsSVC/exp_config.json b/egs/svc/MultipleContentsSVC/exp_config.json deleted file mode 100644 index 7047855abd18c25760fcdd46ec63da5c4b7ad8ba..0000000000000000000000000000000000000000 --- a/egs/svc/MultipleContentsSVC/exp_config.json +++ /dev/null @@ -1,126 +0,0 @@ -{ - "base_config": "config/diffusion.json", - "model_type": "DiffWaveNetSVC", - "dataset": [ - "m4singer", - "opencpop", - "opensinger", - "svcc", - "vctk" - ], - "dataset_path": { - // TODO: Fill in your dataset path - "m4singer": "[M4Singer dataset path]", - "opencpop": "[Opencpop dataset path]", - "opensinger": "[OpenSinger dataset path]", - "svcc": "[SVCC dataset path]", - "vctk": "[VCTK dataset path]" - }, - // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc" - "log_dir": "ckpts/svc", - "preprocess": { - // TODO: Fill in the output data path. The default value is "Amphion/data" - "processed_dir": "data", - // Config for features extraction - "extract_mel": true, - "extract_pitch": true, - "extract_energy": true, - "extract_whisper_feature": true, - "extract_contentvec_feature": true, - "extract_wenet_feature": false, - "whisper_batch_size": 30, // decrease it if your GPU is out of memory - "contentvec_batch_size": 1, - // Fill in the content-based pretrained model's path - "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt", - "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt", - "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml", - "whisper_model": "medium", - "whisper_model_path": "pretrained/whisper/medium.pt", - // Config for features usage - "use_mel": true, - "use_min_max_norm_mel": true, - "use_frame_pitch": true, - "use_frame_energy": true, - "use_spkid": true, - "use_whisper": true, - "use_contentvec": true, - "use_wenet": false, - "n_mel": 100, - "sample_rate": 24000 - }, - "model": { - "condition_encoder": { - // Config for features usage - "use_whisper": true, - "use_contentvec": true, - "use_wenet": false, - "whisper_dim": 1024, - "contentvec_dim": 256, - "wenet_dim": 512, - "use_singer_encoder": false, - "pitch_min": 50, - "pitch_max": 1100 - }, - "diffusion": { - "scheduler": "ddpm", - "scheduler_settings": { - "num_train_timesteps": 1000, - "beta_start": 1.0e-4, - "beta_end": 0.02, - "beta_schedule": "linear" - }, - // Diffusion steps encoder - "step_encoder": { - "dim_raw_embedding": 128, - "dim_hidden_layer": 512, - "activation": "SiLU", - "num_layer": 2, - "max_period": 10000 - }, - // Diffusion decoder - "model_type": "bidilconv", - // bidilconv, unet2d, TODO: unet1d - "bidilconv": { - "base_channel": 512, - "n_res_block": 40, - "conv_kernel_size": 3, - "dilation_cycle_length": 4, - // specially, 1 means no dilation - "conditioner_size": 384 - } - } - }, - "train": { - "batch_size": 32, - "gradient_accumulation_step": 1, - "max_epoch": -1, // -1 means no limit - "save_checkpoint_stride": [ - 3, - 50 - ], - "keep_last": [ - 3, - 2 - ], - "run_eval": [ - true, - true - ], - "adamw": { - "lr": 2.0e-4 - }, - "reducelronplateau": { - "factor": 0.8, - "patience": 30, - "min_lr": 1.0e-4 - }, - "dataloader": { - "num_worker": 8, - "pin_memory": true - }, - "sampler": { - "holistic_shuffle": false, - "drop_last": true - } - } -} \ No newline at end of file diff --git a/egs/svc/MultipleContentsSVC/run.sh b/egs/svc/MultipleContentsSVC/run.sh deleted file mode 100644 index f8daac3da463c177e36cdf041342566cc4243257..0000000000000000000000000000000000000000 --- a/egs/svc/MultipleContentsSVC/run.sh +++ /dev/null @@ -1 +0,0 @@ -../_template/run.sh \ No newline at end of file diff --git a/egs/svc/README.md b/egs/svc/README.md deleted file mode 100644 index 5961eaab3782ff96ddbb65a246527ab768498fa5..0000000000000000000000000000000000000000 --- a/egs/svc/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Amphion Singing Voice Conversion (SVC) Recipe - -## Quick Start - -We provide a **[beginner recipe](MultipleContentsSVC)** to demonstrate how to train a cutting edge SVC model. Specifically, it is also an official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). Some demos can be seen [here](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html). - -## Supported Model Architectures - -The main idea of SVC is to first disentangle the speaker-agnostic representations from the source audio, and then inject the desired speaker information to synthesize the target, which usually utilizes an acoustic decoder and a subsequent waveform synthesizer (vocoder): - -
-
- -
-
- -Until now, Amphion SVC has supported the following features and models: - -- **Speaker-agnostic Representations**: - - Content Features: Sourcing from [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), and [ContentVec](https://github.com/auspicious3000/contentvec). - - Prosody Features: F0 and energy. -- **Speaker Embeddings**: - - Speaker Look-Up Table. - - Reference Encoder (👨‍💻 developing): It can be used for zero-shot SVC. -- **Acoustic Decoders**: - - Diffusion-based models: - - **[DiffWaveNetSVC](MultipleContentsSVC)**: The encoder is based on Bidirectional Non-Causal Dilated CNN, which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219). - - **[DiffComoSVC](DiffComoSVC)** (👨‍💻 developing): The diffusion framework is based on [Consistency Model](https://proceedings.mlr.press/v202/song23a.html). It can significantly accelerate the inference process of the diffusion model. - - Transformer-based models: - - **[TransformerSVC](TransformerSVC)**: Encoder-only and Non-autoregressive Transformer Architecture. - - VAE- and Flow-based models: - - **[VitsSVC]()** (👨‍💻 developing): It is designed as a [VITS](https://arxiv.org/abs/2106.06103)-like model whose textual input is replaced by the content features, which is similar to [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc). -- **Waveform Synthesizers (Vocoders)**: - - The supported vocoders can be seen in [Amphion Vocoder Recipe](../vocoder/README.md). diff --git a/egs/svc/_template/run.sh b/egs/svc/_template/run.sh deleted file mode 100644 index 8dc870fdef8b1464000021def5627f91d1676bbe..0000000000000000000000000000000000000000 --- a/egs/svc/_template/run.sh +++ /dev/null @@ -1,150 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $exp_dir))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,resume_from_ckpt_path:,resume_type:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --resume_from_ckpt_path) shift; resume_from_ckpt_path=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - # [Only for Inference] The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir can be "$work_dir/source_audio" which includes several audio files (*.wav, *.mp3 or *.flac). - --infer_source_file) shift; infer_source_file=$1 ; shift ;; - --infer_source_audio_dir) shift; infer_source_audio_dir=$1 ; shift ;; - # [Only for Inference] Specify the target speaker you want to convert into. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1". - --infer_target_speaker) shift; infer_target_speaker=$1 ; shift ;; - # [Only for Inference] For advanced users, you can modify the trans_key parameters into an integer (which means the semitones you want to transpose). Its default value is "autoshift". - --infer_key_shift) shift; infer_key_shift=$1 ; shift ;; - # [Only for Inference] The vocoder dir. Its default value is Amphion/pretrained/bigvgan. See Amphion/pretrained/README.md to download the pretrained BigVGAN vocoders. - --infer_vocoder_dir) shift; infer_vocoder_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/svc/preprocess.py \ - --config $exp_config \ - --num_workers 4 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/svc/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/svc/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume_from_ckpt_path "$resume_from_ckpt_path" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$expt_dir/result" - fi - - if [ -z "$infer_source_file" ] && [ -z "$infer_source_audio_dir" ]; then - echo "[Error] Please specify the source file/dir. The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir should include several audio files (*.wav, *.mp3 or *.flac)." - exit 1 - fi - - if [ -z "$infer_source_file" ]; then - infer_source=$infer_source_audio_dir - fi - - if [ -z "$infer_source_audio_dir" ]; then - infer_source=$infer_source_file - fi - - if [ -z "$infer_target_speaker" ]; then - echo "[Error] Please specify the target speaker. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1"" - exit 1 - fi - - if [ -z "$infer_key_shift" ]; then - infer_key_shift="autoshift" - fi - - if [ -z "$infer_vocoder_dir" ]; then - infer_vocoder_dir="$work_dir"/pretrained/bigvgan - echo "[Warning] You don't specify the infer_vocoder_dir. It is set $infer_vocoder_dir by default. Make sure that you have followed Amphoion/pretrained/README.md to download the pretrained BigVGAN vocoder checkpoint." - fi - - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/svc/inference.py \ - --config $exp_config \ - --acoustics_dir $infer_expt_dir \ - --vocoder_dir $infer_vocoder_dir \ - --target_singer $infer_target_speaker \ - --trans_key $infer_key_shift \ - --source $infer_source \ - --output_dir $infer_output_dir \ - --log_level debug -fi \ No newline at end of file diff --git a/egs/vocoder/README.md b/egs/vocoder/README.md deleted file mode 100644 index 213253570b19b5c1d3746ee17911c7eb464ecc73..0000000000000000000000000000000000000000 --- a/egs/vocoder/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# Amphion Vocoder Recipe - -## Quick Start - -We provide a [**beginner recipe**](gan/tfr_enhanced_hifigan/README.md) to demonstrate how to train a high quality HiFi-GAN speech vocoder. Specially, it is also an official implementation of our paper "[Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://arxiv.org/abs/2311.14957)". Some demos can be seen [here](https://vocodexelysium.github.io/MS-SB-CQTD/). - -## Supported Models - -Neural vocoder generates audible waveforms from acoustic representations, which is one of the key parts for current audio generation systems. Until now, Amphion has supported various widely-used vocoders according to different vocoder types, including: - -- **GAN-based vocoders**, which we have provided [**a unified recipe**](gan/README.md) : - - [MelGAN](https://arxiv.org/abs/1910.06711) - - [HiFi-GAN](https://arxiv.org/abs/2010.05646) - - [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts) - - [BigVGAN](https://arxiv.org/abs/2206.04658) - - [APNet](https://arxiv.org/abs/2305.07952) -- **Flow-based vocoders** (👨‍💻 developing): - - [WaveGlow](https://arxiv.org/abs/1811.00002) -- **Diffusion-based vocoders** (👨‍💻 developing): - - [Diffwave](https://arxiv.org/abs/2009.09761) -- **Auto-regressive based vocoders** (👨‍💻 developing): - - [WaveNet](https://arxiv.org/abs/1609.03499) - - [WaveRNN](https://arxiv.org/abs/1802.08435v1) \ No newline at end of file diff --git a/egs/vocoder/diffusion/README.md b/egs/vocoder/diffusion/README.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/egs/vocoder/diffusion/exp_config_base.json b/egs/vocoder/diffusion/exp_config_base.json deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/egs/vocoder/gan/README.md b/egs/vocoder/gan/README.md deleted file mode 100644 index f8b0b352729c1f5a7935a588563d93ae30d1ef52..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/README.md +++ /dev/null @@ -1,224 +0,0 @@ -# Amphion GAN-based Vocoder Recipe - -## Supported Model Architectures - -GAN-based Vocoder consists of a generator and multiple discriminators, as illustrated below: - -
-
- -
-
- -Until now, Amphion GAN-based Vocoder has supported the following generators and discriminators. - -- **Generators** - - [MelGAN](https://arxiv.org/abs/1910.06711) - - [HiFi-GAN](https://arxiv.org/abs/2010.05646) - - [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts) - - [BigVGAN](https://arxiv.org/abs/2206.04658) - - [APNet](https://arxiv.org/abs/2305.07952) -- **Discriminators** - - [Multi-Scale Discriminator](https://arxiv.org/abs/2010.05646) - - [Multi-Period Discriminator](https://arxiv.org/abs/2010.05646) - - [Multi-Resolution Discriminator](https://arxiv.org/abs/2011.09631) - - [Multi-Scale Short-Time Fourier Transform Discriminator](https://arxiv.org/abs/2210.13438) - - [**Multi-Scale Constant-Q Transfrom Discriminator (ours)**](https://arxiv.org/abs/2311.14957) - -You can use any vocoder architecture with any dataset you want. There are four steps in total: - -1. Data preparation -2. Feature extraction -3. Training -4. Inference - -> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: -> ```bash -> cd Amphion -> ``` - -## 1. Data Preparation - -You can train the vocoder with any datasets. Amphion's supported open-source datasets are detailed [here](../../../datasets/README.md). - -### Configuration - -Specify the dataset path in `exp_config_base.json`. Note that you can change the `dataset` list to use your preferred datasets. - -```json -"dataset": [ - "csd", - "kising", - "m4singer", - "nus48e", - "opencpop", - "opensinger", - "opera", - "pjs", - "popbutfy", - "popcs", - "ljspeech", - "vctk", - "libritts", -], -"dataset_path": { - // TODO: Fill in your dataset path - "csd": "[dataset path]", - "kising": "[dataset path]", - "m4singer": "[dataset path]", - "nus48e": "[dataset path]", - "opencpop": "[dataset path]", - "opensinger": "[dataset path]", - "opera": "[dataset path]", - "pjs": "[dataset path]", - "popbutfy": "[dataset path]", - "popcs": "[dataset path]", - "ljspeech": "[dataset path]", - "vctk": "[dataset path]", - "libritts": "[dataset path]", -}, -``` - -### 2. Feature Extraction - -The needed features are speficied in the individual vocoder direction so it doesn't require any modification. - -### Configuration - -Specify the dataset path and the output path for saving the processed data and the training model in `exp_config_base.json`: - -```json - // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder" - "log_dir": "ckpts/vocoder", - "preprocess": { - // TODO: Fill in the output data path. The default value is "Amphion/data" - "processed_dir": "data", - ... - }, -``` - -### Run - -Run the `run.sh` as the preproces stage (set `--stage 1`). - -```bash -sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 1 -``` - -> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. - -## 3. Training - -### Configuration - -We provide the default hyparameters in the `exp_config_base.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. - -```json -"train": { - "batch_size": 16, - "max_epoch": 1000000, - "save_checkpoint_stride": [20], - "adamw": { - "lr": 2.0e-4, - "adam_b1": 0.8, - "adam_b2": 0.99 - }, - "exponential_lr": { - "lr_decay": 0.999 - }, -} -``` - -You can also choose any amount of prefered discriminators for training in the `exp_config_base.json`. - -```json -"discriminators": [ - "msd", - "mpd", - "msstftd", - "mssbcqtd", -], -``` - -### Run - -Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/vocoder/[YourExptName]`. - -```bash -sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 2 --name [YourExptName] -``` - -> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. - - -## 4. Inference - -### Run - -Run the `run.sh` as the training stage (set `--stage 3`), we provide three different inference modes, including `infer_from_dataset`, `infer_from_feature`, `and infer_from_audio`. - -```bash -sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ - --infer_mode [Your chosen inference mode] \ - --infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \ - --infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \ - --infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -#### a. Inference from Dataset - -Run the `run.sh` with specified datasets, here is an example. - -```bash -sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ - --infer_mode infer_from_dataset \ - --infer_datasets "libritts vctk ljspeech" \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -#### b. Inference from Features - -If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure: - -```plaintext - ┣ {infer_feature_dir} - ┃ ┣ mels - ┃ ┃ ┣ sample1.npy - ┃ ┃ ┣ sample2.npy - ┃ ┣ f0s (required if you use NSF-HiFiGAN) - ┃ ┃ ┣ sample1.npy - ┃ ┃ ┣ sample2.npy -``` - -Then run the `run.sh` with specificed folder direction, here is an example. - -```bash -sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ - --infer_mode infer_from_feature \ - --infer_feature_dir [Your path to your predicted acoustic features] \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -#### c. Inference from Audios - -If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure: - -```plaintext - ┣ audios - ┃ ┣ sample1.wav - ┃ ┣ sample2.wav -``` - -Then run the `run.sh` with specificed folder direction, here is an example. - -```bash -sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \ - --infer_mode infer_from_audio \ - --infer_audio_dir [Your path to your audio files] \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` diff --git a/egs/vocoder/gan/_template/run.sh b/egs/vocoder/gan/_template/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/_template/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/apnet/exp_config.json b/egs/vocoder/gan/apnet/exp_config.json deleted file mode 100644 index 520d3da3ea36201b8fa71e0fb1fd0d1ef9bbfdc0..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/apnet/exp_config.json +++ /dev/null @@ -1,45 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_audio": true, - "extract_amplitude_phase": true, - - // Features used for model training - "use_mel": true, - "use_audio": true, - "use_amplitude_phase": true - }, - "model": { - "generator": "apnet", - "apnet": { - "ASP_channel": 512, - "ASP_resblock_kernel_sizes": [3,7,11], - "ASP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], - "ASP_input_conv_kernel_size": 7, - "ASP_output_conv_kernel_size": 7, - - "PSP_channel": 512, - "PSP_resblock_kernel_sizes": [3,7,11], - "PSP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], - "PSP_input_conv_kernel_size": 7, - "PSP_output_R_conv_kernel_size": 7, - "PSP_output_I_conv_kernel_size": 7, - } - }, - "train": { - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - "phase", - "amplitude", - "consistency" - ] - }, - "inference": { - "batch_size": 1, - } -} diff --git a/egs/vocoder/gan/apnet/run.sh b/egs/vocoder/gan/apnet/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/apnet/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/bigvgan/exp_config.json b/egs/vocoder/gan/bigvgan/exp_config.json deleted file mode 100644 index cbed8a4bcd082f8a4cfa368a372f35c7e1a94973..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/bigvgan/exp_config.json +++ /dev/null @@ -1,66 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_audio": true, - - // Features used for model training - "use_mel": true, - "use_audio": true - }, - "model": { - "generator": "bigvgan", - "bigvgan": { - "resblock": "1", - "activation": "snakebeta", - "snake_logscale": true, - "upsample_rates": [ - 8, - 8, - 2, - 2, - ], - "upsample_kernel_sizes": [ - 16, - 16, - 4, - 4 - ], - "upsample_initial_channel": 512, - "resblock_kernel_sizes": [ - 3, - 7, - 11 - ], - "resblock_dilation_sizes": [ - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ] - ] - } - }, - "train": { - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - ] - }, - "inference": { - "batch_size": 1, - } -} diff --git a/egs/vocoder/gan/bigvgan/run.sh b/egs/vocoder/gan/bigvgan/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/bigvgan/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/bigvgan_large/exp_config.json b/egs/vocoder/gan/bigvgan_large/exp_config.json deleted file mode 100644 index a15dc3573839cd05085ac4540b651b98f94a1bf4..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/bigvgan_large/exp_config.json +++ /dev/null @@ -1,70 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_audio": true, - - // Features used for model training - "use_mel": true, - "use_audio": true - }, - "model": { - "generator": "bigvgan", - "bigvgan": { - "resblock": "1", - "activation": "snakebeta", - "snake_logscale": true, - "upsample_rates": [ - 4, - 4, - 2, - 2, - 2, - 2 - ], - "upsample_kernel_sizes": [ - 8, - 8, - 4, - 4, - 4, - 4 - ], - "upsample_initial_channel": 1536, - "resblock_kernel_sizes": [ - 3, - 7, - 11 - ], - "resblock_dilation_sizes": [ - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ] - ] - }, - }, - "train": { - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - ] - }, - "inference": { - "batch_size": 1, - } -} diff --git a/egs/vocoder/gan/bigvgan_large/run.sh b/egs/vocoder/gan/bigvgan_large/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/bigvgan_large/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/exp_config_base.json b/egs/vocoder/gan/exp_config_base.json deleted file mode 100644 index 84dbf7bcc0f5044c1e1f1d67b59cf26a338dff3a..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/exp_config_base.json +++ /dev/null @@ -1,111 +0,0 @@ -{ - "base_config": "config/vocoder.json", - "model_type": "GANVocoder", - // TODO: Choose your needed datasets - "dataset": [ - "csd", - "kising", - "m4singer", - "nus48e", - "opencpop", - "opensinger", - "opera", - "pjs", - "popbutfy", - "popcs", - "ljspeech", - "vctk", - "libritts", - ], - "dataset_path": { - // TODO: Fill in your dataset path - "csd": "[dataset path]", - "kising": "[dataset path]", - "m4singer": "[dataset path]", - "nus48e": "[dataset path]", - "opencpop": "[dataset path]", - "opensinger": "[dataset path]", - "opera": "[dataset path]", - "pjs": "[dataset path]", - "popbutfy": "[dataset path]", - "popcs": "[dataset path]", - "ljspeech": "[dataset path]", - "vctk": "[dataset path]", - "libritts": "[dataset path]", - }, - // TODO: Fill in the output log path - "log_dir": "ckpts/vocoder", - "preprocess": { - // Acoustic features - "extract_mel": true, - "extract_audio": true, - "extract_pitch": false, - "extract_uv": false, - "pitch_extractor": "parselmouth", - - // Features used for model training - "use_mel": true, - "use_frame_pitch": false, - "use_uv": false, - "use_audio": true, - - // TODO: Fill in the output data path - "processed_dir": "data/", - "n_mel": 100, - "sample_rate": 24000 - }, - "model": { - // TODO: Choose your needed discriminators - "discriminators": [ - "msd", - "mpd", - "msstftd", - "mssbcqtd", - ], - "mpd": { - "mpd_reshapes": [ - 2, - 3, - 5, - 7, - 11 - ], - "use_spectral_norm": false, - "discriminator_channel_mult_factor": 1 - }, - "mrd": { - "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]], - "use_spectral_norm": false, - "discriminator_channel_mult_factor": 1, - "mrd_override": false - }, - "msstftd": { - "filters": 32 - }, - "mssbcqtd": { - hop_lengths: [512, 256, 256], - filters: 32, - max_filters: 1024, - filters_scale: 1, - dilations: [1, 2, 4], - in_channels: 1, - out_channels: 1, - n_octaves: [9, 9, 9], - bins_per_octaves: [24, 36, 48] - }, - }, - "train": { - // TODO: Choose a suitable batch size, training epoch, and save stride - "batch_size": 32, - "max_epoch": 1000000, - "save_checkpoint_stride": [20], - "adamw": { - "lr": 2.0e-4, - "adam_b1": 0.8, - "adam_b2": 0.99 - }, - "exponential_lr": { - "lr_decay": 0.999 - }, - } -} \ No newline at end of file diff --git a/egs/vocoder/gan/hifigan/exp_config.json b/egs/vocoder/gan/hifigan/exp_config.json deleted file mode 100644 index b06712eac472a8452d7cfbfcf8bdf9bf3b232514..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/hifigan/exp_config.json +++ /dev/null @@ -1,59 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_audio": true, - - // Features used for model training - "use_mel": true, - "use_audio": true - }, - "model": { - "generator": "hifigan", - "hifigan": { - "resblock": "2", - "upsample_rates": [ - 8, - 8, - 4 - ], - "upsample_kernel_sizes": [ - 16, - 16, - 8 - ], - "upsample_initial_channel": 256, - "resblock_kernel_sizes": [ - 3, - 5, - 7 - ], - "resblock_dilation_sizes": [ - [ - 1, - 2 - ], - [ - 2, - 6 - ], - [ - 3, - 12 - ] - ] - } - }, - "train": { - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - ] - }, - "inference": { - "batch_size": 1, - } -} diff --git a/egs/vocoder/gan/hifigan/run.sh b/egs/vocoder/gan/hifigan/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/hifigan/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/melgan/exp_config.json b/egs/vocoder/gan/melgan/exp_config.json deleted file mode 100644 index 9470600bc0d15afd07107f816995e8c67ad52d9a..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/melgan/exp_config.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_audio": true, - - // Features used for model training - "use_mel": true, - "use_audio": true - }, - "model": { - "generator": "melgan", - "melgan": { - "ratios": [8, 8, 2, 2], - "ngf": 32, - "n_residual_layers": 3, - "num_D": 3, - "ndf": 16, - "n_layers": 4, - "downsampling_factor": 4 - }, - }, - "train": { - "criterions": [ - "feature", - "discriminator", - "generator", - ] - }, - "inference": { - "batch_size": 1, - } -} diff --git a/egs/vocoder/gan/melgan/run.sh b/egs/vocoder/gan/melgan/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/melgan/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/nsfhifigan/exp_config.json b/egs/vocoder/gan/nsfhifigan/exp_config.json deleted file mode 100644 index ead9dc8909b6a5283dd12b721cfb4519cc3953ca..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/nsfhifigan/exp_config.json +++ /dev/null @@ -1,83 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "preprocess": { - // acoustic features - "extract_mel": true, - "extract_audio": true, - "extract_pitch": true, - - // Features used for model training - "use_mel": true, - "use_audio": true, - "use_frame_pitch": true - }, - "model": { - "generator": "nsfhifigan", - "nsfhifigan": { - "resblock": "1", - "harmonic_num": 8, - "upsample_rates": [ - 8, - 4, - 2, - 2, - 2 - ], - "upsample_kernel_sizes": [ - 16, - 8, - 4, - 4, - 4 - ], - "upsample_initial_channel": 768, - "resblock_kernel_sizes": [ - 3, - 7, - 11 - ], - "resblock_dilation_sizes": [ - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ] - ] - }, - "mpd": { - "mpd_reshapes": [ - 2, - 3, - 5, - 7, - 11, - 17, - 23, - 37 - ], - "use_spectral_norm": false, - "discriminator_channel_multi": 1 - } - }, - "train": { - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - ] - }, - "inference": { - "batch_size": 1, - } -} diff --git a/egs/vocoder/gan/nsfhifigan/run.sh b/egs/vocoder/gan/nsfhifigan/run.sh deleted file mode 100644 index f50e7fab6f761ba69a5c45131479813b02f4d050..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/nsfhifigan/run.sh +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/README.md b/egs/vocoder/gan/tfr_enhanced_hifigan/README.md deleted file mode 100644 index 71350bab8aa19eaa41369e37faa9c5767241c8ac..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/tfr_enhanced_hifigan/README.md +++ /dev/null @@ -1,185 +0,0 @@ -# Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fedility Vocoder - -[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2311.14957) -[![demo](https://img.shields.io/badge/Vocoder-Demo-red)](https://vocodexelysium.github.io/MS-SB-CQTD/) - -
-
- -
-
- -This is the official implementation of the paper "[Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://arxiv.org/abs/2311.14957)". In this recipe, we will illustrate how to train a high quality HiFi-GAN on LibriTTS, VCTK and LJSpeech via utilizing multiple Time-Frequency-Representation-based Discriminators. - -There are four stages in total: - -1. Data preparation -2. Feature extraction -3. Training -4. Inference - -> **NOTE:** You need to run every command of this recipe in the `Amphion` root path: -> ```bash -> cd Amphion -> ``` - -## 1. Data Preparation - -### Dataset Download - -By default, we utilize the three datasets for training: LibriTTS, VCTK and LJSpeech. How to download them is detailed in [here](../../../datasets/README.md). - -### Configuration - -Specify the dataset path in `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets. - -```json -"dataset": [ - "ljspeech", - "vctk", - "libritts", -], -"dataset_path": { - // TODO: Fill in your dataset path - "ljspeech": "[LJSpeech dataset path]", - "vctk": "[VCTK dataset path]", - "libritts": "[LibriTTS dataset path]", -}, -``` - -## 2. Features Extraction - -For HiFiGAN, only the Mel-Spectrogram and the Output Audio are needed for training. - -### Configuration - -Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`: - -```json - // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder" - "log_dir": "ckpts/vocoder", - "preprocess": { - // TODO: Fill in the output data path. The default value is "Amphion/data" - "processed_dir": "data", - ... - }, -``` - -### Run - -Run the `run.sh` as the preproces stage (set `--stage 1`). - -```bash -sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 1 -``` - -> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`. - -## 3. Training - -### Configuration - -We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines. - -```json -"train": { - "batch_size": 32, - ... -} -``` - -### Run - -Run the `run.sh` as the training stage (set `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/vocoder/[YourExptName]`. - -```bash -sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 2 --name [YourExptName] -``` - -> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`. - -## 4. Inference - -### Pretrained Vocoder Download - -We trained a HiFiGAN checkpoint with around 685 hours Speech data. The final pretrained checkpoint is released [here](../../../../pretrained/hifigan/README.md). - -### Run - -Run the `run.sh` as the training stage (set `--stage 3`), we provide three different inference modes, including `infer_from_dataset`, `infer_from_feature`, `and infer_from audio`. - -```bash -sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ - --infer_mode [Your chosen inference mode] \ - --infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \ - --infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \ - --infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -#### a. Inference from Dataset - -Run the `run.sh` with specified datasets, here is an example. - -```bash -sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ - --infer_mode infer_from_dataset \ - --infer_datasets "libritts vctk ljspeech" \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -#### b. Inference from Features - -If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure: - -```plaintext - ┣ {infer_feature_dir} - ┃ ┣ mels - ┃ ┃ ┣ sample1.npy - ┃ ┃ ┣ sample2.npy -``` - -Then run the `run.sh` with specificed folder direction, here is an example. - -```bash -sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ - --infer_mode infer_from_feature \ - --infer_feature_dir [Your path to your predicted acoustic features] \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -#### c. Inference from Audios - -If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure: - -```plaintext - ┣ audios - ┃ ┣ sample1.wav - ┃ ┣ sample2.wav -``` - -Then run the `run.sh` with specificed folder direction, here is an example. - -```bash -sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \ - --infer_mode infer_from_audio \ - --infer_audio_dir [Your path to your audio files] \ - --infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \ - --infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \ -``` - -## Citations - -```bibtex -@misc{gu2023cqt, - title={Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder}, - author={Yicheng Gu and Xueyao Zhang and Liumeng Xue and Zhizheng Wu}, - year={2023}, - eprint={2311.14957}, - archivePrefix={arXiv}, - primaryClass={cs.SD} -} -``` \ No newline at end of file diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json b/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json deleted file mode 100644 index aada80f98f6e43ccea9c6096c8fba71a46f32b67..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json +++ /dev/null @@ -1,118 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "model_type": "GANVocoder", - "dataset": [ - "ljspeech", - "vctk", - "libritts", - ], - "dataset_path": { - // TODO: Fill in your dataset path - "ljspeech": "[dataset path]", - "vctk": "[dataset path]", - "libritts": "[dataset path]", - }, - // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder" - "log_dir": "ckpts/vocoder", - "preprocess": { - // TODO: Fill in the output data path. The default value is "Amphion/data" - "processed_dir": "data", - // acoustic features - "extract_mel": true, - "extract_audio": true, - "extract_pitch": false, - "extract_uv": false, - "extract_amplitude_phase": false, - "pitch_extractor": "parselmouth", - // Features used for model training - "use_mel": true, - "use_frame_pitch": false, - "use_uv": false, - "use_audio": true, - "n_mel": 100, - "sample_rate": 24000 - }, - "model": { - "generator": "hifigan", - "discriminators": [ - "msd", - "mpd", - "mssbcqtd", - "msstftd", - ], - "hifigan": { - "resblock": "1", - "upsample_rates": [ - 8, - 4, - 2, - 2, - 2 - ], - "upsample_kernel_sizes": [ - 16, - 8, - 4, - 4, - 4 - ], - "upsample_initial_channel": 768, - "resblock_kernel_sizes": [ - 3, - 5, - 7 - ], - "resblock_dilation_sizes": [ - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ], - [ - 1, - 3, - 5 - ] - ] - }, - "mpd": { - "mpd_reshapes": [ - 2, - 3, - 5, - 7, - 11, - 17, - 23, - 37 - ], - "use_spectral_norm": false, - "discriminator_channel_multi": 1 - } - }, - "train": { - "batch_size": 16, - "adamw": { - "lr": 2.0e-4, - "adam_b1": 0.8, - "adam_b2": 0.99 - }, - "exponential_lr": { - "lr_decay": 0.999 - }, - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - ] - }, - "inference": { - "batch_size": 1, - } -} \ No newline at end of file diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh b/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh deleted file mode 100644 index 857d81ff1098a66fcc0b47c876dd5692d1a1e777..0000000000000000000000000000000000000000 --- a/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -######## Build Experiment Environment ########### -exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir)))) - -export WORK_DIR=$work_dir -export PYTHONPATH=$work_dir -export PYTHONIOENCODING=UTF-8 - -######## Parse the Given Parameters from the Commond ########### -options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,checkpoint:,resume_type:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@") -eval set -- "$options" - -while true; do - case $1 in - # Experimental Configuration File - -c | --config) shift; exp_config=$1 ; shift ;; - # Experimental Name - -n | --name) shift; exp_name=$1 ; shift ;; - # Running Stage - -s | --stage) shift; running_stage=$1 ; shift ;; - # Visible GPU machines. The default value is "0". - --gpu) shift; gpu=$1 ; shift ;; - - # [Only for Training] Resume configuration - --resume) shift; resume=$1 ; shift ;; - # [Only for Training] The specific checkpoint path that you want to resume from. - --checkpoint) shift; cehckpoint=$1 ; shift ;; - # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights. - --resume_type) shift; resume_type=$1 ; shift ;; - - # [Only for Inference] The inference mode - --infer_mode) shift; infer_mode=$1 ; shift ;; - # [Only for Inference] The inferenced datasets - --infer_datasets) shift; infer_datasets=$1 ; shift ;; - # [Only for Inference] The feature dir for inference - --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;; - # [Only for Inference] The audio dir for inference - --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;; - # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]" - --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;; - # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result" - --infer_output_dir) shift; infer_output_dir=$1 ; shift ;; - - --) shift ; break ;; - *) echo "Invalid option: $1" exit 1 ;; - esac -done - - -### Value check ### -if [ -z "$running_stage" ]; then - echo "[Error] Please specify the running stage" - exit 1 -fi - -if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config.json -fi -echo "Exprimental Configuration File: $exp_config" - -if [ -z "$gpu" ]; then - gpu="0" -fi - -######## Features Extraction ########### -if [ $running_stage -eq 1 ]; then - CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \ - --config $exp_config \ - --num_workers 8 -fi - -######## Training ########### -if [ $running_stage -eq 2 ]; then - if [ -z "$exp_name" ]; then - echo "[Error] Please specify the experiments name" - exit 1 - fi - echo "Exprimental Name: $exp_name" - - if [ "$resume" = true ]; then - echo "Automatically resume from the experimental dir..." - CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --resume - else - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/vocoder/train.py \ - --config "$exp_config" \ - --exp_name "$exp_name" \ - --log_level info \ - --checkpoint "$checkpoint" \ - --resume_type "$resume_type" - fi -fi - -######## Inference/Conversion ########### -if [ $running_stage -eq 3 ]; then - if [ -z "$infer_expt_dir" ]; then - echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]" - exit 1 - fi - - if [ -z "$infer_output_dir" ]; then - infer_output_dir="$infer_expt_dir/result" - fi - - echo $infer_datasets - - if [ $infer_mode = "infer_from_dataset" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --infer_datasets $infer_datasets \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_feature" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --feature_folder $infer_feature_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - - if [ $infer_mode = "infer_from_audio" ]; then - CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \ - --config $exp_config \ - --infer_mode $infer_mode \ - --audio_folder $infer_audio_dir \ - --vocoder_dir $infer_expt_dir \ - --output_dir $infer_output_dir \ - --log_level debug - fi - -fi \ No newline at end of file diff --git a/examples/chinese_female_recordings.wav b/examples/chinese_female_recordings.wav deleted file mode 100644 index 637c8e46cd5909460ac15fd613bf9686b9a8babe..0000000000000000000000000000000000000000 --- a/examples/chinese_female_recordings.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f710270fe3857211c55aaa1f813e310e68855ff9eabaf5b249537a2d4277cc30 -size 448928 diff --git a/examples/chinese_male_seperated.wav b/examples/chinese_male_seperated.wav deleted file mode 100644 index 16c6fe286cf49c15dbb788509a67934e68823db8..0000000000000000000000000000000000000000 --- a/examples/chinese_male_seperated.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:009077a677b23bff3154078930e6c624d218eb0acbe78990bec88f6bf5a6e5de -size 480044 diff --git a/examples/english_female_seperated.wav b/examples/english_female_seperated.wav deleted file mode 100644 index cbc84f7ba9ef78c69ddcb8afd60114f3940a86be..0000000000000000000000000000000000000000 --- a/examples/english_female_seperated.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:87e75863ffb4e597467a825d019217e73d64dce1e9635de60a32559ffcb97cf4 -size 1509584 diff --git a/examples/english_male_recordings.wav b/examples/english_male_recordings.wav deleted file mode 100644 index 260e40028b5bfcd50f42b4eb8689529212e50df4..0000000000000000000000000000000000000000 --- a/examples/english_male_recordings.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e14ebf1c554ebb25e5169b4bcda36a685538e94c531f303339bad91ff93a2288 -size 251948 diff --git a/examples/output/.DS_Store b/examples/output/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/examples/output/.DS_Store and /dev/null differ diff --git a/examples/output/chinese_female_recordings_vocalist_l1_JohnMayer.wav b/examples/output/chinese_female_recordings_vocalist_l1_JohnMayer.wav deleted file mode 100644 index b9a8962d035943fc74828b5f2fb2d92575e6cb75..0000000000000000000000000000000000000000 --- a/examples/output/chinese_female_recordings_vocalist_l1_JohnMayer.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bf6d6ef89ba2234fbc64c0ee48f81528cf49717a23a919aa8d0767ada2437113 -size 244268 diff --git a/examples/output/chinese_male_seperated_vocalist_l1_TaylorSwift.wav b/examples/output/chinese_male_seperated_vocalist_l1_TaylorSwift.wav deleted file mode 100644 index 6b18bb3c45b520feecc2193657bb5e76f596255b..0000000000000000000000000000000000000000 --- a/examples/output/chinese_male_seperated_vocalist_l1_TaylorSwift.wav +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0e682abb072246f412133bfa313c6edf863f1d6a6db63022749f74c2c7ef01c7 -size 479788 diff --git "a/examples/output/english_female_seperated_vocalist_l1_\346\261\252\345\263\260.wav" "b/examples/output/english_female_seperated_vocalist_l1_\346\261\252\345\263\260.wav" deleted file mode 100644 index b308299cdb1c605c1e31255729b106148fa757da..0000000000000000000000000000000000000000 --- "a/examples/output/english_female_seperated_vocalist_l1_\346\261\252\345\263\260.wav" +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a03755cfc9aef4d26bda6370d9335625482f22f2c1f3c918dbbec3246213cee2 -size 410668 diff --git "a/examples/output/english_male_recordings_vocalist_l1_\347\237\263\345\200\232\346\264\201.wav" "b/examples/output/english_male_recordings_vocalist_l1_\347\237\263\345\200\232\346\264\201.wav" deleted file mode 100644 index e15b6d445ce995c4c9d07eeadc504832afa06ea9..0000000000000000000000000000000000000000 --- "a/examples/output/english_male_recordings_vocalist_l1_\347\237\263\345\200\232\346\264\201.wav" +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e850a0e02f2741185c3d3b642a9c292a3a297cdf262e92333b63adf98af7d450 -size 251948 diff --git a/models/__init__.py b/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/base/__init__.py b/models/base/__init__.py deleted file mode 100644 index fe0221047a62e0b9b3ddd112c79a700c48834fd1..0000000000000000000000000000000000000000 --- a/models/base/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .new_trainer import BaseTrainer -from .new_inference import BaseInference diff --git a/models/base/base_dataset.py b/models/base/base_dataset.py deleted file mode 100644 index e7d9848bb08f29669a51f3dde200d31bafe1d8da..0000000000000000000000000000000000000000 --- a/models/base/base_dataset.py +++ /dev/null @@ -1,350 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import numpy as np -import torch.utils.data -from torch.nn.utils.rnn import pad_sequence -from utils.data_utils import * -from processors.acoustic_extractor import cal_normalized_mel -from text import text_to_sequence -from text.text_token_collation import phoneIDCollation - - -class BaseDataset(torch.utils.data.Dataset): - def __init__(self, cfg, dataset, is_valid=False): - """ - Args: - cfg: config - dataset: dataset name - is_valid: whether to use train or valid dataset - """ - - assert isinstance(dataset, str) - - # self.data_root = processed_data_dir - self.cfg = cfg - - processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) - meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file - self.metafile_path = os.path.join(processed_data_dir, meta_file) - self.metadata = self.get_metadata() - - - - ''' - load spk2id and utt2spk from json file - spk2id: {spk1: 0, spk2: 1, ...} - utt2spk: {dataset_uid: spk1, ...} - ''' - if cfg.preprocess.use_spkid: - spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id) - with open(spk2id_path, "r") as f: - self.spk2id = json.load(f) - - utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk) - self.utt2spk = dict() - with open(utt2spk_path, "r") as f: - for line in f.readlines(): - utt, spk = line.strip().split('\t') - self.utt2spk[utt] = spk - - - if cfg.preprocess.use_uv: - self.utt2uv_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - self.utt2uv_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.uv_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_frame_pitch: - self.utt2frame_pitch_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2frame_pitch_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.pitch_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_frame_energy: - self.utt2frame_energy_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2frame_energy_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.energy_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_mel: - self.utt2mel_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2mel_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.mel_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_linear: - self.utt2linear_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2linear_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.linear_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_audio: - self.utt2audio_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2audio_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.audio_dir, - uid + ".npy", - ) - elif cfg.preprocess.use_label: - self.utt2label_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2label_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.label_dir, - uid + ".npy", - ) - elif cfg.preprocess.use_one_hot: - self.utt2one_hot_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2one_hot_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.one_hot_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_text or cfg.preprocess.use_phone: - self.utt2seq = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - if cfg.preprocess.use_text: - text = utt_info["Text"] - sequence = text_to_sequence(text, cfg.preprocess.text_cleaners) - elif cfg.preprocess.use_phone: - # load phoneme squence from phone file - phone_path = os.path.join(processed_data_dir, - cfg.preprocess.phone_dir, - uid+'.phone' - ) - with open(phone_path, 'r') as fin: - phones = fin.readlines() - assert len(phones) == 1 - phones = phones[0].strip() - phones_seq = phones.split(' ') - - phon_id_collator = phoneIDCollation(cfg, dataset=dataset) - sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq) - - self.utt2seq[utt] = sequence - - - def get_metadata(self): - with open(self.metafile_path, "r", encoding="utf-8") as f: - metadata = json.load(f) - - return metadata - - def get_dataset_name(self): - return self.metadata[0]["Dataset"] - - def __getitem__(self, index): - utt_info = self.metadata[index] - - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - single_feature = dict() - - if self.cfg.preprocess.use_spkid: - single_feature["spk_id"] = np.array( - [self.spk2id[self.utt2spk[utt]]], dtype=np.int32 - ) - - if self.cfg.preprocess.use_mel: - mel = np.load(self.utt2mel_path[utt]) - assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] - if self.cfg.preprocess.use_min_max_norm_mel: - # do mel norm - mel = cal_normalized_mel(mel, utt_info["Dataset"], self.cfg.preprocess) - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = mel.shape[1] - single_feature["mel"] = mel.T # [T, n_mels] - - if self.cfg.preprocess.use_linear: - linear = np.load(self.utt2linear_path[utt]) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = linear.shape[1] - single_feature["linear"] = linear.T # [T, n_linear] - - if self.cfg.preprocess.use_frame_pitch: - frame_pitch_path = self.utt2frame_pitch_path[utt] - frame_pitch = np.load(frame_pitch_path) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_pitch) - aligned_frame_pitch = align_length( - frame_pitch, single_feature["target_len"] - ) - single_feature["frame_pitch"] = aligned_frame_pitch - - if self.cfg.preprocess.use_uv: - frame_uv_path = self.utt2uv_path[utt] - frame_uv = np.load(frame_uv_path) - aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) - aligned_frame_uv = [ - 0 if frame_uv else 1 for frame_uv in aligned_frame_uv - ] - aligned_frame_uv = np.array(aligned_frame_uv) - single_feature["frame_uv"] = aligned_frame_uv - - if self.cfg.preprocess.use_frame_energy: - frame_energy_path = self.utt2frame_energy_path[utt] - frame_energy = np.load(frame_energy_path) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_energy) - aligned_frame_energy = align_length( - frame_energy, single_feature["target_len"] - ) - single_feature["frame_energy"] = aligned_frame_energy - - if self.cfg.preprocess.use_audio: - audio = np.load(self.utt2audio_path[utt]) - single_feature["audio"] = audio - single_feature["audio_len"] = audio.shape[0] - - if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text: - single_feature["phone_seq"] = np.array(self.utt2seq[utt]) - single_feature["phone_len"] = len(self.utt2seq[utt]) - - return single_feature - - def __len__(self): - return len(self.metadata) - - -class BaseCollator(object): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - self.cfg = cfg - - def __call__(self, batch): - packed_batch_features = dict() - - # mel: [b, T, n_mels] - # frame_pitch, frame_energy: [1, T] - # target_len: [1] - # spk_id: [b, 1] - # mask: [b, T, 1] - - for key in batch[0].keys(): - if key == "target_len": - packed_batch_features["target_len"] = torch.LongTensor( - [b["target_len"] for b in batch] - ) - masks = [ - torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch - ] - packed_batch_features["mask"] = pad_sequence( - masks, batch_first=True, padding_value=0 - ) - elif key == "phone_len": - packed_batch_features["phone_len"] = torch.LongTensor( - [b["phone_len"] for b in batch] - ) - masks = [ - torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch - ] - packed_batch_features["phn_mask"] = pad_sequence( - masks, batch_first=True, padding_value=0 - ) - elif key == "audio_len": - packed_batch_features["audio_len"] = torch.LongTensor( - [b["audio_len"] for b in batch] - ) - masks = [ - torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch - ] - else: - values = [torch.from_numpy(b[key]) for b in batch] - packed_batch_features[key] = pad_sequence( - values, batch_first=True, padding_value=0 - ) - return packed_batch_features - - -class BaseTestDataset(torch.utils.data.Dataset): - def __init__(self, cfg, args): - raise NotImplementedError - - - def get_metadata(self): - raise NotImplementedError - - def __getitem__(self, index): - raise NotImplementedError - - def __len__(self): - return len(self.metadata) - - -class BaseTestCollator(object): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - raise NotImplementedError - - def __call__(self, batch): - raise NotImplementedError diff --git a/models/base/base_inference.py b/models/base/base_inference.py deleted file mode 100644 index 2713f19a0d61f06bca1f01de5ccd8a3b4d2cc02f..0000000000000000000000000000000000000000 --- a/models/base/base_inference.py +++ /dev/null @@ -1,220 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import os -import re -import time -from pathlib import Path - -import torch -from torch.utils.data import DataLoader -from tqdm import tqdm - -from models.vocoders.vocoder_inference import synthesis -from torch.utils.data import DataLoader -from utils.util import set_all_random_seed -from utils.util import load_config - - -def parse_vocoder(vocoder_dir): - r"""Parse vocoder config""" - vocoder_dir = os.path.abspath(vocoder_dir) - ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")] - ckpt_list.sort(key=lambda x: int(x.stem), reverse=True) - ckpt_path = str(ckpt_list[0]) - vocoder_cfg = load_config(os.path.join(vocoder_dir, "args.json"), lowercase=True) - vocoder_cfg.model.bigvgan = vocoder_cfg.vocoder - return vocoder_cfg, ckpt_path - - -class BaseInference(object): - def __init__(self, cfg, args): - self.cfg = cfg - self.args = args - self.model_type = cfg.model_type - self.avg_rtf = list() - set_all_random_seed(10086) - os.makedirs(args.output_dir, exist_ok=True) - - if torch.cuda.is_available(): - self.device = torch.device("cuda") - else: - self.device = torch.device("cpu") - torch.set_num_threads(10) # inference on 1 core cpu. - - # Load acoustic model - self.model = self.create_model().to(self.device) - state_dict = self.load_state_dict() - self.load_model(state_dict) - self.model.eval() - - # Load vocoder model if necessary - if self.args.checkpoint_dir_vocoder is not None: - self.get_vocoder_info() - - def create_model(self): - raise NotImplementedError - - def load_state_dict(self): - self.checkpoint_file = self.args.checkpoint_file - if self.checkpoint_file is None: - assert self.args.checkpoint_dir is not None - checkpoint_path = os.path.join(self.args.checkpoint_dir, "checkpoint") - checkpoint_filename = open(checkpoint_path).readlines()[-1].strip() - self.checkpoint_file = os.path.join( - self.args.checkpoint_dir, checkpoint_filename - ) - - self.checkpoint_dir = os.path.split(self.checkpoint_file)[0] - - print("Restore acoustic model from {}".format(self.checkpoint_file)) - raw_state_dict = torch.load(self.checkpoint_file, map_location=self.device) - self.am_restore_step = re.findall(r"step-(.+?)_loss", self.checkpoint_file)[0] - - return raw_state_dict - - def load_model(self, model): - raise NotImplementedError - - def get_vocoder_info(self): - self.checkpoint_dir_vocoder = self.args.checkpoint_dir_vocoder - self.vocoder_cfg = os.path.join( - os.path.dirname(self.checkpoint_dir_vocoder), "args.json" - ) - self.cfg.vocoder = load_config(self.vocoder_cfg, lowercase=True) - self.vocoder_tag = self.checkpoint_dir_vocoder.split("/")[-2].split(":")[-1] - self.vocoder_steps = self.checkpoint_dir_vocoder.split("/")[-1].split(".")[0] - - def build_test_utt_data(self): - raise NotImplementedError - - def build_testdata_loader(self, args, target_speaker=None): - datasets, collate = self.build_test_dataset() - self.test_dataset = datasets(self.cfg, args, target_speaker) - self.test_collate = collate(self.cfg) - self.test_batch_size = min( - self.cfg.train.batch_size, len(self.test_dataset.metadata) - ) - test_loader = DataLoader( - self.test_dataset, - collate_fn=self.test_collate, - num_workers=self.args.num_workers, - batch_size=self.test_batch_size, - shuffle=False, - ) - return test_loader - - def inference_each_batch(self, batch_data): - raise NotImplementedError - - def inference_for_batches(self, args, target_speaker=None): - ###### Construct test_batch ###### - loader = self.build_testdata_loader(args, target_speaker) - - n_batch = len(loader) - now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())) - print( - "Model eval time: {}, batch_size = {}, n_batch = {}".format( - now, self.test_batch_size, n_batch - ) - ) - self.model.eval() - - ###### Inference for each batch ###### - pred_res = [] - with torch.no_grad(): - for i, batch_data in enumerate(loader if n_batch == 1 else tqdm(loader)): - # Put the data to device - for k, v in batch_data.items(): - batch_data[k] = batch_data[k].to(self.device) - - y_pred, stats = self.inference_each_batch(batch_data) - - pred_res += y_pred - - return pred_res - - def inference(self, feature): - raise NotImplementedError - - def synthesis_by_vocoder(self, pred): - audios_pred = synthesis( - self.vocoder_cfg, - self.checkpoint_dir_vocoder, - len(pred), - pred, - ) - return audios_pred - - def __call__(self, utt): - feature = self.build_test_utt_data(utt) - start_time = time.time() - with torch.no_grad(): - outputs = self.inference(feature)[0] - time_used = time.time() - start_time - rtf = time_used / ( - outputs.shape[1] - * self.cfg.preprocess.hop_size - / self.cfg.preprocess.sample_rate - ) - print("Time used: {:.3f}, RTF: {:.4f}".format(time_used, rtf)) - self.avg_rtf.append(rtf) - audios = outputs.cpu().squeeze().numpy().reshape(-1, 1) - return audios - - -def base_parser(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--config", default="config.json", help="json files for configurations." - ) - parser.add_argument("--use_ddp_inference", default=False) - parser.add_argument("--n_workers", default=1, type=int) - parser.add_argument("--local_rank", default=-1, type=int) - parser.add_argument( - "--batch_size", default=1, type=int, help="Batch size for inference" - ) - parser.add_argument( - "--num_workers", - default=1, - type=int, - help="Worker number for inference dataloader", - ) - parser.add_argument( - "--checkpoint_dir", - type=str, - default=None, - help="Checkpoint dir including model file and configuration", - ) - parser.add_argument( - "--checkpoint_file", help="checkpoint file", type=str, default=None - ) - parser.add_argument( - "--test_list", help="test utterance list for testing", type=str, default=None - ) - parser.add_argument( - "--checkpoint_dir_vocoder", - help="Vocoder's checkpoint dir including model file and configuration", - type=str, - default=None, - ) - parser.add_argument( - "--output_dir", - type=str, - default=None, - help="Output dir for saving generated results", - ) - return parser - - -if __name__ == "__main__": - parser = base_parser() - args = parser.parse_args() - cfg = load_config(args.config) - - # Build inference - inference = BaseInference(cfg, args) - inference() diff --git a/models/base/base_sampler.py b/models/base/base_sampler.py deleted file mode 100644 index 149d1437eb1d3a00ca8c9895b150b39b2a3635fa..0000000000000000000000000000000000000000 --- a/models/base/base_sampler.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import random - -from torch.utils.data import ConcatDataset, Dataset -from torch.utils.data.sampler import ( - BatchSampler, - RandomSampler, - Sampler, - SequentialSampler, -) - - -class ScheduledSampler(Sampler): - """A sampler that samples data from a given concat-dataset. - - Args: - concat_dataset (ConcatDataset): a concatenated dataset consisting of all datasets - batch_size (int): batch size - holistic_shuffle (bool): whether to shuffle the whole dataset or not - logger (logging.Logger): logger to print warning message - - Usage: - For cfg.train.batch_size = 3, cfg.train.holistic_shuffle = False, cfg.train.drop_last = True: - >>> list(ScheduledSampler(ConcatDataset([0, 1, 2], [3, 4, 5], [6, 7, 8]]))) - [3, 4, 5, 0, 1, 2, 6, 7, 8] - """ - - def __init__( - self, - concat_dataset, - batch_size, - holistic_shuffle, - logger=None, - loader_type="train", - ): - if not isinstance(concat_dataset, ConcatDataset): - raise ValueError( - "concat_dataset must be an instance of ConcatDataset, but got {}".format( - type(concat_dataset) - ) - ) - if not isinstance(batch_size, int): - raise ValueError( - "batch_size must be an integer, but got {}".format(type(batch_size)) - ) - if not isinstance(holistic_shuffle, bool): - raise ValueError( - "holistic_shuffle must be a boolean, but got {}".format( - type(holistic_shuffle) - ) - ) - - self.concat_dataset = concat_dataset - self.batch_size = batch_size - self.holistic_shuffle = holistic_shuffle - - affected_dataset_name = [] - affected_dataset_len = [] - for dataset in concat_dataset.datasets: - dataset_len = len(dataset) - dataset_name = dataset.get_dataset_name() - if dataset_len < batch_size: - affected_dataset_name.append(dataset_name) - affected_dataset_len.append(dataset_len) - - self.type = loader_type - for dataset_name, dataset_len in zip( - affected_dataset_name, affected_dataset_len - ): - if not loader_type == "valid": - logger.warning( - "The {} dataset {} has a length of {}, which is smaller than the batch size {}. This may cause unexpected behavior.".format( - loader_type, dataset_name, dataset_len, batch_size - ) - ) - - def __len__(self): - # the number of batches with drop last - num_of_batches = sum( - [ - math.floor(len(dataset) / self.batch_size) - for dataset in self.concat_dataset.datasets - ] - ) - # if samples are not enough for one batch, we don't drop last - if self.type == "valid" and num_of_batches < 1: - return len(self.concat_dataset) - return num_of_batches * self.batch_size - - def __iter__(self): - iters = [] - for dataset in self.concat_dataset.datasets: - iters.append( - SequentialSampler(dataset).__iter__() - if not self.holistic_shuffle - else RandomSampler(dataset).__iter__() - ) - # e.g. [0, 200, 400] - init_indices = [0] + self.concat_dataset.cumulative_sizes[:-1] - output_batches = [] - for dataset_idx in range(len(self.concat_dataset.datasets)): - cur_batch = [] - for idx in iters[dataset_idx]: - cur_batch.append(idx + init_indices[dataset_idx]) - if len(cur_batch) == self.batch_size: - output_batches.append(cur_batch) - cur_batch = [] - # if loader_type is valid, we don't need to drop last - if self.type == "valid" and len(cur_batch) > 0: - output_batches.append(cur_batch) - - # force drop last in training - random.shuffle(output_batches) - output_indices = [item for sublist in output_batches for item in sublist] - return iter(output_indices) - - -def build_samplers(concat_dataset: Dataset, cfg, logger, loader_type): - sampler = ScheduledSampler( - concat_dataset, - cfg.train.batch_size, - cfg.train.sampler.holistic_shuffle, - logger, - loader_type, - ) - batch_sampler = BatchSampler( - sampler, - cfg.train.batch_size, - cfg.train.sampler.drop_last if not loader_type == "valid" else False, - ) - return sampler, batch_sampler diff --git a/models/base/base_trainer.py b/models/base/base_trainer.py deleted file mode 100644 index 8782216dc13ce5d9de05ae790faeb82cf7cfd501..0000000000000000000000000000000000000000 --- a/models/base/base_trainer.py +++ /dev/null @@ -1,348 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import collections -import json -import os -import sys -import time - -import torch -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel -from torch.utils.data import ConcatDataset, DataLoader -from torch.utils.tensorboard import SummaryWriter - -from models.base.base_sampler import BatchSampler -from utils.util import ( - Logger, - remove_older_ckpt, - save_config, - set_all_random_seed, - ValueWindow, -) - - -class BaseTrainer(object): - def __init__(self, args, cfg): - self.args = args - self.log_dir = args.log_dir - self.cfg = cfg - - self.checkpoint_dir = os.path.join(args.log_dir, "checkpoints") - os.makedirs(self.checkpoint_dir, exist_ok=True) - if not cfg.train.ddp or args.local_rank == 0: - self.sw = SummaryWriter(os.path.join(args.log_dir, "events")) - self.logger = self.build_logger() - self.time_window = ValueWindow(50) - - self.step = 0 - self.epoch = -1 - self.max_epochs = self.cfg.train.epochs - self.max_steps = self.cfg.train.max_steps - - # set random seed & init distributed training - set_all_random_seed(self.cfg.train.random_seed) - if cfg.train.ddp: - dist.init_process_group(backend="nccl") - - if cfg.model_type not in ["AutoencoderKL", "AudioLDM"]: - self.singers = self.build_singers_lut() - - # setup data_loader - self.data_loader = self.build_data_loader() - - # setup model & enable distributed training - self.model = self.build_model() - print(self.model) - - if isinstance(self.model, dict): - for key, value in self.model.items(): - value.cuda(self.args.local_rank) - if key == "PQMF": - continue - if cfg.train.ddp: - self.model[key] = DistributedDataParallel( - value, device_ids=[self.args.local_rank] - ) - else: - self.model.cuda(self.args.local_rank) - if cfg.train.ddp: - self.model = DistributedDataParallel( - self.model, device_ids=[self.args.local_rank] - ) - - # create criterion - self.criterion = self.build_criterion() - if isinstance(self.criterion, dict): - for key, value in self.criterion.items(): - self.criterion[key].cuda(args.local_rank) - else: - self.criterion.cuda(self.args.local_rank) - - # optimizer - self.optimizer = self.build_optimizer() - self.scheduler = self.build_scheduler() - - # save config file - self.config_save_path = os.path.join(self.checkpoint_dir, "args.json") - - def build_logger(self): - log_file = os.path.join(self.checkpoint_dir, "train.log") - logger = Logger(log_file, level=self.args.log_level).logger - - return logger - - def build_dataset(self): - raise NotImplementedError - - def build_data_loader(self): - Dataset, Collator = self.build_dataset() - # build dataset instance for each dataset and combine them by ConcatDataset - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=False) - datasets_list.append(subdataset) - train_dataset = ConcatDataset(datasets_list) - - train_collate = Collator(self.cfg) - # TODO: multi-GPU training - if self.cfg.train.ddp: - raise NotImplementedError("DDP is not supported yet.") - - # sampler will provide indices to batch_sampler, which will perform batching and yield batch indices - batch_sampler = BatchSampler( - cfg=self.cfg, concat_dataset=train_dataset, dataset_list=datasets_list - ) - - # use batch_sampler argument instead of (sampler, shuffle, drop_last, batch_size) - train_loader = DataLoader( - train_dataset, - collate_fn=train_collate, - num_workers=self.args.num_workers, - batch_sampler=batch_sampler, - pin_memory=False, - ) - if not self.cfg.train.ddp or self.args.local_rank == 0: - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=True) - datasets_list.append(subdataset) - valid_dataset = ConcatDataset(datasets_list) - valid_collate = Collator(self.cfg) - batch_sampler = BatchSampler( - cfg=self.cfg, concat_dataset=valid_dataset, dataset_list=datasets_list - ) - valid_loader = DataLoader( - valid_dataset, - collate_fn=valid_collate, - num_workers=1, - batch_sampler=batch_sampler, - ) - else: - raise NotImplementedError("DDP is not supported yet.") - # valid_loader = None - data_loader = {"train": train_loader, "valid": valid_loader} - return data_loader - - def build_singers_lut(self): - # combine singers - if not os.path.exists(os.path.join(self.log_dir, self.cfg.preprocess.spk2id)): - singers = collections.OrderedDict() - else: - with open( - os.path.join(self.log_dir, self.cfg.preprocess.spk2id), "r" - ) as singer_file: - singers = json.load(singer_file) - singer_count = len(singers) - for dataset in self.cfg.dataset: - singer_lut_path = os.path.join( - self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id - ) - with open(singer_lut_path, "r") as singer_lut_path: - singer_lut = json.load(singer_lut_path) - for singer in singer_lut.keys(): - if singer not in singers: - singers[singer] = singer_count - singer_count += 1 - with open( - os.path.join(self.log_dir, self.cfg.preprocess.spk2id), "w" - ) as singer_file: - json.dump(singers, singer_file, indent=4, ensure_ascii=False) - print( - "singers have been dumped to {}".format( - os.path.join(self.log_dir, self.cfg.preprocess.spk2id) - ) - ) - return singers - - def build_model(self): - raise NotImplementedError() - - def build_optimizer(self): - raise NotImplementedError - - def build_scheduler(self): - raise NotImplementedError() - - def build_criterion(self): - raise NotImplementedError - - def get_state_dict(self): - raise NotImplementedError - - def save_config_file(self): - save_config(self.config_save_path, self.cfg) - - # TODO, save without module. - def save_checkpoint(self, state_dict, saved_model_path): - torch.save(state_dict, saved_model_path) - - def load_checkpoint(self): - checkpoint_path = os.path.join(self.checkpoint_dir, "checkpoint") - assert os.path.exists(checkpoint_path) - checkpoint_filename = open(checkpoint_path).readlines()[-1].strip() - model_path = os.path.join(self.checkpoint_dir, checkpoint_filename) - assert os.path.exists(model_path) - if not self.cfg.train.ddp or self.args.local_rank == 0: - self.logger.info(f"Re(store) from {model_path}") - checkpoint = torch.load(model_path, map_location="cpu") - return checkpoint - - def load_model(self, checkpoint): - raise NotImplementedError - - def restore(self): - checkpoint = self.load_checkpoint() - self.load_model(checkpoint) - - def train_step(self, data): - raise NotImplementedError( - f"Need to implement function {sys._getframe().f_code.co_name} in " - f"your sub-class of {self.__class__.__name__}. " - ) - - @torch.no_grad() - def eval_step(self): - raise NotImplementedError( - f"Need to implement function {sys._getframe().f_code.co_name} in " - f"your sub-class of {self.__class__.__name__}. " - ) - - def write_summary(self, losses, stats): - raise NotImplementedError( - f"Need to implement function {sys._getframe().f_code.co_name} in " - f"your sub-class of {self.__class__.__name__}. " - ) - - def write_valid_summary(self, losses, stats): - raise NotImplementedError( - f"Need to implement function {sys._getframe().f_code.co_name} in " - f"your sub-class of {self.__class__.__name__}. " - ) - - def echo_log(self, losses, mode="Training"): - message = [ - "{} - Epoch {} Step {}: [{:.3f} s/step]".format( - mode, self.epoch + 1, self.step, self.time_window.average - ) - ] - - for key in sorted(losses.keys()): - if isinstance(losses[key], dict): - for k, v in losses[key].items(): - message.append( - str(k).split("/")[-1] + "=" + str(round(float(v), 5)) - ) - else: - message.append( - str(key).split("/")[-1] + "=" + str(round(float(losses[key]), 5)) - ) - self.logger.info(", ".join(message)) - - def eval_epoch(self): - self.logger.info("Validation...") - valid_losses = {} - for i, batch_data in enumerate(self.data_loader["valid"]): - for k, v in batch_data.items(): - if isinstance(v, torch.Tensor): - batch_data[k] = v.cuda() - valid_loss, valid_stats, total_valid_loss = self.eval_step(batch_data, i) - for key in valid_loss: - if key not in valid_losses: - valid_losses[key] = 0 - valid_losses[key] += valid_loss[key] - - # Add mel and audio to the Tensorboard - # Average loss - for key in valid_losses: - valid_losses[key] /= i + 1 - self.echo_log(valid_losses, "Valid") - return valid_losses, valid_stats - - def train_epoch(self): - for i, batch_data in enumerate(self.data_loader["train"]): - start_time = time.time() - # Put the data to cuda device - for k, v in batch_data.items(): - if isinstance(v, torch.Tensor): - batch_data[k] = v.cuda(self.args.local_rank) - - # Training step - train_losses, train_stats, total_loss = self.train_step(batch_data) - self.time_window.append(time.time() - start_time) - - if self.args.local_rank == 0 or not self.cfg.train.ddp: - if self.step % self.args.stdout_interval == 0: - self.echo_log(train_losses, "Training") - - if self.step % self.cfg.train.save_summary_steps == 0: - self.logger.info(f"Save summary as step {self.step}") - self.write_summary(train_losses, train_stats) - - if ( - self.step % self.cfg.train.save_checkpoints_steps == 0 - and self.step != 0 - ): - saved_model_name = "step-{:07d}_loss-{:.4f}.pt".format( - self.step, total_loss - ) - saved_model_path = os.path.join( - self.checkpoint_dir, saved_model_name - ) - saved_state_dict = self.get_state_dict() - self.save_checkpoint(saved_state_dict, saved_model_path) - self.save_config_file() - # keep max n models - remove_older_ckpt( - saved_model_name, - self.checkpoint_dir, - max_to_keep=self.cfg.train.keep_checkpoint_max, - ) - - if self.step != 0 and self.step % self.cfg.train.valid_interval == 0: - if isinstance(self.model, dict): - for key in self.model.keys(): - self.model[key].eval() - else: - self.model.eval() - # Evaluate one epoch and get average loss - valid_losses, valid_stats = self.eval_epoch() - if isinstance(self.model, dict): - for key in self.model.keys(): - self.model[key].train() - else: - self.model.train() - # Write validation losses to summary. - self.write_valid_summary(valid_losses, valid_stats) - self.step += 1 - - def train(self): - for epoch in range(max(0, self.epoch), self.max_epochs): - self.train_epoch() - self.epoch += 1 - if self.step > self.max_steps: - self.logger.info("Training finished!") - break diff --git a/models/base/new_dataset.py b/models/base/new_dataset.py deleted file mode 100644 index 2201bb4132ab86d1110092d7ab9e509296367a22..0000000000000000000000000000000000000000 --- a/models/base/new_dataset.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os -from abc import abstractmethod -from pathlib import Path - -import json5 -import torch -import yaml - - -# TODO: for training and validating -class BaseDataset(torch.utils.data.Dataset): - r"""Base dataset for training and validating.""" - - def __init__(self, args, cfg, is_valid=False): - pass - - -class BaseTestDataset(torch.utils.data.Dataset): - r"""Test dataset for inference.""" - - def __init__(self, args=None, cfg=None, infer_type="from_dataset"): - assert infer_type in ["from_dataset", "from_file"] - - self.args = args - self.cfg = cfg - self.infer_type = infer_type - - @abstractmethod - def __getitem__(self, index): - pass - - def __len__(self): - return len(self.metadata) - - def get_metadata(self): - path = Path(self.args.source) - if path.suffix == ".json" or path.suffix == ".jsonc": - metadata = json5.load(open(self.args.source, "r")) - elif path.suffix == ".yaml" or path.suffix == ".yml": - metadata = yaml.full_load(open(self.args.source, "r")) - else: - raise ValueError(f"Unsupported file type: {path.suffix}") - - return metadata diff --git a/models/base/new_inference.py b/models/base/new_inference.py deleted file mode 100644 index 4813fca4aba192fb8737dd74f37f6d430e1909a4..0000000000000000000000000000000000000000 --- a/models/base/new_inference.py +++ /dev/null @@ -1,249 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import random -import re -import time -from abc import abstractmethod -from pathlib import Path - -import accelerate -import json5 -import numpy as np -import torch -from accelerate.logging import get_logger -from torch.utils.data import DataLoader - -from models.vocoders.vocoder_inference import synthesis -from utils.io import save_audio -from utils.util import load_config -from utils.audio_slicer import is_silence - -EPS = 1.0e-12 - - -class BaseInference(object): - def __init__(self, args=None, cfg=None, infer_type="from_dataset"): - super().__init__() - - start = time.monotonic_ns() - self.args = args - self.cfg = cfg - - assert infer_type in ["from_dataset", "from_file"] - self.infer_type = infer_type - - # init with accelerate - self.accelerator = accelerate.Accelerator() - self.accelerator.wait_for_everyone() - - # Use accelerate logger for distributed inference - with self.accelerator.main_process_first(): - self.logger = get_logger("inference", log_level=args.log_level) - - # Log some info - self.logger.info("=" * 56) - self.logger.info("||\t\t" + "New inference process started." + "\t\t||") - self.logger.info("=" * 56) - self.logger.info("\n") - self.logger.debug(f"Using {args.log_level.upper()} logging level.") - - self.acoustics_dir = args.acoustics_dir - self.logger.debug(f"Acoustic dir: {args.acoustics_dir}") - self.vocoder_dir = args.vocoder_dir - self.logger.debug(f"Vocoder dir: {args.vocoder_dir}") - # should be in svc inferencer - # self.target_singer = args.target_singer - # self.logger.info(f"Target singers: {args.target_singer}") - # self.trans_key = args.trans_key - # self.logger.info(f"Trans key: {args.trans_key}") - - os.makedirs(args.output_dir, exist_ok=True) - - # set random seed - with self.accelerator.main_process_first(): - start = time.monotonic_ns() - self._set_random_seed(self.cfg.train.random_seed) - end = time.monotonic_ns() - self.logger.debug( - f"Setting random seed done in {(end - start) / 1e6:.2f}ms" - ) - self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") - - # setup data_loader - with self.accelerator.main_process_first(): - self.logger.info("Building dataset...") - start = time.monotonic_ns() - self.test_dataloader = self._build_dataloader() - end = time.monotonic_ns() - self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") - - # setup model - with self.accelerator.main_process_first(): - self.logger.info("Building model...") - start = time.monotonic_ns() - self.model = self._build_model() - end = time.monotonic_ns() - # self.logger.debug(self.model) - self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms") - - # init with accelerate - self.logger.info("Initializing accelerate...") - start = time.monotonic_ns() - self.accelerator = accelerate.Accelerator() - self.model = self.accelerator.prepare(self.model) - end = time.monotonic_ns() - self.accelerator.wait_for_everyone() - self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms") - - with self.accelerator.main_process_first(): - self.logger.info("Loading checkpoint...") - start = time.monotonic_ns() - # TODO: Also, suppose only use latest one yet - self.__load_model(os.path.join(args.acoustics_dir, "checkpoint")) - end = time.monotonic_ns() - self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms") - - self.model.eval() - self.accelerator.wait_for_everyone() - - ### Abstract methods ### - @abstractmethod - def _build_test_dataset(self): - pass - - @abstractmethod - def _build_model(self): - pass - - @abstractmethod - @torch.inference_mode() - def _inference_each_batch(self, batch_data): - pass - - ### Abstract methods end ### - - @torch.inference_mode() - def inference(self): - for i, batch in enumerate(self.test_dataloader): - y_pred = self._inference_each_batch(batch).cpu() - mel_min, mel_max = self.test_dataset.target_mel_extrema - y_pred = (y_pred + 1.0) / 2.0 * (mel_max - mel_min + EPS) + mel_min - y_ls = y_pred.chunk(self.test_batch_size) - tgt_ls = batch["target_len"].cpu().chunk(self.test_batch_size) - j = 0 - for it, l in zip(y_ls, tgt_ls): - l = l.item() - it = it.squeeze(0)[:l] - uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"] - torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt")) - j += 1 - - vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir) - - res = synthesis( - cfg=vocoder_cfg, - vocoder_weight_file=vocoder_ckpt, - n_samples=None, - pred=[ - torch.load( - os.path.join(self.args.output_dir, "{}.pt".format(i["Uid"])) - ).numpy(force=True) - for i in self.test_dataset.metadata - ], - ) - - output_audio_files = [] - for it, wav in zip(self.test_dataset.metadata, res): - uid = it["Uid"] - file = os.path.join(self.args.output_dir, f"{uid}.wav") - output_audio_files.append(file) - - wav = wav.numpy(force=True) - save_audio( - file, - wav, - self.cfg.preprocess.sample_rate, - add_silence=False, - turn_up=not is_silence(wav, self.cfg.preprocess.sample_rate), - ) - os.remove(os.path.join(self.args.output_dir, f"{uid}.pt")) - - return sorted(output_audio_files) - - # TODO: LEGACY CODE - def _build_dataloader(self): - datasets, collate = self._build_test_dataset() - self.test_dataset = datasets(self.args, self.cfg, self.infer_type) - self.test_collate = collate(self.cfg) - self.test_batch_size = min( - self.cfg.train.batch_size, len(self.test_dataset.metadata) - ) - test_dataloader = DataLoader( - self.test_dataset, - collate_fn=self.test_collate, - num_workers=1, - batch_size=self.test_batch_size, - shuffle=False, - ) - return test_dataloader - - def __load_model(self, checkpoint_dir: str = None, checkpoint_path: str = None): - r"""Load model from checkpoint. If checkpoint_path is None, it will - load the latest checkpoint in checkpoint_dir. If checkpoint_path is not - None, it will load the checkpoint specified by checkpoint_path. **Only use this - method after** ``accelerator.prepare()``. - """ - if checkpoint_path is None: - ls = [] - for i in Path(checkpoint_dir).iterdir(): - if re.match(r"epoch-\d+_step-\d+_loss-[\d.]+", str(i.stem)): - ls.append(i) - ls.sort( - key=lambda x: int(x.stem.split("_")[-3].split("-")[-1]), reverse=True - ) - checkpoint_path = ls[0] - else: - checkpoint_path = Path(checkpoint_path) - self.accelerator.load_state(str(checkpoint_path)) - # set epoch and step - self.epoch = int(checkpoint_path.stem.split("_")[-3].split("-")[-1]) - self.step = int(checkpoint_path.stem.split("_")[-2].split("-")[-1]) - return str(checkpoint_path) - - @staticmethod - def _set_random_seed(seed): - r"""Set random seed for all possible random modules.""" - random.seed(seed) - np.random.seed(seed) - torch.random.manual_seed(seed) - - @staticmethod - def _parse_vocoder(vocoder_dir): - r"""Parse vocoder config""" - vocoder_dir = os.path.abspath(vocoder_dir) - ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")] - ckpt_list.sort(key=lambda x: int(x.stem), reverse=True) - ckpt_path = str(ckpt_list[0]) - vocoder_cfg = load_config( - os.path.join(vocoder_dir, "args.json"), lowercase=True - ) - return vocoder_cfg, ckpt_path - - @staticmethod - def __count_parameters(model): - return sum(p.numel() for p in model.parameters()) - - def __dump_cfg(self, path): - os.makedirs(os.path.dirname(path), exist_ok=True) - json5.dump( - self.cfg, - open(path, "w"), - indent=4, - sort_keys=True, - ensure_ascii=False, - quote_keys=True, - ) diff --git a/models/base/new_trainer.py b/models/base/new_trainer.py deleted file mode 100644 index 5d013d2bc2f2e47e5c7646cac5c63cc88c04486b..0000000000000000000000000000000000000000 --- a/models/base/new_trainer.py +++ /dev/null @@ -1,722 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os -import random -import shutil -import time -from abc import abstractmethod -from pathlib import Path - -import accelerate -import json5 -import numpy as np -import torch -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration -from torch.utils.data import ConcatDataset, DataLoader -from tqdm import tqdm - -from models.base.base_sampler import build_samplers -from optimizer.optimizers import NoamLR - - -class BaseTrainer(object): - r"""The base trainer for all tasks. Any trainer should inherit from this class.""" - - def __init__(self, args=None, cfg=None): - super().__init__() - - self.args = args - self.cfg = cfg - - cfg.exp_name = args.exp_name - - # init with accelerate - self._init_accelerator() - self.accelerator.wait_for_everyone() - - # Use accelerate logger for distributed training - with self.accelerator.main_process_first(): - self.logger = get_logger(args.exp_name, log_level=args.log_level) - - # Log some info - self.logger.info("=" * 56) - self.logger.info("||\t\t" + "New training process started." + "\t\t||") - self.logger.info("=" * 56) - self.logger.info("\n") - self.logger.debug(f"Using {args.log_level.upper()} logging level.") - self.logger.info(f"Experiment name: {args.exp_name}") - self.logger.info(f"Experiment directory: {self.exp_dir}") - self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") - if self.accelerator.is_main_process: - os.makedirs(self.checkpoint_dir, exist_ok=True) - self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") - - # init counts - self.batch_count: int = 0 - self.step: int = 0 - self.epoch: int = 0 - self.max_epoch = ( - self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") - ) - self.logger.info( - "Max epoch: {}".format( - self.max_epoch if self.max_epoch < float("inf") else "Unlimited" - ) - ) - - # Check values - if self.accelerator.is_main_process: - self.__check_basic_configs() - # Set runtime configs - self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride - self.checkpoints_path = [ - [] for _ in range(len(self.save_checkpoint_stride)) - ] - self.keep_last = [ - i if i > 0 else float("inf") for i in self.cfg.train.keep_last - ] - self.run_eval = self.cfg.train.run_eval - - # set random seed - with self.accelerator.main_process_first(): - start = time.monotonic_ns() - self._set_random_seed(self.cfg.train.random_seed) - end = time.monotonic_ns() - self.logger.debug( - f"Setting random seed done in {(end - start) / 1e6:.2f}ms" - ) - self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") - - # setup data_loader - with self.accelerator.main_process_first(): - self.logger.info("Building dataset...") - start = time.monotonic_ns() - self.train_dataloader, self.valid_dataloader = self._build_dataloader() - end = time.monotonic_ns() - self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") - - # setup model - with self.accelerator.main_process_first(): - self.logger.info("Building model...") - start = time.monotonic_ns() - self.model = self._build_model() - end = time.monotonic_ns() - self.logger.debug(self.model) - self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") - self.logger.info( - f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" - ) - # optimizer & scheduler - with self.accelerator.main_process_first(): - self.logger.info("Building optimizer and scheduler...") - start = time.monotonic_ns() - self.optimizer = self.__build_optimizer() - self.scheduler = self.__build_scheduler() - end = time.monotonic_ns() - self.logger.info( - f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" - ) - - # accelerate prepare - self.logger.info("Initializing accelerate...") - start = time.monotonic_ns() - ( - self.train_dataloader, - self.valid_dataloader, - self.model, - self.optimizer, - self.scheduler, - ) = self.accelerator.prepare( - self.train_dataloader, - self.valid_dataloader, - self.model, - self.optimizer, - self.scheduler, - ) - end = time.monotonic_ns() - self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") - - # create criterion - with self.accelerator.main_process_first(): - self.logger.info("Building criterion...") - start = time.monotonic_ns() - self.criterion = self._build_criterion() - end = time.monotonic_ns() - self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") - - # Resume or Finetune - with self.accelerator.main_process_first(): - if args.resume: - ## Automatically resume according to the current exprimental name - self.logger.info("Resuming from {}...".format(self.checkpoint_dir)) - start = time.monotonic_ns() - ckpt_path = self.__load_model( - checkpoint_dir=self.checkpoint_dir, resume_type=args.resume_type - ) - end = time.monotonic_ns() - self.logger.info( - f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" - ) - self.checkpoints_path = json.load( - open(os.path.join(ckpt_path, "ckpts.json"), "r") - ) - elif args.resume_from_ckpt_path and args.resume_from_ckpt_path != "": - ## Resume from the given checkpoint path - if not os.path.exists(args.resume_from_ckpt_path): - raise ValueError( - "[Error] The resumed checkpoint path {} don't exist.".format( - args.resume_from_ckpt_path - ) - ) - - self.logger.info( - "Resuming from {}...".format(args.resume_from_ckpt_path) - ) - start = time.monotonic_ns() - ckpt_path = self.__load_model( - checkpoint_path=args.resume_from_ckpt_path, - resume_type=args.resume_type, - ) - end = time.monotonic_ns() - self.logger.info( - f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" - ) - - # save config file path - self.config_save_path = os.path.join(self.exp_dir, "args.json") - - ### Following are abstract methods that should be implemented in child classes ### - @abstractmethod - def _build_dataset(self): - r"""Build dataset for model training/validating/evaluating.""" - pass - - @staticmethod - @abstractmethod - def _build_criterion(): - r"""Build criterion function for model loss calculation.""" - pass - - @abstractmethod - def _build_model(self): - r"""Build model for training/validating/evaluating.""" - pass - - @abstractmethod - def _forward_step(self, batch): - r"""One forward step of the neural network. This abstract method is trying to - unify ``_train_step`` and ``_valid_step`` and avoid redundant implementation. - However, for special case that using different forward step pattern for - training and validating, you could just override this method with ``pass`` and - implement ``_train_step`` and ``_valid_step`` separately. - """ - pass - - @abstractmethod - def _save_auxiliary_states(self): - r"""To save some auxiliary states when saving model's ckpt""" - pass - - ### Abstract methods end ### - - ### THIS IS MAIN ENTRY ### - def train_loop(self): - r"""Training loop. The public entry of training process.""" - # Wait everyone to prepare before we move on - self.accelerator.wait_for_everyone() - # dump config file - if self.accelerator.is_main_process: - self.__dump_cfg(self.config_save_path) - self.model.train() - self.optimizer.zero_grad() - # Wait to ensure good to go - self.accelerator.wait_for_everyone() - while self.epoch < self.max_epoch: - self.logger.info("\n") - self.logger.info("-" * 32) - self.logger.info("Epoch {}: ".format(self.epoch)) - - ### TODO: change the return values of _train_epoch() to a loss dict, or (total_loss, loss_dict) - ### It's inconvenient for the model with multiple losses - # Do training & validating epoch - train_loss = self._train_epoch() - self.logger.info(" |- Train/Loss: {:.6f}".format(train_loss)) - valid_loss = self._valid_epoch() - self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_loss)) - self.accelerator.log( - {"Epoch/Train Loss": train_loss, "Epoch/Valid Loss": valid_loss}, - step=self.epoch, - ) - - self.accelerator.wait_for_everyone() - # TODO: what is scheduler? - self.scheduler.step(valid_loss) # FIXME: use epoch track correct? - - # Check if hit save_checkpoint_stride and run_eval - run_eval = False - if self.accelerator.is_main_process: - save_checkpoint = False - hit_dix = [] - for i, num in enumerate(self.save_checkpoint_stride): - if self.epoch % num == 0: - save_checkpoint = True - hit_dix.append(i) - run_eval |= self.run_eval[i] - - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process and save_checkpoint: - path = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, train_loss - ), - ) - self.tmp_checkpoint_save_path = path - self.accelerator.save_state(path) - print(f"save checkpoint in {path}") - json.dump( - self.checkpoints_path, - open(os.path.join(path, "ckpts.json"), "w"), - ensure_ascii=False, - indent=4, - ) - self._save_auxiliary_states() - - # Remove old checkpoints - to_remove = [] - for idx in hit_dix: - self.checkpoints_path[idx].append(path) - while len(self.checkpoints_path[idx]) > self.keep_last[idx]: - to_remove.append((idx, self.checkpoints_path[idx].pop(0))) - - # Search conflicts - total = set() - for i in self.checkpoints_path: - total |= set(i) - do_remove = set() - for idx, path in to_remove[::-1]: - if path in total: - self.checkpoints_path[idx].insert(0, path) - else: - do_remove.add(path) - - # Remove old checkpoints - for path in do_remove: - shutil.rmtree(path, ignore_errors=True) - self.logger.debug(f"Remove old checkpoint: {path}") - - self.accelerator.wait_for_everyone() - if run_eval: - # TODO: run evaluation - pass - - # Update info for each epoch - self.epoch += 1 - - # Finish training and save final checkpoint - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process: - self.accelerator.save_state( - os.path.join( - self.checkpoint_dir, - "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, valid_loss - ), - ) - ) - self._save_auxiliary_states() - - self.accelerator.end_training() - - ### Following are methods that can be used directly in child classes ### - def _train_epoch(self): - r"""Training epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - self.model.train() - epoch_sum_loss: float = 0.0 - epoch_step: int = 0 - for batch in tqdm( - self.train_dataloader, - desc=f"Training Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - # Do training step and BP - with self.accelerator.accumulate(self.model): - loss = self._train_step(batch) - self.accelerator.backward(loss) - self.optimizer.step() - self.optimizer.zero_grad() - self.batch_count += 1 - - # Update info for each step - # TODO: step means BP counts or batch counts? - if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: - epoch_sum_loss += loss - self.accelerator.log( - { - "Step/Train Loss": loss, - "Step/Learning Rate": self.optimizer.param_groups[0]["lr"], - }, - step=self.step, - ) - self.step += 1 - epoch_step += 1 - - self.accelerator.wait_for_everyone() - return ( - epoch_sum_loss - / len(self.train_dataloader) - * self.cfg.train.gradient_accumulation_step - ) - - @torch.inference_mode() - def _valid_epoch(self): - r"""Testing epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - self.model.eval() - epoch_sum_loss = 0.0 - for batch in tqdm( - self.valid_dataloader, - desc=f"Validating Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - batch_loss = self._valid_step(batch) - epoch_sum_loss += batch_loss.item() - - self.accelerator.wait_for_everyone() - return epoch_sum_loss / len(self.valid_dataloader) - - def _train_step(self, batch): - r"""Training forward step. Should return average loss of a sample over - one batch. Provoke ``_forward_step`` is recommended except for special case. - See ``_train_epoch`` for usage. - """ - return self._forward_step(batch) - - @torch.inference_mode() - def _valid_step(self, batch): - r"""Testing forward step. Should return average loss of a sample over - one batch. Provoke ``_forward_step`` is recommended except for special case. - See ``_test_epoch`` for usage. - """ - return self._forward_step(batch) - - def __load_model( - self, - checkpoint_dir: str = None, - checkpoint_path: str = None, - resume_type: str = "", - ): - r"""Load model from checkpoint. If checkpoint_path is None, it will - load the latest checkpoint in checkpoint_dir. If checkpoint_path is not - None, it will load the checkpoint specified by checkpoint_path. **Only use this - method after** ``accelerator.prepare()``. - """ - if checkpoint_path is None: - ls = [str(i) for i in Path(checkpoint_dir).glob("*")] - ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) - checkpoint_path = ls[0] - self.logger.info("Resume from {}...".format(checkpoint_path)) - - if resume_type in ["resume", ""]: - # Load all the things, including model weights, optimizer, scheduler, and random states. - self.accelerator.load_state(input_dir=checkpoint_path) - - # set epoch and step - self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 - self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 - - elif resume_type == "finetune": - # Load only the model weights - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model), - os.path.join(checkpoint_path, "pytorch_model.bin"), - ) - self.logger.info("Load model weights for finetune...") - - else: - raise ValueError("Resume_type must be `resume` or `finetune`.") - - return checkpoint_path - - # TODO: LEGACY CODE - def _build_dataloader(self): - Dataset, Collator = self._build_dataset() - - # build dataset instance for each dataset and combine them by ConcatDataset - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=False) - datasets_list.append(subdataset) - train_dataset = ConcatDataset(datasets_list) - train_collate = Collator(self.cfg) - _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") - self.logger.debug(f"train batch_sampler: {list(batch_sampler)}") - self.logger.debug(f"length: {train_dataset.cumulative_sizes}") - # TODO: use config instead of (sampler, shuffle, drop_last, batch_size) - train_loader = DataLoader( - train_dataset, - collate_fn=train_collate, - batch_sampler=batch_sampler, - num_workers=self.cfg.train.dataloader.num_worker, - pin_memory=self.cfg.train.dataloader.pin_memory, - ) - - # Build valid dataloader - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=True) - datasets_list.append(subdataset) - valid_dataset = ConcatDataset(datasets_list) - valid_collate = Collator(self.cfg) - _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid") - self.logger.debug(f"valid batch_sampler: {list(batch_sampler)}") - self.logger.debug(f"length: {valid_dataset.cumulative_sizes}") - valid_loader = DataLoader( - valid_dataset, - collate_fn=valid_collate, - batch_sampler=batch_sampler, - num_workers=self.cfg.train.dataloader.num_worker, - pin_memory=self.cfg.train.dataloader.pin_memory, - ) - return train_loader, valid_loader - - @staticmethod - def _set_random_seed(seed): - r"""Set random seed for all possible random modules.""" - random.seed(seed) - np.random.seed(seed) - torch.random.manual_seed(seed) - - def _check_nan(self, loss, y_pred, y_gt): - if torch.any(torch.isnan(loss)): - self.logger.fatal("Fatal Error: Training is down since loss has Nan!") - self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True) - if torch.any(torch.isnan(y_pred)): - self.logger.error( - f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True - ) - else: - self.logger.debug( - f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True - ) - if torch.any(torch.isnan(y_gt)): - self.logger.error( - f"y_gt has Nan: {torch.any(torch.isnan(y_gt))}", in_order=True - ) - else: - self.logger.debug( - f"y_gt has nan: {torch.any(torch.isnan(y_gt))}", in_order=True - ) - if torch.any(torch.isnan(y_pred)): - self.logger.error(f"y_pred: {y_pred}", in_order=True) - else: - self.logger.debug(f"y_pred: {y_pred}", in_order=True) - if torch.any(torch.isnan(y_gt)): - self.logger.error(f"y_gt: {y_gt}", in_order=True) - else: - self.logger.debug(f"y_gt: {y_gt}", in_order=True) - - # TODO: still OK to save tracking? - self.accelerator.end_training() - raise RuntimeError("Loss has Nan! See log for more info.") - - ### Protected methods end ### - - ## Following are private methods ## - ## !!! These are inconvenient for GAN-based model training. It'd be better to move these to svc_trainer.py if needed. - def __build_optimizer(self): - r"""Build optimizer for model.""" - # Make case-insensitive matching - if self.cfg.train.optimizer.lower() == "adadelta": - optimizer = torch.optim.Adadelta( - self.model.parameters(), **self.cfg.train.adadelta - ) - self.logger.info("Using Adadelta optimizer.") - elif self.cfg.train.optimizer.lower() == "adagrad": - optimizer = torch.optim.Adagrad( - self.model.parameters(), **self.cfg.train.adagrad - ) - self.logger.info("Using Adagrad optimizer.") - elif self.cfg.train.optimizer.lower() == "adam": - optimizer = torch.optim.Adam(self.model.parameters(), **self.cfg.train.adam) - self.logger.info("Using Adam optimizer.") - elif self.cfg.train.optimizer.lower() == "adamw": - optimizer = torch.optim.AdamW( - self.model.parameters(), **self.cfg.train.adamw - ) - elif self.cfg.train.optimizer.lower() == "sparseadam": - optimizer = torch.optim.SparseAdam( - self.model.parameters(), **self.cfg.train.sparseadam - ) - elif self.cfg.train.optimizer.lower() == "adamax": - optimizer = torch.optim.Adamax( - self.model.parameters(), **self.cfg.train.adamax - ) - elif self.cfg.train.optimizer.lower() == "asgd": - optimizer = torch.optim.ASGD(self.model.parameters(), **self.cfg.train.asgd) - elif self.cfg.train.optimizer.lower() == "lbfgs": - optimizer = torch.optim.LBFGS( - self.model.parameters(), **self.cfg.train.lbfgs - ) - elif self.cfg.train.optimizer.lower() == "nadam": - optimizer = torch.optim.NAdam( - self.model.parameters(), **self.cfg.train.nadam - ) - elif self.cfg.train.optimizer.lower() == "radam": - optimizer = torch.optim.RAdam( - self.model.parameters(), **self.cfg.train.radam - ) - elif self.cfg.train.optimizer.lower() == "rmsprop": - optimizer = torch.optim.RMSprop( - self.model.parameters(), **self.cfg.train.rmsprop - ) - elif self.cfg.train.optimizer.lower() == "rprop": - optimizer = torch.optim.Rprop( - self.model.parameters(), **self.cfg.train.rprop - ) - elif self.cfg.train.optimizer.lower() == "sgd": - optimizer = torch.optim.SGD(self.model.parameters(), **self.cfg.train.sgd) - else: - raise NotImplementedError( - f"Optimizer {self.cfg.train.optimizer} not supported yet!" - ) - return optimizer - - def __build_scheduler(self): - r"""Build scheduler for optimizer.""" - # Make case-insensitive matching - if self.cfg.train.scheduler.lower() == "lambdalr": - scheduler = torch.optim.lr_scheduler.LambdaLR( - self.optimizer, **self.cfg.train.lambdalr - ) - elif self.cfg.train.scheduler.lower() == "multiplicativelr": - scheduler = torch.optim.lr_scheduler.MultiplicativeLR( - self.optimizer, **self.cfg.train.multiplicativelr - ) - elif self.cfg.train.scheduler.lower() == "steplr": - scheduler = torch.optim.lr_scheduler.StepLR( - self.optimizer, **self.cfg.train.steplr - ) - elif self.cfg.train.scheduler.lower() == "multisteplr": - scheduler = torch.optim.lr_scheduler.MultiStepLR( - self.optimizer, **self.cfg.train.multisteplr - ) - elif self.cfg.train.scheduler.lower() == "constantlr": - scheduler = torch.optim.lr_scheduler.ConstantLR( - self.optimizer, **self.cfg.train.constantlr - ) - elif self.cfg.train.scheduler.lower() == "linearlr": - scheduler = torch.optim.lr_scheduler.LinearLR( - self.optimizer, **self.cfg.train.linearlr - ) - elif self.cfg.train.scheduler.lower() == "exponentiallr": - scheduler = torch.optim.lr_scheduler.ExponentialLR( - self.optimizer, **self.cfg.train.exponentiallr - ) - elif self.cfg.train.scheduler.lower() == "polynomiallr": - scheduler = torch.optim.lr_scheduler.PolynomialLR( - self.optimizer, **self.cfg.train.polynomiallr - ) - elif self.cfg.train.scheduler.lower() == "cosineannealinglr": - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - self.optimizer, **self.cfg.train.cosineannealinglr - ) - elif self.cfg.train.scheduler.lower() == "sequentiallr": - scheduler = torch.optim.lr_scheduler.SequentialLR( - self.optimizer, **self.cfg.train.sequentiallr - ) - elif self.cfg.train.scheduler.lower() == "reducelronplateau": - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - self.optimizer, **self.cfg.train.reducelronplateau - ) - elif self.cfg.train.scheduler.lower() == "cycliclr": - scheduler = torch.optim.lr_scheduler.CyclicLR( - self.optimizer, **self.cfg.train.cycliclr - ) - elif self.cfg.train.scheduler.lower() == "onecyclelr": - scheduler = torch.optim.lr_scheduler.OneCycleLR( - self.optimizer, **self.cfg.train.onecyclelr - ) - elif self.cfg.train.scheduler.lower() == "cosineannearingwarmrestarts": - scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( - self.optimizer, **self.cfg.train.cosineannearingwarmrestarts - ) - elif self.cfg.train.scheduler.lower() == "noamlr": - scheduler = NoamLR(self.optimizer, **self.cfg.train.lr_scheduler) - else: - raise NotImplementedError( - f"Scheduler {self.cfg.train.scheduler} not supported yet!" - ) - return scheduler - - def _init_accelerator(self): - self.exp_dir = os.path.join( - os.path.abspath(self.cfg.log_dir), self.args.exp_name - ) - project_config = ProjectConfiguration( - project_dir=self.exp_dir, - logging_dir=os.path.join(self.exp_dir, "log"), - ) - self.accelerator = accelerate.Accelerator( - gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, - log_with=self.cfg.train.tracker, - project_config=project_config, - ) - if self.accelerator.is_main_process: - os.makedirs(project_config.project_dir, exist_ok=True) - os.makedirs(project_config.logging_dir, exist_ok=True) - with self.accelerator.main_process_first(): - self.accelerator.init_trackers(self.args.exp_name) - - def __check_basic_configs(self): - if self.cfg.train.gradient_accumulation_step <= 0: - self.logger.fatal("Invalid gradient_accumulation_step value!") - self.logger.error( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - self.accelerator.end_training() - raise ValueError( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - # TODO: check other values - - @staticmethod - def __count_parameters(model): - model_param = 0.0 - if isinstance(model, dict): - for key, value in model.items(): - model_param += sum(p.numel() for p in model[key].parameters()) - else: - model_param = sum(p.numel() for p in model.parameters()) - return model_param - - def __dump_cfg(self, path): - os.makedirs(os.path.dirname(path), exist_ok=True) - json5.dump( - self.cfg, - open(path, "w"), - indent=4, - sort_keys=True, - ensure_ascii=False, - quote_keys=True, - ) - - ### Private methods end ### diff --git a/models/svc/__init__.py b/models/svc/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/svc/base/__init__.py b/models/svc/base/__init__.py deleted file mode 100644 index 38c2b1686db550b3b9892b8bc6e594cd847aafd1..0000000000000000000000000000000000000000 --- a/models/svc/base/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .svc_inference import SVCInference -from .svc_trainer import SVCTrainer diff --git a/models/svc/base/svc_dataset.py b/models/svc/base/svc_dataset.py deleted file mode 100644 index cad356bd97b92b9917db6c973b31a42552f5fa76..0000000000000000000000000000000000000000 --- a/models/svc/base/svc_dataset.py +++ /dev/null @@ -1,425 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import random -import torch -from torch.nn.utils.rnn import pad_sequence -import json -import os -import numpy as np -from utils.data_utils import * -from processors.acoustic_extractor import cal_normalized_mel, load_mel_extrema -from processors.content_extractor import ( - ContentvecExtractor, - WhisperExtractor, - WenetExtractor, -) -from models.base.base_dataset import ( - BaseCollator, - BaseDataset, -) -from models.base.new_dataset import BaseTestDataset - -EPS = 1.0e-12 - - -class SVCDataset(BaseDataset): - def __init__(self, cfg, dataset, is_valid=False): - BaseDataset.__init__(self, cfg, dataset, is_valid=is_valid) - - cfg = self.cfg - - if cfg.model.condition_encoder.use_whisper: - self.whisper_aligner = WhisperExtractor(self.cfg) - self.utt2whisper_path = load_content_feature_path( - self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.whisper_dir - ) - - if cfg.model.condition_encoder.use_contentvec: - self.contentvec_aligner = ContentvecExtractor(self.cfg) - self.utt2contentVec_path = load_content_feature_path( - self.metadata, - cfg.preprocess.processed_dir, - cfg.preprocess.contentvec_dir, - ) - - if cfg.model.condition_encoder.use_mert: - self.utt2mert_path = load_content_feature_path( - self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.mert_dir - ) - if cfg.model.condition_encoder.use_wenet: - self.wenet_aligner = WenetExtractor(self.cfg) - self.utt2wenet_path = load_content_feature_path( - self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.wenet_dir - ) - - def __getitem__(self, index): - single_feature = BaseDataset.__getitem__(self, index) - - utt_info = self.metadata[index] - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - if self.cfg.model.condition_encoder.use_whisper: - assert "target_len" in single_feature.keys() - aligned_whisper_feat = self.whisper_aligner.offline_align( - np.load(self.utt2whisper_path[utt]), single_feature["target_len"] - ) - single_feature["whisper_feat"] = aligned_whisper_feat - - if self.cfg.model.condition_encoder.use_contentvec: - assert "target_len" in single_feature.keys() - aligned_contentvec = self.contentvec_aligner.offline_align( - np.load(self.utt2contentVec_path[utt]), single_feature["target_len"] - ) - single_feature["contentvec_feat"] = aligned_contentvec - - if self.cfg.model.condition_encoder.use_mert: - assert "target_len" in single_feature.keys() - aligned_mert_feat = align_content_feature_length( - np.load(self.utt2mert_path[utt]), - single_feature["target_len"], - source_hop=self.cfg.preprocess.mert_hop_size, - ) - single_feature["mert_feat"] = aligned_mert_feat - - if self.cfg.model.condition_encoder.use_wenet: - assert "target_len" in single_feature.keys() - aligned_wenet_feat = self.wenet_aligner.offline_align( - np.load(self.utt2wenet_path[utt]), single_feature["target_len"] - ) - single_feature["wenet_feat"] = aligned_wenet_feat - - # print(single_feature.keys()) - # for k, v in single_feature.items(): - # if type(v) in [torch.Tensor, np.ndarray]: - # print(k, v.shape) - # else: - # print(k, v) - # exit() - - return self.clip_if_too_long(single_feature) - - def __len__(self): - return len(self.metadata) - - def random_select(self, feature_seq_len, max_seq_len, ending_ts=2812): - """ - ending_ts: to avoid invalid whisper features for over 30s audios - 2812 = 30 * 24000 // 256 - """ - ts = max(feature_seq_len - max_seq_len, 0) - ts = min(ts, ending_ts - max_seq_len) - - start = random.randint(0, ts) - end = start + max_seq_len - return start, end - - def clip_if_too_long(self, sample, max_seq_len=512): - """ - sample : - { - 'spk_id': (1,), - 'target_len': int - 'mel': (seq_len, dim), - 'frame_pitch': (seq_len,) - 'frame_energy': (seq_len,) - 'content_vector_feat': (seq_len, dim) - } - """ - if sample["target_len"] <= max_seq_len: - return sample - - start, end = self.random_select(sample["target_len"], max_seq_len) - sample["target_len"] = end - start - - for k in sample.keys(): - if k not in ["spk_id", "target_len"]: - sample[k] = sample[k][start:end] - - return sample - - -class SVCCollator(BaseCollator): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - BaseCollator.__init__(self, cfg) - - def __call__(self, batch): - parsed_batch_features = BaseCollator.__call__(self, batch) - return parsed_batch_features - - -class SVCTestDataset(BaseTestDataset): - def __init__(self, args, cfg, infer_type): - BaseTestDataset.__init__(self, args, cfg, infer_type) - self.metadata = self.get_metadata() - - target_singer = args.target_singer - self.cfg = cfg - self.trans_key = args.trans_key - assert type(target_singer) == str - - self.target_singer = target_singer.split("_")[-1] - self.target_dataset = target_singer.replace( - "_{}".format(self.target_singer), "" - ) - - self.target_mel_extrema = load_mel_extrema(cfg.preprocess, self.target_dataset) - self.target_mel_extrema = torch.as_tensor( - self.target_mel_extrema[0] - ), torch.as_tensor(self.target_mel_extrema[1]) - - ######### Load source acoustic features ######### - if cfg.preprocess.use_spkid: - spk2id_path = os.path.join(args.acoustics_dir, cfg.preprocess.spk2id) - # utt2sp_path = os.path.join(self.data_root, cfg.preprocess.utt2spk) - - with open(spk2id_path, "r") as f: - self.spk2id = json.load(f) - # print("self.spk2id", self.spk2id) - - if cfg.preprocess.use_uv: - self.utt2uv_path = { - f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( - cfg.preprocess.processed_dir, - utt_info["Dataset"], - cfg.preprocess.uv_dir, - utt_info["Uid"] + ".npy", - ) - for utt_info in self.metadata - } - - if cfg.preprocess.use_frame_pitch: - self.utt2frame_pitch_path = { - f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( - cfg.preprocess.processed_dir, - utt_info["Dataset"], - cfg.preprocess.pitch_dir, - utt_info["Uid"] + ".npy", - ) - for utt_info in self.metadata - } - - # Target F0 median - target_f0_statistics_path = os.path.join( - cfg.preprocess.processed_dir, - self.target_dataset, - cfg.preprocess.pitch_dir, - "statistics.json", - ) - self.target_pitch_median = json.load(open(target_f0_statistics_path, "r"))[ - f"{self.target_dataset}_{self.target_singer}" - ]["voiced_positions"]["median"] - - # Source F0 median (if infer from file) - if infer_type == "from_file": - source_audio_name = cfg.inference.source_audio_name - source_f0_statistics_path = os.path.join( - cfg.preprocess.processed_dir, - source_audio_name, - cfg.preprocess.pitch_dir, - "statistics.json", - ) - self.source_pitch_median = json.load( - open(source_f0_statistics_path, "r") - )[f"{source_audio_name}_{source_audio_name}"]["voiced_positions"][ - "median" - ] - else: - self.source_pitch_median = None - - if cfg.preprocess.use_frame_energy: - self.utt2frame_energy_path = { - f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( - cfg.preprocess.processed_dir, - utt_info["Dataset"], - cfg.preprocess.energy_dir, - utt_info["Uid"] + ".npy", - ) - for utt_info in self.metadata - } - - if cfg.preprocess.use_mel: - self.utt2mel_path = { - f'{utt_info["Dataset"]}_{utt_info["Uid"]}': os.path.join( - cfg.preprocess.processed_dir, - utt_info["Dataset"], - cfg.preprocess.mel_dir, - utt_info["Uid"] + ".npy", - ) - for utt_info in self.metadata - } - - ######### Load source content features' path ######### - if cfg.model.condition_encoder.use_whisper: - self.whisper_aligner = WhisperExtractor(cfg) - self.utt2whisper_path = load_content_feature_path( - self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.whisper_dir - ) - - if cfg.model.condition_encoder.use_contentvec: - self.contentvec_aligner = ContentvecExtractor(cfg) - self.utt2contentVec_path = load_content_feature_path( - self.metadata, - cfg.preprocess.processed_dir, - cfg.preprocess.contentvec_dir, - ) - - if cfg.model.condition_encoder.use_mert: - self.utt2mert_path = load_content_feature_path( - self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.mert_dir - ) - if cfg.model.condition_encoder.use_wenet: - self.wenet_aligner = WenetExtractor(cfg) - self.utt2wenet_path = load_content_feature_path( - self.metadata, cfg.preprocess.processed_dir, cfg.preprocess.wenet_dir - ) - - def __getitem__(self, index): - single_feature = {} - - utt_info = self.metadata[index] - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - source_dataset = self.metadata[index]["Dataset"] - - if self.cfg.preprocess.use_spkid: - single_feature["spk_id"] = np.array( - [self.spk2id[f"{self.target_dataset}_{self.target_singer}"]], - dtype=np.int32, - ) - - ######### Get Acoustic Features Item ######### - if self.cfg.preprocess.use_mel: - mel = np.load(self.utt2mel_path[utt]) - assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] - if self.cfg.preprocess.use_min_max_norm_mel: - # mel norm - mel = cal_normalized_mel(mel, source_dataset, self.cfg.preprocess) - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = mel.shape[1] - single_feature["mel"] = mel.T # [T, n_mels] - - if self.cfg.preprocess.use_frame_pitch: - frame_pitch_path = self.utt2frame_pitch_path[utt] - frame_pitch = np.load(frame_pitch_path) - - if self.trans_key: - try: - self.trans_key = int(self.trans_key) - except: - pass - if type(self.trans_key) == int: - frame_pitch = transpose_key(frame_pitch, self.trans_key) - elif self.trans_key: - assert self.target_singer - - frame_pitch = pitch_shift_to_target( - frame_pitch, self.target_pitch_median, self.source_pitch_median - ) - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_pitch) - aligned_frame_pitch = align_length( - frame_pitch, single_feature["target_len"] - ) - single_feature["frame_pitch"] = aligned_frame_pitch - - if self.cfg.preprocess.use_uv: - frame_uv_path = self.utt2uv_path[utt] - frame_uv = np.load(frame_uv_path) - aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) - aligned_frame_uv = [ - 0 if frame_uv else 1 for frame_uv in aligned_frame_uv - ] - aligned_frame_uv = np.array(aligned_frame_uv) - single_feature["frame_uv"] = aligned_frame_uv - - if self.cfg.preprocess.use_frame_energy: - frame_energy_path = self.utt2frame_energy_path[utt] - frame_energy = np.load(frame_energy_path) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_energy) - aligned_frame_energy = align_length( - frame_energy, single_feature["target_len"] - ) - single_feature["frame_energy"] = aligned_frame_energy - - ######### Get Content Features Item ######### - if self.cfg.model.condition_encoder.use_whisper: - assert "target_len" in single_feature.keys() - aligned_whisper_feat = self.whisper_aligner.offline_align( - np.load(self.utt2whisper_path[utt]), single_feature["target_len"] - ) - single_feature["whisper_feat"] = aligned_whisper_feat - - if self.cfg.model.condition_encoder.use_contentvec: - assert "target_len" in single_feature.keys() - aligned_contentvec = self.contentvec_aligner.offline_align( - np.load(self.utt2contentVec_path[utt]), single_feature["target_len"] - ) - single_feature["contentvec_feat"] = aligned_contentvec - - if self.cfg.model.condition_encoder.use_mert: - assert "target_len" in single_feature.keys() - aligned_mert_feat = align_content_feature_length( - np.load(self.utt2mert_path[utt]), - single_feature["target_len"], - source_hop=self.cfg.preprocess.mert_hop_size, - ) - single_feature["mert_feat"] = aligned_mert_feat - - if self.cfg.model.condition_encoder.use_wenet: - assert "target_len" in single_feature.keys() - aligned_wenet_feat = self.wenet_aligner.offline_align( - np.load(self.utt2wenet_path[utt]), single_feature["target_len"] - ) - single_feature["wenet_feat"] = aligned_wenet_feat - - return single_feature - - def __len__(self): - return len(self.metadata) - - -class SVCTestCollator: - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - self.cfg = cfg - - def __call__(self, batch): - packed_batch_features = dict() - - # mel: [b, T, n_mels] - # frame_pitch, frame_energy: [1, T] - # target_len: [1] - # spk_id: [b, 1] - # mask: [b, T, 1] - - for key in batch[0].keys(): - if key == "target_len": - packed_batch_features["target_len"] = torch.LongTensor( - [b["target_len"] for b in batch] - ) - masks = [ - torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch - ] - packed_batch_features["mask"] = pad_sequence( - masks, batch_first=True, padding_value=0 - ) - else: - values = [torch.from_numpy(b[key]) for b in batch] - packed_batch_features[key] = pad_sequence( - values, batch_first=True, padding_value=0 - ) - - return packed_batch_features diff --git a/models/svc/base/svc_inference.py b/models/svc/base/svc_inference.py deleted file mode 100644 index 52f88d5d915e1616292c03927b4f51557351f58b..0000000000000000000000000000000000000000 --- a/models/svc/base/svc_inference.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from models.base.new_inference import BaseInference -from models.svc.base.svc_dataset import SVCTestCollator, SVCTestDataset - - -class SVCInference(BaseInference): - def __init__(self, args=None, cfg=None, infer_type="from_dataset"): - BaseInference.__init__(self, args, cfg, infer_type) - - def _build_test_dataset(self): - return SVCTestDataset, SVCTestCollator diff --git a/models/svc/base/svc_trainer.py b/models/svc/base/svc_trainer.py deleted file mode 100644 index a2a093a86712bb7ccfa786a6c18dd1683ffc013c..0000000000000000000000000000000000000000 --- a/models/svc/base/svc_trainer.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os - -import torch -import torch.nn as nn - -from models.base.new_trainer import BaseTrainer -from models.svc.base.svc_dataset import SVCCollator, SVCDataset - - -class SVCTrainer(BaseTrainer): - r"""The base trainer for all SVC models. It inherits from BaseTrainer and implements - ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this - class, and implement ``_build_model``, ``_forward_step``. - """ - - def __init__(self, args=None, cfg=None): - self.args = args - self.cfg = cfg - - self._init_accelerator() - - # Only for SVC tasks - with self.accelerator.main_process_first(): - self.singers = self._build_singer_lut() - - # Super init - BaseTrainer.__init__(self, args, cfg) - - # Only for SVC tasks - self.task_type = "SVC" - self.logger.info("Task type: {}".format(self.task_type)) - - ### Following are methods only for SVC tasks ### - # TODO: LEGACY CODE, NEED TO BE REFACTORED - def _build_dataset(self): - return SVCDataset, SVCCollator - - @staticmethod - def _build_criterion(): - criterion = nn.MSELoss(reduction="none") - return criterion - - @staticmethod - def _compute_loss(criterion, y_pred, y_gt, loss_mask): - """ - Args: - criterion: MSELoss(reduction='none') - y_pred, y_gt: (bs, seq_len, D) - loss_mask: (bs, seq_len, 1) - Returns: - loss: Tensor of shape [] - """ - - # (bs, seq_len, D) - loss = criterion(y_pred, y_gt) - # expand loss_mask to (bs, seq_len, D) - loss_mask = loss_mask.repeat(1, 1, loss.shape[-1]) - - loss = torch.sum(loss * loss_mask) / torch.sum(loss_mask) - return loss - - def _save_auxiliary_states(self): - """ - To save the singer's look-up table in the checkpoint saving path - """ - with open( - os.path.join(self.tmp_checkpoint_save_path, self.cfg.preprocess.spk2id), "w" - ) as f: - json.dump(self.singers, f, indent=4, ensure_ascii=False) - - def _build_singer_lut(self): - resumed_singer_path = None - if self.args.resume_from_ckpt_path and self.args.resume_from_ckpt_path != "": - resumed_singer_path = os.path.join( - self.args.resume_from_ckpt_path, self.cfg.preprocess.spk2id - ) - if os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)): - resumed_singer_path = os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) - - if resumed_singer_path: - with open(resumed_singer_path, "r") as f: - singers = json.load(f) - else: - singers = dict() - - for dataset in self.cfg.dataset: - singer_lut_path = os.path.join( - self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id - ) - with open(singer_lut_path, "r") as singer_lut_path: - singer_lut = json.load(singer_lut_path) - for singer in singer_lut.keys(): - if singer not in singers: - singers[singer] = len(singers) - - with open( - os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "w" - ) as singer_file: - json.dump(singers, singer_file, indent=4, ensure_ascii=False) - print( - "singers have been dumped to {}".format( - os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) - ) - ) - return singers diff --git a/models/svc/comosvc/__init__.py b/models/svc/comosvc/__init__.py deleted file mode 100644 index 19f1cb162e95d8a992002beaa0c0d8bada9cddd5..0000000000000000000000000000000000000000 --- a/models/svc/comosvc/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. diff --git a/models/svc/comosvc/comosvc.py b/models/svc/comosvc/comosvc.py deleted file mode 100644 index 6cecd7a3f40f3a78f0df06ef2340159d321d6117..0000000000000000000000000000000000000000 --- a/models/svc/comosvc/comosvc.py +++ /dev/null @@ -1,377 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -"""Adapted from https://github.com/zhenye234/CoMoSpeech""" - -import torch -import torch.nn as nn -import copy -import numpy as np -import math -from tqdm.auto import tqdm - -from utils.ssim import SSIM - -from models.svc.transformer.conformer import Conformer, BaseModule -from models.svc.diffusion.diffusion_wrapper import DiffusionWrapper -from models.svc.comosvc.utils import slice_segments, rand_ids_segments - - -class Consistency(nn.Module): - def __init__(self, cfg, distill=False): - super().__init__() - self.cfg = cfg - # self.denoise_fn = GradLogPEstimator2d(96) - self.denoise_fn = DiffusionWrapper(self.cfg) - self.cfg = cfg.model.comosvc - self.teacher = not distill - self.P_mean = self.cfg.P_mean - self.P_std = self.cfg.P_std - self.sigma_data = self.cfg.sigma_data - self.sigma_min = self.cfg.sigma_min - self.sigma_max = self.cfg.sigma_max - self.rho = self.cfg.rho - self.N = self.cfg.n_timesteps - self.ssim_loss = SSIM() - - # Time step discretization - step_indices = torch.arange(self.N) - # karras boundaries formula - t_steps = ( - self.sigma_min ** (1 / self.rho) - + step_indices - / (self.N - 1) - * (self.sigma_max ** (1 / self.rho) - self.sigma_min ** (1 / self.rho)) - ) ** self.rho - self.t_steps = torch.cat( - [torch.zeros_like(t_steps[:1]), self.round_sigma(t_steps)] - ) - - def init_consistency_training(self): - self.denoise_fn_ema = copy.deepcopy(self.denoise_fn) - self.denoise_fn_pretrained = copy.deepcopy(self.denoise_fn) - - def EDMPrecond(self, x, sigma, cond, denoise_fn, mask, spk=None): - """ - karras diffusion reverse process - - Args: - x: noisy mel-spectrogram [B x n_mel x L] - sigma: noise level [B x 1 x 1] - cond: output of conformer encoder [B x n_mel x L] - denoise_fn: denoiser neural network e.g. DilatedCNN - mask: mask of padded frames [B x n_mel x L] - - Returns: - denoised mel-spectrogram [B x n_mel x L] - """ - sigma = sigma.reshape(-1, 1, 1) - - c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2) - c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2).sqrt() - c_in = 1 / (self.sigma_data**2 + sigma**2).sqrt() - c_noise = sigma.log() / 4 - - x_in = c_in * x - x_in = x_in.transpose(1, 2) - x = x.transpose(1, 2) - cond = cond.transpose(1, 2) - F_x = denoise_fn(x_in, c_noise.squeeze(), cond) - # F_x = denoise_fn((c_in * x), mask, cond, c_noise.flatten()) - D_x = c_skip * x + c_out * (F_x) - D_x = D_x.transpose(1, 2) - return D_x - - def EDMLoss(self, x_start, cond, mask): - """ - compute loss for EDM model - - Args: - x_start: ground truth mel-spectrogram [B x n_mel x L] - cond: output of conformer encoder [B x n_mel x L] - mask: mask of padded frames [B x n_mel x L] - """ - rnd_normal = torch.randn([x_start.shape[0], 1, 1], device=x_start.device) - sigma = (rnd_normal * self.P_std + self.P_mean).exp() - weight = (sigma**2 + self.sigma_data**2) / (sigma * self.sigma_data) ** 2 - - # follow Grad-TTS, start from Gaussian noise with mean cond and std I - noise = (torch.randn_like(x_start) + cond) * sigma - D_yn = self.EDMPrecond(x_start + noise, sigma, cond, self.denoise_fn, mask) - loss = weight * ((D_yn - x_start) ** 2) - loss = torch.sum(loss * mask) / torch.sum(mask) - return loss - - def round_sigma(self, sigma): - return torch.as_tensor(sigma) - - def edm_sampler( - self, - latents, - cond, - nonpadding, - num_steps=50, - sigma_min=0.002, - sigma_max=80, - rho=7, - S_churn=0, - S_min=0, - S_max=float("inf"), - S_noise=1, - # S_churn=40 ,S_min=0.05,S_max=50,S_noise=1.003,# S_churn=0, S_min=0, S_max=float('inf'), S_noise=1, - # S_churn=30 ,S_min=0.01,S_max=30,S_noise=1.007, - # S_churn=30 ,S_min=0.01,S_max=1,S_noise=1.007, - # S_churn=80 ,S_min=0.05,S_max=50,S_noise=1.003, - ): - """ - karras diffusion sampler - - Args: - latents: noisy mel-spectrogram [B x n_mel x L] - cond: output of conformer encoder [B x n_mel x L] - nonpadding: mask of padded frames [B x n_mel x L] - num_steps: number of steps for diffusion inference - - Returns: - denoised mel-spectrogram [B x n_mel x L] - """ - # Time step discretization. - step_indices = torch.arange(num_steps, device=latents.device) - - num_steps = num_steps + 1 - t_steps = ( - sigma_max ** (1 / rho) - + step_indices - / (num_steps - 1) - * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho)) - ) ** rho - t_steps = torch.cat([self.round_sigma(t_steps), torch.zeros_like(t_steps[:1])]) - - # Main sampling loop. - x_next = latents * t_steps[0] - # wrap in tqdm for progress bar - bar = tqdm(enumerate(zip(t_steps[:-1], t_steps[1:]))) - for i, (t_cur, t_next) in bar: - x_cur = x_next - # Increase noise temporarily. - gamma = ( - min(S_churn / num_steps, np.sqrt(2) - 1) - if S_min <= t_cur <= S_max - else 0 - ) - t_hat = self.round_sigma(t_cur + gamma * t_cur) - t = torch.zeros((x_cur.shape[0], 1, 1), device=x_cur.device) - t[:, 0, 0] = t_hat - t_hat = t - x_hat = x_cur + ( - t_hat**2 - t_cur**2 - ).sqrt() * S_noise * torch.randn_like(x_cur) - # Euler step. - denoised = self.EDMPrecond(x_hat, t_hat, cond, self.denoise_fn, nonpadding) - d_cur = (x_hat - denoised) / t_hat - x_next = x_hat + (t_next - t_hat) * d_cur - - return x_next - - def CTLoss_D(self, y, cond, mask): - """ - compute loss for consistency distillation - - Args: - y: ground truth mel-spectrogram [B x n_mel x L] - cond: output of conformer encoder [B x n_mel x L] - mask: mask of padded frames [B x n_mel x L] - """ - with torch.no_grad(): - mu = 0.95 - for p, ema_p in zip( - self.denoise_fn.parameters(), self.denoise_fn_ema.parameters() - ): - ema_p.mul_(mu).add_(p, alpha=1 - mu) - - n = torch.randint(1, self.N, (y.shape[0],)) - z = torch.randn_like(y) + cond - - tn_1 = self.t_steps[n + 1].reshape(-1, 1, 1).to(y.device) - f_theta = self.EDMPrecond(y + tn_1 * z, tn_1, cond, self.denoise_fn, mask) - - with torch.no_grad(): - tn = self.t_steps[n].reshape(-1, 1, 1).to(y.device) - - # euler step - x_hat = y + tn_1 * z - denoised = self.EDMPrecond( - x_hat, tn_1, cond, self.denoise_fn_pretrained, mask - ) - d_cur = (x_hat - denoised) / tn_1 - y_tn = x_hat + (tn - tn_1) * d_cur - - f_theta_ema = self.EDMPrecond(y_tn, tn, cond, self.denoise_fn_ema, mask) - - # loss = (f_theta - f_theta_ema.detach()) ** 2 - # loss = torch.sum(loss * mask) / torch.sum(mask) - loss = self.ssim_loss(f_theta, f_theta_ema.detach()) - loss = torch.sum(loss * mask) / torch.sum(mask) - - return loss - - def get_t_steps(self, N): - N = N + 1 - step_indices = torch.arange(N) # , device=latents.device) - t_steps = ( - self.sigma_min ** (1 / self.rho) - + step_indices - / (N - 1) - * (self.sigma_max ** (1 / self.rho) - self.sigma_min ** (1 / self.rho)) - ) ** self.rho - - return t_steps.flip(0) - - def CT_sampler(self, latents, cond, nonpadding, t_steps=1): - """ - consistency distillation sampler - - Args: - latents: noisy mel-spectrogram [B x n_mel x L] - cond: output of conformer encoder [B x n_mel x L] - nonpadding: mask of padded frames [B x n_mel x L] - t_steps: number of steps for diffusion inference - - Returns: - denoised mel-spectrogram [B x n_mel x L] - """ - # one-step - if t_steps == 1: - t_steps = [80] - # multi-step - else: - t_steps = self.get_t_steps(t_steps) - - t_steps = torch.as_tensor(t_steps).to(latents.device) - latents = latents * t_steps[0] - _t = torch.zeros((latents.shape[0], 1, 1), device=latents.device) - _t[:, 0, 0] = t_steps - x = self.EDMPrecond(latents, _t, cond, self.denoise_fn_ema, nonpadding) - - for t in t_steps[1:-1]: - z = torch.randn_like(x) + cond - x_tn = x + (t**2 - self.sigma_min**2).sqrt() * z - _t = torch.zeros((x.shape[0], 1, 1), device=x.device) - _t[:, 0, 0] = t - t = _t - print(t) - x = self.EDMPrecond(x_tn, t, cond, self.denoise_fn_ema, nonpadding) - return x - - def forward(self, x, nonpadding, cond, t_steps=1, infer=False): - """ - calculate loss or sample mel-spectrogram - - Args: - x: - training: ground truth mel-spectrogram [B x n_mel x L] - inference: output of encoder [B x n_mel x L] - """ - if self.teacher: # teacher model -- karras diffusion - if not infer: - loss = self.EDMLoss(x, cond, nonpadding) - return loss - else: - shape = (cond.shape[0], self.cfg.n_mel, cond.shape[2]) - x = torch.randn(shape, device=x.device) + cond - x = self.edm_sampler(x, cond, nonpadding, t_steps) - - return x - else: # Consistency distillation - if not infer: - loss = self.CTLoss_D(x, cond, nonpadding) - return loss - - else: - shape = (cond.shape[0], self.cfg.n_mel, cond.shape[2]) - x = torch.randn(shape, device=x.device) + cond - x = self.CT_sampler(x, cond, nonpadding, t_steps=1) - - return x - - -class ComoSVC(BaseModule): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - self.cfg.model.comosvc.n_mel = self.cfg.preprocess.n_mel - self.distill = self.cfg.model.comosvc.distill - self.encoder = Conformer(self.cfg.model.comosvc) - self.decoder = Consistency(self.cfg, distill=self.distill) - self.ssim_loss = SSIM() - - @torch.no_grad() - def forward(self, x_mask, x, n_timesteps, temperature=1.0): - """ - Generates mel-spectrogram from pitch, content vector, energy. Returns: - 1. encoder outputs (from conformer) - 2. decoder outputs (from diffusion-based decoder) - - Args: - x_mask : mask of padded frames in mel-spectrogram. [B x L x n_mel] - x : output of encoder framework. [B x L x d_condition] - n_timesteps : number of steps to use for reverse diffusion in decoder. - temperature : controls variance of terminal distribution. - """ - - # Get encoder_outputs `mu_x` - mu_x = self.encoder(x, x_mask) - encoder_outputs = mu_x - - mu_x = mu_x.transpose(1, 2) - x_mask = x_mask.transpose(1, 2) - - # Generate sample by performing reverse dynamics - decoder_outputs = self.decoder( - mu_x, x_mask, mu_x, t_steps=n_timesteps, infer=True - ) - decoder_outputs = decoder_outputs.transpose(1, 2) - return encoder_outputs, decoder_outputs - - def compute_loss(self, x_mask, x, mel, out_size=None, skip_diff=False): - """ - Computes 2 losses: - 1. prior loss: loss between mel-spectrogram and encoder outputs. - 2. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder. - - Args: - x_mask : mask of padded frames in mel-spectrogram. [B x L x n_mel] - x : output of encoder framework. [B x L x d_condition] - mel : ground truth mel-spectrogram. [B x L x n_mel] - """ - - mu_x = self.encoder(x, x_mask) - # prior loss - prior_loss = torch.sum( - 0.5 * ((mel - mu_x) ** 2 + math.log(2 * math.pi)) * x_mask - ) - prior_loss = prior_loss / (torch.sum(x_mask) * self.cfg.model.comosvc.n_mel) - # ssim loss - ssim_loss = self.ssim_loss(mu_x, mel) - ssim_loss = torch.sum(ssim_loss * x_mask) / torch.sum(x_mask) - - x_mask = x_mask.transpose(1, 2) - mu_x = mu_x.transpose(1, 2) - mel = mel.transpose(1, 2) - if not self.distill and skip_diff: - diff_loss = prior_loss.clone() - diff_loss.fill_(0) - - # Cut a small segment of mel-spectrogram in order to increase batch size - else: - if self.distill: - mu_y = mu_x.detach() - else: - mu_y = mu_x - mask_y = x_mask - - diff_loss = self.decoder(mel, mask_y, mu_y, infer=False) - - return ssim_loss, prior_loss, diff_loss diff --git a/models/svc/comosvc/comosvc_inference.py b/models/svc/comosvc/comosvc_inference.py deleted file mode 100644 index 2783ec7e468c367c7d2f5f8988ed1f7e272d4cb7..0000000000000000000000000000000000000000 --- a/models/svc/comosvc/comosvc_inference.py +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from models.svc.base import SVCInference -from modules.encoder.condition_encoder import ConditionEncoder -from models.svc.comosvc.comosvc import ComoSVC - - -class ComoSVCInference(SVCInference): - def __init__(self, args, cfg, infer_type="from_dataset"): - SVCInference.__init__(self, args, cfg, infer_type) - - def _build_model(self): - # TODO: sort out the config - self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min - self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max - self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) - self.acoustic_mapper = ComoSVC(self.cfg) - if self.cfg.model.comosvc.distill: - self.acoustic_mapper.decoder.init_consistency_training() - model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) - return model - - def _inference_each_batch(self, batch_data): - device = self.accelerator.device - for k, v in batch_data.items(): - batch_data[k] = v.to(device) - - cond = self.condition_encoder(batch_data) - mask = batch_data["mask"] - encoder_pred, decoder_pred = self.acoustic_mapper( - mask, cond, self.cfg.inference.comosvc.inference_steps - ) - - return decoder_pred diff --git a/models/svc/comosvc/comosvc_trainer.py b/models/svc/comosvc/comosvc_trainer.py deleted file mode 100644 index 5ba49fd4539b8ae351137a85595ff9cfba1f4677..0000000000000000000000000000000000000000 --- a/models/svc/comosvc/comosvc_trainer.py +++ /dev/null @@ -1,295 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import os -import json5 -from collections import OrderedDict -from tqdm import tqdm -import json -import shutil - -from models.svc.base import SVCTrainer -from modules.encoder.condition_encoder import ConditionEncoder -from models.svc.comosvc.comosvc import ComoSVC - - -class ComoSVCTrainer(SVCTrainer): - r"""The base trainer for all diffusion models. It inherits from SVCTrainer and - implements ``_build_model`` and ``_forward_step`` methods. - """ - - def __init__(self, args=None, cfg=None): - SVCTrainer.__init__(self, args, cfg) - self.distill = cfg.model.comosvc.distill - self.skip_diff = True - if self.distill: # and args.resume is None: - self.teacher_model_path = cfg.model.teacher_model_path - self.teacher_state_dict = self._load_teacher_state_dict() - self._load_teacher_model(self.teacher_state_dict) - self.acoustic_mapper.decoder.init_consistency_training() - - ### Following are methods only for comoSVC models ### - def _load_teacher_state_dict(self): - self.checkpoint_file = self.teacher_model_path - print("Load teacher acoustic model from {}".format(self.checkpoint_file)) - raw_state_dict = torch.load(self.checkpoint_file) # , map_location=self.device) - return raw_state_dict - - def _load_teacher_model(self, state_dict): - raw_dict = state_dict - clean_dict = OrderedDict() - for k, v in raw_dict.items(): - if k.startswith("module."): - clean_dict[k[7:]] = v - else: - clean_dict[k] = v - self.model.load_state_dict(clean_dict) - - def _build_model(self): - r"""Build the model for training. This function is called in ``__init__`` function.""" - - # TODO: sort out the config - self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min - self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max - self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) - self.acoustic_mapper = ComoSVC(self.cfg) - model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) - return model - - def _forward_step(self, batch): - r"""Forward step for training and inference. This function is called - in ``_train_step`` & ``_test_step`` function. - """ - loss = {} - mask = batch["mask"] - mel_input = batch["mel"] - cond = self.condition_encoder(batch) - if self.distill: - cond = cond.detach() - self.skip_diff = True if self.step < self.cfg.train.fast_steps else False - ssim_loss, prior_loss, diff_loss = self.acoustic_mapper.compute_loss( - mask, cond, mel_input, skip_diff=self.skip_diff - ) - if self.distill: - loss["distil_loss"] = diff_loss - else: - loss["ssim_loss_encoder"] = ssim_loss - loss["prior_loss_encoder"] = prior_loss - loss["diffusion_loss_decoder"] = diff_loss - - return loss - - def _train_epoch(self): - r"""Training epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - self.model.train() - epoch_sum_loss: float = 0.0 - epoch_step: int = 0 - for batch in tqdm( - self.train_dataloader, - desc=f"Training Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - # Do training step and BP - with self.accelerator.accumulate(self.model): - loss = self._train_step(batch) - total_loss = 0 - for k, v in loss.items(): - total_loss += v - self.accelerator.backward(total_loss) - enc_grad_norm = torch.nn.utils.clip_grad_norm_( - self.acoustic_mapper.encoder.parameters(), max_norm=1 - ) - dec_grad_norm = torch.nn.utils.clip_grad_norm_( - self.acoustic_mapper.decoder.parameters(), max_norm=1 - ) - self.optimizer.step() - self.optimizer.zero_grad() - self.batch_count += 1 - - # Update info for each step - # TODO: step means BP counts or batch counts? - if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: - epoch_sum_loss += total_loss - log_info = {} - for k, v in loss.items(): - key = "Step/Train Loss/{}".format(k) - log_info[key] = v - log_info["Step/Learning Rate"]: self.optimizer.param_groups[0]["lr"] - self.accelerator.log( - log_info, - step=self.step, - ) - self.step += 1 - epoch_step += 1 - - self.accelerator.wait_for_everyone() - return ( - epoch_sum_loss - / len(self.train_dataloader) - * self.cfg.train.gradient_accumulation_step, - loss, - ) - - def train_loop(self): - r"""Training loop. The public entry of training process.""" - # Wait everyone to prepare before we move on - self.accelerator.wait_for_everyone() - # dump config file - if self.accelerator.is_main_process: - self.__dump_cfg(self.config_save_path) - self.model.train() - self.optimizer.zero_grad() - # Wait to ensure good to go - self.accelerator.wait_for_everyone() - while self.epoch < self.max_epoch: - self.logger.info("\n") - self.logger.info("-" * 32) - self.logger.info("Epoch {}: ".format(self.epoch)) - - ### TODO: change the return values of _train_epoch() to a loss dict, or (total_loss, loss_dict) - ### It's inconvenient for the model with multiple losses - # Do training & validating epoch - train_loss, loss = self._train_epoch() - self.logger.info(" |- Train/Loss: {:.6f}".format(train_loss)) - for k, v in loss.items(): - self.logger.info(" |- Train/Loss/{}: {:.6f}".format(k, v)) - valid_loss = self._valid_epoch() - self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_loss)) - self.accelerator.log( - {"Epoch/Train Loss": train_loss, "Epoch/Valid Loss": valid_loss}, - step=self.epoch, - ) - - self.accelerator.wait_for_everyone() - # TODO: what is scheduler? - self.scheduler.step(valid_loss) # FIXME: use epoch track correct? - - # Check if hit save_checkpoint_stride and run_eval - run_eval = False - if self.accelerator.is_main_process: - save_checkpoint = False - hit_dix = [] - for i, num in enumerate(self.save_checkpoint_stride): - if self.epoch % num == 0: - save_checkpoint = True - hit_dix.append(i) - run_eval |= self.run_eval[i] - - self.accelerator.wait_for_everyone() - if ( - self.accelerator.is_main_process - and save_checkpoint - and (self.distill or not self.skip_diff) - ): - path = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, train_loss - ), - ) - self.accelerator.save_state(path) - json.dump( - self.checkpoints_path, - open(os.path.join(path, "ckpts.json"), "w"), - ensure_ascii=False, - indent=4, - ) - - # Remove old checkpoints - to_remove = [] - for idx in hit_dix: - self.checkpoints_path[idx].append(path) - while len(self.checkpoints_path[idx]) > self.keep_last[idx]: - to_remove.append((idx, self.checkpoints_path[idx].pop(0))) - - # Search conflicts - total = set() - for i in self.checkpoints_path: - total |= set(i) - do_remove = set() - for idx, path in to_remove[::-1]: - if path in total: - self.checkpoints_path[idx].insert(0, path) - else: - do_remove.add(path) - - # Remove old checkpoints - for path in do_remove: - shutil.rmtree(path, ignore_errors=True) - self.logger.debug(f"Remove old checkpoint: {path}") - - self.accelerator.wait_for_everyone() - if run_eval: - # TODO: run evaluation - pass - - # Update info for each epoch - self.epoch += 1 - - # Finish training and save final checkpoint - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process: - self.accelerator.save_state( - os.path.join( - self.checkpoint_dir, - "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, valid_loss - ), - ) - ) - self.accelerator.end_training() - - @torch.inference_mode() - def _valid_epoch(self): - r"""Testing epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - self.model.eval() - epoch_sum_loss = 0.0 - for batch in tqdm( - self.valid_dataloader, - desc=f"Validating Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - batch_loss = self._valid_step(batch) - for k, v in batch_loss.items(): - epoch_sum_loss += v - - self.accelerator.wait_for_everyone() - return epoch_sum_loss / len(self.valid_dataloader) - - @staticmethod - def __count_parameters(model): - model_param = 0.0 - if isinstance(model, dict): - for key, value in model.items(): - model_param += sum(p.numel() for p in model[key].parameters()) - else: - model_param = sum(p.numel() for p in model.parameters()) - return model_param - - def __dump_cfg(self, path): - os.makedirs(os.path.dirname(path), exist_ok=True) - json5.dump( - self.cfg, - open(path, "w"), - indent=4, - sort_keys=True, - ensure_ascii=False, - quote_keys=True, - ) diff --git a/models/svc/comosvc/utils.py b/models/svc/comosvc/utils.py deleted file mode 100644 index 3f576f9a237d0a22ddfdb160122b906da9bcf889..0000000000000000000000000000000000000000 --- a/models/svc/comosvc/utils.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - - -def slice_segments(x, ids_str, segment_size=200): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_ids_segments(lengths, segment_size=200): - b = lengths.shape[0] - ids_str_max = lengths - segment_size - ids_str = (torch.rand([b]).to(device=lengths.device) * ids_str_max).to( - dtype=torch.long - ) - return ids_str - - -def fix_len_compatibility(length, num_downsamplings_in_unet=2): - while True: - if length % (2**num_downsamplings_in_unet) == 0: - return length - length += 1 diff --git a/models/svc/diffusion/__init__.py b/models/svc/diffusion/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/svc/diffusion/diffusion_inference.py b/models/svc/diffusion/diffusion_inference.py deleted file mode 100644 index a752ef8f195b59d0ac0ad402dc35ce5840626ab9..0000000000000000000000000000000000000000 --- a/models/svc/diffusion/diffusion_inference.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from diffusers import DDIMScheduler, DDPMScheduler, PNDMScheduler - -from models.svc.base import SVCInference -from models.svc.diffusion.diffusion_inference_pipeline import DiffusionInferencePipeline -from models.svc.diffusion.diffusion_wrapper import DiffusionWrapper -from modules.encoder.condition_encoder import ConditionEncoder - - -class DiffusionInference(SVCInference): - def __init__(self, args=None, cfg=None, infer_type="from_dataset"): - SVCInference.__init__(self, args, cfg, infer_type) - - settings = { - **cfg.model.diffusion.scheduler_settings, - **cfg.inference.diffusion.scheduler_settings, - } - settings.pop("num_inference_timesteps") - - if cfg.inference.diffusion.scheduler.lower() == "ddpm": - self.scheduler = DDPMScheduler(**settings) - self.logger.info("Using DDPM scheduler.") - elif cfg.inference.diffusion.scheduler.lower() == "ddim": - self.scheduler = DDIMScheduler(**settings) - self.logger.info("Using DDIM scheduler.") - elif cfg.inference.diffusion.scheduler.lower() == "pndm": - self.scheduler = PNDMScheduler(**settings) - self.logger.info("Using PNDM scheduler.") - else: - raise NotImplementedError( - "Unsupported scheduler type: {}".format( - cfg.inference.diffusion.scheduler.lower() - ) - ) - - self.pipeline = DiffusionInferencePipeline( - self.model[1], - self.scheduler, - args.diffusion_inference_steps, - ) - - def _build_model(self): - self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min - self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max - self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) - self.acoustic_mapper = DiffusionWrapper(self.cfg) - model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) - return model - - def _inference_each_batch(self, batch_data): - device = self.accelerator.device - for k, v in batch_data.items(): - batch_data[k] = v.to(device) - - conditioner = self.model[0](batch_data) - noise = torch.randn_like(batch_data["mel"], device=device) - y_pred = self.pipeline(noise, conditioner) - return y_pred diff --git a/models/svc/diffusion/diffusion_inference_pipeline.py b/models/svc/diffusion/diffusion_inference_pipeline.py deleted file mode 100644 index e2461aada99179ac17a2aaffebdb24864af1f5ee..0000000000000000000000000000000000000000 --- a/models/svc/diffusion/diffusion_inference_pipeline.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from diffusers import DiffusionPipeline - - -class DiffusionInferencePipeline(DiffusionPipeline): - def __init__(self, network, scheduler, num_inference_timesteps=1000): - super().__init__() - - self.register_modules(network=network, scheduler=scheduler) - self.num_inference_timesteps = num_inference_timesteps - - @torch.inference_mode() - def __call__( - self, - initial_noise: torch.Tensor, - conditioner: torch.Tensor = None, - ): - r""" - Args: - initial_noise: The initial noise to be denoised. - conditioner:The conditioner. - n_inference_steps: The number of denoising steps. More denoising steps - usually lead to a higher quality at the expense of slower inference. - """ - - mel = initial_noise - batch_size = mel.size(0) - self.scheduler.set_timesteps(self.num_inference_timesteps) - - for t in self.progress_bar(self.scheduler.timesteps): - timestep = torch.full((batch_size,), t, device=mel.device, dtype=torch.long) - - # 1. predict noise model_output - model_output = self.network(mel, timestep, conditioner) - - # 2. denoise, compute previous step: x_t -> x_t-1 - mel = self.scheduler.step(model_output, t, mel).prev_sample - - # 3. clamp - mel = mel.clamp(-1.0, 1.0) - - return mel diff --git a/models/svc/diffusion/diffusion_trainer.py b/models/svc/diffusion/diffusion_trainer.py deleted file mode 100644 index 6f5aeb56a825f84c57bb1d2ba9a5ff5a32d5f486..0000000000000000000000000000000000000000 --- a/models/svc/diffusion/diffusion_trainer.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from diffusers import DDPMScheduler - -from models.svc.base import SVCTrainer -from modules.encoder.condition_encoder import ConditionEncoder -from .diffusion_wrapper import DiffusionWrapper - - -class DiffusionTrainer(SVCTrainer): - r"""The base trainer for all diffusion models. It inherits from SVCTrainer and - implements ``_build_model`` and ``_forward_step`` methods. - """ - - def __init__(self, args=None, cfg=None): - SVCTrainer.__init__(self, args, cfg) - - # Only for SVC tasks using diffusion - self.noise_scheduler = DDPMScheduler( - **self.cfg.model.diffusion.scheduler_settings, - ) - self.diffusion_timesteps = ( - self.cfg.model.diffusion.scheduler_settings.num_train_timesteps - ) - - ### Following are methods only for diffusion models ### - def _build_model(self): - r"""Build the model for training. This function is called in ``__init__`` function.""" - - # TODO: sort out the config - self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min - self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max - self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) - self.acoustic_mapper = DiffusionWrapper(self.cfg) - model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) - - num_of_params_encoder = self.count_parameters(self.condition_encoder) - num_of_params_am = self.count_parameters(self.acoustic_mapper) - num_of_params = num_of_params_encoder + num_of_params_am - log = "Diffusion Model's Parameters: #Encoder is {:.2f}M, #Diffusion is {:.2f}M. The total is {:.2f}M".format( - num_of_params_encoder / 1e6, num_of_params_am / 1e6, num_of_params / 1e6 - ) - self.logger.info(log) - - return model - - def count_parameters(self, model): - model_param = 0.0 - if isinstance(model, dict): - for key, value in model.items(): - model_param += sum(p.numel() for p in model[key].parameters()) - else: - model_param = sum(p.numel() for p in model.parameters()) - return model_param - - def _forward_step(self, batch): - r"""Forward step for training and inference. This function is called - in ``_train_step`` & ``_test_step`` function. - """ - - device = self.accelerator.device - - mel_input = batch["mel"] - noise = torch.randn_like(mel_input, device=device, dtype=torch.float32) - batch_size = mel_input.size(0) - timesteps = torch.randint( - 0, - self.diffusion_timesteps, - (batch_size,), - device=device, - dtype=torch.long, - ) - - noisy_mel = self.noise_scheduler.add_noise(mel_input, noise, timesteps) - conditioner = self.condition_encoder(batch) - - y_pred = self.acoustic_mapper(noisy_mel, timesteps, conditioner) - - # TODO: Predict noise or gt should be configurable - loss = self._compute_loss(self.criterion, y_pred, noise, batch["mask"]) - self._check_nan(loss, y_pred, noise) - - # FIXME: Clarify that we should not divide it with batch size here - return loss diff --git a/models/svc/diffusion/diffusion_wrapper.py b/models/svc/diffusion/diffusion_wrapper.py deleted file mode 100644 index ef66c2b6b85ceb8fe7a2cf9b53c62edc6b3ef6bc..0000000000000000000000000000000000000000 --- a/models/svc/diffusion/diffusion_wrapper.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn - -from modules.diffusion import BiDilConv -from modules.encoder.position_encoder import PositionEncoder - - -class DiffusionWrapper(nn.Module): - def __init__(self, cfg): - super().__init__() - - self.cfg = cfg - self.diff_cfg = cfg.model.diffusion - - self.diff_encoder = PositionEncoder( - d_raw_emb=self.diff_cfg.step_encoder.dim_raw_embedding, - d_out=self.diff_cfg.bidilconv.base_channel, - d_mlp=self.diff_cfg.step_encoder.dim_hidden_layer, - activation_function=self.diff_cfg.step_encoder.activation, - n_layer=self.diff_cfg.step_encoder.num_layer, - max_period=self.diff_cfg.step_encoder.max_period, - ) - - # FIXME: Only support BiDilConv now for debug - if self.diff_cfg.model_type.lower() == "bidilconv": - self.neural_network = BiDilConv( - input_channel=self.cfg.preprocess.n_mel, **self.diff_cfg.bidilconv - ) - else: - raise ValueError( - f"Unsupported diffusion model type: {self.diff_cfg.model_type}" - ) - - def forward(self, x, t, c): - """ - Args: - x: [N, T, mel_band] of mel spectrogram - t: Diffusion time step with shape of [N] - c: [N, T, conditioner_size] of conditioner - - Returns: - [N, T, mel_band] of mel spectrogram - """ - - assert ( - x.size()[:-1] == c.size()[:-1] - ), "x mismatch with c, got \n x: {} \n c: {}".format(x.size(), c.size()) - assert x.size(0) == t.size( - 0 - ), "x mismatch with t, got \n x: {} \n t: {}".format(x.size(), t.size()) - assert t.dim() == 1, "t must be 1D tensor, got {}".format(t.dim()) - - N, T, mel_band = x.size() - - x = x.transpose(1, 2).contiguous() # [N, mel_band, T] - c = c.transpose(1, 2).contiguous() # [N, conditioner_size, T] - t = self.diff_encoder(t).contiguous() # [N, base_channel] - - h = self.neural_network(x, t, c) - h = h.transpose(1, 2).contiguous() # [N, T, mel_band] - - assert h.size() == ( - N, - T, - mel_band, - ), "h mismatch with input x, got \n h: {} \n x: {}".format( - h.size(), (N, T, mel_band) - ) - return h diff --git a/models/svc/transformer/__init__.py b/models/svc/transformer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/svc/transformer/conformer.py b/models/svc/transformer/conformer.py deleted file mode 100644 index 5e48019cfc17d5f3825ce989f4852cec55fe1daa..0000000000000000000000000000000000000000 --- a/models/svc/transformer/conformer.py +++ /dev/null @@ -1,405 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import torch -import numpy as np -import torch.nn as nn -from utils.util import convert_pad_shape - - -class BaseModule(torch.nn.Module): - def __init__(self): - super(BaseModule, self).__init__() - - @property - def nparams(self): - """ - Returns number of trainable parameters of the module. - """ - num_params = 0 - for name, param in self.named_parameters(): - if param.requires_grad: - num_params += np.prod(param.detach().cpu().numpy().shape) - return num_params - - def relocate_input(self, x: list): - """ - Relocates provided tensors to the same device set for the module. - """ - device = next(self.parameters()).device - for i in range(len(x)): - if isinstance(x[i], torch.Tensor) and x[i].device != device: - x[i] = x[i].to(device) - return x - - -class LayerNorm(BaseModule): - def __init__(self, channels, eps=1e-4): - super(LayerNorm, self).__init__() - self.channels = channels - self.eps = eps - - self.gamma = torch.nn.Parameter(torch.ones(channels)) - self.beta = torch.nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - n_dims = len(x.shape) - mean = torch.mean(x, 1, keepdim=True) - variance = torch.mean((x - mean) ** 2, 1, keepdim=True) - - x = (x - mean) * torch.rsqrt(variance + self.eps) - - shape = [1, -1] + [1] * (n_dims - 2) - x = x * self.gamma.view(*shape) + self.beta.view(*shape) - return x - - -class ConvReluNorm(BaseModule): - def __init__( - self, - in_channels, - hidden_channels, - out_channels, - kernel_size, - n_layers, - p_dropout, - eps=1e-5, - ): - super(ConvReluNorm, self).__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - self.eps = eps - - self.conv_layers = torch.nn.ModuleList() - self.conv_layers.append( - torch.nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.relu_drop = torch.nn.Sequential( - torch.nn.ReLU(), torch.nn.Dropout(p_dropout) - ) - for _ in range(n_layers - 1): - self.conv_layers.append( - torch.nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.instance_norm(x, x_mask) - x = self.relu_drop(x) - x = self.proj(x) - return x * x_mask - - def instance_norm(self, x, mask, return_mean_std=False): - mean, std = self.calc_mean_std(x, mask) - x = (x - mean) / std - if return_mean_std: - return x, mean, std - else: - return x - - def calc_mean_std(self, x, mask=None): - x = x * mask - B, C = x.shape[:2] - mn = x.view(B, C, -1).mean(-1) - sd = (x.view(B, C, -1).var(-1) + self.eps).sqrt() - mn = mn.view(B, C, *((len(x.shape) - 2) * [1])) - sd = sd.view(B, C, *((len(x.shape) - 2) * [1])) - return mn, sd - - -class MultiHeadAttention(BaseModule): - def __init__( - self, - channels, - out_channels, - n_heads, - window_size=None, - heads_share=True, - p_dropout=0.0, - proximal_bias=False, - proximal_init=False, - ): - super(MultiHeadAttention, self).__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.window_size = window_size - self.heads_share = heads_share - self.proximal_bias = proximal_bias - self.p_dropout = p_dropout - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = torch.nn.Conv1d(channels, channels, 1) - self.conv_k = torch.nn.Conv1d(channels, channels, 1) - self.conv_v = torch.nn.Conv1d(channels, channels, 1) - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = torch.nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.emb_rel_v = torch.nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) - self.drop = torch.nn.Dropout(p_dropout) - - torch.nn.init.xavier_uniform_(self.conv_q.weight) - torch.nn.init.xavier_uniform_(self.conv_k.weight) - if proximal_init: - self.conv_k.weight.data.copy_(self.conv_q.weight.data) - self.conv_k.bias.data.copy_(self.conv_q.bias.data) - torch.nn.init.xavier_uniform_(self.conv_v.weight) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) - if self.window_size is not None: - assert ( - t_s == t_t - ), "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) - rel_logits = self._relative_position_to_absolute_position(rel_logits) - scores_local = rel_logits / math.sqrt(self.k_channels) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to( - device=scores.device, dtype=scores.dtype - ) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - p_attn = torch.nn.functional.softmax(scores, dim=-1) - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings( - self.emb_rel_v, t_s - ) - output = output + self._matmul_with_relative_values( - relative_weights, value_relative_embeddings - ) - output = output.transpose(2, 3).contiguous().view(b, d, t_t) - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = torch.nn.functional.pad( - relative_embeddings, - convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), - ) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[ - :, slice_start_position:slice_end_position - ] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - batch, heads, length, _ = x.size() - x = torch.nn.functional.pad( - x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]) - ) - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = torch.nn.functional.pad( - x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) - ) - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ - :, :, :length, length - 1 : - ] - return x_final - - def _absolute_position_to_relative_position(self, x): - batch, heads, length, _ = x.size() - x = torch.nn.functional.pad( - x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) - ) - x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) - x_flat = torch.nn.functional.pad( - x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]) - ) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(BaseModule): - def __init__( - self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0 - ): - super(FFN, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.conv_1 = torch.nn.Conv1d( - in_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.conv_2 = torch.nn.Conv1d( - filter_channels, out_channels, kernel_size, padding=kernel_size // 2 - ) - self.drop = torch.nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - return x * x_mask - - -class Encoder(BaseModule): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads=2, - n_layers=6, - kernel_size=3, - p_dropout=0.1, - window_size=4, - **kwargs - ): - super(Encoder, self).__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - - self.drop = torch.nn.Dropout(p_dropout) - self.attn_layers = torch.nn.ModuleList() - self.norm_layers_1 = torch.nn.ModuleList() - self.ffn_layers = torch.nn.ModuleList() - self.norm_layers_2 = torch.nn.ModuleList() - for _ in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - window_size=window_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - for i in range(self.n_layers): - x = x * x_mask - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Conformer(BaseModule): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - self.n_heads = self.cfg.n_heads - self.n_layers = self.cfg.n_layers - self.hidden_channels = self.cfg.input_dim - self.filter_channels = self.cfg.filter_channels - self.output_dim = self.cfg.output_dim - self.dropout = self.cfg.dropout - - self.conformer_encoder = Encoder( - self.hidden_channels, - self.filter_channels, - n_heads=self.n_heads, - n_layers=self.n_layers, - kernel_size=3, - p_dropout=self.dropout, - window_size=4, - ) - self.projection = nn.Conv1d(self.hidden_channels, self.output_dim, 1) - - def forward(self, x, x_mask): - """ - Args: - x: (N, seq_len, input_dim) - Returns: - output: (N, seq_len, output_dim) - """ - # (N, seq_len, d_model) - x = x.transpose(1, 2) - x_mask = x_mask.transpose(1, 2) - output = self.conformer_encoder(x, x_mask) - # (N, seq_len, output_dim) - output = self.projection(output) - output = output.transpose(1, 2) - return output diff --git a/models/svc/transformer/transformer.py b/models/svc/transformer/transformer.py deleted file mode 100644 index fd3cdb6c2d0fc93534d005b9f67a3058c9185c60..0000000000000000000000000000000000000000 --- a/models/svc/transformer/transformer.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import torch -import torch.nn as nn -from torch.nn import TransformerEncoder, TransformerEncoderLayer - - -class Transformer(nn.Module): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - - dropout = self.cfg.dropout - nhead = self.cfg.n_heads - nlayers = self.cfg.n_layers - input_dim = self.cfg.input_dim - output_dim = self.cfg.output_dim - - d_model = input_dim - self.pos_encoder = PositionalEncoding(d_model, dropout) - encoder_layers = TransformerEncoderLayer( - d_model, nhead, dropout=dropout, batch_first=True - ) - self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) - - self.output_mlp = nn.Linear(d_model, output_dim) - - def forward(self, x, mask=None): - """ - Args: - x: (N, seq_len, input_dim) - Returns: - output: (N, seq_len, output_dim) - """ - # (N, seq_len, d_model) - src = self.pos_encoder(x) - # model_stats["pos_embedding"] = x - # (N, seq_len, d_model) - output = self.transformer_encoder(src) - # (N, seq_len, output_dim) - output = self.output_mlp(output) - return output - - -class PositionalEncoding(nn.Module): - def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): - super().__init__() - self.dropout = nn.Dropout(p=dropout) - - position = torch.arange(max_len).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) - ) - - # Assume that x is (seq_len, N, d) - # pe = torch.zeros(max_len, 1, d_model) - # pe[:, 0, 0::2] = torch.sin(position * div_term) - # pe[:, 0, 1::2] = torch.cos(position * div_term) - - # Assume that x in (N, seq_len, d) - pe = torch.zeros(1, max_len, d_model) - pe[0, :, 0::2] = torch.sin(position * div_term) - pe[0, :, 1::2] = torch.cos(position * div_term) - - self.register_buffer("pe", pe) - - def forward(self, x): - """ - Args: - x: Tensor, shape [N, seq_len, d] - """ - # Old: Assume that x is (seq_len, N, d), and self.pe is (max_len, 1, d_model) - # x = x + self.pe[: x.size(0)] - - # Now: self.pe is (1, max_len, d) - x = x + self.pe[:, : x.size(1), :] - - return self.dropout(x) diff --git a/models/svc/transformer/transformer_inference.py b/models/svc/transformer/transformer_inference.py deleted file mode 100644 index f6299c532aec6cb9283ee87ee9f0142f0b5c981b..0000000000000000000000000000000000000000 --- a/models/svc/transformer/transformer_inference.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import time -import numpy as np -import torch -from tqdm import tqdm -import torch.nn as nn -from collections import OrderedDict - -from models.svc.base import SVCInference -from modules.encoder.condition_encoder import ConditionEncoder -from models.svc.transformer.transformer import Transformer -from models.svc.transformer.conformer import Conformer - - -class TransformerInference(SVCInference): - def __init__(self, args=None, cfg=None, infer_type="from_dataset"): - SVCInference.__init__(self, args, cfg, infer_type) - - def _build_model(self): - self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min - self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max - self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) - if self.cfg.model.transformer.type == "transformer": - self.acoustic_mapper = Transformer(self.cfg.model.transformer) - elif self.cfg.model.transformer.type == "conformer": - self.acoustic_mapper = Conformer(self.cfg.model.transformer) - else: - raise NotImplementedError - model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) - return model - - def _inference_each_batch(self, batch_data): - device = self.accelerator.device - for k, v in batch_data.items(): - batch_data[k] = v.to(device) - - condition = self.condition_encoder(batch_data) - y_pred = self.acoustic_mapper(condition, batch_data["mask"]) - - return y_pred diff --git a/models/svc/transformer/transformer_trainer.py b/models/svc/transformer/transformer_trainer.py deleted file mode 100644 index 3633078475d26e708280bc354f091bb9ab01ae45..0000000000000000000000000000000000000000 --- a/models/svc/transformer/transformer_trainer.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from models.svc.base import SVCTrainer -from modules.encoder.condition_encoder import ConditionEncoder -from models.svc.transformer.transformer import Transformer -from models.svc.transformer.conformer import Conformer -from utils.ssim import SSIM - - -class TransformerTrainer(SVCTrainer): - def __init__(self, args, cfg): - SVCTrainer.__init__(self, args, cfg) - self.ssim_loss = SSIM() - - def _build_model(self): - self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min - self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max - self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) - if self.cfg.model.transformer.type == "transformer": - self.acoustic_mapper = Transformer(self.cfg.model.transformer) - elif self.cfg.model.transformer.type == "conformer": - self.acoustic_mapper = Conformer(self.cfg.model.transformer) - else: - raise NotImplementedError - model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) - return model - - def _forward_step(self, batch): - total_loss = 0 - device = self.accelerator.device - mel = batch["mel"] - mask = batch["mask"] - - condition = self.condition_encoder(batch) - mel_pred = self.acoustic_mapper(condition, mask) - - l1_loss = torch.sum(torch.abs(mel_pred - mel) * batch["mask"]) / torch.sum( - batch["mask"] - ) - self._check_nan(l1_loss, mel_pred, mel) - total_loss += l1_loss - ssim_loss = self.ssim_loss(mel_pred, mel) - ssim_loss = torch.sum(ssim_loss * batch["mask"]) / torch.sum(batch["mask"]) - self._check_nan(ssim_loss, mel_pred, mel) - total_loss += ssim_loss - - return total_loss diff --git a/models/vocoders/autoregressive/autoregressive_vocoder_dataset.py b/models/vocoders/autoregressive/autoregressive_vocoder_dataset.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/autoregressive/autoregressive_vocoder_inference.py b/models/vocoders/autoregressive/autoregressive_vocoder_inference.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/autoregressive/autoregressive_vocoder_trainer.py b/models/vocoders/autoregressive/autoregressive_vocoder_trainer.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/autoregressive/wavenet/conv.py b/models/vocoders/autoregressive/wavenet/conv.py deleted file mode 100644 index a095aad5d7203f6e5fb5a4d585b894e34dbe63c7..0000000000000000000000000000000000000000 --- a/models/vocoders/autoregressive/wavenet/conv.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from torch import nn -from torch.nn import functional as F - - -class Conv1d(nn.Conv1d): - """Extended nn.Conv1d for incremental dilated convolutions""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.clear_buffer() - self._linearized_weight = None - self.register_backward_hook(self._clear_linearized_weight) - - def incremental_forward(self, input): - # input (B, T, C) - # run forward pre hooks - for hook in self._forward_pre_hooks.values(): - hook(self, input) - - # reshape weight - weight = self._get_linearized_weight() - kw = self.kernel_size[0] - dilation = self.dilation[0] - - bsz = input.size(0) - if kw > 1: - input = input.data - if self.input_buffer is None: - self.input_buffer = input.new( - bsz, kw + (kw - 1) * (dilation - 1), input.size(2) - ) - self.input_buffer.zero_() - else: - # shift buffer - self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone() - # append next input - self.input_buffer[:, -1, :] = input[:, -1, :] - input = self.input_buffer - if dilation > 1: - input = input[:, 0::dilation, :].contiguous() - output = F.linear(input.view(bsz, -1), weight, self.bias) - return output.view(bsz, 1, -1) - - def clear_buffer(self): - self.input_buffer = None - - def _get_linearized_weight(self): - if self._linearized_weight is None: - kw = self.kernel_size[0] - # nn.Conv1d - if self.weight.size() == (self.out_channels, self.in_channels, kw): - weight = self.weight.transpose(1, 2).contiguous() - else: - # fairseq.modules.conv_tbc.ConvTBC - weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous() - assert weight.size() == (self.out_channels, kw, self.in_channels) - self._linearized_weight = weight.view(self.out_channels, -1) - return self._linearized_weight - - def _clear_linearized_weight(self, *args): - self._linearized_weight = None diff --git a/models/vocoders/autoregressive/wavenet/modules.py b/models/vocoders/autoregressive/wavenet/modules.py deleted file mode 100644 index 13d51e52a50af3bc1f7fe9627aeae8d2b1b28b7d..0000000000000000000000000000000000000000 --- a/models/vocoders/autoregressive/wavenet/modules.py +++ /dev/null @@ -1,152 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import math - -from torch import nn -from torch.nn import functional as F - -from .conv import Conv1d as conv_Conv1d - - -def Conv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs): - m = conv_Conv1d(in_channels, out_channels, kernel_size, **kwargs) - nn.init.kaiming_normal_(m.weight, nonlinearity="relu") - if m.bias is not None: - nn.init.constant_(m.bias, 0) - return nn.utils.weight_norm(m) - - -def Conv1d1x1(in_channels, out_channels, bias=True): - return Conv1d( - in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias - ) - - -def _conv1x1_forward(conv, x, is_incremental): - if is_incremental: - x = conv.incremental_forward(x) - else: - x = conv(x) - return x - - -class ResidualConv1dGLU(nn.Module): - """Residual dilated conv1d + Gated linear unit - - Args: - residual_channels (int): Residual input / output channels - gate_channels (int): Gated activation channels. - kernel_size (int): Kernel size of convolution layers. - skip_out_channels (int): Skip connection channels. If None, set to same - as ``residual_channels``. - cin_channels (int): Local conditioning channels. If negative value is - set, local conditioning is disabled. - dropout (float): Dropout probability. - padding (int): Padding for convolution layers. If None, proper padding - is computed depends on dilation and kernel_size. - dilation (int): Dilation factor. - """ - - def __init__( - self, - residual_channels, - gate_channels, - kernel_size, - skip_out_channels=None, - cin_channels=-1, - dropout=1 - 0.95, - padding=None, - dilation=1, - causal=True, - bias=True, - *args, - **kwargs, - ): - super(ResidualConv1dGLU, self).__init__() - self.dropout = dropout - - if skip_out_channels is None: - skip_out_channels = residual_channels - if padding is None: - # no future time stamps available - if causal: - padding = (kernel_size - 1) * dilation - else: - padding = (kernel_size - 1) // 2 * dilation - self.causal = causal - - self.conv = Conv1d( - residual_channels, - gate_channels, - kernel_size, - padding=padding, - dilation=dilation, - bias=bias, - *args, - **kwargs, - ) - - # mel conditioning - self.conv1x1c = Conv1d1x1(cin_channels, gate_channels, bias=False) - - gate_out_channels = gate_channels // 2 - self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias) - self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_out_channels, bias=bias) - - def forward(self, x, c=None): - return self._forward(x, c, False) - - def incremental_forward(self, x, c=None): - return self._forward(x, c, True) - - def clear_buffer(self): - for c in [ - self.conv, - self.conv1x1_out, - self.conv1x1_skip, - self.conv1x1c, - ]: - if c is not None: - c.clear_buffer() - - def _forward(self, x, c, is_incremental): - """Forward - - Args: - x (Tensor): B x C x T - c (Tensor): B x C x T, Mel conditioning features - Returns: - Tensor: output - """ - residual = x - x = F.dropout(x, p=self.dropout, training=self.training) - if is_incremental: - splitdim = -1 - x = self.conv.incremental_forward(x) - else: - splitdim = 1 - x = self.conv(x) - # remove future time steps - x = x[:, :, : residual.size(-1)] if self.causal else x - - a, b = x.split(x.size(splitdim) // 2, dim=splitdim) - - assert self.conv1x1c is not None - c = _conv1x1_forward(self.conv1x1c, c, is_incremental) - ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim) - a, b = a + ca, b + cb - - x = torch.tanh(a) * torch.sigmoid(b) - - # For skip connection - s = _conv1x1_forward(self.conv1x1_skip, x, is_incremental) - - # For residual connection - x = _conv1x1_forward(self.conv1x1_out, x, is_incremental) - - x = (x + residual) * math.sqrt(0.5) - return x, s diff --git a/models/vocoders/autoregressive/wavenet/upsample.py b/models/vocoders/autoregressive/wavenet/upsample.py deleted file mode 100644 index b664302cd56545f1709a4f1874ebadd8e9375a9c..0000000000000000000000000000000000000000 --- a/models/vocoders/autoregressive/wavenet/upsample.py +++ /dev/null @@ -1,109 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -import numpy as np - -from torch import nn -from torch.nn import functional as F - - -class Stretch2d(nn.Module): - def __init__(self, x_scale, y_scale, mode="nearest"): - super(Stretch2d, self).__init__() - self.x_scale = x_scale - self.y_scale = y_scale - self.mode = mode - - def forward(self, x): - return F.interpolate( - x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode - ) - - -def _get_activation(upsample_activation): - nonlinear = getattr(nn, upsample_activation) - return nonlinear - - -class UpsampleNetwork(nn.Module): - def __init__( - self, - upsample_scales, - upsample_activation="none", - upsample_activation_params={}, - mode="nearest", - freq_axis_kernel_size=1, - cin_pad=0, - cin_channels=128, - ): - super(UpsampleNetwork, self).__init__() - self.up_layers = nn.ModuleList() - total_scale = np.prod(upsample_scales) - self.indent = cin_pad * total_scale - for scale in upsample_scales: - freq_axis_padding = (freq_axis_kernel_size - 1) // 2 - k_size = (freq_axis_kernel_size, scale * 2 + 1) - padding = (freq_axis_padding, scale) - stretch = Stretch2d(scale, 1, mode) - conv = nn.Conv2d(1, 1, kernel_size=k_size, padding=padding, bias=False) - conv.weight.data.fill_(1.0 / np.prod(k_size)) - conv = nn.utils.weight_norm(conv) - self.up_layers.append(stretch) - self.up_layers.append(conv) - if upsample_activation != "none": - nonlinear = _get_activation(upsample_activation) - self.up_layers.append(nonlinear(**upsample_activation_params)) - - def forward(self, c): - """ - Args: - c : B x C x T - """ - - # B x 1 x C x T - c = c.unsqueeze(1) - for f in self.up_layers: - c = f(c) - # B x C x T - c = c.squeeze(1) - - if self.indent > 0: - c = c[:, :, self.indent : -self.indent] - return c - - -class ConvInUpsampleNetwork(nn.Module): - def __init__( - self, - upsample_scales, - upsample_activation="none", - upsample_activation_params={}, - mode="nearest", - freq_axis_kernel_size=1, - cin_pad=0, - cin_channels=128, - ): - super(ConvInUpsampleNetwork, self).__init__() - # To capture wide-context information in conditional features - # meaningless if cin_pad == 0 - ks = 2 * cin_pad + 1 - self.conv_in = nn.Conv1d( - cin_channels, cin_channels, kernel_size=ks, padding=cin_pad, bias=False - ) - self.upsample = UpsampleNetwork( - upsample_scales, - upsample_activation, - upsample_activation_params, - mode, - freq_axis_kernel_size, - cin_pad=cin_pad, - cin_channels=cin_channels, - ) - - def forward(self, c): - c_up = self.upsample(self.conv_in(c)) - return c_up diff --git a/models/vocoders/autoregressive/wavenet/wavenet.py b/models/vocoders/autoregressive/wavenet/wavenet.py deleted file mode 100644 index d63f22c2600fd0f83e5bdf339ebb121b3d2f35e6..0000000000000000000000000000000000000000 --- a/models/vocoders/autoregressive/wavenet/wavenet.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -from torch import nn -from torch.nn import functional as F - -from .modules import Conv1d1x1, ResidualConv1dGLU -from .upsample import ConvInUpsampleNetwork - - -def receptive_field_size( - total_layers, num_cycles, kernel_size, dilation=lambda x: 2**x -): - """Compute receptive field size - - Args: - total_layers (int): total layers - num_cycles (int): cycles - kernel_size (int): kernel size - dilation (lambda): lambda to compute dilation factor. ``lambda x : 1`` - to disable dilated convolution. - - Returns: - int: receptive field size in sample - - """ - assert total_layers % num_cycles == 0 - - layers_per_cycle = total_layers // num_cycles - dilations = [dilation(i % layers_per_cycle) for i in range(total_layers)] - return (kernel_size - 1) * sum(dilations) + 1 - - -class WaveNet(nn.Module): - """The WaveNet model that supports local and global conditioning. - - Args: - out_channels (int): Output channels. If input_type is mu-law quantized - one-hot vecror. this must equal to the quantize channels. Other wise - num_mixtures x 3 (pi, mu, log_scale). - layers (int): Number of total layers - stacks (int): Number of dilation cycles - residual_channels (int): Residual input / output channels - gate_channels (int): Gated activation channels. - skip_out_channels (int): Skip connection channels. - kernel_size (int): Kernel size of convolution layers. - dropout (float): Dropout probability. - input_dim (int): Number of mel-spec dimension. - upsample_scales (list): List of upsample scale. - ``np.prod(upsample_scales)`` must equal to hop size. Used only if - upsample_conditional_features is enabled. - freq_axis_kernel_size (int): Freq-axis kernel_size for transposed - convolution layers for upsampling. If you only care about time-axis - upsampling, set this to 1. - scalar_input (Bool): If True, scalar input ([-1, 1]) is expected, otherwise - quantized one-hot vector is expected.. - """ - - def __init__(self, cfg): - super(WaveNet, self).__init__() - self.cfg = cfg - self.scalar_input = self.cfg.VOCODER.SCALAR_INPUT - self.out_channels = self.cfg.VOCODER.OUT_CHANNELS - self.cin_channels = self.cfg.VOCODER.INPUT_DIM - self.residual_channels = self.cfg.VOCODER.RESIDUAL_CHANNELS - self.layers = self.cfg.VOCODER.LAYERS - self.stacks = self.cfg.VOCODER.STACKS - self.gate_channels = self.cfg.VOCODER.GATE_CHANNELS - self.kernel_size = self.cfg.VOCODER.KERNEL_SIZE - self.skip_out_channels = self.cfg.VOCODER.SKIP_OUT_CHANNELS - self.dropout = self.cfg.VOCODER.DROPOUT - self.upsample_scales = self.cfg.VOCODER.UPSAMPLE_SCALES - self.mel_frame_pad = self.cfg.VOCODER.MEL_FRAME_PAD - - assert self.layers % self.stacks == 0 - - layers_per_stack = self.layers // self.stacks - if self.scalar_input: - self.first_conv = Conv1d1x1(1, self.residual_channels) - else: - self.first_conv = Conv1d1x1(self.out_channels, self.residual_channels) - - self.conv_layers = nn.ModuleList() - for layer in range(self.layers): - dilation = 2 ** (layer % layers_per_stack) - conv = ResidualConv1dGLU( - self.residual_channels, - self.gate_channels, - kernel_size=self.kernel_size, - skip_out_channels=self.skip_out_channels, - bias=True, - dilation=dilation, - dropout=self.dropout, - cin_channels=self.cin_channels, - ) - self.conv_layers.append(conv) - - self.last_conv_layers = nn.ModuleList( - [ - nn.ReLU(inplace=True), - Conv1d1x1(self.skip_out_channels, self.skip_out_channels), - nn.ReLU(inplace=True), - Conv1d1x1(self.skip_out_channels, self.out_channels), - ] - ) - - self.upsample_net = ConvInUpsampleNetwork( - upsample_scales=self.upsample_scales, - cin_pad=self.mel_frame_pad, - cin_channels=self.cin_channels, - ) - - self.receptive_field = receptive_field_size( - self.layers, self.stacks, self.kernel_size - ) - - def forward(self, x, mel, softmax=False): - """Forward step - - Args: - x (Tensor): One-hot encoded audio signal, shape (B x C x T) - mel (Tensor): Local conditioning features, - shape (B x cin_channels x T) - softmax (bool): Whether applies softmax or not. - - Returns: - Tensor: output, shape B x out_channels x T - """ - B, _, T = x.size() - - mel = self.upsample_net(mel) - assert mel.shape[-1] == x.shape[-1] - - x = self.first_conv(x) - skips = 0 - for f in self.conv_layers: - x, h = f(x, mel) - skips += h - skips *= math.sqrt(1.0 / len(self.conv_layers)) - - x = skips - for f in self.last_conv_layers: - x = f(x) - - x = F.softmax(x, dim=1) if softmax else x - - return x - - def clear_buffer(self): - self.first_conv.clear_buffer() - for f in self.conv_layers: - f.clear_buffer() - for f in self.last_conv_layers: - try: - f.clear_buffer() - except AttributeError: - pass - - def make_generation_fast_(self): - def remove_weight_norm(m): - try: - nn.utils.remove_weight_norm(m) - except ValueError: # this module didn't have weight norm - return - - self.apply(remove_weight_norm) diff --git a/models/vocoders/autoregressive/wavernn/wavernn.py b/models/vocoders/autoregressive/wavernn/wavernn.py deleted file mode 100644 index c7475fa8fe8b4575bf714e615349582ff98bbc27..0000000000000000000000000000000000000000 --- a/models/vocoders/autoregressive/wavernn/wavernn.py +++ /dev/null @@ -1,188 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - -import numpy as np - - -class ResBlock(nn.Module): - def __init__(self, dims): - super().__init__() - self.conv1 = nn.Conv1d(dims, dims, kernel_size=1, bias=False) - self.conv2 = nn.Conv1d(dims, dims, kernel_size=1, bias=False) - self.batch_norm1 = nn.BatchNorm1d(dims) - self.batch_norm2 = nn.BatchNorm1d(dims) - - def forward(self, x): - residual = x - x = self.conv1(x) - x = self.batch_norm1(x) - x = F.relu(x) - x = self.conv2(x) - x = self.batch_norm2(x) - x = x + residual - return x - - -class MelResNet(nn.Module): - def __init__(self, res_blocks, in_dims, compute_dims, res_out_dims, pad): - super().__init__() - kernel_size = pad * 2 + 1 - self.conv_in = nn.Conv1d( - in_dims, compute_dims, kernel_size=kernel_size, bias=False - ) - self.batch_norm = nn.BatchNorm1d(compute_dims) - self.layers = nn.ModuleList() - for i in range(res_blocks): - self.layers.append(ResBlock(compute_dims)) - self.conv_out = nn.Conv1d(compute_dims, res_out_dims, kernel_size=1) - - def forward(self, x): - x = self.conv_in(x) - x = self.batch_norm(x) - x = F.relu(x) - for f in self.layers: - x = f(x) - x = self.conv_out(x) - return x - - -class Stretch2d(nn.Module): - def __init__(self, x_scale, y_scale): - super().__init__() - self.x_scale = x_scale - self.y_scale = y_scale - - def forward(self, x): - b, c, h, w = x.size() - x = x.unsqueeze(-1).unsqueeze(3) - x = x.repeat(1, 1, 1, self.y_scale, 1, self.x_scale) - return x.view(b, c, h * self.y_scale, w * self.x_scale) - - -class UpsampleNetwork(nn.Module): - def __init__( - self, feat_dims, upsample_scales, compute_dims, res_blocks, res_out_dims, pad - ): - super().__init__() - total_scale = np.cumproduct(upsample_scales)[-1] - self.indent = pad * total_scale - self.resnet = MelResNet(res_blocks, feat_dims, compute_dims, res_out_dims, pad) - self.resnet_stretch = Stretch2d(total_scale, 1) - self.up_layers = nn.ModuleList() - for scale in upsample_scales: - kernel_size = (1, scale * 2 + 1) - padding = (0, scale) - stretch = Stretch2d(scale, 1) - conv = nn.Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False) - conv.weight.data.fill_(1.0 / kernel_size[1]) - self.up_layers.append(stretch) - self.up_layers.append(conv) - - def forward(self, m): - aux = self.resnet(m).unsqueeze(1) - aux = self.resnet_stretch(aux) - aux = aux.squeeze(1) - m = m.unsqueeze(1) - for f in self.up_layers: - m = f(m) - m = m.squeeze(1)[:, :, self.indent : -self.indent] - return m.transpose(1, 2), aux.transpose(1, 2) - - -class WaveRNN(nn.Module): - def __init__(self, cfg): - super().__init__() - - self.cfg = cfg - self.pad = self.cfg.VOCODER.MEL_FRAME_PAD - - if self.cfg.VOCODER.MODE == "mu_law_quantize": - self.n_classes = 2**self.cfg.VOCODER.BITS - elif self.cfg.VOCODER.MODE == "mu_law" or self.cfg.VOCODER: - self.n_classes = 30 - - self._to_flatten = [] - - self.rnn_dims = self.cfg.VOCODER.RNN_DIMS - self.aux_dims = self.cfg.VOCODER.RES_OUT_DIMS // 4 - self.hop_length = self.cfg.VOCODER.HOP_LENGTH - self.fc_dims = self.cfg.VOCODER.FC_DIMS - self.upsample_factors = self.cfg.VOCODER.UPSAMPLE_FACTORS - self.feat_dims = self.cfg.VOCODER.INPUT_DIM - self.compute_dims = self.cfg.VOCODER.COMPUTE_DIMS - self.res_out_dims = self.cfg.VOCODER.RES_OUT_DIMS - self.res_blocks = self.cfg.VOCODER.RES_BLOCKS - - self.upsample = UpsampleNetwork( - self.feat_dims, - self.upsample_factors, - self.compute_dims, - self.res_blocks, - self.res_out_dims, - self.pad, - ) - self.I = nn.Linear(self.feat_dims + self.aux_dims + 1, self.rnn_dims) - - self.rnn1 = nn.GRU(self.rnn_dims, self.rnn_dims, batch_first=True) - self.rnn2 = nn.GRU( - self.rnn_dims + self.aux_dims, self.rnn_dims, batch_first=True - ) - self._to_flatten += [self.rnn1, self.rnn2] - - self.fc1 = nn.Linear(self.rnn_dims + self.aux_dims, self.fc_dims) - self.fc2 = nn.Linear(self.fc_dims + self.aux_dims, self.fc_dims) - self.fc3 = nn.Linear(self.fc_dims, self.n_classes) - - self.num_params() - - self._flatten_parameters() - - def forward(self, x, mels): - device = next(self.parameters()).device - - self._flatten_parameters() - - batch_size = x.size(0) - h1 = torch.zeros(1, batch_size, self.rnn_dims, device=device) - h2 = torch.zeros(1, batch_size, self.rnn_dims, device=device) - mels, aux = self.upsample(mels) - - aux_idx = [self.aux_dims * i for i in range(5)] - a1 = aux[:, :, aux_idx[0] : aux_idx[1]] - a2 = aux[:, :, aux_idx[1] : aux_idx[2]] - a3 = aux[:, :, aux_idx[2] : aux_idx[3]] - a4 = aux[:, :, aux_idx[3] : aux_idx[4]] - - x = torch.cat([x.unsqueeze(-1), mels, a1], dim=2) - x = self.I(x) - res = x - x, _ = self.rnn1(x, h1) - - x = x + res - res = x - x = torch.cat([x, a2], dim=2) - x, _ = self.rnn2(x, h2) - - x = x + res - x = torch.cat([x, a3], dim=2) - x = F.relu(self.fc1(x)) - - x = torch.cat([x, a4], dim=2) - x = F.relu(self.fc2(x)) - return self.fc3(x) - - def num_params(self, print_out=True): - parameters = filter(lambda p: p.requires_grad, self.parameters()) - parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 - if print_out: - print("Trainable Parameters: %.3fM" % parameters) - return parameters - - def _flatten_parameters(self): - [m.flatten_parameters() for m in self._to_flatten] diff --git a/models/vocoders/diffusion/diffusion_vocoder_dataset.py b/models/vocoders/diffusion/diffusion_vocoder_dataset.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/diffusion/diffusion_vocoder_inference.py b/models/vocoders/diffusion/diffusion_vocoder_inference.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/diffusion/diffusion_vocoder_trainer.py b/models/vocoders/diffusion/diffusion_vocoder_trainer.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/diffusion/diffwave/diffwave.py b/models/vocoders/diffusion/diffwave/diffwave.py deleted file mode 100644 index c9379b0b622c6da8a754f2cc87fd7723eacfa995..0000000000000000000000000000000000000000 --- a/models/vocoders/diffusion/diffwave/diffwave.py +++ /dev/null @@ -1,173 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F - -from math import sqrt - - -Linear = nn.Linear -ConvTranspose2d = nn.ConvTranspose2d - - -def Conv1d(*args, **kwargs): - layer = nn.Conv1d(*args, **kwargs) - nn.init.kaiming_normal_(layer.weight) - return layer - - -@torch.jit.script -def silu(x): - return x * torch.sigmoid(x) - - -class DiffusionEmbedding(nn.Module): - def __init__(self, max_steps): - super().__init__() - self.register_buffer( - "embedding", self._build_embedding(max_steps), persistent=False - ) - self.projection1 = Linear(128, 512) - self.projection2 = Linear(512, 512) - - def forward(self, diffusion_step): - if diffusion_step.dtype in [torch.int32, torch.int64]: - x = self.embedding[diffusion_step] - else: - x = self._lerp_embedding(diffusion_step) - x = self.projection1(x) - x = silu(x) - x = self.projection2(x) - x = silu(x) - return x - - def _lerp_embedding(self, t): - low_idx = torch.floor(t).long() - high_idx = torch.ceil(t).long() - low = self.embedding[low_idx] - high = self.embedding[high_idx] - return low + (high - low) * (t - low_idx) - - def _build_embedding(self, max_steps): - steps = torch.arange(max_steps).unsqueeze(1) # [T,1] - dims = torch.arange(64).unsqueeze(0) # [1,64] - table = steps * 10.0 ** (dims * 4.0 / 63.0) # [T,64] - table = torch.cat([torch.sin(table), torch.cos(table)], dim=1) - return table - - -class SpectrogramUpsampler(nn.Module): - def __init__(self, upsample_factors): - super().__init__() - self.conv1 = ConvTranspose2d( - 1, - 1, - [3, upsample_factors[0] * 2], - stride=[1, upsample_factors[0]], - padding=[1, upsample_factors[0] // 2], - ) - self.conv2 = ConvTranspose2d( - 1, - 1, - [3, upsample_factors[1] * 2], - stride=[1, upsample_factors[1]], - padding=[1, upsample_factors[1] // 2], - ) - - def forward(self, x): - x = torch.unsqueeze(x, 1) - x = self.conv1(x) - x = F.leaky_relu(x, 0.4) - x = self.conv2(x) - x = F.leaky_relu(x, 0.4) - x = torch.squeeze(x, 1) - return x - - -class ResidualBlock(nn.Module): - def __init__(self, n_mels, residual_channels, dilation): - super().__init__() - self.dilated_conv = Conv1d( - residual_channels, - 2 * residual_channels, - 3, - padding=dilation, - dilation=dilation, - ) - self.diffusion_projection = Linear(512, residual_channels) - - self.conditioner_projection = Conv1d(n_mels, 2 * residual_channels, 1) - - self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) - - def forward(self, x, diffusion_step, conditioner): - diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) - y = x + diffusion_step - - conditioner = self.conditioner_projection(conditioner) - y = self.dilated_conv(y) + conditioner - - gate, filter = torch.chunk(y, 2, dim=1) - y = torch.sigmoid(gate) * torch.tanh(filter) - - y = self.output_projection(y) - residual, skip = torch.chunk(y, 2, dim=1) - return (x + residual) / sqrt(2.0), skip - - -class DiffWave(nn.Module): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - self.cfg.VOCODER.NOISE_SCHEDULE = np.linspace( - self.cfg.VOCODER.NOISE_SCHEDULE_FACTORS[0], - self.cfg.VOCODER.NOISE_SCHEDULE_FACTORS[1], - self.cfg.VOCODER.NOISE_SCHEDULE_FACTORS[2], - ).tolist() - self.input_projection = Conv1d(1, self.cfg.VOCODER.RESIDUAL_CHANNELS, 1) - self.diffusion_embedding = DiffusionEmbedding( - len(self.cfg.VOCODER.NOISE_SCHEDULE) - ) - self.spectrogram_upsampler = SpectrogramUpsampler( - self.cfg.VOCODER.UPSAMPLE_FACTORS - ) - - self.residual_layers = nn.ModuleList( - [ - ResidualBlock( - self.cfg.VOCODER.INPUT_DIM, - self.cfg.VOCODER.RESIDUAL_CHANNELS, - 2 ** (i % self.cfg.VOCODER.DILATION_CYCLE_LENGTH), - ) - for i in range(self.cfg.VOCODER.RESIDUAL_LAYERS) - ] - ) - self.skip_projection = Conv1d( - self.cfg.VOCODER.RESIDUAL_CHANNELS, self.cfg.VOCODER.RESIDUAL_CHANNELS, 1 - ) - self.output_projection = Conv1d(self.cfg.VOCODER.RESIDUAL_CHANNELS, 1, 1) - nn.init.zeros_(self.output_projection.weight) - - def forward(self, audio, diffusion_step, spectrogram): - x = audio.unsqueeze(1) - x = self.input_projection(x) - x = F.relu(x) - - diffusion_step = self.diffusion_embedding(diffusion_step) - spectrogram = self.spectrogram_upsampler(spectrogram) - - skip = None - for layer in self.residual_layers: - x, skip_connection = layer(x, diffusion_step, spectrogram) - skip = skip_connection if skip is None else skip_connection + skip - - x = skip / sqrt(len(self.residual_layers)) - x = self.skip_projection(x) - x = F.relu(x) - x = self.output_projection(x) - return x diff --git a/models/vocoders/dsp/world/world.py b/models/vocoders/dsp/world/world.py deleted file mode 100644 index 59f28e8e896f883fe6ce243dfb7f254e78fd09c6..0000000000000000000000000000000000000000 --- a/models/vocoders/dsp/world/world.py +++ /dev/null @@ -1,183 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# 1. Extract WORLD features including F0, AP, SP -# 2. Transform between SP and MCEP -import torchaudio -import pyworld as pw -import numpy as np -import torch -import diffsptk -import os -from tqdm import tqdm -import pickle -import json -import re -import torchaudio - -from cuhkszsvc.configs.config_parse import get_wav_path, get_wav_file_path -from utils.io import has_existed - - -def get_mcep_params(fs): - """Hyperparameters of transformation between SP and MCEP - - Reference: - https://github.com/CSTR-Edinburgh/merlin/blob/master/misc/scripts/vocoder/world_v2/copy_synthesis.sh - - """ - if fs in [44100, 48000]: - fft_size = 2048 - alpha = 0.77 - if fs in [16000]: - fft_size = 1024 - alpha = 0.58 - return fft_size, alpha - - -def extract_world_features(wave_file, fs, frameshift): - # waveform: (1, seq) - waveform, sample_rate = torchaudio.load(wave_file) - if sample_rate != fs: - waveform = torchaudio.functional.resample( - waveform, orig_freq=sample_rate, new_freq=fs - ) - # x: (seq,) - x = np.array(torch.clamp(waveform[0], -1.0, 1.0), dtype=np.double) - - _f0, t = pw.dio(x, fs, frame_period=frameshift) # raw pitch extractor - f0 = pw.stonemask(x, _f0, t, fs) # pitch refinement - sp = pw.cheaptrick(x, f0, t, fs) # extract smoothed spectrogram - ap = pw.d4c(x, f0, t, fs) # extract aperiodicity - - return f0, sp, ap, fs - - -def sp2mcep(x, mcsize, fs): - fft_size, alpha = get_mcep_params(fs) - x = torch.as_tensor(x, dtype=torch.float) - - tmp = diffsptk.ScalarOperation("SquareRoot")(x) - tmp = diffsptk.ScalarOperation("Multiplication", 32768.0)(tmp) - mgc = diffsptk.MelCepstralAnalysis( - cep_order=mcsize - 1, fft_length=fft_size, alpha=alpha, n_iter=1 - )(tmp) - return mgc.numpy() - - -def mcep2sp(x, mcsize, fs): - fft_size, alpha = get_mcep_params(fs) - x = torch.as_tensor(x, dtype=torch.float) - - tmp = diffsptk.MelGeneralizedCepstrumToSpectrum( - alpha=alpha, - cep_order=mcsize - 1, - fft_length=fft_size, - )(x) - tmp = diffsptk.ScalarOperation("Division", 32768.0)(tmp) - sp = diffsptk.ScalarOperation("Power", 2)(tmp) - return sp.double().numpy() - - -def extract_mcep_features_of_dataset( - output_path, dataset_path, dataset, mcsize, fs, frameshift, splits=None -): - output_dir = os.path.join(output_path, dataset, "mcep/{}".format(fs)) - - if not splits: - splits = ["train", "test"] if dataset != "m4singer" else ["test"] - - for dataset_type in splits: - print("-" * 20) - print("Dataset: {}, {}".format(dataset, dataset_type)) - - output_file = os.path.join(output_dir, "{}.pkl".format(dataset_type)) - if has_existed(output_file): - continue - - # Extract SP features - print("\nExtracting SP featuers...") - sp_features = get_world_features_of_dataset( - output_path, dataset_path, dataset, dataset_type, fs, frameshift - ) - - # SP to MCEP - print("\nTransform SP to MCEP...") - mcep_features = [sp2mcep(sp, mcsize=mcsize, fs=fs) for sp in tqdm(sp_features)] - - # Save - os.makedirs(output_dir, exist_ok=True) - with open(output_file, "wb") as f: - pickle.dump(mcep_features, f) - - -def get_world_features_of_dataset( - output_path, - dataset_path, - dataset, - dataset_type, - fs, - frameshift, - save_sp_feature=False, -): - data_dir = os.path.join(output_path, dataset) - wave_dir = get_wav_path(dataset_path, dataset) - - # Dataset - dataset_file = os.path.join(data_dir, "{}.json".format(dataset_type)) - if not os.path.exists(dataset_file): - print("File {} has not existed.".format(dataset_file)) - return None - - with open(dataset_file, "r") as f: - datasets = json.load(f) - - # Save dir - f0_dir = os.path.join(output_path, dataset, "f0") - os.makedirs(f0_dir, exist_ok=True) - - # Extract - f0_features = [] - sp_features = [] - for utt in tqdm(datasets): - wave_file = get_wav_file_path(dataset, wave_dir, utt) - f0, sp, _, _ = extract_world_features(wave_file, fs, frameshift) - - sp_features.append(sp) - f0_features.append(f0) - - # Save sp - if save_sp_feature: - sp_dir = os.path.join(output_path, dataset, "sp") - os.makedirs(sp_dir, exist_ok=True) - with open(os.path.join(sp_dir, "{}.pkl".format(dataset_type)), "wb") as f: - pickle.dump(sp_features, f) - - # F0 statistics - f0_statistics_file = os.path.join(f0_dir, "{}_f0.pkl".format(dataset_type)) - f0_statistics(f0_features, f0_statistics_file) - - return sp_features - - -def f0_statistics(f0_features, path): - print("\nF0 statistics...") - - total_f0 = [] - for f0 in tqdm(f0_features): - total_f0 += [f for f in f0 if f != 0] - - mean = sum(total_f0) / len(total_f0) - print("Min = {}, Max = {}, Mean = {}".format(min(total_f0), max(total_f0), mean)) - - with open(path, "wb") as f: - pickle.dump([mean, total_f0], f) - - -def world_synthesis(f0, sp, ap, fs, frameshift): - y = pw.synthesize( - f0, sp, ap, fs, frame_period=frameshift - ) # synthesize an utterance using the parameters - return y diff --git a/models/vocoders/flow/flow_vocoder_dataset.py b/models/vocoders/flow/flow_vocoder_dataset.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/flow/flow_vocoder_inference.py b/models/vocoders/flow/flow_vocoder_inference.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/flow/flow_vocoder_trainer.py b/models/vocoders/flow/flow_vocoder_trainer.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/flow/waveglow/waveglow.py b/models/vocoders/flow/waveglow/waveglow.py deleted file mode 100644 index 13e2a1bf8f5e3c3d47a031ceec87e4ff111cd5fe..0000000000000000000000000000000000000000 --- a/models/vocoders/flow/waveglow/waveglow.py +++ /dev/null @@ -1,249 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from torch.autograd import Variable -import torch.nn.functional as F - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -class Invertible1x1Conv(torch.nn.Module): - """ - The layer outputs both the convolution, and the log determinant - of its weight matrix. If reverse=True it does convolution with - inverse - """ - - def __init__(self, c): - super(Invertible1x1Conv, self).__init__() - self.conv = torch.nn.Conv1d( - c, c, kernel_size=1, stride=1, padding=0, bias=False - ) - - # Sample a random orthonormal matrix to initialize weights - W = torch.linalg.qr(torch.FloatTensor(c, c).normal_())[0] - - # Ensure determinant is 1.0 not -1.0 - if torch.det(W) < 0: - W[:, 0] = -1 * W[:, 0] - W = W.view(c, c, 1) - self.conv.weight.data = W - - def forward(self, z, reverse=False): - # shape - batch_size, group_size, n_of_groups = z.size() - - W = self.conv.weight.squeeze() - - if reverse: - if not hasattr(self, "W_inverse"): - # Reverse computation - W_inverse = W.float().inverse() - W_inverse = Variable(W_inverse[..., None]) - if z.type() == "torch.cuda.HalfTensor": - W_inverse = W_inverse.half() - self.W_inverse = W_inverse - z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0) - return z - else: - # Forward computation - log_det_W = batch_size * n_of_groups * torch.logdet(W) - z = self.conv(z) - return z, log_det_W - - -class WN(torch.nn.Module): - """ - This is the WaveNet like layer for the affine coupling. The primary difference - from WaveNet is the convolutions need not be causal. There is also no dilation - size reset. The dilation only doubles on each layer - """ - - def __init__( - self, n_in_channels, n_mel_channels, n_layers, n_channels, kernel_size - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - assert n_channels % 2 == 0 - self.n_layers = n_layers - self.n_channels = n_channels - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - - start = torch.nn.Conv1d(n_in_channels, n_channels, 1) - start = torch.nn.utils.weight_norm(start, name="weight") - self.start = start - - # Initializing last layer to 0 makes the affine coupling layers - # do nothing at first. This helps with training stability - end = torch.nn.Conv1d(n_channels, 2 * n_in_channels, 1) - end.weight.data.zero_() - end.bias.data.zero_() - self.end = end - - cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels * n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = 2**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - n_channels, - 2 * n_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * n_channels - else: - res_skip_channels = n_channels - res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, forward_input): - audio, spect = forward_input - audio = self.start(audio) - output = torch.zeros_like(audio) - n_channels_tensor = torch.IntTensor([self.n_channels]) - - spect = self.cond_layer(spect) - - for i in range(self.n_layers): - spect_offset = i * 2 * self.n_channels - acts = fused_add_tanh_sigmoid_multiply( - self.in_layers[i](audio), - spect[:, spect_offset : spect_offset + 2 * self.n_channels, :], - n_channels_tensor, - ) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - audio = audio + res_skip_acts[:, : self.n_channels, :] - output = output + res_skip_acts[:, self.n_channels :, :] - else: - output = output + res_skip_acts - - return self.end(output) - - -class WaveGlow(torch.nn.Module): - def __init__(self, cfg): - super(WaveGlow, self).__init__() - - self.cfg = cfg - - self.upsample = torch.nn.ConvTranspose1d( - self.cfg.VOCODER.INPUT_DIM, - self.cfg.VOCODER.INPUT_DIM, - 1024, - stride=256, - ) - assert self.cfg.VOCODER.N_GROUP % 2 == 0 - self.n_flows = self.cfg.VOCODER.N_FLOWS - self.n_group = self.cfg.VOCODER.N_GROUP - self.n_early_every = self.cfg.VOCODER.N_EARLY_EVERY - self.n_early_size = self.cfg.VOCODER.N_EARLY_SIZE - self.WN = torch.nn.ModuleList() - self.convinv = torch.nn.ModuleList() - - n_half = int(self.cfg.VOCODER.N_GROUP / 2) - - # Set up layers with the right sizes based on how many dimensions - # have been output already - n_remaining_channels = self.cfg.VOCODER.N_GROUP - for k in range(self.cfg.VOCODER.N_FLOWS): - if k % self.n_early_every == 0 and k > 0: - n_half = n_half - int(self.n_early_size / 2) - n_remaining_channels = n_remaining_channels - self.n_early_size - self.convinv.append(Invertible1x1Conv(n_remaining_channels)) - self.WN.append( - WN( - n_half, - self.cfg.VOCODER.INPUT_DIM * self.cfg.VOCODER.N_GROUP, - self.cfg.VOCODER.N_LAYERS, - self.cfg.VOCODER.N_CHANNELS, - self.cfg.VOCODER.KERNEL_SIZE, - ) - ) - self.n_remaining_channels = n_remaining_channels # Useful during inference - - def forward(self, forward_input): - """ - forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames - forward_input[1] = audio: batch x time - """ - spect, audio = forward_input - - # Upsample spectrogram to size of audio - spect = self.upsample(spect) - assert spect.size(2) >= audio.size(1) - if spect.size(2) > audio.size(1): - spect = spect[:, :, : audio.size(1)] - - spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3) - spect = ( - spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1) - ) - - audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1) - output_audio = [] - log_s_list = [] - log_det_W_list = [] - - for k in range(self.n_flows): - if k % self.n_early_every == 0 and k > 0: - output_audio.append(audio[:, : self.n_early_size, :]) - audio = audio[:, self.n_early_size :, :] - - audio, log_det_W = self.convinv[k](audio) - log_det_W_list.append(log_det_W) - - n_half = int(audio.size(1) / 2) - audio_0 = audio[:, :n_half, :] - audio_1 = audio[:, n_half:, :] - - output = self.WN[k]((audio_0, spect)) - log_s = output[:, n_half:, :] - b = output[:, :n_half, :] - audio_1 = torch.exp(log_s) * audio_1 + b - log_s_list.append(log_s) - - audio = torch.cat([audio_0, audio_1], 1) - - output_audio.append(audio) - return torch.cat(output_audio, 1), log_s_list, log_det_W_list - - @staticmethod - def remove_weightnorm(model): - waveglow = model - for WN in waveglow.WN: - WN.start = torch.nn.utils.remove_weight_norm(WN.start) - WN.in_layers = remove(WN.in_layers) - WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer) - WN.res_skip_layers = remove(WN.res_skip_layers) - return waveglow - - -def remove(conv_list): - new_conv_list = torch.nn.ModuleList() - for old_conv in conv_list: - old_conv = torch.nn.utils.remove_weight_norm(old_conv) - new_conv_list.append(old_conv) - return new_conv_list diff --git a/models/vocoders/gan/discriminator/mpd.py b/models/vocoders/gan/discriminator/mpd.py deleted file mode 100644 index f28711d18847a106a998cab90871fe6303a4fd08..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/discriminator/mpd.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv2d, Conv1d -from torch.nn.utils import weight_norm, spectral_norm -from torch import nn -from modules.vocoder_blocks import * - -LRELU_SLOPE = 0.1 - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, cfg, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.d_mult = cfg.model.mpd.discriminator_channel_mult_factor - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - int(32 * self.d_mult), - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - int(32 * self.d_mult), - int(128 * self.d_mult), - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - int(128 * self.d_mult), - int(512 * self.d_mult), - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - int(512 * self.d_mult), - int(1024 * self.d_mult), - (kernel_size, 1), - (stride, 1), - padding=(get_padding(5, 1), 0), - ) - ), - norm_f( - Conv2d( - int(1024 * self.d_mult), - int(1024 * self.d_mult), - (kernel_size, 1), - (stride, 1), - padding=(2, 0), - ) - ), - ] - ) - self.conv_post = norm_f( - Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)) - ) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, cfg): - super(MultiPeriodDiscriminator, self).__init__() - self.mpd_reshapes = cfg.model.mpd.mpd_reshapes - print("mpd_reshapes: {}".format(self.mpd_reshapes)) - discriminators = [ - DiscriminatorP(cfg, rs, use_spectral_norm=cfg.model.mpd.use_spectral_norm) - for rs in self.mpd_reshapes - ] - self.discriminators = nn.ModuleList(discriminators) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -# TODO: merge with DiscriminatorP (lmxue, yicheng) -class DiscriminatorP_vits(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP_vits, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -# TODO: merge with MultiPeriodDiscriminator (lmxue, yicheng) -class MultiPeriodDiscriminator_vits(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator_vits, self).__init__() - periods = [2, 3, 5, 7, 11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP_vits(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - outputs = { - "y_d_hat_r": y_d_rs, - "y_d_hat_g": y_d_gs, - "fmap_rs": fmap_rs, - "fmap_gs": fmap_gs, - } - - return outputs diff --git a/models/vocoders/gan/discriminator/mrd.py b/models/vocoders/gan/discriminator/mrd.py deleted file mode 100644 index 38ee80bfbf82b6aa63c80dbc2c6ffed8cb50a924..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/discriminator/mrd.py +++ /dev/null @@ -1,160 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from torch import nn - -LRELU_SLOPE = 0.1 - - -# This code is a refined MRD adopted from BigVGAN under the MIT License -# https://github.com/NVIDIA/BigVGAN - - -class DiscriminatorR(nn.Module): - def __init__(self, cfg, resolution): - super().__init__() - - self.resolution = resolution - assert ( - len(self.resolution) == 3 - ), "MRD layer requires list with len=3, got {}".format(self.resolution) - self.lrelu_slope = LRELU_SLOPE - - norm_f = ( - weight_norm if cfg.model.mrd.use_spectral_norm == False else spectral_norm - ) - if cfg.model.mrd.mrd_override: - print( - "INFO: overriding MRD use_spectral_norm as {}".format( - cfg.model.mrd.mrd_use_spectral_norm - ) - ) - norm_f = ( - weight_norm - if cfg.model.mrd.mrd_use_spectral_norm == False - else spectral_norm - ) - self.d_mult = cfg.model.mrd.discriminator_channel_mult_factor - if cfg.model.mrd.mrd_override: - print( - "INFO: overriding mrd channel multiplier as {}".format( - cfg.model.mrd.mrd_channel_mult - ) - ) - self.d_mult = cfg.model.mrd.mrd_channel_mult - - self.convs = nn.ModuleList( - [ - norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))), - norm_f( - nn.Conv2d( - int(32 * self.d_mult), - int(32 * self.d_mult), - (3, 9), - stride=(1, 2), - padding=(1, 4), - ) - ), - norm_f( - nn.Conv2d( - int(32 * self.d_mult), - int(32 * self.d_mult), - (3, 9), - stride=(1, 2), - padding=(1, 4), - ) - ), - norm_f( - nn.Conv2d( - int(32 * self.d_mult), - int(32 * self.d_mult), - (3, 9), - stride=(1, 2), - padding=(1, 4), - ) - ), - norm_f( - nn.Conv2d( - int(32 * self.d_mult), - int(32 * self.d_mult), - (3, 3), - padding=(1, 1), - ) - ), - ] - ) - self.conv_post = norm_f( - nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)) - ) - - def forward(self, x): - fmap = [] - - x = self.spectrogram(x) - x = x.unsqueeze(1) - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, self.lrelu_slope) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - def spectrogram(self, x): - n_fft, hop_length, win_length = self.resolution - x = F.pad( - x, - (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), - mode="reflect", - ) - x = x.squeeze(1) - x = torch.stft( - x, - n_fft=n_fft, - hop_length=hop_length, - win_length=win_length, - center=False, - return_complex=True, - ) - x = torch.view_as_real(x) # [B, F, TT, 2] - mag = torch.norm(x, p=2, dim=-1) # [B, F, TT] - - return mag - - -class MultiResolutionDiscriminator(nn.Module): - def __init__(self, cfg, debug=False): - super().__init__() - self.resolutions = cfg.model.mrd.resolutions - assert ( - len(self.resolutions) == 3 - ), "MRD requires list of list with len=3, each element having a list with len=3. got {}".format( - self.resolutions - ) - self.discriminators = nn.ModuleList( - [DiscriminatorR(cfg, resolution) for resolution in self.resolutions] - ) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(x=y) - y_d_g, fmap_g = d(x=y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/discriminator/msd.py b/models/vocoders/gan/discriminator/msd.py deleted file mode 100644 index 4c1556aea581878dcbe10f7a3bdebc33a4972e2c..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/discriminator/msd.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, AvgPool1d -from torch.nn.utils import weight_norm, spectral_norm -from torch import nn -from modules.vocoder_blocks import * - - -LRELU_SLOPE = 0.1 - - -class DiscriminatorS(nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 128, 15, 1, padding=7)), - norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), - norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), - norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiScaleDiscriminator(nn.Module): - def __init__(self, cfg): - super(MultiScaleDiscriminator, self).__init__() - - self.cfg = cfg - - self.discriminators = nn.ModuleList( - [ - DiscriminatorS(use_spectral_norm=True), - DiscriminatorS(), - DiscriminatorS(), - ] - ) - - self.meanpools = nn.ModuleList( - [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] - ) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/discriminator/mssbcqtd.py b/models/vocoders/gan/discriminator/mssbcqtd.py deleted file mode 100644 index 213de5441754944a360707e99a3734ad035d9077..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/discriminator/mssbcqtd.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch import nn -from modules.vocoder_blocks import * - -from einops import rearrange -import torchaudio.transforms as T - -from nnAudio import features - -LRELU_SLOPE = 0.1 - - -class DiscriminatorCQT(nn.Module): - def __init__(self, cfg, hop_length, n_octaves, bins_per_octave): - super(DiscriminatorCQT, self).__init__() - self.cfg = cfg - - self.filters = cfg.model.mssbcqtd.filters - self.max_filters = cfg.model.mssbcqtd.max_filters - self.filters_scale = cfg.model.mssbcqtd.filters_scale - self.kernel_size = (3, 9) - self.dilations = cfg.model.mssbcqtd.dilations - self.stride = (1, 2) - - self.in_channels = cfg.model.mssbcqtd.in_channels - self.out_channels = cfg.model.mssbcqtd.out_channels - self.fs = cfg.preprocess.sample_rate - self.hop_length = hop_length - self.n_octaves = n_octaves - self.bins_per_octave = bins_per_octave - - self.cqt_transform = features.cqt.CQT2010v2( - sr=self.fs * 2, - hop_length=self.hop_length, - n_bins=self.bins_per_octave * self.n_octaves, - bins_per_octave=self.bins_per_octave, - output_format="Complex", - pad_mode="constant", - ) - - self.conv_pres = nn.ModuleList() - for i in range(self.n_octaves): - self.conv_pres.append( - NormConv2d( - self.in_channels * 2, - self.in_channels * 2, - kernel_size=self.kernel_size, - padding=get_2d_padding(self.kernel_size), - ) - ) - - self.convs = nn.ModuleList() - - self.convs.append( - NormConv2d( - self.in_channels * 2, - self.filters, - kernel_size=self.kernel_size, - padding=get_2d_padding(self.kernel_size), - ) - ) - - in_chs = min(self.filters_scale * self.filters, self.max_filters) - for i, dilation in enumerate(self.dilations): - out_chs = min( - (self.filters_scale ** (i + 1)) * self.filters, self.max_filters - ) - self.convs.append( - NormConv2d( - in_chs, - out_chs, - kernel_size=self.kernel_size, - stride=self.stride, - dilation=(dilation, 1), - padding=get_2d_padding(self.kernel_size, (dilation, 1)), - norm="weight_norm", - ) - ) - in_chs = out_chs - out_chs = min( - (self.filters_scale ** (len(self.dilations) + 1)) * self.filters, - self.max_filters, - ) - self.convs.append( - NormConv2d( - in_chs, - out_chs, - kernel_size=(self.kernel_size[0], self.kernel_size[0]), - padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])), - norm="weight_norm", - ) - ) - - self.conv_post = NormConv2d( - out_chs, - self.out_channels, - kernel_size=(self.kernel_size[0], self.kernel_size[0]), - padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])), - norm="weight_norm", - ) - - self.activation = torch.nn.LeakyReLU(negative_slope=LRELU_SLOPE) - self.resample = T.Resample(orig_freq=self.fs, new_freq=self.fs * 2) - - def forward(self, x): - fmap = [] - - x = self.resample(x) - - z = self.cqt_transform(x) - - z_amplitude = z[:, :, :, 0].unsqueeze(1) - z_phase = z[:, :, :, 1].unsqueeze(1) - - z = torch.cat([z_amplitude, z_phase], dim=1) - z = rearrange(z, "b c w t -> b c t w") - - latent_z = [] - for i in range(self.n_octaves): - latent_z.append( - self.conv_pres[i]( - z[ - :, - :, - :, - i * self.bins_per_octave : (i + 1) * self.bins_per_octave, - ] - ) - ) - latent_z = torch.cat(latent_z, dim=-1) - - for i, l in enumerate(self.convs): - latent_z = l(latent_z) - - latent_z = self.activation(latent_z) - fmap.append(latent_z) - - latent_z = self.conv_post(latent_z) - - return latent_z, fmap - - -class MultiScaleSubbandCQTDiscriminator(nn.Module): - def __init__(self, cfg): - super(MultiScaleSubbandCQTDiscriminator, self).__init__() - - self.cfg = cfg - - self.discriminators = nn.ModuleList( - [ - DiscriminatorCQT( - cfg, - hop_length=cfg.model.mssbcqtd.hop_lengths[i], - n_octaves=cfg.model.mssbcqtd.n_octaves[i], - bins_per_octave=cfg.model.mssbcqtd.bins_per_octaves[i], - ) - for i in range(len(cfg.model.mssbcqtd.hop_lengths)) - ] - ) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - - for disc in self.discriminators: - y_d_r, fmap_r = disc(y) - y_d_g, fmap_g = disc(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/discriminator/msstftd.py b/models/vocoders/gan/discriminator/msstftd.py deleted file mode 100644 index 83dedb78848d2d73ac667e7a191f05de1ed7bf21..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/discriminator/msstftd.py +++ /dev/null @@ -1,226 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is adopted from META's Encodec under MIT License -# https://github.com/facebookresearch/encodec - -"""MS-STFT discriminator, provided here for reference.""" - -import typing as tp - -import torchaudio -import torch -from torch import nn -from einops import rearrange - -from modules.vocoder_blocks import * - - -FeatureMapType = tp.List[torch.Tensor] -LogitsType = torch.Tensor -DiscriminatorOutput = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]] - - -def get_2d_padding( - kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1) -): - return ( - ((kernel_size[0] - 1) * dilation[0]) // 2, - ((kernel_size[1] - 1) * dilation[1]) // 2, - ) - - -class DiscriminatorSTFT(nn.Module): - """STFT sub-discriminator. - Args: - filters (int): Number of filters in convolutions - in_channels (int): Number of input channels. Default: 1 - out_channels (int): Number of output channels. Default: 1 - n_fft (int): Size of FFT for each scale. Default: 1024 - hop_length (int): Length of hop between STFT windows for each scale. Default: 256 - kernel_size (tuple of int): Inner Conv2d kernel sizes. Default: ``(3, 9)`` - stride (tuple of int): Inner Conv2d strides. Default: ``(1, 2)`` - dilations (list of int): Inner Conv2d dilation on the time dimension. Default: ``[1, 2, 4]`` - win_length (int): Window size for each scale. Default: 1024 - normalized (bool): Whether to normalize by magnitude after stft. Default: True - norm (str): Normalization method. Default: `'weight_norm'` - activation (str): Activation function. Default: `'LeakyReLU'` - activation_params (dict): Parameters to provide to the activation function. - growth (int): Growth factor for the filters. Default: 1 - """ - - def __init__( - self, - filters: int, - in_channels: int = 1, - out_channels: int = 1, - n_fft: int = 1024, - hop_length: int = 256, - win_length: int = 1024, - max_filters: int = 1024, - filters_scale: int = 1, - kernel_size: tp.Tuple[int, int] = (3, 9), - dilations: tp.List = [1, 2, 4], - stride: tp.Tuple[int, int] = (1, 2), - normalized: bool = True, - norm: str = "weight_norm", - activation: str = "LeakyReLU", - activation_params: dict = {"negative_slope": 0.2}, - ): - super().__init__() - assert len(kernel_size) == 2 - assert len(stride) == 2 - self.filters = filters - self.in_channels = in_channels - self.out_channels = out_channels - self.n_fft = n_fft - self.hop_length = hop_length - self.win_length = win_length - self.normalized = normalized - self.activation = getattr(torch.nn, activation)(**activation_params) - self.spec_transform = torchaudio.transforms.Spectrogram( - n_fft=self.n_fft, - hop_length=self.hop_length, - win_length=self.win_length, - window_fn=torch.hann_window, - normalized=self.normalized, - center=False, - pad_mode=None, - power=None, - ) - spec_channels = 2 * self.in_channels - self.convs = nn.ModuleList() - self.convs.append( - NormConv2d( - spec_channels, - self.filters, - kernel_size=kernel_size, - padding=get_2d_padding(kernel_size), - ) - ) - in_chs = min(filters_scale * self.filters, max_filters) - for i, dilation in enumerate(dilations): - out_chs = min((filters_scale ** (i + 1)) * self.filters, max_filters) - self.convs.append( - NormConv2d( - in_chs, - out_chs, - kernel_size=kernel_size, - stride=stride, - dilation=(dilation, 1), - padding=get_2d_padding(kernel_size, (dilation, 1)), - norm=norm, - ) - ) - in_chs = out_chs - out_chs = min( - (filters_scale ** (len(dilations) + 1)) * self.filters, max_filters - ) - self.convs.append( - NormConv2d( - in_chs, - out_chs, - kernel_size=(kernel_size[0], kernel_size[0]), - padding=get_2d_padding((kernel_size[0], kernel_size[0])), - norm=norm, - ) - ) - self.conv_post = NormConv2d( - out_chs, - self.out_channels, - kernel_size=(kernel_size[0], kernel_size[0]), - padding=get_2d_padding((kernel_size[0], kernel_size[0])), - norm=norm, - ) - - def forward(self, x: torch.Tensor): - """Discriminator STFT Module is the sub module of MultiScaleSTFTDiscriminator. - - Args: - x (torch.Tensor): input tensor of shape [B, 1, Time] - - Returns: - z: z is the output of the last convolutional layer of shape - fmap: fmap is the list of feature maps of every convolutional layer of shape - """ - fmap = [] - z = self.spec_transform(x) # [B, 2, Freq, Frames, 2] - z = torch.cat([z.real, z.imag], dim=1) - z = rearrange(z, "b c w t -> b c t w") - for i, layer in enumerate(self.convs): - z = layer(z) - - z = self.activation(z) - fmap.append(z) - z = self.conv_post(z) - return z, fmap - - -class MultiScaleSTFTDiscriminator(nn.Module): - """Multi-Scale STFT (MS-STFT) discriminator. - Args: - filters (int): Number of filters in convolutions - in_channels (int): Number of input channels. Default: 1 - out_channels (int): Number of output channels. Default: 1 - n_ffts (Sequence[int]): Size of FFT for each scale - hop_lengths (Sequence[int]): Length of hop between STFT windows for each scale - win_lengths (Sequence[int]): Window size for each scale - **kwargs: additional args for STFTDiscriminator - """ - - def __init__( - self, - cfg, - in_channels: int = 1, - out_channels: int = 1, - n_ffts: tp.List[int] = [1024, 2048, 512], - hop_lengths: tp.List[int] = [256, 512, 256], - win_lengths: tp.List[int] = [1024, 2048, 512], - **kwargs, - ): - self.cfg = cfg - super().__init__() - assert len(n_ffts) == len(hop_lengths) == len(win_lengths) - self.discriminators = nn.ModuleList( - [ - DiscriminatorSTFT( - filters=self.cfg.model.msstftd.filters, - in_channels=in_channels, - out_channels=out_channels, - n_fft=n_ffts[i], - win_length=win_lengths[i], - hop_length=hop_lengths[i], - **kwargs, - ) - for i in range(len(n_ffts)) - ] - ) - self.num_discriminators = len(self.discriminators) - - def forward(self, y, y_hat) -> DiscriminatorOutput: - """Multi-Scale STFT (MS-STFT) discriminator. - - Args: - x (torch.Tensor): input waveform - - Returns: - logits: list of every discriminator's output - fmaps: list of every discriminator's feature maps, - each feature maps is a list of Discriminator STFT's every layer - """ - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - - for disc in self.discriminators: - y_d_r, fmap_r = disc(y) - y_d_g, fmap_g = disc(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/models/vocoders/gan/gan_vocoder_dataset.py b/models/vocoders/gan/gan_vocoder_dataset.py deleted file mode 100644 index 5bf87c371647a44fb5bcae33701eda65616e5fd7..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/gan_vocoder_dataset.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import random - -import numpy as np - -from torch.nn import functional as F - -from torch.nn.utils.rnn import pad_sequence -from utils.data_utils import * -from models.vocoders.vocoder_dataset import VocoderDataset - - -class GANVocoderDataset(VocoderDataset): - def __init__(self, cfg, dataset, is_valid=False): - """ - Args: - cfg: config - dataset: dataset name - is_valid: whether to use train or valid dataset - """ - super().__init__(cfg, dataset, is_valid) - - eval_index = random.randint(0, len(self.metadata) - 1) - eval_utt_info = self.metadata[eval_index] - eval_utt = "{}_{}".format(eval_utt_info["Dataset"], eval_utt_info["Uid"]) - self.eval_audio = np.load(self.utt2audio_path[eval_utt]) - if cfg.preprocess.use_mel: - self.eval_mel = np.load(self.utt2mel_path[eval_utt]) - if cfg.preprocess.use_frame_pitch: - self.eval_pitch = np.load(self.utt2frame_pitch_path[eval_utt]) - - def __getitem__(self, index): - utt_info = self.metadata[index] - - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - single_feature = dict() - - if self.cfg.preprocess.use_mel: - mel = np.load(self.utt2mel_path[utt]) - assert mel.shape[0] == self.cfg.preprocess.n_mel - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = mel.shape[1] - - if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame: - mel = np.pad( - mel, - ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), - mode="constant", - ) - else: - if "start" not in single_feature.keys(): - start = random.randint( - 0, mel.shape[-1] - self.cfg.preprocess.cut_mel_frame - ) - end = start + self.cfg.preprocess.cut_mel_frame - single_feature["start"] = start - single_feature["end"] = end - mel = mel[:, single_feature["start"] : single_feature["end"]] - single_feature["mel"] = mel - - if self.cfg.preprocess.use_frame_pitch: - frame_pitch = np.load(self.utt2frame_pitch_path[utt]) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_pitch) - aligned_frame_pitch = align_length( - frame_pitch, single_feature["target_len"] - ) - - if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame: - aligned_frame_pitch = np.pad( - aligned_frame_pitch, - ( - ( - 0, - self.cfg.preprocess.cut_mel_frame - * self.cfg.preprocess.hop_size - - audio.shape[-1], - ) - ), - mode="constant", - ) - else: - if "start" not in single_feature.keys(): - start = random.randint( - 0, - aligned_frame_pitch.shape[-1] - - self.cfg.preprocess.cut_mel_frame, - ) - end = start + self.cfg.preprocess.cut_mel_frame - single_feature["start"] = start - single_feature["end"] = end - aligned_frame_pitch = aligned_frame_pitch[ - single_feature["start"] : single_feature["end"] - ] - single_feature["frame_pitch"] = aligned_frame_pitch - - if self.cfg.preprocess.use_audio: - audio = np.load(self.utt2audio_path[utt]) - - assert "target_len" in single_feature.keys() - - if ( - audio.shape[-1] - <= self.cfg.preprocess.cut_mel_frame * self.cfg.preprocess.hop_size - ): - audio = np.pad( - audio, - ( - ( - 0, - self.cfg.preprocess.cut_mel_frame - * self.cfg.preprocess.hop_size - - audio.shape[-1], - ) - ), - mode="constant", - ) - else: - if "start" not in single_feature.keys(): - audio = audio[ - 0 : self.cfg.preprocess.cut_mel_frame - * self.cfg.preprocess.hop_size - ] - else: - audio = audio[ - single_feature["start"] - * self.cfg.preprocess.hop_size : single_feature["end"] - * self.cfg.preprocess.hop_size, - ] - single_feature["audio"] = audio - - if self.cfg.preprocess.use_amplitude_phase: - logamp = np.load(self.utt2logamp_path[utt]) - pha = np.load(self.utt2pha_path[utt]) - rea = np.load(self.utt2rea_path[utt]) - imag = np.load(self.utt2imag_path[utt]) - - assert "target_len" in single_feature.keys() - - if single_feature["target_len"] <= self.cfg.preprocess.cut_mel_frame: - logamp = np.pad( - logamp, - ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), - mode="constant", - ) - pha = np.pad( - pha, - ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), - mode="constant", - ) - rea = np.pad( - rea, - ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), - mode="constant", - ) - imag = np.pad( - imag, - ((0, 0), (0, self.cfg.preprocess.cut_mel_frame - mel.shape[-1])), - mode="constant", - ) - else: - logamp = logamp[:, single_feature["start"] : single_feature["end"]] - pha = pha[:, single_feature["start"] : single_feature["end"]] - rea = rea[:, single_feature["start"] : single_feature["end"]] - imag = imag[:, single_feature["start"] : single_feature["end"]] - single_feature["logamp"] = logamp - single_feature["pha"] = pha - single_feature["rea"] = rea - single_feature["imag"] = imag - - return single_feature - - -class GANVocoderCollator(object): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - self.cfg = cfg - - def __call__(self, batch): - packed_batch_features = dict() - - # mel: [b, n_mels, frame] - # frame_pitch: [b, frame] - # audios: [b, frame * hop_size] - - for key in batch[0].keys(): - if key in ["target_len", "start", "end"]: - continue - else: - values = [torch.from_numpy(b[key]) for b in batch] - packed_batch_features[key] = pad_sequence( - values, batch_first=True, padding_value=0 - ) - - return packed_batch_features diff --git a/models/vocoders/gan/gan_vocoder_inference.py b/models/vocoders/gan/gan_vocoder_inference.py deleted file mode 100644 index cb69631662dedf4fc73a29f675f0a4bc361b03ec..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/gan_vocoder_inference.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from utils.util import pad_mels_to_tensors, pad_f0_to_tensors - - -def vocoder_inference(cfg, model, mels, f0s=None, device=None, fast_inference=False): - """Inference the vocoder - Args: - mels: A tensor of mel-specs with the shape (batch_size, num_mels, frames) - Returns: - audios: A tensor of audios with the shape (batch_size, seq_len) - """ - model.eval() - - with torch.no_grad(): - mels = mels.to(device) - if f0s != None: - f0s = f0s.to(device) - - if f0s == None and not cfg.preprocess.extract_amplitude_phase: - output = model.forward(mels) - elif cfg.preprocess.extract_amplitude_phase: - ( - _, - _, - _, - _, - output, - ) = model.forward(mels) - else: - output = model.forward(mels, f0s) - - return output.squeeze(1).detach().cpu() - - -def synthesis_audios(cfg, model, mels, f0s=None, batch_size=None, fast_inference=False): - """Inference the vocoder - Args: - mels: A list of mel-specs - Returns: - audios: A list of audios - """ - # Get the device - device = next(model.parameters()).device - - audios = [] - - # Pad the given list into tensors - mel_batches, mel_frames = pad_mels_to_tensors(mels, batch_size) - if f0s != None: - f0_batches = pad_f0_to_tensors(f0s, batch_size) - - if f0s == None: - for mel_batch, mel_frame in zip(mel_batches, mel_frames): - for i in range(mel_batch.shape[0]): - mel = mel_batch[i] - frame = mel_frame[i] - audio = vocoder_inference( - cfg, - model, - mel.unsqueeze(0), - device=device, - fast_inference=fast_inference, - ).squeeze(0) - - # # Apply fade_out to make the sound more natural - # fade_out = torch.linspace( - # 1, 0, steps=20 * model.cfg.preprocess.hop_size - # ).cpu() - - # calculate the audio length - audio_length = frame * model.cfg.preprocess.hop_size - audio = audio[:audio_length] - - # audio[-20 * model.cfg.preprocess.hop_size :] *= fade_out - - audios.append(audio) - else: - for mel_batch, f0_batch, mel_frame in zip(mel_batches, f0_batches, mel_frames): - for i in range(mel_batch.shape[0]): - mel = mel_batch[i] - f0 = f0_batch[i] - frame = mel_frame[i] - audio = vocoder_inference( - cfg, - model, - mel.unsqueeze(0), - f0s=f0.unsqueeze(0), - device=device, - fast_inference=fast_inference, - ).squeeze(0) - - # # Apply fade_out to make the sound more natural - # fade_out = torch.linspace( - # 1, 0, steps=20 * model.cfg.preprocess.hop_size - # ).cpu() - - # calculate the audio length - audio_length = frame * model.cfg.preprocess.hop_length - audio = audio[:audio_length] - - # audio[-20 * model.cfg.preprocess.hop_size :] *= fade_out - - audios.append(audio) - return audios diff --git a/models/vocoders/gan/gan_vocoder_trainer.py b/models/vocoders/gan/gan_vocoder_trainer.py deleted file mode 100644 index 2fb9c8f03a7de14d0162bfd671d33b76890293a5..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/gan_vocoder_trainer.py +++ /dev/null @@ -1,1112 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import sys -import time -import torch -import json -import itertools -import accelerate -import torch.distributed as dist -import torch.nn.functional as F -from tqdm import tqdm -from torch.nn.parallel import DistributedDataParallel -from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler -from torch.utils.tensorboard import SummaryWriter - -from torch.optim import AdamW -from torch.optim.lr_scheduler import ExponentialLR - -from librosa.filters import mel as librosa_mel_fn - -from accelerate.logging import get_logger -from pathlib import Path - -from utils.io import save_audio -from utils.data_utils import * -from utils.util import ( - Logger, - ValueWindow, - remove_older_ckpt, - set_all_random_seed, - save_config, -) -from utils.mel import extract_mel_features -from models.vocoders.vocoder_trainer import VocoderTrainer -from models.vocoders.gan.gan_vocoder_dataset import ( - GANVocoderDataset, - GANVocoderCollator, -) - -from models.vocoders.gan.generator.bigvgan import BigVGAN -from models.vocoders.gan.generator.hifigan import HiFiGAN -from models.vocoders.gan.generator.melgan import MelGAN -from models.vocoders.gan.generator.nsfhifigan import NSFHiFiGAN -from models.vocoders.gan.generator.apnet import APNet - -from models.vocoders.gan.discriminator.mpd import MultiPeriodDiscriminator -from models.vocoders.gan.discriminator.mrd import MultiResolutionDiscriminator -from models.vocoders.gan.discriminator.mssbcqtd import MultiScaleSubbandCQTDiscriminator -from models.vocoders.gan.discriminator.msd import MultiScaleDiscriminator -from models.vocoders.gan.discriminator.msstftd import MultiScaleSTFTDiscriminator - -from models.vocoders.gan.gan_vocoder_inference import vocoder_inference - -supported_generators = { - "bigvgan": BigVGAN, - "hifigan": HiFiGAN, - "melgan": MelGAN, - "nsfhifigan": NSFHiFiGAN, - "apnet": APNet, -} - -supported_discriminators = { - "mpd": MultiPeriodDiscriminator, - "msd": MultiScaleDiscriminator, - "mrd": MultiResolutionDiscriminator, - "msstftd": MultiScaleSTFTDiscriminator, - "mssbcqtd": MultiScaleSubbandCQTDiscriminator, -} - - -class GANVocoderTrainer(VocoderTrainer): - def __init__(self, args, cfg): - super().__init__() - - self.args = args - self.cfg = cfg - - cfg.exp_name = args.exp_name - - # Init accelerator - self._init_accelerator() - self.accelerator.wait_for_everyone() - - # Init logger - with self.accelerator.main_process_first(): - self.logger = get_logger(args.exp_name, log_level=args.log_level) - - self.logger.info("=" * 56) - self.logger.info("||\t\t" + "New training process started." + "\t\t||") - self.logger.info("=" * 56) - self.logger.info("\n") - self.logger.debug(f"Using {args.log_level.upper()} logging level.") - self.logger.info(f"Experiment name: {args.exp_name}") - self.logger.info(f"Experiment directory: {self.exp_dir}") - self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") - if self.accelerator.is_main_process: - os.makedirs(self.checkpoint_dir, exist_ok=True) - self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") - - # Init training status - self.batch_count: int = 0 - self.step: int = 0 - self.epoch: int = 0 - - self.max_epoch = ( - self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") - ) - self.logger.info( - "Max epoch: {}".format( - self.max_epoch if self.max_epoch < float("inf") else "Unlimited" - ) - ) - - # Check potential erorrs - if self.accelerator.is_main_process: - self._check_basic_configs() - self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride - self.checkpoints_path = [ - [] for _ in range(len(self.save_checkpoint_stride)) - ] - self.run_eval = self.cfg.train.run_eval - - # Set random seed - with self.accelerator.main_process_first(): - start = time.monotonic_ns() - self._set_random_seed(self.cfg.train.random_seed) - end = time.monotonic_ns() - self.logger.debug( - f"Setting random seed done in {(end - start) / 1e6:.2f}ms" - ) - self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") - - # Build dataloader - with self.accelerator.main_process_first(): - self.logger.info("Building dataset...") - start = time.monotonic_ns() - self.train_dataloader, self.valid_dataloader = self._build_dataloader() - end = time.monotonic_ns() - self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") - - # Build model - with self.accelerator.main_process_first(): - self.logger.info("Building model...") - start = time.monotonic_ns() - self.generator, self.discriminators = self._build_model() - end = time.monotonic_ns() - self.logger.debug(self.generator) - for _, discriminator in self.discriminators.items(): - self.logger.debug(discriminator) - self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") - self.logger.info(f"Model parameters: {self._count_parameters()/1e6:.2f}M") - - # Build optimizers and schedulers - with self.accelerator.main_process_first(): - self.logger.info("Building optimizer and scheduler...") - start = time.monotonic_ns() - ( - self.generator_optimizer, - self.discriminator_optimizer, - ) = self._build_optimizer() - ( - self.generator_scheduler, - self.discriminator_scheduler, - ) = self._build_scheduler() - end = time.monotonic_ns() - self.logger.info( - f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" - ) - - # Accelerator preparing - self.logger.info("Initializing accelerate...") - start = time.monotonic_ns() - ( - self.train_dataloader, - self.valid_dataloader, - self.generator, - self.generator_optimizer, - self.discriminator_optimizer, - self.generator_scheduler, - self.discriminator_scheduler, - ) = self.accelerator.prepare( - self.train_dataloader, - self.valid_dataloader, - self.generator, - self.generator_optimizer, - self.discriminator_optimizer, - self.generator_scheduler, - self.discriminator_scheduler, - ) - for key, discriminator in self.discriminators.items(): - self.discriminators[key] = self.accelerator.prepare_model(discriminator) - end = time.monotonic_ns() - self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") - - # Build criterions - with self.accelerator.main_process_first(): - self.logger.info("Building criterion...") - start = time.monotonic_ns() - self.criterions = self._build_criterion() - end = time.monotonic_ns() - self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") - - # Resume checkpoints - with self.accelerator.main_process_first(): - if args.resume_type: - self.logger.info("Resuming from checkpoint...") - start = time.monotonic_ns() - ckpt_path = Path(args.checkpoint) - if self._is_valid_pattern(ckpt_path.parts[-1]): - ckpt_path = self._load_model( - None, args.checkpoint, args.resume_type - ) - else: - ckpt_path = self._load_model( - args.checkpoint, resume_type=args.resume_type - ) - end = time.monotonic_ns() - self.logger.info( - f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" - ) - self.checkpoints_path = json.load( - open(os.path.join(ckpt_path, "ckpts.json"), "r") - ) - - self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") - if self.accelerator.is_main_process: - os.makedirs(self.checkpoint_dir, exist_ok=True) - self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") - - # Save config - self.config_save_path = os.path.join(self.exp_dir, "args.json") - - def _build_dataset(self): - return GANVocoderDataset, GANVocoderCollator - - def _build_criterion(self): - class feature_criterion(torch.nn.Module): - def __init__(self, cfg): - super(feature_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, fmap_r, fmap_g): - loss = 0 - - if self.cfg.model.generator in [ - "hifigan", - "nsfhifigan", - "bigvgan", - "apnet", - ]: - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - loss = loss * 2 - elif self.cfg.model.generator in ["melgan"]: - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += self.l1Loss(rl, gl) - - loss = loss * 10 - elif self.cfg.model.generator in ["codec"]: - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss = loss + self.l1Loss(rl, gl) / torch.mean( - torch.abs(rl) - ) - - KL_scale = len(fmap_r) * len(fmap_r[0]) - - loss = 3 * loss / KL_scale - else: - raise NotImplementedError - - return loss - - class discriminator_criterion(torch.nn.Module): - def __init__(self, cfg): - super(discriminator_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - - if self.cfg.model.generator in [ - "hifigan", - "nsfhifigan", - "bigvgan", - "apnet", - ]: - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg**2) - loss += r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - elif self.cfg.model.generator in ["melgan"]: - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean(self.relu(1 - dr)) - g_loss = torch.mean(self.relu(1 + dg)) - loss = loss + r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - elif self.cfg.model.generator in ["codec"]: - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean(self.relu(1 - dr)) - g_loss = torch.mean(self.relu(1 + dg)) - loss = loss + r_loss + g_loss - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - loss = loss / len(disc_real_outputs) - else: - raise NotImplementedError - - return loss, r_losses, g_losses - - class generator_criterion(torch.nn.Module): - def __init__(self, cfg): - super(generator_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, disc_outputs): - loss = 0 - gen_losses = [] - - if self.cfg.model.generator in [ - "hifigan", - "nsfhifigan", - "bigvgan", - "apnet", - ]: - for dg in disc_outputs: - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - elif self.cfg.model.generator in ["melgan"]: - for dg in disc_outputs: - l = -torch.mean(dg) - gen_losses.append(l) - loss += l - elif self.cfg.model.generator in ["codec"]: - for dg in disc_outputs: - l = torch.mean(self.relu(1 - dg)) / len(disc_outputs) - gen_losses.append(l) - loss += l - else: - raise NotImplementedError - - return loss, gen_losses - - class mel_criterion(torch.nn.Module): - def __init__(self, cfg): - super(mel_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, y_gt, y_pred): - loss = 0 - - if self.cfg.model.generator in [ - "hifigan", - "nsfhifigan", - "bigvgan", - "melgan", - "codec", - "apnet", - ]: - y_gt_mel = extract_mel_features(y_gt, self.cfg.preprocess) - y_pred_mel = extract_mel_features( - y_pred.squeeze(1), self.cfg.preprocess - ) - - loss = self.l1Loss(y_gt_mel, y_pred_mel) * 45 - else: - raise NotImplementedError - - return loss - - class wav_criterion(torch.nn.Module): - def __init__(self, cfg): - super(wav_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, y_gt, y_pred): - loss = 0 - - if self.cfg.model.generator in [ - "hifigan", - "nsfhifigan", - "bigvgan", - "apnet", - ]: - loss = self.l2Loss(y_gt, y_pred.squeeze(1)) * 100 - elif self.cfg.model.generator in ["melgan"]: - loss = self.l1Loss(y_gt, y_pred.squeeze(1)) / 10 - elif self.cfg.model.generator in ["codec"]: - loss = self.l1Loss(y_gt, y_pred.squeeze(1)) + self.l2Loss( - y_gt, y_pred.squeeze(1) - ) - loss /= 10 - else: - raise NotImplementedError - - return loss - - class phase_criterion(torch.nn.Module): - def __init__(self, cfg): - super(phase_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, phase_gt, phase_pred): - n_fft = self.cfg.preprocess.n_fft - frames = phase_gt.size()[-1] - - GD_matrix = ( - torch.triu(torch.ones(n_fft // 2 + 1, n_fft // 2 + 1), diagonal=1) - - torch.triu(torch.ones(n_fft // 2 + 1, n_fft // 2 + 1), diagonal=2) - - torch.eye(n_fft // 2 + 1) - ) - GD_matrix = GD_matrix.to(phase_pred.device) - - GD_r = torch.matmul(phase_gt.permute(0, 2, 1), GD_matrix) - GD_g = torch.matmul(phase_pred.permute(0, 2, 1), GD_matrix) - - PTD_matrix = ( - torch.triu(torch.ones(frames, frames), diagonal=1) - - torch.triu(torch.ones(frames, frames), diagonal=2) - - torch.eye(frames) - ) - PTD_matrix = PTD_matrix.to(phase_pred.device) - - PTD_r = torch.matmul(phase_gt, PTD_matrix) - PTD_g = torch.matmul(phase_pred, PTD_matrix) - - IP_loss = torch.mean(-torch.cos(phase_gt - phase_pred)) - GD_loss = torch.mean(-torch.cos(GD_r - GD_g)) - PTD_loss = torch.mean(-torch.cos(PTD_r - PTD_g)) - - return 100 * (IP_loss + GD_loss + PTD_loss) - - class amplitude_criterion(torch.nn.Module): - def __init__(self, cfg): - super(amplitude_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__(self, log_amplitude_gt, log_amplitude_pred): - amplitude_loss = self.l2Loss(log_amplitude_gt, log_amplitude_pred) - - return 45 * amplitude_loss - - class consistency_criterion(torch.nn.Module): - def __init__(self, cfg): - super(consistency_criterion, self).__init__() - self.cfg = cfg - self.l1Loss = torch.nn.L1Loss(reduction="mean") - self.l2Loss = torch.nn.MSELoss(reduction="mean") - self.relu = torch.nn.ReLU() - - def __call__( - self, - rea_gt, - rea_pred, - rea_pred_final, - imag_gt, - imag_pred, - imag_pred_final, - ): - C_loss = torch.mean( - torch.mean( - (rea_pred - rea_pred_final) ** 2 - + (imag_pred - imag_pred_final) ** 2, - (1, 2), - ) - ) - - L_R = self.l1Loss(rea_gt, rea_pred) - L_I = self.l1Loss(imag_gt, imag_pred) - - return 20 * (C_loss + 2.25 * (L_R + L_I)) - - criterions = dict() - for key in self.cfg.train.criterions: - if key == "feature": - criterions["feature"] = feature_criterion(self.cfg) - elif key == "discriminator": - criterions["discriminator"] = discriminator_criterion(self.cfg) - elif key == "generator": - criterions["generator"] = generator_criterion(self.cfg) - elif key == "mel": - criterions["mel"] = mel_criterion(self.cfg) - elif key == "wav": - criterions["wav"] = wav_criterion(self.cfg) - elif key == "phase": - criterions["phase"] = phase_criterion(self.cfg) - elif key == "amplitude": - criterions["amplitude"] = amplitude_criterion(self.cfg) - elif key == "consistency": - criterions["consistency"] = consistency_criterion(self.cfg) - else: - raise NotImplementedError - - return criterions - - def _build_model(self): - generator = supported_generators[self.cfg.model.generator](self.cfg) - discriminators = dict() - for key in self.cfg.model.discriminators: - discriminators[key] = supported_discriminators[key](self.cfg) - - return generator, discriminators - - def _build_optimizer(self): - optimizer_params_generator = [dict(params=self.generator.parameters())] - generator_optimizer = AdamW( - optimizer_params_generator, - lr=self.cfg.train.adamw.lr, - betas=(self.cfg.train.adamw.adam_b1, self.cfg.train.adamw.adam_b2), - ) - - optimizer_params_discriminator = [] - for discriminator in self.discriminators.keys(): - optimizer_params_discriminator.append( - dict(params=self.discriminators[discriminator].parameters()) - ) - discriminator_optimizer = AdamW( - optimizer_params_discriminator, - lr=self.cfg.train.adamw.lr, - betas=(self.cfg.train.adamw.adam_b1, self.cfg.train.adamw.adam_b2), - ) - - return generator_optimizer, discriminator_optimizer - - def _build_scheduler(self): - discriminator_scheduler = ExponentialLR( - self.discriminator_optimizer, - gamma=self.cfg.train.exponential_lr.lr_decay, - last_epoch=self.epoch - 1, - ) - - generator_scheduler = ExponentialLR( - self.generator_optimizer, - gamma=self.cfg.train.exponential_lr.lr_decay, - last_epoch=self.epoch - 1, - ) - - return generator_scheduler, discriminator_scheduler - - def train_loop(self): - """Training process""" - self.accelerator.wait_for_everyone() - - # Dump config - if self.accelerator.is_main_process: - self._dump_cfg(self.config_save_path) - self.generator.train() - for key in self.discriminators.keys(): - self.discriminators[key].train() - self.generator_optimizer.zero_grad() - self.discriminator_optimizer.zero_grad() - - # Sync and start training - self.accelerator.wait_for_everyone() - while self.epoch < self.max_epoch: - self.logger.info("\n") - self.logger.info("-" * 32) - self.logger.info("Epoch {}: ".format(self.epoch)) - - # Train and Validate - train_total_loss, train_losses = self._train_epoch() - for key, loss in train_losses.items(): - self.logger.info(" |- Train/{} Loss: {:.6f}".format(key, loss)) - self.accelerator.log( - {"Epoch/Train {} Loss".format(key): loss}, - step=self.epoch, - ) - valid_total_loss, valid_losses = self._valid_epoch() - for key, loss in valid_losses.items(): - self.logger.info(" |- Valid/{} Loss: {:.6f}".format(key, loss)) - self.accelerator.log( - {"Epoch/Valid {} Loss".format(key): loss}, - step=self.epoch, - ) - self.accelerator.log( - { - "Epoch/Train Total Loss": train_total_loss, - "Epoch/Valid Total Loss": valid_total_loss, - }, - step=self.epoch, - ) - - # Update scheduler - self.accelerator.wait_for_everyone() - self.generator_scheduler.step() - self.discriminator_scheduler.step() - - # Check save checkpoint interval - run_eval = False - if self.accelerator.is_main_process: - save_checkpoint = False - for i, num in enumerate(self.save_checkpoint_stride): - if self.epoch % num == 0: - save_checkpoint = True - run_eval |= self.run_eval[i] - - # Save checkpoints - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process and save_checkpoint: - path = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, valid_total_loss - ), - ) - self.accelerator.save_state(path) - json.dump( - self.checkpoints_path, - open(os.path.join(path, "ckpts.json"), "w"), - ensure_ascii=False, - indent=4, - ) - - # Save eval audios - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process and run_eval: - for i in range(len(self.valid_dataloader.dataset.eval_audios)): - if self.cfg.preprocess.use_frame_pitch: - eval_audio = self._inference( - self.valid_dataloader.dataset.eval_mels[i], - eval_pitch=self.valid_dataloader.dataset.eval_pitchs[i], - use_pitch=True, - ) - else: - eval_audio = self._inference( - self.valid_dataloader.dataset.eval_mels[i] - ) - path = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}.wav".format( - self.epoch, - self.step, - valid_total_loss, - self.valid_dataloader.dataset.eval_dataset_names[i], - ), - ) - path_gt = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}_gt.wav".format( - self.epoch, - self.step, - valid_total_loss, - self.valid_dataloader.dataset.eval_dataset_names[i], - ), - ) - save_audio(path, eval_audio, self.cfg.preprocess.sample_rate) - save_audio( - path_gt, - self.valid_dataloader.dataset.eval_audios[i], - self.cfg.preprocess.sample_rate, - ) - - self.accelerator.wait_for_everyone() - - self.epoch += 1 - - # Finish training - self.accelerator.wait_for_everyone() - path = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, valid_total_loss - ), - ) - self.accelerator.save_state(path) - - def _train_epoch(self): - """Training epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - self.generator.train() - for key, _ in self.discriminators.items(): - self.discriminators[key].train() - - epoch_losses: dict = {} - epoch_total_loss: int = 0 - - for batch in tqdm( - self.train_dataloader, - desc=f"Training Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - # Get losses - total_loss, losses = self._train_step(batch) - self.batch_count += 1 - - # Log info - if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: - self.accelerator.log( - { - "Step/Generator Learning Rate": self.generator_optimizer.param_groups[ - 0 - ][ - "lr" - ], - "Step/Discriminator Learning Rate": self.discriminator_optimizer.param_groups[ - 0 - ][ - "lr" - ], - }, - step=self.step, - ) - for key, _ in losses.items(): - self.accelerator.log( - { - "Step/Train {} Loss".format(key): losses[key], - }, - step=self.step, - ) - - if not epoch_losses: - epoch_losses = losses - else: - for key, value in losses.items(): - epoch_losses[key] += value - epoch_total_loss += total_loss - self.step += 1 - - # Get and log total losses - self.accelerator.wait_for_everyone() - epoch_total_loss = ( - epoch_total_loss - / len(self.train_dataloader) - * self.cfg.train.gradient_accumulation_step - ) - for key in epoch_losses.keys(): - epoch_losses[key] = ( - epoch_losses[key] - / len(self.train_dataloader) - * self.cfg.train.gradient_accumulation_step - ) - return epoch_total_loss, epoch_losses - - def _train_step(self, data): - """Training forward step. Should return average loss of a sample over - one batch. Provoke ``_forward_step`` is recommended except for special case. - See ``_train_epoch`` for usage. - """ - # Init losses - train_losses = {} - total_loss = 0 - - generator_losses = {} - generator_total_loss = 0 - discriminator_losses = {} - discriminator_total_loss = 0 - - # Use input feature to get predictions - mel_input = data["mel"] - audio_gt = data["audio"] - - if self.cfg.preprocess.extract_amplitude_phase: - logamp_gt = data["logamp"] - pha_gt = data["pha"] - rea_gt = data["rea"] - imag_gt = data["imag"] - - if self.cfg.preprocess.use_frame_pitch: - pitch_input = data["frame_pitch"] - - if self.cfg.preprocess.use_frame_pitch: - pitch_input = pitch_input.float() - audio_pred = self.generator.forward(mel_input, pitch_input) - elif self.cfg.preprocess.extract_amplitude_phase: - ( - logamp_pred, - pha_pred, - rea_pred, - imag_pred, - audio_pred, - ) = self.generator.forward(mel_input) - from utils.mel import amplitude_phase_spectrum - - _, _, rea_pred_final, imag_pred_final = amplitude_phase_spectrum( - audio_pred.squeeze(1), self.cfg.preprocess - ) - else: - audio_pred = self.generator.forward(mel_input) - - # Calculate and BP Discriminator losses - self.discriminator_optimizer.zero_grad() - for key, _ in self.discriminators.items(): - y_r, y_g, _, _ = self.discriminators[key].forward( - audio_gt.unsqueeze(1), audio_pred.detach() - ) - ( - discriminator_losses["{}_discriminator".format(key)], - _, - _, - ) = self.criterions["discriminator"](y_r, y_g) - discriminator_total_loss += discriminator_losses[ - "{}_discriminator".format(key) - ] - - self.accelerator.backward(discriminator_total_loss) - self.discriminator_optimizer.step() - - # Calculate and BP Generator losses - self.generator_optimizer.zero_grad() - for key, _ in self.discriminators.items(): - y_r, y_g, f_r, f_g = self.discriminators[key].forward( - audio_gt.unsqueeze(1), audio_pred - ) - generator_losses["{}_feature".format(key)] = self.criterions["feature"]( - f_r, f_g - ) - generator_losses["{}_generator".format(key)], _ = self.criterions[ - "generator" - ](y_g) - generator_total_loss += generator_losses["{}_feature".format(key)] - generator_total_loss += generator_losses["{}_generator".format(key)] - - if "mel" in self.criterions.keys(): - generator_losses["mel"] = self.criterions["mel"](audio_gt, audio_pred) - generator_total_loss += generator_losses["mel"] - - if "wav" in self.criterions.keys(): - generator_losses["wav"] = self.criterions["wav"](audio_gt, audio_pred) - generator_total_loss += generator_losses["wav"] - - if "amplitude" in self.criterions.keys(): - generator_losses["amplitude"] = self.criterions["amplitude"]( - logamp_gt, logamp_pred - ) - generator_total_loss += generator_losses["amplitude"] - - if "phase" in self.criterions.keys(): - generator_losses["phase"] = self.criterions["phase"](pha_gt, pha_pred) - generator_total_loss += generator_losses["phase"] - - if "consistency" in self.criterions.keys(): - generator_losses["consistency"] = self.criterions["consistency"]( - rea_gt, rea_pred, rea_pred_final, imag_gt, imag_pred, imag_pred_final - ) - generator_total_loss += generator_losses["consistency"] - - self.accelerator.backward(generator_total_loss) - self.generator_optimizer.step() - - # Get the total losses - total_loss = discriminator_total_loss + generator_total_loss - train_losses.update(discriminator_losses) - train_losses.update(generator_losses) - - for key, _ in train_losses.items(): - train_losses[key] = train_losses[key].item() - - return total_loss.item(), train_losses - - def _valid_epoch(self): - """Testing epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - self.generator.eval() - for key, _ in self.discriminators.items(): - self.discriminators[key].eval() - - epoch_losses: dict = {} - epoch_total_loss: int = 0 - - for batch in tqdm( - self.valid_dataloader, - desc=f"Validating Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - # Get losses - total_loss, losses = self._valid_step(batch) - - # Log info - for key, _ in losses.items(): - self.accelerator.log( - { - "Step/Valid {} Loss".format(key): losses[key], - }, - step=self.step, - ) - - if not epoch_losses: - epoch_losses = losses - else: - for key, value in losses.items(): - epoch_losses[key] += value - epoch_total_loss += total_loss - - # Get and log total losses - self.accelerator.wait_for_everyone() - epoch_total_loss = epoch_total_loss / len(self.valid_dataloader) - for key in epoch_losses.keys(): - epoch_losses[key] = epoch_losses[key] / len(self.valid_dataloader) - return epoch_total_loss, epoch_losses - - def _valid_step(self, data): - """Testing forward step. Should return average loss of a sample over - one batch. Provoke ``_forward_step`` is recommended except for special case. - See ``_test_epoch`` for usage. - """ - # Init losses - valid_losses = {} - total_loss = 0 - - generator_losses = {} - generator_total_loss = 0 - discriminator_losses = {} - discriminator_total_loss = 0 - - # Use feature inputs to get the predicted audio - mel_input = data["mel"] - audio_gt = data["audio"] - - if self.cfg.preprocess.extract_amplitude_phase: - logamp_gt = data["logamp"] - pha_gt = data["pha"] - rea_gt = data["rea"] - imag_gt = data["imag"] - - if self.cfg.preprocess.use_frame_pitch: - pitch_input = data["frame_pitch"] - - if self.cfg.preprocess.use_frame_pitch: - pitch_input = pitch_input.float() - audio_pred = self.generator.forward(mel_input, pitch_input) - elif self.cfg.preprocess.extract_amplitude_phase: - ( - logamp_pred, - pha_pred, - rea_pred, - imag_pred, - audio_pred, - ) = self.generator.forward(mel_input) - from utils.mel import amplitude_phase_spectrum - - _, _, rea_pred_final, imag_pred_final = amplitude_phase_spectrum( - audio_pred.squeeze(1), self.cfg.preprocess - ) - else: - audio_pred = self.generator.forward(mel_input) - - # Get Discriminator losses - for key, _ in self.discriminators.items(): - y_r, y_g, _, _ = self.discriminators[key].forward( - audio_gt.unsqueeze(1), audio_pred - ) - ( - discriminator_losses["{}_discriminator".format(key)], - _, - _, - ) = self.criterions["discriminator"](y_r, y_g) - discriminator_total_loss += discriminator_losses[ - "{}_discriminator".format(key) - ] - - for key, _ in self.discriminators.items(): - y_r, y_g, f_r, f_g = self.discriminators[key].forward( - audio_gt.unsqueeze(1), audio_pred - ) - generator_losses["{}_feature".format(key)] = self.criterions["feature"]( - f_r, f_g - ) - generator_losses["{}_generator".format(key)], _ = self.criterions[ - "generator" - ](y_g) - generator_total_loss += generator_losses["{}_feature".format(key)] - generator_total_loss += generator_losses["{}_generator".format(key)] - - if "mel" in self.criterions.keys(): - generator_losses["mel"] = self.criterions["mel"](audio_gt, audio_pred) - generator_total_loss += generator_losses["mel"] - if "mel" in self.criterions.keys(): - generator_losses["mel"] = self.criterions["mel"](audio_gt, audio_pred) - generator_total_loss += generator_losses["mel"] - - if "wav" in self.criterions.keys(): - generator_losses["wav"] = self.criterions["wav"](audio_gt, audio_pred) - generator_total_loss += generator_losses["wav"] - if "wav" in self.criterions.keys(): - generator_losses["wav"] = self.criterions["wav"](audio_gt, audio_pred) - generator_total_loss += generator_losses["wav"] - - if "amplitude" in self.criterions.keys(): - generator_losses["amplitude"] = self.criterions["amplitude"]( - logamp_gt, logamp_pred - ) - generator_total_loss += generator_losses["amplitude"] - - if "phase" in self.criterions.keys(): - generator_losses["phase"] = self.criterions["phase"](pha_gt, pha_pred) - generator_total_loss += generator_losses["phase"] - - if "consistency" in self.criterions.keys(): - generator_losses["consistency"] = self.criterions["consistency"]( - rea_gt, - rea_pred, - rea_pred_final, - imag_gt, - imag_pred, - imag_pred_final, - ) - generator_total_loss += generator_losses["consistency"] - - total_loss = discriminator_total_loss + generator_total_loss - valid_losses.update(discriminator_losses) - valid_losses.update(generator_losses) - - for item in valid_losses: - valid_losses[item] = valid_losses[item].item() - for item in valid_losses: - valid_losses[item] = valid_losses[item].item() - - return total_loss.item(), valid_losses - return total_loss.item(), valid_losses - - def _inference(self, eval_mel, eval_pitch=None, use_pitch=False): - """Inference during training for test audios.""" - if use_pitch: - eval_pitch = align_length(eval_pitch, eval_mel.shape[1]) - eval_audio = vocoder_inference( - self.cfg, - self.generator, - torch.from_numpy(eval_mel).unsqueeze(0), - f0s=torch.from_numpy(eval_pitch).unsqueeze(0).float(), - device=next(self.generator.parameters()).device, - ).squeeze(0) - else: - eval_audio = vocoder_inference( - self.cfg, - self.generator, - torch.from_numpy(eval_mel).unsqueeze(0), - device=next(self.generator.parameters()).device, - ).squeeze(0) - return eval_audio - - def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): - """Load model from checkpoint. If checkpoint_path is None, it will - load the latest checkpoint in checkpoint_dir. If checkpoint_path is not - None, it will load the checkpoint specified by checkpoint_path. **Only use this - method after** ``accelerator.prepare()``. - """ - if checkpoint_path is None: - ls = [str(i) for i in Path(checkpoint_dir).glob("*")] - ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) - checkpoint_path = ls[0] - if resume_type == "resume": - self.accelerator.load_state(checkpoint_path) - self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 - self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 - elif resume_type == "finetune": - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.generator), - os.path.join(checkpoint_path, "pytorch_model.bin"), - ) - for key, _ in self.discriminators.items(): - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.discriminators[key]), - os.path.join(checkpoint_path, "pytorch_model.bin"), - ) - self.logger.info("Load model weights for finetune SUCCESS!") - else: - raise ValueError("Unsupported resume type: {}".format(resume_type)) - return checkpoint_path - - def _count_parameters(self): - result = sum(p.numel() for p in self.generator.parameters()) - for _, discriminator in self.discriminators.items(): - result += sum(p.numel() for p in discriminator.parameters()) - return result diff --git a/models/vocoders/gan/generator/apnet.py b/models/vocoders/gan/generator/apnet.py deleted file mode 100644 index f39dd6b01f6be5a6bdd2e04ca822a4a9c9b4c9b4..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/generator/apnet.py +++ /dev/null @@ -1,395 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import torch.nn as nn -from modules.vocoder_blocks import * - -LRELU_SLOPE = 0.1 - - -class ISTFT(nn.Module): - """ - Custom implementation of ISTFT since torch.istft doesn't allow custom padding (other than `center=True`) with - windowing. This is because the NOLA (Nonzero Overlap Add) check fails at the edges. - See issue: https://github.com/pytorch/pytorch/issues/62323 - Specifically, in the context of neural vocoding we are interested in "same" padding analogous to CNNs. - The NOLA constraint is met as we trim padded samples anyway. - - Args: - n_fft (int): Size of Fourier transform. - hop_length (int): The distance between neighboring sliding window frames. - win_length (int): The size of window frame and STFT filter. - padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same". - """ - - def __init__( - self, - n_fft: int, - hop_length: int, - win_length: int, - padding: str = "same", - ): - super().__init__() - if padding not in ["center", "same"]: - raise ValueError("Padding must be 'center' or 'same'.") - self.padding = padding - self.n_fft = n_fft - self.hop_length = hop_length - self.win_length = win_length - - def forward(self, spec: torch.Tensor, window) -> torch.Tensor: - """ - Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram. - - Args: - spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size, - N is the number of frequency bins, and T is the number of time frames. - - Returns: - Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal. - """ - if self.padding == "center": - # Fallback to pytorch native implementation - return torch.istft( - spec, - self.n_fft, - self.hop_length, - self.win_length, - window, - center=True, - ) - elif self.padding == "same": - pad = (self.win_length - self.hop_length) // 2 - else: - raise ValueError("Padding must be 'center' or 'same'.") - - assert spec.dim() == 3, "Expected a 3D tensor as input" - B, N, T = spec.shape - - # Inverse FFT - ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward") - ifft = ifft * window[None, :, None] - - # Overlap and Add - output_size = (T - 1) * self.hop_length + self.win_length - y = torch.nn.functional.fold( - ifft, - output_size=(1, output_size), - kernel_size=(1, self.win_length), - stride=(1, self.hop_length), - )[:, 0, 0, pad:-pad] - - # Window envelope - window_sq = window.square().expand(1, T, -1).transpose(1, 2) - window_envelope = torch.nn.functional.fold( - window_sq, - output_size=(1, output_size), - kernel_size=(1, self.win_length), - stride=(1, self.hop_length), - ).squeeze()[pad:-pad] - - # Normalize - assert (window_envelope > 1e-11).all() - y = y / window_envelope - - return y - -# The ASP and PSP Module are adopted from APNet under the MIT License -# https://github.com/YangAi520/APNet/blob/main/models.py - -class ASPResBlock(torch.nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ASPResBlock, self).__init__() - self.cfg = cfg - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - -class PSPResBlock(torch.nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): - super(PSPResBlock, self).__init__() - self.cfg = cfg - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - -class APNet(torch.nn.Module): - def __init__(self, cfg): - super(APNet, self).__init__() - self.cfg = cfg - self.ASP_num_kernels = len(cfg.model.apnet.ASP_resblock_kernel_sizes) - self.PSP_num_kernels = len(cfg.model.apnet.PSP_resblock_kernel_sizes) - - self.ASP_input_conv = weight_norm( - Conv1d( - cfg.preprocess.n_mel, - cfg.model.apnet.ASP_channel, - cfg.model.apnet.ASP_input_conv_kernel_size, - 1, - padding=get_padding(cfg.model.apnet.ASP_input_conv_kernel_size, 1), - ) - ) - self.PSP_input_conv = weight_norm( - Conv1d( - cfg.preprocess.n_mel, - cfg.model.apnet.PSP_channel, - cfg.model.apnet.PSP_input_conv_kernel_size, - 1, - padding=get_padding(cfg.model.apnet.PSP_input_conv_kernel_size, 1), - ) - ) - - self.ASP_ResNet = nn.ModuleList() - for j, (k, d) in enumerate( - zip( - cfg.model.apnet.ASP_resblock_kernel_sizes, - cfg.model.apnet.ASP_resblock_dilation_sizes, - ) - ): - self.ASP_ResNet.append(ASPResBlock(cfg, cfg.model.apnet.ASP_channel, k, d)) - - self.PSP_ResNet = nn.ModuleList() - for j, (k, d) in enumerate( - zip( - cfg.model.apnet.PSP_resblock_kernel_sizes, - cfg.model.apnet.PSP_resblock_dilation_sizes, - ) - ): - self.PSP_ResNet.append(PSPResBlock(cfg, cfg.model.apnet.PSP_channel, k, d)) - - self.ASP_output_conv = weight_norm( - Conv1d( - cfg.model.apnet.ASP_channel, - cfg.preprocess.n_fft // 2 + 1, - cfg.model.apnet.ASP_output_conv_kernel_size, - 1, - padding=get_padding(cfg.model.apnet.ASP_output_conv_kernel_size, 1), - ) - ) - self.PSP_output_R_conv = weight_norm( - Conv1d( - cfg.model.apnet.PSP_channel, - cfg.preprocess.n_fft // 2 + 1, - cfg.model.apnet.PSP_output_R_conv_kernel_size, - 1, - padding=get_padding(cfg.model.apnet.PSP_output_R_conv_kernel_size, 1), - ) - ) - self.PSP_output_I_conv = weight_norm( - Conv1d( - cfg.model.apnet.PSP_channel, - cfg.preprocess.n_fft // 2 + 1, - cfg.model.apnet.PSP_output_I_conv_kernel_size, - 1, - padding=get_padding(cfg.model.apnet.PSP_output_I_conv_kernel_size, 1), - ) - ) - - self.iSTFT = ISTFT( - self.cfg.preprocess.n_fft, - hop_length=self.cfg.preprocess.hop_size, - win_length=self.cfg.preprocess.win_size, - ) - - self.ASP_output_conv.apply(init_weights) - self.PSP_output_R_conv.apply(init_weights) - self.PSP_output_I_conv.apply(init_weights) - - def forward(self, mel): - logamp = self.ASP_input_conv(mel) - logamps = None - for j in range(self.ASP_num_kernels): - if logamps is None: - logamps = self.ASP_ResNet[j](logamp) - else: - logamps += self.ASP_ResNet[j](logamp) - logamp = logamps / self.ASP_num_kernels - logamp = F.leaky_relu(logamp) - logamp = self.ASP_output_conv(logamp) - - pha = self.PSP_input_conv(mel) - phas = None - for j in range(self.PSP_num_kernels): - if phas is None: - phas = self.PSP_ResNet[j](pha) - else: - phas += self.PSP_ResNet[j](pha) - pha = phas / self.PSP_num_kernels - pha = F.leaky_relu(pha) - R = self.PSP_output_R_conv(pha) - I = self.PSP_output_I_conv(pha) - - pha = torch.atan2(I, R) - - rea = torch.exp(logamp) * torch.cos(pha) - imag = torch.exp(logamp) * torch.sin(pha) - - spec = torch.cat((rea.unsqueeze(-1), imag.unsqueeze(-1)), -1) - - spec = torch.view_as_complex(spec) - - audio = self.iSTFT.forward( - spec, torch.hann_window(self.cfg.preprocess.win_size).to(mel.device) - ) - - return logamp, pha, rea, imag, audio.unsqueeze(1) diff --git a/models/vocoders/gan/generator/bigvgan.py b/models/vocoders/gan/generator/bigvgan.py deleted file mode 100644 index 205f8d697b9a49aa4ac3f4c46015fef861d4fe1b..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/generator/bigvgan.py +++ /dev/null @@ -1,344 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -import torch.nn as nn - -from torch.nn import Conv1d, ConvTranspose1d -from torch.nn.utils import weight_norm, remove_weight_norm - -from modules.vocoder_blocks import * -from modules.activation_functions import * -from modules.anti_aliasing import * - -LRELU_SLOPE = 0.1 - -# The AMPBlock Module is adopted from BigVGAN under the MIT License -# https://github.com/NVIDIA/BigVGAN - -class AMPBlock1(torch.nn.Module): - def __init__( - self, cfg, channels, kernel_size=3, dilation=(1, 3, 5), activation=None - ): - super(AMPBlock1, self).__init__() - self.cfg = cfg - - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - self.num_layers = len(self.convs1) + len( - self.convs2 - ) # total number of conv layers - - if ( - activation == "snake" - ): # periodic nonlinearity with snake function and anti-aliasing - self.activations = nn.ModuleList( - [ - Activation1d( - activation=Snake( - channels, alpha_logscale=cfg.model.bigvgan.snake_logscale - ) - ) - for _ in range(self.num_layers) - ] - ) - elif ( - activation == "snakebeta" - ): # periodic nonlinearity with snakebeta function and anti-aliasing - self.activations = nn.ModuleList( - [ - Activation1d( - activation=SnakeBeta( - channels, alpha_logscale=cfg.model.bigvgan.snake_logscale - ) - ) - for _ in range(self.num_layers) - ] - ) - else: - raise NotImplementedError( - "activation incorrectly specified. check the config file and look for 'activation'." - ) - - def forward(self, x): - acts1, acts2 = self.activations[::2], self.activations[1::2] - for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): - xt = a1(x) - xt = c1(xt) - xt = a2(xt) - xt = c2(xt) - x = xt + x - - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class AMPBlock2(torch.nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3), activation=None): - super(AMPBlock2, self).__init__() - self.cfg = cfg - - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - self.num_layers = len(self.convs) # total number of conv layers - - if ( - activation == "snake" - ): # periodic nonlinearity with snake function and anti-aliasing - self.activations = nn.ModuleList( - [ - Activation1d( - activation=Snake( - channels, alpha_logscale=cfg.model.bigvgan.snake_logscale - ) - ) - for _ in range(self.num_layers) - ] - ) - elif ( - activation == "snakebeta" - ): # periodic nonlinearity with snakebeta function and anti-aliasing - self.activations = nn.ModuleList( - [ - Activation1d( - activation=SnakeBeta( - channels, alpha_logscale=cfg.model.bigvgan.snake_logscale - ) - ) - for _ in range(self.num_layers) - ] - ) - else: - raise NotImplementedError( - "activation incorrectly specified. check the config file and look for 'activation'." - ) - - def forward(self, x): - for c, a in zip(self.convs, self.activations): - xt = a(x) - xt = c(xt) - x = xt + x - - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class BigVGAN(torch.nn.Module): - def __init__(self, cfg): - super(BigVGAN, self).__init__() - self.cfg = cfg - - self.num_kernels = len(cfg.model.bigvgan.resblock_kernel_sizes) - self.num_upsamples = len(cfg.model.bigvgan.upsample_rates) - - # Conv pre to boost channels - self.conv_pre = weight_norm( - Conv1d( - cfg.preprocess.n_mel, - cfg.model.bigvgan.upsample_initial_channel, - 7, - 1, - padding=3, - ) - ) - - resblock = AMPBlock1 if cfg.model.bigvgan.resblock == "1" else AMPBlock2 - - # Upsamplers - self.ups = nn.ModuleList() - for i, (u, k) in enumerate( - zip( - cfg.model.bigvgan.upsample_rates, - cfg.model.bigvgan.upsample_kernel_sizes, - ) - ): - self.ups.append( - nn.ModuleList( - [ - weight_norm( - ConvTranspose1d( - cfg.model.bigvgan.upsample_initial_channel // (2**i), - cfg.model.bigvgan.upsample_initial_channel - // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ] - ) - ) - - # Res Blocks with AMP and Anti-aliasing - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = cfg.model.bigvgan.upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip( - cfg.model.bigvgan.resblock_kernel_sizes, - cfg.model.bigvgan.resblock_dilation_sizes, - ) - ): - self.resblocks.append( - resblock(cfg, ch, k, d, activation=cfg.model.bigvgan.activation) - ) - - # Conv post for result - if ( - cfg.model.bigvgan.activation == "snake" - ): - activation_post = Snake(ch, alpha_logscale=cfg.model.bigvgan.snake_logscale) - self.activation_post = Activation1d(activation=activation_post) - elif ( - cfg.model.bigvgan.activation == "snakebeta" - ): - activation_post = SnakeBeta( - ch, alpha_logscale=cfg.model.bigvgan.snake_logscale - ) - self.activation_post = Activation1d(activation=activation_post) - else: - raise NotImplementedError( - "activation incorrectly specified. check the config file and look for 'activation'." - ) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - - # Weight Norm - for i in range(len(self.ups)): - self.ups[i].apply(init_weights) - self.conv_post.apply(init_weights) - - def forward(self, x): - x = self.conv_pre(x) - - for i in range(self.num_upsamples): - for i_up in range(len(self.ups[i])): - x = self.ups[i][i_up](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - - x = self.activation_post(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print("Removing weight norm...") - for l in self.ups: - for l_i in l: - remove_weight_norm(l_i) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) diff --git a/models/vocoders/gan/generator/hifigan.py b/models/vocoders/gan/generator/hifigan.py deleted file mode 100644 index 9f5f32498f5eb6441db787b0ae204a1eeff36aa3..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/generator/hifigan.py +++ /dev/null @@ -1,449 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d -from torch.nn.utils import weight_norm, remove_weight_norm -from modules.vocoder_blocks import * - - -LRELU_SLOPE = 0.1 - - -class ResBlock1(torch.nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.cfg = cfg - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.cfg = cfg - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class HiFiGAN(torch.nn.Module): - def __init__(self, cfg): - super(HiFiGAN, self).__init__() - self.cfg = cfg - self.num_kernels = len(self.cfg.model.hifigan.resblock_kernel_sizes) - self.num_upsamples = len(self.cfg.model.hifigan.upsample_rates) - self.conv_pre = weight_norm( - Conv1d( - cfg.preprocess.n_mel, - self.cfg.model.hifigan.upsample_initial_channel, - 7, - 1, - padding=3, - ) - ) - resblock = ResBlock1 if self.cfg.model.hifigan.resblock == "1" else ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate( - zip( - self.cfg.model.hifigan.upsample_rates, - self.cfg.model.hifigan.upsample_kernel_sizes, - ) - ): - self.ups.append( - weight_norm( - ConvTranspose1d( - self.cfg.model.hifigan.upsample_initial_channel // (2**i), - self.cfg.model.hifigan.upsample_initial_channel - // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = self.cfg.model.hifigan.upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip( - self.cfg.model.hifigan.resblock_kernel_sizes, - self.cfg.model.hifigan.resblock_dilation_sizes, - ) - ): - self.resblocks.append(resblock(self.cfg, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - - def forward(self, x): - x = self.conv_pre(x) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print("Removing weight norm...") - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -# todo: merge with ResBlock1 (lmxue, yicheng) -class ResBlock1_vits(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1_vits, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -# todo: merge with ResBlock2 (lmxue, yicheng) -class ResBlock2_vits(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2_vits, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -# todo: merge with HiFiGAN (lmxue, yicheng) -class HiFiGAN_vits(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(HiFiGAN_vits, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = ResBlock1_vits if resblock == "1" else ResBlock2_vits - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() diff --git a/models/vocoders/gan/generator/melgan.py b/models/vocoders/gan/generator/melgan.py deleted file mode 100644 index ca90d684ca1f5a0a813db4192540adac0cee2558..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/generator/melgan.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np - -from torch.nn.utils import weight_norm - -# This code is adopted from MelGAN under the MIT License -# https://github.com/descriptinc/melgan-neurips - -def weights_init(m): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(0.0, 0.02) - elif classname.find("BatchNorm2d") != -1: - m.weight.data.normal_(1.0, 0.02) - m.bias.data.fill_(0) - - -def WNConv1d(*args, **kwargs): - return weight_norm(nn.Conv1d(*args, **kwargs)) - - -def WNConvTranspose1d(*args, **kwargs): - return weight_norm(nn.ConvTranspose1d(*args, **kwargs)) - - -class ResnetBlock(nn.Module): - def __init__(self, dim, dilation=1): - super().__init__() - self.block = nn.Sequential( - nn.LeakyReLU(0.2), - nn.ReflectionPad1d(dilation), - WNConv1d(dim, dim, kernel_size=3, dilation=dilation), - nn.LeakyReLU(0.2), - WNConv1d(dim, dim, kernel_size=1), - ) - self.shortcut = WNConv1d(dim, dim, kernel_size=1) - - def forward(self, x): - return self.shortcut(x) + self.block(x) - - -class MelGAN(nn.Module): - def __init__(self, cfg): - super().__init__() - - self.cfg = cfg - - self.hop_length = np.prod(self.cfg.model.melgan.ratios) - mult = int(2 ** len(self.cfg.model.melgan.ratios)) - - model = [ - nn.ReflectionPad1d(3), - WNConv1d( - self.cfg.preprocess.n_mel, - mult * self.cfg.model.melgan.ngf, - kernel_size=7, - padding=0, - ), - ] - - # Upsample to raw audio scale - for i, r in enumerate(self.cfg.model.melgan.ratios): - model += [ - nn.LeakyReLU(0.2), - WNConvTranspose1d( - mult * self.cfg.model.melgan.ngf, - mult * self.cfg.model.melgan.ngf // 2, - kernel_size=r * 2, - stride=r, - padding=r // 2 + r % 2, - output_padding=r % 2, - ), - ] - - for j in range(self.cfg.model.melgan.n_residual_layers): - model += [ - ResnetBlock(mult * self.cfg.model.melgan.ngf // 2, dilation=3**j) - ] - - mult //= 2 - - model += [ - nn.LeakyReLU(0.2), - nn.ReflectionPad1d(3), - WNConv1d(self.cfg.model.melgan.ngf, 1, kernel_size=7, padding=0), - nn.Tanh(), - ] - - self.model = nn.Sequential(*model) - self.apply(weights_init) - - def forward(self, x): - return self.model(x) diff --git a/models/vocoders/gan/generator/nsfhifigan.py b/models/vocoders/gan/generator/nsfhifigan.py deleted file mode 100644 index 8deb4ce7a9348f4a03bfef55fd21c634b3f25a78..0000000000000000000000000000000000000000 --- a/models/vocoders/gan/generator/nsfhifigan.py +++ /dev/null @@ -1,281 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -import numpy as np -import torch.nn as nn -import torch.nn.functional as F - -from torch.nn import Conv1d, ConvTranspose1d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm - -from modules.neural_source_filter import * -from modules.vocoder_blocks import * - - -LRELU_SLOPE = 0.1 - - -class ResBlock1(nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.cfg = cfg - - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(nn.Module): - def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock1, self).__init__() - self.cfg = cfg - - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - -# This NSF Module is adopted from Xin Wang's NSF under the MIT License -# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts - -class SourceModuleHnNSF(nn.Module): - def __init__( - self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0 - ): - super(SourceModuleHnNSF, self).__init__() - - self.amp = amp - self.noise_std = noise_std - self.l_sin_gen = SineGen(fs, harmonic_num, amp, noise_std, voiced_threshold) - - self.l_linear = nn.Linear(harmonic_num + 1, 1) - self.l_tanh = nn.Tanh() - - def forward(self, x, upp): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge - - -class NSFHiFiGAN(nn.Module): - def __init__(self, cfg): - super(NSFHiFiGAN, self).__init__() - - self.cfg = cfg - self.num_kernels = len(self.cfg.model.nsfhifigan.resblock_kernel_sizes) - self.num_upsamples = len(self.cfg.model.nsfhifigan.upsample_rates) - self.m_source = SourceModuleHnNSF( - fs=self.cfg.preprocess.sample_rate, - harmonic_num=self.cfg.model.nsfhifigan.harmonic_num, - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = weight_norm( - Conv1d( - self.cfg.preprocess.n_mel, - self.cfg.model.nsfhifigan.upsample_initial_channel, - 7, - 1, - padding=3, - ) - ) - - resblock = ResBlock1 if self.cfg.model.nsfhifigan.resblock == "1" else ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate( - zip( - self.cfg.model.nsfhifigan.upsample_rates, - self.cfg.model.nsfhifigan.upsample_kernel_sizes, - ) - ): - c_cur = self.cfg.model.nsfhifigan.upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - self.cfg.model.nsfhifigan.upsample_initial_channel // (2**i), - self.cfg.model.nsfhifigan.upsample_initial_channel - // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(self.cfg.model.nsfhifigan.upsample_rates): - stride_f0 = int( - np.prod(self.cfg.model.nsfhifigan.upsample_rates[i + 1 :]) - ) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - ch = self.cfg.model.nsfhifigan.upsample_initial_channel - for i in range(len(self.ups)): - ch //= 2 - for j, (k, d) in enumerate( - zip( - self.cfg.model.nsfhifigan.resblock_kernel_sizes, - self.cfg.model.nsfhifigan.resblock_dilation_sizes, - ) - ): - self.resblocks.append(resblock(cfg, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - self.upp = int(np.prod(self.cfg.model.nsfhifigan.upsample_rates)) - - def forward(self, x, f0): - har_source = self.m_source(f0, self.upp).transpose(1, 2) - x = self.conv_pre(x) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - - length = min(x.shape[-1], x_source.shape[-1]) - x = x[:, :, :length] - x_source = x[:, :, :length] - - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x diff --git a/models/vocoders/gan/generator/sifigan.py b/models/vocoders/gan/generator/sifigan.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/models/vocoders/vocoder_dataset.py b/models/vocoders/vocoder_dataset.py deleted file mode 100644 index 7df17b97ba7a4f770f01971324126eca4a2db272..0000000000000000000000000000000000000000 --- a/models/vocoders/vocoder_dataset.py +++ /dev/null @@ -1,264 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Iterable -import torch -import numpy as np -import torch.utils.data -from torch.nn.utils.rnn import pad_sequence -from utils.data_utils import * -from torch.utils.data import ConcatDataset, Dataset - - -class VocoderDataset(torch.utils.data.Dataset): - def __init__(self, cfg, dataset, is_valid=False): - """ - Args: - cfg: config - dataset: dataset name - is_valid: whether to use train or valid dataset - """ - assert isinstance(dataset, str) - - processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) - - meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file - self.metafile_path = os.path.join(processed_data_dir, meta_file) - self.metadata = self.get_metadata() - - self.data_root = processed_data_dir - self.cfg = cfg - - if cfg.preprocess.use_audio: - self.utt2audio_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2audio_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.audio_dir, - uid + ".npy", - ) - elif cfg.preprocess.use_label: - self.utt2label_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2label_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.label_dir, - uid + ".npy", - ) - elif cfg.preprocess.use_one_hot: - self.utt2one_hot_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2one_hot_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.one_hot_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_mel: - self.utt2mel_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2mel_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.mel_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_frame_pitch: - self.utt2frame_pitch_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2frame_pitch_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.pitch_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_uv: - self.utt2uv_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - self.utt2uv_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.uv_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_amplitude_phase: - self.utt2logamp_path = {} - self.utt2pha_path = {} - self.utt2rea_path = {} - self.utt2imag_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - self.utt2logamp_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.log_amplitude_dir, - uid + ".npy", - ) - self.utt2pha_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.phase_dir, - uid + ".npy", - ) - self.utt2rea_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.real_dir, - uid + ".npy", - ) - self.utt2imag_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.imaginary_dir, - uid + ".npy", - ) - - def __getitem__(self, index): - utt_info = self.metadata[index] - - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - single_feature = dict() - - if self.cfg.preprocess.use_mel: - mel = np.load(self.utt2mel_path[utt]) - assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = mel.shape[1] - - single_feature["mel"] = mel - - if self.cfg.preprocess.use_frame_pitch: - frame_pitch = np.load(self.utt2frame_pitch_path[utt]) - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_pitch) - - aligned_frame_pitch = align_length( - frame_pitch, single_feature["target_len"] - ) - - single_feature["frame_pitch"] = aligned_frame_pitch - - if self.cfg.preprocess.use_audio: - audio = np.load(self.utt2audio_path[utt]) - - single_feature["audio"] = audio - - return single_feature - - def get_metadata(self): - with open(self.metafile_path, "r", encoding="utf-8") as f: - metadata = json.load(f) - - return metadata - - def get_dataset_name(self): - return self.metadata[0]["Dataset"] - - def __len__(self): - return len(self.metadata) - - -class VocoderConcatDataset(ConcatDataset): - def __init__(self, datasets: Iterable[Dataset], full_audio_inference=False): - """Concatenate a series of datasets with their random inference audio merged.""" - super().__init__(datasets) - - self.cfg = self.datasets[0].cfg - - self.metadata = [] - - # Merge metadata - for dataset in self.datasets: - self.metadata += dataset.metadata - - # Merge random inference features - if full_audio_inference: - self.eval_audios = [] - self.eval_dataset_names = [] - if self.cfg.preprocess.use_mel: - self.eval_mels = [] - if self.cfg.preprocess.use_frame_pitch: - self.eval_pitchs = [] - for dataset in self.datasets: - self.eval_audios.append(dataset.eval_audio) - self.eval_dataset_names.append(dataset.get_dataset_name()) - if self.cfg.preprocess.use_mel: - self.eval_mels.append(dataset.eval_mel) - if self.cfg.preprocess.use_frame_pitch: - self.eval_pitchs.append(dataset.eval_pitch) - - -class VocoderCollator(object): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - self.cfg = cfg - - def __call__(self, batch): - packed_batch_features = dict() - - # mel: [b, n_mels, frame] - # frame_pitch: [b, frame] - # audios: [b, frame * hop_size] - - for key in batch[0].keys(): - if key == "target_len": - packed_batch_features["target_len"] = torch.LongTensor( - [b["target_len"] for b in batch] - ) - masks = [ - torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch - ] - packed_batch_features["mask"] = pad_sequence( - masks, batch_first=True, padding_value=0 - ) - elif key == "mel": - values = [torch.from_numpy(b[key]).T for b in batch] - packed_batch_features[key] = pad_sequence( - values, batch_first=True, padding_value=0 - ) - else: - values = [torch.from_numpy(b[key]) for b in batch] - packed_batch_features[key] = pad_sequence( - values, batch_first=True, padding_value=0 - ) - - return packed_batch_features diff --git a/models/vocoders/vocoder_inference.py b/models/vocoders/vocoder_inference.py deleted file mode 100644 index dfd09ee6aa44c544c51a62c0e014cca4260cc6a8..0000000000000000000000000000000000000000 --- a/models/vocoders/vocoder_inference.py +++ /dev/null @@ -1,488 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import torch -import json -import json5 -import time -import accelerate -import random -import numpy as np -import shutil - -from pathlib import Path -from tqdm import tqdm -from glob import glob -from accelerate.logging import get_logger -from torch.utils.data import DataLoader - -from models.vocoders.vocoder_dataset import ( - VocoderDataset, - VocoderCollator, - VocoderConcatDataset, -) - -from models.vocoders.gan.generator import bigvgan, hifigan, melgan, nsfhifigan, apnet -from models.vocoders.flow.waveglow import waveglow -from models.vocoders.diffusion.diffwave import diffwave -from models.vocoders.autoregressive.wavenet import wavenet -from models.vocoders.autoregressive.wavernn import wavernn -from models.vocoders.gan import gan_vocoder_inference -from utils.io import save_audio - -_vocoders = { - "diffwave": diffwave.DiffWave, - "wavernn": wavernn.WaveRNN, - "wavenet": wavenet.WaveNet, - "waveglow": waveglow.WaveGlow, - "nsfhifigan": nsfhifigan.NSFHiFiGAN, - "bigvgan": bigvgan.BigVGAN, - "hifigan": hifigan.HiFiGAN, - "melgan": melgan.MelGAN, - "apnet": apnet.APNet, -} - -_vocoder_infer_funcs = { - # "world": world_inference.synthesis_audios, - # "wavernn": wavernn_inference.synthesis_audios, - # "wavenet": wavenet_inference.synthesis_audios, - # "diffwave": diffwave_inference.synthesis_audios, - "nsfhifigan": gan_vocoder_inference.synthesis_audios, - "bigvgan": gan_vocoder_inference.synthesis_audios, - "melgan": gan_vocoder_inference.synthesis_audios, - "hifigan": gan_vocoder_inference.synthesis_audios, - "apnet": gan_vocoder_inference.synthesis_audios, -} - - -class VocoderInference(object): - def __init__(self, args=None, cfg=None, infer_type="from_dataset"): - super().__init__() - - start = time.monotonic_ns() - self.args = args - self.cfg = cfg - self.infer_type = infer_type - - # Init accelerator - self.accelerator = accelerate.Accelerator() - self.accelerator.wait_for_everyone() - - # Get logger - with self.accelerator.main_process_first(): - self.logger = get_logger("inference", log_level=args.log_level) - - # Log some info - self.logger.info("=" * 56) - self.logger.info("||\t\t" + "New inference process started." + "\t\t||") - self.logger.info("=" * 56) - self.logger.info("\n") - - self.vocoder_dir = args.vocoder_dir - self.logger.debug(f"Vocoder dir: {args.vocoder_dir}") - - os.makedirs(args.output_dir, exist_ok=True) - if os.path.exists(os.path.join(args.output_dir, "pred")): - shutil.rmtree(os.path.join(args.output_dir, "pred")) - if os.path.exists(os.path.join(args.output_dir, "gt")): - shutil.rmtree(os.path.join(args.output_dir, "gt")) - os.makedirs(os.path.join(args.output_dir, "pred"), exist_ok=True) - os.makedirs(os.path.join(args.output_dir, "gt"), exist_ok=True) - - # Set random seed - with self.accelerator.main_process_first(): - start = time.monotonic_ns() - self._set_random_seed(self.cfg.train.random_seed) - end = time.monotonic_ns() - self.logger.debug( - f"Setting random seed done in {(end - start) / 1e6:.2f}ms" - ) - self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") - - # Setup inference mode - if self.infer_type == "infer_from_dataset": - self.cfg.dataset = self.args.infer_datasets - elif self.infer_type == "infer_from_feature": - self._build_tmp_dataset_from_feature() - self.cfg.dataset = ["tmp"] - elif self.infer_type == "infer_from_audio": - self._build_tmp_dataset_from_audio() - self.cfg.dataset = ["tmp"] - - # Setup data loader - with self.accelerator.main_process_first(): - self.logger.info("Building dataset...") - start = time.monotonic_ns() - self.test_dataloader = self._build_dataloader() - end = time.monotonic_ns() - self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") - - # Build model - with self.accelerator.main_process_first(): - self.logger.info("Building model...") - start = time.monotonic_ns() - self.model = self._build_model() - end = time.monotonic_ns() - self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms") - - # Init with accelerate - self.logger.info("Initializing accelerate...") - start = time.monotonic_ns() - self.accelerator = accelerate.Accelerator() - (self.model, self.test_dataloader) = self.accelerator.prepare( - self.model, self.test_dataloader - ) - end = time.monotonic_ns() - self.accelerator.wait_for_everyone() - self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms") - - with self.accelerator.main_process_first(): - self.logger.info("Loading checkpoint...") - start = time.monotonic_ns() - if os.path.isdir(args.vocoder_dir): - if os.path.isdir(os.path.join(args.vocoder_dir, "checkpoint")): - self._load_model(os.path.join(args.vocoder_dir, "checkpoint")) - else: - self._load_model(os.path.join(args.vocoder_dir)) - else: - self._load_model(os.path.join(args.vocoder_dir)) - end = time.monotonic_ns() - self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms") - - self.model.eval() - self.accelerator.wait_for_everyone() - - def _build_tmp_dataset_from_feature(self): - if os.path.exists(os.path.join(self.cfg.preprocess.processed_dir, "tmp")): - shutil.rmtree(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) - - utts = [] - mels = glob(os.path.join(self.args.feature_folder, "mels", "*.npy")) - for i, mel in enumerate(mels): - uid = mel.split("/")[-1].split(".")[0] - utt = {"Dataset": "tmp", "Uid": uid, "index": i} - utts.append(utt) - - os.makedirs(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) - with open( - os.path.join(self.cfg.preprocess.processed_dir, "tmp", "test.json"), "w" - ) as f: - json.dump(utts, f) - - meta_info = {"dataset": "tmp", "test": {"size": len(utts)}} - - with open( - os.path.join(self.cfg.preprocess.processed_dir, "tmp", "meta_info.json"), - "w", - ) as f: - json.dump(meta_info, f) - - features = glob(os.path.join(self.args.feature_folder, "*")) - for feature in features: - feature_name = feature.split("/")[-1] - if os.path.isfile(feature): - continue - shutil.copytree( - os.path.join(self.args.feature_folder, feature_name), - os.path.join(self.cfg.preprocess.processed_dir, "tmp", feature_name), - ) - - def _build_tmp_dataset_from_audio(self): - if os.path.exists(os.path.join(self.cfg.preprocess.processed_dir, "tmp")): - shutil.rmtree(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) - - utts = [] - audios = glob(os.path.join(self.args.audio_folder, "*")) - for i, audio in enumerate(audios): - uid = audio.split("/")[-1].split(".")[0] - utt = {"Dataset": "tmp", "Uid": uid, "index": i, "Path": audio} - utts.append(utt) - - os.makedirs(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) - with open( - os.path.join(self.cfg.preprocess.processed_dir, "tmp", "test.json"), "w" - ) as f: - json.dump(utts, f) - - meta_info = {"dataset": "tmp", "test": {"size": len(utts)}} - - with open( - os.path.join(self.cfg.preprocess.processed_dir, "tmp", "meta_info.json"), - "w", - ) as f: - json.dump(meta_info, f) - - from processors import acoustic_extractor - - acoustic_extractor.extract_utt_acoustic_features_serial( - utts, os.path.join(self.cfg.preprocess.processed_dir, "tmp"), self.cfg - ) - - def _build_test_dataset(self): - return VocoderDataset, VocoderCollator - - def _build_model(self): - model = _vocoders[self.cfg.model.generator](self.cfg) - return model - - def _build_dataloader(self): - """Build dataloader which merges a series of datasets.""" - Dataset, Collator = self._build_test_dataset() - - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=True) - datasets_list.append(subdataset) - test_dataset = VocoderConcatDataset(datasets_list, full_audio_inference=False) - test_collate = Collator(self.cfg) - test_batch_size = min(self.cfg.inference.batch_size, len(test_dataset)) - test_dataloader = DataLoader( - test_dataset, - collate_fn=test_collate, - num_workers=1, - batch_size=test_batch_size, - shuffle=False, - ) - self.test_batch_size = test_batch_size - self.test_dataset = test_dataset - return test_dataloader - - def _load_model(self, checkpoint_dir, from_multi_gpu=False): - """Load model from checkpoint. If a folder is given, it will - load the latest checkpoint in checkpoint_dir. If a path is given - it will load the checkpoint specified by checkpoint_path. - **Only use this method after** ``accelerator.prepare()``. - """ - if os.path.isdir(checkpoint_dir): - if "epoch" in checkpoint_dir and "step" in checkpoint_dir: - checkpoint_path = checkpoint_dir - else: - # Load the latest accelerator state dicts - ls = [ - str(i) - for i in Path(checkpoint_dir).glob("*") - if not "audio" in str(i) - ] - ls.sort( - key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True - ) - checkpoint_path = ls[0] - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model), - os.path.join(checkpoint_path, "pytorch_model.bin"), - ) - return str(checkpoint_path) - else: - # Load old .pt checkpoints - if self.cfg.model.generator in [ - "bigvgan", - "hifigan", - "melgan", - "nsfhifigan", - ]: - ckpt = torch.load( - checkpoint_dir, - map_location=torch.device("cuda") - if torch.cuda.is_available() - else torch.device("cpu"), - ) - if from_multi_gpu: - pretrained_generator_dict = ckpt["generator_state_dict"] - generator_dict = self.model.state_dict() - - new_generator_dict = { - k.split("module.")[-1]: v - for k, v in pretrained_generator_dict.items() - if ( - k.split("module.")[-1] in generator_dict - and v.shape == generator_dict[k.split("module.")[-1]].shape - ) - } - - generator_dict.update(new_generator_dict) - - self.model.load_state_dict(generator_dict) - else: - self.model.load_state_dict(ckpt["generator_state_dict"]) - else: - self.model.load_state_dict(torch.load(checkpoint_dir)["state_dict"]) - return str(checkpoint_dir) - - def inference(self): - """Inference via batches""" - for i, batch in tqdm(enumerate(self.test_dataloader)): - if self.cfg.preprocess.use_frame_pitch: - audio_pred = self.model.forward( - batch["mel"].transpose(-1, -2), batch["frame_pitch"].float() - ).cpu() - elif self.cfg.preprocess.extract_amplitude_phase: - audio_pred = self.model.forward(batch["mel"].transpose(-1, -2))[-1] - else: - audio_pred = ( - self.model.forward(batch["mel"].transpose(-1, -2)).detach().cpu() - ) - audio_ls = audio_pred.chunk(self.test_batch_size) - audio_gt_ls = batch["audio"].cpu().chunk(self.test_batch_size) - length_ls = batch["target_len"].cpu().chunk(self.test_batch_size) - j = 0 - for it, it_gt, l in zip(audio_ls, audio_gt_ls, length_ls): - l = l.item() - it = it.squeeze(0).squeeze(0)[: l * self.cfg.preprocess.hop_size] - it_gt = it_gt.squeeze(0)[: l * self.cfg.preprocess.hop_size] - uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"] - save_audio( - os.path.join(self.args.output_dir, "pred", "{}.wav").format(uid), - it, - self.cfg.preprocess.sample_rate, - ) - save_audio( - os.path.join(self.args.output_dir, "gt", "{}.wav").format(uid), - it_gt, - self.cfg.preprocess.sample_rate, - ) - j += 1 - - if os.path.exists(os.path.join(self.cfg.preprocess.processed_dir, "tmp")): - shutil.rmtree(os.path.join(self.cfg.preprocess.processed_dir, "tmp")) - - def _set_random_seed(self, seed): - """Set random seed for all possible random modules.""" - random.seed(seed) - np.random.seed(seed) - torch.random.manual_seed(seed) - - def _count_parameters(self, model): - return sum(p.numel() for p in model.parameters()) - - def _dump_cfg(self, path): - os.makedirs(os.path.dirname(path), exist_ok=True) - json5.dump( - self.cfg, - open(path, "w"), - indent=4, - sort_keys=True, - ensure_ascii=False, - quote_keys=True, - ) - - -def load_nnvocoder( - cfg, - vocoder_name, - weights_file, - from_multi_gpu=False, -): - """Load the specified vocoder. - cfg: the vocoder config filer. - weights_file: a folder or a .pt path. - from_multi_gpu: automatically remove the "module" string in state dicts if "True". - """ - print("Loading Vocoder from Weights file: {}".format(weights_file)) - - # Build model - model = _vocoders[vocoder_name](cfg) - if not os.path.isdir(weights_file): - # Load from .pt file - if vocoder_name in ["bigvgan", "hifigan", "melgan", "nsfhifigan"]: - ckpt = torch.load( - weights_file, - map_location=torch.device("cuda") - if torch.cuda.is_available() - else torch.device("cpu"), - ) - if from_multi_gpu: - pretrained_generator_dict = ckpt["generator_state_dict"] - generator_dict = model.state_dict() - - new_generator_dict = { - k.split("module.")[-1]: v - for k, v in pretrained_generator_dict.items() - if ( - k.split("module.")[-1] in generator_dict - and v.shape == generator_dict[k.split("module.")[-1]].shape - ) - } - - generator_dict.update(new_generator_dict) - - model.load_state_dict(generator_dict) - else: - model.load_state_dict(ckpt["generator_state_dict"]) - else: - model.load_state_dict(torch.load(weights_file)["state_dict"]) - else: - # Load from accelerator state dict - weights_file = os.path.join(weights_file, "checkpoint") - ls = [str(i) for i in Path(weights_file).glob("*") if not "audio" in str(i)] - ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) - checkpoint_path = ls[0] - accelerator = accelerate.Accelerator() - model = accelerator.prepare(model) - accelerator.load_state(checkpoint_path) - - if torch.cuda.is_available(): - model = model.cuda() - - model = model.eval() - return model - - -def tensorize(data, device, n_samples): - """ - data: a list of numpy array - """ - assert type(data) == list - if n_samples: - data = data[:n_samples] - data = [torch.as_tensor(x, device=device) for x in data] - return data - - -def synthesis( - cfg, - vocoder_weight_file, - n_samples, - pred, - f0s=None, - batch_size=64, - fast_inference=False, -): - """Synthesis audios from a given vocoder and series of given features. - cfg: vocoder config. - vocoder_weight_file: a folder of accelerator state dict or a path to the .pt file. - pred: a list of numpy arrays. [(seq_len1, acoustic_features_dim), (seq_len2, acoustic_features_dim), ...] - """ - - vocoder_name = cfg.model.generator - - print("Synthesis audios using {} vocoder...".format(vocoder_name)) - - ###### TODO: World Vocoder Refactor ###### - # if vocoder_name == "world": - # world_inference.synthesis_audios( - # cfg, dataset_name, split, n_samples, pred, save_dir, tag - # ) - # return - - # ====== Loading neural vocoder model ====== - vocoder = load_nnvocoder( - cfg, vocoder_name, weights_file=vocoder_weight_file, from_multi_gpu=True - ) - device = next(vocoder.parameters()).device - - # ====== Inference for predicted acoustic features ====== - # pred: (frame_len, n_mels) -> (n_mels, frame_len) - mels_pred = tensorize([p.T for p in pred], device, n_samples) - print("For predicted mels, #sample = {}...".format(len(mels_pred))) - audios_pred = _vocoder_infer_funcs[vocoder_name]( - cfg, - vocoder, - mels_pred, - f0s=f0s, - batch_size=batch_size, - fast_inference=fast_inference, - ) - return audios_pred diff --git a/models/vocoders/vocoder_sampler.py b/models/vocoders/vocoder_sampler.py deleted file mode 100644 index 9d29f88a291dcf7386cadaeae0d990c8e76ebf98..0000000000000000000000000000000000000000 --- a/models/vocoders/vocoder_sampler.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import random - -from torch.utils.data import ConcatDataset, Dataset -from torch.utils.data.sampler import ( - BatchSampler, - RandomSampler, - Sampler, - SequentialSampler, -) - - -class ScheduledSampler(Sampler): - """A sampler that samples data from a given concat-dataset. - - Args: - concat_dataset (ConcatDataset): a concatenated dataset consisting of all datasets - batch_size (int): batch size - holistic_shuffle (bool): whether to shuffle the whole dataset or not - logger (logging.Logger): logger to print warning message - - Usage: - For cfg.train.batch_size = 3, cfg.train.holistic_shuffle = False, cfg.train.drop_last = True: - >>> list(ScheduledSampler(ConcatDataset([0, 1, 2], [3, 4, 5], [6, 7, 8]]))) - [3, 4, 5, 0, 1, 2, 6, 7, 8] - """ - - def __init__( - self, concat_dataset, batch_size, holistic_shuffle, logger=None, type="train" - ): - if not isinstance(concat_dataset, ConcatDataset): - raise ValueError( - "concat_dataset must be an instance of ConcatDataset, but got {}".format( - type(concat_dataset) - ) - ) - if not isinstance(batch_size, int): - raise ValueError( - "batch_size must be an integer, but got {}".format(type(batch_size)) - ) - if not isinstance(holistic_shuffle, bool): - raise ValueError( - "holistic_shuffle must be a boolean, but got {}".format( - type(holistic_shuffle) - ) - ) - - self.concat_dataset = concat_dataset - self.batch_size = batch_size - self.holistic_shuffle = holistic_shuffle - - affected_dataset_name = [] - affected_dataset_len = [] - for dataset in concat_dataset.datasets: - dataset_len = len(dataset) - dataset_name = dataset.get_dataset_name() - if dataset_len < batch_size: - affected_dataset_name.append(dataset_name) - affected_dataset_len.append(dataset_len) - - self.type = type - for dataset_name, dataset_len in zip( - affected_dataset_name, affected_dataset_len - ): - if not type == "valid": - logger.warning( - "The {} dataset {} has a length of {}, which is smaller than the batch size {}. This may cause unexpected behavior.".format( - type, dataset_name, dataset_len, batch_size - ) - ) - - def __len__(self): - # the number of batches with drop last - num_of_batches = sum( - [ - math.floor(len(dataset) / self.batch_size) - for dataset in self.concat_dataset.datasets - ] - ) - return num_of_batches * self.batch_size - - def __iter__(self): - iters = [] - for dataset in self.concat_dataset.datasets: - iters.append( - SequentialSampler(dataset).__iter__() - if self.holistic_shuffle - else RandomSampler(dataset).__iter__() - ) - init_indices = [0] + self.concat_dataset.cumulative_sizes[:-1] - output_batches = [] - for dataset_idx in range(len(self.concat_dataset.datasets)): - cur_batch = [] - for idx in iters[dataset_idx]: - cur_batch.append(idx + init_indices[dataset_idx]) - if len(cur_batch) == self.batch_size: - output_batches.append(cur_batch) - cur_batch = [] - if self.type == "valid" and len(cur_batch) > 0: - output_batches.append(cur_batch) - cur_batch = [] - # force drop last in training - random.shuffle(output_batches) - output_indices = [item for sublist in output_batches for item in sublist] - return iter(output_indices) - - -def build_samplers(concat_dataset: Dataset, cfg, logger, type): - sampler = ScheduledSampler( - concat_dataset, - cfg.train.batch_size, - cfg.train.sampler.holistic_shuffle, - logger, - type, - ) - batch_sampler = BatchSampler( - sampler, - cfg.train.batch_size, - cfg.train.sampler.drop_last if not type == "valid" else False, - ) - return sampler, batch_sampler diff --git a/models/vocoders/vocoder_trainer.py b/models/vocoders/vocoder_trainer.py deleted file mode 100644 index 5821e735a64f07fcf9c782712670e24ce6a91c04..0000000000000000000000000000000000000000 --- a/models/vocoders/vocoder_trainer.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import random -from pathlib import Path -import re - -import accelerate -import json5 -import numpy as np -import torch -from accelerate.utils import ProjectConfiguration -from torch.utils.data import DataLoader -from tqdm import tqdm - -from models.vocoders.vocoder_dataset import VocoderConcatDataset -from models.vocoders.vocoder_sampler import build_samplers - - -class VocoderTrainer: - def __init__(self): - super().__init__() - - def _init_accelerator(self): - """Initialize the accelerator components.""" - self.exp_dir = os.path.join( - os.path.abspath(self.cfg.log_dir), self.args.exp_name - ) - project_config = ProjectConfiguration( - project_dir=self.exp_dir, logging_dir=os.path.join(self.exp_dir, "log") - ) - self.accelerator = accelerate.Accelerator( - gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, - log_with=self.cfg.train.tracker, - project_config=project_config, - ) - if self.accelerator.is_main_process: - os.makedirs(project_config.project_dir, exist_ok=True) - os.makedirs(project_config.logging_dir, exist_ok=True) - with self.accelerator.main_process_first(): - self.accelerator.init_trackers(self.args.exp_name) - - def _build_dataset(self): - pass - - def _build_criterion(self): - pass - - def _build_model(self): - pass - - def _build_dataloader(self): - """Build dataloader which merges a series of datasets.""" - # Build dataset instance for each dataset and combine them by ConcatDataset - Dataset, Collator = self._build_dataset() - - # Build train set - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=False) - datasets_list.append(subdataset) - train_dataset = VocoderConcatDataset(datasets_list, full_audio_inference=True) - train_collate = Collator(self.cfg) - _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") - train_loader = DataLoader( - train_dataset, - collate_fn=train_collate, - batch_sampler=batch_sampler, - num_workers=self.cfg.train.dataloader.num_worker, - pin_memory=self.cfg.train.dataloader.pin_memory, - ) - - # Build test set - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=True) - datasets_list.append(subdataset) - valid_dataset = VocoderConcatDataset(datasets_list, full_audio_inference=True) - valid_collate = Collator(self.cfg) - _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "train") - valid_loader = DataLoader( - valid_dataset, - collate_fn=valid_collate, - batch_sampler=batch_sampler, - num_workers=self.cfg.train.dataloader.num_worker, - pin_memory=self.cfg.train.dataloader.pin_memory, - ) - return train_loader, valid_loader - - def _build_optimizer(self): - pass - - def _build_scheduler(self): - pass - - def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): - """Load model from checkpoint. If a folder is given, it will - load the latest checkpoint in checkpoint_dir. If a path is given - it will load the checkpoint specified by checkpoint_path. - **Only use this method after** ``accelerator.prepare()``. - """ - if checkpoint_path is None: - ls = [str(i) for i in Path(checkpoint_dir).glob("*")] - ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) - checkpoint_path = ls[0] - if resume_type == "resume": - self.accelerator.load_state(checkpoint_path) - elif resume_type == "finetune": - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model), - os.path.join(checkpoint_path, "pytorch_model.bin"), - ) - self.logger.info("Load model weights for finetune SUCCESS!") - else: - raise ValueError("Unsupported resume type: {}".format(resume_type)) - self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 - self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 - return checkpoint_path - - def train_loop(self): - pass - - def _train_epoch(self): - pass - - def _valid_epoch(self): - pass - - def _train_step(self): - pass - - def _valid_step(self): - pass - - def _inference(self): - pass - - def _set_random_seed(self, seed): - """Set random seed for all possible random modules.""" - random.seed(seed) - np.random.seed(seed) - torch.random.manual_seed(seed) - - def _check_nan(self, loss): - if torch.any(torch.isnan(loss)): - self.logger.fatal("Fatal Error: NaN!") - self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True) - - def _check_basic_configs(self): - if self.cfg.train.gradient_accumulation_step <= 0: - self.logger.fatal("Invalid gradient_accumulation_step value!") - self.logger.error( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - self.accelerator.end_training() - raise ValueError( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - - def _count_parameters(self): - pass - - def _dump_cfg(self, path): - os.makedirs(os.path.dirname(path), exist_ok=True) - json5.dump( - self.cfg, - open(path, "w"), - indent=4, - sort_keys=True, - ensure_ascii=False, - quote_keys=True, - ) - - def _is_valid_pattern(self, directory_name): - directory_name = str(directory_name) - pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}" - return re.match(pattern, directory_name) is not None diff --git a/modules/__init__.py b/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/activation_functions/__init__.py b/modules/activation_functions/__init__.py deleted file mode 100644 index d35dfbc27a825ea8ed286074d40971764b890468..0000000000000000000000000000000000000000 --- a/modules/activation_functions/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .gated_activation_unit import GaU -from .snake import Snake, SnakeBeta diff --git a/modules/activation_functions/gated_activation_unit.py b/modules/activation_functions/gated_activation_unit.py deleted file mode 100644 index e55956981fa99299059dd1c02236011fbadac049..0000000000000000000000000000000000000000 --- a/modules/activation_functions/gated_activation_unit.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn - -from modules.general.utils import Conv1d - - -class GaU(nn.Module): - r"""Gated Activation Unit (GaU) proposed in `Gated Activation Units for Neural - Networks `_. - - Args: - channels: number of input channels. - kernel_size: kernel size of the convolution. - dilation: dilation rate of the convolution. - d_context: dimension of context tensor, None if don't use context. - """ - - def __init__( - self, - channels: int, - kernel_size: int = 3, - dilation: int = 1, - d_context: int = None, - ): - super().__init__() - - self.context = d_context - - self.conv = Conv1d( - channels, - channels * 2, - kernel_size, - dilation=dilation, - padding=dilation * (kernel_size - 1) // 2, - ) - - if self.context: - self.context_proj = Conv1d(d_context, channels * 2, 1) - - def forward(self, x: torch.Tensor, context: torch.Tensor = None): - r"""Calculate forward propagation. - - Args: - x: input tensor with shape [B, C, T]. - context: context tensor with shape [B, ``d_context``, T], default to None. - """ - - h = self.conv(x) - - if self.context: - h = h + self.context_proj(context) - - h1, h2 = h.chunk(2, 1) - h = torch.tanh(h1) * torch.sigmoid(h2) - - return h diff --git a/modules/activation_functions/snake.py b/modules/activation_functions/snake.py deleted file mode 100644 index e5398819f2e7c836e02189321cbf048bedcd9e91..0000000000000000000000000000000000000000 --- a/modules/activation_functions/snake.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from torch import nn, pow, sin -from torch.nn import Parameter - - -class Snake(nn.Module): - r"""Implementation of a sine-based periodic activation function. - Alpha is initialized to 1 by default, higher values means higher frequency. - It will be trained along with the rest of your model. - - Args: - in_features: shape of the input - alpha: trainable parameter - - Shape: - - Input: (B, C, T) - - Output: (B, C, T), same shape as the input - - References: - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, - Masahito Ueda: https://arxiv.org/abs/2006.08195 - - Examples: - >>> a1 = Snake(256) - >>> x = torch.randn(256) - >>> x = a1(x) - """ - - def __init__( - self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False - ): - super(Snake, self).__init__() - self.in_features = in_features - - # initialize alpha - self.alpha_logscale = alpha_logscale - if self.alpha_logscale: # log scale alphas initialized to zeros - self.alpha = Parameter(torch.zeros(in_features) * alpha) - else: # linear scale alphas initialized to ones - self.alpha = Parameter(torch.ones(in_features) * alpha) - - self.alpha.requires_grad = alpha_trainable - - self.no_div_by_zero = 0.000000001 - - def forward(self, x): - r"""Forward pass of the function. Applies the function to the input elementwise. - Snake ∶= x + 1/a * sin^2 (ax) - """ - - alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] - if self.alpha_logscale: - alpha = torch.exp(alpha) - x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) - - return x - - -class SnakeBeta(nn.Module): - r"""A modified Snake function which uses separate parameters for the magnitude - of the periodic components. Alpha is initialized to 1 by default, - higher values means higher frequency. Beta is initialized to 1 by default, - higher values means higher magnitude. Both will be trained along with the - rest of your model. - - Args: - in_features: shape of the input - alpha: trainable parameter that controls frequency - beta: trainable parameter that controls magnitude - - Shape: - - Input: (B, C, T) - - Output: (B, C, T), same shape as the input - - References: - This activation function is a modified version based on this paper by Liu Ziyin, - Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195 - - Examples: - >>> a1 = SnakeBeta(256) - >>> x = torch.randn(256) - >>> x = a1(x) - """ - - def __init__( - self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False - ): - super(SnakeBeta, self).__init__() - self.in_features = in_features - - # initialize alpha - self.alpha_logscale = alpha_logscale - if self.alpha_logscale: # log scale alphas initialized to zeros - self.alpha = Parameter(torch.zeros(in_features) * alpha) - self.beta = Parameter(torch.zeros(in_features) * alpha) - else: # linear scale alphas initialized to ones - self.alpha = Parameter(torch.ones(in_features) * alpha) - self.beta = Parameter(torch.ones(in_features) * alpha) - - self.alpha.requires_grad = alpha_trainable - self.beta.requires_grad = alpha_trainable - - self.no_div_by_zero = 0.000000001 - - def forward(self, x): - r"""Forward pass of the function. Applies the function to the input elementwise. - SnakeBeta ∶= x + 1/b * sin^2 (xa) - """ - - alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] - beta = self.beta.unsqueeze(0).unsqueeze(-1) - if self.alpha_logscale: - alpha = torch.exp(alpha) - beta = torch.exp(beta) - x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) - - return x diff --git a/modules/anti_aliasing/__init__.py b/modules/anti_aliasing/__init__.py deleted file mode 100644 index 193756bfa04218d10f65fbed8665a8a0abad1cd4..0000000000000000000000000000000000000000 --- a/modules/anti_aliasing/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .act import * -from .filter import * -from .resample import * diff --git a/modules/anti_aliasing/act.py b/modules/anti_aliasing/act.py deleted file mode 100644 index 3404fa138650c7a16bf918b6cb51b5912b656e8c..0000000000000000000000000000000000000000 --- a/modules/anti_aliasing/act.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn - -from .resample import * - -# This code is adopted from BigVGAN under the MIT License -# https://github.com/NVIDIA/BigVGAN - -class Activation1d(nn.Module): - def __init__( - self, - activation, - up_ratio: int = 2, - down_ratio: int = 2, - up_kernel_size: int = 12, - down_kernel_size: int = 12, - ): - super().__init__() - self.up_ratio = up_ratio - self.down_ratio = down_ratio - self.act = activation - self.upsample = UpSample1d(up_ratio, up_kernel_size) - self.downsample = DownSample1d(down_ratio, down_kernel_size) - - # x: [B,C,T] - def forward(self, x): - x = self.upsample(x) - x = self.act(x) - x = self.downsample(x) - - return x diff --git a/modules/anti_aliasing/filter.py b/modules/anti_aliasing/filter.py deleted file mode 100644 index b91688866bd073c7ce268004791ec1263a6d76d7..0000000000000000000000000000000000000000 --- a/modules/anti_aliasing/filter.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -import math - -if "sinc" in dir(torch): - sinc = torch.sinc -else: - # This code is adopted from adefossez's julius.core.sinc under the MIT License - # https://adefossez.github.io/julius/julius/core.html - def sinc(x: torch.Tensor): - """ - Implementation of sinc, i.e. sin(pi * x) / (pi * x) - __Warning__: Different to julius.sinc, the input is multiplied by `pi`! - """ - return torch.where( - x == 0, - torch.tensor(1.0, device=x.device, dtype=x.dtype), - torch.sin(math.pi * x) / math.pi / x, - ) - - -# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License -# https://adefossez.github.io/julius/julius/lowpass.html -def kaiser_sinc_filter1d( - cutoff, half_width, kernel_size -): # return filter [1,1,kernel_size] - even = kernel_size % 2 == 0 - half_size = kernel_size // 2 - - # For kaiser window - delta_f = 4 * half_width - A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 - if A > 50.0: - beta = 0.1102 * (A - 8.7) - elif A >= 21.0: - beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0) - else: - beta = 0.0 - window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) - - # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio - if even: - time = torch.arange(-half_size, half_size) + 0.5 - else: - time = torch.arange(kernel_size) - half_size - if cutoff == 0: - filter_ = torch.zeros_like(time) - else: - filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) - # Normalize filter to have sum = 1, otherwise we will have a small leakage - # of the constant component in the input signal. - filter_ /= filter_.sum() - filter = filter_.view(1, 1, kernel_size) - - return filter - - -class LowPassFilter1d(nn.Module): - def __init__( - self, - cutoff=0.5, - half_width=0.6, - stride: int = 1, - padding: bool = True, - padding_mode: str = "replicate", - kernel_size: int = 12, - ): - # kernel_size should be even number for stylegan3 setup, - # in this implementation, odd number is also possible. - super().__init__() - if cutoff < -0.0: - raise ValueError("Minimum cutoff must be larger than zero.") - if cutoff > 0.5: - raise ValueError("A cutoff above 0.5 does not make sense.") - self.kernel_size = kernel_size - self.even = kernel_size % 2 == 0 - self.pad_left = kernel_size // 2 - int(self.even) - self.pad_right = kernel_size // 2 - self.stride = stride - self.padding = padding - self.padding_mode = padding_mode - filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) - self.register_buffer("filter", filter) - - # input [B, C, T] - def forward(self, x): - _, C, _ = x.shape - - if self.padding: - x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) - out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) - - return out diff --git a/modules/anti_aliasing/resample.py b/modules/anti_aliasing/resample.py deleted file mode 100644 index 735cb86e2fcf12f86317d972844e04ae3bacf14e..0000000000000000000000000000000000000000 --- a/modules/anti_aliasing/resample.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -#################### Anti-aliasing #################### - -import torch.nn as nn -from torch.nn import functional as F - -from .filter import * - -# This code is adopted from BigVGAN under the MIT License -# https://github.com/NVIDIA/BigVGAN - -class UpSample1d(nn.Module): - def __init__(self, ratio=2, kernel_size=None): - super().__init__() - self.ratio = ratio - self.kernel_size = ( - int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size - ) - self.stride = ratio - self.pad = self.kernel_size // ratio - 1 - self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 - self.pad_right = ( - self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 - ) - filter = kaiser_sinc_filter1d( - cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size - ) - self.register_buffer("filter", filter) - - # x: [B, C, T] - def forward(self, x): - _, C, _ = x.shape - - x = F.pad(x, (self.pad, self.pad), mode="replicate") - x = self.ratio * F.conv_transpose1d( - x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C - ) - x = x[..., self.pad_left : -self.pad_right] - - return x - - -class DownSample1d(nn.Module): - def __init__(self, ratio=2, kernel_size=None): - super().__init__() - self.ratio = ratio - self.kernel_size = ( - int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size - ) - self.lowpass = LowPassFilter1d( - cutoff=0.5 / ratio, - half_width=0.6 / ratio, - stride=ratio, - kernel_size=self.kernel_size, - ) - - def forward(self, x): - xx = self.lowpass(x) - - return xx diff --git a/modules/base/base_module.py b/modules/base/base_module.py deleted file mode 100644 index c3c81206f4f130b706fffd45e93a054ce1231ddd..0000000000000000000000000000000000000000 --- a/modules/base/base_module.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from torch import nn -from torch.nn import functional as F - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__( - self, - in_channels, - hidden_channels, - out_channels, - kernel_size, - n_layers, - p_dropout, - ): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append( - nn.Conv1d( - in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) - for _ in range(n_layers - 1): - self.conv_layers.append( - nn.Conv1d( - hidden_channels, - hidden_channels, - kernel_size, - padding=kernel_size // 2, - ) - ) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask diff --git a/modules/diffusion/__init__.py b/modules/diffusion/__init__.py deleted file mode 100644 index bf1e4f9e71ef3b0ef2d677a9c0db8e03b022eaea..0000000000000000000000000000000000000000 --- a/modules/diffusion/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .bidilconv.bidilated_conv import BiDilConv -from .unet.unet import UNet diff --git a/modules/diffusion/bidilconv/bidilated_conv.py b/modules/diffusion/bidilconv/bidilated_conv.py deleted file mode 100644 index ac75522a3da8a40ae1b76656dd2a78097d6d94f3..0000000000000000000000000000000000000000 --- a/modules/diffusion/bidilconv/bidilated_conv.py +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch.nn as nn - -from modules.general.utils import Conv1d, zero_module -from .residual_block import ResidualBlock - - -class BiDilConv(nn.Module): - r"""Dilated CNN architecture with residual connections, default diffusion decoder. - - Args: - input_channel: The number of input channels. - base_channel: The number of base channels. - n_res_block: The number of residual blocks. - conv_kernel_size: The kernel size of convolutional layers. - dilation_cycle_length: The cycle length of dilation. - conditioner_size: The size of conditioner. - """ - - def __init__( - self, - input_channel, - base_channel, - n_res_block, - conv_kernel_size, - dilation_cycle_length, - conditioner_size, - output_channel: int = -1, - ): - super().__init__() - - self.input_channel = input_channel - self.base_channel = base_channel - self.n_res_block = n_res_block - self.conv_kernel_size = conv_kernel_size - self.dilation_cycle_length = dilation_cycle_length - self.conditioner_size = conditioner_size - self.output_channel = output_channel if output_channel > 0 else input_channel - - self.input = nn.Sequential( - Conv1d( - input_channel, - base_channel, - 1, - ), - nn.ReLU(), - ) - - self.residual_blocks = nn.ModuleList( - [ - ResidualBlock( - channels=base_channel, - kernel_size=conv_kernel_size, - dilation=2 ** (i % dilation_cycle_length), - d_context=conditioner_size, - ) - for i in range(n_res_block) - ] - ) - - self.out_proj = nn.Sequential( - Conv1d( - base_channel, - base_channel, - 1, - ), - nn.ReLU(), - zero_module( - Conv1d( - base_channel, - self.output_channel, - 1, - ), - ), - ) - - def forward(self, x, y, context=None): - """ - Args: - x: Noisy mel-spectrogram [B x ``n_mel`` x L] - y: FILM embeddings with the shape of (B, ``base_channel``) - context: Context with the shape of [B x ``d_context`` x L], default to None. - """ - - h = self.input(x) - - skip = None - for i in range(self.n_res_block): - h, skip_connection = self.residual_blocks[i](h, y, context) - skip = skip_connection if skip is None else skip_connection + skip - - out = skip / math.sqrt(self.n_res_block) - - out = self.out_proj(out) - - return out diff --git a/modules/diffusion/bidilconv/residual_block.py b/modules/diffusion/bidilconv/residual_block.py deleted file mode 100644 index df75d4fcb307af58fafe7258ba6e4a7a8cedd2f6..0000000000000000000000000000000000000000 --- a/modules/diffusion/bidilconv/residual_block.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn as nn - -from modules.activation_functions import GaU -from modules.general.utils import Conv1d - - -class ResidualBlock(nn.Module): - r"""Residual block with dilated convolution, main portion of ``BiDilConv``. - - Args: - channels: The number of channels of input and output. - kernel_size: The kernel size of dilated convolution. - dilation: The dilation rate of dilated convolution. - d_context: The dimension of content encoder output, None if don't use context. - """ - - def __init__( - self, - channels: int = 256, - kernel_size: int = 3, - dilation: int = 1, - d_context: int = None, - ): - super().__init__() - - self.context = d_context - - self.gau = GaU( - channels, - kernel_size, - dilation, - d_context, - ) - - self.out_proj = Conv1d( - channels, - channels * 2, - 1, - ) - - def forward( - self, - x: torch.Tensor, - y_emb: torch.Tensor, - context: torch.Tensor = None, - ): - """ - Args: - x: Latent representation inherited from previous residual block - with the shape of [B x C x T]. - y_emb: Embeddings with the shape of [B x C], which will be FILM on the x. - context: Context with the shape of [B x ``d_context`` x T], default to None. - """ - - h = x + y_emb[..., None] - - if self.context: - h = self.gau(h, context) - else: - h = self.gau(h) - - h = self.out_proj(h) - res, skip = h.chunk(2, 1) - - return (res + x) / math.sqrt(2.0), skip diff --git a/modules/diffusion/karras/karras_diffusion.py b/modules/diffusion/karras/karras_diffusion.py deleted file mode 100644 index f590c25a04a3db9cc16308de4e43f31d77e13f11..0000000000000000000000000000000000000000 --- a/modules/diffusion/karras/karras_diffusion.py +++ /dev/null @@ -1,979 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -Based on: https://github.com/crowsonkb/k-diffusion -""" -import random - -import numpy as np -import torch as th -import torch.nn as nn -import torch.nn.functional as F - -# from piq import LPIPS -from utils.ssim import SSIM - -from modules.diffusion.karras.random_utils import get_generator - - -def mean_flat(tensor): - """ - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def append_dims(x, target_dims): - """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError( - f"input has {x.ndim} dims but target_dims is {target_dims}, which is less" - ) - return x[(...,) + (None,) * dims_to_append] - - -def append_zero(x): - return th.cat([x, x.new_zeros([1])]) - - -def get_weightings(weight_schedule, snrs, sigma_data): - if weight_schedule == "snr": - weightings = snrs - elif weight_schedule == "snr+1": - weightings = snrs + 1 - elif weight_schedule == "karras": - weightings = snrs + 1.0 / sigma_data**2 - elif weight_schedule == "truncated-snr": - weightings = th.clamp(snrs, min=1.0) - elif weight_schedule == "uniform": - weightings = th.ones_like(snrs) - else: - raise NotImplementedError() - return weightings - - -class KarrasDenoiser: - def __init__( - self, - sigma_data: float = 0.5, - sigma_max=80.0, - sigma_min=0.002, - rho=7.0, - weight_schedule="karras", - distillation=False, - loss_norm="l2", - ): - self.sigma_data = sigma_data - self.sigma_max = sigma_max - self.sigma_min = sigma_min - self.weight_schedule = weight_schedule - self.distillation = distillation - self.loss_norm = loss_norm - # if loss_norm == "lpips": - # self.lpips_loss = LPIPS(replace_pooling=True, reduction="none") - if loss_norm == "ssim": - self.ssim_loss = SSIM() - self.rho = rho - self.num_timesteps = 40 - - def get_snr(self, sigmas): - return sigmas**-2 - - def get_sigmas(self, sigmas): - return sigmas - - def get_scalings(self, sigma): - c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2) - c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5 - c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5 - return c_skip, c_out, c_in - - def get_scalings_for_boundary_condition(self, sigma): - c_skip = self.sigma_data**2 / ( - (sigma - self.sigma_min) ** 2 + self.sigma_data**2 - ) - c_out = ( - (sigma - self.sigma_min) - * self.sigma_data - / (sigma**2 + self.sigma_data**2) ** 0.5 - ) - c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5 - return c_skip, c_out, c_in - - def training_losses(self, model, x_start, sigmas, condition=None, noise=None): - if noise is None: - noise = th.randn_like(x_start) - - terms = {} - - dims = x_start.ndim - x_t = x_start + noise * append_dims(sigmas, dims) - model_output, denoised = self.denoise(model, x_t, sigmas, condition) - - snrs = self.get_snr(sigmas) - weights = append_dims( - get_weightings(self.weight_schedule, snrs, self.sigma_data), dims - ) - # terms["xs_mse"] = mean_flat((denoised - x_start) ** 2) - terms["mse"] = mean_flat(weights * (denoised - x_start) ** 2) - # terms["mae"] = mean_flat(weights * th.abs(denoised - x_start)) - # terms["mse"] = nn.MSELoss(reduction="none")(denoised, x_start) - - # if "vb" in terms: - # terms["loss"] = terms["mse"] + terms["vb"] - # else: - terms["loss"] = terms["mse"] - - return terms - - def consistency_losses( - self, - model, - x_start, - num_scales, - # model_kwargs=None, - condition=None, - target_model=None, - teacher_model=None, - teacher_diffusion=None, - noise=None, - ): - if noise is None: - noise = th.randn_like(x_start) - - dims = x_start.ndim - - def denoise_fn(x, t): - return self.denoise(model, x, t, condition)[1] - - if target_model: - - @th.no_grad() - def target_denoise_fn(x, t): - return self.denoise(target_model, x, t, condition)[1] - - else: - raise NotImplementedError("Must have a target model") - - if teacher_model: - - @th.no_grad() - def teacher_denoise_fn(x, t): - return teacher_diffusion.denoise(teacher_model, x, t, condition)[1] - - @th.no_grad() - def heun_solver(samples, t, next_t, x0): - x = samples - if teacher_model is None: - denoiser = x0 - else: - denoiser = teacher_denoise_fn(x, t) - - d = (x - denoiser) / append_dims(t, dims) - samples = x + d * append_dims(next_t - t, dims) - if teacher_model is None: - denoiser = x0 - else: - denoiser = teacher_denoise_fn(samples, next_t) - - next_d = (samples - denoiser) / append_dims(next_t, dims) - samples = x + (d + next_d) * append_dims((next_t - t) / 2, dims) - - return samples - - @th.no_grad() - def euler_solver(samples, t, next_t, x0): - x = samples - if teacher_model is None: - denoiser = x0 - else: - denoiser = teacher_denoise_fn(x, t) - d = (x - denoiser) / append_dims(t, dims) - samples = x + d * append_dims(next_t - t, dims) - - return samples - - indices = th.randint( - 0, num_scales - 1, (x_start.shape[0],), device=x_start.device - ) - - t = self.sigma_max ** (1 / self.rho) + indices / (num_scales - 1) * ( - self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) - ) - t = t**self.rho - - t2 = self.sigma_max ** (1 / self.rho) + (indices + 1) / (num_scales - 1) * ( - self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) - ) - t2 = t2**self.rho - - x_t = x_start + noise * append_dims(t, dims) - - dropout_state = th.get_rng_state() - distiller = denoise_fn(x_t, t) - - if teacher_model is None: - x_t2 = euler_solver(x_t, t, t2, x_start).detach() - else: - x_t2 = heun_solver(x_t, t, t2, x_start).detach() - - th.set_rng_state(dropout_state) - distiller_target = target_denoise_fn(x_t2, t2) - distiller_target = distiller_target.detach() - - snrs = self.get_snr(t) - weights = get_weightings(self.weight_schedule, snrs, self.sigma_data) - if self.loss_norm == "l1": - diffs = th.abs(distiller - distiller_target) - loss = mean_flat(diffs) * weights - elif self.loss_norm == "l2": - # diffs = (distiller - distiller_target) ** 2 - loss = F.mse_loss(distiller, distiller_target) - # loss = mean_flat(diffs) * weights - elif self.loss_norm == "ssim": - loss = self.ssim_loss(distiller, distiller_target) * weights - # elif self.loss_norm == "l2-32": - # distiller = F.interpolate(distiller, size=32, mode="bilinear") - # distiller_target = F.interpolate( - # distiller_target, - # size=32, - # mode="bilinear", - # ) - # diffs = (distiller - distiller_target) ** 2 - # loss = mean_flat(diffs) * weights - # elif self.loss_norm == "lpips": - # if x_start.shape[-1] < 256: - # distiller = F.interpolate(distiller, size=224, mode="bilinear") - # distiller_target = F.interpolate( - # distiller_target, size=224, mode="bilinear" - # ) - - # loss = ( - # self.lpips_loss( - # (distiller + 1) / 2.0, - # (distiller_target + 1) / 2.0, - # ) - # * weights - # ) - else: - raise ValueError(f"Unknown loss norm {self.loss_norm}") - - terms = {} - terms["loss"] = loss - - return terms - - # def progdist_losses( - # self, - # model, - # x_start, - # num_scales, - # model_kwargs=None, - # teacher_model=None, - # teacher_diffusion=None, - # noise=None, - # ): - # if model_kwargs is None: - # model_kwargs = {} - # if noise is None: - # noise = th.randn_like(x_start) - - # dims = x_start.ndim - - # def denoise_fn(x, t): - # return self.denoise(model, x, t, **model_kwargs)[1] - - # @th.no_grad() - # def teacher_denoise_fn(x, t): - # return teacher_diffusion.denoise(teacher_model, x, t, **model_kwargs)[1] - - # @th.no_grad() - # def euler_solver(samples, t, next_t): - # x = samples - # denoiser = teacher_denoise_fn(x, t) - # d = (x - denoiser) / append_dims(t, dims) - # samples = x + d * append_dims(next_t - t, dims) - - # return samples - - # @th.no_grad() - # def euler_to_denoiser(x_t, t, x_next_t, next_t): - # denoiser = x_t - append_dims(t, dims) * (x_next_t - x_t) / append_dims( - # next_t - t, dims - # ) - # return denoiser - - # indices = th.randint(0, num_scales, (x_start.shape[0],), device=x_start.device) - - # t = self.sigma_max ** (1 / self.rho) + indices / num_scales * ( - # self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) - # ) - # t = t**self.rho - - # t2 = self.sigma_max ** (1 / self.rho) + (indices + 0.5) / num_scales * ( - # self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) - # ) - # t2 = t2**self.rho - - # t3 = self.sigma_max ** (1 / self.rho) + (indices + 1) / num_scales * ( - # self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho) - # ) - # t3 = t3**self.rho - - # x_t = x_start + noise * append_dims(t, dims) - - # denoised_x = denoise_fn(x_t, t) - - # x_t2 = euler_solver(x_t, t, t2).detach() - # x_t3 = euler_solver(x_t2, t2, t3).detach() - - # target_x = euler_to_denoiser(x_t, t, x_t3, t3).detach() - - # snrs = self.get_snr(t) - # weights = get_weightings(self.weight_schedule, snrs, self.sigma_data) - # if self.loss_norm == "l1": - # diffs = th.abs(denoised_x - target_x) - # loss = mean_flat(diffs) * weights - # elif self.loss_norm == "l2": - # diffs = (denoised_x - target_x) ** 2 - # loss = mean_flat(diffs) * weights - # elif self.loss_norm == "lpips": - # if x_start.shape[-1] < 256: - # denoised_x = F.interpolate(denoised_x, size=224, mode="bilinear") - # target_x = F.interpolate(target_x, size=224, mode="bilinear") - # loss = ( - # self.lpips_loss( - # (denoised_x + 1) / 2.0, - # (target_x + 1) / 2.0, - # ) - # * weights - # ) - # else: - # raise ValueError(f"Unknown loss norm {self.loss_norm}") - - # terms = {} - # terms["loss"] = loss - - # return terms - - def denoise(self, model, x_t, sigmas, condition): - if not self.distillation: - c_skip, c_out, c_in = [ - append_dims(x, x_t.ndim) for x in self.get_scalings(sigmas) - ] - else: - c_skip, c_out, c_in = [ - append_dims(x, x_t.ndim) - for x in self.get_scalings_for_boundary_condition(sigmas) - ] - rescaled_t = 1000 * 0.25 * th.log(sigmas + 1e-44) - # rescaled_t = rescaled_t[:, None] - model_output = model(c_in * x_t, rescaled_t, condition) - denoised = c_out * model_output + c_skip * x_t - return model_output, denoised - - -def karras_sample( - diffusion, - model, - shape, - steps, - clip_denoised=True, - progress=True, - callback=None, - # model_kwargs=None, - condition=None, - device=None, - sigma_min=0.002, - sigma_max=80, # higher for highres? - rho=7.0, - sampler="heun", - s_churn=0.0, - s_tmin=0.0, - s_tmax=float("inf"), - s_noise=1.0, - generator=None, - ts=None, -): - if generator is None: - generator = get_generator("dummy") - - if sampler == "progdist": - sigmas = get_sigmas_karras(steps + 1, sigma_min, sigma_max, rho, device=device) - else: - sigmas = get_sigmas_karras(steps, sigma_min, sigma_max, rho, device=device) - th.manual_seed(42) - x_T = generator.randn(*shape, device=device) * sigma_max - sigmas = sigmas.unsqueeze(-1) - sample_fn = { - "heun": sample_heun, - "dpm": sample_dpm, - "ancestral": sample_euler_ancestral, - "onestep": sample_onestep, - "progdist": sample_progdist, - "euler": sample_euler, - "multistep": stochastic_iterative_sampler, - }[sampler] - - if sampler in ["heun", "dpm"]: - sampler_args = dict( - s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise - ) - elif sampler == "multistep": - sampler_args = dict( - ts=ts, t_min=sigma_min, t_max=sigma_max, rho=diffusion.rho, steps=steps - ) - else: - sampler_args = {} - - def denoiser(x_t, sigma): - _, denoised = diffusion.denoise(model, x_t, sigma, condition) - if clip_denoised: - denoised = denoised.clamp(-1, 1) - return denoised - - x_0 = sample_fn( - denoiser, - x_T, - sigmas, - generator, - progress=progress, - callback=callback, - **sampler_args, - ) - return x_0.clamp(-1, 1) - - -def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"): - """Constructs the noise schedule of Karras et al. (2022).""" - ramp = th.linspace(0, 1, n) - min_inv_rho = sigma_min ** (1 / rho) - max_inv_rho = sigma_max ** (1 / rho) - sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho - return append_zero(sigmas).to(device) - - -def to_d(x, sigma, denoised): - """Converts a denoiser output to a Karras ODE derivative.""" - return (x - denoised) / append_dims(sigma, x.ndim) - - -def get_ancestral_step(sigma_from, sigma_to): - """Calculates the noise level (sigma_down) to step down to and the amount - of noise to add (sigma_up) when doing an ancestral sampling step.""" - sigma_up = ( - sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2 - ) ** 0.5 - sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5 - return sigma_down, sigma_up - - -@th.no_grad() -def sample_euler_ancestral(model, x, sigmas, generator, progress=False, callback=None): - """Ancestral sampling with Euler method steps.""" - s_in = x.new_ones([x.shape[0]]) - indices = range(len(sigmas) - 1) - if progress: - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - denoised = model(x, sigmas[i] * s_in) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) - if callback is not None: - callback( - { - "x": x, - "i": i, - "sigma": sigmas[i], - "sigma_hat": sigmas[i], - "denoised": denoised, - } - ) - d = to_d(x, sigmas[i], denoised) - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - x = x + generator.randn_like(x) * sigma_up - return x - - -@th.no_grad() -def sample_midpoint_ancestral(model, x, ts, generator, progress=False, callback=None): - """Ancestral sampling with midpoint method steps.""" - s_in = x.new_ones([x.shape[0]]) - step_size = 1 / len(ts) - if progress: - from tqdm.auto import tqdm - - ts = tqdm(ts) - - for tn in ts: - dn = model(x, tn * s_in) - dn_2 = model(x + (step_size / 2) * dn, (tn + step_size / 2) * s_in) - x = x + step_size * dn_2 - if callback is not None: - callback({"x": x, "tn": tn, "dn": dn, "dn_2": dn_2}) - return x - - -@th.no_grad() -def sample_heun( - denoiser, - x, - sigmas, - generator, - progress=False, - callback=None, - s_churn=0.0, - s_tmin=0.0, - s_tmax=float("inf"), - s_noise=1.0, -): - """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" - s_in = x.new_ones([x.shape[0]]) - indices = range(len(sigmas) - 1) - if progress: - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - gamma = ( - min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) - if s_tmin <= sigmas[i] <= s_tmax - else 0.0 - ) - eps = generator.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5 - denoised = denoiser(x, sigma_hat * s_in) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback( - { - "x": x, - "i": i, - "sigma": sigmas[i], - "sigma_hat": sigma_hat, - "denoised": denoised, - } - ) - dt = sigmas[i + 1] - sigma_hat - if sigmas[i + 1] == 0: - # Euler method - x = x + d * dt - else: - # Heun's method - x_2 = x + d * dt - denoised_2 = denoiser(x_2, sigmas[i + 1] * s_in) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - d_prime = (d + d_2) / 2 - x = x + d_prime * dt - return x - - -@th.no_grad() -def sample_euler( - denoiser, - x, - sigmas, - generator, - progress=False, - callback=None, -): - """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" - s_in = x.new_ones([x.shape[0]]) - indices = range(len(sigmas) - 1) - if progress: - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - sigma = sigmas[i] - denoised = denoiser(x, sigma * s_in) - d = to_d(x, sigma, denoised) - if callback is not None: - callback( - { - "x": x, - "i": i, - "sigma": sigmas[i], - "denoised": denoised, - } - ) - dt = sigmas[i + 1] - sigma - x = x + d * dt - return x - - -@th.no_grad() -def sample_dpm( - denoiser, - x, - sigmas, - generator, - progress=False, - callback=None, - s_churn=0.0, - s_tmin=0.0, - s_tmax=float("inf"), - s_noise=1.0, -): - """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" - s_in = x.new_ones([x.shape[0]]) - indices = range(len(sigmas) - 1) - if progress: - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - gamma = ( - min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) - if s_tmin <= sigmas[i] <= s_tmax - else 0.0 - ) - eps = generator.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5 - denoised = denoiser(x, sigma_hat * s_in) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback( - { - "x": x, - "i": i, - "sigma": sigmas[i], - "sigma_hat": sigma_hat, - "denoised": denoised, - } - ) - # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule - sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3 - dt_1 = sigma_mid - sigma_hat - dt_2 = sigmas[i + 1] - sigma_hat - x_2 = x + d * dt_1 - denoised_2 = denoiser(x_2, sigma_mid * s_in) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - return x - - -@th.no_grad() -def sample_onestep( - distiller, - x, - sigmas, - generator=None, - progress=False, - callback=None, -): - """Single-step generation from a distilled model.""" - s_in = x.new_ones([x.shape[0]]) - return distiller(x, sigmas[0] * s_in) - - -@th.no_grad() -def stochastic_iterative_sampler( - distiller, - x, - sigmas, - generator, - ts, - progress=False, - callback=None, - t_min=0.002, - t_max=80.0, - rho=7.0, - steps=40, -): - t_max_rho = t_max ** (1 / rho) - t_min_rho = t_min ** (1 / rho) - s_in = x.new_ones([x.shape[0]]) - - for i in range(len(ts) - 1): - t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho - x0 = distiller(x, t * s_in) - next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho - next_t = np.clip(next_t, t_min, t_max) - x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) - - return x - - -@th.no_grad() -def sample_progdist( - denoiser, - x, - sigmas, - generator=None, - progress=False, - callback=None, -): - s_in = x.new_ones([x.shape[0]]) - sigmas = sigmas[:-1] # skip the zero sigma - - indices = range(len(sigmas) - 1) - if progress: - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - sigma = sigmas[i] - denoised = denoiser(x, sigma * s_in) - d = to_d(x, sigma, denoised) - if callback is not None: - callback( - { - "x": x, - "i": i, - "sigma": sigma, - "denoised": denoised, - } - ) - dt = sigmas[i + 1] - sigma - x = x + d * dt - - return x - - -# @th.no_grad() -# def iterative_colorization( -# distiller, -# images, -# x, -# ts, -# t_min=0.002, -# t_max=80.0, -# rho=7.0, -# steps=40, -# generator=None, -# ): -# def obtain_orthogonal_matrix(): -# vector = np.asarray([0.2989, 0.5870, 0.1140]) -# vector = vector / np.linalg.norm(vector) -# matrix = np.eye(3) -# matrix[:, 0] = vector -# matrix = np.linalg.qr(matrix)[0] -# if np.sum(matrix[:, 0]) < 0: -# matrix = -matrix -# return matrix - -# Q = th.from_numpy(obtain_orthogonal_matrix()).to(dist_util.dev()).to(th.float32) -# mask = th.zeros(*x.shape[1:], device=dist_util.dev()) -# mask[0, ...] = 1.0 - -# def replacement(x0, x1): -# x0 = th.einsum("bchw,cd->bdhw", x0, Q) -# x1 = th.einsum("bchw,cd->bdhw", x1, Q) - -# x_mix = x0 * mask + x1 * (1.0 - mask) -# x_mix = th.einsum("bdhw,cd->bchw", x_mix, Q) -# return x_mix - -# t_max_rho = t_max ** (1 / rho) -# t_min_rho = t_min ** (1 / rho) -# s_in = x.new_ones([x.shape[0]]) -# images = replacement(images, th.zeros_like(images)) - -# for i in range(len(ts) - 1): -# t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho -# x0 = distiller(x, t * s_in) -# x0 = th.clamp(x0, -1.0, 1.0) -# x0 = replacement(images, x0) -# next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho -# next_t = np.clip(next_t, t_min, t_max) -# x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) - -# return x, images - - -# @th.no_grad() -# def iterative_inpainting( -# distiller, -# images, -# x, -# ts, -# t_min=0.002, -# t_max=80.0, -# rho=7.0, -# steps=40, -# generator=None, -# ): -# from PIL import Image, ImageDraw, ImageFont - -# image_size = x.shape[-1] - -# # create a blank image with a white background -# img = Image.new("RGB", (image_size, image_size), color="white") - -# # get a drawing context for the image -# draw = ImageDraw.Draw(img) - -# # load a font -# font = ImageFont.truetype("arial.ttf", 250) - -# # draw the letter "C" in black -# draw.text((50, 0), "S", font=font, fill=(0, 0, 0)) - -# # convert the image to a numpy array -# img_np = np.array(img) -# img_np = img_np.transpose(2, 0, 1) -# img_th = th.from_numpy(img_np).to(dist_util.dev()) - -# mask = th.zeros(*x.shape, device=dist_util.dev()) -# mask = mask.reshape(-1, 7, 3, image_size, image_size) - -# mask[::2, :, img_th > 0.5] = 1.0 -# mask[1::2, :, img_th < 0.5] = 1.0 -# mask = mask.reshape(-1, 3, image_size, image_size) - -# def replacement(x0, x1): -# x_mix = x0 * mask + x1 * (1 - mask) -# return x_mix - -# t_max_rho = t_max ** (1 / rho) -# t_min_rho = t_min ** (1 / rho) -# s_in = x.new_ones([x.shape[0]]) -# images = replacement(images, -th.ones_like(images)) - -# for i in range(len(ts) - 1): -# t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho -# x0 = distiller(x, t * s_in) -# x0 = th.clamp(x0, -1.0, 1.0) -# x0 = replacement(images, x0) -# next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho -# next_t = np.clip(next_t, t_min, t_max) -# x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) - -# return x, images - - -# @th.no_grad() -# def iterative_superres( -# distiller, -# images, -# x, -# ts, -# t_min=0.002, -# t_max=80.0, -# rho=7.0, -# steps=40, -# generator=None, -# ): -# patch_size = 8 - -# def obtain_orthogonal_matrix(): -# vector = np.asarray([1] * patch_size**2) -# vector = vector / np.linalg.norm(vector) -# matrix = np.eye(patch_size**2) -# matrix[:, 0] = vector -# matrix = np.linalg.qr(matrix)[0] -# if np.sum(matrix[:, 0]) < 0: -# matrix = -matrix -# return matrix - -# Q = th.from_numpy(obtain_orthogonal_matrix()).to(dist_util.dev()).to(th.float32) - -# image_size = x.shape[-1] - -# def replacement(x0, x1): -# x0_flatten = ( -# x0.reshape(-1, 3, image_size, image_size) -# .reshape( -# -1, -# 3, -# image_size // patch_size, -# patch_size, -# image_size // patch_size, -# patch_size, -# ) -# .permute(0, 1, 2, 4, 3, 5) -# .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2) -# ) -# x1_flatten = ( -# x1.reshape(-1, 3, image_size, image_size) -# .reshape( -# -1, -# 3, -# image_size // patch_size, -# patch_size, -# image_size // patch_size, -# patch_size, -# ) -# .permute(0, 1, 2, 4, 3, 5) -# .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2) -# ) -# x0 = th.einsum("bcnd,de->bcne", x0_flatten, Q) -# x1 = th.einsum("bcnd,de->bcne", x1_flatten, Q) -# x_mix = x0.new_zeros(x0.shape) -# x_mix[..., 0] = x0[..., 0] -# x_mix[..., 1:] = x1[..., 1:] -# x_mix = th.einsum("bcne,de->bcnd", x_mix, Q) -# x_mix = ( -# x_mix.reshape( -# -1, -# 3, -# image_size // patch_size, -# image_size // patch_size, -# patch_size, -# patch_size, -# ) -# .permute(0, 1, 2, 4, 3, 5) -# .reshape(-1, 3, image_size, image_size) -# ) -# return x_mix - -# def average_image_patches(x): -# x_flatten = ( -# x.reshape(-1, 3, image_size, image_size) -# .reshape( -# -1, -# 3, -# image_size // patch_size, -# patch_size, -# image_size // patch_size, -# patch_size, -# ) -# .permute(0, 1, 2, 4, 3, 5) -# .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2) -# ) -# x_flatten[..., :] = x_flatten.mean(dim=-1, keepdim=True) -# return ( -# x_flatten.reshape( -# -1, -# 3, -# image_size // patch_size, -# image_size // patch_size, -# patch_size, -# patch_size, -# ) -# .permute(0, 1, 2, 4, 3, 5) -# .reshape(-1, 3, image_size, image_size) -# ) - -# t_max_rho = t_max ** (1 / rho) -# t_min_rho = t_min ** (1 / rho) -# s_in = x.new_ones([x.shape[0]]) -# images = average_image_patches(images) - -# for i in range(len(ts) - 1): -# t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho -# x0 = distiller(x, t * s_in) -# x0 = th.clamp(x0, -1.0, 1.0) -# x0 = replacement(images, x0) -# next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho -# next_t = np.clip(next_t, t_min, t_max) -# x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2) - -# return x, images diff --git a/modules/diffusion/karras/random_utils.py b/modules/diffusion/karras/random_utils.py deleted file mode 100644 index 7ebf4265934edad153adaf83777731afa313d000..0000000000000000000000000000000000000000 --- a/modules/diffusion/karras/random_utils.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch as th - - -def get_generator(generator, num_samples=0, seed=0): - if generator == "dummy": - return DummyGenerator() - elif generator == "determ": - return DeterministicGenerator(num_samples, seed) - elif generator == "determ-indiv": - return DeterministicIndividualGenerator(num_samples, seed) - else: - raise NotImplementedError - - -class DummyGenerator: - def randn(self, *args, **kwargs): - return th.randn(*args, **kwargs) - - def randint(self, *args, **kwargs): - return th.randint(*args, **kwargs) - - def randn_like(self, *args, **kwargs): - return th.randn_like(*args, **kwargs) - - -class DeterministicGenerator: - """ - RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines - Uses a single rng and samples num_samples sized randomness and subsamples the current indices - """ - - def __init__(self, num_samples, seed=0): - print("Warning: Distributed not initialised, using single rank") - self.rank = 0 - self.world_size = 1 - self.num_samples = num_samples - self.done_samples = 0 - self.seed = seed - self.rng_cpu = th.Generator() - if th.cuda.is_available(): - self.rng_cuda = th.Generator(dist_util.dev()) - self.set_seed(seed) - - def get_global_size_and_indices(self, size): - global_size = (self.num_samples, *size[1:]) - indices = th.arange( - self.done_samples + self.rank, - self.done_samples + self.world_size * int(size[0]), - self.world_size, - ) - indices = th.clamp(indices, 0, self.num_samples - 1) - assert ( - len(indices) == size[0] - ), f"rank={self.rank}, ws={self.world_size}, l={len(indices)}, bs={size[0]}" - return global_size, indices - - def get_generator(self, device): - return self.rng_cpu if th.device(device).type == "cpu" else self.rng_cuda - - def randn(self, *size, dtype=th.float, device="cpu"): - global_size, indices = self.get_global_size_and_indices(size) - generator = self.get_generator(device) - return th.randn(*global_size, generator=generator, dtype=dtype, device=device)[ - indices - ] - - def randint(self, low, high, size, dtype=th.long, device="cpu"): - global_size, indices = self.get_global_size_and_indices(size) - generator = self.get_generator(device) - return th.randint( - low, high, generator=generator, size=global_size, dtype=dtype, device=device - )[indices] - - def randn_like(self, tensor): - size, dtype, device = tensor.size(), tensor.dtype, tensor.device - return self.randn(*size, dtype=dtype, device=device) - - def set_done_samples(self, done_samples): - self.done_samples = done_samples - self.set_seed(self.seed) - - def get_seed(self): - return self.seed - - def set_seed(self, seed): - self.rng_cpu.manual_seed(seed) - if th.cuda.is_available(): - self.rng_cuda.manual_seed(seed) - - -class DeterministicIndividualGenerator: - """ - RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines - Uses a separate rng for each sample to reduce memoery usage - """ - - def __init__(self, num_samples, seed=0): - print("Warning: Distributed not initialised, using single rank") - self.rank = 0 - self.world_size = 1 - self.num_samples = num_samples - self.done_samples = 0 - self.seed = seed - self.rng_cpu = [th.Generator() for _ in range(num_samples)] - if th.cuda.is_available(): - self.rng_cuda = [th.Generator(dist_util.dev()) for _ in range(num_samples)] - self.set_seed(seed) - - def get_size_and_indices(self, size): - indices = th.arange( - self.done_samples + self.rank, - self.done_samples + self.world_size * int(size[0]), - self.world_size, - ) - indices = th.clamp(indices, 0, self.num_samples - 1) - assert ( - len(indices) == size[0] - ), f"rank={self.rank}, ws={self.world_size}, l={len(indices)}, bs={size[0]}" - return (1, *size[1:]), indices - - def get_generator(self, device): - return self.rng_cpu if th.device(device).type == "cpu" else self.rng_cuda - - def randn(self, *size, dtype=th.float, device="cpu"): - size, indices = self.get_size_and_indices(size) - generator = self.get_generator(device) - return th.cat( - [ - th.randn(*size, generator=generator[i], dtype=dtype, device=device) - for i in indices - ], - dim=0, - ) - - def randint(self, low, high, size, dtype=th.long, device="cpu"): - size, indices = self.get_size_and_indices(size) - generator = self.get_generator(device) - return th.cat( - [ - th.randint( - low, - high, - generator=generator[i], - size=size, - dtype=dtype, - device=device, - ) - for i in indices - ], - dim=0, - ) - - def randn_like(self, tensor): - size, dtype, device = tensor.size(), tensor.dtype, tensor.device - return self.randn(*size, dtype=dtype, device=device) - - def set_done_samples(self, done_samples): - self.done_samples = done_samples - - def get_seed(self): - return self.seed - - def set_seed(self, seed): - [ - rng_cpu.manual_seed(i + self.num_samples * seed) - for i, rng_cpu in enumerate(self.rng_cpu) - ] - if th.cuda.is_available(): - [ - rng_cuda.manual_seed(i + self.num_samples * seed) - for i, rng_cuda in enumerate(self.rng_cuda) - ] diff --git a/modules/diffusion/karras/sample.py b/modules/diffusion/karras/sample.py deleted file mode 100644 index 6d7472a37342c8e8eb06ccd6e292a28b80e8dfef..0000000000000000000000000000000000000000 --- a/modules/diffusion/karras/sample.py +++ /dev/null @@ -1,185 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from abc import ABC, abstractmethod - -import numpy as np -import torch as th -from scipy.stats import norm -import torch.distributed as dist - - -def create_named_schedule_sampler(name, diffusion): - """ - Create a ScheduleSampler from a library of pre-defined samplers. - - :param name: the name of the sampler. - :param diffusion: the diffusion object to sample for. - """ - if name == "uniform": - return UniformSampler(diffusion) - elif name == "loss-second-moment": - return LossSecondMomentResampler(diffusion) - elif name == "lognormal": - return LogNormalSampler() - else: - raise NotImplementedError(f"unknown schedule sampler: {name}") - - -class ScheduleSampler(ABC): - """ - A distribution over timesteps in the diffusion process, intended to reduce - variance of the objective. - - By default, samplers perform unbiased importance sampling, in which the - objective's mean is unchanged. - However, subclasses may override sample() to change how the resampled - terms are reweighted, allowing for actual changes in the objective. - """ - - @abstractmethod - def weights(self): - """ - Get a numpy array of weights, one per diffusion step. - - The weights needn't be normalized, but must be positive. - """ - - def sample(self, batch_size, device): - """ - Importance-sample timesteps for a batch. - - :param batch_size: the number of timesteps. - :param device: the torch device to save to. - :return: a tuple (timesteps, weights): - - timesteps: a tensor of timestep indices. - - weights: a tensor of weights to scale the resulting losses. - """ - w = self.weights() - p = w / np.sum(w) - indices_np = np.random.choice(len(p), size=(batch_size,), p=p) - indices = th.from_numpy(indices_np).long().to(device) - weights_np = 1 / (len(p) * p[indices_np]) - weights = th.from_numpy(weights_np).float().to(device) - return indices, weights - - -class UniformSampler(ScheduleSampler): - def __init__(self, diffusion): - self.diffusion = diffusion - self._weights = np.ones([diffusion.num_timesteps]) - - def weights(self): - return self._weights - - -class LossAwareSampler(ScheduleSampler): - def update_with_local_losses(self, local_ts, local_losses): - """ - Update the reweighting using losses from a model. - - Call this method from each rank with a batch of timesteps and the - corresponding losses for each of those timesteps. - This method will perform synchronization to make sure all of the ranks - maintain the exact same reweighting. - - :param local_ts: an integer Tensor of timesteps. - :param local_losses: a 1D Tensor of losses. - """ - batch_sizes = [ - th.tensor([0], dtype=th.int32, device=local_ts.device) - for _ in range(dist.get_world_size()) - ] - dist.all_gather( - batch_sizes, - th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device), - ) - - # Pad all_gather batches to be the maximum batch size. - batch_sizes = [x.item() for x in batch_sizes] - max_bs = max(batch_sizes) - - timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes] - loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes] - dist.all_gather(timestep_batches, local_ts) - dist.all_gather(loss_batches, local_losses) - timesteps = [ - x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs] - ] - losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]] - self.update_with_all_losses(timesteps, losses) - - @abstractmethod - def update_with_all_losses(self, ts, losses): - """ - Update the reweighting using losses from a model. - - Sub-classes should override this method to update the reweighting - using losses from the model. - - This method directly updates the reweighting without synchronizing - between workers. It is called by update_with_local_losses from all - ranks with identical arguments. Thus, it should have deterministic - behavior to maintain state across workers. - - :param ts: a list of int timesteps. - :param losses: a list of float losses, one per timestep. - """ - - -class LossSecondMomentResampler(LossAwareSampler): - def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001): - self.diffusion = diffusion - self.history_per_term = history_per_term - self.uniform_prob = uniform_prob - self._loss_history = np.zeros( - [diffusion.num_timesteps, history_per_term], dtype=np.float64 - ) - self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int) - - def weights(self): - if not self._warmed_up(): - return np.ones([self.diffusion.num_timesteps], dtype=np.float64) - weights = np.sqrt(np.mean(self._loss_history**2, axis=-1)) - weights /= np.sum(weights) - weights *= 1 - self.uniform_prob - weights += self.uniform_prob / len(weights) - return weights - - def update_with_all_losses(self, ts, losses): - for t, loss in zip(ts, losses): - if self._loss_counts[t] == self.history_per_term: - # Shift out the oldest loss term. - self._loss_history[t, :-1] = self._loss_history[t, 1:] - self._loss_history[t, -1] = loss - else: - self._loss_history[t, self._loss_counts[t]] = loss - self._loss_counts[t] += 1 - - def _warmed_up(self): - return (self._loss_counts == self.history_per_term).all() - - -class LogNormalSampler: - def __init__(self, p_mean=-1.2, p_std=1.2, even=False): - self.p_mean = p_mean - self.p_std = p_std - self.even = even - if self.even: - self.inv_cdf = lambda x: norm.ppf(x, loc=p_mean, scale=p_std) - self.rank, self.size = dist.get_rank(), dist.get_world_size() - - def sample(self, bs, device): - if self.even: - # buckets = [1/G] - start_i, end_i = self.rank * bs, (self.rank + 1) * bs - global_batch_size = self.size * bs - locs = (th.arange(start_i, end_i) + th.rand(bs)) / global_batch_size - log_sigmas = th.tensor(self.inv_cdf(locs), dtype=th.float32, device=device) - else: - log_sigmas = self.p_mean + self.p_std * th.randn(bs, device=device) - sigmas = th.exp(log_sigmas) - weights = th.ones_like(sigmas) - return sigmas, weights diff --git a/modules/diffusion/unet/attention.py b/modules/diffusion/unet/attention.py deleted file mode 100644 index f5a210cb392e3e1beb9f8c2884978f6b0b71ced4..0000000000000000000000000000000000000000 --- a/modules/diffusion/unet/attention.py +++ /dev/null @@ -1,241 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from modules.general.utils import Conv1d, normalization, zero_module -from .basic import UNetBlock - - -class AttentionBlock(UNetBlock): - r"""A spatial transformer encoder block that allows spatial positions to attend - to each other. Reference from `latent diffusion repo - `_. - - Args: - channels: Number of channels in the input. - num_head_channels: Number of channels per attention head. - num_heads: Number of attention heads. Overrides ``num_head_channels`` if set. - encoder_channels: Number of channels in the encoder output for cross-attention. - If ``None``, then self-attention is performed. - use_self_attention: Whether to use self-attention before cross-attention, only applicable if encoder_channels is set. - dims: Number of spatial dimensions, i.e. 1 for temporal signals, 2 for images. - h_dim: The dimension of the height, would be applied if ``dims`` is 2. - encoder_hdim: The dimension of the height of the encoder output, would be applied if ``dims`` is 2. - p_dropout: Dropout probability. - """ - - def __init__( - self, - channels: int, - num_head_channels: int = 32, - num_heads: int = -1, - encoder_channels: int = None, - use_self_attention: bool = False, - dims: int = 1, - h_dim: int = 100, - encoder_hdim: int = 384, - p_dropout: float = 0.0, - ): - super().__init__() - - self.channels = channels - self.p_dropout = p_dropout - self.dims = dims - - if dims == 1: - self.channels = channels - elif dims == 2: - # We consider the channel as product of channel and height, i.e. C x H - # This is because we want to apply attention on the audio signal, which is 1D - self.channels = channels * h_dim - else: - raise ValueError(f"invalid number of dimensions: {dims}") - - if num_head_channels == -1: - assert ( - self.channels % num_heads == 0 - ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}" - self.num_heads = num_heads - self.num_head_channels = self.channels // num_heads - else: - assert ( - self.channels % num_head_channels == 0 - ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}" - self.num_heads = self.channels // num_head_channels - self.num_head_channels = num_head_channels - - if encoder_channels is not None: - self.use_self_attention = use_self_attention - - if dims == 1: - self.encoder_channels = encoder_channels - elif dims == 2: - self.encoder_channels = encoder_channels * encoder_hdim - else: - raise ValueError(f"invalid number of dimensions: {dims}") - - if use_self_attention: - self.self_attention = BasicAttentionBlock( - self.channels, - self.num_head_channels, - self.num_heads, - p_dropout=self.p_dropout, - ) - self.cross_attention = BasicAttentionBlock( - self.channels, - self.num_head_channels, - self.num_heads, - self.encoder_channels, - p_dropout=self.p_dropout, - ) - else: - self.encoder_channels = None - self.self_attention = BasicAttentionBlock( - self.channels, - self.num_head_channels, - self.num_heads, - p_dropout=self.p_dropout, - ) - - def forward(self, x: torch.Tensor, encoder_output: torch.Tensor = None): - r""" - Args: - x: input tensor with shape [B x ``channels`` x ...] - encoder_output: feature tensor with shape [B x ``encoder_channels`` x ...], if ``None``, then self-attention is performed. - - Returns: - output tensor with shape [B x ``channels`` x ...] - """ - shape = x.size() - x = x.reshape(shape[0], self.channels, -1).contiguous() - - if self.encoder_channels is None: - assert ( - encoder_output is None - ), "encoder_output must be None for self-attention." - h = self.self_attention(x) - - else: - assert ( - encoder_output is not None - ), "encoder_output must be given for cross-attention." - encoder_output = encoder_output.reshape( - shape[0], self.encoder_channels, -1 - ).contiguous() - - if self.use_self_attention: - x = self.self_attention(x) - h = self.cross_attention(x, encoder_output) - - return h.reshape(*shape).contiguous() - - -class BasicAttentionBlock(nn.Module): - def __init__( - self, - channels: int, - num_head_channels: int = 32, - num_heads: int = -1, - context_channels: int = None, - p_dropout: float = 0.0, - ): - super().__init__() - - self.channels = channels - self.p_dropout = p_dropout - self.context_channels = context_channels - - if num_head_channels == -1: - assert ( - self.channels % num_heads == 0 - ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}" - self.num_heads = num_heads - self.num_head_channels = self.channels // num_heads - else: - assert ( - self.channels % num_head_channels == 0 - ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}" - self.num_heads = self.channels // num_head_channels - self.num_head_channels = num_head_channels - - if context_channels is not None: - self.to_q = nn.Sequential( - normalization(self.channels), - Conv1d(self.channels, self.channels, 1), - ) - self.to_kv = Conv1d(context_channels, 2 * self.channels, 1) - else: - self.to_qkv = nn.Sequential( - normalization(self.channels), - Conv1d(self.channels, 3 * self.channels, 1), - ) - - self.linear = Conv1d(self.channels, self.channels) - - self.proj_out = nn.Sequential( - normalization(self.channels), - Conv1d(self.channels, self.channels, 1), - nn.GELU(), - nn.Dropout(p=self.p_dropout), - zero_module(Conv1d(self.channels, self.channels, 1)), - ) - - def forward(self, q: torch.Tensor, kv: torch.Tensor = None): - r""" - Args: - q: input tensor with shape [B, ``channels``, L] - kv: feature tensor with shape [B, ``context_channels``, T], if ``None``, then self-attention is performed. - - Returns: - output tensor with shape [B, ``channels``, L] - """ - N, C, L = q.size() - - if self.context_channels is not None: - assert kv is not None, "kv must be given for cross-attention." - - q = ( - self.to_q(q) - .reshape(self.num_heads, self.num_head_channels, -1) - .transpose(-1, -2) - .contiguous() - ) - kv = ( - self.to_kv(kv) - .reshape(2, self.num_heads, self.num_head_channels, -1) - .transpose(-1, -2) - .chunk(2) - ) - k, v = ( - kv[0].squeeze(0).contiguous(), - kv[1].squeeze(0).contiguous(), - ) - - else: - qkv = ( - self.to_qkv(q) - .reshape(3, self.num_heads, self.num_head_channels, -1) - .transpose(-1, -2) - .chunk(3) - ) - q, k, v = ( - qkv[0].squeeze(0).contiguous(), - qkv[1].squeeze(0).contiguous(), - qkv[2].squeeze(0).contiguous(), - ) - - h = F.scaled_dot_product_attention(q, k, v, dropout_p=self.p_dropout).transpose( - -1, -2 - ) - h = h.reshape(N, -1, L).contiguous() - h = self.linear(h) - - x = q + h - h = self.proj_out(x) - - return x + h diff --git a/modules/diffusion/unet/basic.py b/modules/diffusion/unet/basic.py deleted file mode 100644 index 9cd4bc55ab58b3ebb7f9b2c0ec8dd37ad6d43721..0000000000000000000000000000000000000000 --- a/modules/diffusion/unet/basic.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn -from abc import abstractmethod - - -class UNetBlock(nn.Module): - r"""Any module where forward() takes timestep embeddings as a second argument.""" - - @abstractmethod - def forward(self, x, emb): - r"""Apply the module to `x` given `emb` timestep embeddings.""" diff --git a/modules/diffusion/unet/resblock.py b/modules/diffusion/unet/resblock.py deleted file mode 100644 index 144c867785bbf76fa877ea6e24dfbdcffb78008e..0000000000000000000000000000000000000000 --- a/modules/diffusion/unet/resblock.py +++ /dev/null @@ -1,178 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from .basic import UNetBlock -from modules.general.utils import ( - append_dims, - ConvNd, - normalization, - zero_module, -) - - -class ResBlock(UNetBlock): - r"""A residual block that can optionally change the number of channels. - - Args: - channels: the number of input channels. - emb_channels: the number of timestep embedding channels. - dropout: the rate of dropout. - out_channels: if specified, the number of out channels. - use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - dims: determines if the signal is 1D, 2D, or 3D. - up: if True, use this block for upsampling. - down: if True, use this block for downsampling. - """ - - def __init__( - self, - channels, - emb_channels, - dropout: float = 0.0, - out_channels=None, - use_conv=False, - use_scale_shift_norm=False, - dims=2, - up=False, - down=False, - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_scale_shift_norm = use_scale_shift_norm - - self.in_layers = nn.Sequential( - normalization(channels), - nn.SiLU(), - ConvNd(dims, channels, self.out_channels, 3, padding=1), - ) - - self.updown = up or down - - if up: - self.h_upd = Upsample(channels, False, dims) - self.x_upd = Upsample(channels, False, dims) - elif down: - self.h_upd = Downsample(channels, False, dims) - self.x_upd = Downsample(channels, False, dims) - else: - self.h_upd = self.x_upd = nn.Identity() - - self.emb_layers = nn.Sequential( - nn.SiLU(), - ConvNd( - dims, - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, - 1, - ), - ) - self.out_layers = nn.Sequential( - normalization(self.out_channels), - nn.SiLU(), - nn.Dropout(p=dropout), - zero_module( - ConvNd(dims, self.out_channels, self.out_channels, 3, padding=1) - ), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - elif use_conv: - self.skip_connection = ConvNd( - dims, channels, self.out_channels, 3, padding=1 - ) - else: - self.skip_connection = ConvNd(dims, channels, self.out_channels, 1) - - def forward(self, x, emb): - """ - Apply the block to a Tensor, conditioned on a timestep embedding. - - x: an [N x C x ...] Tensor of features. - emb: an [N x emb_channels x ...] Tensor of timestep embeddings. - :return: an [N x C x ...] Tensor of outputs. - """ - if self.updown: - in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] - h = in_rest(x) - h = self.h_upd(h) - x = self.x_upd(x) - h = in_conv(h) - else: - h = self.in_layers(x) - emb_out = self.emb_layers(emb) - emb_out = append_dims(emb_out, h.dim()) - if self.use_scale_shift_norm: - out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - scale, shift = torch.chunk(emb_out, 2, dim=1) - h = out_norm(h) * (1 + scale) + shift - h = out_rest(h) - else: - h = h + emb_out - h = self.out_layers(h) - return self.skip_connection(x) + h - - -class Upsample(nn.Module): - r"""An upsampling layer with an optional convolution. - - Args: - channels: channels in the inputs and outputs. - dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - upsampling occurs in the inner-two dimensions. - out_channels: if specified, the number of out channels. - """ - - def __init__(self, channels, dims=2, out_channels=None): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.dims = dims - self.conv = ConvNd(dims, self.channels, self.out_channels, 3, padding=1) - - def forward(self, x): - assert x.shape[1] == self.channels - if self.dims == 3: - x = F.interpolate( - x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" - ) - else: - x = F.interpolate(x, scale_factor=2, mode="nearest") - x = self.conv(x) - return x - - -class Downsample(nn.Module): - r"""A downsampling layer with an optional convolution. - - Args: - channels: channels in the inputs and outputs. - dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - out_channels: if specified, the number of output channels. - """ - - def __init__(self, channels, dims=2, out_channels=None): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - self.op = ConvNd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=1 - ) - - def forward(self, x): - assert x.shape[1] == self.channels - return self.op(x) diff --git a/modules/diffusion/unet/unet.py b/modules/diffusion/unet/unet.py deleted file mode 100644 index 2d39f5d1e07f64012ba08268a0a4a5e71ad01e88..0000000000000000000000000000000000000000 --- a/modules/diffusion/unet/unet.py +++ /dev/null @@ -1,310 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn - -from modules.encoder.position_encoder import PositionEncoder -from modules.general.utils import append_dims, ConvNd, normalization, zero_module -from .attention import AttentionBlock -from .resblock import Downsample, ResBlock, Upsample - - -class UNet(nn.Module): - r"""The full UNet model with attention and timestep embedding. - - Args: - dims: determines if the signal is 1D (temporal), 2D(spatial). - in_channels: channels in the input Tensor. - model_channels: base channel count for the model. - out_channels: channels in the output Tensor. - num_res_blocks: number of residual blocks per downsample. - channel_mult: channel multiplier for each level of the UNet. - num_attn_blocks: number of attention blocks at place. - attention_resolutions: a collection of downsample rates at which attention will - take place. May be a set, list, or tuple. For example, if this contains 4, - then at 4x downsampling, attention will be used. - num_heads: the number of attention heads in each attention layer. - num_head_channels: if specified, ignore num_heads and instead use a fixed - channel width per attention head. - d_context: if specified, use for cross-attention channel project. - p_dropout: the dropout probability. - use_self_attention: Apply self attention before cross attention. - num_classes: if specified (as an int), then this model will be class-conditional - with ``num_classes`` classes. - use_extra_film: if specified, use an extra FiLM-like conditioning mechanism. - d_emb: if specified, use for FiLM-like conditioning. - use_scale_shift_norm: use a FiLM-like conditioning mechanism. - resblock_updown: use residual blocks for up/downsampling. - """ - - def __init__( - self, - dims: int = 1, - in_channels: int = 100, - model_channels: int = 128, - out_channels: int = 100, - h_dim: int = 128, - num_res_blocks: int = 1, - channel_mult: tuple = (1, 2, 4), - num_attn_blocks: int = 1, - attention_resolutions: tuple = (1, 2, 4), - num_heads: int = 1, - num_head_channels: int = -1, - d_context: int = None, - context_hdim: int = 128, - p_dropout: float = 0.0, - num_classes: int = -1, - use_extra_film: str = None, - d_emb: int = None, - use_scale_shift_norm: bool = True, - resblock_updown: bool = False, - ): - super().__init__() - - self.dims = dims - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - self.num_res_blocks = num_res_blocks - self.channel_mult = channel_mult - self.num_attn_blocks = num_attn_blocks - self.attention_resolutions = attention_resolutions - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.d_context = d_context - self.p_dropout = p_dropout - self.num_classes = num_classes - self.use_extra_film = use_extra_film - self.d_emb = d_emb - self.use_scale_shift_norm = use_scale_shift_norm - self.resblock_updown = resblock_updown - - time_embed_dim = model_channels * 4 - self.pos_enc = PositionEncoder(model_channels, time_embed_dim) - - assert ( - num_classes == -1 or use_extra_film is None - ), "You cannot set both num_classes and use_extra_film." - - if self.num_classes > 0: - # TODO: if used for singer, norm should be 1, correct? - self.label_emb = nn.Embedding(num_classes, time_embed_dim, max_norm=1.0) - elif use_extra_film is not None: - assert ( - d_emb is not None - ), "d_emb must be specified if use_extra_film is not None" - assert use_extra_film in [ - "add", - "concat", - ], f"use_extra_film only supported by add or concat. Your input is {use_extra_film}" - self.use_extra_film = use_extra_film - self.film_emb = ConvNd(dims, d_emb, time_embed_dim, 1) - if use_extra_film == "concat": - time_embed_dim *= 2 - - # Input blocks - ch = input_ch = int(channel_mult[0] * model_channels) - self.input_blocks = nn.ModuleList( - [UNetSequential(ConvNd(dims, in_channels, ch, 3, padding=1))] - ) - self._feature_size = ch - input_block_chans = [ch] - ds = 1 - for level, mult in enumerate(channel_mult): - for _ in range(num_res_blocks): - layers = [ - ResBlock( - ch, - time_embed_dim, - p_dropout, - out_channels=int(mult * model_channels), - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = int(mult * model_channels) - if ds in attention_resolutions: - for _ in range(num_attn_blocks): - layers.append( - AttentionBlock( - ch, - num_heads=num_heads, - num_head_channels=num_head_channels, - encoder_channels=d_context, - dims=dims, - h_dim=h_dim // (level + 1), - encoder_hdim=context_hdim, - p_dropout=p_dropout, - ) - ) - self.input_blocks.append(UNetSequential(*layers)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - UNetSequential( - ResBlock( - ch, - time_embed_dim, - p_dropout, - out_channels=out_ch, - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - ) - if resblock_updown - else Downsample(ch, dims=dims, out_channels=out_ch) - ) - ) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - # Middle blocks - self.middle_block = UNetSequential( - ResBlock( - ch, - time_embed_dim, - p_dropout, - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - ), - AttentionBlock( - ch, - num_heads=num_heads, - num_head_channels=num_head_channels, - encoder_channels=d_context, - dims=dims, - h_dim=h_dim // (level + 1), - encoder_hdim=context_hdim, - p_dropout=p_dropout, - ), - ResBlock( - ch, - time_embed_dim, - p_dropout, - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - ), - ) - self._feature_size += ch - - # Output blocks - self.output_blocks = nn.ModuleList([]) - for level, mult in tuple(enumerate(channel_mult))[::-1]: - for i in range(num_res_blocks + 1): - ich = input_block_chans.pop() - layers = [ - ResBlock( - ch + ich, - time_embed_dim, - p_dropout, - out_channels=int(model_channels * mult), - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - ) - ] - ch = int(model_channels * mult) - if ds in attention_resolutions: - for _ in range(num_attn_blocks): - layers.append( - AttentionBlock( - ch, - num_heads=num_heads, - num_head_channels=num_head_channels, - encoder_channels=d_context, - dims=dims, - h_dim=h_dim // (level + 1), - encoder_hdim=context_hdim, - p_dropout=p_dropout, - ) - ) - if level and i == num_res_blocks: - out_ch = ch - layers.append( - ResBlock( - ch, - time_embed_dim, - p_dropout, - out_channels=out_ch, - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - ) - if resblock_updown - else Upsample(ch, dims=dims, out_channels=out_ch) - ) - ds //= 2 - self.output_blocks.append(UNetSequential(*layers)) - self._feature_size += ch - - # Final proj out - self.out = nn.Sequential( - normalization(ch), - nn.SiLU(), - zero_module(ConvNd(dims, input_ch, out_channels, 3, padding=1)), - ) - - def forward(self, x, timesteps=None, context=None, y=None, **kwargs): - r"""Apply the model to an input batch. - - Args: - x: an [N x C x ...] Tensor of inputs. - timesteps: a 1-D batch of timesteps, i.e. [N]. - context: conditioning Tensor with shape of [N x ``d_context`` x ...] plugged - in via cross attention. - y: an [N] Tensor of labels, if **class-conditional**. - an [N x ``d_emb`` x ...] Tensor if **film-embed conditional**. - - Returns: - an [N x C x ...] Tensor of outputs. - """ - assert (y is None) or ( - (y is not None) - and ((self.num_classes > 0) or (self.use_extra_film is not None)) - ), f"y must be specified if num_classes or use_extra_film is not None. \nGot num_classes: {self.num_classes}\t\nuse_extra_film: {self.use_extra_film}\t\n" - - hs = [] - emb = self.pos_enc(timesteps) - emb = append_dims(emb, x.dim()) - - if self.num_classes > 0: - assert y.size() == (x.size(0),) - emb = emb + self.label_emb(y) - elif self.use_extra_film is not None: - assert y.size() == (x.size(0), self.d_emb, *x.size()[2:]) - y = self.film_emb(y) - if self.use_extra_film == "add": - emb = emb + y - elif self.use_extra_film == "concat": - emb = torch.cat([emb, y], dim=1) - - h = x - for module in self.input_blocks: - h = module(h, emb, context) - hs.append(h) - h = self.middle_block(h, emb, context) - for module in self.output_blocks: - h = torch.cat([h, hs.pop()], dim=1) - h = module(h, emb, context) - - return self.out(h) - - -class UNetSequential(nn.Sequential): - r"""A sequential module that passes embeddings to the children that support it.""" - - def forward(self, x, emb=None, context=None): - for layer in self: - if isinstance(layer, ResBlock): - x = layer(x, emb) - elif isinstance(layer, AttentionBlock): - x = layer(x, context) - else: - x = layer(x) - return x diff --git a/modules/distributions/__init__.py b/modules/distributions/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/distributions/distributions.py b/modules/distributions/distributions.py deleted file mode 100644 index 0287d76c7ff2d0c1436c8e8b24ccd43513d38714..0000000000000000000000000000000000000000 --- a/modules/distributions/distributions.py +++ /dev/null @@ -1,107 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import numpy as np - - -class AbstractDistribution: - def sample(self): - raise NotImplementedError() - - def mode(self): - raise NotImplementedError() - - -class DiracDistribution(AbstractDistribution): - def __init__(self, value): - self.value = value - - def sample(self): - return self.value - - def mode(self): - return self.value - - -class DiagonalGaussianDistribution(object): - def __init__(self, parameters, deterministic=False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - if self.deterministic: - self.var = self.std = torch.zeros_like(self.mean).to( - device=self.parameters.device - ) - - def sample(self): - x = self.mean + self.std * torch.randn(self.mean.shape).to( - device=self.parameters.device - ) - return x - - def kl(self, other=None): - if self.deterministic: - return torch.Tensor([0.0]) - else: - if other is None: - return 0.5 * torch.sum( - torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, - dim=[1, 2, 3], - ) - else: - return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var - + self.var / other.var - - 1.0 - - self.logvar - + other.logvar, - dim=[1, 2, 3], - ) - - def nll(self, sample, dims=[1, 2, 3]): - if self.deterministic: - return torch.Tensor([0.0]) - logtwopi = np.log(2.0 * np.pi) - return 0.5 * torch.sum( - logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, - dim=dims, - ) - - def mode(self): - return self.mean - - -def normal_kl(mean1, logvar1, mean2, logvar2): - """ - source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 - Compute the KL divergence between two gaussians. - Shapes are automatically broadcasted, so batches can be compared to - scalars, among other use cases. - """ - tensor = None - for obj in (mean1, logvar1, mean2, logvar2): - if isinstance(obj, torch.Tensor): - tensor = obj - break - assert tensor is not None, "at least one argument must be a Tensor" - - # Force variances to be Tensors. Broadcasting helps convert scalars to - # Tensors, but it does not work for torch.exp(). - logvar1, logvar2 = [ - x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) - for x in (logvar1, logvar2) - ] - - return 0.5 * ( - -1.0 - + logvar2 - - logvar1 - + torch.exp(logvar1 - logvar2) - + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) - ) diff --git a/modules/duration_predictor/__init__.py b/modules/duration_predictor/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/duration_predictor/standard_duration_predictor.py b/modules/duration_predictor/standard_duration_predictor.py deleted file mode 100644 index 9924e70a7571cef0359019d6d083db7d9bd6e9cf..0000000000000000000000000000000000000000 --- a/modules/duration_predictor/standard_duration_predictor.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.py - -import torch -from torch import nn -from modules.base.base_module import LayerNorm - - -class DurationPredictor(nn.Module): - def __init__( - self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 - ): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d( - in_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_1 = LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d( - filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 - ) - self.norm_2 = LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask diff --git a/modules/duration_predictor/stochastic_duration_predictor.py b/modules/duration_predictor/stochastic_duration_predictor.py deleted file mode 100644 index 60bbb95547bb89a24bb8852c57a462a2d2326a32..0000000000000000000000000000000000000000 --- a/modules/duration_predictor/stochastic_duration_predictor.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.pyimport torch - -from torch import nn -from torch.nn import functional as F -import math -from modules.flow.modules import * - - -class StochasticDurationPredictor(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - p_dropout, - n_flows=4, - gin_channels=0, - ): - super().__init__() - filter_channels = in_channels - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = Log() - self.flows = nn.ModuleList() - self.flows.append(ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = DDSConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - self.post_flows = nn.ModuleList() - self.post_flows.append(ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append( - ConvFlow(2, filter_channels, kernel_size, n_layers=3) - ) - self.post_flows.append(Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = DDSConv( - filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout - ) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = ( - torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) - * x_mask - ) - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum( - (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2] - ) - logq = ( - torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - - logdet_tot_q - ) - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = ( - torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - - logdet_tot - ) - return nll + logq - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] - z = ( - torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) - * noise_scale - ) - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw diff --git a/modules/encoder/__init__.py b/modules/encoder/__init__.py deleted file mode 100644 index da72ddbeaac062191c7e8e04b62824a46d5ae9aa..0000000000000000000000000000000000000000 --- a/modules/encoder/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .token_encoder import TokenEmbedding \ No newline at end of file diff --git a/modules/encoder/condition_encoder.py b/modules/encoder/condition_encoder.py deleted file mode 100644 index 88ee892efcdfb7538eb1dca8ffc84cd452b073bc..0000000000000000000000000000000000000000 --- a/modules/encoder/condition_encoder.py +++ /dev/null @@ -1,251 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -import torch.nn as nn -from torchaudio.models import Conformer -from models.svc.transformer.transformer import PositionalEncoding - -from utils.f0 import f0_to_coarse - - -class ContentEncoder(nn.Module): - def __init__(self, cfg, input_dim, output_dim): - super().__init__() - self.cfg = cfg - - assert input_dim != 0 - self.nn = nn.Linear(input_dim, output_dim) - - # Introduce conformer or not - if ( - "use_conformer_for_content_features" in cfg - and cfg.use_conformer_for_content_features - ): - self.pos_encoder = PositionalEncoding(input_dim) - self.conformer = Conformer( - input_dim=input_dim, - num_heads=2, - ffn_dim=256, - num_layers=6, - depthwise_conv_kernel_size=3, - ) - else: - self.conformer = None - - def forward(self, x, length=None): - # x: (N, seq_len, input_dim) -> (N, seq_len, output_dim) - if self.conformer: - x = self.pos_encoder(x) - x, _ = self.conformer(x, length) - return self.nn(x) - - -class MelodyEncoder(nn.Module): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - - self.input_dim = self.cfg.input_melody_dim - self.output_dim = self.cfg.output_melody_dim - self.n_bins = self.cfg.n_bins_melody - self.pitch_min = self.cfg.pitch_min - self.pitch_max = self.cfg.pitch_max - - if self.input_dim != 0: - if self.n_bins == 0: - # Not use quantization - self.nn = nn.Linear(self.input_dim, self.output_dim) - else: - self.f0_min = cfg.f0_min - self.f0_max = cfg.f0_max - - self.nn = nn.Embedding( - num_embeddings=self.n_bins, - embedding_dim=self.output_dim, - padding_idx=None, - ) - self.uv_embedding = nn.Embedding(2, self.output_dim) - # self.conformer = Conformer( - # input_dim=self.output_dim, - # num_heads=4, - # ffn_dim=128, - # num_layers=4, - # depthwise_conv_kernel_size=3, - # ) - - def forward(self, x, uv=None, length=None): - # x: (N, frame_len) - # print(x.shape) - if self.n_bins == 0: - x = x.unsqueeze(-1) - else: - x = f0_to_coarse(x, self.n_bins, self.f0_min, self.f0_max) - x = self.nn(x) - if uv is not None: - uv = self.uv_embedding(uv) - x = x + uv - # x, _ = self.conformer(x, length) - return x - - -class LoudnessEncoder(nn.Module): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - - self.input_dim = self.cfg.input_loudness_dim - self.output_dim = self.cfg.output_loudness_dim - self.n_bins = self.cfg.n_bins_loudness - - if self.input_dim != 0: - if self.n_bins == 0: - # Not use quantization - self.nn = nn.Linear(self.input_dim, self.output_dim) - else: - # TODO: set trivially now - self.loudness_min = 1e-30 - self.loudness_max = 1.5 - - if cfg.use_log_loudness: - self.energy_bins = nn.Parameter( - torch.exp( - torch.linspace( - np.log(self.loudness_min), - np.log(self.loudness_max), - self.n_bins - 1, - ) - ), - requires_grad=False, - ) - - self.nn = nn.Embedding( - num_embeddings=self.n_bins, - embedding_dim=self.output_dim, - padding_idx=None, - ) - - def forward(self, x): - # x: (N, frame_len) - if self.n_bins == 0: - x = x.unsqueeze(-1) - else: - x = torch.bucketize(x, self.energy_bins) - return self.nn(x) - - -class SingerEncoder(nn.Module): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - - self.input_dim = 1 - self.output_dim = self.cfg.output_singer_dim - - self.nn = nn.Embedding( - num_embeddings=cfg.singer_table_size, - embedding_dim=self.output_dim, - padding_idx=None, - ) - - def forward(self, x): - # x: (N, 1) -> (N, 1, output_dim) - return self.nn(x) - - -class ConditionEncoder(nn.Module): - def __init__(self, cfg): - super().__init__() - self.cfg = cfg - - self.merge_mode = cfg.merge_mode - - if cfg.use_whisper: - self.whisper_encoder = ContentEncoder( - self.cfg, self.cfg.whisper_dim, self.cfg.content_encoder_dim - ) - - if cfg.use_contentvec: - self.contentvec_encoder = ContentEncoder( - self.cfg, self.cfg.contentvec_dim, self.cfg.content_encoder_dim - ) - - if cfg.use_mert: - self.mert_encoder = ContentEncoder( - self.cfg, self.cfg.mert_dim, self.cfg.content_encoder_dim - ) - - if cfg.use_wenet: - self.wenet_encoder = ContentEncoder( - self.cfg, self.cfg.wenet_dim, self.cfg.content_encoder_dim - ) - - self.melody_encoder = MelodyEncoder(self.cfg) - self.loudness_encoder = LoudnessEncoder(self.cfg) - if cfg.use_spkid: - self.singer_encoder = SingerEncoder(self.cfg) - - def forward(self, x): - outputs = [] - - if "frame_pitch" in x.keys(): - if "frame_uv" not in x.keys(): - x["frame_uv"] = None - pitch_enc_out = self.melody_encoder( - x["frame_pitch"], uv=x["frame_uv"], length=x["target_len"] - ) - outputs.append(pitch_enc_out) - - if "frame_energy" in x.keys(): - loudness_enc_out = self.loudness_encoder(x["frame_energy"]) - outputs.append(loudness_enc_out) - - if "whisper_feat" in x.keys(): - # whisper_feat: [b, T, 1024] - whiser_enc_out = self.whisper_encoder( - x["whisper_feat"], length=x["target_len"] - ) - outputs.append(whiser_enc_out) - seq_len = whiser_enc_out.shape[1] - - if "contentvec_feat" in x.keys(): - contentvec_enc_out = self.contentvec_encoder( - x["contentvec_feat"], length=x["target_len"] - ) - outputs.append(contentvec_enc_out) - seq_len = contentvec_enc_out.shape[1] - - if "mert_feat" in x.keys(): - mert_enc_out = self.mert_encoder(x["mert_feat"], length=x["target_len"]) - outputs.append(mert_enc_out) - seq_len = mert_enc_out.shape[1] - - if "wenet_feat" in x.keys(): - wenet_enc_out = self.wenet_encoder(x["wenet_feat"], length=x["target_len"]) - outputs.append(wenet_enc_out) - seq_len = wenet_enc_out.shape[1] - - if "spk_id" in x.keys(): - speaker_enc_out = self.singer_encoder(x["spk_id"]) # [b, 1, 384] - assert ( - "whisper_feat" in x.keys() - or "contentvec_feat" in x.keys() - or "mert_feat" in x.keys() - or "wenet_feat" in x.keys() - ) - singer_info = speaker_enc_out.expand(-1, seq_len, -1) - outputs.append(singer_info) - - encoder_output = None - if self.merge_mode == "concat": - encoder_output = torch.cat(outputs, dim=-1) - if self.merge_mode == "add": - # (#modules, N, seq_len, output_dim) - outputs = torch.cat([out[None, :, :, :] for out in outputs], dim=0) - # (N, seq_len, output_dim) - encoder_output = torch.sum(outputs, dim=0) - - return encoder_output diff --git a/modules/encoder/conv_encoder.py b/modules/encoder/conv_encoder.py deleted file mode 100644 index cfbd2e290a678e1e5ec1332f73d654c7f13e9fc5..0000000000000000000000000000000000000000 --- a/modules/encoder/conv_encoder.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils import spectral_norm -from modules.generic.conv import Conv1d - - -class ConvEncoder(nn.Module): - def __init__(self, in_channels, z_channels, spk_channels, num_dilation_layer=10): - super(ConvEncoder, self).__init__() - - self.in_channels = in_channels - self.z_channels = z_channels - self.spk_channels = spk_channels - - self.pre_process = Conv1d(in_channels, 512, kernel_size=3) - - self.dilated_conv_layers = nn.ModuleList() - for i in range(num_dilation_layer): - dilation = 2**i - self.dilated_conv_layers.append( - DilatedConvBlock(512, 512, z_channels, spk_channels, dilation) - ) - - def forward(self, inputs, z, s): - inputs = inputs.transpose(1, 2) - outputs = self.pre_process(inputs) - print(inputs.shape) - for layer in self.dilated_conv_layers: - outputs = layer(outputs, z, s) - - encoder_outputs = outputs.transpose(1, 2) - return encoder_outputs - - -class DilatedConvBlock(nn.Module): - """A stack of dilated convolutions interspersed - with batch normalisation and ReLU activations""" - - def __init__(self, in_channels, out_channels, z_channels, s_channels, dilation): - super(DilatedConvBlock, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.z_channels = z_channels - self.s_channels = s_channels - - self.conv1d = Conv1d( - in_channels, out_channels, kernel_size=3, dilation=dilation - ) - self.batch_layer = BatchNorm1dLayer(out_channels, s_channels, z_channels) - - def forward(self, inputs, z, s): - outputs = self.conv1d(inputs) - outputs = self.batch_layer(outputs, z, s) - return F.relu(outputs) - - -class BatchNorm1dLayer(nn.Module): - """The latents z and speaker embedding s modulate the scale and - shift parameters of the batch normalisation layers""" - - def __init__(self, num_features, s_channels=128, z_channels=128): - super().__init__() - - self.num_features = num_features - self.s_channels = s_channels - self.z_channels = z_channels - self.batch_nrom = nn.BatchNorm1d(num_features, affine=False) - - self.scale_layer = spectral_norm(nn.Linear(z_channels, num_features)) - self.scale_layer.weight.data.normal_(1, 0.02) # Initialise scale at N(1, 0.02) - self.scale_layer.bias.data.zero_() # Initialise bias at 0 - - self.shift_layer = spectral_norm(nn.Linear(s_channels, num_features)) - self.shift_layer.weight.data.normal_(1, 0.02) # Initialise scale at N(1, 0.02) - self.shift_layer.bias.data.zero_() # Initialise bias at 0 - - def forward(self, inputs, z, s): - outputs = self.batch_nrom(inputs) - scale = self.scale_layer(z) - scale = scale.view(-1, self.num_features, 1) - - shift = self.shift_layer(s) - shift = shift.view(-1, self.num_features, 1) - - outputs = scale * outputs + shift - - return outputs - - -if __name__ == "__main__": - model = ConvEncoder(256, 64, 64) - encoder_inputs = torch.randn(2, 256, 10) - z = torch.randn(2, 64) - speaker = torch.randn(1, 64) - outputs, duration = model(encoder_inputs, z, speaker) - print(outputs.shape, duration.shape) diff --git a/modules/encoder/position_encoder.py b/modules/encoder/position_encoder.py deleted file mode 100644 index 0f67c688a2b55390c75142ba57d159aca4a31c91..0000000000000000000000000000000000000000 --- a/modules/encoder/position_encoder.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -import torch.nn as nn - -from modules.general.utils import Linear - - -class PositionEncoder(nn.Module): - r"""Encoder of positional embedding, generates PE and then - feed into 2 full-connected layers with ``SiLU``. - - Args: - d_raw_emb: The dimension of raw embedding vectors. - d_out: The dimension of output embedding vectors, default to ``d_raw_emb``. - d_mlp: The dimension of hidden layer in MLP, default to ``d_raw_emb`` * 4. - activation_function: The activation function used in MLP, default to ``SiLU``. - n_layer: The number of layers in MLP, default to 2. - max_period: controls the minimum frequency of the embeddings. - """ - - def __init__( - self, - d_raw_emb: int = 128, - d_out: int = None, - d_mlp: int = None, - activation_function: str = "SiLU", - n_layer: int = 2, - max_period: int = 10000, - ): - super().__init__() - - self.d_raw_emb = d_raw_emb - self.d_out = d_raw_emb if d_out is None else d_out - self.d_mlp = d_raw_emb * 4 if d_mlp is None else d_mlp - self.n_layer = n_layer - self.max_period = max_period - - if activation_function.lower() == "silu": - self.activation_function = "SiLU" - elif activation_function.lower() == "relu": - self.activation_function = "ReLU" - elif activation_function.lower() == "gelu": - self.activation_function = "GELU" - else: - raise ValueError("activation_function must be one of SiLU, ReLU, GELU") - self.activation_function = activation_function - - tmp = [Linear(self.d_raw_emb, self.d_mlp), getattr(nn, activation_function)()] - for _ in range(self.n_layer - 1): - tmp.append(Linear(self.d_mlp, self.d_mlp)) - tmp.append(getattr(nn, activation_function)()) - tmp.append(Linear(self.d_mlp, self.d_out)) - - self.out = nn.Sequential(*tmp) - - def forward(self, steps: torch.Tensor) -> torch.Tensor: - r"""Create and return sinusoidal timestep embeddings directly. - - Args: - steps: a 1D Tensor of N indices, one per batch element. - These may be fractional. - - Returns: - an [N x ``d_out``] Tensor of positional embeddings. - """ - - half = self.d_raw_emb // 2 - freqs = torch.exp( - -math.log(self.max_period) - / half - * torch.arange(half, dtype=torch.float32, device=steps.device) - ) - args = steps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if self.d_raw_emb % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return self.out(embedding) diff --git a/modules/encoder/token_encoder.py b/modules/encoder/token_encoder.py deleted file mode 100644 index 9f556013000c7bc060a4934885d36cd4c5b72201..0000000000000000000000000000000000000000 --- a/modules/encoder/token_encoder.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/lifeiteng/vall-e - -import torch -import torch.nn as nn - -class TokenEmbedding(nn.Module): - def __init__(self, dim_model: int, vocab_size: int, dropout: float = 0.0): - super().__init__() - self.dropout = nn.Dropout(p=dropout) - self.word_embeddings = nn.Embedding(vocab_size, dim_model) - - @property - def weight(self) -> torch.Tensor: - return self.word_embeddings.weight - - def forward(self, x: torch.Tensor): - x = self.word_embeddings(x) - x = self.dropout(x) - return x diff --git a/modules/flow/modules.py b/modules/flow/modules.py deleted file mode 100644 index f4d897a4ae151a90d4a1fb886108903511ff14b7..0000000000000000000000000000000000000000 --- a/modules/flow/modules.py +++ /dev/null @@ -1,457 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/jaywalnut310/vits/ - -import math -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d -from torch.nn.utils import weight_norm, remove_weight_norm - -from utils.util import * -from modules.transformer.transforms import ( - piecewise_rational_quadratic_transform, -) -from modules.base.base_module import LayerNorm - -LRELU_SLOPE = 0.1 - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size**i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append( - nn.Conv1d( - channels, - channels, - kernel_size, - groups=channels, - dilation=dilation, - padding=padding, - ) - ) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__( - self, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - p_dropout=0, - ): - super(WN, self).__init__() - assert kernel_size % 2 == 1 - self.hidden_channels = hidden_channels - self.kernel_size = (kernel_size,) - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d( - gin_channels, 2 * hidden_channels * n_layers, 1 - ) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight") - - for i in range(n_layers): - dilation = dilation_rate**i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d( - hidden_channels, - 2 * hidden_channels, - kernel_size, - dilation=dilation, - padding=padding, - ) - in_layer = torch.nn.utils.weight_norm(in_layer, name="weight") - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight") - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :] - else: - g_l = torch.zeros_like(x_in) - - acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:, : self.hidden_channels, :] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:, self.hidden_channels :, :] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList( - [ - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - weight_norm( - Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - ] - ) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels, 1)) - self.logs = nn.Parameter(torch.zeros(channels, 1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1, 2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False, - ): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=p_dropout, - gin_channels=gin_channels, - ) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels] * 2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1, 2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__( - self, - in_channels, - filter_channels, - kernel_size, - n_layers, - num_bins=10, - tail_bound=5.0, - ): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0) - self.proj = nn.Conv1d( - filter_channels, self.half_channels * (num_bins * 3 - 1), 1 - ) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels] * 2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt( - self.filter_channels - ) - unnormalized_derivatives = h[..., 2 * self.num_bins :] - - x1, logabsdet = piecewise_rational_quadratic_transform( - x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails="linear", - tail_bound=self.tail_bound, - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1, 2]) - if not reverse: - return x, logdet - else: - return x diff --git a/modules/general/__init__.py b/modules/general/__init__.py deleted file mode 100644 index 7904fc1893708d17477220b6b6296127b232b8c1..0000000000000000000000000000000000000000 --- a/modules/general/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .input_strategies import PromptedFeatures, PromptedPrecomputedFeatures -from .scaling import BalancedDoubleSwish -from .utils import Transpose \ No newline at end of file diff --git a/modules/general/input_strategies.py b/modules/general/input_strategies.py deleted file mode 100644 index 51d57879ff3b56996d0488f7b1d019096e9ae350..0000000000000000000000000000000000000000 --- a/modules/general/input_strategies.py +++ /dev/null @@ -1,129 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -# This code is modified from -# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/input_strategies.py -import random -from collections import defaultdict -from concurrent.futures import ThreadPoolExecutor -from typing import Tuple, Type - -from lhotse import CutSet -from lhotse.dataset.collation import collate_features -from lhotse.dataset.input_strategies import ( - ExecutorType, - PrecomputedFeatures, - _get_executor, -) -from lhotse.utils import fastcopy - - -class PromptedFeatures: - def __init__(self, prompts, features): - self.prompts = prompts - self.features = features - - def to(self, device): - return PromptedFeatures( - self.prompts.to(device), self.features.to(device) - ) - - def sum(self): - return self.features.sum() - - @property - def ndim(self): - return self.features.ndim - - @property - def data(self): - return (self.prompts, self.features) - - -class PromptedPrecomputedFeatures(PrecomputedFeatures): - - def __init__( - self, - dataset: str, - cuts: CutSet, - num_workers: int = 0, - executor_type: Type[ExecutorType] = ThreadPoolExecutor, - ) -> None: - super().__init__(num_workers, executor_type) - self.utt2neighbors = self._create_utt2neighbors(dataset, cuts) - - def __call__( - self, cuts: CutSet - ) -> Tuple[PromptedFeatures, PromptedFeatures]: - features, features_lens = self._collate_features(cuts) - prompts, prompts_lens = self._collate_prompts(cuts) - return PromptedFeatures(prompts, features), PromptedFeatures(prompts_lens, features_lens) - - def _create_utt2neighbors(self, dataset, cuts): - utt2neighbors = defaultdict(lambda: []) - utt2cut = {cut.id: cut for cut in cuts} - if dataset.lower() == "libritts": - self._process_libritts_dataset(utt2neighbors, utt2cut, cuts) - elif dataset.lower() == "ljspeech": - self._process_ljspeech_dataset(utt2neighbors, utt2cut, cuts) - else: - raise ValueError("Unsupported dataset") - return utt2neighbors - - def _process_libritts_dataset(self, utt2neighbors, utt2cut, cuts): - speaker2utts = defaultdict(lambda: []) - for cut in cuts: - speaker = cut.supervisions[0].speaker - speaker2utts[speaker].append(cut.id) - - for spk, uttids in speaker2utts.items(): - sorted_uttids = sorted(uttids) - if len(sorted_uttids) == 1: - utt2neighbors[sorted_uttids[0]].append(utt2cut[sorted_uttids[0]]) - continue - - utt2prevutt = dict(zip(sorted_uttids, [sorted_uttids[1]] + sorted_uttids[:-1])) - utt2postutt = dict(zip(sorted_uttids[:-1], sorted_uttids[1:])) - for utt in sorted_uttids: - if utt in utt2prevutt: - utt2neighbors[utt].append(utt2cut[utt2prevutt[utt]]) - if utt in utt2postutt: - utt2neighbors[utt].append(utt2cut[utt2postutt[utt]]) - - def _process_ljspeech_dataset(self, utt2neighbors, utt2cut, cuts): - uttids = [cut.id for cut in cuts] - if len(uttids) == 1: - utt2neighbors[uttids[0]].append(utt2cut[uttids[0]]) - return - - utt2prevutt = dict(zip(uttids, [uttids[1]] + uttids[:-1])) - utt2postutt = dict(zip(uttids[:-1], uttids[1:])) - for utt in uttids: - prevutt, postutt = utt2prevutt.get(utt), utt2postutt.get(utt) - if prevutt and utt[:5] == prevutt[:5]: - utt2neighbors[utt].append(utt2cut[prevutt]) - if postutt and utt[:5] == postutt[:5]: - utt2neighbors[utt].append(utt2cut[postutt]) - - def _collate_features(self, cuts): - return collate_features( - cuts, executor=_get_executor(self.num_workers, executor_type=self._executor_type) - ) - - def _collate_prompts(self, cuts): - prompts_cuts = [] - for k, cut in enumerate(cuts): - prompts_cut = random.choice(self.utt2neighbors[cut.id]) - prompts_cuts.append(fastcopy(prompts_cut, id=f"{cut.id}-{str(k)}")) - - mini_duration = min([cut.duration for cut in prompts_cuts] + [3.0]) - prompts_cuts = CutSet( - cuts={k: cut for k, cut in enumerate(prompts_cuts)} - ).truncate(max_duration=mini_duration, offset_type="random", preserve_id=False) - - return collate_features( - prompts_cuts, executor=_get_executor(self.num_workers, executor_type=self._executor_type) - ) diff --git a/modules/general/scaling.py b/modules/general/scaling.py deleted file mode 100644 index de49a28f816f4cd21a037d6121f12959af54c4bd..0000000000000000000000000000000000000000 --- a/modules/general/scaling.py +++ /dev/null @@ -1,1386 +0,0 @@ -# This module is modified from https://github.com/Plachtaa/VALL-E-X/blob/3faaf8ccadb154d63b38070caf518ce9309ea0f4/modules/scaling.py - - -import logging -import random -import math -from typing import Optional, Tuple, Union - -import torch -import torch.nn as nn -from torch import Tensor - -class Transpose(nn.Identity): - """(N, T, D) -> (N, D, T)""" - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return input.transpose(1, 2) - - -class ActivationBalancerFunction(torch.autograd.Function): - @staticmethod - def forward( - ctx, - x: Tensor, - scale_factor: Tensor, - sign_factor: Optional[Tensor], - channel_dim: int, - ) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - ctx.channel_dim = channel_dim - xgt0 = x > 0 - if sign_factor is None: - ctx.save_for_backward(xgt0, scale_factor) - else: - ctx.save_for_backward(xgt0, scale_factor, sign_factor) - return x - - @staticmethod - def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]: - if len(ctx.saved_tensors) == 3: - xgt0, scale_factor, sign_factor = ctx.saved_tensors - for _ in range(ctx.channel_dim, x_grad.ndim - 1): - scale_factor = scale_factor.unsqueeze(-1) - sign_factor = sign_factor.unsqueeze(-1) - factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5) - else: - xgt0, scale_factor = ctx.saved_tensors - for _ in range(ctx.channel_dim, x_grad.ndim - 1): - scale_factor = scale_factor.unsqueeze(-1) - factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5) - neg_delta_grad = x_grad.abs() * factor - return ( - x_grad - neg_delta_grad, - None, - None, - None, - ) - - -def _compute_scale_factor( - x: Tensor, - channel_dim: int, - min_abs: float, - max_abs: float, - gain_factor: float, - max_factor: float, -) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - sum_dims = [d for d in range(x.ndim) if d != channel_dim] - x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32) - - if min_abs == 0.0: - below_threshold = 0.0 - else: - # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if - # x_abs)_mean , min_abs. - below_threshold = ( - (min_abs - x_abs_mean) * (gain_factor / min_abs) - ).clamp(min=0, max=max_factor) - - above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp( - min=0, max=max_factor - ) - - return below_threshold - above_threshold - - -def _compute_sign_factor( - x: Tensor, - channel_dim: int, - min_positive: float, - max_positive: float, - gain_factor: float, - max_factor: float, -) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - sum_dims = [d for d in range(x.ndim) if d != channel_dim] - proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims) - if min_positive == 0.0: - factor1 = 0.0 - else: - # 0 if proportion_positive >= min_positive, else can be - # as large as max_factor. - factor1 = ( - (min_positive - proportion_positive) * (gain_factor / min_positive) - ).clamp_(min=0, max=max_factor) - - if max_positive == 1.0: - factor2 = 0.0 - else: - # 0 if self.proportion_positive <= max_positive, else can be - # as large as -max_factor. - factor2 = ( - (proportion_positive - max_positive) - * (gain_factor / (1.0 - max_positive)) - ).clamp_(min=0, max=max_factor) - sign_factor = factor1 - factor2 - # require min_positive != 0 or max_positive != 1: - assert not isinstance(sign_factor, float) - return sign_factor - - -class ActivationScaleBalancerFunction(torch.autograd.Function): - """ - This object is used in class ActivationBalancer when the user specified - min_positive=0, max_positive=1, so there are no constraints on the signs - of the activations and only the absolute value has a constraint. - """ - - @staticmethod - def forward( - ctx, - x: Tensor, - sign_factor: Tensor, - scale_factor: Tensor, - channel_dim: int, - ) -> Tensor: - if channel_dim < 0: - channel_dim += x.ndim - ctx.channel_dim = channel_dim - xgt0 = x > 0 - ctx.save_for_backward(xgt0, sign_factor, scale_factor) - return x - - @staticmethod - def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]: - xgt0, sign_factor, scale_factor = ctx.saved_tensors - for _ in range(ctx.channel_dim, x_grad.ndim - 1): - sign_factor = sign_factor.unsqueeze(-1) - scale_factor = scale_factor.unsqueeze(-1) - - factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5) - neg_delta_grad = x_grad.abs() * factor - return ( - x_grad - neg_delta_grad, - None, - None, - None, - ) - - -class RandomClampFunction(torch.autograd.Function): - @staticmethod - def forward( - ctx, - x: Tensor, - min: Optional[float], - max: Optional[float], - prob: float, - reflect: float, - ) -> Tensor: - x_clamped = torch.clamp(x, min=min, max=max) - mask = torch.rand_like(x) < prob - ans = torch.where(mask, x_clamped, x) - if x.requires_grad: - ctx.save_for_backward(ans == x) - ctx.reflect = reflect - if reflect != 0.0: - ans = ans * (1.0 + reflect) - (x * reflect) - return ans - - @staticmethod - def backward( - ctx, ans_grad: Tensor - ) -> Tuple[Tensor, None, None, None, None]: - (is_same,) = ctx.saved_tensors - x_grad = ans_grad * is_same.to(ans_grad.dtype) - reflect = ctx.reflect - if reflect != 0.0: - x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect) - return x_grad, None, None, None, None - - -def random_clamp( - x: Tensor, - min: Optional[float] = None, - max: Optional[float] = None, - prob: float = 0.5, - reflect: float = 0.0, -): - return RandomClampFunction.apply(x, min, max, prob, reflect) - - -def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor: - """ - A randomized way of casting a floating point value to half precision. - """ - if x.dtype == torch.float16: - return x - x_abs = x.abs() - is_too_small = x_abs < min_abs - # for elements where is_too_small is true, random_val will contain +-min_abs with - # probability (x.abs() / min_abs), and 0.0 otherwise. [so this preserves expectations, - # for those elements]. - random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs) - return torch.where(is_too_small, random_val, x).to(torch.float16) - - -class RandomGradFunction(torch.autograd.Function): - """ - Does nothing in forward pass; in backward pass, gets rid of very small grads using - randomized approach that preserves expectations (intended to reduce roundoff). - """ - - @staticmethod - def forward(ctx, x: Tensor, min_abs: float) -> Tensor: - ctx.min_abs = min_abs - return x - - @staticmethod - def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None]: - if ans_grad.dtype == torch.float16: - return ( - random_cast_to_half( - ans_grad.to(torch.float32), min_abs=ctx.min_abs - ), - None, - ) - else: - return ans_grad, None - - -class RandomGrad(torch.nn.Module): - """ - Gets rid of very small gradients using an expectation-preserving method, intended to increase - accuracy of training when using amp (automatic mixed precision) - """ - - def __init__(self, min_abs: float = 5.0e-06): - super(RandomGrad, self).__init__() - self.min_abs = min_abs - - def forward(self, x: Tensor): - if ( - torch.jit.is_scripting() - or not self.training - or torch.jit.is_tracing() - ): - return x - else: - return RandomGradFunction.apply(x, self.min_abs) - - -class SoftmaxFunction(torch.autograd.Function): - """ - Tries to handle half-precision derivatives in a randomized way that should - be more accurate for training than the default behavior. - """ - - @staticmethod - def forward(ctx, x: Tensor, dim: int): - ans = x.softmax(dim=dim) - # if x dtype is float16, x.softmax() returns a float32 because - # (presumably) that op does not support float16, and autocast - # is enabled. - if torch.is_autocast_enabled(): - ans = ans.to(torch.float16) - ctx.save_for_backward(ans) - ctx.x_dtype = x.dtype - ctx.dim = dim - return ans - - @staticmethod - def backward(ctx, ans_grad: Tensor): - (ans,) = ctx.saved_tensors - with torch.cuda.amp.autocast(enabled=False): - ans_grad = ans_grad.to(torch.float32) - ans = ans.to(torch.float32) - x_grad = ans_grad * ans - x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True) - return x_grad, None - - -def softmax(x: Tensor, dim: int): - if torch.jit.is_scripting() or torch.jit.is_tracing(): - return x.softmax(dim) - - return SoftmaxFunction.apply(x, dim) - - -class MaxEigLimiterFunction(torch.autograd.Function): - @staticmethod - def forward( - ctx, - x: Tensor, - coeffs: Tensor, - direction: Tensor, - channel_dim: int, - grad_scale: float, - ) -> Tensor: - ctx.channel_dim = channel_dim - ctx.grad_scale = grad_scale - ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach()) - return x - - @staticmethod - def backward(ctx, x_grad, *args): - with torch.enable_grad(): - (x_orig, coeffs, new_direction) = ctx.saved_tensors - x_orig.requires_grad = True - num_channels = x_orig.shape[ctx.channel_dim] - x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels) - new_direction.requires_grad = False - x = x - x.mean(dim=0) - x_var = (x ** 2).mean() - x_residual = x - coeffs * new_direction - x_residual_var = (x_residual ** 2).mean() - # `variance_proportion` is the proportion of the variance accounted for - # by the top eigen-direction. This is to be minimized. - variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20) - variance_proportion.backward() - x_orig_grad = x_orig.grad - x_extra_grad = ( - x_orig.grad - * ctx.grad_scale - * x_grad.norm() - / (x_orig_grad.norm() + 1.0e-20) - ) - return x_grad + x_extra_grad.detach(), None, None, None, None - - -class BasicNorm(torch.nn.Module): - """ - This is intended to be a simpler, and hopefully cheaper, replacement for - LayerNorm. The observation this is based on, is that Transformer-type - networks, especially with pre-norm, sometimes seem to set one of the - feature dimensions to a large constant value (e.g. 50), which "defeats" - the LayerNorm because the output magnitude is then not strongly dependent - on the other (useful) features. Presumably the weight and bias of the - LayerNorm are required to allow it to do this. - - So the idea is to introduce this large constant value as an explicit - parameter, that takes the role of the "eps" in LayerNorm, so the network - doesn't have to do this trick. We make the "eps" learnable. - - Args: - num_channels: the number of channels, e.g. 512. - channel_dim: the axis/dimension corresponding to the channel, - interprted as an offset from the input's ndim if negative. - shis is NOT the num_channels; it should typically be one of - {-2, -1, 0, 1, 2, 3}. - eps: the initial "epsilon" that we add as ballast in: - scale = ((input_vec**2).mean() + epsilon)**-0.5 - Note: our epsilon is actually large, but we keep the name - to indicate the connection with conventional LayerNorm. - learn_eps: if true, we learn epsilon; if false, we keep it - at the initial value. - eps_min: float - eps_max: float - """ - - def __init__( - self, - num_channels: int, - channel_dim: int = -1, # CAUTION: see documentation. - eps: float = 0.25, - learn_eps: bool = True, - eps_min: float = -3.0, - eps_max: float = 3.0, - ) -> None: - super(BasicNorm, self).__init__() - self.num_channels = num_channels - self.channel_dim = channel_dim - if learn_eps: - self.eps = nn.Parameter(torch.tensor(eps).log().detach()) - else: - self.register_buffer("eps", torch.tensor(eps).log().detach()) - self.eps_min = eps_min - self.eps_max = eps_max - - def forward(self, x: Tensor) -> Tensor: - assert x.shape[self.channel_dim] == self.num_channels - eps = self.eps - if self.training and random.random() < 0.25: - # with probability 0.25, in training mode, clamp eps between the min - # and max; this will encourage it to learn parameters within the - # allowed range by making parameters that are outside the allowed - # range noisy. - - # gradients to allow the parameter to get back into the allowed - # region if it happens to exit it. - eps = eps.clamp(min=self.eps_min, max=self.eps_max) - scales = ( - torch.mean(x ** 2, dim=self.channel_dim, keepdim=True) + eps.exp() - ) ** -0.5 - return x * scales - - -def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear: - """ - Behaves like a constructor of a modified version of nn.Linear - that gives an easy way to set the default initial parameter scale. - - Args: - Accepts the standard args and kwargs that nn.Linear accepts - e.g. in_features, out_features, bias=False. - - initial_scale: you can override this if you want to increase - or decrease the initial magnitude of the module's output - (affects the initialization of weight_scale and bias_scale). - Another option, if you want to do something like this, is - to re-initialize the parameters. - """ - ans = nn.Linear(*args, **kwargs) - with torch.no_grad(): - ans.weight[:] *= initial_scale - if ans.bias is not None: - torch.nn.init.uniform_( - ans.bias, -0.1 * initial_scale, 0.1 * initial_scale - ) - return ans - - -def ScaledConv1d( - *args, - initial_scale: float = 1.0, - kernel_size: int = 3, - padding: str = "same", - **kwargs, -) -> nn.Conv1d: - """ - Behaves like a constructor of a modified version of nn.Conv1d - that gives an easy way to set the default initial parameter scale. - - Args: - Accepts the standard args and kwargs that nn.Linear accepts - e.g. in_features, out_features, bias=False. - - initial_scale: you can override this if you want to increase - or decrease the initial magnitude of the module's output - (affects the initialization of weight_scale and bias_scale). - Another option, if you want to do something like this, is - to re-initialize the parameters. - """ - ans = nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs) - with torch.no_grad(): - ans.weight[:] *= initial_scale - if ans.bias is not None: - torch.nn.init.uniform_( - ans.bias, -0.1 * initial_scale, 0.1 * initial_scale - ) - return ans - - -def TransposeScaledConv1d( - *args, - initial_scale: float = 1.0, - kernel_size: int = 3, - padding: str = "same", - **kwargs, -) -> nn.Sequential: - """ - Transpose -> ScaledConv1d - """ - return nn.Sequential( - Transpose(), - ScaledConv1d( - *args, - initial_scale=initial_scale, - kernel_size=kernel_size, - padding=padding, - **kwargs, - ), - ) - - -def ScaledConv1dTranspose( - *args, - initial_scale: float = 1.0, - kernel_size: int = 3, - padding: str = "same", - **kwargs, -) -> nn.Sequential: - """ - Transpose -> ScaledConv1d - """ - return nn.Sequential( - ScaledConv1d( - *args, - initial_scale=initial_scale, - kernel_size=kernel_size, - padding=padding, - **kwargs, - ), - Transpose(), - ) - - -def TransposeConv1d( - *args, kernel_size: int = 3, padding: str = "same", **kwargs -) -> nn.Sequential: - """ - Transpose -> Conv1d - """ - return nn.Sequential( - Transpose(), - nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), - ) - - -def Conv1dTranspose( - *args, kernel_size: int = 3, padding: str = "same", **kwargs -) -> nn.Sequential: - """ - ScaledConv1d -> Transpose - """ - return nn.Sequential( - nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), - Transpose(), - ) - - -class SRLinear(nn.Linear): - """https://arxiv.org/abs/2303.06296 - Stabilizing Transformer Training by Preventing Attention Entropy Collapse - """ - - def __init__(self, in_features, out_features, bias=True, **kwargs): - super().__init__(in_features, out_features, bias=bias, **kwargs) - self.register_buffer( - "u", nn.functional.normalize(torch.randn(in_features), dim=0) - ) - with torch.no_grad(): - sigma = self.get_sigma() - self.register_buffer("spectral_norm", sigma) - self.sigma = nn.Parameter(torch.ones(1)) - - def get_sigma(self): - with torch.no_grad(): - u = self.u - v = self.weight.mv(u) - v = nn.functional.normalize(v, dim=0) - u = self.weight.T.mv(v) - u = nn.functional.normalize(u, dim=0) - self.u.data.copy_(u) - return torch.einsum("c,cd,d->", v, self.weight, u) - - def get_weight(self): - sigma = self.get_sigma() - if self.training: - self.spectral_norm.data.copy_(sigma) - weight = (self.sigma / sigma) * self.weight - return weight - - def forward(self, x): - return nn.functional.linear(x, self.get_weight(), self.bias) - - -class SRConv1d(SRLinear): - def __init__( - self, - in_features, - out_features, - kernel_size, - stride: int = 1, - padding: str = "same", - bias: bool = True, - **kwargs, - ): - in_features = in_features * kernel_size - super().__init__(in_features, out_features, bias=bias, **kwargs) - nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5)) - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - - def forward(self, x): - in_features = self.in_features // self.kernel_size - weight = self.get_weight().view( - self.out_features, in_features, self.kernel_size - ) - return nn.functional.conv1d( - x, weight, bias=self.bias, stride=self.stride, padding=self.padding - ) - - -def TransposeSRConv1d( - *args, kernel_size: int = 3, padding: str = "same", **kwargs -) -> nn.Sequential: - """ - Transpose -> SRConv1d - """ - return nn.Sequential( - Transpose(), - SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), - ) - - -def SRConv1dTranspose( - *args, kernel_size: int = 3, padding: str = "same", **kwargs -) -> nn.Sequential: - """ - SRConv1d -> Transpose - """ - return nn.Sequential( - SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs), - Transpose(), - ) - - -class ActivationBalancer(torch.nn.Module): - """ - Modifies the backpropped derivatives of a function to try to encourage, for - each channel, that it is positive at least a proportion `threshold` of the - time. It does this by multiplying negative derivative values by up to - (1+max_factor), and positive derivative values by up to (1-max_factor), - interpolated from 1 at the threshold to those extremal values when none - of the inputs are positive. - - Args: - num_channels: the number of channels - channel_dim: the dimension/axis corresponding to the channel, e.g. - -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative. - min_positive: the minimum, per channel, of the proportion of the time - that (x > 0), below which we start to modify the derivatives. - max_positive: the maximum, per channel, of the proportion of the time - that (x > 0), above which we start to modify the derivatives. - max_factor: the maximum factor by which we modify the derivatives for - either the sign constraint or the magnitude constraint; - e.g. with max_factor=0.02, the the derivatives would be multiplied by - values in the range [0.98..1.02]. - sign_gain_factor: determines the 'gain' with which we increase the - change in gradient once the constraints on min_positive and max_positive - are violated. - scale_gain_factor: determines the 'gain' with which we increase the - change in gradient once the constraints on min_abs and max_abs - are violated. - min_abs: the minimum average-absolute-value difference from the mean - value per channel, which we allow, before we start to modify - the derivatives to prevent this. - max_abs: the maximum average-absolute-value difference from the mean - value per channel, which we allow, before we start to modify - the derivatives to prevent this. - min_prob: determines the minimum probability with which we modify the - gradients for the {min,max}_positive and {min,max}_abs constraints, - on each forward(). This is done randomly to prevent all layers - from doing it at the same time. Early in training we may use - higher probabilities than this; it will decay to this value. - """ - - def __init__( - self, - num_channels: int, - channel_dim: int, - min_positive: float = 0.05, - max_positive: float = 0.95, - max_factor: float = 0.04, - sign_gain_factor: float = 0.01, - scale_gain_factor: float = 0.02, - min_abs: float = 0.2, - max_abs: float = 100.0, - min_prob: float = 0.1, - ): - super(ActivationBalancer, self).__init__() - self.num_channels = num_channels - self.channel_dim = channel_dim - self.min_positive = min_positive - self.max_positive = max_positive - self.max_factor = max_factor - self.min_abs = min_abs - self.max_abs = max_abs - self.min_prob = min_prob - self.sign_gain_factor = sign_gain_factor - self.scale_gain_factor = scale_gain_factor - - # count measures how many times the forward() function has been called. - # We occasionally sync this to a tensor called `count`, that exists to - # make sure it is synced to disk when we load and save the model. - self.cpu_count = 0 - self.register_buffer("count", torch.tensor(0, dtype=torch.int64)) - - def forward(self, x: Tensor) -> Tensor: - if ( - torch.jit.is_scripting() - or not x.requires_grad - or torch.jit.is_tracing() - ): - return _no_op(x) - - count = self.cpu_count - self.cpu_count += 1 - - if random.random() < 0.01: - # Occasionally sync self.cpu_count with self.count. - # count affects the decay of 'prob'. don't do this on every iter, - # because syncing with the GPU is slow. - self.cpu_count = max(self.cpu_count, self.count.item()) - self.count.fill_(self.cpu_count) - - # the prob of doing some work exponentially decreases from 0.5 till it hits - # a floor at min_prob (==0.1, by default) - prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0))) - - if random.random() < prob: - sign_gain_factor = 0.5 - if self.min_positive != 0.0 or self.max_positive != 1.0: - sign_factor = _compute_sign_factor( - x, - self.channel_dim, - self.min_positive, - self.max_positive, - gain_factor=self.sign_gain_factor / prob, - max_factor=self.max_factor, - ) - else: - sign_factor = None - - scale_factor = _compute_scale_factor( - x.detach(), - self.channel_dim, - min_abs=self.min_abs, - max_abs=self.max_abs, - gain_factor=self.scale_gain_factor / prob, - max_factor=self.max_factor, - ) - return ActivationBalancerFunction.apply( - x, - scale_factor, - sign_factor, - self.channel_dim, - ) - else: - return _no_op(x) - - -def penalize_abs_values_gt(x: Tensor, limit: float, penalty: float) -> Tensor: - """ - Returns x unmodified, but in backprop will put a penalty for the excess of - the absolute values of elements of x over the limit "limit". E.g. if - limit == 10.0, then if x has any values over 10 it will get a penalty. - - Caution: the value of this penalty will be affected by grad scaling used - in automatic mixed precision training. For this reasons we use this, - it shouldn't really matter, or may even be helpful; we just use this - to disallow really implausible values of scores to be given to softmax. - """ - x_sign = x.sign() - over_limit = (x.abs() - limit) > 0 - # The following is a memory efficient way to penalize the absolute values of - # x that's over the limit. (The memory efficiency comes when you think - # about which items torch needs to cache for the autograd, and which ones it - # can throw away). The numerical value of aux_loss as computed here will - # actually be larger than it should be, by limit * over_limit.sum(), but it - # has the same derivative as the real aux_loss which is penalty * (x.abs() - - # limit).relu(). - aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x) - # note: we don't do sum() here on aux)_loss, but it's as if we had done - # sum() due to how with_loss() works. - x = with_loss(x, aux_loss) - # you must use x for something, or this will be ineffective. - return x - - -def _diag(x: Tensor): # like .diag(), but works for tensors with 3 dims. - if x.ndim == 2: - return x.diag() - else: - (batch, dim, dim) = x.shape - x = x.reshape(batch, dim * dim) - x = x[:, :: dim + 1] - assert x.shape == (batch, dim) - return x - - -def _whitening_metric(x: Tensor, num_groups: int): - """ - Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of - of the centered feature covariance are the same within each group's covariance matrix - and also between groups. - Args: - x: a Tensor of shape (*, num_channels) - num_groups: the number of groups of channels, a number >=1 that divides num_channels - Returns: - Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and - greater than 1.0 otherwise. - """ - assert x.dtype != torch.float16 - x = x.reshape(-1, x.shape[-1]) - (num_frames, num_channels) = x.shape - assert num_channels % num_groups == 0 - channels_per_group = num_channels // num_groups - x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1) - # x now has shape (num_groups, num_frames, channels_per_group) - # subtract the mean so we use the centered, not uncentered, covariance. - # My experience has been that when we "mess with the gradients" like this, - # it's better not do anything that tries to move the mean around, because - # that can easily cause instability. - x = x - x.mean(dim=1, keepdim=True) - # x_covar: (num_groups, channels_per_group, channels_per_group) - x_covar = torch.matmul(x.transpose(1, 2), x) - x_covar_mean_diag = _diag(x_covar).mean() - # the following expression is what we'd get if we took the matrix product - # of each covariance and measured the mean of its trace, i.e. - # the same as _diag(torch.matmul(x_covar, x_covar)).mean(). - x_covarsq_mean_diag = (x_covar ** 2).sum() / ( - num_groups * channels_per_group - ) - # this metric will be >= 1.0; the larger it is, the less 'white' the data was. - metric = x_covarsq_mean_diag / (x_covar_mean_diag ** 2 + 1.0e-20) - return metric - - -class WhiteningPenaltyFunction(torch.autograd.Function): - @staticmethod - def forward( - ctx, - x: Tensor, - num_groups: int, - whitening_limit: float, - grad_scale: float, - ) -> Tensor: - ctx.save_for_backward(x) - ctx.num_groups = num_groups - ctx.whitening_limit = whitening_limit - ctx.grad_scale = grad_scale - return x - - @staticmethod - def backward(ctx, x_grad: Tensor): - (x_orig,) = ctx.saved_tensors - with torch.enable_grad(): - with torch.cuda.amp.autocast(enabled=False): - x_detached = x_orig.to(torch.float32).detach() - x_detached.requires_grad = True - - metric = _whitening_metric(x_detached, ctx.num_groups) - - if random.random() < 0.005 or __name__ == "__main__": - logging.info( - f"Whitening: num_groups={ctx.num_groups}, num_channels={x_orig.shape[-1]}, " - f"metric={metric.item():.2f} vs. limit={ctx.whitening_limit}" - ) - - (metric - ctx.whitening_limit).relu().backward() - penalty_grad = x_detached.grad - scale = ctx.grad_scale * ( - x_grad.to(torch.float32).norm() - / (penalty_grad.norm() + 1.0e-20) - ) - penalty_grad = penalty_grad * scale - return x_grad + penalty_grad.to(x_grad.dtype), None, None, None - - -class Whiten(nn.Module): - def __init__( - self, - num_groups: int, - whitening_limit: float, - prob: Union[float, Tuple[float, float]], - grad_scale: float, - ): - """ - Args: - num_groups: the number of groups to divide the channel dim into before - whitening. We will attempt to make the feature covariance - within each group, after mean subtraction, as "white" as possible, - while having the same trace across all groups. - whitening_limit: a value greater than 1.0, that dictates how much - freedom we have to violate the constraints. 1.0 would mean perfectly - white, with exactly the same trace across groups; larger values - give more freedom. E.g. 2.0. - prob: the probability with which we apply the gradient modification - (also affects the grad scale). May be supplied as a float, - or as a pair (min_prob, max_prob) - - grad_scale: determines the scale on the gradient term from this object, - relative to the rest of the gradient on the attention weights. - E.g. 0.02 (you may want to use smaller values than this if prob is large) - """ - super(Whiten, self).__init__() - assert num_groups >= 1 - assert whitening_limit >= 1 - assert grad_scale >= 0 - self.num_groups = num_groups - self.whitening_limit = whitening_limit - if isinstance(prob, float): - assert 0 < prob <= 1 - self.prob = prob - else: - (self.min_prob, self.max_prob) = prob - assert 0 < self.min_prob < self.max_prob <= 1 - self.prob = self.max_prob - - self.grad_scale = grad_scale - - def forward(self, x: Tensor) -> Tensor: - """ - In the forward pass, this function just returns the input unmodified. - In the backward pass, it will modify the gradients to ensure that the - distribution in each group has close to (lambda times I) as the covariance - after mean subtraction, with the same lambda across groups. - For whitening_limit > 1, there will be more freedom to violate this - constraint. - - Args: - x: the input of shape (*, num_channels) - - Returns: - x, unmodified. You should make sure - you use the returned value, or the graph will be freed - and nothing will happen in backprop. - """ - if ( - not x.requires_grad - or random.random() > self.prob - or self.grad_scale == 0 - ): - return _no_op(x) - else: - if hasattr(self, "min_prob") and random.random() < 0.25: - # occasionally switch between min_prob and max_prob, based on whether - # we are above or below the threshold. - if ( - _whitening_metric(x.to(torch.float32), self.num_groups) - > self.whitening_limit - ): - # there would be a change to the grad. - self.prob = self.max_prob - else: - self.prob = self.min_prob - - return WhiteningPenaltyFunction.apply( - x, self.num_groups, self.whitening_limit, self.grad_scale - ) - - -class WithLoss(torch.autograd.Function): - @staticmethod - def forward(ctx, x: Tensor, y: Tensor): - ctx.y_shape = y.shape - return x - - @staticmethod - def backward(ctx, ans_grad: Tensor): - return ans_grad, torch.ones( - ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device - ) - - -def with_loss(x, y): - if torch.jit.is_scripting() or torch.jit.is_tracing(): - return x - # returns x but adds y.sum() to the loss function. - return WithLoss.apply(x, y) - - -def _no_op(x: Tensor) -> Tensor: - if torch.jit.is_scripting() or torch.jit.is_tracing(): - return x - else: - # a no-op function that will have a node in the autograd graph, - # to avoid certain bugs relating to backward hooks - return x.chunk(1, dim=-1)[0] - - -class Identity(torch.nn.Module): - def __init__(self): - super(Identity, self).__init__() - - def forward(self, x): - return _no_op(x) - - -class MaxEig(torch.nn.Module): - """ - Modifies the backpropped derivatives of a function to try to discourage - that any given direction in activation space accounts for more than - a specified proportion of the covariance (e.g. 0.2). - - - Args: - num_channels: the number of channels - channel_dim: the dimension/axis corresponding to the channel, e.g. - -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative. - max_var_per_eig: the maximum proportion of the variance of the - features/channels, after mean subtraction, that can come from - any given eigenvalue. - min_prob: the minimum probability with which we apply this during any invocation - of forward(), assuming last time we applied the constraint it was - not active; supplied for speed. - scale: determines the scale with which we modify the gradients, relative - to the existing / unmodified gradients - """ - - def __init__( - self, - num_channels: int, - channel_dim: int, - max_var_per_eig: float = 0.2, - min_prob: float = 0.01, - scale: float = 0.01, - ): - super(MaxEig, self).__init__() - self.num_channels = num_channels - self.channel_dim = channel_dim - self.scale = scale - assert max_var_per_eig == 0.0 or max_var_per_eig > 1.0 / num_channels - self.max_var_per_eig = max_var_per_eig - - # we figure out the dominant direction using the power method: starting with - # a random vector, keep multiplying by the covariance and renormalizing. - with torch.no_grad(): - # arbitrary.. would use randn() but want to leave the rest of the model's - # random parameters unchanged for comparison - direction = torch.arange(num_channels).to(torch.float) - direction = direction / direction.norm() - self.register_buffer("max_eig_direction", direction) - - self.min_prob = min_prob - # cur_prob is the current probability we'll use to apply the ActivationBalancer. - # We'll regress this towards prob, each tiem we try to apply it and it is not - # active. - self.cur_prob = 1.0 - - def forward(self, x: Tensor) -> Tensor: - if ( - torch.jit.is_scripting() - or self.max_var_per_eig <= 0 - or random.random() > self.cur_prob - or torch.jit.is_tracing() - ): - return _no_op(x) - - with torch.cuda.amp.autocast(enabled=False): - eps = 1.0e-20 - orig_x = x - x = x.to(torch.float32) - with torch.no_grad(): - x = x.transpose(self.channel_dim, -1).reshape( - -1, self.num_channels - ) - x = x - x.mean(dim=0) - new_direction, coeffs = self._find_direction_coeffs( - x, self.max_eig_direction - ) - x_var = (x ** 2).mean() - x_residual = x - coeffs * new_direction - x_residual_var = (x_residual ** 2).mean() - - # `variance_proportion` is the proportion of the variance accounted for - # by the top eigen-direction. - variance_proportion = (x_var - x_residual_var) / ( - x_var + 1.0e-20 - ) - - # ensure new direction is nonzero even if x == 0, by including `direction`. - self._set_direction( - 0.1 * self.max_eig_direction + new_direction - ) - - if random.random() < 0.01 or __name__ == "__main__": - logging.info( - f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}" - ) - - if variance_proportion >= self.max_var_per_eig: - # The constraint is active. Note, we should quite rarely - # reach here, only near the beginning of training if we are - # starting to diverge, should this constraint be active. - cur_prob = self.cur_prob - self.cur_prob = ( - 1.0 # next time, do the update with probability 1.0. - ) - return MaxEigLimiterFunction.apply( - orig_x, coeffs, new_direction, self.channel_dim, self.scale - ) - else: - # let self.cur_prob exponentially approach self.min_prob, as - # long as the constraint is inactive. - self.cur_prob = 0.75 * self.cur_prob + 0.25 * self.min_prob - return orig_x - - def _set_direction(self, direction: Tensor): - """ - Sets self.max_eig_direction to a normalized version of `direction` - """ - direction = direction.detach() - direction = direction / direction.norm() - direction_sum = direction.sum().item() - if direction_sum - direction_sum == 0: # no inf/nan - self.max_eig_direction[:] = direction - else: - logging.info( - f"Warning: sum of direction in MaxEig is {direction_sum}, " - "num_channels={self.num_channels}, channel_dim={self.channel_dim}" - ) - - def _find_direction_coeffs( - self, x: Tensor, prev_direction: Tensor - ) -> Tuple[Tensor, Tensor, Tensor]: - """ - Figure out (an approximation to) the proportion of the variance of a set of - feature vectors that can be attributed to the top eigen-direction. - Args: - x: a Tensor of shape (num_frames, num_channels), with num_frames > 1. - prev_direction: a Tensor of shape (num_channels,), that is our previous estimate - of the top eigen-direction, or a random direction if this is the first - iteration. Does not have to be normalized, but should be nonzero. - - Returns: (cur_direction, coeffs), where: - cur_direction: a Tensor of shape (num_channels,) that is the current - estimate of the top eigen-direction. - coeffs: a Tensor of shape (num_frames, 1) that minimizes, or - approximately minimizes, (x - coeffs * cur_direction).norm() - """ - (num_frames, num_channels) = x.shape - assert num_channels > 1 and num_frames > 1 - assert prev_direction.shape == (num_channels,) - # `coeffs` are the coefficients of `prev_direction` in x. - # actually represent the coeffs up to a constant positive factor. - coeffs = (x * prev_direction).sum(dim=1, keepdim=True) + 1.0e-10 - cur_direction = (x * coeffs).sum(dim=0) / ( - (coeffs ** 2).sum() + 1.0e-20 - ) - return cur_direction, coeffs - - -class DoubleSwishFunction(torch.autograd.Function): - """ - double_swish(x) = x * torch.sigmoid(x-1) - This is a definition, originally motivated by its close numerical - similarity to swish(swish(x)), where swish(x) = x * sigmoid(x). - - Memory-efficient derivative computation: - double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1) - double_swish'(x) = d/dx double_swish(x) = x * s'(x) + x' * s(x) = x * s'(x) + s(x). - Now, s'(x) = s(x) * (1-s(x)). - double_swish'(x) = x * s'(x) + s(x). - = x * s(x) * (1-s(x)) + s(x). - = double_swish(x) * (1-s(x)) + s(x) - ... so we just need to remember s(x) but not x itself. - """ - - @staticmethod - def forward(ctx, x: Tensor) -> Tensor: - requires_grad = x.requires_grad - x_dtype = x.dtype - if x.dtype == torch.float16: - x = x.to(torch.float32) - - s = torch.sigmoid(x - 1.0) - y = x * s - - if requires_grad: - deriv = y * (1 - s) + s - # notes on derivative of x * sigmoid(x - 1): - # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29 - # min \simeq -0.043638. Take floor as -0.043637 so it's a lower bund - # max \simeq 1.1990. Take ceil to be 1.2 so it's an upper bound. - # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which - # floors), should be expectation-preserving. - floor = -0.043637 - ceil = 1.2 - d_scaled = (deriv - floor) * ( - 255.0 / (ceil - floor) - ) + torch.rand_like(deriv) - if __name__ == "__main__": - # for self-testing only. - assert d_scaled.min() >= 0.0 - assert d_scaled.max() < 256.0 - d_int = d_scaled.to(torch.uint8) - ctx.save_for_backward(d_int) - if x.dtype == torch.float16 or torch.is_autocast_enabled(): - y = y.to(torch.float16) - return y - - @staticmethod - def backward(ctx, y_grad: Tensor) -> Tensor: - (d,) = ctx.saved_tensors - # the same constants as used in forward pass. - floor = -0.043637 - ceil = 1.2 - d = d * ((ceil - floor) / 255.0) + floor - return y_grad * d - - -class DoubleSwish(torch.nn.Module): - def forward(self, x: Tensor) -> Tensor: - """Return double-swish activation function which is an approximation to Swish(Swish(x)), - that we approximate closely with x * sigmoid(x-1). - """ - if torch.jit.is_scripting() or torch.jit.is_tracing(): - return x * torch.sigmoid(x - 1.0) - return DoubleSwishFunction.apply(x) - - -def BalancedDoubleSwish( - d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25 -) -> nn.Sequential: - """ - ActivationBalancer -> DoubleSwish - """ - balancer = ActivationBalancer( - d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob - ) - return nn.Sequential( - balancer, - DoubleSwish(), - ) - - -def _test_max_eig(): - for proportion in [0.1, 0.5, 10.0]: - logging.info(f"proportion = {proportion}") - x = torch.randn(100, 128) - direction = torch.randn(128) - coeffs = torch.randn(100, 1) - x += proportion * direction * coeffs - - x.requires_grad = True - - num_channels = 128 - m = MaxEig( - num_channels, 1, 0.5, scale=0.1 # channel_dim # max_var_per_eig - ) # grad_scale - - for _ in range(4): - y = m(x) - - y_grad = torch.randn_like(x) - y.backward(gradient=y_grad) - - if proportion < 0.2: - assert torch.allclose(x.grad, y_grad, atol=1.0e-02) - elif proportion > 1.0: - assert not torch.allclose(x.grad, y_grad) - - -def _test_whiten(): - for proportion in [0.1, 0.5, 10.0]: - logging.info(f"_test_whiten(): proportion = {proportion}") - x = torch.randn(100, 128) - direction = torch.randn(128) - coeffs = torch.randn(100, 1) - x += proportion * direction * coeffs - - x.requires_grad = True - - num_channels = 128 - m = Whiten( - 1, 5.0, prob=1.0, grad_scale=0.1 # num_groups # whitening_limit, - ) # grad_scale - - for _ in range(4): - y = m(x) - - y_grad = torch.randn_like(x) - y.backward(gradient=y_grad) - - if proportion < 0.2: - assert torch.allclose(x.grad, y_grad) - elif proportion > 1.0: - assert not torch.allclose(x.grad, y_grad) - - -def _test_activation_balancer_sign(): - probs = torch.arange(0, 1, 0.01) - N = 1000 - x = 1.0 * ( - (2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0 - ) - x = x.detach() - x.requires_grad = True - m = ActivationBalancer( - probs.numel(), - channel_dim=0, - min_positive=0.05, - max_positive=0.95, - max_factor=0.2, - min_abs=0.0, - ) - - y_grad = torch.sign(torch.randn(probs.numel(), N)) - - y = m(x) - y.backward(gradient=y_grad) - print("_test_activation_balancer_sign: x = ", x) - print("_test_activation_balancer_sign: y grad = ", y_grad) - print("_test_activation_balancer_sign: x grad = ", x.grad) - - -def _test_activation_balancer_magnitude(): - magnitudes = torch.arange(0, 1, 0.01) - N = 1000 - x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze( - -1 - ) - x = x.detach() - x.requires_grad = True - m = ActivationBalancer( - magnitudes.numel(), - channel_dim=0, - min_positive=0.0, - max_positive=1.0, - max_factor=0.2, - min_abs=0.2, - max_abs=0.8, - min_prob=1.0, - ) - - y_grad = torch.sign(torch.randn(magnitudes.numel(), N)) - - y = m(x) - y.backward(gradient=y_grad) - print("_test_activation_balancer_magnitude: x = ", x) - print("_test_activation_balancer_magnitude: y grad = ", y_grad) - print("_test_activation_balancer_magnitude: x grad = ", x.grad) - - -def _test_basic_norm(): - num_channels = 128 - m = BasicNorm(num_channels=num_channels, channel_dim=1) - - x = torch.randn(500, num_channels) - - y = m(x) - - assert y.shape == x.shape - x_rms = (x ** 2).mean().sqrt() - y_rms = (y ** 2).mean().sqrt() - print("x rms = ", x_rms) - print("y rms = ", y_rms) - assert y_rms < x_rms - assert y_rms > 0.5 * x_rms - - -def _test_double_swish_deriv(): - x = torch.randn(10, 12, dtype=torch.double) * 3.0 - x.requires_grad = True - m = DoubleSwish() - - tol = (1.2 - (-0.043637)) / 255.0 - torch.autograd.gradcheck(m, x, atol=tol) - - # for self-test. - x = torch.randn(1000, 1000, dtype=torch.double) * 3.0 - x.requires_grad = True - y = m(x) - - -def _test_softmax(): - a = torch.randn(2, 10, dtype=torch.float64) - b = a.clone() - a.requires_grad = True - b.requires_grad = True - a.softmax(dim=1)[:, 0].sum().backward() - print("a grad = ", a.grad) - softmax(b, dim=1)[:, 0].sum().backward() - print("b grad = ", b.grad) - assert torch.allclose(a.grad, b.grad) - - -if __name__ == "__main__": - logging.getLogger().setLevel(logging.INFO) - torch.set_num_threads(1) - torch.set_num_interop_threads(1) - _test_softmax() - _test_whiten() - _test_max_eig() - _test_activation_balancer_sign() - _test_activation_balancer_magnitude() - _test_basic_norm() - _test_double_swish_deriv() diff --git a/modules/general/utils.py b/modules/general/utils.py deleted file mode 100644 index 38c94dfbb9859661322f3d2e8204b423fffae736..0000000000000000000000000000000000000000 --- a/modules/general/utils.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn - - -def normalization(channels: int, groups: int = 32): - r"""Make a standard normalization layer, i.e. GroupNorm. - - Args: - channels: number of input channels. - groups: number of groups for group normalization. - - Returns: - a ``nn.Module`` for normalization. - """ - assert groups > 0, f"invalid number of groups: {groups}" - return nn.GroupNorm(groups, channels) - - -def Linear(*args, **kwargs): - r"""Wrapper of ``nn.Linear`` with kaiming_normal_ initialization.""" - layer = nn.Linear(*args, **kwargs) - nn.init.kaiming_normal_(layer.weight) - return layer - - -def Conv1d(*args, **kwargs): - r"""Wrapper of ``nn.Conv1d`` with kaiming_normal_ initialization.""" - layer = nn.Conv1d(*args, **kwargs) - nn.init.kaiming_normal_(layer.weight) - return layer - - -def Conv2d(*args, **kwargs): - r"""Wrapper of ``nn.Conv2d`` with kaiming_normal_ initialization.""" - layer = nn.Conv2d(*args, **kwargs) - nn.init.kaiming_normal_(layer.weight) - return layer - - -def ConvNd(dims: int = 1, *args, **kwargs): - r"""Wrapper of N-dimension convolution with kaiming_normal_ initialization. - - Args: - dims: number of dimensions of the convolution. - """ - if dims == 1: - return Conv1d(*args, **kwargs) - elif dims == 2: - return Conv2d(*args, **kwargs) - else: - raise ValueError(f"invalid number of dimensions: {dims}") - - -def zero_module(module: nn.Module): - r"""Zero out the parameters of a module and return it.""" - nn.init.zeros_(module.weight) - nn.init.zeros_(module.bias) - return module - - -def scale_module(module: nn.Module, scale): - r"""Scale the parameters of a module and return it.""" - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def mean_flat(tensor: torch.Tensor): - r"""Take the mean over all non-batch dimensions.""" - return tensor.mean(dim=tuple(range(1, tensor.dim()))) - - -def append_dims(x, target_dims): - r"""Appends dimensions to the end of a tensor until - it has target_dims dimensions. - """ - dims_to_append = target_dims - x.dim() - if dims_to_append < 0: - raise ValueError( - f"input has {x.dim()} dims but target_dims is {target_dims}, which is less" - ) - return x[(...,) + (None,) * dims_to_append] - - -def append_zero(x, count=1): - r"""Appends ``count`` zeros to the end of a tensor along the last dimension.""" - assert count > 0, f"invalid count: {count}" - return torch.cat([x, x.new_zeros((*x.size()[:-1], count))], dim=-1) - - -class Transpose(nn.Identity): - """(N, T, D) -> (N, D, T)""" - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return input.transpose(1, 2) diff --git a/modules/monotonic_align/__init__.py b/modules/monotonic_align/__init__.py deleted file mode 100644 index 784d9081ca1aa1355b893dbd91d159d6f911cb4b..0000000000000000000000000000000000000000 --- a/modules/monotonic_align/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -# This code from https://github.com/jaywalnut310/vits/ - -import numpy as np -import torch -from .monotonic_align.core import maximum_path_c - - -def maximum_path(neg_cent, mask): - """Cython optimized version. - neg_cent: [b, t_t, t_s] - mask: [b, t_t, t_s] - """ - device = neg_cent.device - dtype = neg_cent.dtype - neg_cent = neg_cent.data.cpu().numpy().astype(np.float32) - path = np.zeros(neg_cent.shape, dtype=np.int32) - - t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32) - t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32) - maximum_path_c(path, neg_cent, t_t_max, t_s_max) - return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/modules/monotonic_align/core.pyx b/modules/monotonic_align/core.pyx deleted file mode 100644 index bfaabd4d21c2299cdd978f0cc0caefa20ad186e5..0000000000000000000000000000000000000000 --- a/modules/monotonic_align/core.pyx +++ /dev/null @@ -1,42 +0,0 @@ -cimport cython -from cython.parallel import prange - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil: - cdef int x - cdef int y - cdef float v_prev - cdef float v_cur - cdef float tmp - cdef int index = t_x - 1 - - for y in range(t_y): - for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): - if x == y: - v_cur = max_neg_val - else: - v_cur = value[y-1, x] - if x == 0: - if y == 0: - v_prev = 0. - else: - v_prev = max_neg_val - else: - v_prev = value[y-1, x-1] - value[y, x] += max(v_prev, v_cur) - - for y in range(t_y - 1, -1, -1): - path[y, index] = 1 - if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]): - index = index - 1 - - -@cython.boundscheck(False) -@cython.wraparound(False) -cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil: - cdef int b = paths.shape[0] - cdef int i - for i in prange(b, nogil=True): - maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i]) diff --git a/modules/monotonic_align/setup.py b/modules/monotonic_align/setup.py deleted file mode 100644 index 773fafd96fb0f1bac88037187542c84eccb26ef5..0000000000000000000000000000000000000000 --- a/modules/monotonic_align/setup.py +++ /dev/null @@ -1,22 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/jaywalnut310/vits/ - -from distutils.core import setup, Extension -from Cython.Build import cythonize -import numpy - -extension = Extension( - name="monotonic_align.core", - sources=["core.pyx"], - include_dirs=[numpy.get_include()], - # Define additional arguments if needed -) - -setup( - name="monotonic_align", - ext_modules=cythonize([extension]), -) diff --git a/modules/neural_source_filter/__init__.py b/modules/neural_source_filter/__init__.py deleted file mode 100644 index aa2749078427c312fa046f354ded858bae82f666..0000000000000000000000000000000000000000 --- a/modules/neural_source_filter/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .sine_excitation import * diff --git a/modules/neural_source_filter/sine_excitation.py b/modules/neural_source_filter/sine_excitation.py deleted file mode 100644 index 08b72b2fc4f5e3065e32b2891bdd7f510c727e70..0000000000000000000000000000000000000000 --- a/modules/neural_source_filter/sine_excitation.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -#################### NSF #################### - -import torch - -import numpy as np -import torch.nn as nn -import torch.nn.functional as F - -# This code is adopted from Xin Wang's NSF under the MIT License -# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts - - -class SineGen(nn.Module): - def __init__( - self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0 - ): - super(SineGen, self).__init__() - self.amp = amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = harmonic_num + 1 - self.fs = fs - self.voice_threshold = voiced_threshold - - def _f0toUnvoiced(self, f0): - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voice_threshold) - return uv - - @torch.no_grad() - def forward(self, f0, upp): - f0 = f0.unsqueeze(-1) - fn = torch.multiply( - f0, torch.arange(1, self.dim + 1, device=f0.device).reshape(1, 1, -1) - ) - rad_values = (fn / self.fs) % 1 - rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - is_half = rad_values.dtype is not torch.float32 - tmp_over_one = torch.cumsum(rad_values.double(), 1) - if is_half: - tmp_over_one = tmp_over_one.half() - else: - tmp_over_one = tmp_over_one.float() - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * (-1.0) - rad_values = rad_values.double() - cumsum_shift = cumsum_shift.double() - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - if is_half: - sine_waves = sine_waves.half() - else: - sine_waves = sine_waves.float() - sine_waves = sine_waves * self.amp - uv = self._f0toUnvoiced(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise diff --git a/modules/norms/__init__.py b/modules/norms/__init__.py deleted file mode 100644 index d8c2d90b9abd8375822d40e0b9714e6eab581f5a..0000000000000000000000000000000000000000 --- a/modules/norms/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .norm import AdaptiveLayerNorm, LayerNorm, BalancedBasicNorm, IdentityNorm \ No newline at end of file diff --git a/modules/norms/norm.py b/modules/norms/norm.py deleted file mode 100644 index 4f74ecd7a43c3cd9419a60c0acd1f439975c98b4..0000000000000000000000000000000000000000 --- a/modules/norms/norm.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import copy -import numbers -from typing import Any, List, Tuple, Union - -import torch -from torch import Tensor, nn -from torch.nn import functional as F - -from modules.general.scaling import ActivationBalancer -from modules.general.scaling import BasicNorm as _BasicNorm - - -_shape_t = Union[int, List[int], torch.Size] - - -class LayerNorm(nn.Module): - __constants__ = ["normalized_shape", "eps", "elementwise_affine"] - normalized_shape: Tuple[int, ...] - eps: float - elementwise_affine: bool - - def __init__( - self, - normalized_shape: _shape_t, - eps: float = 1e-5, - elementwise_affine: bool = True, - device=None, - dtype=None, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(LayerNorm, self).__init__() - if isinstance(normalized_shape, numbers.Integral): - normalized_shape = (normalized_shape,) - self.normalized_shape = tuple(normalized_shape) - self.eps = eps - self.elementwise_affine = elementwise_affine - if self.elementwise_affine: - self.weight = nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - self.bias = nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - else: - self.register_parameter("weight", None) - self.register_parameter("bias", None) - - self.reset_parameters() - - def reset_parameters(self) -> None: - if self.elementwise_affine: - nn.init.ones_(self.weight) - nn.init.zeros_(self.bias) - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - output = F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps) - return output, embedding - - assert embedding is None - return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps) - - - def extra_repr(self) -> str: - return ( - "{normalized_shape}, eps={eps}, " - "elementwise_affine={elementwise_affine}".format(**self.__dict__) - ) - - -class AdaptiveLayerNorm(nn.Module): - r"""Adaptive Layer Normalization""" - - def __init__(self, d_model, norm) -> None: - super(AdaptiveLayerNorm, self).__init__() - self.project_layer = nn.Linear(d_model, 2 * d_model) - self.norm = norm - self.d_model = d_model - self.eps = self.norm.eps - - def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - weight, bias = torch.split( - self.project_layer(embedding), - split_size_or_sections=self.d_model, - dim=-1, - ) - return (weight * self.norm(input) + bias, embedding) - - weight, bias = torch.split( - self.project_layer(embedding), - split_size_or_sections=self.d_model, - dim=-1, - ) - return weight * self.norm(input) + bias - - -class BasicNorm(_BasicNorm): - def __init__( - self, - d_model: int, - eps: float = 1e-5, - device=None, - dtype=None, - ): - super(BasicNorm, self).__init__(d_model, eps=eps) - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - return ( - super(BasicNorm, self).forward(input), - embedding, - ) - - assert embedding is None - return super(BasicNorm, self).forward(input) - - -class BalancedBasicNorm(nn.Module): - def __init__( - self, - d_model: int, - eps: float = 1e-5, - device=None, - dtype=None, - ): - super(BalancedBasicNorm, self).__init__() - self.balancer = ActivationBalancer( - d_model, - channel_dim=-1, - min_positive=0.45, - max_positive=0.55, - max_abs=6.0, - ) - self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype) - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - input, embedding = input - return self.norm((self.balancer(input), embedding)) - - assert embedding is None - return self.norm(self.balancer(input)) - - -class IdentityNorm(nn.Module): - def __init__( - self, - d_model: int, - eps: float = 1e-5, - device=None, - dtype=None, - ) -> None: - super(IdentityNorm, self).__init__() - - def forward(self, input: Tensor, embedding: Any = None) -> Tensor: - if isinstance(input, tuple): - return input - - assert embedding is None - return input - diff --git a/modules/transformer/Constants.py b/modules/transformer/Constants.py deleted file mode 100644 index 5bd586f3185114b7ab22f9a2479e93088d74ced0..0000000000000000000000000000000000000000 --- a/modules/transformer/Constants.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -PAD = 0 -UNK = 1 -BOS = 2 -EOS = 3 - -PAD_WORD = "" -UNK_WORD = "" -BOS_WORD = "" -EOS_WORD = "" diff --git a/modules/transformer/Layers.py b/modules/transformer/Layers.py deleted file mode 100644 index 915b86a7f434456f24643e28fd93813bd6e13d52..0000000000000000000000000000000000000000 --- a/modules/transformer/Layers.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -from torch.nn import functional as F -from .SubLayers import MultiHeadAttention, PositionwiseFeedForward - - -class FFTBlock(torch.nn.Module): - """FFT Block""" - - def __init__(self, d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=0.1): - super(FFTBlock, self).__init__() - self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout) - self.pos_ffn = PositionwiseFeedForward( - d_model, d_inner, kernel_size, dropout=dropout - ) - - def forward(self, enc_input, mask=None, slf_attn_mask=None): - enc_output, enc_slf_attn = self.slf_attn( - enc_input, enc_input, enc_input, mask=slf_attn_mask - ) - enc_output = enc_output.masked_fill(mask.unsqueeze(-1), 0) - - enc_output = self.pos_ffn(enc_output) - enc_output = enc_output.masked_fill(mask.unsqueeze(-1), 0) - - return enc_output, enc_slf_attn - - -class ConvNorm(torch.nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size=1, - stride=1, - padding=None, - dilation=1, - bias=True, - w_init_gain="linear", - ): - super(ConvNorm, self).__init__() - - if padding is None: - assert kernel_size % 2 == 1 - padding = int(dilation * (kernel_size - 1) / 2) - - self.conv = torch.nn.Conv1d( - in_channels, - out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - bias=bias, - ) - - def forward(self, signal): - conv_signal = self.conv(signal) - - return conv_signal - - -class PostNet(nn.Module): - """ - PostNet: Five 1-d convolution with 512 channels and kernel size 5 - """ - - def __init__( - self, - n_mel_channels=80, - postnet_embedding_dim=512, - postnet_kernel_size=5, - postnet_n_convolutions=5, - ): - super(PostNet, self).__init__() - self.convolutions = nn.ModuleList() - - self.convolutions.append( - nn.Sequential( - ConvNorm( - n_mel_channels, - postnet_embedding_dim, - kernel_size=postnet_kernel_size, - stride=1, - padding=int((postnet_kernel_size - 1) / 2), - dilation=1, - w_init_gain="tanh", - ), - nn.BatchNorm1d(postnet_embedding_dim), - ) - ) - - for i in range(1, postnet_n_convolutions - 1): - self.convolutions.append( - nn.Sequential( - ConvNorm( - postnet_embedding_dim, - postnet_embedding_dim, - kernel_size=postnet_kernel_size, - stride=1, - padding=int((postnet_kernel_size - 1) / 2), - dilation=1, - w_init_gain="tanh", - ), - nn.BatchNorm1d(postnet_embedding_dim), - ) - ) - - self.convolutions.append( - nn.Sequential( - ConvNorm( - postnet_embedding_dim, - n_mel_channels, - kernel_size=postnet_kernel_size, - stride=1, - padding=int((postnet_kernel_size - 1) / 2), - dilation=1, - w_init_gain="linear", - ), - nn.BatchNorm1d(n_mel_channels), - ) - ) - - def forward(self, x): - x = x.contiguous().transpose(1, 2) - - for i in range(len(self.convolutions) - 1): - x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training) - x = F.dropout(self.convolutions[-1](x), 0.5, self.training) - - x = x.contiguous().transpose(1, 2) - return x diff --git a/modules/transformer/Models.py b/modules/transformer/Models.py deleted file mode 100644 index 0f2bf3796da5324dbdacd50297563d135e08af6c..0000000000000000000000000000000000000000 --- a/modules/transformer/Models.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import numpy as np - -from .Layers import FFTBlock -from text.symbols import symbols - -PAD = 0 -UNK = 1 -BOS = 2 -EOS = 3 - -PAD_WORD = "" -UNK_WORD = "" -BOS_WORD = "" -EOS_WORD = "" - - -def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None): - """Sinusoid position encoding table""" - - def cal_angle(position, hid_idx): - return position / np.power(10000, 2 * (hid_idx // 2) / d_hid) - - def get_posi_angle_vec(position): - return [cal_angle(position, hid_j) for hid_j in range(d_hid)] - - sinusoid_table = np.array( - [get_posi_angle_vec(pos_i) for pos_i in range(n_position)] - ) - - sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i - sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 - - if padding_idx is not None: - # zero vector for padding dimension - sinusoid_table[padding_idx] = 0.0 - - return torch.FloatTensor(sinusoid_table) - - -class Encoder(nn.Module): - """Encoder""" - - def __init__(self, config): - super(Encoder, self).__init__() - - n_position = config["max_seq_len"] + 1 - n_src_vocab = len(symbols) + 1 - d_word_vec = config["transformer"]["encoder_hidden"] - n_layers = config["transformer"]["encoder_layer"] - n_head = config["transformer"]["encoder_head"] - d_k = d_v = ( - config["transformer"]["encoder_hidden"] - // config["transformer"]["encoder_head"] - ) - d_model = config["transformer"]["encoder_hidden"] - d_inner = config["transformer"]["conv_filter_size"] - kernel_size = config["transformer"]["conv_kernel_size"] - dropout = config["transformer"]["encoder_dropout"] - - self.max_seq_len = config["max_seq_len"] - self.d_model = d_model - - self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=PAD) - self.position_enc = nn.Parameter( - get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0), - requires_grad=False, - ) - - self.layer_stack = nn.ModuleList( - [ - FFTBlock( - d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout - ) - for _ in range(n_layers) - ] - ) - - def forward(self, src_seq, mask, return_attns=False): - enc_slf_attn_list = [] - batch_size, max_len = src_seq.shape[0], src_seq.shape[1] - - # -- Prepare masks - slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) - - # -- Forward - if not self.training and src_seq.shape[1] > self.max_seq_len: - enc_output = self.src_word_emb(src_seq) + get_sinusoid_encoding_table( - src_seq.shape[1], self.d_model - )[: src_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to( - src_seq.device - ) - else: - enc_output = self.src_word_emb(src_seq) + self.position_enc[ - :, :max_len, : - ].expand(batch_size, -1, -1) - - for enc_layer in self.layer_stack: - enc_output, enc_slf_attn = enc_layer( - enc_output, mask=mask, slf_attn_mask=slf_attn_mask - ) - if return_attns: - enc_slf_attn_list += [enc_slf_attn] - - return enc_output - - -class Decoder(nn.Module): - """Decoder""" - - def __init__(self, config): - super(Decoder, self).__init__() - - n_position = config["max_seq_len"] + 1 - d_word_vec = config["transformer"]["decoder_hidden"] - n_layers = config["transformer"]["decoder_layer"] - n_head = config["transformer"]["decoder_head"] - d_k = d_v = ( - config["transformer"]["decoder_hidden"] - // config["transformer"]["decoder_head"] - ) - d_model = config["transformer"]["decoder_hidden"] - d_inner = config["transformer"]["conv_filter_size"] - kernel_size = config["transformer"]["conv_kernel_size"] - dropout = config["transformer"]["decoder_dropout"] - - self.max_seq_len = config["max_seq_len"] - self.d_model = d_model - - self.position_enc = nn.Parameter( - get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0), - requires_grad=False, - ) - - self.layer_stack = nn.ModuleList( - [ - FFTBlock( - d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout - ) - for _ in range(n_layers) - ] - ) - - def forward(self, enc_seq, mask, return_attns=False): - dec_slf_attn_list = [] - batch_size, max_len = enc_seq.shape[0], enc_seq.shape[1] - - # -- Forward - if not self.training and enc_seq.shape[1] > self.max_seq_len: - # -- Prepare masks - slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) - dec_output = enc_seq + get_sinusoid_encoding_table( - enc_seq.shape[1], self.d_model - )[: enc_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to( - enc_seq.device - ) - else: - max_len = min(max_len, self.max_seq_len) - - # -- Prepare masks - slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) - dec_output = enc_seq[:, :max_len, :] + self.position_enc[ - :, :max_len, : - ].expand(batch_size, -1, -1) - mask = mask[:, :max_len] - slf_attn_mask = slf_attn_mask[:, :, :max_len] - - for dec_layer in self.layer_stack: - dec_output, dec_slf_attn = dec_layer( - dec_output, mask=mask, slf_attn_mask=slf_attn_mask - ) - if return_attns: - dec_slf_attn_list += [dec_slf_attn] - - return dec_output, mask diff --git a/modules/transformer/Modules.py b/modules/transformer/Modules.py deleted file mode 100644 index 339aedd0cd3ec603724204e98aced5372cd0a334..0000000000000000000000000000000000000000 --- a/modules/transformer/Modules.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import numpy as np - - -class ScaledDotProductAttention(nn.Module): - """Scaled Dot-Product Attention""" - - def __init__(self, temperature): - super().__init__() - self.temperature = temperature - self.softmax = nn.Softmax(dim=2) - - def forward(self, q, k, v, mask=None): - attn = torch.bmm(q, k.transpose(1, 2)) - attn = attn / self.temperature - - if mask is not None: - attn = attn.masked_fill(mask, -np.inf) - - attn = self.softmax(attn) - output = torch.bmm(attn, v) - - return output, attn diff --git a/modules/transformer/SubLayers.py b/modules/transformer/SubLayers.py deleted file mode 100644 index c1d1b88dfee5eaaec4cc9bfd2017d88847b18b60..0000000000000000000000000000000000000000 --- a/modules/transformer/SubLayers.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn -import torch.nn.functional as F -import numpy as np - -from .Modules import ScaledDotProductAttention - - -class MultiHeadAttention(nn.Module): - """Multi-Head Attention module""" - - def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): - super().__init__() - - self.n_head = n_head - self.d_k = d_k - self.d_v = d_v - - self.w_qs = nn.Linear(d_model, n_head * d_k) - self.w_ks = nn.Linear(d_model, n_head * d_k) - self.w_vs = nn.Linear(d_model, n_head * d_v) - - self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5)) - self.layer_norm = nn.LayerNorm(d_model) - - self.fc = nn.Linear(n_head * d_v, d_model) - - self.dropout = nn.Dropout(dropout) - - def forward(self, q, k, v, mask=None): - d_k, d_v, n_head = self.d_k, self.d_v, self.n_head - - sz_b, len_q, _ = q.size() - sz_b, len_k, _ = k.size() - sz_b, len_v, _ = v.size() - - residual = q - - q = self.w_qs(q).view(sz_b, len_q, n_head, d_k) - k = self.w_ks(k).view(sz_b, len_k, n_head, d_k) - v = self.w_vs(v).view(sz_b, len_v, n_head, d_v) - q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk - k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk - v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv - - mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x .. - output, attn = self.attention(q, k, v, mask=mask) - - output = output.view(n_head, sz_b, len_q, d_v) - output = ( - output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) - ) # b x lq x (n*dv) - - output = self.dropout(self.fc(output)) - output = self.layer_norm(output + residual) - - return output, attn - - -class PositionwiseFeedForward(nn.Module): - """A two-feed-forward-layer module""" - - def __init__(self, d_in, d_hid, kernel_size, dropout=0.1): - super().__init__() - - # Use Conv1D - # position-wise - self.w_1 = nn.Conv1d( - d_in, - d_hid, - kernel_size=kernel_size[0], - padding=(kernel_size[0] - 1) // 2, - ) - # position-wise - self.w_2 = nn.Conv1d( - d_hid, - d_in, - kernel_size=kernel_size[1], - padding=(kernel_size[1] - 1) // 2, - ) - - self.layer_norm = nn.LayerNorm(d_in) - self.dropout = nn.Dropout(dropout) - - def forward(self, x): - residual = x - output = x.transpose(1, 2) - output = self.w_2(F.relu(self.w_1(output))) - output = output.transpose(1, 2) - output = self.dropout(output) - output = self.layer_norm(output + residual) - - return output diff --git a/modules/transformer/__init__.py b/modules/transformer/__init__.py deleted file mode 100644 index fa7f8736235e79a02b4fab47c4b53dd4f4311469..0000000000000000000000000000000000000000 --- a/modules/transformer/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .mh_attention import MultiheadAttention -from .position_embedding import SinePositionalEmbedding \ No newline at end of file diff --git a/modules/transformer/attentions.py b/modules/transformer/attentions.py deleted file mode 100644 index 2b0cfc54ae038d4ca35ba32732dc69c776f4c4f0..0000000000000000000000000000000000000000 --- a/modules/transformer/attentions.py +++ /dev/null @@ -1,416 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import torch -from torch import nn -from torch.nn import functional as F - -from utils.util import * -from modules.base.base_module import * -from modules.base.base_module import LayerNorm - - -class Encoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - window_size=4, - **kwargs, - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - window_size=window_size, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - proximal_bias=False, - proximal_init=True, - **kwargs, - ): - super().__init__() - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - proximal_bias=proximal_bias, - proximal_init=proximal_init, - ) - ) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append( - MultiHeadAttention( - hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - causal=True, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = subsequent_mask(x_mask.size(2)).to( - device=x.device, dtype=x.dtype - ) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__( - self, - channels, - out_channels, - n_heads, - p_dropout=0.0, - window_size=None, - heads_share=True, - block_length=None, - proximal_bias=False, - proximal_init=False, - ): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.emb_rel_v = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert ( - t_s == t_t - ), "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys( - query / math.sqrt(self.k_channels), key_relative_embeddings - ) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to( - device=scores.device, dtype=scores.dtype - ) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert ( - t_s == t_t - ), "Local attention is only available for self-attention." - block_mask = ( - torch.ones_like(scores) - .triu(-self.block_length) - .tril(self.block_length) - ) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings( - self.emb_rel_v, t_s - ) - output = output + self._matmul_with_relative_values( - relative_weights, value_relative_embeddings - ) - output = ( - output.transpose(2, 3).contiguous().view(b, d, t_t) - ) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), - ) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[ - :, slice_start_position:slice_end_position - ] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ - :, :, :length, length - 1 : - ] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) - x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__( - self, - in_channels, - out_channels, - filter_channels, - kernel_size, - p_dropout=0.0, - activation=None, - causal=False, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, convert_pad_shape(padding)) - return x diff --git a/modules/transformer/mh_attention.py b/modules/transformer/mh_attention.py deleted file mode 100644 index 32654d9e2cf064e6a0afb0cdea321ca46a42d306..0000000000000000000000000000000000000000 --- a/modules/transformer/mh_attention.py +++ /dev/null @@ -1,430 +0,0 @@ -from typing import Optional, Tuple - -import torch -from torch import Tensor -from torch.nn import Linear, Module -from torch.nn import functional as F -from torch.nn.init import constant_, xavier_normal_, xavier_uniform_ -from torch.nn.modules.linear import NonDynamicallyQuantizableLinear -from torch.nn.parameter import Parameter - - -class MultiheadAttention(Module): - r"""Allows the model to jointly attend to information - from different representation subspaces as described in the paper: - `Attention Is All You Need `_. - - Multi-Head Attention is defined as: - - .. math:: - \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O - - where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`. - - ``forward()`` will use a special optimized implementation if all of the following - conditions are met: - - - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This - restriction will be loosened in the future.) - - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad`` - - training is disabled (using ``.eval()``) - - dropout is 0 - - ``add_bias_kv`` is ``False`` - - ``add_zero_attn`` is ``False`` - - ``batch_first`` is ``True`` and the input is batched - - ``kdim`` and ``vdim`` are equal to ``embed_dim`` - - at most one of ``key_padding_mask`` or ``attn_mask`` is passed - - if a `NestedTensor `_ is passed, neither ``key_padding_mask`` - nor ``attn_mask`` is passed - - If the optimized implementation is in use, a - `NestedTensor `_ can be passed for - ``query``/``key``/``value`` to represent padding more efficiently than using a - padding mask. In this case, a `NestedTensor `_ - will be returned, and an additional speedup proportional to the fraction of the input - that is padding can be expected. - - Args: - embed_dim: Total dimension of the model. - num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split - across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``). - dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout). - bias: If specified, adds bias to input / output projection layers. Default: ``True``. - add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``. - add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1. - Default: ``False``. - kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``). - vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``). - batch_first: If ``True``, then the input and output tensors are provided - as (batch, seq, feature). Default: ``False`` (seq, batch, feature). - - Examples:: - - >>> # xdoctest: +SKIP - >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) - >>> attn_output, attn_output_weights = multihead_attn(query, key, value) - - """ - __constants__ = ["batch_first"] - bias_k: Optional[torch.Tensor] - bias_v: Optional[torch.Tensor] - - def __init__( - self, - embed_dim, - num_heads, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - kdim=None, - vdim=None, - batch_first=False, - linear1_cls=Linear, - linear2_cls=Linear, - device=None, - dtype=None, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(MultiheadAttention, self).__init__() - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self._qkv_same_embed_dim = ( - self.kdim == embed_dim and self.vdim == embed_dim - ) - - self.num_heads = num_heads - self.dropout = dropout - self.batch_first = batch_first - self.head_dim = embed_dim // num_heads - assert ( - self.head_dim * num_heads == self.embed_dim - ), "embed_dim must be divisible by num_heads" - - if add_bias_kv: - self.bias_k = Parameter( - torch.empty((1, 1, embed_dim), **factory_kwargs) - ) - self.bias_v = Parameter( - torch.empty((1, 1, embed_dim), **factory_kwargs) - ) - else: - self.bias_k = self.bias_v = None - - if linear1_cls == Linear: - if not self._qkv_same_embed_dim: - self.q_proj_weight = Parameter( - torch.empty((embed_dim, embed_dim), **factory_kwargs) - ) - self.k_proj_weight = Parameter( - torch.empty((embed_dim, self.kdim), **factory_kwargs) - ) - self.v_proj_weight = Parameter( - torch.empty((embed_dim, self.vdim), **factory_kwargs) - ) - self.register_parameter("in_proj_weight", None) - else: - self.in_proj_weight = Parameter( - torch.empty((3 * embed_dim, embed_dim), **factory_kwargs) - ) - self.register_parameter("q_proj_weight", None) - self.register_parameter("k_proj_weight", None) - self.register_parameter("v_proj_weight", None) - - if bias: - self.in_proj_bias = Parameter( - torch.empty(3 * embed_dim, **factory_kwargs) - ) - else: - self.register_parameter("in_proj_bias", None) - self.out_proj = NonDynamicallyQuantizableLinear( - embed_dim, embed_dim, bias=bias, **factory_kwargs - ) - - self._reset_parameters() - else: - if not self._qkv_same_embed_dim: - raise NotImplementedError - else: - self.in_proj_linear = linear1_cls( - embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs - ) - self.in_proj_weight = self.in_proj_linear.weight - - self.register_parameter("q_proj_weight", None) - self.register_parameter("k_proj_weight", None) - self.register_parameter("v_proj_weight", None) - - if bias: - self.in_proj_bias = self.in_proj_linear.bias - else: - self.register_parameter("in_proj_bias", None) - - self.out_proj = linear2_cls( - embed_dim, embed_dim, bias=bias, **factory_kwargs - ) - - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - self.add_zero_attn = add_zero_attn - - def _reset_parameters(self): - if self._qkv_same_embed_dim: - xavier_uniform_(self.in_proj_weight) - else: - xavier_uniform_(self.q_proj_weight) - xavier_uniform_(self.k_proj_weight) - xavier_uniform_(self.v_proj_weight) - - if self.in_proj_bias is not None: - constant_(self.in_proj_bias, 0.0) - constant_(self.out_proj.bias, 0.0) - - if self.bias_k is not None: - xavier_normal_(self.bias_k) - if self.bias_v is not None: - xavier_normal_(self.bias_v) - - def __setstate__(self, state): - if "_qkv_same_embed_dim" not in state: - state["_qkv_same_embed_dim"] = True - - super(MultiheadAttention, self).__setstate__(state) - - def forward( - self, - query: Tensor, - key: Tensor, - value: Tensor, - key_padding_mask: Optional[Tensor] = None, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - average_attn_weights: bool = True, - ) -> Tuple[Tensor, Optional[Tensor]]: - r""" - Args: - query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False`` - or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length, - :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``. - Queries are compared against key-value pairs to produce the output. - See "Attention Is All You Need" for more details. - key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False`` - or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length, - :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``. - See "Attention Is All You Need" for more details. - value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when - ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source - sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``. - See "Attention Is All You Need" for more details. - key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key`` - to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`. - Binary and byte masks are supported. - For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for - the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value. - need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``. - Default: ``True``. - attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape - :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size, - :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be - broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch. - Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the - corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the - corresponding position is not allowed to attend. For a float mask, the mask values will be added to - the attention weight. - average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across - heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an - effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads) - - Outputs: - - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched, - :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``, - where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the - embedding dimension ``embed_dim``. - - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``, - returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or - :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and - :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per - head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`. - - .. note:: - `batch_first` argument is ignored for unbatched inputs. - """ - is_batched = query.dim() == 3 - if key_padding_mask is not None: - _kpm_dtype = key_padding_mask.dtype - if _kpm_dtype != torch.bool and not torch.is_floating_point( - key_padding_mask - ): - raise AssertionError( - "only bool and floating types of key_padding_mask are supported" - ) - why_not_fast_path = "" - if not is_batched: - why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}" - elif query is not key or key is not value: - # When lifting this restriction, don't forget to either - # enforce that the dtypes all match or test cases where - # they don't! - why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)" - elif ( - self.in_proj_bias is not None - and query.dtype != self.in_proj_bias.dtype - ): - why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match" - elif ( - self.in_proj_weight is not None - and query.dtype != self.in_proj_weight.dtype - ): - # this case will fail anyway, but at least they'll get a useful error message. - why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match" - elif self.training: - why_not_fast_path = "training is enabled" - elif not self.batch_first: - why_not_fast_path = "batch_first was not True" - elif self.bias_k is not None: - why_not_fast_path = "self.bias_k was not None" - elif self.bias_v is not None: - why_not_fast_path = "self.bias_v was not None" - elif self.dropout: - why_not_fast_path = f"dropout was {self.dropout}, required zero" - elif self.add_zero_attn: - why_not_fast_path = "add_zero_attn was enabled" - elif not self._qkv_same_embed_dim: - why_not_fast_path = "_qkv_same_embed_dim was not True" - elif attn_mask is not None: - why_not_fast_path = "attn_mask was not None" - elif query.is_nested and key_padding_mask is not None: - why_not_fast_path = ( - "key_padding_mask is not supported with NestedTensor input" - ) - elif self.num_heads % 2 == 1: - why_not_fast_path = "num_heads is odd" - elif torch.is_autocast_enabled(): - why_not_fast_path = "autocast is enabled" - - if not why_not_fast_path: - tensor_args = ( - query, - key, - value, - self.in_proj_weight, - self.in_proj_bias, - self.out_proj.weight, - self.out_proj.bias, - ) - # We have to use list comprehensions below because TorchScript does not support - # generator expressions. - if torch.overrides.has_torch_function(tensor_args): - why_not_fast_path = "some Tensor argument has_torch_function" - elif not all( - [ - (x is None or x.is_cuda or "cpu" in str(x.device)) - for x in tensor_args - ] - ): - why_not_fast_path = ( - "some Tensor argument is neither CUDA nor CPU" - ) - elif torch.is_grad_enabled() and any( - [x is not None and x.requires_grad for x in tensor_args] - ): - why_not_fast_path = ( - "grad is enabled and at least one of query or the " - "input/output projection weights or biases requires_grad" - ) - if not why_not_fast_path: - return torch._native_multi_head_attention( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.out_proj.weight, - self.out_proj.bias, - key_padding_mask - if key_padding_mask is not None - else attn_mask, - need_weights, - average_attn_weights, - 1 - if key_padding_mask is not None - else 0 - if attn_mask is not None - else None, - ) - - any_nested = query.is_nested or key.is_nested or value.is_nested - assert not any_nested, ( - "MultiheadAttention does not support NestedTensor outside of its fast path. " - + f"The fast path was not hit because {why_not_fast_path}" - ) - - if self.batch_first and is_batched: - # make sure that the transpose op does not affect the "is" property - if key is value: - if query is key: - query = key = value = query.transpose(1, 0) - else: - query, key = [x.transpose(1, 0) for x in (query, key)] - value = key - else: - query, key, value = [ - x.transpose(1, 0) for x in (query, key, value) - ] - - if not self._qkv_same_embed_dim: - attn_output, attn_output_weights = F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout, - self.out_proj.weight, - self.out_proj.bias, - training=self.training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - use_separate_proj_weight=True, - q_proj_weight=self.q_proj_weight, - k_proj_weight=self.k_proj_weight, - v_proj_weight=self.v_proj_weight, - average_attn_weights=average_attn_weights, - ) - else: - attn_output, attn_output_weights = F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - self.in_proj_weight, - self.in_proj_bias, - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout, - self.out_proj.weight, - self.out_proj.bias, - training=self.training, - key_padding_mask=key_padding_mask, - need_weights=need_weights, - attn_mask=attn_mask, - average_attn_weights=average_attn_weights, - ) - if self.batch_first and is_batched: - return attn_output.transpose(1, 0), attn_output_weights - else: - return attn_output, attn_output_weights diff --git a/modules/transformer/position_embedding.py b/modules/transformer/position_embedding.py deleted file mode 100644 index 3271acd60969ab05cc3cf06245a27aebec77f88b..0000000000000000000000000000000000000000 --- a/modules/transformer/position_embedding.py +++ /dev/null @@ -1,109 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn as nn -import math - -class SinePositionalEmbedding(nn.Module): - def __init__( - self, - dim_model: int, - dropout: float = 0.0, - scale: bool = False, - alpha: bool = False, - ): - super().__init__() - self.dim_model = dim_model - self.x_scale = math.sqrt(dim_model) if scale else 1.0 - self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) - self.dropout = torch.nn.Dropout(p=dropout) - - self.reverse = False - self.pe = None - self.extend_pe(torch.tensor(0.0).expand(1, 4000)) - - def extend_pe(self, x): - """Reset the positional encodings.""" - if self.pe is not None: - if self.pe.size(1) >= x.size(1): - if self.pe.dtype != x.dtype or self.pe.device != x.device: - self.pe = self.pe.to(dtype=x.dtype, device=x.device) - return - pe = torch.zeros(x.size(1), self.dim_model) - if self.reverse: - position = torch.arange( - x.size(1) - 1, -1, -1.0, dtype=torch.float32 - ).unsqueeze(1) - else: - position = torch.arange( - 0, x.size(1), dtype=torch.float32 - ).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, self.dim_model, 2, dtype=torch.float32) - * -(math.log(10000.0) / self.dim_model) - ) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) - self.pe = pe.to(device=x.device, dtype=x.dtype).detach() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - self.extend_pe(x) - output = x.unsqueeze(-1) if x.ndim == 2 else x - output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)] - return self.dropout(output) - - -# import torch -# import torch.nn as nn -# import math - -# class SinePositionalEmbedding(nn.Module): -# def __init__( -# self, -# dim_model: int, -# dropout: float = 0.0, -# scale: bool = False, -# alpha: bool = False, -# ): -# super().__init__() -# self.dim_model = dim_model -# self.x_scale = math.sqrt(dim_model) if scale else 1.0 -# self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha) -# self.dropout = torch.nn.Dropout(p=dropout) - -# self.reverse = False -# self.pe = None -# self.extend_pe(torch.zeros(1, 4000)) - -# def extend_pe(self, x): -# """Reset the positional encodings.""" -# if self._pe_needs_extension(x): -# self.pe = self._generate_positional_encodings(x) - -# def _pe_needs_extension(self, x): -# return self.pe is None or self.pe.size(1) < x.size(1) or self.pe.dtype != x.dtype or self.pe.device != x.device - -# def _generate_positional_encodings(self, x): -# pe = torch.zeros(x.size(1), self.dim_model) -# position = self._get_position_tensor(x) -# div_term = self._get_div_term() -# pe[:, 0::2] = torch.sin(position * div_term) -# pe[:, 1::2] = torch.cos(position * div_term) -# return pe.unsqueeze(0).to(device=x.device, dtype=x.dtype).detach() - -# def _get_position_tensor(self, x): -# position = torch.arange(x.size(1), dtype=torch.float32).unsqueeze(1) -# return position.flip(0) if self.reverse else position - -# def _get_div_term(self): -# return torch.exp(torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model)) - -# def forward(self, x: torch.Tensor) -> torch.Tensor: -# self.extend_pe(x) -# output = x.unsqueeze(-1) if x.ndim == 2 else x -# output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)] -# return self.dropout(output) diff --git a/modules/transformer/transformer.py b/modules/transformer/transformer.py deleted file mode 100644 index b0d39365c09d3dcbd47710c6bd493829e29aeb23..0000000000000000000000000000000000000000 --- a/modules/transformer/transformer.py +++ /dev/null @@ -1,400 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import copy -from functools import partial -from typing import Any, Callable, List, Optional, Union - -import torch -from torch import Tensor, nn -from torch.nn import functional as F - -from modules.norms import AdaptiveLayerNorm, LayerNorm, BalancedBasicNorm, IdentityNorm -from modules.transformer import MultiheadAttention -from modules.general.scaling import BalancedDoubleSwish - - -class TransformerEncoderLayer(nn.Module): - __constants__ = ["batch_first", "norm_first"] - - def __init__( - self, - d_model: int, - nhead: int, - dim_feedforward: int = 2048, - dropout: float = 0.1, - activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, - batch_first: bool = False, - norm_first: bool = False, - device=None, - dtype=None, - linear1_self_attention_cls: nn.Module = nn.Linear, - linear2_self_attention_cls: nn.Module = nn.Linear, - linear1_feedforward_cls: nn.Module = nn.Linear, - linear2_feedforward_cls: nn.Module = nn.Linear, - layer_norm_cls: nn.Module = LayerNorm, - layer_norm_eps: float = 1e-5, - adaptive_layer_norm=False, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(TransformerEncoderLayer, self).__init__() - self.self_attn = MultiheadAttention( - d_model, - nhead, - dropout=dropout, - batch_first=batch_first, - linear1_cls=linear1_self_attention_cls, - linear2_cls=linear2_self_attention_cls, - **factory_kwargs, - ) - - # Implementation of Feedforward model - self.linear1 = linear1_feedforward_cls( - d_model, dim_feedforward, **factory_kwargs - ) - self.dropout = nn.Dropout(dropout) - self.linear2 = linear2_feedforward_cls( - dim_feedforward, d_model, **factory_kwargs - ) - - self.norm_first = norm_first - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - if isinstance(activation, str): - activation = _get_activation_fn(activation) - elif isinstance(activation, partial): - activation = activation(d_model) - elif activation == BalancedDoubleSwish: - activation = BalancedDoubleSwish(d_model) - - self.activation = activation - - norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) - if layer_norm_cls == IdentityNorm: - norm2 = BalancedBasicNorm( - d_model, eps=layer_norm_eps, **factory_kwargs - ) - else: - norm2 = layer_norm_cls( - d_model, eps=layer_norm_eps, **factory_kwargs - ) - - if adaptive_layer_norm: - self.norm1 = AdaptiveLayerNorm(d_model, norm1) - self.norm2 = AdaptiveLayerNorm(d_model, norm2) - else: - self.norm1 = norm1 - self.norm2 = norm2 - - def __setstate__(self, state): - super(TransformerEncoderLayer, self).__setstate__(state) - if not hasattr(self, "activation"): - self.activation = F.relu - - def forward( - self, - src: Tensor, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - ) -> Tensor: - r"""Pass the input through the encoder layer. - - Args: - src: the sequence to the encoder layer (required). - src_mask: the mask for the src sequence (optional). - src_key_padding_mask: the mask for the src keys per batch (optional). - - Shape: - see the docs in Transformer class. - """ - x, stage_embedding = src, None - is_src_tuple = False - if isinstance(src, tuple): - x, stage_embedding = src - is_src_tuple = True - - if src_key_padding_mask is not None: - _skpm_dtype = src_key_padding_mask.dtype - if _skpm_dtype != torch.bool and not torch.is_floating_point( - src_key_padding_mask - ): - raise AssertionError( - "only bool and floating types of key_padding_mask are supported" - ) - - if self.norm_first: - x = x + self._sa_block( - self.norm1(x, stage_embedding), - src_mask, - src_key_padding_mask, - ) - x = x + self._ff_block(self.norm2(x, stage_embedding)) - else: - x = self.norm1( - x + self._sa_block(x, src_mask, src_key_padding_mask), - stage_embedding, - ) - x = self.norm2(x + self._ff_block(x), stage_embedding) - - if is_src_tuple: - return (x, stage_embedding) - return x - - def _sa_block( - self, - x: Tensor, - attn_mask: Optional[Tensor], - key_padding_mask: Optional[Tensor], - ) -> Tensor: - x = self.self_attn( - x, - x, - x, - attn_mask=attn_mask, - key_padding_mask=key_padding_mask, - need_weights=False, - )[0] - return self.dropout1(x) - - def _ff_block(self, x: Tensor) -> Tensor: - x = self.linear2(self.dropout(self.activation(self.linear1(x)))) - return self.dropout2(x) - -class TransformerEncoder(nn.Module): - """TransformerEncoder is a stack of N encoder layers.""" - - def __init__(self, encoder_layer, num_layers, norm=None): - super(TransformerEncoder, self).__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - src: Tensor, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - return_layer_states: bool = False, - ) -> Tensor: - - # Pass the input through the encoder layers - output = src - layer_states = [] if return_layer_states else None - - for mod in self.layers: - output = self._apply_module(mod, output, mask, src_key_padding_mask, layer_states) - - if self.norm is not None: - output = self.norm(output) - - return (layer_states, output) if return_layer_states else output - - def _apply_module(self, module, output, mask, key_padding_mask, layer_states): - # Apply a single transformer module - output = module(output, src_mask=mask, src_key_padding_mask=key_padding_mask) - if layer_states is not None: - layer_states.append(output) - return output - -class TransformerDecoderLayer(nn.Module): - __constants__ = ["batch_first", "norm_first"] - - def __init__( - self, - d_model: int, - nhead: int, - dim_feedforward: int = 2048, - dropout: float = 0.1, - activation: Union[str, Callable[[Tensor], Tensor]] = F.relu, - linear1_self_attention_cls: nn.Module = nn.Linear, - linear2_self_attention_cls: nn.Module = nn.Linear, - linear1_feedforward_cls: nn.Module = nn.Linear, - linear2_feedforward_cls: nn.Module = nn.Linear, - batch_first: bool = False, - norm_first: bool = False, - device=None, - dtype=None, - layer_norm_cls: nn.Module = LayerNorm, - layer_norm_eps: float = 1e-5, - adaptive_layer_norm=False, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - super(TransformerDecoderLayer, self).__init__() - self.self_attn = MultiheadAttention( - d_model, - nhead, - dropout=dropout, - batch_first=batch_first, - linear1_cls=linear1_self_attention_cls, - linear2_cls=linear2_self_attention_cls, - **factory_kwargs, - ) - self.multihead_attn = MultiheadAttention( - d_model, - nhead, - dropout=dropout, - batch_first=batch_first, - linear1_cls=linear1_self_attention_cls, - linear2_cls=linear2_self_attention_cls, - **factory_kwargs, - ) - self.linear1 = linear1_feedforward_cls( - d_model, dim_feedforward, **factory_kwargs - ) - self.dropout = nn.Dropout(dropout) - self.linear2 = linear2_feedforward_cls( - dim_feedforward, d_model, **factory_kwargs - ) - - self.norm_first = norm_first - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - self.dropout3 = nn.Dropout(dropout) - - self.activation = self._get_activation_fn(activation) - self.norm1, self.norm2, self.norm3 = self._init_norm_layers( - d_model, layer_norm_cls, layer_norm_eps, adaptive_layer_norm, factory_kwargs - ) - - def forward( - self, - tgt: Tensor, - memory: Tensor, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - ) -> Tensor: - r"""Pass the inputs (and mask) through the decoder layer. - - Args: - tgt: the sequence to the decoder layer (required). - memory: the sequence from the last layer of the encoder (required). - tgt_mask: the mask for the tgt sequence (optional). - memory_mask: the mask for the memory sequence (optional). - tgt_key_padding_mask: the mask for the tgt keys per batch (optional). - memory_key_padding_mask: the mask for the memory keys per batch (optional). - - Shape: - see the docs in Transformer class. - """ - tgt_is_tuple = False - if isinstance(tgt, tuple): - x, stage_embedding = tgt - tgt_is_tuple = True - else: - x, stage_embedding = tgt, None - - if self.norm_first: - x = x + self._sa_block( - self.norm1(x, stage_embedding), tgt_mask, tgt_key_padding_mask - ) - x = x + self._mha_block( - self.norm2(x, stage_embedding), - memory, - memory_mask, - memory_key_padding_mask, - ) - x = x + self._ff_block(self.norm3(x, stage_embedding)) - else: - x = self.norm1( - x + self._sa_block(x, tgt_mask, tgt_key_padding_mask), - stage_embedding, - ) - x = self.norm2( - x - + self._mha_block( - x, memory, memory_mask, memory_key_padding_mask - ), - stage_embedding, - ) - x = self.norm3(x + self._ff_block(x), stage_embedding) - - if tgt_is_tuple: - return (x, stage_embedding) - return x - - def _sa_block( - self, - x: Tensor, - attn_mask: Optional[Tensor], - key_padding_mask: Optional[Tensor], - ) -> Tensor: - x = self.self_attn( - x, - x, - x, - attn_mask=attn_mask, - key_padding_mask=key_padding_mask, - need_weights=False, - )[0] - return self.dropout1(x) - - def _mha_block( - self, - x: Tensor, - mem: Tensor, - attn_mask: Optional[Tensor], - key_padding_mask: Optional[Tensor], - ) -> Tensor: - x = self.multihead_attn( - x, - mem, - mem, - attn_mask=attn_mask, - key_padding_mask=key_padding_mask, - need_weights=False, - )[0] - return self.dropout2(x) - - def _ff_block(self, x: Tensor) -> Tensor: - x = self.linear2(self.dropout(self.activation(self.linear1(x)))) - return self.dropout3(x) - - def _get_activation_fn(self, activation): - if isinstance(activation, str): - return _get_activation_fn(activation) - elif callable(activation): - return activation - else: - raise ValueError("Unsupported activation type") - - def _init_norm_layers(self, d_model, layer_norm_cls, layer_norm_eps, adaptive_layer_norm, factory_kwargs): - if adaptive_layer_norm: - return ( - AdaptiveLayerNorm(d_model, layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)), - AdaptiveLayerNorm(d_model, layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)), - AdaptiveLayerNorm(d_model, layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)) - ) - else: - return ( - layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs), - layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs), - layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs) if layer_norm_cls != IdentityNorm - else BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs) - ) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]: - if activation == "relu": - return F.relu - elif activation == "gelu": - return F.gelu - - raise RuntimeError( - "activation should be relu/gelu, not {}".format(activation) - ) - - -class Transpose(nn.Identity): - """(N, T, D) -> (N, D, T)""" - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return input.transpose(1, 2) diff --git a/modules/transformer/transforms.py b/modules/transformer/transforms.py deleted file mode 100644 index d28b2e037e80f81ab75665c1f397c930bd0eca20..0000000000000000000000000000000000000000 --- a/modules/transformer/transforms.py +++ /dev/null @@ -1,214 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = {"tails": tails, "tail_bound": tail_bound} - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs, - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 - - -def unconstrained_rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails="linear", - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == "linear": - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError("{} tails are not implemented.".format(tails)) - - ( - outputs[inside_interval_mask], - logabsdet[inside_interval_mask], - ) = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, - right=tail_bound, - bottom=-tail_bound, - top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - ) - - return outputs, logabsdet - - -def rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0.0, - right=1.0, - bottom=0.0, - top=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError("Input to a transform is not within its domain") - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError("Minimal bin width too large for the number of bins") - if min_bin_height * num_bins > 1.0: - raise ValueError("Minimal bin height too large for the number of bins") - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) + input_heights * (input_delta - input_derivatives) - b = input_heights * input_derivatives - (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) - c = -input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * ( - input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta - ) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/modules/vocoder_blocks/__init__.py b/modules/vocoder_blocks/__init__.py deleted file mode 100644 index b678da73bf7b15cf9c42a1f4f36379f62fc43896..0000000000000000000000000000000000000000 --- a/modules/vocoder_blocks/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .gan_utils import * -from .norm2d import * diff --git a/modules/vocoder_blocks/gan_utils.py b/modules/vocoder_blocks/gan_utils.py deleted file mode 100644 index 0e6fc38cb3c047ea856f8d63b0f3a755644e98ee..0000000000000000000000000000000000000000 --- a/modules/vocoder_blocks/gan_utils.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -#################### GAN utils #################### - - -import typing as tp - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def get_2d_padding( - kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1) -): - return ( - ((kernel_size[0] - 1) * dilation[0]) // 2, - ((kernel_size[1] - 1) * dilation[1]) // 2, - ) - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) diff --git a/modules/vocoder_blocks/norm2d.py b/modules/vocoder_blocks/norm2d.py deleted file mode 100644 index a907f907faae2ce9cf9e695b4a8b103ab39773ce..0000000000000000000000000000000000000000 --- a/modules/vocoder_blocks/norm2d.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -#################### Norm2D for Discriminators #################### - -import torch -import torch.nn as nn -import einops -from torch.nn.utils import spectral_norm, weight_norm - -CONV_NORMALIZATIONS = frozenset( - [ - "none", - "weight_norm", - "spectral_norm", - "time_layer_norm", - "layer_norm", - "time_group_norm", - ] -) - - -class ConvLayerNorm(nn.LayerNorm): - """ - Convolution-friendly LayerNorm that moves channels to last dimensions - before running the normalization and moves them back to original position right after. - """ - - def __init__(self, normalized_shape, **kwargs): - super().__init__(normalized_shape, **kwargs) - - def forward(self, x): - x = einops.rearrange(x, "b ... t -> b t ...") - x = super().forward(x) - x = einops.rearrange(x, "b t ... -> b ... t") - return - - -def apply_parametrization_norm(module: nn.Module, norm: str = "none") -> nn.Module: - assert norm in CONV_NORMALIZATIONS - if norm == "weight_norm": - return weight_norm(module) - elif norm == "spectral_norm": - return spectral_norm(module) - else: - # We already check was in CONV_NORMALIZATION, so any other choice - # doesn't need reparametrization. - return module - - -def get_norm_module( - module: nn.Module, causal: bool = False, norm: str = "none", **norm_kwargs -) -> nn.Module: - """Return the proper normalization module. If causal is True, this will ensure the returned - module is causal, or return an error if the normalization doesn't support causal evaluation. - """ - assert norm in CONV_NORMALIZATIONS - if norm == "layer_norm": - assert isinstance(module, nn.modules.conv._ConvNd) - return ConvLayerNorm(module.out_channels, **norm_kwargs) - elif norm == "time_group_norm": - if causal: - raise ValueError("GroupNorm doesn't support causal evaluation.") - assert isinstance(module, nn.modules.conv._ConvNd) - return nn.GroupNorm(1, module.out_channels, **norm_kwargs) - else: - return nn.Identity() - - -class NormConv2d(nn.Module): - """Wrapper around Conv2d and normalization applied to this conv - to provide a uniform interface across normalization approaches. - """ - - def __init__( - self, - *args, - norm: str = "none", - norm_kwargs={}, - **kwargs, - ): - super().__init__() - self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm) - self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs) - self.norm_type = norm - - def forward(self, x): - x = self.conv(x) - x = self.norm(x) - return x diff --git a/modules/wenet_extractor/README.md b/modules/wenet_extractor/README.md deleted file mode 100644 index b8f0abc4d5d9fda42edba488d01bd40222a78304..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/README.md +++ /dev/null @@ -1,23 +0,0 @@ -## Acknowledgement - -This module borrows some codes from [WeNet](https://github.com/wenet-e2e/wenet). - -## Citations - -```bibtex -@inproceedings{yao2021wenet, - title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, - author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, - booktitle={Proc. Interspeech}, - year={2021}, - address={Brno, Czech Republic }, - organization={IEEE} -} - -@article{zhang2022wenet, - title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, - author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, - journal={arXiv preprint arXiv:2203.15455}, - year={2022} -} -``` \ No newline at end of file diff --git a/modules/wenet_extractor/__init__.py b/modules/wenet_extractor/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/bin/alignment.py b/modules/wenet_extractor/bin/alignment.py deleted file mode 100644 index 0f3fcb4ea01b29efdc87aff79778a21d335d699e..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/alignment.py +++ /dev/null @@ -1,245 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import copy -import logging -import os -import sys - -import torch -import yaml -from torch.utils.data import DataLoader -from textgrid import TextGrid, IntervalTier - -from wenet.dataset.dataset import Dataset -from wenet.utils.checkpoint import load_checkpoint -from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols -from wenet.utils.ctc_util import forced_align -from wenet.utils.common import get_subsample -from wenet.utils.init_model import init_model - - -def generator_textgrid(maxtime, lines, output): - # Download Praat: https://www.fon.hum.uva.nl/praat/ - interval = maxtime / (len(lines) + 1) - margin = 0.0001 - - tg = TextGrid(maxTime=maxtime) - linetier = IntervalTier(name="line", maxTime=maxtime) - - i = 0 - for l in lines: - s, e, w = l.split() - linetier.add(minTime=float(s) + margin, maxTime=float(e), mark=w) - - tg.append(linetier) - print("successfully generator {}".format(output)) - tg.write(output) - - -def get_frames_timestamp(alignment): - # convert alignment to a praat format, which is a doing phonetics - # by computer and helps analyzing alignment - timestamp = [] - # get frames level duration for each token - start = 0 - end = 0 - while end < len(alignment): - while end < len(alignment) and alignment[end] == 0: - end += 1 - if end == len(alignment): - timestamp[-1] += alignment[start:] - break - end += 1 - while end < len(alignment) and alignment[end - 1] == alignment[end]: - end += 1 - timestamp.append(alignment[start:end]) - start = end - return timestamp - - -def get_labformat(timestamp, subsample): - begin = 0 - duration = 0 - labformat = [] - for idx, t in enumerate(timestamp): - # 25ms frame_length,10ms hop_length, 1/subsample - subsample = get_subsample(configs) - # time duration - duration = len(t) * 0.01 * subsample - if idx < len(timestamp) - 1: - print("{:.2f} {:.2f} {}".format(begin, begin + duration, char_dict[t[-1]])) - labformat.append( - "{:.2f} {:.2f} {}\n".format(begin, begin + duration, char_dict[t[-1]]) - ) - else: - non_blank = 0 - for i in t: - if i != 0: - token = i - break - print("{:.2f} {:.2f} {}".format(begin, begin + duration, char_dict[token])) - labformat.append( - "{:.2f} {:.2f} {}\n".format(begin, begin + duration, char_dict[token]) - ) - begin = begin + duration - return labformat - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="use ctc to generate alignment") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument("--input_file", required=True, help="format data file") - parser.add_argument( - "--data_type", - default="raw", - choices=["raw", "shard"], - help="train and cv data type", - ) - parser.add_argument( - "--gpu", type=int, default=-1, help="gpu id for this rank, -1 for cpu" - ) - parser.add_argument("--checkpoint", required=True, help="checkpoint model") - parser.add_argument("--dict", required=True, help="dict file") - parser.add_argument( - "--non_lang_syms", help="non-linguistic symbol file. One symbol per line." - ) - parser.add_argument("--result_file", required=True, help="alignment result file") - parser.add_argument("--batch_size", type=int, default=1, help="batch size") - parser.add_argument( - "--gen_praat", action="store_true", help="convert alignment to a praat format" - ) - parser.add_argument( - "--bpe_model", default=None, type=str, help="bpe model for english part" - ) - - args = parser.parse_args() - print(args) - logging.basicConfig( - level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" - ) - os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) - - if args.batch_size > 1: - logging.fatal("alignment mode must be running with batch_size == 1") - sys.exit(1) - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - - # Load dict - char_dict = {} - with open(args.dict, "r") as fin: - for line in fin: - arr = line.strip().split() - assert len(arr) == 2 - char_dict[int(arr[1])] = arr[0] - eos = len(char_dict) - 1 - - symbol_table = read_symbol_table(args.dict) - - # Init dataset and data loader - ali_conf = copy.deepcopy(configs["dataset_conf"]) - - ali_conf["filter_conf"]["max_length"] = 102400 - ali_conf["filter_conf"]["min_length"] = 0 - ali_conf["filter_conf"]["token_max_length"] = 102400 - ali_conf["filter_conf"]["token_min_length"] = 0 - ali_conf["filter_conf"]["max_output_input_ratio"] = 102400 - ali_conf["filter_conf"]["min_output_input_ratio"] = 0 - ali_conf["speed_perturb"] = False - ali_conf["spec_aug"] = False - ali_conf["shuffle"] = False - ali_conf["sort"] = False - ali_conf["fbank_conf"]["dither"] = 0.0 - ali_conf["batch_conf"]["batch_type"] = "static" - ali_conf["batch_conf"]["batch_size"] = args.batch_size - non_lang_syms = read_non_lang_symbols(args.non_lang_syms) - - ali_dataset = Dataset( - args.data_type, - args.input_file, - symbol_table, - ali_conf, - args.bpe_model, - non_lang_syms, - partition=False, - ) - - ali_data_loader = DataLoader(ali_dataset, batch_size=None, num_workers=0) - - # Init asr model from configs - model = init_model(configs) - - load_checkpoint(model, args.checkpoint) - use_cuda = args.gpu >= 0 and torch.cuda.is_available() - device = torch.device("cuda" if use_cuda else "cpu") - model = model.to(device) - - model.eval() - with torch.no_grad(), open(args.result_file, "w", encoding="utf-8") as fout: - for batch_idx, batch in enumerate(ali_data_loader): - print("#" * 80) - key, feat, target, feats_length, target_length = batch - print(key) - - feat = feat.to(device) - target = target.to(device) - feats_length = feats_length.to(device) - target_length = target_length.to(device) - # Let's assume B = batch_size and N = beam_size - # 1. Encoder - encoder_out, encoder_mask = model._forward_encoder( - feat, feats_length - ) # (B, maxlen, encoder_dim) - maxlen = encoder_out.size(1) - ctc_probs = model.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) - # print(ctc_probs.size(1)) - ctc_probs = ctc_probs.squeeze(0) - target = target.squeeze(0) - alignment = forced_align(ctc_probs, target) - print(alignment) - fout.write("{} {}\n".format(key[0], alignment)) - - if args.gen_praat: - timestamp = get_frames_timestamp(alignment) - print(timestamp) - subsample = get_subsample(configs) - labformat = get_labformat(timestamp, subsample) - - lab_path = os.path.join( - os.path.dirname(args.result_file), key[0] + ".lab" - ) - with open(lab_path, "w", encoding="utf-8") as f: - f.writelines(labformat) - - textgrid_path = os.path.join( - os.path.dirname(args.result_file), key[0] + ".TextGrid" - ) - generator_textgrid( - maxtime=(len(alignment) + 1) * 0.01 * subsample, - lines=labformat, - output=textgrid_path, - ) diff --git a/modules/wenet_extractor/bin/average_model.py b/modules/wenet_extractor/bin/average_model.py deleted file mode 100644 index 25998b01136b077f93d8f70abf7e4a1fccf981a1..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/average_model.py +++ /dev/null @@ -1,104 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import os -import argparse -import glob - -import yaml -import numpy as np -import torch - - -def get_args(): - parser = argparse.ArgumentParser(description="average model") - parser.add_argument("--dst_model", required=True, help="averaged model") - parser.add_argument("--src_path", required=True, help="src model path for average") - parser.add_argument("--val_best", action="store_true", help="averaged model") - parser.add_argument("--num", default=5, type=int, help="nums for averaged model") - parser.add_argument( - "--min_epoch", default=0, type=int, help="min epoch used for averaging model" - ) - parser.add_argument( - "--max_epoch", - default=65536, - type=int, - help="max epoch used for averaging model", - ) - - args = parser.parse_args() - print(args) - return args - - -def main(): - args = get_args() - checkpoints = [] - val_scores = [] - if args.val_best: - yamls = glob.glob("{}/[!train]*.yaml".format(args.src_path)) - for y in yamls: - with open(y, "r") as f: - dic_yaml = yaml.load(f, Loader=yaml.FullLoader) - loss = dic_yaml["cv_loss"] - epoch = dic_yaml["epoch"] - if epoch >= args.min_epoch and epoch <= args.max_epoch: - val_scores += [[epoch, loss]] - val_scores = np.array(val_scores) - sort_idx = np.argsort(val_scores[:, -1]) - sorted_val_scores = val_scores[sort_idx][::1] - print("best val scores = " + str(sorted_val_scores[: args.num, 1])) - print( - "selected epochs = " - + str(sorted_val_scores[: args.num, 0].astype(np.int64)) - ) - path_list = [ - args.src_path + "/{}.pt".format(int(epoch)) - for epoch in sorted_val_scores[: args.num, 0] - ] - else: - path_list = glob.glob("{}/[0-9]*.pt".format(args.src_path)) - path_list = sorted(path_list, key=os.path.getmtime) - path_list = path_list[-args.num :] - print(path_list) - avg = None - num = args.num - assert num == len(path_list) - for path in path_list: - print("Processing {}".format(path)) - states = torch.load(path, map_location=torch.device("cpu")) - if avg is None: - avg = states - else: - for k in avg.keys(): - avg[k] += states[k] - # average - for k in avg.keys(): - if avg[k] is not None: - # pytorch 1.6 use true_divide instead of /= - avg[k] = torch.true_divide(avg[k], num) - print("Saving to {}".format(args.dst_model)) - torch.save(avg, args.dst_model) - - -if __name__ == "__main__": - main() diff --git a/modules/wenet_extractor/bin/export_jit.py b/modules/wenet_extractor/bin/export_jit.py deleted file mode 100644 index 1e2b7558c87a3991548bf5d9cf26543776599894..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/export_jit.py +++ /dev/null @@ -1,80 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import os - -import torch -import yaml - -from wenet.utils.checkpoint import load_checkpoint -from wenet.utils.init_model import init_model - - -def get_args(): - parser = argparse.ArgumentParser(description="export your script model") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument("--checkpoint", required=True, help="checkpoint model") - parser.add_argument("--output_file", default=None, help="output file") - parser.add_argument( - "--output_quant_file", default=None, help="output quantized model file" - ) - args = parser.parse_args() - return args - - -def main(): - args = get_args() - # No need gpu for model export - os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - model = init_model(configs) - print(model) - - load_checkpoint(model, args.checkpoint) - # Export jit torch script model - - if args.output_file: - script_model = torch.jit.script(model) - script_model.save(args.output_file) - print("Export model successfully, see {}".format(args.output_file)) - - # Export quantized jit torch script model - if args.output_quant_file: - quantized_model = torch.quantization.quantize_dynamic( - model, {torch.nn.Linear}, dtype=torch.qint8 - ) - print(quantized_model) - script_quant_model = torch.jit.script(quantized_model) - script_quant_model.save(args.output_quant_file) - print( - "Export quantized model successfully, " - "see {}".format(args.output_quant_file) - ) - - -if __name__ == "__main__": - main() diff --git a/modules/wenet_extractor/bin/export_onnx_bpu.py b/modules/wenet_extractor/bin/export_onnx_bpu.py deleted file mode 100644 index ed21b39d3a38f4f2b967bc9ae8a6b148e0a9fde7..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/export_onnx_bpu.py +++ /dev/null @@ -1,1122 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - - -from __future__ import print_function - -import os -import sys -import copy -import math -import yaml -import logging -from typing import Tuple - -import torch -import numpy as np - -from wenet.transformer.embedding import NoPositionalEncoding -from wenet.utils.checkpoint import load_checkpoint -from wenet.utils.init_model import init_model -from wenet.bin.export_onnx_cpu import get_args, to_numpy, print_input_output_info - - -try: - import onnx - import onnxruntime -except ImportError: - print("Please install onnx and onnxruntime!") - sys.exit(1) - - -logger = logging.getLogger(__file__) -logger.setLevel(logging.INFO) - - -class BPULayerNorm(torch.nn.Module): - """Refactor torch.nn.LayerNorm to meet 4-D dataflow.""" - - def __init__(self, module, chunk_size=8, run_on_bpu=False): - super().__init__() - original = copy.deepcopy(module) - self.hidden = module.weight.size(0) - self.chunk_size = chunk_size - self.run_on_bpu = run_on_bpu - - if self.run_on_bpu: - self.weight = torch.nn.Parameter( - module.weight.reshape(1, self.hidden, 1, 1).repeat(1, 1, 1, chunk_size) - ) - self.bias = torch.nn.Parameter( - module.bias.reshape(1, self.hidden, 1, 1).repeat(1, 1, 1, chunk_size) - ) - self.negtive = torch.nn.Parameter( - torch.ones((1, self.hidden, 1, chunk_size)) * -1.0 - ) - self.eps = torch.nn.Parameter( - torch.zeros((1, self.hidden, 1, chunk_size)) + module.eps - ) - self.mean_conv_1 = torch.nn.Conv2d(self.hidden, 1, 1, bias=False) - self.mean_conv_1.weight = torch.nn.Parameter( - torch.ones(self.hidden, self.hidden, 1, 1) / (1.0 * self.hidden) - ) - self.mean_conv_2 = torch.nn.Conv2d(self.hidden, 1, 1, bias=False) - self.mean_conv_2.weight = torch.nn.Parameter( - torch.ones(self.hidden, self.hidden, 1, 1) / (1.0 * self.hidden) - ) - else: - self.norm = module - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, self.chunk_size, self.hidden) - orig_out = module(random_data) - new_out = self.forward(random_data.transpose(1, 2).unsqueeze(2)) - np.testing.assert_allclose( - to_numpy(orig_out), - to_numpy(new_out.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if self.run_on_bpu: - u = self.mean_conv_1(x) # (1, h, 1, c) - numerator = x + u * self.negtive # (1, h, 1, c) - s = torch.pow(numerator, 2) # (1, h, 1, c) - s = self.mean_conv_2(s) # (1, h, 1, c) - denominator = torch.sqrt(s + self.eps) # (1, h, 1, c) - x = torch.div(numerator, denominator) # (1, h, 1, c) - x = x * self.weight + self.bias - else: - x = x.squeeze(2).transpose(1, 2).contiguous() - x = self.norm(x) - x = x.transpose(1, 2).contiguous().unsqueeze(2) - return x - - -class BPUIdentity(torch.nn.Module): - """Refactor torch.nn.Identity(). - For inserting BPU node whose input == output. - """ - - def __init__(self, channels): - super().__init__() - self.channels = channels - self.identity_conv = torch.nn.Conv2d( - channels, channels, 1, groups=channels, bias=False - ) - torch.nn.init.dirac_(self.identity_conv.weight.data, groups=channels) - - self.check_equal() - - def check_equal(self): - random_data = torch.randn(1, self.channels, 1, 10) - result = self.forward(random_data) - np.testing.assert_allclose( - to_numpy(random_data), to_numpy(result), rtol=1e-02, atol=1e-03 - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Identity with 4-D dataflow, input == output. - Args: - x (torch.Tensor): (batch, in_channel, 1, time) - - Returns: - (torch.Tensor): (batch, in_channel, 1, time). - """ - return self.identity_conv(x) - - -class BPULinear(torch.nn.Module): - """Refactor torch.nn.Linear or pointwise_conv""" - - def __init__(self, module, is_pointwise_conv=False): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.idim = module.weight.size(1) - self.odim = module.weight.size(0) - self.is_pointwise_conv = is_pointwise_conv - - # Modify weight & bias - self.linear = torch.nn.Conv2d(self.idim, self.odim, 1, 1) - if is_pointwise_conv: - # (odim, idim, kernel=1) -> (odim, idim, 1, 1) - self.linear.weight = torch.nn.Parameter(module.weight.unsqueeze(-1)) - else: - # (odim, idim) -> (odim, idim, 1, 1) - self.linear.weight = torch.nn.Parameter( - module.weight.unsqueeze(2).unsqueeze(3) - ) - self.linear.bias = module.bias - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, 8, self.idim) - if self.is_pointwise_conv: - random_data = random_data.transpose(1, 2) - original_result = module(random_data) - if self.is_pointwise_conv: - random_data = random_data.transpose(1, 2) - original_result = original_result.transpose(1, 2) - random_data = random_data.transpose(1, 2).unsqueeze(2) - new_result = self.forward(random_data) - np.testing.assert_allclose( - to_numpy(original_result), - to_numpy(new_result.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Linear with 4-D dataflow. - Args: - x (torch.Tensor): (batch, in_channel, 1, time) - Returns: - (torch.Tensor): (batch, out_channel, 1, time). - """ - return self.linear(x) - - -class BPUGlobalCMVN(torch.nn.Module): - """Refactor wenet/transformer/cmvn.py::GlobalCMVN""" - - def __init__(self, module): - super().__init__() - # Unchanged submodules and attributes - self.norm_var = module.norm_var - - # NOTE(xcsong): Expand to 4-D tensor, (mel_dim) -> (1, 1, mel_dim, 1) - self.mean = module.mean.unsqueeze(-1).unsqueeze(0).unsqueeze(0) - self.istd = module.istd.unsqueeze(-1).unsqueeze(0).unsqueeze(0) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """CMVN with 4-D dataflow. - Args: - x (torch.Tensor): (batch, 1, mel_dim, time) - Returns: - (torch.Tensor): normalized feature with same shape. - """ - x = x - self.mean - if self.norm_var: - x = x * self.istd - return x - - -class BPUConv2dSubsampling8(torch.nn.Module): - """Refactor wenet/transformer/subsampling.py::Conv2dSubsampling8 - - NOTE(xcsong): Only support pos_enc_class == NoPositionalEncoding - """ - - def __init__(self, module): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.right_context = module.right_context - self.subsampling_rate = module.subsampling_rate - assert isinstance(module.pos_enc, NoPositionalEncoding) - - # 1. Modify self.conv - # NOTE(xcsong): We change input shape from (1, 1, frames, mel_dim) - # to (1, 1, mel_dim, frames) for more efficient computation. - self.conv = module.conv - for idx in [0, 2, 4]: - self.conv[idx].weight = torch.nn.Parameter( - module.conv[idx].weight.transpose(2, 3) - ) - - # 2. Modify self.linear - # NOTE(xcsong): Split final projection to meet the requirment of - # maximum kernel_size (7 for XJ3) - self.linear = torch.nn.ModuleList() - odim = module.linear.weight.size(0) # 512, in this case - freq = module.linear.weight.size(1) // odim # 4608 // 512 == 9 - self.odim, self.freq = odim, freq - weight = module.linear.weight.reshape( - odim, odim, freq, 1 - ) # (odim, odim * freq) -> (odim, odim, freq, 1) - self.split_size = [] - num_split = (freq - 1) // 7 + 1 # XJ3 requires kernel_size <= 7 - slice_begin = 0 - for idx in range(num_split): - kernel_size = min(freq, (idx + 1) * 7) - idx * 7 - conv_ele = torch.nn.Conv2d(odim, odim, (kernel_size, 1), (kernel_size, 1)) - conv_ele.weight = torch.nn.Parameter( - weight[:, :, slice_begin : slice_begin + kernel_size, :] - ) - conv_ele.bias = torch.nn.Parameter(torch.zeros_like(conv_ele.bias)) - self.linear.append(conv_ele) - self.split_size.append(kernel_size) - slice_begin += kernel_size - self.linear[0].bias = torch.nn.Parameter(module.linear.bias) - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, 67, 80) - mask = torch.zeros(1, 1, 67) - original_result, _, _ = module(random_data, mask) # (1, 8, 512) - random_data = random_data.transpose(1, 2).unsqueeze(0) # (1, 1, 80, 67) - new_result = self.forward(random_data) # (1, 512, 1, 8) - np.testing.assert_allclose( - to_numpy(original_result), - to_numpy(new_result.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Subsample x with 4-D dataflow. - Args: - x (torch.Tensor): Input tensor (#batch, 1, mel_dim, time). - - Returns: - torch.Tensor: Subsampled tensor (#batch, odim, 1, time'), - where time' = time // 8. - """ - x = self.conv(x) # (1, odim, freq, time') - x_out = torch.zeros(x.size(0), self.odim, 1, x.size(3)) - x = torch.split(x, self.split_size, dim=2) - for idx, (x_part, layer) in enumerate(zip(x, self.linear)): - x_out += layer(x_part) - return x_out - - -class BPUMultiHeadedAttention(torch.nn.Module): - """Refactor wenet/transformer/attention.py::MultiHeadedAttention - - NOTE(xcsong): Only support attention_class == MultiHeadedAttention, - we do not consider RelPositionMultiHeadedAttention currently. - """ - - def __init__(self, module, chunk_size, left_chunks): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.d_k = module.d_k - self.h = module.h - n_feat = self.d_k * self.h - self.chunk_size = chunk_size - self.left_chunks = left_chunks - self.time = chunk_size * (left_chunks + 1) - self.activation = torch.nn.Softmax(dim=-1) - - # 1. Modify self.linear_x - self.linear_q = BPULinear(module.linear_q) - self.linear_k = BPULinear(module.linear_k) - self.linear_v = BPULinear(module.linear_v) - self.linear_out = BPULinear(module.linear_out) - # 2. denom - self.register_buffer( - "denom", torch.full((1, self.h, 1, 1), 1.0 / math.sqrt(self.d_k)) - ) - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, self.chunk_size, self.d_k * self.h) - mask = torch.ones((1, self.h, self.chunk_size, self.time), dtype=torch.bool) - cache = torch.zeros(1, self.h, self.chunk_size * self.left_chunks, self.d_k * 2) - original_out, original_cache = module( - random_data, - random_data, - random_data, - mask[:, 0, :, :], - torch.empty(0), - cache, - ) - random_data = random_data.transpose(1, 2).unsqueeze(2) - cache = cache.reshape( - 1, self.h, self.d_k * 2, self.chunk_size * self.left_chunks - ) - new_out, new_cache = self.forward( - random_data, random_data, random_data, mask, cache - ) - np.testing.assert_allclose( - to_numpy(original_out), - to_numpy(new_out.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - np.testing.assert_allclose( - to_numpy(original_cache), - to_numpy(new_cache.transpose(2, 3)), - rtol=1e-02, - atol=1e-03, - ) - - def forward( - self, - q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - mask: torch.Tensor, - cache: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute scaled dot product attention. - - Args: - q (torch.Tensor): Query tensor (#batch, size, 1, chunk_size). - k (torch.Tensor): Key tensor (#batch, size, 1, chunk_size). - v (torch.Tensor): Value tensor (#batch, size, 1, chunk_size). - mask (torch.Tensor): Mask tensor, - (#batch, head, chunk_size, cache_t + chunk_size). - cache (torch.Tensor): Cache tensor - (1, head, d_k * 2, cache_t), - where `cache_t == chunk_size * left_chunks`. - - - Returns: - torch.Tensor: Output tensor (#batch, size, 1, chunk_size). - torch.Tensor: Cache tensor - (1, head, d_k * 2, cache_t + chunk_size) - where `cache_t == chunk_size * left_chunks` - """ - # 1. Forward QKV - q = self.linear_q(q) # (1, d, 1, c) d == size, c == chunk_size - k = self.linear_k(k) # (1, d, 1, c) - v = self.linear_v(v) # (1, d, 1, c) - q = q.view(1, self.h, self.d_k, self.chunk_size) - k = k.view(1, self.h, self.d_k, self.chunk_size) - v = v.view(1, self.h, self.d_k, self.chunk_size) - q = q.transpose(2, 3) # (batch, head, time1, d_k) - k_cache, v_cache = torch.split(cache, cache.size(2) // 2, dim=2) - k = torch.cat((k_cache, k), dim=3) - v = torch.cat((v_cache, v), dim=3) - new_cache = torch.cat((k, v), dim=2) - # 2. (Q^T)K - scores = torch.matmul(q, k) * self.denom # (#b, n_head, time1, time2) - # 3. Forward attention - mask = mask.eq(0) - scores = scores.masked_fill(mask, -float("inf")) - attn = self.activation(scores).masked_fill(mask, 0.0) - attn = attn.transpose(2, 3) - x = torch.matmul(v, attn) - x = x.view(1, self.d_k * self.h, 1, self.chunk_size) - x_out = self.linear_out(x) - return x_out, new_cache - - -class BPUConvolution(torch.nn.Module): - """Refactor wenet/transformer/convolution.py::ConvolutionModule - - NOTE(xcsong): Only suport use_layer_norm == False - """ - - def __init__(self, module): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.lorder = module.lorder - self.use_layer_norm = False - self.activation = module.activation - channels = module.pointwise_conv1.weight.size(1) - self.channels = channels - kernel_size = module.depthwise_conv.weight.size(2) - assert module.use_layer_norm is False - - # 1. Modify self.pointwise_conv1 - self.pointwise_conv1 = BPULinear(module.pointwise_conv1, True) - - # 2. Modify self.depthwise_conv - self.depthwise_conv = torch.nn.Conv2d( - channels, channels, (1, kernel_size), stride=1, groups=channels - ) - self.depthwise_conv.weight = torch.nn.Parameter( - module.depthwise_conv.weight.unsqueeze(-2) - ) - self.depthwise_conv.bias = torch.nn.Parameter(module.depthwise_conv.bias) - - # 3. Modify self.norm, Only support batchnorm2d - self.norm = torch.nn.BatchNorm2d(channels) - self.norm.training = False - self.norm.num_features = module.norm.num_features - self.norm.eps = module.norm.eps - self.norm.momentum = module.norm.momentum - self.norm.weight = torch.nn.Parameter(module.norm.weight) - self.norm.bias = torch.nn.Parameter(module.norm.bias) - self.norm.running_mean = module.norm.running_mean - self.norm.running_var = module.norm.running_var - - # 4. Modify self.pointwise_conv2 - self.pointwise_conv2 = BPULinear(module.pointwise_conv2, True) - - # 5. Identity conv, for running `concat` on BPU - self.identity = BPUIdentity(channels) - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, 8, self.channels) - cache = torch.zeros((1, self.channels, self.lorder)) - original_out, original_cache = module(random_data, cache=cache) - random_data = random_data.transpose(1, 2).unsqueeze(2) - cache = cache.unsqueeze(2) - new_out, new_cache = self.forward(random_data, cache) - np.testing.assert_allclose( - to_numpy(original_out), - to_numpy(new_out.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - np.testing.assert_allclose( - to_numpy(original_cache), - to_numpy(new_cache.squeeze(2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward( - self, x: torch.Tensor, cache: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute convolution module. - Args: - x (torch.Tensor): Input tensor (#batch, channels, 1, chunk_size). - cache (torch.Tensor): left context cache, it is only - used in causal convolution (#batch, channels, 1, cache_t). - Returns: - torch.Tensor: Output tensor (#batch, channels, 1, chunk_size). - torch.Tensor: Cache tensor (#batch, channels, 1, cache_t). - """ - # Concat cache - x = torch.cat((self.identity(cache), self.identity(x)), dim=3) - new_cache = x[:, :, :, -self.lorder :] - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channel, 1, dim) - x = torch.nn.functional.glu(x, dim=1) # (b, channel, 1, dim) - - # Depthwise Conv - x = self.depthwise_conv(x) - x = self.activation(self.norm(x)) - x = self.pointwise_conv2(x) - return x, new_cache - - -class BPUFFN(torch.nn.Module): - """Refactor wenet/transformer/positionwise_feed_forward.py::PositionwiseFeedForward""" - - def __init__(self, module): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.activation = module.activation - - # 1. Modify self.w_x - self.w_1 = BPULinear(module.w_1) - self.w_2 = BPULinear(module.w_2) - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, 8, self.w_1.idim) - original_out = module(random_data) - random_data = random_data.transpose(1, 2).unsqueeze(2) - new_out = self.forward(random_data) - np.testing.assert_allclose( - to_numpy(original_out), - to_numpy(new_out.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Forward function. - - Args: - xs: input tensor (B, D, 1, L) - Returns: - output tensor, (B, D, 1, L) - """ - return self.w_2(self.activation(self.w_1(x))) - - -class BPUConformerEncoderLayer(torch.nn.Module): - """Refactor wenet/transformer/encoder_layer.py::ConformerEncoderLayer""" - - def __init__(self, module, chunk_size, left_chunks, ln_run_on_bpu=False): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.size = module.size - assert module.normalize_before is True - assert module.concat_after is False - - # 1. Modify submodules - self.feed_forward_macaron = BPUFFN(module.feed_forward_macaron) - self.self_attn = BPUMultiHeadedAttention( - module.self_attn, chunk_size, left_chunks - ) - self.conv_module = BPUConvolution(module.conv_module) - self.feed_forward = BPUFFN(module.feed_forward) - - # 2. Modify norms - self.norm_ff = BPULayerNorm(module.norm_ff, chunk_size, ln_run_on_bpu) - self.norm_mha = BPULayerNorm(module.norm_mha, chunk_size, ln_run_on_bpu) - self.norm_ff_macron = BPULayerNorm( - module.norm_ff_macaron, chunk_size, ln_run_on_bpu - ) - self.norm_conv = BPULayerNorm(module.norm_conv, chunk_size, ln_run_on_bpu) - self.norm_final = BPULayerNorm(module.norm_final, chunk_size, ln_run_on_bpu) - - # 3. 4-D ff_scale - self.register_buffer( - "ff_scale", torch.full((1, self.size, 1, 1), module.ff_scale) - ) - - self.check_equal(original) - - def check_equal(self, module): - time1 = self.self_attn.chunk_size - time2 = self.self_attn.time - h, d_k = self.self_attn.h, self.self_attn.d_k - random_x = torch.randn(1, time1, self.size) - att_mask = torch.ones(1, h, time1, time2) - att_cache = torch.zeros(1, h, time2 - time1, d_k * 2) - cnn_cache = torch.zeros(1, self.size, self.conv_module.lorder) - original_x, _, original_att_cache, original_cnn_cache = module( - random_x, - att_mask[:, 0, :, :], - torch.empty(0), - att_cache=att_cache, - cnn_cache=cnn_cache, - ) - random_x = random_x.transpose(1, 2).unsqueeze(2) - att_cache = att_cache.reshape(1, h, d_k * 2, time2 - time1) - cnn_cache = cnn_cache.unsqueeze(2) - new_x, new_att_cache, new_cnn_cache = self.forward( - random_x, att_mask, att_cache, cnn_cache - ) - np.testing.assert_allclose( - to_numpy(original_att_cache), - to_numpy(new_att_cache.transpose(2, 3)), - rtol=1e-02, - atol=1e-03, - ) - np.testing.assert_allclose( - to_numpy(original_x), - to_numpy(new_x.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - np.testing.assert_allclose( - to_numpy(original_cnn_cache), - to_numpy(new_cnn_cache.squeeze(2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward( - self, - x: torch.Tensor, - att_mask: torch.Tensor, - att_cache: torch.Tensor, - cnn_cache: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute encoded features. - - Args: - x (torch.Tensor): (#batch, size, 1, chunk_size) - att_mask (torch.Tensor): Mask tensor for the input - (#batch, head, chunk_size, cache_t1 + chunk_size), - att_cache (torch.Tensor): Cache tensor of the KEY & VALUE - (#batch=1, head, d_k * 2, cache_t1), head * d_k == size. - cnn_cache (torch.Tensor): Convolution cache in conformer layer - (#batch=1, size, 1, cache_t2) - Returns: - torch.Tensor: Output tensor (#batch, size, 1, chunk_size). - torch.Tensor: att_cache tensor, - (1, head, d_k * 2, cache_t1 + chunk_size). - torch.Tensor: cnn_cahce tensor (#batch, size, 1, cache_t2). - """ - # 1. ffn_macaron - residual = x - x = self.norm_ff_macron(x) - x = residual + self.ff_scale * self.feed_forward_macaron(x) - - # 2. attention - residual = x - x = self.norm_mha(x) - x_att, new_att_cache = self.self_attn(x, x, x, att_mask, att_cache) - x = residual + x_att - - # 3. convolution - residual = x - x = self.norm_conv(x) - x, new_cnn_cache = self.conv_module(x, cnn_cache) - x = residual + x - - # 4. ffn - residual = x - x = self.norm_ff(x) - x = residual + self.ff_scale * self.feed_forward(x) - - # 5. final post-norm - x = self.norm_final(x) - - return x, new_att_cache, new_cnn_cache - - -class BPUConformerEncoder(torch.nn.Module): - """Refactor wenet/transformer/encoder.py::ConformerEncoder""" - - def __init__(self, module, chunk_size, left_chunks, ln_run_on_bpu=False): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - output_size = module.output_size() - self._output_size = module.output_size() - self.after_norm = module.after_norm - self.chunk_size = chunk_size - self.left_chunks = left_chunks - self.head = module.encoders[0].self_attn.h - self.layers = len(module.encoders) - - # 1. Modify submodules - self.global_cmvn = BPUGlobalCMVN(module.global_cmvn) - self.embed = BPUConv2dSubsampling8(module.embed) - self.encoders = torch.nn.ModuleList() - for layer in module.encoders: - self.encoders.append( - BPUConformerEncoderLayer(layer, chunk_size, left_chunks, ln_run_on_bpu) - ) - - # 2. Auxiliary conv - self.identity_cnncache = BPUIdentity(output_size) - - self.check_equal(original) - - def check_equal(self, module): - time1 = self.encoders[0].self_attn.chunk_size - time2 = self.encoders[0].self_attn.time - layers = self.layers - h, d_k = self.head, self.encoders[0].self_attn.d_k - decoding_window = ( - (self.chunk_size - 1) * module.embed.subsampling_rate - + module.embed.right_context - + 1 - ) - lorder = self.encoders[0].conv_module.lorder - random_x = torch.randn(1, decoding_window, 80) - att_mask = torch.ones(1, h, time1, time2) - att_cache = torch.zeros(layers, h, time2 - time1, d_k * 2) - cnn_cache = torch.zeros(layers, 1, self._output_size, lorder) - orig_x, orig_att_cache, orig_cnn_cache = module.forward_chunk( - random_x, - 0, - time2 - time1, - att_mask=att_mask[:, 0, :, :], - att_cache=att_cache, - cnn_cache=cnn_cache, - ) - random_x = random_x.unsqueeze(0) - att_cache = att_cache.reshape(1, h * layers, d_k * 2, time2 - time1) - cnn_cache = cnn_cache.reshape(1, self._output_size, layers, lorder) - new_x, new_att_cache, new_cnn_cache = self.forward( - random_x, att_cache, cnn_cache, att_mask - ) - caches = torch.split(new_att_cache, h, dim=1) - caches = [c.transpose(2, 3) for c in caches] - np.testing.assert_allclose( - to_numpy(orig_att_cache), - to_numpy(torch.cat(caches, dim=0)), - rtol=1e-02, - atol=1e-03, - ) - np.testing.assert_allclose( - to_numpy(orig_x), - to_numpy(new_x.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - np.testing.assert_allclose( - to_numpy(orig_cnn_cache), - to_numpy(new_cnn_cache.transpose(0, 2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward( - self, - xs: torch.Tensor, - att_cache: torch.Tensor, - cnn_cache: torch.Tensor, - att_mask: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ Forward just one chunk - - Args: - xs (torch.Tensor): chunk input, with shape (b=1, 1, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (1, head * elayers, d_k * 2, cache_t1), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (1, hidden-dim, elayers, cache_t2), where - `cache_t2 == cnn.lorder - 1` - att_mask (torch.Tensor): Mask tensor for the input - (#batch, head, chunk_size, cache_t1 + chunk_size), - - Returns: - torch.Tensor: output of current input xs, - with shape (b=1, hidden-dim, 1, chunk_size). - torch.Tensor: new attention cache required for next chunk, with - same shape as the original att_cache. - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - """ - # xs: (B, 1, time, mel_dim) -> (B, 1, mel_dim, time) - xs = xs.transpose(2, 3) - xs = self.global_cmvn(xs) - # xs: (B, 1, mel_dim, time) -> (B, hidden_dim, 1, chunk_size) - xs = self.embed(xs) - - att_cache = torch.split(att_cache, self.head, dim=1) - cnn_cache = self.identity_cnncache(cnn_cache) - cnn_cache = torch.split(cnn_cache, 1, dim=2) - r_att_cache = [] - r_cnn_cache = [] - for i, layer in enumerate(self.encoders): - xs, new_att_cache, new_cnn_cache = layer( - xs, att_mask, att_cache=att_cache[i], cnn_cache=cnn_cache[i] - ) - r_att_cache.append(new_att_cache[:, :, :, self.chunk_size :]) - r_cnn_cache.append(new_cnn_cache) - r_att_cache = torch.cat(r_att_cache, dim=1) - r_cnn_cache = self.identity_cnncache(torch.cat(r_cnn_cache, dim=2)) - - xs = xs.squeeze(2).transpose(1, 2).contiguous() - xs = self.after_norm(xs) - # NOTE(xcsong): 4D in, 4D out to meet the requirment of CTC input. - xs = xs.transpose(1, 2).contiguous().unsqueeze(2) # (B, C, 1, T) - - return (xs, r_att_cache, r_cnn_cache) - - -class BPUCTC(torch.nn.Module): - """Refactor wenet/transformer/ctc.py::CTC""" - - def __init__(self, module): - super().__init__() - # Unchanged submodules and attributes - original = copy.deepcopy(module) - self.idim = module.ctc_lo.weight.size(1) - num_class = module.ctc_lo.weight.size(0) - - # 1. Modify self.ctc_lo, Split final projection to meet the - # requirment of maximum in/out channels (2048 for XJ3) - self.ctc_lo = torch.nn.ModuleList() - self.split_size = [] - num_split = (num_class - 1) // 2048 + 1 - for idx in range(num_split): - out_channel = min(num_class, (idx + 1) * 2048) - idx * 2048 - conv_ele = torch.nn.Conv2d(self.idim, out_channel, 1, 1) - self.ctc_lo.append(conv_ele) - self.split_size.append(out_channel) - orig_weight = torch.split(module.ctc_lo.weight, self.split_size, dim=0) - orig_bias = torch.split(module.ctc_lo.bias, self.split_size, dim=0) - for i, (w, b) in enumerate(zip(orig_weight, orig_bias)): - w = w.unsqueeze(2).unsqueeze(3) - self.ctc_lo[i].weight = torch.nn.Parameter(w) - self.ctc_lo[i].bias = torch.nn.Parameter(b) - - self.check_equal(original) - - def check_equal(self, module): - random_data = torch.randn(1, 100, self.idim) - original_result = module.ctc_lo(random_data) - random_data = random_data.transpose(1, 2).unsqueeze(2) - new_result = self.forward(random_data) - np.testing.assert_allclose( - to_numpy(original_result), - to_numpy(new_result.squeeze(2).transpose(1, 2)), - rtol=1e-02, - atol=1e-03, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """frame activations, without softmax. - - Args: - Tensor x: 4d tensor (B, hidden_dim, 1, chunk_size) - Returns: - torch.Tensor: (B, num_class, 1, chunk_size) - """ - out = [] - for i, layer in enumerate(self.ctc_lo): - out.append(layer(x)) - out = torch.cat(out, dim=1) - return out - - -def export_encoder(asr_model, args): - logger.info("Stage-1: export encoder") - decode_window, mel_dim = args.decoding_window, args.feature_size - encoder = BPUConformerEncoder( - asr_model.encoder, - args.chunk_size, - args.num_decoding_left_chunks, - args.ln_run_on_bpu, - ) - encoder.eval() - encoder_outpath = os.path.join(args.output_dir, "encoder.onnx") - - logger.info("Stage-1.1: prepare inputs for encoder") - chunk = torch.randn((1, 1, decode_window, mel_dim)) - required_cache_size = encoder.chunk_size * encoder.left_chunks - kv_time = required_cache_size + encoder.chunk_size - hidden, layers = encoder._output_size, len(encoder.encoders) - head = encoder.encoders[0].self_attn.h - d_k = hidden // head - lorder = encoder.encoders[0].conv_module.lorder - att_cache = torch.zeros(1, layers * head, d_k * 2, required_cache_size) - att_mask = torch.ones((1, head, encoder.chunk_size, kv_time)) - att_mask[:, :, :, :required_cache_size] = 0 - cnn_cache = torch.zeros((1, hidden, layers, lorder)) - inputs = (chunk, att_cache, cnn_cache, att_mask) - logger.info( - "chunk.size(): {} att_cache.size(): {} " - "cnn_cache.size(): {} att_mask.size(): {}".format( - list(chunk.size()), - list(att_cache.size()), - list(cnn_cache.size()), - list(att_mask.size()), - ) - ) - - logger.info("Stage-1.2: torch.onnx.export") - # NOTE(xcsong): Below attributes will be used in - # onnx2horizonbin.py::generate_config() - attributes = {} - attributes["input_name"] = "chunk;att_cache;cnn_cache;att_mask" - attributes["output_name"] = "output;r_att_cache;r_cnn_cache" - attributes["input_type"] = "featuremap;featuremap;featuremap;featuremap" - attributes["norm_type"] = "no_preprocess;no_preprocess;no_preprocess;no_preprocess" - attributes["input_layout_train"] = "NCHW;NCHW;NCHW;NCHW" - attributes["input_layout_rt"] = "NCHW;NCHW;NCHW;NCHW" - attributes[ - "input_shape" - ] = "{}x{}x{}x{};{}x{}x{}x{};{}x{}x{}x{};{}x{}x{}x{}".format( - chunk.size(0), - chunk.size(1), - chunk.size(2), - chunk.size(3), - att_cache.size(0), - att_cache.size(1), - att_cache.size(2), - att_cache.size(3), - cnn_cache.size(0), - cnn_cache.size(1), - cnn_cache.size(2), - cnn_cache.size(3), - att_mask.size(0), - att_mask.size(1), - att_mask.size(2), - att_mask.size(3), - ) - torch.onnx.export( # NOTE(xcsong): only support opset==11 - encoder, - inputs, - encoder_outpath, - opset_version=11, - export_params=True, - do_constant_folding=True, - input_names=attributes["input_name"].split(";"), - output_names=attributes["output_name"].split(";"), - dynamic_axes=None, - verbose=False, - ) - onnx_encoder = onnx.load(encoder_outpath) - for k in vars(args): - meta = onnx_encoder.metadata_props.add() - meta.key, meta.value = str(k), str(getattr(args, k)) - for k in attributes: - meta = onnx_encoder.metadata_props.add() - meta.key, meta.value = str(k), str(attributes[k]) - onnx.checker.check_model(onnx_encoder) - onnx.helper.printable_graph(onnx_encoder.graph) - onnx.save(onnx_encoder, encoder_outpath) - print_input_output_info(onnx_encoder, "onnx_encoder") - logger.info("Export onnx_encoder, done! see {}".format(encoder_outpath)) - - logger.info("Stage-1.3: check onnx_encoder and torch_encoder") - torch_output = [] - torch_chunk, torch_att_mask = copy.deepcopy(chunk), copy.deepcopy(att_mask) - torch_att_cache = copy.deepcopy(att_cache) - torch_cnn_cache = copy.deepcopy(cnn_cache) - for i in range(10): - logger.info( - "torch chunk-{}: {}, att_cache: {}, cnn_cache: {}" - ", att_mask: {}".format( - i, - list(torch_chunk.size()), - list(torch_att_cache.size()), - list(torch_cnn_cache.size()), - list(torch_att_mask.size()), - ) - ) - torch_att_mask[:, :, :, -(encoder.chunk_size * (i + 1)) :] = 1 - out, torch_att_cache, torch_cnn_cache = encoder( - torch_chunk, torch_att_cache, torch_cnn_cache, torch_att_mask - ) - torch_output.append(out) - torch_output = torch.cat(torch_output, dim=-1) - - onnx_output = [] - onnx_chunk, onnx_att_mask = to_numpy(chunk), to_numpy(att_mask) - onnx_att_cache = to_numpy(att_cache) - onnx_cnn_cache = to_numpy(cnn_cache) - ort_session = onnxruntime.InferenceSession(encoder_outpath) - input_names = [node.name for node in onnx_encoder.graph.input] - for i in range(10): - logger.info( - "onnx chunk-{}: {}, att_cache: {}, cnn_cache: {}," - " att_mask: {}".format( - i, - onnx_chunk.shape, - onnx_att_cache.shape, - onnx_cnn_cache.shape, - onnx_att_mask.shape, - ) - ) - onnx_att_mask[:, :, :, -(encoder.chunk_size * (i + 1)) :] = 1 - ort_inputs = { - "chunk": onnx_chunk, - "att_cache": onnx_att_cache, - "cnn_cache": onnx_cnn_cache, - "att_mask": onnx_att_mask, - } - ort_outs = ort_session.run(None, ort_inputs) - onnx_att_cache, onnx_cnn_cache = ort_outs[1], ort_outs[2] - onnx_output.append(ort_outs[0]) - onnx_output = np.concatenate(onnx_output, axis=-1) - - np.testing.assert_allclose( - to_numpy(torch_output), onnx_output, rtol=1e-03, atol=1e-04 - ) - meta = ort_session.get_modelmeta() - logger.info("custom_metadata_map={}".format(meta.custom_metadata_map)) - logger.info("Check onnx_encoder, pass!") - return encoder, ort_session - - -def export_ctc(asr_model, args): - logger.info("Stage-2: export ctc") - ctc = BPUCTC(asr_model.ctc).eval() - ctc_outpath = os.path.join(args.output_dir, "ctc.onnx") - - logger.info("Stage-2.1: prepare inputs for ctc") - hidden = torch.randn((1, args.output_size, 1, args.chunk_size)) - - logger.info("Stage-2.2: torch.onnx.export") - # NOTE(xcsong): Below attributes will be used in - # onnx2horizonbin.py::generate_config() - attributes = {} - attributes["input_name"], attributes["input_type"] = "hidden", "featuremap" - attributes["norm_type"] = "no_preprocess" - attributes["input_layout_train"] = "NCHW" - attributes["input_layout_rt"] = "NCHW" - attributes["input_shape"] = "{}x{}x{}x{}".format( - hidden.size(0), - hidden.size(1), - hidden.size(2), - hidden.size(3), - ) - torch.onnx.export( - ctc, - hidden, - ctc_outpath, - opset_version=11, - export_params=True, - do_constant_folding=True, - input_names=["hidden"], - output_names=["probs"], - dynamic_axes=None, - verbose=False, - ) - onnx_ctc = onnx.load(ctc_outpath) - for k in vars(args): - meta = onnx_ctc.metadata_props.add() - meta.key, meta.value = str(k), str(getattr(args, k)) - for k in attributes: - meta = onnx_ctc.metadata_props.add() - meta.key, meta.value = str(k), str(attributes[k]) - onnx.checker.check_model(onnx_ctc) - onnx.helper.printable_graph(onnx_ctc.graph) - onnx.save(onnx_ctc, ctc_outpath) - print_input_output_info(onnx_ctc, "onnx_ctc") - logger.info("Export onnx_ctc, done! see {}".format(ctc_outpath)) - - logger.info("Stage-2.3: check onnx_ctc and torch_ctc") - torch_output = ctc(hidden) - ort_session = onnxruntime.InferenceSession(ctc_outpath) - onnx_output = ort_session.run(None, {"hidden": to_numpy(hidden)}) - - np.testing.assert_allclose( - to_numpy(torch_output), onnx_output[0], rtol=1e-03, atol=1e-04 - ) - meta = ort_session.get_modelmeta() - logger.info("custom_metadata_map={}".format(meta.custom_metadata_map)) - logger.info("Check onnx_ctc, pass!") - return ctc, ort_session - - -def export_decoder(asr_model, args): - logger.info("Currently, Decoder is not supported.") - - -if __name__ == "__main__": - torch.manual_seed(777) - args = get_args() - args.ln_run_on_bpu = False - # NOTE(xcsong): XJ3 BPU only support static shapes - assert args.chunk_size > 0 - assert args.num_decoding_left_chunks > 0 - os.system("mkdir -p " + args.output_dir) - os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - - model = init_model(configs) - load_checkpoint(model, args.checkpoint) - model.eval() - print(model) - - args.feature_size = configs["input_dim"] - args.output_size = model.encoder.output_size() - args.decoding_window = ( - (args.chunk_size - 1) * model.encoder.embed.subsampling_rate - + model.encoder.embed.right_context - + 1 - ) - - export_encoder(model, args) - export_ctc(model, args) - export_decoder(model, args) diff --git a/modules/wenet_extractor/bin/export_onnx_cpu.py b/modules/wenet_extractor/bin/export_onnx_cpu.py deleted file mode 100644 index 9797b79e2ea6db1e6691c0656aaabc2308df4d22..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/export_onnx_cpu.py +++ /dev/null @@ -1,500 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import os -import copy -import sys - -import torch -import yaml -import numpy as np - -from wenet.utils.checkpoint import load_checkpoint -from wenet.utils.init_model import init_model - -try: - import onnx - import onnxruntime - from onnxruntime.quantization import quantize_dynamic, QuantType -except ImportError: - print("Please install onnx and onnxruntime!") - sys.exit(1) - - -def get_args(): - parser = argparse.ArgumentParser(description="export your script model") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument("--checkpoint", required=True, help="checkpoint model") - parser.add_argument("--output_dir", required=True, help="output directory") - parser.add_argument( - "--chunk_size", required=True, type=int, help="decoding chunk size" - ) - parser.add_argument( - "--num_decoding_left_chunks", required=True, type=int, help="cache chunks" - ) - parser.add_argument( - "--reverse_weight", - default=0.5, - type=float, - help="reverse_weight in attention_rescoing", - ) - args = parser.parse_args() - return args - - -def to_numpy(tensor): - if tensor.requires_grad: - return tensor.detach().cpu().numpy() - else: - return tensor.cpu().numpy() - - -def print_input_output_info(onnx_model, name, prefix="\t\t"): - input_names = [node.name for node in onnx_model.graph.input] - input_shapes = [ - [d.dim_value for d in node.type.tensor_type.shape.dim] - for node in onnx_model.graph.input - ] - output_names = [node.name for node in onnx_model.graph.output] - output_shapes = [ - [d.dim_value for d in node.type.tensor_type.shape.dim] - for node in onnx_model.graph.output - ] - print("{}{} inputs : {}".format(prefix, name, input_names)) - print("{}{} input shapes : {}".format(prefix, name, input_shapes)) - print("{}{} outputs: {}".format(prefix, name, output_names)) - print("{}{} output shapes : {}".format(prefix, name, output_shapes)) - - -def export_encoder(asr_model, args): - print("Stage-1: export encoder") - encoder = asr_model.encoder - encoder.forward = encoder.forward_chunk - encoder_outpath = os.path.join(args["output_dir"], "encoder.onnx") - - print("\tStage-1.1: prepare inputs for encoder") - chunk = torch.randn((args["batch"], args["decoding_window"], args["feature_size"])) - offset = 0 - # NOTE(xcsong): The uncertainty of `next_cache_start` only appears - # in the first few chunks, this is caused by dynamic att_cache shape, i,e - # (0, 0, 0, 0) for 1st chunk and (elayers, head, ?, d_k*2) for subsequent - # chunks. One way to ease the ONNX export is to keep `next_cache_start` - # as a fixed value. To do this, for the **first** chunk, if - # left_chunks > 0, we feed real cache & real mask to the model, otherwise - # fake cache & fake mask. In this way, we get: - # 1. 16/-1 mode: next_cache_start == 0 for all chunks - # 2. 16/4 mode: next_cache_start == chunk_size for all chunks - # 3. 16/0 mode: next_cache_start == chunk_size for all chunks - # 4. -1/-1 mode: next_cache_start == 0 for all chunks - # NO MORE DYNAMIC CHANGES!! - # - # NOTE(Mddct): We retain the current design for the convenience of supporting some - # inference frameworks without dynamic shapes. If you're interested in all-in-one - # model that supports different chunks please see: - # https://github.com/wenet-e2e/wenet/pull/1174 - - if args["left_chunks"] > 0: # 16/4 - required_cache_size = args["chunk_size"] * args["left_chunks"] - offset = required_cache_size - # Real cache - att_cache = torch.zeros( - ( - args["num_blocks"], - args["head"], - required_cache_size, - args["output_size"] // args["head"] * 2, - ) - ) - # Real mask - att_mask = torch.ones( - (args["batch"], 1, required_cache_size + args["chunk_size"]), - dtype=torch.bool, - ) - att_mask[:, :, :required_cache_size] = 0 - elif args["left_chunks"] <= 0: # 16/-1, -1/-1, 16/0 - required_cache_size = -1 if args["left_chunks"] < 0 else 0 - # Fake cache - att_cache = torch.zeros( - ( - args["num_blocks"], - args["head"], - 0, - args["output_size"] // args["head"] * 2, - ) - ) - # Fake mask - att_mask = torch.ones((0, 0, 0), dtype=torch.bool) - cnn_cache = torch.zeros( - ( - args["num_blocks"], - args["batch"], - args["output_size"], - args["cnn_module_kernel"] - 1, - ) - ) - inputs = (chunk, offset, required_cache_size, att_cache, cnn_cache, att_mask) - print( - "\t\tchunk.size(): {}\n".format(chunk.size()), - "\t\toffset: {}\n".format(offset), - "\t\trequired_cache: {}\n".format(required_cache_size), - "\t\tatt_cache.size(): {}\n".format(att_cache.size()), - "\t\tcnn_cache.size(): {}\n".format(cnn_cache.size()), - "\t\tatt_mask.size(): {}\n".format(att_mask.size()), - ) - - print("\tStage-1.2: torch.onnx.export") - dynamic_axes = { - "chunk": {1: "T"}, - "att_cache": {2: "T_CACHE"}, - "att_mask": {2: "T_ADD_T_CACHE"}, - "output": {1: "T"}, - "r_att_cache": {2: "T_CACHE"}, - } - # NOTE(xcsong): We keep dynamic axes even if in 16/4 mode, this is - # to avoid padding the last chunk (which usually contains less - # frames than required). For users who want static axes, just pop - # out specific axis. - # if args['chunk_size'] > 0: # 16/4, 16/-1, 16/0 - # dynamic_axes.pop('chunk') - # dynamic_axes.pop('output') - # if args['left_chunks'] >= 0: # 16/4, 16/0 - # # NOTE(xsong): since we feed real cache & real mask into the - # # model when left_chunks > 0, the shape of cache will never - # # be changed. - # dynamic_axes.pop('att_cache') - # dynamic_axes.pop('r_att_cache') - torch.onnx.export( - encoder, - inputs, - encoder_outpath, - opset_version=13, - export_params=True, - do_constant_folding=True, - input_names=[ - "chunk", - "offset", - "required_cache_size", - "att_cache", - "cnn_cache", - "att_mask", - ], - output_names=["output", "r_att_cache", "r_cnn_cache"], - dynamic_axes=dynamic_axes, - verbose=False, - ) - onnx_encoder = onnx.load(encoder_outpath) - for k, v in args.items(): - meta = onnx_encoder.metadata_props.add() - meta.key, meta.value = str(k), str(v) - onnx.checker.check_model(onnx_encoder) - onnx.helper.printable_graph(onnx_encoder.graph) - # NOTE(xcsong): to add those metadatas we need to reopen - # the file and resave it. - onnx.save(onnx_encoder, encoder_outpath) - print_input_output_info(onnx_encoder, "onnx_encoder") - # Dynamic quantization - model_fp32 = encoder_outpath - model_quant = os.path.join(args["output_dir"], "encoder.quant.onnx") - quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8) - print("\t\tExport onnx_encoder, done! see {}".format(encoder_outpath)) - - print("\tStage-1.3: check onnx_encoder and torch_encoder") - torch_output = [] - torch_chunk = copy.deepcopy(chunk) - torch_offset = copy.deepcopy(offset) - torch_required_cache_size = copy.deepcopy(required_cache_size) - torch_att_cache = copy.deepcopy(att_cache) - torch_cnn_cache = copy.deepcopy(cnn_cache) - torch_att_mask = copy.deepcopy(att_mask) - for i in range(10): - print( - "\t\ttorch chunk-{}: {}, offset: {}, att_cache: {}," - " cnn_cache: {}, att_mask: {}".format( - i, - list(torch_chunk.size()), - torch_offset, - list(torch_att_cache.size()), - list(torch_cnn_cache.size()), - list(torch_att_mask.size()), - ) - ) - # NOTE(xsong): att_mask of the first few batches need changes if - # we use 16/4 mode. - if args["left_chunks"] > 0: # 16/4 - torch_att_mask[:, :, -(args["chunk_size"] * (i + 1)) :] = 1 - out, torch_att_cache, torch_cnn_cache = encoder( - torch_chunk, - torch_offset, - torch_required_cache_size, - torch_att_cache, - torch_cnn_cache, - torch_att_mask, - ) - torch_output.append(out) - torch_offset += out.size(1) - torch_output = torch.cat(torch_output, dim=1) - - onnx_output = [] - onnx_chunk = to_numpy(chunk) - onnx_offset = np.array((offset)).astype(np.int64) - onnx_required_cache_size = np.array((required_cache_size)).astype(np.int64) - onnx_att_cache = to_numpy(att_cache) - onnx_cnn_cache = to_numpy(cnn_cache) - onnx_att_mask = to_numpy(att_mask) - ort_session = onnxruntime.InferenceSession(encoder_outpath) - input_names = [node.name for node in onnx_encoder.graph.input] - for i in range(10): - print( - "\t\tonnx chunk-{}: {}, offset: {}, att_cache: {}," - " cnn_cache: {}, att_mask: {}".format( - i, - onnx_chunk.shape, - onnx_offset, - onnx_att_cache.shape, - onnx_cnn_cache.shape, - onnx_att_mask.shape, - ) - ) - # NOTE(xsong): att_mask of the first few batches need changes if - # we use 16/4 mode. - if args["left_chunks"] > 0: # 16/4 - onnx_att_mask[:, :, -(args["chunk_size"] * (i + 1)) :] = 1 - ort_inputs = { - "chunk": onnx_chunk, - "offset": onnx_offset, - "required_cache_size": onnx_required_cache_size, - "att_cache": onnx_att_cache, - "cnn_cache": onnx_cnn_cache, - "att_mask": onnx_att_mask, - } - # NOTE(xcsong): If we use 16/-1, -1/-1 or 16/0 mode, `next_cache_start` - # will be hardcoded to 0 or chunk_size by ONNX, thus - # required_cache_size and att_mask are no more needed and they will - # be removed by ONNX automatically. - for k in list(ort_inputs): - if k not in input_names: - ort_inputs.pop(k) - ort_outs = ort_session.run(None, ort_inputs) - onnx_att_cache, onnx_cnn_cache = ort_outs[1], ort_outs[2] - onnx_output.append(ort_outs[0]) - onnx_offset += ort_outs[0].shape[1] - onnx_output = np.concatenate(onnx_output, axis=1) - - np.testing.assert_allclose( - to_numpy(torch_output), onnx_output, rtol=1e-03, atol=1e-05 - ) - meta = ort_session.get_modelmeta() - print("\t\tcustom_metadata_map={}".format(meta.custom_metadata_map)) - print("\t\tCheck onnx_encoder, pass!") - - -def export_ctc(asr_model, args): - print("Stage-2: export ctc") - ctc = asr_model.ctc - ctc.forward = ctc.log_softmax - ctc_outpath = os.path.join(args["output_dir"], "ctc.onnx") - - print("\tStage-2.1: prepare inputs for ctc") - hidden = torch.randn( - ( - args["batch"], - args["chunk_size"] if args["chunk_size"] > 0 else 16, - args["output_size"], - ) - ) - - print("\tStage-2.2: torch.onnx.export") - dynamic_axes = {"hidden": {1: "T"}, "probs": {1: "T"}} - torch.onnx.export( - ctc, - hidden, - ctc_outpath, - opset_version=13, - export_params=True, - do_constant_folding=True, - input_names=["hidden"], - output_names=["probs"], - dynamic_axes=dynamic_axes, - verbose=False, - ) - onnx_ctc = onnx.load(ctc_outpath) - for k, v in args.items(): - meta = onnx_ctc.metadata_props.add() - meta.key, meta.value = str(k), str(v) - onnx.checker.check_model(onnx_ctc) - onnx.helper.printable_graph(onnx_ctc.graph) - onnx.save(onnx_ctc, ctc_outpath) - print_input_output_info(onnx_ctc, "onnx_ctc") - # Dynamic quantization - model_fp32 = ctc_outpath - model_quant = os.path.join(args["output_dir"], "ctc.quant.onnx") - quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8) - print("\t\tExport onnx_ctc, done! see {}".format(ctc_outpath)) - - print("\tStage-2.3: check onnx_ctc and torch_ctc") - torch_output = ctc(hidden) - ort_session = onnxruntime.InferenceSession(ctc_outpath) - onnx_output = ort_session.run(None, {"hidden": to_numpy(hidden)}) - - np.testing.assert_allclose( - to_numpy(torch_output), onnx_output[0], rtol=1e-03, atol=1e-05 - ) - print("\t\tCheck onnx_ctc, pass!") - - -def export_decoder(asr_model, args): - print("Stage-3: export decoder") - decoder = asr_model - # NOTE(lzhin): parameters of encoder will be automatically removed - # since they are not used during rescoring. - decoder.forward = decoder.forward_attention_decoder - decoder_outpath = os.path.join(args["output_dir"], "decoder.onnx") - - print("\tStage-3.1: prepare inputs for decoder") - # hardcode time->200 nbest->10 len->20, they are dynamic axes. - encoder_out = torch.randn((1, 200, args["output_size"])) - hyps = torch.randint(low=0, high=args["vocab_size"], size=[10, 20]) - hyps[:, 0] = args["vocab_size"] - 1 # - hyps_lens = torch.randint(low=15, high=21, size=[10]) - - print("\tStage-3.2: torch.onnx.export") - dynamic_axes = { - "hyps": {0: "NBEST", 1: "L"}, - "hyps_lens": {0: "NBEST"}, - "encoder_out": {1: "T"}, - "score": {0: "NBEST", 1: "L"}, - "r_score": {0: "NBEST", 1: "L"}, - } - inputs = (hyps, hyps_lens, encoder_out, args["reverse_weight"]) - torch.onnx.export( - decoder, - inputs, - decoder_outpath, - opset_version=13, - export_params=True, - do_constant_folding=True, - input_names=["hyps", "hyps_lens", "encoder_out", "reverse_weight"], - output_names=["score", "r_score"], - dynamic_axes=dynamic_axes, - verbose=False, - ) - onnx_decoder = onnx.load(decoder_outpath) - for k, v in args.items(): - meta = onnx_decoder.metadata_props.add() - meta.key, meta.value = str(k), str(v) - onnx.checker.check_model(onnx_decoder) - onnx.helper.printable_graph(onnx_decoder.graph) - onnx.save(onnx_decoder, decoder_outpath) - print_input_output_info(onnx_decoder, "onnx_decoder") - model_fp32 = decoder_outpath - model_quant = os.path.join(args["output_dir"], "decoder.quant.onnx") - quantize_dynamic(model_fp32, model_quant, weight_type=QuantType.QUInt8) - print("\t\tExport onnx_decoder, done! see {}".format(decoder_outpath)) - - print("\tStage-3.3: check onnx_decoder and torch_decoder") - torch_score, torch_r_score = decoder( - hyps, hyps_lens, encoder_out, args["reverse_weight"] - ) - ort_session = onnxruntime.InferenceSession(decoder_outpath) - input_names = [node.name for node in onnx_decoder.graph.input] - ort_inputs = { - "hyps": to_numpy(hyps), - "hyps_lens": to_numpy(hyps_lens), - "encoder_out": to_numpy(encoder_out), - "reverse_weight": np.array((args["reverse_weight"])), - } - for k in list(ort_inputs): - if k not in input_names: - ort_inputs.pop(k) - onnx_output = ort_session.run(None, ort_inputs) - - np.testing.assert_allclose( - to_numpy(torch_score), onnx_output[0], rtol=1e-03, atol=1e-05 - ) - if args["is_bidirectional_decoder"] and args["reverse_weight"] > 0.0: - np.testing.assert_allclose( - to_numpy(torch_r_score), onnx_output[1], rtol=1e-03, atol=1e-05 - ) - print("\t\tCheck onnx_decoder, pass!") - - -def main(): - torch.manual_seed(777) - args = get_args() - output_dir = args.output_dir - os.system("mkdir -p " + output_dir) - os.environ["CUDA_VISIBLE_DEVICES"] = "-1" - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - - model = init_model(configs) - load_checkpoint(model, args.checkpoint) - model.eval() - print(model) - - arguments = {} - arguments["output_dir"] = output_dir - arguments["batch"] = 1 - arguments["chunk_size"] = args.chunk_size - arguments["left_chunks"] = args.num_decoding_left_chunks - arguments["reverse_weight"] = args.reverse_weight - arguments["output_size"] = configs["encoder_conf"]["output_size"] - arguments["num_blocks"] = configs["encoder_conf"]["num_blocks"] - arguments["cnn_module_kernel"] = configs["encoder_conf"].get("cnn_module_kernel", 1) - arguments["head"] = configs["encoder_conf"]["attention_heads"] - arguments["feature_size"] = configs["input_dim"] - arguments["vocab_size"] = configs["output_dim"] - # NOTE(xcsong): if chunk_size == -1, hardcode to 67 - arguments["decoding_window"] = ( - (args.chunk_size - 1) * model.encoder.embed.subsampling_rate - + model.encoder.embed.right_context - + 1 - if args.chunk_size > 0 - else 67 - ) - arguments["encoder"] = configs["encoder"] - arguments["decoder"] = configs["decoder"] - arguments["subsampling_rate"] = model.subsampling_rate() - arguments["right_context"] = model.right_context() - arguments["sos_symbol"] = model.sos_symbol() - arguments["eos_symbol"] = model.eos_symbol() - arguments["is_bidirectional_decoder"] = 1 if model.is_bidirectional_decoder() else 0 - - # NOTE(xcsong): Please note that -1/-1 means non-streaming model! It is - # not a [16/4 16/-1 16/0] all-in-one model and it should not be used in - # streaming mode (i.e., setting chunk_size=16 in `decoder_main`). If you - # want to use 16/-1 or any other streaming mode in `decoder_main`, - # please export onnx in the same config. - if arguments["left_chunks"] > 0: - assert arguments["chunk_size"] > 0 # -1/4 not supported - - export_encoder(model, arguments) - export_ctc(model, arguments) - export_decoder(model, arguments) - - -if __name__ == "__main__": - main() diff --git a/modules/wenet_extractor/bin/export_onnx_gpu.py b/modules/wenet_extractor/bin/export_onnx_gpu.py deleted file mode 100644 index 0b3d9be24633680cbeac1e2261e68bb7cabdffcb..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/export_onnx_gpu.py +++ /dev/null @@ -1,1121 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import os -import sys - -import torch -import yaml -import logging - -import torch.nn.functional as F -from wenet.utils.checkpoint import load_checkpoint -from wenet.transformer.ctc import CTC -from wenet.transformer.decoder import TransformerDecoder -from wenet.transformer.encoder import BaseEncoder -from wenet.utils.init_model import init_model -from wenet.utils.mask import make_pad_mask - -try: - import onnxruntime -except ImportError: - print("Please install onnxruntime-gpu!") - sys.exit(1) - -logger = logging.getLogger(__file__) -logger.setLevel(logging.INFO) - - -class Encoder(torch.nn.Module): - def __init__(self, encoder: BaseEncoder, ctc: CTC, beam_size: int = 10): - super().__init__() - self.encoder = encoder - self.ctc = ctc - self.beam_size = beam_size - - def forward( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - ): - """Encoder - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - Returns: - encoder_out: B x T x F - encoder_out_lens: B - ctc_log_probs: B x T x V - beam_log_probs: B x T x beam_size - beam_log_probs_idx: B x T x beam_size - """ - encoder_out, encoder_mask = self.encoder(speech, speech_lengths, -1, -1) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - ctc_log_probs = self.ctc.log_softmax(encoder_out) - encoder_out_lens = encoder_out_lens.int() - beam_log_probs, beam_log_probs_idx = torch.topk( - ctc_log_probs, self.beam_size, dim=2 - ) - return ( - encoder_out, - encoder_out_lens, - ctc_log_probs, - beam_log_probs, - beam_log_probs_idx, - ) - - -class StreamingEncoder(torch.nn.Module): - def __init__(self, model, required_cache_size, beam_size, transformer=False): - super().__init__() - self.ctc = model.ctc - self.subsampling_rate = model.encoder.embed.subsampling_rate - self.embed = model.encoder.embed - self.global_cmvn = model.encoder.global_cmvn - self.required_cache_size = required_cache_size - self.beam_size = beam_size - self.encoder = model.encoder - self.transformer = transformer - - def forward(self, chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask): - """Streaming Encoder - Args: - xs (torch.Tensor): chunk input, with shape (b, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - offset (torch.Tensor): offset with shape (b, 1) - 1 is retained for triton deployment - required_cache_size (int): cache size required for next chunk - compuation - > 0: actual cache size - <= 0: not allowed in streaming gpu encoder ` - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (b, elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (b, elayers, b, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - cache_mask: (torch.Tensor): cache mask with shape (b, required_cache_size) - in a batch of request, each request may have different - history cache. Cache mask is used to indidate the effective - cache for each request - Returns: - torch.Tensor: log probabilities of ctc output and cutoff by beam size - with shape (b, chunk_size, beam) - torch.Tensor: index of top beam size probabilities for each timestep - with shape (b, chunk_size, beam) - torch.Tensor: output of current input xs, - with shape (b, chunk_size, hidden-dim). - torch.Tensor: new attention cache required for next chunk, with - same shape (b, elayers, head, cache_t1, d_k * 2) - as the original att_cache - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - torch.Tensor: new cache mask, with same shape as the original - cache mask - """ - offset = offset.squeeze(1) - T = chunk_xs.size(1) - chunk_mask = ~make_pad_mask(chunk_lens, T).unsqueeze(1) - # B X 1 X T - chunk_mask = chunk_mask.to(chunk_xs.dtype) - # transpose batch & num_layers dim - att_cache = torch.transpose(att_cache, 0, 1) - cnn_cache = torch.transpose(cnn_cache, 0, 1) - - # rewrite encoder.forward_chunk - # <---------forward_chunk START---------> - xs = self.global_cmvn(chunk_xs) - # chunk mask is important for batch inferencing since - # different sequence in a batch has different length - xs, pos_emb, chunk_mask = self.embed(xs, chunk_mask, offset) - cache_size = att_cache.size(3) # required cache size - masks = torch.cat((cache_mask, chunk_mask), dim=2) - index = offset - cache_size - - pos_emb = self.embed.position_encoding(index, cache_size + xs.size(1)) - pos_emb = pos_emb.to(dtype=xs.dtype) - - next_cache_start = -self.required_cache_size - r_cache_mask = masks[:, :, next_cache_start:] - - r_att_cache = [] - r_cnn_cache = [] - for i, layer in enumerate(self.encoder.encoders): - xs, _, new_att_cache, new_cnn_cache = layer( - xs, masks, pos_emb, att_cache=att_cache[i], cnn_cache=cnn_cache[i] - ) - # shape(new_att_cache) is (B, head, attention_key_size, d_k * 2), - # shape(new_cnn_cache) is (B, hidden-dim, cache_t2) - r_att_cache.append(new_att_cache[:, :, next_cache_start:, :].unsqueeze(1)) - if not self.transformer: - r_cnn_cache.append(new_cnn_cache.unsqueeze(1)) - if self.encoder.normalize_before: - chunk_out = self.encoder.after_norm(xs) - else: - chunk_out = xs - - r_att_cache = torch.cat(r_att_cache, dim=1) # concat on layers idx - if not self.transformer: - r_cnn_cache = torch.cat(r_cnn_cache, dim=1) # concat on layers - - # <---------forward_chunk END---------> - - log_ctc_probs = self.ctc.log_softmax(chunk_out) - log_probs, log_probs_idx = torch.topk(log_ctc_probs, self.beam_size, dim=2) - log_probs = log_probs.to(chunk_xs.dtype) - - r_offset = offset + chunk_out.shape[1] - # the below ops not supported in Tensorrt - # chunk_out_lens = torch.div(chunk_lens, subsampling_rate, - # rounding_mode='floor') - chunk_out_lens = chunk_lens // self.subsampling_rate - r_offset = r_offset.unsqueeze(1) - - return ( - log_probs, - log_probs_idx, - chunk_out, - chunk_out_lens, - r_offset, - r_att_cache, - r_cnn_cache, - r_cache_mask, - ) - - -class StreamingSqueezeformerEncoder(torch.nn.Module): - def __init__(self, model, required_cache_size, beam_size): - super().__init__() - self.ctc = model.ctc - self.subsampling_rate = model.encoder.embed.subsampling_rate - self.embed = model.encoder.embed - self.global_cmvn = model.encoder.global_cmvn - self.required_cache_size = required_cache_size - self.beam_size = beam_size - self.encoder = model.encoder - self.reduce_idx = model.encoder.reduce_idx - self.recover_idx = model.encoder.recover_idx - if self.reduce_idx is None: - self.time_reduce = None - else: - if self.recover_idx is None: - self.time_reduce = "normal" # no recovery at the end - else: - self.time_reduce = "recover" # recovery at the end - assert len(self.reduce_idx) == len(self.recover_idx) - - def calculate_downsampling_factor(self, i: int) -> int: - if self.reduce_idx is None: - return 1 - else: - reduce_exp, recover_exp = 0, 0 - for exp, rd_idx in enumerate(self.reduce_idx): - if i >= rd_idx: - reduce_exp = exp + 1 - if self.recover_idx is not None: - for exp, rc_idx in enumerate(self.recover_idx): - if i >= rc_idx: - recover_exp = exp + 1 - return int(2 ** (reduce_exp - recover_exp)) - - def forward(self, chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask): - """Streaming Encoder - Args: - xs (torch.Tensor): chunk input, with shape (b, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - offset (torch.Tensor): offset with shape (b, 1) - 1 is retained for triton deployment - required_cache_size (int): cache size required for next chunk - compuation - > 0: actual cache size - <= 0: not allowed in streaming gpu encoder ` - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (b, elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (b, elayers, b, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - cache_mask: (torch.Tensor): cache mask with shape (b, required_cache_size) - in a batch of request, each request may have different - history cache. Cache mask is used to indidate the effective - cache for each request - Returns: - torch.Tensor: log probabilities of ctc output and cutoff by beam size - with shape (b, chunk_size, beam) - torch.Tensor: index of top beam size probabilities for each timestep - with shape (b, chunk_size, beam) - torch.Tensor: output of current input xs, - with shape (b, chunk_size, hidden-dim). - torch.Tensor: new attention cache required for next chunk, with - same shape (b, elayers, head, cache_t1, d_k * 2) - as the original att_cache - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - torch.Tensor: new cache mask, with same shape as the original - cache mask - """ - offset = offset.squeeze(1) - T = chunk_xs.size(1) - chunk_mask = ~make_pad_mask(chunk_lens, T).unsqueeze(1) - # B X 1 X T - chunk_mask = chunk_mask.to(chunk_xs.dtype) - # transpose batch & num_layers dim - att_cache = torch.transpose(att_cache, 0, 1) - cnn_cache = torch.transpose(cnn_cache, 0, 1) - - # rewrite encoder.forward_chunk - # <---------forward_chunk START---------> - xs = self.global_cmvn(chunk_xs) - # chunk mask is important for batch inferencing since - # different sequence in a batch has different length - xs, pos_emb, chunk_mask = self.embed(xs, chunk_mask, offset) - elayers, cache_size = att_cache.size(0), att_cache.size(3) - att_mask = torch.cat((cache_mask, chunk_mask), dim=2) - index = offset - cache_size - - pos_emb = self.embed.position_encoding(index, cache_size + xs.size(1)) - pos_emb = pos_emb.to(dtype=xs.dtype) - - next_cache_start = -self.required_cache_size - r_cache_mask = att_mask[:, :, next_cache_start:] - - r_att_cache = [] - r_cnn_cache = [] - mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) - mask_pad = mask_pad.unsqueeze(1) - max_att_len: int = 0 - recover_activations: List[ - Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] - ] = [] - index = 0 - xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int) - xs = self.encoder.preln(xs) - for i, layer in enumerate(self.encoder.encoders): - if self.reduce_idx is not None: - if self.time_reduce is not None and i in self.reduce_idx: - recover_activations.append((xs, att_mask, pos_emb, mask_pad)) - xs, xs_lens, att_mask, mask_pad = self.encoder.time_reduction_layer( - xs, xs_lens, att_mask, mask_pad - ) - pos_emb = pos_emb[:, ::2, :] - if self.encoder.pos_enc_layer_type == "rel_pos_repaired": - pos_emb = pos_emb[:, : xs.size(1) * 2 - 1, :] - index += 1 - - if self.recover_idx is not None: - if self.time_reduce == "recover" and i in self.recover_idx: - index -= 1 - ( - recover_tensor, - recover_att_mask, - recover_pos_emb, - recover_mask_pad, - ) = recover_activations[index] - # recover output length for ctc decode - xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2) - xs = self.encoder.time_recover_layer(xs) - recoverd_t = recover_tensor.size(1) - xs = recover_tensor + xs[:, :recoverd_t, :].contiguous() - att_mask = recover_att_mask - pos_emb = recover_pos_emb - mask_pad = recover_mask_pad - - factor = self.calculate_downsampling_factor(i) - - xs, _, new_att_cache, new_cnn_cache = layer( - xs, - att_mask, - pos_emb, - att_cache=att_cache[i][:, :, ::factor, :][ - :, :, : pos_emb.size(1) - xs.size(1), : - ] - if elayers > 0 - else att_cache[:, :, ::factor, :], - cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache, - ) - cached_att = new_att_cache[:, :, next_cache_start // factor :, :] - cached_cnn = new_cnn_cache.unsqueeze(1) - cached_att = ( - cached_att.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3) - ) - if i == 0: - # record length for the first block as max length - max_att_len = cached_att.size(2) - r_att_cache.append(cached_att[:, :, :max_att_len, :].unsqueeze(1)) - r_cnn_cache.append(cached_cnn) - - chunk_out = xs - r_att_cache = torch.cat(r_att_cache, dim=1) # concat on layers idx - r_cnn_cache = torch.cat(r_cnn_cache, dim=1) # concat on layers - - # <---------forward_chunk END---------> - - log_ctc_probs = self.ctc.log_softmax(chunk_out) - log_probs, log_probs_idx = torch.topk(log_ctc_probs, self.beam_size, dim=2) - log_probs = log_probs.to(chunk_xs.dtype) - - r_offset = offset + chunk_out.shape[1] - # the below ops not supported in Tensorrt - # chunk_out_lens = torch.div(chunk_lens, subsampling_rate, - # rounding_mode='floor') - chunk_out_lens = chunk_lens // self.subsampling_rate - r_offset = r_offset.unsqueeze(1) - - return ( - log_probs, - log_probs_idx, - chunk_out, - chunk_out_lens, - r_offset, - r_att_cache, - r_cnn_cache, - r_cache_mask, - ) - - -class StreamingEfficientConformerEncoder(torch.nn.Module): - def __init__(self, model, required_cache_size, beam_size): - super().__init__() - self.ctc = model.ctc - self.subsampling_rate = model.encoder.embed.subsampling_rate - self.embed = model.encoder.embed - self.global_cmvn = model.encoder.global_cmvn - self.required_cache_size = required_cache_size - self.beam_size = beam_size - self.encoder = model.encoder - - # Efficient Conformer - self.stride_layer_idx = model.encoder.stride_layer_idx - self.stride = model.encoder.stride - self.num_blocks = model.encoder.num_blocks - self.cnn_module_kernel = model.encoder.cnn_module_kernel - - def calculate_downsampling_factor(self, i: int) -> int: - factor = 1 - for idx, stride_idx in enumerate(self.stride_layer_idx): - if i > stride_idx: - factor *= self.stride[idx] - return factor - - def forward(self, chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask): - """Streaming Encoder - Args: - chunk_xs (torch.Tensor): chunk input, with shape (b, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - chunk_lens (torch.Tensor): - offset (torch.Tensor): offset with shape (b, 1) - 1 is retained for triton deployment - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (b, elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (b, elayers, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - cache_mask: (torch.Tensor): cache mask with shape (b, required_cache_size) - in a batch of request, each request may have different - history cache. Cache mask is used to indidate the effective - cache for each request - Returns: - torch.Tensor: log probabilities of ctc output and cutoff by beam size - with shape (b, chunk_size, beam) - torch.Tensor: index of top beam size probabilities for each timestep - with shape (b, chunk_size, beam) - torch.Tensor: output of current input xs, - with shape (b, chunk_size, hidden-dim). - torch.Tensor: new attention cache required for next chunk, with - same shape (b, elayers, head, cache_t1, d_k * 2) - as the original att_cache - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - torch.Tensor: new cache mask, with same shape as the original - cache mask - """ - offset = offset.squeeze(1) # (b, ) - offset *= self.calculate_downsampling_factor(self.num_blocks + 1) - - T = chunk_xs.size(1) - chunk_mask = ~make_pad_mask(chunk_lens, T).unsqueeze(1) # (b, 1, T) - # B X 1 X T - chunk_mask = chunk_mask.to(chunk_xs.dtype) - # transpose batch & num_layers dim - # Shape(att_cache): (elayers, b, head, cache_t1, d_k * 2) - # Shape(cnn_cache): (elayers, b, outsize, cnn_kernel) - att_cache = torch.transpose(att_cache, 0, 1) - cnn_cache = torch.transpose(cnn_cache, 0, 1) - - # rewrite encoder.forward_chunk - # <---------forward_chunk START---------> - xs = self.global_cmvn(chunk_xs) - # chunk mask is important for batch inferencing since - # different sequence in a batch has different length - xs, pos_emb, chunk_mask = self.embed(xs, chunk_mask, offset) - cache_size = att_cache.size(3) # required cache size - masks = torch.cat((cache_mask, chunk_mask), dim=2) - att_mask = torch.cat((cache_mask, chunk_mask), dim=2) - index = offset - cache_size - - pos_emb = self.embed.position_encoding(index, cache_size + xs.size(1)) - pos_emb = pos_emb.to(dtype=xs.dtype) - - next_cache_start = -self.required_cache_size - r_cache_mask = masks[:, :, next_cache_start:] - - r_att_cache = [] - r_cnn_cache = [] - mask_pad = chunk_mask.to(torch.bool) - max_att_len, max_cnn_len = 0, 0 # for repeat_interleave of new_att_cache - for i, layer in enumerate(self.encoder.encoders): - factor = self.calculate_downsampling_factor(i) - # NOTE(xcsong): Before layer.forward - # shape(att_cache[i:i + 1]) is (b, head, cache_t1, d_k * 2), - # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) - # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ] - att_cache_trunc = 0 - if xs.size(1) + att_cache.size(3) / factor > pos_emb.size(1): - # The time step is not divisible by the downsampling multiple - # We propose to double the chunk_size. - att_cache_trunc = ( - xs.size(1) + att_cache.size(3) // factor - pos_emb.size(1) + 1 - ) - xs, _, new_att_cache, new_cnn_cache = layer( - xs, - att_mask, - pos_emb, - mask_pad=mask_pad, - att_cache=att_cache[i][:, :, ::factor, :][:, :, att_cache_trunc:, :], - cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache, - ) - - if i in self.stride_layer_idx: - # compute time dimension for next block - efficient_index = self.stride_layer_idx.index(i) - att_mask = att_mask[ - :, :: self.stride[efficient_index], :: self.stride[efficient_index] - ] - mask_pad = mask_pad[ - :, :: self.stride[efficient_index], :: self.stride[efficient_index] - ] - pos_emb = pos_emb[:, :: self.stride[efficient_index], :] - - # shape(new_att_cache) = [batch, head, time2, outdim] - new_att_cache = new_att_cache[:, :, next_cache_start // factor :, :] - # shape(new_cnn_cache) = [batch, 1, outdim, cache_t2] - new_cnn_cache = new_cnn_cache.unsqueeze(1) # shape(1):layerID - - # use repeat_interleave to new_att_cache - # new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2) - new_att_cache = ( - new_att_cache.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3) - ) - # padding new_cnn_cache to cnn.lorder for casual convolution - new_cnn_cache = F.pad( - new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0) - ) - - if i == 0: - # record length for the first block as max length - max_att_len = new_att_cache.size(2) - max_cnn_len = new_cnn_cache.size(3) - - # update real shape of att_cache and cnn_cache - r_att_cache.append(new_att_cache[:, :, -max_att_len:, :].unsqueeze(1)) - r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:]) - - if self.encoder.normalize_before: - chunk_out = self.encoder.after_norm(xs) - else: - chunk_out = xs - - # shape of r_att_cache: (b, elayers, head, time2, outdim) - r_att_cache = torch.cat(r_att_cache, dim=1) # concat on layers idx - # shape of r_cnn_cache: (b, elayers, outdim, cache_t2) - r_cnn_cache = torch.cat(r_cnn_cache, dim=1) # concat on layers - - # <---------forward_chunk END---------> - - log_ctc_probs = self.ctc.log_softmax(chunk_out) - log_probs, log_probs_idx = torch.topk(log_ctc_probs, self.beam_size, dim=2) - log_probs = log_probs.to(chunk_xs.dtype) - - r_offset = offset + chunk_out.shape[1] - # the below ops not supported in Tensorrt - # chunk_out_lens = torch.div(chunk_lens, subsampling_rate, - # rounding_mode='floor') - chunk_out_lens = ( - chunk_lens - // self.subsampling_rate - // self.calculate_downsampling_factor(self.num_blocks + 1) - ) - chunk_out_lens += 1 - r_offset = r_offset.unsqueeze(1) - - return ( - log_probs, - log_probs_idx, - chunk_out, - chunk_out_lens, - r_offset, - r_att_cache, - r_cnn_cache, - r_cache_mask, - ) - - -class Decoder(torch.nn.Module): - def __init__( - self, - decoder: TransformerDecoder, - ctc_weight: float = 0.5, - reverse_weight: float = 0.0, - beam_size: int = 10, - decoder_fastertransformer: bool = False, - ): - super().__init__() - self.decoder = decoder - self.ctc_weight = ctc_weight - self.reverse_weight = reverse_weight - self.beam_size = beam_size - self.decoder_fastertransformer = decoder_fastertransformer - - def forward( - self, - encoder_out: torch.Tensor, - encoder_lens: torch.Tensor, - hyps_pad_sos_eos: torch.Tensor, - hyps_lens_sos: torch.Tensor, - r_hyps_pad_sos_eos: torch.Tensor, - ctc_score: torch.Tensor, - ): - """Encoder - Args: - encoder_out: B x T x F - encoder_lens: B - hyps_pad_sos_eos: B x beam x (T2+1), - hyps with sos & eos and padded by ignore id - hyps_lens_sos: B x beam, length for each hyp with sos - r_hyps_pad_sos_eos: B x beam x (T2+1), - reversed hyps with sos & eos and padded by ignore id - ctc_score: B x beam, ctc score for each hyp - Returns: - decoder_out: B x beam x T2 x V - r_decoder_out: B x beam x T2 x V - best_index: B - """ - B, T, F = encoder_out.shape - bz = self.beam_size - B2 = B * bz - encoder_out = encoder_out.repeat(1, bz, 1).view(B2, T, F) - encoder_mask = ~make_pad_mask(encoder_lens, T).unsqueeze(1) - encoder_mask = encoder_mask.repeat(1, bz, 1).view(B2, 1, T) - T2 = hyps_pad_sos_eos.shape[2] - 1 - hyps_pad = hyps_pad_sos_eos.view(B2, T2 + 1) - hyps_lens = hyps_lens_sos.view( - B2, - ) - hyps_pad_sos = hyps_pad[:, :-1].contiguous() - hyps_pad_eos = hyps_pad[:, 1:].contiguous() - - r_hyps_pad = r_hyps_pad_sos_eos.view(B2, T2 + 1) - r_hyps_pad_sos = r_hyps_pad[:, :-1].contiguous() - r_hyps_pad_eos = r_hyps_pad[:, 1:].contiguous() - - decoder_out, r_decoder_out, _ = self.decoder( - encoder_out, - encoder_mask, - hyps_pad_sos, - hyps_lens, - r_hyps_pad_sos, - self.reverse_weight, - ) - decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) - V = decoder_out.shape[-1] - decoder_out = decoder_out.view(B2, T2, V) - mask = ~make_pad_mask(hyps_lens, T2) # B2 x T2 - # mask index, remove ignore id - index = torch.unsqueeze(hyps_pad_eos * mask, 2) - score = decoder_out.gather(2, index).squeeze(2) # B2 X T2 - # mask padded part - score = score * mask - decoder_out = decoder_out.view(B, bz, T2, V) - if self.reverse_weight > 0: - r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) - r_decoder_out = r_decoder_out.view(B2, T2, V) - index = torch.unsqueeze(r_hyps_pad_eos * mask, 2) - r_score = r_decoder_out.gather(2, index).squeeze(2) - r_score = r_score * mask - score = score * (1 - self.reverse_weight) + self.reverse_weight * r_score - r_decoder_out = r_decoder_out.view(B, bz, T2, V) - score = torch.sum(score, axis=1) # B2 - score = torch.reshape(score, (B, bz)) + self.ctc_weight * ctc_score - best_index = torch.argmax(score, dim=1) - if self.decoder_fastertransformer: - return decoder_out, best_index - else: - return best_index - - -def to_numpy(tensors): - out = [] - if type(tensors) == torch.tensor: - tensors = [tensors] - for tensor in tensors: - if tensor.requires_grad: - tensor = tensor.detach().cpu().numpy() - else: - tensor = tensor.cpu().numpy() - out.append(tensor) - return out - - -def test(xlist, blist, rtol=1e-3, atol=1e-5, tolerate_small_mismatch=True): - for a, b in zip(xlist, blist): - try: - torch.testing.assert_allclose(a, b, rtol=rtol, atol=atol) - except AssertionError as error: - if tolerate_small_mismatch: - print(error) - else: - raise - - -def export_offline_encoder(model, configs, args, logger, encoder_onnx_path): - bz = 32 - seq_len = 100 - beam_size = args.beam_size - feature_size = configs["input_dim"] - - speech = torch.randn(bz, seq_len, feature_size, dtype=torch.float32) - speech_lens = torch.randint(low=10, high=seq_len, size=(bz,), dtype=torch.int32) - encoder = Encoder(model.encoder, model.ctc, beam_size) - encoder.eval() - - torch.onnx.export( - encoder, - (speech, speech_lens), - encoder_onnx_path, - export_params=True, - opset_version=13, - do_constant_folding=True, - input_names=["speech", "speech_lengths"], - output_names=[ - "encoder_out", - "encoder_out_lens", - "ctc_log_probs", - "beam_log_probs", - "beam_log_probs_idx", - ], - dynamic_axes={ - "speech": {0: "B", 1: "T"}, - "speech_lengths": {0: "B"}, - "encoder_out": {0: "B", 1: "T_OUT"}, - "encoder_out_lens": {0: "B"}, - "ctc_log_probs": {0: "B", 1: "T_OUT"}, - "beam_log_probs": {0: "B", 1: "T_OUT"}, - "beam_log_probs_idx": {0: "B", 1: "T_OUT"}, - }, - verbose=False, - ) - - with torch.no_grad(): - o0, o1, o2, o3, o4 = encoder(speech, speech_lens) - - providers = ["CUDAExecutionProvider"] - ort_session = onnxruntime.InferenceSession(encoder_onnx_path, providers=providers) - ort_inputs = {"speech": to_numpy(speech), "speech_lengths": to_numpy(speech_lens)} - ort_outs = ort_session.run(None, ort_inputs) - - # check encoder output - test(to_numpy([o0, o1, o2, o3, o4]), ort_outs) - logger.info("export offline onnx encoder succeed!") - onnx_config = { - "beam_size": args.beam_size, - "reverse_weight": args.reverse_weight, - "ctc_weight": args.ctc_weight, - "fp16": args.fp16, - } - return onnx_config - - -def export_online_encoder(model, configs, args, logger, encoder_onnx_path): - decoding_chunk_size = args.decoding_chunk_size - subsampling = model.encoder.embed.subsampling_rate - context = model.encoder.embed.right_context + 1 - decoding_window = (decoding_chunk_size - 1) * subsampling + context - batch_size = 32 - audio_len = decoding_window - feature_size = configs["input_dim"] - output_size = configs["encoder_conf"]["output_size"] - num_layers = configs["encoder_conf"]["num_blocks"] - # in transformer the cnn module will not be available - transformer = False - cnn_module_kernel = configs["encoder_conf"].get("cnn_module_kernel", 1) - 1 - if not cnn_module_kernel: - transformer = True - num_decoding_left_chunks = args.num_decoding_left_chunks - required_cache_size = decoding_chunk_size * num_decoding_left_chunks - if configs["encoder"] == "squeezeformer": - encoder = StreamingSqueezeformerEncoder( - model, required_cache_size, args.beam_size - ) - elif configs["encoder"] == "efficientConformer": - encoder = StreamingEfficientConformerEncoder( - model, required_cache_size, args.beam_size - ) - else: - encoder = StreamingEncoder( - model, required_cache_size, args.beam_size, transformer - ) - encoder.eval() - - # begin to export encoder - chunk_xs = torch.randn(batch_size, audio_len, feature_size, dtype=torch.float32) - chunk_lens = torch.ones(batch_size, dtype=torch.int32) * audio_len - - offset = torch.arange(0, batch_size).unsqueeze(1) - # (elayers, b, head, cache_t1, d_k * 2) - head = configs["encoder_conf"]["attention_heads"] - d_k = configs["encoder_conf"]["output_size"] // head - att_cache = torch.randn( - batch_size, num_layers, head, required_cache_size, d_k * 2, dtype=torch.float32 - ) - cnn_cache = torch.randn( - batch_size, num_layers, output_size, cnn_module_kernel, dtype=torch.float32 - ) - - cache_mask = torch.ones(batch_size, 1, required_cache_size, dtype=torch.float32) - input_names = [ - "chunk_xs", - "chunk_lens", - "offset", - "att_cache", - "cnn_cache", - "cache_mask", - ] - output_names = [ - "log_probs", - "log_probs_idx", - "chunk_out", - "chunk_out_lens", - "r_offset", - "r_att_cache", - "r_cnn_cache", - "r_cache_mask", - ] - input_tensors = (chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask) - if transformer: - output_names.pop(6) - - all_names = input_names + output_names - dynamic_axes = {} - for name in all_names: - # only the first dimension is dynamic - # all other dimension is fixed - dynamic_axes[name] = {0: "B"} - - torch.onnx.export( - encoder, - input_tensors, - encoder_onnx_path, - export_params=True, - opset_version=14, - do_constant_folding=True, - input_names=input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - verbose=False, - ) - - with torch.no_grad(): - torch_outs = encoder( - chunk_xs, chunk_lens, offset, att_cache, cnn_cache, cache_mask - ) - if transformer: - torch_outs = list(torch_outs).pop(6) - ort_session = onnxruntime.InferenceSession( - encoder_onnx_path, providers=["CUDAExecutionProvider"] - ) - ort_inputs = {} - - input_tensors = to_numpy(input_tensors) - for idx, name in enumerate(input_names): - ort_inputs[name] = input_tensors[idx] - if transformer: - del ort_inputs["cnn_cache"] - ort_outs = ort_session.run(None, ort_inputs) - test(to_numpy(torch_outs), ort_outs, rtol=1e-03, atol=1e-05) - logger.info("export to onnx streaming encoder succeed!") - onnx_config = { - "subsampling_rate": subsampling, - "context": context, - "decoding_chunk_size": decoding_chunk_size, - "num_decoding_left_chunks": num_decoding_left_chunks, - "beam_size": args.beam_size, - "fp16": args.fp16, - "feat_size": feature_size, - "decoding_window": decoding_window, - "cnn_module_kernel_cache": cnn_module_kernel, - } - return onnx_config - - -def export_rescoring_decoder( - model, configs, args, logger, decoder_onnx_path, decoder_fastertransformer -): - bz, seq_len = 32, 100 - beam_size = args.beam_size - decoder = Decoder( - model.decoder, - model.ctc_weight, - model.reverse_weight, - beam_size, - decoder_fastertransformer, - ) - decoder.eval() - - hyps_pad_sos_eos = torch.randint(low=3, high=1000, size=(bz, beam_size, seq_len)) - hyps_lens_sos = torch.randint( - low=3, high=seq_len, size=(bz, beam_size), dtype=torch.int32 - ) - r_hyps_pad_sos_eos = torch.randint(low=3, high=1000, size=(bz, beam_size, seq_len)) - - output_size = configs["encoder_conf"]["output_size"] - encoder_out = torch.randn(bz, seq_len, output_size, dtype=torch.float32) - encoder_out_lens = torch.randint(low=3, high=seq_len, size=(bz,), dtype=torch.int32) - ctc_score = torch.randn(bz, beam_size, dtype=torch.float32) - - input_names = [ - "encoder_out", - "encoder_out_lens", - "hyps_pad_sos_eos", - "hyps_lens_sos", - "r_hyps_pad_sos_eos", - "ctc_score", - ] - output_names = ["best_index"] - if decoder_fastertransformer: - output_names.insert(0, "decoder_out") - - torch.onnx.export( - decoder, - ( - encoder_out, - encoder_out_lens, - hyps_pad_sos_eos, - hyps_lens_sos, - r_hyps_pad_sos_eos, - ctc_score, - ), - decoder_onnx_path, - export_params=True, - opset_version=13, - do_constant_folding=True, - input_names=input_names, - output_names=output_names, - dynamic_axes={ - "encoder_out": {0: "B", 1: "T"}, - "encoder_out_lens": {0: "B"}, - "hyps_pad_sos_eos": {0: "B", 2: "T2"}, - "hyps_lens_sos": {0: "B"}, - "r_hyps_pad_sos_eos": {0: "B", 2: "T2"}, - "ctc_score": {0: "B"}, - "best_index": {0: "B"}, - }, - verbose=False, - ) - with torch.no_grad(): - o0 = decoder( - encoder_out, - encoder_out_lens, - hyps_pad_sos_eos, - hyps_lens_sos, - r_hyps_pad_sos_eos, - ctc_score, - ) - providers = ["CUDAExecutionProvider"] - ort_session = onnxruntime.InferenceSession(decoder_onnx_path, providers=providers) - - input_tensors = [ - encoder_out, - encoder_out_lens, - hyps_pad_sos_eos, - hyps_lens_sos, - r_hyps_pad_sos_eos, - ctc_score, - ] - ort_inputs = {} - input_tensors = to_numpy(input_tensors) - for idx, name in enumerate(input_names): - ort_inputs[name] = input_tensors[idx] - - # if model.reverse weight == 0, - # the r_hyps_pad will be removed - # from the onnx decoder since it doen't play any role - if model.reverse_weight == 0: - del ort_inputs["r_hyps_pad_sos_eos"] - ort_outs = ort_session.run(None, ort_inputs) - - # check decoder output - if decoder_fastertransformer: - test(to_numpy(o0), ort_outs, rtol=1e-03, atol=1e-05) - else: - test(to_numpy([o0]), ort_outs, rtol=1e-03, atol=1e-05) - logger.info("export to onnx decoder succeed!") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="export x86_gpu model") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument("--checkpoint", required=True, help="checkpoint model") - parser.add_argument( - "--cmvn_file", - required=False, - default="", - type=str, - help="global_cmvn file, default path is in config file", - ) - parser.add_argument( - "--reverse_weight", - default=-1.0, - type=float, - required=False, - help="reverse weight for bitransformer," + "default value is in config file", - ) - parser.add_argument( - "--ctc_weight", - default=-1.0, - type=float, - required=False, - help="ctc weight, default value is in config file", - ) - parser.add_argument( - "--beam_size", - default=10, - type=int, - required=False, - help="beam size would be ctc output size", - ) - parser.add_argument( - "--output_onnx_dir", - default="onnx_model", - help="output onnx encoder and decoder directory", - ) - parser.add_argument( - "--fp16", - action="store_true", - help="whether to export fp16 model, default false", - ) - # arguments for streaming encoder - parser.add_argument( - "--streaming", - action="store_true", - help="whether to export streaming encoder, default false", - ) - parser.add_argument( - "--decoding_chunk_size", - default=16, - type=int, - required=False, - help="the decoding chunk size, <=0 is not supported", - ) - parser.add_argument( - "--num_decoding_left_chunks", - default=5, - type=int, - required=False, - help="number of left chunks, <= 0 is not supported", - ) - parser.add_argument( - "--decoder_fastertransformer", - action="store_true", - help="return decoder_out and best_index for ft", - ) - args = parser.parse_args() - - torch.manual_seed(0) - torch.set_printoptions(precision=10) - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - if args.cmvn_file and os.path.exists(args.cmvn_file): - configs["cmvn_file"] = args.cmvn_file - if args.reverse_weight != -1.0 and "reverse_weight" in configs["model_conf"]: - configs["model_conf"]["reverse_weight"] = args.reverse_weight - print("Update reverse weight to", args.reverse_weight) - if args.ctc_weight != -1: - print("Update ctc weight to ", args.ctc_weight) - configs["model_conf"]["ctc_weight"] = args.ctc_weight - configs["encoder_conf"]["use_dynamic_chunk"] = False - - model = init_model(configs) - load_checkpoint(model, args.checkpoint) - model.eval() - - if not os.path.exists(args.output_onnx_dir): - os.mkdir(args.output_onnx_dir) - encoder_onnx_path = os.path.join(args.output_onnx_dir, "encoder.onnx") - export_enc_func = None - if args.streaming: - assert args.decoding_chunk_size > 0 - assert args.num_decoding_left_chunks > 0 - export_enc_func = export_online_encoder - else: - export_enc_func = export_offline_encoder - - onnx_config = export_enc_func(model, configs, args, logger, encoder_onnx_path) - - decoder_onnx_path = os.path.join(args.output_onnx_dir, "decoder.onnx") - export_rescoring_decoder( - model, configs, args, logger, decoder_onnx_path, args.decoder_fastertransformer - ) - - if args.fp16: - try: - import onnxmltools - from onnxmltools.utils.float16_converter import convert_float_to_float16 - except ImportError: - print("Please install onnxmltools!") - sys.exit(1) - encoder_onnx_model = onnxmltools.utils.load_model(encoder_onnx_path) - encoder_onnx_model = convert_float_to_float16(encoder_onnx_model) - encoder_onnx_path = os.path.join(args.output_onnx_dir, "encoder_fp16.onnx") - onnxmltools.utils.save_model(encoder_onnx_model, encoder_onnx_path) - decoder_onnx_model = onnxmltools.utils.load_model(decoder_onnx_path) - decoder_onnx_model = convert_float_to_float16(decoder_onnx_model) - decoder_onnx_path = os.path.join(args.output_onnx_dir, "decoder_fp16.onnx") - onnxmltools.utils.save_model(decoder_onnx_model, decoder_onnx_path) - # dump configurations - - config_dir = os.path.join(args.output_onnx_dir, "config.yaml") - with open(config_dir, "w") as out: - yaml.dump(onnx_config, out) diff --git a/modules/wenet_extractor/bin/recognize.py b/modules/wenet_extractor/bin/recognize.py deleted file mode 100644 index b78463b78e49790faaf72811ec76d44453eb52ff..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/recognize.py +++ /dev/null @@ -1,435 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import copy -import logging -import os -import sys - -import torch -import yaml -from torch.utils.data import DataLoader - -from wenet.dataset.dataset import Dataset -from wenet.paraformer.search.beam_search import build_beam_search -from wenet.utils.checkpoint import load_checkpoint -from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols -from wenet.utils.config import override_config -from wenet.utils.init_model import init_model - - -def get_args(): - parser = argparse.ArgumentParser(description="recognize with your model") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument("--test_data", required=True, help="test data file") - parser.add_argument( - "--data_type", - default="raw", - choices=["raw", "shard"], - help="train and cv data type", - ) - parser.add_argument( - "--gpu", type=int, default=-1, help="gpu id for this rank, -1 for cpu" - ) - parser.add_argument("--checkpoint", required=True, help="checkpoint model") - parser.add_argument("--dict", required=True, help="dict file") - parser.add_argument( - "--non_lang_syms", help="non-linguistic symbol file. One symbol per line." - ) - parser.add_argument( - "--beam_size", type=int, default=10, help="beam size for search" - ) - parser.add_argument("--penalty", type=float, default=0.0, help="length penalty") - parser.add_argument("--result_file", required=True, help="asr result file") - parser.add_argument("--batch_size", type=int, default=16, help="asr result file") - parser.add_argument( - "--mode", - choices=[ - "attention", - "ctc_greedy_search", - "ctc_prefix_beam_search", - "attention_rescoring", - "rnnt_greedy_search", - "rnnt_beam_search", - "rnnt_beam_attn_rescoring", - "ctc_beam_td_attn_rescoring", - "hlg_onebest", - "hlg_rescore", - "paraformer_greedy_search", - "paraformer_beam_search", - ], - default="attention", - help="decoding mode", - ) - - parser.add_argument( - "--search_ctc_weight", - type=float, - default=1.0, - help="ctc weight for nbest generation", - ) - parser.add_argument( - "--search_transducer_weight", - type=float, - default=0.0, - help="transducer weight for nbest generation", - ) - parser.add_argument( - "--ctc_weight", - type=float, - default=0.0, - help="ctc weight for rescoring weight in \ - attention rescoring decode mode \ - ctc weight for rescoring weight in \ - transducer attention rescore decode mode", - ) - - parser.add_argument( - "--transducer_weight", - type=float, - default=0.0, - help="transducer weight for rescoring weight in " - "transducer attention rescore mode", - ) - parser.add_argument( - "--attn_weight", - type=float, - default=0.0, - help="attention weight for rescoring weight in " - "transducer attention rescore mode", - ) - parser.add_argument( - "--decoding_chunk_size", - type=int, - default=-1, - help="""decoding chunk size, - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here""", - ) - parser.add_argument( - "--num_decoding_left_chunks", - type=int, - default=-1, - help="number of left chunks for decoding", - ) - parser.add_argument( - "--simulate_streaming", action="store_true", help="simulate streaming inference" - ) - parser.add_argument( - "--reverse_weight", - type=float, - default=0.0, - help="""right to left weight for attention rescoring - decode mode""", - ) - parser.add_argument( - "--bpe_model", default=None, type=str, help="bpe model for english part" - ) - parser.add_argument( - "--override_config", action="append", default=[], help="override yaml config" - ) - parser.add_argument( - "--connect_symbol", - default="", - type=str, - help="used to connect the output characters", - ) - - parser.add_argument( - "--word", default="", type=str, help="word file, only used for hlg decode" - ) - parser.add_argument( - "--hlg", default="", type=str, help="hlg file, only used for hlg decode" - ) - parser.add_argument( - "--lm_scale", - type=float, - default=0.0, - help="lm scale for hlg attention rescore decode", - ) - parser.add_argument( - "--decoder_scale", - type=float, - default=0.0, - help="lm scale for hlg attention rescore decode", - ) - parser.add_argument( - "--r_decoder_scale", - type=float, - default=0.0, - help="lm scale for hlg attention rescore decode", - ) - - args = parser.parse_args() - print(args) - return args - - -def main(): - args = get_args() - logging.basicConfig( - level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" - ) - os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) - - if ( - args.mode - in [ - "ctc_prefix_beam_search", - "attention_rescoring", - "paraformer_beam_search", - ] - and args.batch_size > 1 - ): - logging.fatal( - "decoding mode {} must be running with batch_size == 1".format(args.mode) - ) - sys.exit(1) - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - if len(args.override_config) > 0: - configs = override_config(configs, args.override_config) - - symbol_table = read_symbol_table(args.dict) - test_conf = copy.deepcopy(configs["dataset_conf"]) - - test_conf["filter_conf"]["max_length"] = 102400 - test_conf["filter_conf"]["min_length"] = 0 - test_conf["filter_conf"]["token_max_length"] = 102400 - test_conf["filter_conf"]["token_min_length"] = 0 - test_conf["filter_conf"]["max_output_input_ratio"] = 102400 - test_conf["filter_conf"]["min_output_input_ratio"] = 0 - test_conf["speed_perturb"] = False - test_conf["spec_aug"] = False - test_conf["spec_sub"] = False - test_conf["spec_trim"] = False - test_conf["shuffle"] = False - test_conf["sort"] = False - if "fbank_conf" in test_conf: - test_conf["fbank_conf"]["dither"] = 0.0 - elif "mfcc_conf" in test_conf: - test_conf["mfcc_conf"]["dither"] = 0.0 - test_conf["batch_conf"]["batch_type"] = "static" - test_conf["batch_conf"]["batch_size"] = args.batch_size - non_lang_syms = read_non_lang_symbols(args.non_lang_syms) - - test_dataset = Dataset( - args.data_type, - args.test_data, - symbol_table, - test_conf, - args.bpe_model, - non_lang_syms, - partition=False, - ) - - test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0) - - # Init asr model from configs - model = init_model(configs) - - # Load dict - char_dict = {v: k for k, v in symbol_table.items()} - eos = len(char_dict) - 1 - - load_checkpoint(model, args.checkpoint) - use_cuda = args.gpu >= 0 and torch.cuda.is_available() - device = torch.device("cuda" if use_cuda else "cpu") - model = model.to(device) - - model.eval() - - # Build BeamSearchCIF object - if args.mode == "paraformer_beam_search": - paraformer_beam_search = build_beam_search(model, args, device) - else: - paraformer_beam_search = None - - with torch.no_grad(), open(args.result_file, "w") as fout: - for batch_idx, batch in enumerate(test_data_loader): - keys, feats, target, feats_lengths, target_lengths = batch - feats = feats.to(device) - target = target.to(device) - feats_lengths = feats_lengths.to(device) - target_lengths = target_lengths.to(device) - if args.mode == "attention": - hyps, _ = model.recognize( - feats, - feats_lengths, - beam_size=args.beam_size, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ) - hyps = [hyp.tolist() for hyp in hyps] - elif args.mode == "ctc_greedy_search": - hyps, _ = model.ctc_greedy_search( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ) - elif args.mode == "rnnt_greedy_search": - assert feats.size(0) == 1 - assert "predictor" in configs - hyps = model.greedy_search( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ) - elif args.mode == "rnnt_beam_search": - assert feats.size(0) == 1 - assert "predictor" in configs - hyps = model.beam_search( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - beam_size=args.beam_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ctc_weight=args.search_ctc_weight, - transducer_weight=args.search_transducer_weight, - ) - elif args.mode == "rnnt_beam_attn_rescoring": - assert feats.size(0) == 1 - assert "predictor" in configs - hyps = model.transducer_attention_rescoring( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - beam_size=args.beam_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ctc_weight=args.ctc_weight, - transducer_weight=args.transducer_weight, - attn_weight=args.attn_weight, - reverse_weight=args.reverse_weight, - search_ctc_weight=args.search_ctc_weight, - search_transducer_weight=args.search_transducer_weight, - ) - elif args.mode == "ctc_beam_td_attn_rescoring": - assert feats.size(0) == 1 - assert "predictor" in configs - hyps = model.transducer_attention_rescoring( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - beam_size=args.beam_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ctc_weight=args.ctc_weight, - transducer_weight=args.transducer_weight, - attn_weight=args.attn_weight, - reverse_weight=args.reverse_weight, - search_ctc_weight=args.search_ctc_weight, - search_transducer_weight=args.search_transducer_weight, - beam_search_type="ctc", - ) - # ctc_prefix_beam_search and attention_rescoring only return one - # result in List[int], change it to List[List[int]] for compatible - # with other batch decoding mode - elif args.mode == "ctc_prefix_beam_search": - assert feats.size(0) == 1 - hyp, _ = model.ctc_prefix_beam_search( - feats, - feats_lengths, - args.beam_size, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ) - hyps = [hyp] - elif args.mode == "attention_rescoring": - assert feats.size(0) == 1 - hyp, _ = model.attention_rescoring( - feats, - feats_lengths, - args.beam_size, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - ctc_weight=args.ctc_weight, - simulate_streaming=args.simulate_streaming, - reverse_weight=args.reverse_weight, - ) - hyps = [hyp] - elif args.mode == "hlg_onebest": - hyps = model.hlg_onebest( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - hlg=args.hlg, - word=args.word, - symbol_table=symbol_table, - ) - elif args.mode == "hlg_rescore": - hyps = model.hlg_rescore( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - lm_scale=args.lm_scale, - decoder_scale=args.decoder_scale, - r_decoder_scale=args.r_decoder_scale, - hlg=args.hlg, - word=args.word, - symbol_table=symbol_table, - ) - elif args.mode == "paraformer_beam_search": - hyps = model.paraformer_beam_search( - feats, - feats_lengths, - beam_search=paraformer_beam_search, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ) - elif args.mode == "paraformer_greedy_search": - hyps = model.paraformer_greedy_search( - feats, - feats_lengths, - decoding_chunk_size=args.decoding_chunk_size, - num_decoding_left_chunks=args.num_decoding_left_chunks, - simulate_streaming=args.simulate_streaming, - ) - for i, key in enumerate(keys): - content = [] - for w in hyps[i]: - if w == eos: - break - content.append(char_dict[w]) - logging.info("{} {}".format(key, args.connect_symbol.join(content))) - fout.write("{} {}\n".format(key, args.connect_symbol.join(content))) - - -if __name__ == "__main__": - main() diff --git a/modules/wenet_extractor/bin/recognize_onnx_gpu.py b/modules/wenet_extractor/bin/recognize_onnx_gpu.py deleted file mode 100644 index 2b42788b1947ce4f58a2e8ef51d10ec0e7bca2d7..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/recognize_onnx_gpu.py +++ /dev/null @@ -1,285 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import copy -import logging -import os -import sys - -import torch -import yaml -from torch.utils.data import DataLoader - -from wenet.dataset.dataset import Dataset -from wenet.utils.common import IGNORE_ID -from wenet.utils.file_utils import read_symbol_table -from wenet.utils.config import override_config - -import onnxruntime as rt -import multiprocessing -import numpy as np - -try: - from swig_decoders import ( - map_batch, - ctc_beam_search_decoder_batch, - TrieVector, - PathTrie, - ) -except ImportError: - print( - "Please install ctc decoders first by refering to\n" - + "https://github.com/Slyne/ctc_decoder.git" - ) - sys.exit(1) - - -def get_args(): - parser = argparse.ArgumentParser(description="recognize with your model") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument("--test_data", required=True, help="test data file") - parser.add_argument( - "--data_type", - default="raw", - choices=["raw", "shard"], - help="train and cv data type", - ) - parser.add_argument( - "--gpu", type=int, default=-1, help="gpu id for this rank, -1 for cpu" - ) - parser.add_argument("--dict", required=True, help="dict file") - parser.add_argument("--encoder_onnx", required=True, help="encoder onnx file") - parser.add_argument("--decoder_onnx", required=True, help="decoder onnx file") - parser.add_argument("--result_file", required=True, help="asr result file") - parser.add_argument("--batch_size", type=int, default=32, help="asr result file") - parser.add_argument( - "--mode", - choices=["ctc_greedy_search", "ctc_prefix_beam_search", "attention_rescoring"], - default="attention_rescoring", - help="decoding mode", - ) - parser.add_argument( - "--bpe_model", default=None, type=str, help="bpe model for english part" - ) - parser.add_argument( - "--override_config", action="append", default=[], help="override yaml config" - ) - parser.add_argument( - "--fp16", - action="store_true", - help="whether to export fp16 model, default false", - ) - args = parser.parse_args() - print(args) - return args - - -def main(): - args = get_args() - logging.basicConfig( - level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" - ) - os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) - - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - if len(args.override_config) > 0: - configs = override_config(configs, args.override_config) - - reverse_weight = configs["model_conf"].get("reverse_weight", 0.0) - symbol_table = read_symbol_table(args.dict) - test_conf = copy.deepcopy(configs["dataset_conf"]) - test_conf["filter_conf"]["max_length"] = 102400 - test_conf["filter_conf"]["min_length"] = 0 - test_conf["filter_conf"]["token_max_length"] = 102400 - test_conf["filter_conf"]["token_min_length"] = 0 - test_conf["filter_conf"]["max_output_input_ratio"] = 102400 - test_conf["filter_conf"]["min_output_input_ratio"] = 0 - test_conf["speed_perturb"] = False - test_conf["spec_aug"] = False - test_conf["spec_sub"] = False - test_conf["spec_trim"] = False - test_conf["shuffle"] = False - test_conf["sort"] = False - test_conf["fbank_conf"]["dither"] = 0.0 - test_conf["batch_conf"]["batch_type"] = "static" - test_conf["batch_conf"]["batch_size"] = args.batch_size - - test_dataset = Dataset( - args.data_type, - args.test_data, - symbol_table, - test_conf, - args.bpe_model, - partition=False, - ) - - test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0) - - # Init asr model from configs - use_cuda = args.gpu >= 0 and torch.cuda.is_available() - if use_cuda: - EP_list = ["CUDAExecutionProvider", "CPUExecutionProvider"] - else: - EP_list = ["CPUExecutionProvider"] - - encoder_ort_session = rt.InferenceSession(args.encoder_onnx, providers=EP_list) - decoder_ort_session = None - if args.mode == "attention_rescoring": - decoder_ort_session = rt.InferenceSession(args.decoder_onnx, providers=EP_list) - - # Load dict - vocabulary = [] - char_dict = {} - with open(args.dict, "r") as fin: - for line in fin: - arr = line.strip().split() - assert len(arr) == 2 - char_dict[int(arr[1])] = arr[0] - vocabulary.append(arr[0]) - eos = sos = len(char_dict) - 1 - with torch.no_grad(), open(args.result_file, "w") as fout: - for _, batch in enumerate(test_data_loader): - keys, feats, _, feats_lengths, _ = batch - feats, feats_lengths = feats.numpy(), feats_lengths.numpy() - if args.fp16: - feats = feats.astype(np.float16) - ort_inputs = { - encoder_ort_session.get_inputs()[0].name: feats, - encoder_ort_session.get_inputs()[1].name: feats_lengths, - } - ort_outs = encoder_ort_session.run(None, ort_inputs) - ( - encoder_out, - encoder_out_lens, - ctc_log_probs, - beam_log_probs, - beam_log_probs_idx, - ) = ort_outs - beam_size = beam_log_probs.shape[-1] - batch_size = beam_log_probs.shape[0] - num_processes = min(multiprocessing.cpu_count(), batch_size) - if args.mode == "ctc_greedy_search": - if beam_size != 1: - log_probs_idx = beam_log_probs_idx[:, :, 0] - batch_sents = [] - for idx, seq in enumerate(log_probs_idx): - batch_sents.append(seq[0 : encoder_out_lens[idx]].tolist()) - hyps = map_batch(batch_sents, vocabulary, num_processes, True, 0) - elif args.mode in ("ctc_prefix_beam_search", "attention_rescoring"): - batch_log_probs_seq_list = beam_log_probs.tolist() - batch_log_probs_idx_list = beam_log_probs_idx.tolist() - batch_len_list = encoder_out_lens.tolist() - batch_log_probs_seq = [] - batch_log_probs_ids = [] - batch_start = [] # only effective in streaming deployment - batch_root = TrieVector() - root_dict = {} - for i in range(len(batch_len_list)): - num_sent = batch_len_list[i] - batch_log_probs_seq.append(batch_log_probs_seq_list[i][0:num_sent]) - batch_log_probs_ids.append(batch_log_probs_idx_list[i][0:num_sent]) - root_dict[i] = PathTrie() - batch_root.append(root_dict[i]) - batch_start.append(True) - score_hyps = ctc_beam_search_decoder_batch( - batch_log_probs_seq, - batch_log_probs_ids, - batch_root, - batch_start, - beam_size, - num_processes, - 0, - -2, - 0.99999, - ) - if args.mode == "ctc_prefix_beam_search": - hyps = [] - for cand_hyps in score_hyps: - hyps.append(cand_hyps[0][1]) - hyps = map_batch(hyps, vocabulary, num_processes, False, 0) - if args.mode == "attention_rescoring": - ctc_score, all_hyps = [], [] - max_len = 0 - for hyps in score_hyps: - cur_len = len(hyps) - if len(hyps) < beam_size: - hyps += (beam_size - cur_len) * [(-float("INF"), (0,))] - cur_ctc_score = [] - for hyp in hyps: - cur_ctc_score.append(hyp[0]) - all_hyps.append(list(hyp[1])) - if len(hyp[1]) > max_len: - max_len = len(hyp[1]) - ctc_score.append(cur_ctc_score) - if args.fp16: - ctc_score = np.array(ctc_score, dtype=np.float16) - else: - ctc_score = np.array(ctc_score, dtype=np.float32) - hyps_pad_sos_eos = ( - np.ones((batch_size, beam_size, max_len + 2), dtype=np.int64) - * IGNORE_ID - ) - r_hyps_pad_sos_eos = ( - np.ones((batch_size, beam_size, max_len + 2), dtype=np.int64) - * IGNORE_ID - ) - hyps_lens_sos = np.ones((batch_size, beam_size), dtype=np.int32) - k = 0 - for i in range(batch_size): - for j in range(beam_size): - cand = all_hyps[k] - l = len(cand) + 2 - hyps_pad_sos_eos[i][j][0:l] = [sos] + cand + [eos] - r_hyps_pad_sos_eos[i][j][0:l] = [sos] + cand[::-1] + [eos] - hyps_lens_sos[i][j] = len(cand) + 1 - k += 1 - decoder_ort_inputs = { - decoder_ort_session.get_inputs()[0].name: encoder_out, - decoder_ort_session.get_inputs()[1].name: encoder_out_lens, - decoder_ort_session.get_inputs()[2].name: hyps_pad_sos_eos, - decoder_ort_session.get_inputs()[3].name: hyps_lens_sos, - decoder_ort_session.get_inputs()[-1].name: ctc_score, - } - if reverse_weight > 0: - r_hyps_pad_sos_eos_name = decoder_ort_session.get_inputs()[4].name - decoder_ort_inputs[r_hyps_pad_sos_eos_name] = r_hyps_pad_sos_eos - best_index = decoder_ort_session.run(None, decoder_ort_inputs)[0] - best_sents = [] - k = 0 - for idx in best_index: - cur_best_sent = all_hyps[k : k + beam_size][idx] - best_sents.append(cur_best_sent) - k += beam_size - hyps = map_batch(best_sents, vocabulary, num_processes) - - for i, key in enumerate(keys): - content = hyps[i] - logging.info("{} {}".format(key, content)) - fout.write("{} {}\n".format(key, content)) - - -if __name__ == "__main__": - main() diff --git a/modules/wenet_extractor/bin/train.py b/modules/wenet_extractor/bin/train.py deleted file mode 100644 index eba3d91304331806e99840a044a8537e9127a08c..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/bin/train.py +++ /dev/null @@ -1,358 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from __future__ import print_function - -import argparse -import copy -import logging -import os - -import torch -import torch.distributed as dist -import torch.optim as optim -import yaml -from tensorboardX import SummaryWriter -from torch.utils.data import DataLoader - -from wenet.dataset.dataset import Dataset -from wenet.utils.checkpoint import ( - load_checkpoint, - save_checkpoint, - load_trained_modules, -) -from wenet.utils.executor import Executor -from wenet.utils.file_utils import read_symbol_table, read_non_lang_symbols -from wenet.utils.scheduler import WarmupLR, NoamHoldAnnealing -from wenet.utils.config import override_config -from wenet.utils.init_model import init_model - - -def get_args(): - parser = argparse.ArgumentParser(description="training your network") - parser.add_argument("--config", required=True, help="config file") - parser.add_argument( - "--data_type", - default="raw", - choices=["raw", "shard"], - help="train and cv data type", - ) - parser.add_argument("--train_data", required=True, help="train data file") - parser.add_argument("--cv_data", required=True, help="cv data file") - parser.add_argument( - "--gpu", type=int, default=-1, help="gpu id for this local rank, -1 for cpu" - ) - parser.add_argument("--model_dir", required=True, help="save model dir") - parser.add_argument("--checkpoint", help="checkpoint model") - parser.add_argument( - "--tensorboard_dir", default="tensorboard", help="tensorboard log dir" - ) - parser.add_argument( - "--ddp.rank", - dest="rank", - default=0, - type=int, - help="global rank for distributed training", - ) - parser.add_argument( - "--ddp.world_size", - dest="world_size", - default=-1, - type=int, - help="""number of total processes/gpus for - distributed training""", - ) - parser.add_argument( - "--ddp.dist_backend", - dest="dist_backend", - default="nccl", - choices=["nccl", "gloo"], - help="distributed backend", - ) - parser.add_argument( - "--ddp.init_method", dest="init_method", default=None, help="ddp init method" - ) - parser.add_argument( - "--num_workers", - default=0, - type=int, - help="num of subprocess workers for reading", - ) - parser.add_argument( - "--pin_memory", - action="store_true", - default=False, - help="Use pinned memory buffers used for reading", - ) - parser.add_argument( - "--use_amp", - action="store_true", - default=False, - help="Use automatic mixed precision training", - ) - parser.add_argument( - "--fp16_grad_sync", - action="store_true", - default=False, - help="Use fp16 gradient sync for ddp", - ) - parser.add_argument("--cmvn", default=None, help="global cmvn file") - parser.add_argument( - "--symbol_table", required=True, help="model unit symbol table for training" - ) - parser.add_argument( - "--non_lang_syms", help="non-linguistic symbol file. One symbol per line." - ) - parser.add_argument("--prefetch", default=100, type=int, help="prefetch number") - parser.add_argument( - "--bpe_model", default=None, type=str, help="bpe model for english part" - ) - parser.add_argument( - "--override_config", action="append", default=[], help="override yaml config" - ) - parser.add_argument( - "--enc_init", - default=None, - type=str, - help="Pre-trained model to initialize encoder", - ) - parser.add_argument( - "--enc_init_mods", - default="encoder.", - type=lambda s: [str(mod) for mod in s.split(",") if s != ""], - help="List of encoder modules \ - to initialize ,separated by a comma", - ) - parser.add_argument("--lfmmi_dir", default="", required=False, help="LF-MMI dir") - - args = parser.parse_args() - return args - - -def main(): - args = get_args() - logging.basicConfig( - level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s" - ) - os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) - - # Set random seed - torch.manual_seed(777) - with open(args.config, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - if len(args.override_config) > 0: - configs = override_config(configs, args.override_config) - - distributed = args.world_size > 1 - if distributed: - logging.info("training on multiple gpus, this gpu {}".format(args.gpu)) - dist.init_process_group( - args.dist_backend, - init_method=args.init_method, - world_size=args.world_size, - rank=args.rank, - ) - - symbol_table = read_symbol_table(args.symbol_table) - - train_conf = configs["dataset_conf"] - cv_conf = copy.deepcopy(train_conf) - cv_conf["speed_perturb"] = False - cv_conf["spec_aug"] = False - cv_conf["spec_sub"] = False - cv_conf["spec_trim"] = False - cv_conf["shuffle"] = False - non_lang_syms = read_non_lang_symbols(args.non_lang_syms) - - train_dataset = Dataset( - args.data_type, - args.train_data, - symbol_table, - train_conf, - args.bpe_model, - non_lang_syms, - True, - ) - cv_dataset = Dataset( - args.data_type, - args.cv_data, - symbol_table, - cv_conf, - args.bpe_model, - non_lang_syms, - partition=False, - ) - - train_data_loader = DataLoader( - train_dataset, - batch_size=None, - pin_memory=args.pin_memory, - num_workers=args.num_workers, - prefetch_factor=args.prefetch, - ) - cv_data_loader = DataLoader( - cv_dataset, - batch_size=None, - pin_memory=args.pin_memory, - num_workers=args.num_workers, - prefetch_factor=args.prefetch, - ) - - if "fbank_conf" in configs["dataset_conf"]: - input_dim = configs["dataset_conf"]["fbank_conf"]["num_mel_bins"] - else: - input_dim = configs["dataset_conf"]["mfcc_conf"]["num_mel_bins"] - vocab_size = len(symbol_table) - - # Save configs to model_dir/train.yaml for inference and export - configs["input_dim"] = input_dim - configs["output_dim"] = vocab_size - configs["cmvn_file"] = args.cmvn - configs["is_json_cmvn"] = True - configs["lfmmi_dir"] = args.lfmmi_dir - - if args.rank == 0: - saved_config_path = os.path.join(args.model_dir, "train.yaml") - with open(saved_config_path, "w") as fout: - data = yaml.dump(configs) - fout.write(data) - - # Init asr model from configs - model = init_model(configs) - print(model) - num_params = sum(p.numel() for p in model.parameters()) - print("the number of model params: {:,d}".format(num_params)) - - # !!!IMPORTANT!!! - # Try to export the model by script, if fails, we should refine - # the code to satisfy the script export requirements - if args.rank == 0: - script_model = torch.jit.script(model) - script_model.save(os.path.join(args.model_dir, "init.zip")) - executor = Executor() - # If specify checkpoint, load some info from checkpoint - if args.checkpoint is not None: - infos = load_checkpoint(model, args.checkpoint) - elif args.enc_init is not None: - logging.info("load pretrained encoders: {}".format(args.enc_init)) - infos = load_trained_modules(model, args) - else: - infos = {} - start_epoch = infos.get("epoch", -1) + 1 - cv_loss = infos.get("cv_loss", 0.0) - step = infos.get("step", -1) - - num_epochs = configs.get("max_epoch", 100) - model_dir = args.model_dir - writer = None - if args.rank == 0: - os.makedirs(model_dir, exist_ok=True) - exp_id = os.path.basename(model_dir) - writer = SummaryWriter(os.path.join(args.tensorboard_dir, exp_id)) - - if distributed: - assert torch.cuda.is_available() - # cuda model is required for nn.parallel.DistributedDataParallel - model.cuda() - model = torch.nn.parallel.DistributedDataParallel( - model, find_unused_parameters=True - ) - device = torch.device("cuda") - if args.fp16_grad_sync: - from torch.distributed.algorithms.ddp_comm_hooks import ( - default as comm_hooks, - ) - - model.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) - else: - use_cuda = args.gpu >= 0 and torch.cuda.is_available() - device = torch.device("cuda" if use_cuda else "cpu") - model = model.to(device) - - if configs["optim"] == "adam": - optimizer = optim.Adam(model.parameters(), **configs["optim_conf"]) - elif configs["optim"] == "adamw": - optimizer = optim.AdamW(model.parameters(), **configs["optim_conf"]) - else: - raise ValueError("unknown optimizer: " + configs["optim"]) - if configs["scheduler"] == "warmuplr": - scheduler = WarmupLR(optimizer, **configs["scheduler_conf"]) - elif configs["scheduler"] == "NoamHoldAnnealing": - scheduler = NoamHoldAnnealing(optimizer, **configs["scheduler_conf"]) - else: - raise ValueError("unknown scheduler: " + configs["scheduler"]) - - final_epoch = None - configs["rank"] = args.rank - configs["is_distributed"] = distributed - configs["use_amp"] = args.use_amp - if start_epoch == 0 and args.rank == 0: - save_model_path = os.path.join(model_dir, "init.pt") - save_checkpoint(model, save_model_path) - - # Start training loop - executor.step = step - scheduler.set_step(step) - # used for pytorch amp mixed precision training - scaler = None - if args.use_amp: - scaler = torch.cuda.amp.GradScaler() - - for epoch in range(start_epoch, num_epochs): - train_dataset.set_epoch(epoch) - configs["epoch"] = epoch - lr = optimizer.param_groups[0]["lr"] - logging.info("Epoch {} TRAIN info lr {}".format(epoch, lr)) - executor.train( - model, - optimizer, - scheduler, - train_data_loader, - device, - writer, - configs, - scaler, - ) - total_loss, num_seen_utts = executor.cv(model, cv_data_loader, device, configs) - cv_loss = total_loss / num_seen_utts - - logging.info("Epoch {} CV info cv_loss {}".format(epoch, cv_loss)) - if args.rank == 0: - save_model_path = os.path.join(model_dir, "{}.pt".format(epoch)) - save_checkpoint( - model, - save_model_path, - {"epoch": epoch, "lr": lr, "cv_loss": cv_loss, "step": executor.step}, - ) - writer.add_scalar("epoch/cv_loss", cv_loss, epoch) - writer.add_scalar("epoch/lr", lr, epoch) - final_epoch = epoch - - if final_epoch is not None and args.rank == 0: - final_model_path = os.path.join(model_dir, "final.pt") - os.remove(final_model_path) if os.path.exists(final_model_path) else None - os.symlink("{}.pt".format(final_epoch), final_model_path) - writer.close() - - -if __name__ == "__main__": - main() diff --git a/modules/wenet_extractor/cif/predictor.py b/modules/wenet_extractor/cif/predictor.py deleted file mode 100644 index 91428c4c418da3026e6865f334e4b4cdafd02900..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/cif/predictor.py +++ /dev/null @@ -1,274 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from typing import Optional - -import torch -from torch import nn -from modules.wenet_extractor.utils.mask import make_pad_mask - - -class Predictor(nn.Module): - def __init__( - self, - idim, - l_order, - r_order, - threshold=1.0, - dropout=0.1, - smooth_factor=1.0, - noise_threshold=0, - tail_threshold=0.45, - ): - super().__init__() - - self.pad = nn.ConstantPad1d((l_order, r_order), 0.0) - self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim) - self.cif_output = nn.Linear(idim, 1) - self.dropout = torch.nn.Dropout(p=dropout) - self.threshold = threshold - self.smooth_factor = smooth_factor - self.noise_threshold = noise_threshold - self.tail_threshold = tail_threshold - - def forward( - self, - hidden, - target_label: Optional[torch.Tensor] = None, - mask: torch.Tensor = torch.tensor(0), - ignore_id: int = -1, - mask_chunk_predictor: Optional[torch.Tensor] = None, - target_label_length: Optional[torch.Tensor] = None, - ): - h = hidden - context = h.transpose(1, 2) - queries = self.pad(context) - memory = self.cif_conv1d(queries) - output = memory + context - output = self.dropout(output) - output = output.transpose(1, 2) - output = torch.relu(output) - output = self.cif_output(output) - alphas = torch.sigmoid(output) - alphas = torch.nn.functional.relu( - alphas * self.smooth_factor - self.noise_threshold - ) - if mask is not None: - mask = mask.transpose(-1, -2).float() - alphas = alphas * mask - if mask_chunk_predictor is not None: - alphas = alphas * mask_chunk_predictor - alphas = alphas.squeeze(-1) - mask = mask.squeeze(-1) - if target_label_length is not None: - target_length = target_label_length - elif target_label is not None: - target_length = (target_label != ignore_id).float().sum(-1) - else: - target_length = None - token_num = alphas.sum(-1) - if target_length is not None: - alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1)) - elif self.tail_threshold > 0.0: - hidden, alphas, token_num = self.tail_process_fn( - hidden, alphas, token_num, mask=mask - ) - - acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) - - if target_length is None and self.tail_threshold > 0.0: - token_num_int = torch.max(token_num).type(torch.int32).item() - acoustic_embeds = acoustic_embeds[:, :token_num_int, :] - - return acoustic_embeds, token_num, alphas, cif_peak - - def tail_process_fn( - self, - hidden, - alphas, - token_num: Optional[torch.Tensor] = None, - mask: Optional[torch.Tensor] = None, - ): - b, t, d = hidden.size() - tail_threshold = self.tail_threshold - if mask is not None: - zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device) - ones_t = torch.ones_like(zeros_t) - mask_1 = torch.cat([mask, zeros_t], dim=1) - mask_2 = torch.cat([ones_t, mask], dim=1) - mask = mask_2 - mask_1 - tail_threshold = mask * tail_threshold - alphas = torch.cat([alphas, zeros_t], dim=1) - alphas = torch.add(alphas, tail_threshold) - else: - tail_threshold_tensor = torch.tensor( - [tail_threshold], dtype=alphas.dtype - ).to(alphas.device) - tail_threshold_tensor = torch.reshape(tail_threshold_tensor, (1, 1)) - alphas = torch.cat([alphas, tail_threshold_tensor], dim=1) - zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device) - hidden = torch.cat([hidden, zeros], dim=1) - token_num = alphas.sum(dim=-1) - token_num_floor = torch.floor(token_num) - - return hidden, alphas, token_num_floor - - def gen_frame_alignments( - self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None - ): - batch_size, maximum_length = alphas.size() - int_type = torch.int32 - - is_training = self.training - if is_training: - token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type) - else: - token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type) - - max_token_num = torch.max(token_num).item() - - alphas_cumsum = torch.cumsum(alphas, dim=1) - alphas_cumsum = torch.floor(alphas_cumsum).type(int_type) - alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1) - - index = torch.ones([batch_size, max_token_num], dtype=int_type) - index = torch.cumsum(index, dim=1) - index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device) - - index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type) - index_div_bool_zeros = index_div.eq(0) - index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1 - index_div_bool_zeros_count = torch.clamp( - index_div_bool_zeros_count, 0, encoder_sequence_length.max() - ) - token_num_mask = (~make_pad_mask(token_num, max_len=max_token_num)).to( - token_num.device - ) - index_div_bool_zeros_count *= token_num_mask - - index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat( - 1, 1, maximum_length - ) - ones = torch.ones_like(index_div_bool_zeros_count_tile) - zeros = torch.zeros_like(index_div_bool_zeros_count_tile) - ones = torch.cumsum(ones, dim=2) - cond = index_div_bool_zeros_count_tile == ones - index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones) - - index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type( - torch.bool - ) - index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type( - int_type - ) - index_div_bool_zeros_count_tile_out = torch.sum( - index_div_bool_zeros_count_tile, dim=1 - ) - index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type( - int_type - ) - predictor_mask = ( - ( - ~make_pad_mask( - encoder_sequence_length, max_len=encoder_sequence_length.max() - ) - ) - .type(int_type) - .to(encoder_sequence_length.device) - ) - index_div_bool_zeros_count_tile_out = ( - index_div_bool_zeros_count_tile_out * predictor_mask - ) - - predictor_alignments = index_div_bool_zeros_count_tile_out - predictor_alignments_length = predictor_alignments.sum(-1).type( - encoder_sequence_length.dtype - ) - return predictor_alignments.detach(), predictor_alignments_length.detach() - - -class MAELoss(nn.Module): - def __init__(self, normalize_length=False): - super(MAELoss, self).__init__() - self.normalize_length = normalize_length - self.criterion = torch.nn.L1Loss(reduction="sum") - - def forward(self, token_length, pre_token_length): - loss_token_normalizer = token_length.size(0) - if self.normalize_length: - loss_token_normalizer = token_length.sum().type(torch.float32) - loss = self.criterion(token_length, pre_token_length) - loss = loss / loss_token_normalizer - return loss - - -def cif(hidden: torch.Tensor, alphas: torch.Tensor, threshold: float): - batch_size, len_time, hidden_size = hidden.size() - - # loop varss - integrate = torch.zeros([batch_size], device=hidden.device) - frame = torch.zeros([batch_size, hidden_size], device=hidden.device) - # intermediate vars along time - list_fires = [] - list_frames = [] - - for t in range(len_time): - alpha = alphas[:, t] - distribution_completion = ( - torch.ones([batch_size], device=hidden.device) - integrate - ) - - integrate += alpha - list_fires.append(integrate) - - fire_place = integrate >= threshold - integrate = torch.where( - fire_place, - integrate - torch.ones([batch_size], device=hidden.device), - integrate, - ) - cur = torch.where(fire_place, distribution_completion, alpha) - remainds = alpha - cur - - frame += cur[:, None] * hidden[:, t, :] - list_frames.append(frame) - frame = torch.where( - fire_place[:, None].repeat(1, hidden_size), - remainds[:, None] * hidden[:, t, :], - frame, - ) - - fires = torch.stack(list_fires, 1) - frames = torch.stack(list_frames, 1) - list_ls = [] - len_labels = torch.round(alphas.sum(-1)).int() - max_label_len = len_labels.max() - for b in range(batch_size): - fire = fires[b, :] - l = torch.index_select( - frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze() - ) - pad_l = torch.zeros( - [int(max_label_len - l.size(0)), hidden_size], device=hidden.device - ) - list_ls.append(torch.cat([l, pad_l], 0)) - return torch.stack(list_ls, 0), fires diff --git a/modules/wenet_extractor/dataset/__init__.py b/modules/wenet_extractor/dataset/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/dataset/dataset.py b/modules/wenet_extractor/dataset/dataset.py deleted file mode 100644 index c4ef135bf083be9a18be62d2981196b26b81b394..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/dataset/dataset.py +++ /dev/null @@ -1,211 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import random - -import torch -import torch.distributed as dist -from torch.utils.data import IterableDataset - -import wenet.dataset.processor as processor -from wenet.utils.file_utils import read_lists - - -class Processor(IterableDataset): - def __init__(self, source, f, *args, **kw): - assert callable(f) - self.source = source - self.f = f - self.args = args - self.kw = kw - - def set_epoch(self, epoch): - self.source.set_epoch(epoch) - - def __iter__(self): - """Return an iterator over the source dataset processed by the - given processor. - """ - assert self.source is not None - assert callable(self.f) - return self.f(iter(self.source), *self.args, **self.kw) - - def apply(self, f): - assert callable(f) - return Processor(self, f, *self.args, **self.kw) - - -class DistributedSampler: - def __init__(self, shuffle=True, partition=True): - self.epoch = -1 - self.update() - self.shuffle = shuffle - self.partition = partition - - def update(self): - assert dist.is_available() - if dist.is_initialized(): - self.rank = dist.get_rank() - self.world_size = dist.get_world_size() - else: - self.rank = 0 - self.world_size = 1 - worker_info = torch.utils.data.get_worker_info() - if worker_info is None: - self.worker_id = 0 - self.num_workers = 1 - else: - self.worker_id = worker_info.id - self.num_workers = worker_info.num_workers - return dict( - rank=self.rank, - world_size=self.world_size, - worker_id=self.worker_id, - num_workers=self.num_workers, - ) - - def set_epoch(self, epoch): - self.epoch = epoch - - def sample(self, data): - """Sample data according to rank/world_size/num_workers - - Args: - data(List): input data list - - Returns: - List: data list after sample - """ - data = list(range(len(data))) - # TODO(Binbin Zhang): fix this - # We can not handle uneven data for CV on DDP, so we don't - # sample data by rank, that means every GPU gets the same - # and all the CV data - if self.partition: - if self.shuffle: - random.Random(self.epoch).shuffle(data) - data = data[self.rank :: self.world_size] - data = data[self.worker_id :: self.num_workers] - return data - - -class DataList(IterableDataset): - def __init__(self, lists, shuffle=True, partition=True): - self.lists = lists - self.sampler = DistributedSampler(shuffle, partition) - - def set_epoch(self, epoch): - self.sampler.set_epoch(epoch) - - def __iter__(self): - sampler_info = self.sampler.update() - indexes = self.sampler.sample(self.lists) - for index in indexes: - # yield dict(src=src) - data = dict(src=self.lists[index]) - data.update(sampler_info) - yield data - - -def Dataset( - data_type, - data_list_file, - symbol_table, - conf, - bpe_model=None, - non_lang_syms=None, - partition=True, -): - """Construct dataset from arguments - - We have two shuffle stage in the Dataset. The first is global - shuffle at shards tar/raw file level. The second is global shuffle - at training samples level. - - Args: - data_type(str): raw/shard - bpe_model(str): model for english bpe part - partition(bool): whether to do data partition in terms of rank - """ - assert data_type in ["raw", "shard"] - lists = read_lists(data_list_file) - shuffle = conf.get("shuffle", True) - dataset = DataList(lists, shuffle=shuffle, partition=partition) - if data_type == "shard": - dataset = Processor(dataset, processor.url_opener) - dataset = Processor(dataset, processor.tar_file_and_group) - else: - dataset = Processor(dataset, processor.parse_raw) - - dataset = Processor( - dataset, - processor.tokenize, - symbol_table, - bpe_model, - non_lang_syms, - conf.get("split_with_space", False), - ) - filter_conf = conf.get("filter_conf", {}) - dataset = Processor(dataset, processor.filter, **filter_conf) - - resample_conf = conf.get("resample_conf", {}) - dataset = Processor(dataset, processor.resample, **resample_conf) - - speed_perturb = conf.get("speed_perturb", False) - if speed_perturb: - dataset = Processor(dataset, processor.speed_perturb) - - feats_type = conf.get("feats_type", "fbank") - assert feats_type in ["fbank", "mfcc"] - if feats_type == "fbank": - fbank_conf = conf.get("fbank_conf", {}) - dataset = Processor(dataset, processor.compute_fbank, **fbank_conf) - elif feats_type == "mfcc": - mfcc_conf = conf.get("mfcc_conf", {}) - dataset = Processor(dataset, processor.compute_mfcc, **mfcc_conf) - - spec_aug = conf.get("spec_aug", True) - spec_sub = conf.get("spec_sub", False) - spec_trim = conf.get("spec_trim", False) - if spec_aug: - spec_aug_conf = conf.get("spec_aug_conf", {}) - dataset = Processor(dataset, processor.spec_aug, **spec_aug_conf) - if spec_sub: - spec_sub_conf = conf.get("spec_sub_conf", {}) - dataset = Processor(dataset, processor.spec_sub, **spec_sub_conf) - if spec_trim: - spec_trim_conf = conf.get("spec_trim_conf", {}) - dataset = Processor(dataset, processor.spec_trim, **spec_trim_conf) - - if shuffle: - shuffle_conf = conf.get("shuffle_conf", {}) - dataset = Processor(dataset, processor.shuffle, **shuffle_conf) - - sort = conf.get("sort", True) - if sort: - sort_conf = conf.get("sort_conf", {}) - dataset = Processor(dataset, processor.sort, **sort_conf) - - batch_conf = conf.get("batch_conf", {}) - dataset = Processor(dataset, processor.batch, **batch_conf) - dataset = Processor(dataset, processor.padding) - return dataset diff --git a/modules/wenet_extractor/dataset/kaldi_io.py b/modules/wenet_extractor/dataset/kaldi_io.py deleted file mode 100644 index 1bddd67e69411270b7d9d799fb35a2f30c6fd744..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/dataset/kaldi_io.py +++ /dev/null @@ -1,852 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import numpy as np -import sys, os, re, gzip, struct - -################################################# -# Adding kaldi tools to shell path, - -# Select kaldi, -if not "KALDI_ROOT" in os.environ: - # Default! To change run python with 'export KALDI_ROOT=/some_dir python' - os.environ["KALDI_ROOT"] = "/mnt/matylda5/iveselyk/Tools/kaldi-trunk" - -# Add kaldi tools to path, -os.environ["PATH"] = ( - os.popen( - "echo $KALDI_ROOT/src/bin:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/src/fstbin/:$KALDI_ROOT/src/gmmbin/:$KALDI_ROOT/src/featbin/:$KALDI_ROOT/src/lm/:$KALDI_ROOT/src/sgmmbin/:$KALDI_ROOT/src/sgmm2bin/:$KALDI_ROOT/src/fgmmbin/:$KALDI_ROOT/src/latbin/:$KALDI_ROOT/src/nnetbin:$KALDI_ROOT/src/nnet2bin:$KALDI_ROOT/src/nnet3bin:$KALDI_ROOT/src/online2bin/:$KALDI_ROOT/src/ivectorbin/:$KALDI_ROOT/src/lmbin/" - ) - .readline() - .strip() - + ":" - + os.environ["PATH"] -) - - -################################################# -# Define all custom exceptions, -class UnsupportedDataType(Exception): - pass - - -class UnknownVectorHeader(Exception): - pass - - -class UnknownMatrixHeader(Exception): - pass - - -class BadSampleSize(Exception): - pass - - -class BadInputFormat(Exception): - pass - - -class SubprocessFailed(Exception): - pass - - -################################################# -# Data-type independent helper functions, - - -def open_or_fd(file, mode="rb"): - """fd = open_or_fd(file) - Open file, gzipped file, pipe, or forward the file-descriptor. - Eventually seeks in the 'file' argument contains ':offset' suffix. - """ - offset = None - try: - # strip 'ark:' prefix from r{x,w}filename (optional), - if re.search("^(ark|scp)(,scp|,b|,t|,n?f|,n?p|,b?o|,n?s|,n?cs)*:", file): - (prefix, file) = file.split(":", 1) - # separate offset from filename (optional), - if re.search(":[0-9]+$", file): - (file, offset) = file.rsplit(":", 1) - # input pipe? - if file[-1] == "|": - fd = popen(file[:-1], "rb") # custom, - # output pipe? - elif file[0] == "|": - fd = popen(file[1:], "wb") # custom, - # is it gzipped? - elif file.split(".")[-1] == "gz": - fd = gzip.open(file, mode) - # a normal file... - else: - fd = open(file, mode) - except TypeError: - # 'file' is opened file descriptor, - fd = file - # Eventually seek to offset, - if offset != None: - fd.seek(int(offset)) - return fd - - -# based on '/usr/local/lib/python3.4/os.py' -def popen(cmd, mode="rb"): - if not isinstance(cmd, str): - raise TypeError("invalid cmd type (%s, expected string)" % type(cmd)) - - import subprocess, io, threading - - # cleanup function for subprocesses, - def cleanup(proc, cmd): - ret = proc.wait() - if ret > 0: - raise SubprocessFailed("cmd %s returned %d !" % (cmd, ret)) - return - - # text-mode, - if mode == "r": - proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE) - threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, - return io.TextIOWrapper(proc.stdout) - elif mode == "w": - proc = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE) - threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, - return io.TextIOWrapper(proc.stdin) - # binary, - elif mode == "rb": - proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE) - threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, - return proc.stdout - elif mode == "wb": - proc = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE) - threading.Thread(target=cleanup, args=(proc, cmd)).start() # clean-up thread, - return proc.stdin - # sanity, - else: - raise ValueError("invalid mode %s" % mode) - - -def read_key(fd): - """[key] = read_key(fd) - Read the utterance-key from the opened ark/stream descriptor 'fd'. - """ - key = "" - while 1: - char = fd.read(1).decode("latin1") - if char == "": - break - if char == " ": - break - key += char - key = key.strip() - if key == "": - return None # end of file, - assert re.match("^\S+$", key) != None # check format (no whitespace!) - return key - - -################################################# -# Integer vectors (alignments, ...), - - -def read_ali_ark(file_or_fd): - """Alias to 'read_vec_int_ark()'""" - return read_vec_int_ark(file_or_fd) - - -def read_vec_int_ark(file_or_fd): - """generator(key,vec) = read_vec_int_ark(file_or_fd) - Create generator of (key,vector) tuples, which reads from the ark file/stream. - file_or_fd : ark, gzipped ark, pipe or opened file descriptor. - - Read ark to a 'dictionary': - d = { u:d for u,d in kaldi_io.read_vec_int_ark(file) } - """ - fd = open_or_fd(file_or_fd) - try: - key = read_key(fd) - while key: - ali = read_vec_int(fd) - yield key, ali - key = read_key(fd) - finally: - if fd is not file_or_fd: - fd.close() - - -def read_vec_int_scp(file_or_fd): - """generator(key,vec) = read_vec_int_scp(file_or_fd) - Returns generator of (key,vector) tuples, read according to kaldi scp. - file_or_fd : scp, gzipped scp, pipe or opened file descriptor. - - Iterate the scp: - for key,vec in kaldi_io.read_vec_int_scp(file): - ... - - Read scp to a 'dictionary': - d = { key:vec for key,mat in kaldi_io.read_vec_int_scp(file) } - """ - fd = open_or_fd(file_or_fd) - try: - for line in fd: - (key, rxfile) = line.decode().split(" ") - vec = read_vec_int(rxfile) - yield key, vec - finally: - if fd is not file_or_fd: - fd.close() - - -def read_vec_int(file_or_fd): - """[int-vec] = read_vec_int(file_or_fd) - Read kaldi integer vector, ascii or binary input, - """ - fd = open_or_fd(file_or_fd) - binary = fd.read(2).decode() - if binary == "\0B": # binary flag - assert fd.read(1).decode() == "\4" - # int-size - vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[0] # vector dim - # Elements from int32 vector are sored in tuples: (sizeof(int32), value), - vec = np.frombuffer( - fd.read(vec_size * 5), - dtype=[("size", "int8"), ("value", "int32")], - count=vec_size, - ) - assert vec[0]["size"] == 4 # int32 size, - ans = vec[:]["value"] # values are in 2nd column, - else: # ascii, - arr = (binary + fd.readline().decode()).strip().split() - try: - arr.remove("[") - arr.remove("]") # optionally - except ValueError: - pass - ans = np.array(arr, dtype=int) - if fd is not file_or_fd: - fd.close() # cleanup - return ans - - -# Writing, -def write_vec_int(file_or_fd, v, key=""): - """write_vec_int(f, v, key='') - Write a binary kaldi integer vector to filename or stream. - Arguments: - file_or_fd : filename or opened file descriptor for writing, - v : the vector to be stored, - key (optional) : used for writing ark-file, the utterance-id gets written before the vector. - - Example of writing single vector: - kaldi_io.write_vec_int(filename, vec) - - Example of writing arkfile: - with open(ark_file,'w') as f: - for key,vec in dict.iteritems(): - kaldi_io.write_vec_flt(f, vec, key=key) - """ - fd = open_or_fd(file_or_fd, mode="wb") - if sys.version_info[0] == 3: - assert fd.mode == "wb" - try: - if key != "": - fd.write( - (key + " ").encode("latin1") - ) # ark-files have keys (utterance-id), - fd.write("\0B".encode()) # we write binary! - # dim, - fd.write("\4".encode()) # int32 type, - fd.write(struct.pack(np.dtype("int32").char, v.shape[0])) - # data, - for i in range(len(v)): - fd.write("\4".encode()) # int32 type, - fd.write(struct.pack(np.dtype("int32").char, v[i])) # binary, - finally: - if fd is not file_or_fd: - fd.close() - - -################################################# -# Float vectors (confidences, ivectors, ...), - - -# Reading, -def read_vec_flt_scp(file_or_fd): - """generator(key,mat) = read_vec_flt_scp(file_or_fd) - Returns generator of (key,vector) tuples, read according to kaldi scp. - file_or_fd : scp, gzipped scp, pipe or opened file descriptor. - - Iterate the scp: - for key,vec in kaldi_io.read_vec_flt_scp(file): - ... - - Read scp to a 'dictionary': - d = { key:mat for key,mat in kaldi_io.read_mat_scp(file) } - """ - fd = open_or_fd(file_or_fd) - try: - for line in fd: - (key, rxfile) = line.decode().split(" ") - vec = read_vec_flt(rxfile) - yield key, vec - finally: - if fd is not file_or_fd: - fd.close() - - -def read_vec_flt_ark(file_or_fd): - """generator(key,vec) = read_vec_flt_ark(file_or_fd) - Create generator of (key,vector) tuples, reading from an ark file/stream. - file_or_fd : ark, gzipped ark, pipe or opened file descriptor. - - Read ark to a 'dictionary': - d = { u:d for u,d in kaldi_io.read_vec_flt_ark(file) } - """ - fd = open_or_fd(file_or_fd) - try: - key = read_key(fd) - while key: - ali = read_vec_flt(fd) - yield key, ali - key = read_key(fd) - finally: - if fd is not file_or_fd: - fd.close() - - -def read_vec_flt(file_or_fd): - """[flt-vec] = read_vec_flt(file_or_fd) - Read kaldi float vector, ascii or binary input, - """ - fd = open_or_fd(file_or_fd) - binary = fd.read(2).decode() - if binary == "\0B": # binary flag - # Data type, - header = fd.read(3).decode() - if header == "FV ": - sample_size = 4 # floats - elif header == "DV ": - sample_size = 8 # doubles - else: - raise UnknownVectorHeader("The header contained '%s'" % header) - assert sample_size > 0 - # Dimension, - assert fd.read(1).decode() == "\4" - # int-size - vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[0] # vector dim - # Read whole vector, - buf = fd.read(vec_size * sample_size) - if sample_size == 4: - ans = np.frombuffer(buf, dtype="float32") - elif sample_size == 8: - ans = np.frombuffer(buf, dtype="float64") - else: - raise BadSampleSize - return ans - else: # ascii, - arr = (binary + fd.readline().decode()).strip().split() - try: - arr.remove("[") - arr.remove("]") # optionally - except ValueError: - pass - ans = np.array(arr, dtype=float) - if fd is not file_or_fd: - fd.close() # cleanup - return ans - - -# Writing, -def write_vec_flt(file_or_fd, v, key=""): - """write_vec_flt(f, v, key='') - Write a binary kaldi vector to filename or stream. Supports 32bit and 64bit floats. - Arguments: - file_or_fd : filename or opened file descriptor for writing, - v : the vector to be stored, - key (optional) : used for writing ark-file, the utterance-id gets written before the vector. - - Example of writing single vector: - kaldi_io.write_vec_flt(filename, vec) - - Example of writing arkfile: - with open(ark_file,'w') as f: - for key,vec in dict.iteritems(): - kaldi_io.write_vec_flt(f, vec, key=key) - """ - fd = open_or_fd(file_or_fd, mode="wb") - if sys.version_info[0] == 3: - assert fd.mode == "wb" - try: - if key != "": - fd.write( - (key + " ").encode("latin1") - ) # ark-files have keys (utterance-id), - fd.write("\0B".encode()) # we write binary! - # Data-type, - if v.dtype == "float32": - fd.write("FV ".encode()) - elif v.dtype == "float64": - fd.write("DV ".encode()) - else: - raise UnsupportedDataType( - "'%s', please use 'float32' or 'float64'" % v.dtype - ) - # Dim, - fd.write("\04".encode()) - fd.write(struct.pack(np.dtype("uint32").char, v.shape[0])) # dim - # Data, - fd.write(v.tobytes()) - finally: - if fd is not file_or_fd: - fd.close() - - -################################################# -# Float matrices (features, transformations, ...), - - -# Reading, -def read_mat_scp(file_or_fd): - """generator(key,mat) = read_mat_scp(file_or_fd) - Returns generator of (key,matrix) tuples, read according to kaldi scp. - file_or_fd : scp, gzipped scp, pipe or opened file descriptor. - - Iterate the scp: - for key,mat in kaldi_io.read_mat_scp(file): - ... - - Read scp to a 'dictionary': - d = { key:mat for key,mat in kaldi_io.read_mat_scp(file) } - """ - fd = open_or_fd(file_or_fd) - try: - for line in fd: - (key, rxfile) = line.decode().split(" ") - mat = read_mat(rxfile) - yield key, mat - finally: - if fd is not file_or_fd: - fd.close() - - -def read_mat_ark(file_or_fd): - """generator(key,mat) = read_mat_ark(file_or_fd) - Returns generator of (key,matrix) tuples, read from ark file/stream. - file_or_fd : scp, gzipped scp, pipe or opened file descriptor. - - Iterate the ark: - for key,mat in kaldi_io.read_mat_ark(file): - ... - - Read ark to a 'dictionary': - d = { key:mat for key,mat in kaldi_io.read_mat_ark(file) } - """ - fd = open_or_fd(file_or_fd) - try: - key = read_key(fd) - while key: - mat = read_mat(fd) - yield key, mat - key = read_key(fd) - finally: - if fd is not file_or_fd: - fd.close() - - -def read_mat(file_or_fd): - """[mat] = read_mat(file_or_fd) - Reads single kaldi matrix, supports ascii and binary. - file_or_fd : file, gzipped file, pipe or opened file descriptor. - """ - fd = open_or_fd(file_or_fd) - try: - binary = fd.read(2).decode() - if binary == "\0B": - mat = _read_mat_binary(fd) - else: - assert binary == " [" - mat = _read_mat_ascii(fd) - finally: - if fd is not file_or_fd: - fd.close() - return mat - - -def _read_mat_binary(fd): - # Data type - header = fd.read(3).decode() - # 'CM', 'CM2', 'CM3' are possible values, - if header.startswith("CM"): - return _read_compressed_mat(fd, header) - elif header == "FM ": - sample_size = 4 # floats - elif header == "DM ": - sample_size = 8 # doubles - else: - raise UnknownMatrixHeader("The header contained '%s'" % header) - assert sample_size > 0 - # Dimensions - s1, rows, s2, cols = np.frombuffer( - fd.read(10), dtype="int8,int32,int8,int32", count=1 - )[0] - # Read whole matrix - buf = fd.read(rows * cols * sample_size) - if sample_size == 4: - vec = np.frombuffer(buf, dtype="float32") - elif sample_size == 8: - vec = np.frombuffer(buf, dtype="float64") - else: - raise BadSampleSize - mat = np.reshape(vec, (rows, cols)) - return mat - - -def _read_mat_ascii(fd): - rows = [] - while 1: - line = fd.readline().decode() - if len(line) == 0: - raise BadInputFormat # eof, should not happen! - if len(line.strip()) == 0: - continue # skip empty line - arr = line.strip().split() - if arr[-1] != "]": - rows.append(np.array(arr, dtype="float32")) # not last line - else: - rows.append(np.array(arr[:-1], dtype="float32")) # last line - mat = np.vstack(rows) - return mat - - -def _read_compressed_mat(fd, format): - """Read a compressed matrix, - see: https://github.com/kaldi-asr/kaldi/blob/master/src/matrix/compressed-matrix.h - methods: CompressedMatrix::Read(...), CompressedMatrix::CopyToMat(...), - """ - assert format == "CM " # The formats CM2, CM3 are not supported... - - # Format of header 'struct', - global_header = np.dtype( - [ - ("minvalue", "float32"), - ("range", "float32"), - ("num_rows", "int32"), - ("num_cols", "int32"), - ] - ) # member '.format' is not written, - per_col_header = np.dtype( - [ - ("percentile_0", "uint16"), - ("percentile_25", "uint16"), - ("percentile_75", "uint16"), - ("percentile_100", "uint16"), - ] - ) - - # Mapping for percentiles in col-headers, - def uint16_to_float(value, min, range): - return np.float32(min + range * 1.52590218966964e-05 * value) - - # Mapping for matrix elements, - def uint8_to_float_v2(vec, p0, p25, p75, p100): - # Split the vector by masks, - mask_0_64 = vec <= 64 - mask_193_255 = vec > 192 - mask_65_192 = ~(mask_0_64 | mask_193_255) - # Sanity check (useful but slow...), - # assert(len(vec) == np.sum(np.hstack([mask_0_64,mask_65_192,mask_193_255]))) - # assert(len(vec) == np.sum(np.any([mask_0_64,mask_65_192,mask_193_255], axis=0))) - # Build the float vector, - ans = np.empty(len(vec), dtype="float32") - ans[mask_0_64] = p0 + (p25 - p0) / 64.0 * vec[mask_0_64] - ans[mask_65_192] = p25 + (p75 - p25) / 128.0 * (vec[mask_65_192] - 64) - ans[mask_193_255] = p75 + (p100 - p75) / 63.0 * (vec[mask_193_255] - 192) - return ans - - # Read global header, - globmin, globrange, rows, cols = np.frombuffer( - fd.read(16), dtype=global_header, count=1 - )[0] - - # The data is structed as [Colheader, ... , Colheader, Data, Data , .... ] - # { cols }{ size } - col_headers = np.frombuffer(fd.read(cols * 8), dtype=per_col_header, count=cols) - data = np.reshape( - np.frombuffer(fd.read(cols * rows), dtype="uint8", count=cols * rows), - newshape=(cols, rows), - ) # stored as col-major, - - mat = np.empty((cols, rows), dtype="float32") - for i, col_header in enumerate(col_headers): - col_header_flt = [ - uint16_to_float(percentile, globmin, globrange) for percentile in col_header - ] - mat[i] = uint8_to_float_v2(data[i], *col_header_flt) - - return mat.T # transpose! col-major -> row-major, - - -def write_ark_scp(key, mat, ark_fout, scp_out): - mat_offset = write_mat(ark_fout, mat, key) - scp_line = "{}\t{}:{}".format(key, ark_fout.name, mat_offset) - scp_out.write(scp_line) - scp_out.write("\n") - - -# Writing, -def write_mat(file_or_fd, m, key=""): - """write_mat(f, m, key='') - Write a binary kaldi matrix to filename or stream. Supports 32bit and 64bit floats. - Arguments: - file_or_fd : filename of opened file descriptor for writing, - m : the matrix to be stored, - key (optional) : used for writing ark-file, the utterance-id gets written before the matrix. - - Example of writing single matrix: - kaldi_io.write_mat(filename, mat) - - Example of writing arkfile: - with open(ark_file,'w') as f: - for key,mat in dict.iteritems(): - kaldi_io.write_mat(f, mat, key=key) - """ - mat_offset = 0 - fd = open_or_fd(file_or_fd, mode="wb") - if sys.version_info[0] == 3: - assert fd.mode == "wb" - try: - if key != "": - fd.write( - (key + " ").encode("latin1") - ) # ark-files have keys (utterance-id), - mat_offset = fd.tell() - fd.write("\0B".encode()) # we write binary! - # Data-type, - if m.dtype == "float32": - fd.write("FM ".encode()) - elif m.dtype == "float64": - fd.write("DM ".encode()) - else: - raise UnsupportedDataType( - "'%s', please use 'float32' or 'float64'" % m.dtype - ) - # Dims, - fd.write("\04".encode()) - fd.write(struct.pack(np.dtype("uint32").char, m.shape[0])) # rows - fd.write("\04".encode()) - fd.write(struct.pack(np.dtype("uint32").char, m.shape[1])) # cols - # Data, - fd.write(m.tobytes()) - finally: - if fd is not file_or_fd: - fd.close() - return mat_offset - - -################################################# -# 'Posterior' kaldi type (posteriors, confusion network, nnet1 training targets, ...) -# Corresponds to: vector > > -# - outer vector: time axis -# - inner vector: records at the time -# - tuple: int = index, float = value -# - - -def read_cnet_ark(file_or_fd): - """Alias of function 'read_post_ark()', 'cnet' = confusion network""" - return read_post_ark(file_or_fd) - - -def read_post_ark(file_or_fd): - """generator(key,vec>) = read_post_ark(file) - Returns generator of (key,posterior) tuples, read from ark file. - file_or_fd : ark, gzipped ark, pipe or opened file descriptor. - - Iterate the ark: - for key,post in kaldi_io.read_post_ark(file): - ... - - Read ark to a 'dictionary': - d = { key:post for key,post in kaldi_io.read_post_ark(file) } - """ - fd = open_or_fd(file_or_fd) - try: - key = read_key(fd) - while key: - post = read_post(fd) - yield key, post - key = read_key(fd) - finally: - if fd is not file_or_fd: - fd.close() - - -def read_post(file_or_fd): - """[post] = read_post(file_or_fd) - Reads single kaldi 'Posterior' in binary format. - - The 'Posterior' is C++ type 'vector > >', - the outer-vector is usually time axis, inner-vector are the records - at given time, and the tuple is composed of an 'index' (integer) - and a 'float-value'. The 'float-value' can represent a probability - or any other numeric value. - - Returns vector of vectors of tuples. - """ - fd = open_or_fd(file_or_fd) - ans = [] - binary = fd.read(2).decode() - assert binary == "\0B" - # binary flag - assert fd.read(1).decode() == "\4" - # int-size - outer_vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[ - 0 - ] # number of frames (or bins) - - # Loop over 'outer-vector', - for i in range(outer_vec_size): - assert fd.read(1).decode() == "\4" - # int-size - inner_vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[ - 0 - ] # number of records for frame (or bin) - data = np.frombuffer( - fd.read(inner_vec_size * 10), - dtype=[ - ("size_idx", "int8"), - ("idx", "int32"), - ("size_post", "int8"), - ("post", "float32"), - ], - count=inner_vec_size, - ) - assert data[0]["size_idx"] == 4 - assert data[0]["size_post"] == 4 - ans.append(data[["idx", "post"]].tolist()) - - if fd is not file_or_fd: - fd.close() - return ans - - -################################################# -# Kaldi Confusion Network bin begin/end times, -# (kaldi stores CNs time info separately from the Posterior). -# - - -def read_cntime_ark(file_or_fd): - """generator(key,vec>) = read_cntime_ark(file_or_fd) - Returns generator of (key,cntime) tuples, read from ark file. - file_or_fd : file, gzipped file, pipe or opened file descriptor. - - Iterate the ark: - for key,time in kaldi_io.read_cntime_ark(file): - ... - - Read ark to a 'dictionary': - d = { key:time for key,time in kaldi_io.read_post_ark(file) } - """ - fd = open_or_fd(file_or_fd) - try: - key = read_key(fd) - while key: - cntime = read_cntime(fd) - yield key, cntime - key = read_key(fd) - finally: - if fd is not file_or_fd: - fd.close() - - -def read_cntime(file_or_fd): - """[cntime] = read_cntime(file_or_fd) - Reads single kaldi 'Confusion Network time info', in binary format: - C++ type: vector >. - (begin/end times of bins at the confusion network). - - Binary layout is ' ...' - - file_or_fd : file, gzipped file, pipe or opened file descriptor. - - Returns vector of tuples. - """ - fd = open_or_fd(file_or_fd) - binary = fd.read(2).decode() - assert binary == "\0B" - # assuming it's binary - - assert fd.read(1).decode() == "\4" - # int-size - vec_size = np.frombuffer(fd.read(4), dtype="int32", count=1)[ - 0 - ] # number of frames (or bins) - - data = np.frombuffer( - fd.read(vec_size * 10), - dtype=[ - ("size_beg", "int8"), - ("t_beg", "float32"), - ("size_end", "int8"), - ("t_end", "float32"), - ], - count=vec_size, - ) - assert data[0]["size_beg"] == 4 - assert data[0]["size_end"] == 4 - ans = data[["t_beg", "t_end"]].tolist() # Return vector of tuples (t_beg,t_end), - - if fd is not file_or_fd: - fd.close() - return ans - - -################################################# -# Segments related, -# - - -# Segments as 'Bool vectors' can be handy, -# - for 'superposing' the segmentations, -# - for frame-selection in Speaker-ID experiments, -def read_segments_as_bool_vec(segments_file): - """[ bool_vec ] = read_segments_as_bool_vec(segments_file) - using kaldi 'segments' file for 1 wav, format : ' ' - - t-beg, t-end is in seconds, - - assumed 100 frames/second, - """ - segs = np.loadtxt(segments_file, dtype="object,object,f,f", ndmin=1) - # Sanity checks, - assert len(segs) > 0 # empty segmentation is an error, - assert ( - len(np.unique([rec[1] for rec in segs])) == 1 - ) # segments with only 1 wav-file, - # Convert time to frame-indexes, - start = np.rint([100 * rec[2] for rec in segs]).astype(int) - end = np.rint([100 * rec[3] for rec in segs]).astype(int) - # Taken from 'read_lab_to_bool_vec', htk.py, - frms = np.repeat( - np.r_[np.tile([False, True], len(end)), False], - np.r_[np.c_[start - np.r_[0, end[:-1]], end - start].flat, 0], - ) - assert np.sum(end - start) == np.sum(frms) - return frms diff --git a/modules/wenet_extractor/dataset/processor.py b/modules/wenet_extractor/dataset/processor.py deleted file mode 100644 index df66f60affce9b5f22dcec0b283ed2c3599031c0..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/dataset/processor.py +++ /dev/null @@ -1,656 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import logging -import json -import random -import re -import tarfile -from subprocess import PIPE, Popen -from urllib.parse import urlparse - -import torch -import torchaudio -import torchaudio.compliance.kaldi as kaldi -from torch.nn.utils.rnn import pad_sequence - -AUDIO_FORMAT_SETS = set(["flac", "mp3", "m4a", "ogg", "opus", "wav", "wma"]) - - -def url_opener(data): - """Give url or local file, return file descriptor - Inplace operation. - - Args: - data(Iterable[str]): url or local file list - - Returns: - Iterable[{src, stream}] - """ - for sample in data: - assert "src" in sample - # TODO(Binbin Zhang): support HTTP - url = sample["src"] - try: - pr = urlparse(url) - # local file - if pr.scheme == "" or pr.scheme == "file": - stream = open(url, "rb") - # network file, such as HTTP(HDFS/OSS/S3)/HTTPS/SCP - else: - cmd = f"wget -q -O - {url}" - process = Popen(cmd, shell=True, stdout=PIPE) - sample.update(process=process) - stream = process.stdout - sample.update(stream=stream) - yield sample - except Exception as ex: - logging.warning("Failed to open {}".format(url)) - - -def tar_file_and_group(data): - """Expand a stream of open tar files into a stream of tar file contents. - And groups the file with same prefix - - Args: - data: Iterable[{src, stream}] - - Returns: - Iterable[{key, wav, txt, sample_rate}] - """ - for sample in data: - assert "stream" in sample - stream = tarfile.open(fileobj=sample["stream"], mode="r|*") - prev_prefix = None - example = {} - valid = True - for tarinfo in stream: - name = tarinfo.name - pos = name.rfind(".") - assert pos > 0 - prefix, postfix = name[:pos], name[pos + 1 :] - if prev_prefix is not None and prefix != prev_prefix: - example["key"] = prev_prefix - if valid: - yield example - example = {} - valid = True - with stream.extractfile(tarinfo) as file_obj: - try: - if postfix == "txt": - example["txt"] = file_obj.read().decode("utf8").strip() - elif postfix in AUDIO_FORMAT_SETS: - waveform, sample_rate = torchaudio.load(file_obj) - example["wav"] = waveform - example["sample_rate"] = sample_rate - else: - example[postfix] = file_obj.read() - except Exception as ex: - valid = False - logging.warning("error to parse {}".format(name)) - prev_prefix = prefix - if prev_prefix is not None: - example["key"] = prev_prefix - yield example - stream.close() - if "process" in sample: - sample["process"].communicate() - sample["stream"].close() - - -def parse_raw(data): - """Parse key/wav/txt from json line - - Args: - data: Iterable[str], str is a json line has key/wav/txt - - Returns: - Iterable[{key, wav, txt, sample_rate}] - """ - for sample in data: - assert "src" in sample - json_line = sample["src"] - obj = json.loads(json_line) - assert "key" in obj - assert "wav" in obj - assert "txt" in obj - key = obj["key"] - wav_file = obj["wav"] - txt = obj["txt"] - try: - if "start" in obj: - assert "end" in obj - sample_rate = torchaudio.backend.sox_io_backend.info( - wav_file - ).sample_rate - start_frame = int(obj["start"] * sample_rate) - end_frame = int(obj["end"] * sample_rate) - waveform, _ = torchaudio.backend.sox_io_backend.load( - filepath=wav_file, - num_frames=end_frame - start_frame, - frame_offset=start_frame, - ) - else: - waveform, sample_rate = torchaudio.load(wav_file) - example = dict(key=key, txt=txt, wav=waveform, sample_rate=sample_rate) - yield example - except Exception as ex: - logging.warning("Failed to read {}".format(wav_file)) - - -def filter( - data, - max_length=10240, - min_length=10, - token_max_length=200, - token_min_length=1, - min_output_input_ratio=0.0005, - max_output_input_ratio=1, -): - """Filter sample according to feature and label length - Inplace operation. - - Args:: - data: Iterable[{key, wav, label, sample_rate}] - max_length: drop utterance which is greater than max_length(10ms) - min_length: drop utterance which is less than min_length(10ms) - token_max_length: drop utterance which is greater than - token_max_length, especially when use char unit for - english modeling - token_min_length: drop utterance which is - less than token_max_length - min_output_input_ratio: minimal ration of - token_length / feats_length(10ms) - max_output_input_ratio: maximum ration of - token_length / feats_length(10ms) - - Returns: - Iterable[{key, wav, label, sample_rate}] - """ - for sample in data: - assert "sample_rate" in sample - assert "wav" in sample - assert "label" in sample - # sample['wav'] is torch.Tensor, we have 100 frames every second - num_frames = sample["wav"].size(1) / sample["sample_rate"] * 100 - if num_frames < min_length: - continue - if num_frames > max_length: - continue - if len(sample["label"]) < token_min_length: - continue - if len(sample["label"]) > token_max_length: - continue - if num_frames != 0: - if len(sample["label"]) / num_frames < min_output_input_ratio: - continue - if len(sample["label"]) / num_frames > max_output_input_ratio: - continue - yield sample - - -def resample(data, resample_rate=16000): - """Resample data. - Inplace operation. - - Args: - data: Iterable[{key, wav, label, sample_rate}] - resample_rate: target resample rate - - Returns: - Iterable[{key, wav, label, sample_rate}] - """ - print("resample...") - for sample in data: - assert "sample_rate" in sample - assert "wav" in sample - sample_rate = sample["sample_rate"] - print("sample_rate: ", sample_rate) - print("resample_rate: ", resample_rate) - waveform = sample["wav"] - if sample_rate != resample_rate: - sample["sample_rate"] = resample_rate - sample["wav"] = torchaudio.transforms.Resample( - orig_freq=sample_rate, new_freq=resample_rate - )(waveform) - yield sample - - -def speed_perturb(data, speeds=None): - """Apply speed perturb to the data. - Inplace operation. - - Args: - data: Iterable[{key, wav, label, sample_rate}] - speeds(List[float]): optional speed - - Returns: - Iterable[{key, wav, label, sample_rate}] - """ - if speeds is None: - speeds = [0.9, 1.0, 1.1] - for sample in data: - assert "sample_rate" in sample - assert "wav" in sample - sample_rate = sample["sample_rate"] - waveform = sample["wav"] - speed = random.choice(speeds) - if speed != 1.0: - wav, _ = torchaudio.sox_effects.apply_effects_tensor( - waveform, - sample_rate, - [["speed", str(speed)], ["rate", str(sample_rate)]], - ) - sample["wav"] = wav - - yield sample - - -def compute_fbank(data, num_mel_bins=23, frame_length=25, frame_shift=10, dither=0.0): - """Extract fbank - - Args: - data: Iterable[{key, wav, label, sample_rate}] - - Returns: - Iterable[{key, feat, label}] - """ - for sample in data: - assert "sample_rate" in sample - assert "wav" in sample - assert "key" in sample - assert "label" in sample - sample_rate = sample["sample_rate"] - waveform = sample["wav"] - waveform = waveform * (1 << 15) - # Only keep key, feat, label - mat = kaldi.fbank( - waveform, - num_mel_bins=num_mel_bins, - frame_length=frame_length, - frame_shift=frame_shift, - dither=dither, - energy_floor=0.0, - sample_frequency=sample_rate, - ) - yield dict(key=sample["key"], label=sample["label"], feat=mat) - - -def compute_mfcc( - data, - num_mel_bins=23, - frame_length=25, - frame_shift=10, - dither=0.0, - num_ceps=40, - high_freq=0.0, - low_freq=20.0, -): - """Extract mfcc - - Args: - data: Iterable[{key, wav, label, sample_rate}] - - Returns: - Iterable[{key, feat, label}] - """ - for sample in data: - assert "sample_rate" in sample - assert "wav" in sample - assert "key" in sample - assert "label" in sample - sample_rate = sample["sample_rate"] - waveform = sample["wav"] - waveform = waveform * (1 << 15) - # Only keep key, feat, label - mat = kaldi.mfcc( - waveform, - num_mel_bins=num_mel_bins, - frame_length=frame_length, - frame_shift=frame_shift, - dither=dither, - num_ceps=num_ceps, - high_freq=high_freq, - low_freq=low_freq, - sample_frequency=sample_rate, - ) - yield dict(key=sample["key"], label=sample["label"], feat=mat) - - -def __tokenize_by_bpe_model(sp, txt): - tokens = [] - # CJK(China Japan Korea) unicode range is [U+4E00, U+9FFF], ref: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - pattern = re.compile(r"([\u4e00-\u9fff])") - # Example: - # txt = "你好 ITS'S OKAY 的" - # chars = ["你", "好", " ITS'S OKAY ", "的"] - chars = pattern.split(txt.upper()) - mix_chars = [w for w in chars if len(w.strip()) > 0] - for ch_or_w in mix_chars: - # ch_or_w is a single CJK charater(i.e., "你"), do nothing. - if pattern.fullmatch(ch_or_w) is not None: - tokens.append(ch_or_w) - # ch_or_w contains non-CJK charaters(i.e., " IT'S OKAY "), - # encode ch_or_w using bpe_model. - else: - for p in sp.encode_as_pieces(ch_or_w): - tokens.append(p) - - return tokens - - -def tokenize( - data, symbol_table, bpe_model=None, non_lang_syms=None, split_with_space=False -): - """Decode text to chars or BPE - Inplace operation - - Args: - data: Iterable[{key, wav, txt, sample_rate}] - - Returns: - Iterable[{key, wav, txt, tokens, label, sample_rate}] - """ - if non_lang_syms is not None: - non_lang_syms_pattern = re.compile(r"(\[[^\[\]]+\]|<[^<>]+>|{[^{}]+})") - else: - non_lang_syms = {} - non_lang_syms_pattern = None - - if bpe_model is not None: - import sentencepiece as spm - - sp = spm.SentencePieceProcessor() - sp.load(bpe_model) - else: - sp = None - - for sample in data: - assert "txt" in sample - txt = sample["txt"].strip() - if non_lang_syms_pattern is not None: - parts = non_lang_syms_pattern.split(txt.upper()) - parts = [w for w in parts if len(w.strip()) > 0] - else: - parts = [txt] - - label = [] - tokens = [] - for part in parts: - if part in non_lang_syms: - tokens.append(part) - else: - if bpe_model is not None: - tokens.extend(__tokenize_by_bpe_model(sp, part)) - else: - if split_with_space: - part = part.split(" ") - for ch in part: - if ch == " ": - ch = "▁" - tokens.append(ch) - - for ch in tokens: - if ch in symbol_table: - label.append(symbol_table[ch]) - elif "" in symbol_table: - label.append(symbol_table[""]) - - sample["tokens"] = tokens - sample["label"] = label - yield sample - - -def spec_aug(data, num_t_mask=2, num_f_mask=2, max_t=50, max_f=10, max_w=80): - """Do spec augmentation - Inplace operation - - Args: - data: Iterable[{key, feat, label}] - num_t_mask: number of time mask to apply - num_f_mask: number of freq mask to apply - max_t: max width of time mask - max_f: max width of freq mask - max_w: max width of time warp - - Returns - Iterable[{key, feat, label}] - """ - for sample in data: - assert "feat" in sample - x = sample["feat"] - assert isinstance(x, torch.Tensor) - y = x.clone().detach() - max_frames = y.size(0) - max_freq = y.size(1) - # time mask - for i in range(num_t_mask): - start = random.randint(0, max_frames - 1) - length = random.randint(1, max_t) - end = min(max_frames, start + length) - y[start:end, :] = 0 - # freq mask - for i in range(num_f_mask): - start = random.randint(0, max_freq - 1) - length = random.randint(1, max_f) - end = min(max_freq, start + length) - y[:, start:end] = 0 - sample["feat"] = y - yield sample - - -def spec_sub(data, max_t=20, num_t_sub=3): - """Do spec substitute - Inplace operation - ref: U2++, section 3.2.3 [https://arxiv.org/abs/2106.05642] - - Args: - data: Iterable[{key, feat, label}] - max_t: max width of time substitute - num_t_sub: number of time substitute to apply - - Returns - Iterable[{key, feat, label}] - """ - for sample in data: - assert "feat" in sample - x = sample["feat"] - assert isinstance(x, torch.Tensor) - y = x.clone().detach() - max_frames = y.size(0) - for i in range(num_t_sub): - start = random.randint(0, max_frames - 1) - length = random.randint(1, max_t) - end = min(max_frames, start + length) - # only substitute the earlier time chosen randomly for current time - pos = random.randint(0, start) - y[start:end, :] = x[start - pos : end - pos, :] - sample["feat"] = y - yield sample - - -def spec_trim(data, max_t=20): - """Trim tailing frames. Inplace operation. - ref: TrimTail [https://arxiv.org/abs/2211.00522] - - Args: - data: Iterable[{key, feat, label}] - max_t: max width of length trimming - - Returns - Iterable[{key, feat, label}] - """ - for sample in data: - assert "feat" in sample - x = sample["feat"] - assert isinstance(x, torch.Tensor) - max_frames = x.size(0) - length = random.randint(1, max_t) - if length < max_frames / 2: - y = x.clone().detach()[: max_frames - length] - sample["feat"] = y - yield sample - - -def shuffle(data, shuffle_size=10000): - """Local shuffle the data - - Args: - data: Iterable[{key, feat, label}] - shuffle_size: buffer size for shuffle - - Returns: - Iterable[{key, feat, label}] - """ - buf = [] - for sample in data: - buf.append(sample) - if len(buf) >= shuffle_size: - random.shuffle(buf) - for x in buf: - yield x - buf = [] - # The sample left over - random.shuffle(buf) - for x in buf: - yield x - - -def sort(data, sort_size=500): - """Sort the data by feature length. - Sort is used after shuffle and before batch, so we can group - utts with similar lengths into a batch, and `sort_size` should - be less than `shuffle_size` - - Args: - data: Iterable[{key, feat, label}] - sort_size: buffer size for sort - - Returns: - Iterable[{key, feat, label}] - """ - - buf = [] - for sample in data: - buf.append(sample) - if len(buf) >= sort_size: - buf.sort(key=lambda x: x["feat"].size(0)) - for x in buf: - yield x - buf = [] - # The sample left over - buf.sort(key=lambda x: x["feat"].size(0)) - for x in buf: - yield x - - -def static_batch(data, batch_size=16): - """Static batch the data by `batch_size` - - Args: - data: Iterable[{key, feat, label}] - batch_size: batch size - - Returns: - Iterable[List[{key, feat, label}]] - """ - buf = [] - for sample in data: - buf.append(sample) - if len(buf) >= batch_size: - yield buf - buf = [] - if len(buf) > 0: - yield buf - - -def dynamic_batch(data, max_frames_in_batch=12000): - """Dynamic batch the data until the total frames in batch - reach `max_frames_in_batch` - - Args: - data: Iterable[{key, feat, label}] - max_frames_in_batch: max_frames in one batch - - Returns: - Iterable[List[{key, feat, label}]] - """ - buf = [] - longest_frames = 0 - for sample in data: - assert "feat" in sample - assert isinstance(sample["feat"], torch.Tensor) - new_sample_frames = sample["feat"].size(0) - longest_frames = max(longest_frames, new_sample_frames) - frames_after_padding = longest_frames * (len(buf) + 1) - if frames_after_padding > max_frames_in_batch: - yield buf - buf = [sample] - longest_frames = new_sample_frames - else: - buf.append(sample) - if len(buf) > 0: - yield buf - - -def batch(data, batch_type="static", batch_size=16, max_frames_in_batch=12000): - """Wrapper for static/dynamic batch""" - if batch_type == "static": - return static_batch(data, batch_size) - elif batch_type == "dynamic": - return dynamic_batch(data, max_frames_in_batch) - else: - logging.fatal("Unsupported batch type {}".format(batch_type)) - - -def padding(data): - """Padding the data into training data - - Args: - data: Iterable[List[{key, feat, label}]] - - Returns: - Iterable[Tuple(keys, feats, labels, feats lengths, label lengths)] - """ - for sample in data: - assert isinstance(sample, list) - feats_length = torch.tensor( - [x["feat"].size(0) for x in sample], dtype=torch.int32 - ) - order = torch.argsort(feats_length, descending=True) - feats_lengths = torch.tensor( - [sample[i]["feat"].size(0) for i in order], dtype=torch.int32 - ) - sorted_feats = [sample[i]["feat"] for i in order] - sorted_keys = [sample[i]["key"] for i in order] - sorted_labels = [ - torch.tensor(sample[i]["label"], dtype=torch.int64) for i in order - ] - label_lengths = torch.tensor( - [x.size(0) for x in sorted_labels], dtype=torch.int32 - ) - - padded_feats = pad_sequence(sorted_feats, batch_first=True, padding_value=0) - padding_labels = pad_sequence(sorted_labels, batch_first=True, padding_value=-1) - - yield (sorted_keys, padded_feats, padding_labels, feats_lengths, label_lengths) diff --git a/modules/wenet_extractor/dataset/wav_distortion.py b/modules/wenet_extractor/dataset/wav_distortion.py deleted file mode 100644 index ee2dd8cedae66b25410e8fdd322e0a70675a2040..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/dataset/wav_distortion.py +++ /dev/null @@ -1,345 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import sys -import random -import math - -import torchaudio -import torch - -torchaudio.set_audio_backend("sox_io") - - -def db2amp(db): - return pow(10, db / 20) - - -def amp2db(amp): - return 20 * math.log10(amp) - - -def make_poly_distortion(conf): - """Generate a db-domain ploynomial distortion function - - f(x) = a * x^m * (1-x)^n + x - - Args: - conf: a dict {'a': #int, 'm': #int, 'n': #int} - - Returns: - The ploynomial function, which could be applied on - a float amplitude value - """ - a = conf["a"] - m = conf["m"] - n = conf["n"] - - def poly_distortion(x): - abs_x = abs(x) - if abs_x < 0.000001: - x = x - else: - db_norm = amp2db(abs_x) / 100 + 1 - if db_norm < 0: - db_norm = 0 - db_norm = a * pow(db_norm, m) * pow((1 - db_norm), n) + db_norm - if db_norm > 1: - db_norm = 1 - db = (db_norm - 1) * 100 - amp = db2amp(db) - if amp >= 0.9997: - amp = 0.9997 - if x > 0: - x = amp - else: - x = -amp - return x - - return poly_distortion - - -def make_quad_distortion(): - return make_poly_distortion({"a": 1, "m": 1, "n": 1}) - - -# the amplitude are set to max for all non-zero point -def make_max_distortion(conf): - """Generate a max distortion function - - Args: - conf: a dict {'max_db': float } - 'max_db': the maxium value. - - Returns: - The max function, which could be applied on - a float amplitude value - """ - max_db = conf["max_db"] - if max_db: - max_amp = db2amp(max_db) # < 0.997 - else: - max_amp = 0.997 - - def max_distortion(x): - if x > 0: - x = max_amp - elif x < 0: - x = -max_amp - else: - x = 0.0 - return x - - return max_distortion - - -def make_amp_mask(db_mask=None): - """Get a amplitude domain mask from db domain mask - - Args: - db_mask: Optional. A list of tuple. if None, using default value. - - Returns: - A list of tuple. The amplitude domain mask - """ - if db_mask is None: - db_mask = [(-110, -95), (-90, -80), (-65, -60), (-50, -30), (-15, 0)] - amp_mask = [(db2amp(db[0]), db2amp(db[1])) for db in db_mask] - return amp_mask - - -default_mask = make_amp_mask() - - -def generate_amp_mask(mask_num): - """Generate amplitude domain mask randomly in [-100db, 0db] - - Args: - mask_num: the slot number of the mask - - Returns: - A list of tuple. each tuple defines a slot. - e.g. [(-100, -80), (-65, -60), (-50, -30), (-15, 0)] - for #mask_num = 4 - """ - a = [0] * 2 * mask_num - a[0] = 0 - m = [] - for i in range(1, 2 * mask_num): - a[i] = a[i - 1] + random.uniform(0.5, 1) - max_val = a[2 * mask_num - 1] - for i in range(0, mask_num): - l = ((a[2 * i] - max_val) / max_val) * 100 - r = ((a[2 * i + 1] - max_val) / max_val) * 100 - m.append((l, r)) - return make_amp_mask(m) - - -def make_fence_distortion(conf): - """Generate a fence distortion function - - In this fence-like shape function, the values in mask slots are - set to maxium, while the values not in mask slots are set to 0. - Use seperated masks for Positive and negetive amplitude. - - Args: - conf: a dict {'mask_number': int,'max_db': float } - 'mask_number': the slot number in mask. - 'max_db': the maxium value. - - Returns: - The fence function, which could be applied on - a float amplitude value - """ - mask_number = conf["mask_number"] - max_db = conf["max_db"] - max_amp = db2amp(max_db) # 0.997 - if mask_number <= 0: - positive_mask = default_mask - negative_mask = make_amp_mask([(-50, 0)]) - else: - positive_mask = generate_amp_mask(mask_number) - negative_mask = generate_amp_mask(mask_number) - - def fence_distortion(x): - is_in_mask = False - if x > 0: - for mask in positive_mask: - if x >= mask[0] and x <= mask[1]: - is_in_mask = True - return max_amp - if not is_in_mask: - return 0.0 - elif x < 0: - abs_x = abs(x) - for mask in negative_mask: - if abs_x >= mask[0] and abs_x <= mask[1]: - is_in_mask = True - return max_amp - if not is_in_mask: - return 0.0 - return x - - return fence_distortion - - -# -def make_jag_distortion(conf): - """Generate a jag distortion function - - In this jag-like shape function, the values in mask slots are - not changed, while the values not in mask slots are set to 0. - Use seperated masks for Positive and negetive amplitude. - - Args: - conf: a dict {'mask_number': #int} - 'mask_number': the slot number in mask. - - Returns: - The jag function,which could be applied on - a float amplitude value - """ - mask_number = conf["mask_number"] - if mask_number <= 0: - positive_mask = default_mask - negative_mask = make_amp_mask([(-50, 0)]) - else: - positive_mask = generate_amp_mask(mask_number) - negative_mask = generate_amp_mask(mask_number) - - def jag_distortion(x): - is_in_mask = False - if x > 0: - for mask in positive_mask: - if x >= mask[0] and x <= mask[1]: - is_in_mask = True - return x - if not is_in_mask: - return 0.0 - elif x < 0: - abs_x = abs(x) - for mask in negative_mask: - if abs_x >= mask[0] and abs_x <= mask[1]: - is_in_mask = True - return x - if not is_in_mask: - return 0.0 - return x - - return jag_distortion - - -# gaining 20db means amp = amp * 10 -# gaining -20db means amp = amp / 10 -def make_gain_db(conf): - """Generate a db domain gain function - - Args: - conf: a dict {'db': #float} - 'db': the gaining value - - Returns: - The db gain function, which could be applied on - a float amplitude value - """ - db = conf["db"] - - def gain_db(x): - return min(0.997, x * pow(10, db / 20)) - - return gain_db - - -def distort(x, func, rate=0.8): - """Distort a waveform in sample point level - - Args: - x: the origin wavefrom - func: the distort function - rate: sample point-level distort probability - - Returns: - the distorted waveform - """ - for i in range(0, x.shape[1]): - a = random.uniform(0, 1) - if a < rate: - x[0][i] = func(float(x[0][i])) - return x - - -def distort_chain(x, funcs, rate=0.8): - for i in range(0, x.shape[1]): - a = random.uniform(0, 1) - if a < rate: - for func in funcs: - x[0][i] = func(float(x[0][i])) - return x - - -# x is numpy -def distort_wav_conf(x, distort_type, distort_conf, rate=0.1): - if distort_type == "gain_db": - gain_db = make_gain_db(distort_conf) - x = distort(x, gain_db) - elif distort_type == "max_distortion": - max_distortion = make_max_distortion(distort_conf) - x = distort(x, max_distortion, rate=rate) - elif distort_type == "fence_distortion": - fence_distortion = make_fence_distortion(distort_conf) - x = distort(x, fence_distortion, rate=rate) - elif distort_type == "jag_distortion": - jag_distortion = make_jag_distortion(distort_conf) - x = distort(x, jag_distortion, rate=rate) - elif distort_type == "poly_distortion": - poly_distortion = make_poly_distortion(distort_conf) - x = distort(x, poly_distortion, rate=rate) - elif distort_type == "quad_distortion": - quad_distortion = make_quad_distortion() - x = distort(x, quad_distortion, rate=rate) - elif distort_type == "none_distortion": - pass - else: - print("unsupport type") - return x - - -def distort_wav_conf_and_save(distort_type, distort_conf, rate, wav_in, wav_out): - x, sr = torchaudio.load(wav_in) - x = x.detach().numpy() - out = distort_wav_conf(x, distort_type, distort_conf, rate) - torchaudio.save(wav_out, torch.from_numpy(out), sr) - - -if __name__ == "__main__": - distort_type = sys.argv[1] - wav_in = sys.argv[2] - wav_out = sys.argv[3] - conf = None - rate = 0.1 - if distort_type == "new_jag_distortion": - conf = {"mask_number": 4} - elif distort_type == "new_fence_distortion": - conf = {"mask_number": 1, "max_db": -30} - elif distort_type == "poly_distortion": - conf = {"a": 4, "m": 2, "n": 2} - distort_wav_conf_and_save(distort_type, conf, rate, wav_in, wav_out) diff --git a/modules/wenet_extractor/efficient_conformer/__init__.py b/modules/wenet_extractor/efficient_conformer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/efficient_conformer/attention.py b/modules/wenet_extractor/efficient_conformer/attention.py deleted file mode 100644 index 96f5ac10d56e44c0873e6d659ed501fdedf43192..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/efficient_conformer/attention.py +++ /dev/null @@ -1,273 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Multi-Head Attention layer definition.""" - -import math -from typing import Tuple, Optional - -import torch -from torch import nn -import torch.nn.functional as F -from modules.wenet_extractor.transformer.attention import MultiHeadedAttention - - -class GroupedRelPositionMultiHeadedAttention(MultiHeadedAttention): - """Multi-Head Attention layer with relative position encoding. - Paper: - https://arxiv.org/abs/1901.02860 - https://arxiv.org/abs/2109.01163 - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - """ - - def __init__(self, n_head, n_feat, dropout_rate, group_size=3): - """Construct an RelPositionMultiHeadedAttention object.""" - super().__init__(n_head, n_feat, dropout_rate) - # linear transformation for positional encoding - self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) - self.group_size = group_size - self.d_k = n_feat // n_head # for GroupedAttention - self.n_feat = n_feat - # these two learnable bias are used in matrix c and matrix d - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size)) - self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size)) - torch.nn.init.xavier_uniform_(self.pos_bias_u) - torch.nn.init.xavier_uniform_(self.pos_bias_v) - - def rel_shift(self, x, zero_triu: bool = False): - """Compute relative positinal encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, size). - zero_triu (bool): If true, return the lower triangular part of - the matrix. - Returns: - torch.Tensor: Output tensor. - """ - - zero_pad = torch.zeros( - (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype - ) - x_padded = torch.cat([zero_pad, x], dim=-1) - - x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) - x = x_padded[:, :, 1:].view_as(x) - - if zero_triu: - ones = torch.ones((x.size(2), x.size(3))) - x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] - - return x - - def pad4group(self, Q, K, V, P, mask, group_size: int = 3): - """ - q: (#batch, time1, size) -> (#batch, head, time1, size/head) - k,v: (#batch, time2, size) -> (#batch, head, time2, size/head) - p: (#batch, time2, size) - """ - # Compute Overflows - overflow_Q = Q.size(2) % group_size - overflow_KV = K.size(2) % group_size - - # if-else for ONNX export - # 0 // 0.00000000000000001 = 0 - # 1 // 1.00000000000000001 = 1 - padding_Q = (group_size - overflow_Q) * int( - overflow_Q // (overflow_Q + 0.00000000000000001) - ) - padding_KV = (group_size - overflow_KV) * int( - overflow_KV // (overflow_KV + 0.00000000000000001) - ) - - batch_size, _, seq_len_KV, _ = K.size() - - # Input Padding (B, T, D) -> (B, T + P, D) - Q = F.pad(Q, (0, 0, 0, padding_Q), value=0.0) - K = F.pad(K, (0, 0, 0, padding_KV), value=0.0) - V = F.pad(V, (0, 0, 0, padding_KV), value=0.0) - - if mask is not None and mask.size(2) > 0: # time2 > 0: - mask = mask[:, ::group_size, ::group_size] - - Q = ( - Q.transpose(1, 2) - .contiguous() - .view(batch_size, -1, self.h, self.d_k * group_size) - .transpose(1, 2) - ) - K = ( - K.transpose(1, 2) - .contiguous() - .view(batch_size, -1, self.h, self.d_k * group_size) - .transpose(1, 2) - ) - V = ( - V.transpose(1, 2) - .contiguous() - .view(batch_size, -1, self.h, self.d_k * group_size) - .transpose(1, 2) - ) - - # process pos_emb - P_batch_size = P.size(0) - overflow_P = P.size(1) % group_size - padding_P = group_size - overflow_P if overflow_P else 0 - P = F.pad(P, (0, 0, 0, padding_P), value=0.0) - P = P.view(P_batch_size, -1, self.h, self.d_k * group_size).transpose(1, 2) - - return Q, K, V, P, mask, padding_Q - - def forward_attention( - self, - value: torch.Tensor, - scores: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - padding_q: Optional[int] = None, - ) -> torch.Tensor: - """Compute attention context vector. - - Args: - value (torch.Tensor): Transformed value, size - (#batch, n_head, time2, d_k). - scores (torch.Tensor): Attention score, size - (#batch, n_head, time1, time2). - mask (torch.Tensor): Mask, size (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - padding_q : for GroupedAttention in efficent conformer - - Returns: - torch.Tensor: Transformed value (#batch, time1, d_model) - weighted by the attention score (#batch, time1, time2). - - """ - n_batch = value.size(0) - # NOTE(xcsong): When will `if mask.size(2) > 0` be True? - # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the - # 1st chunk to ease the onnx export.] - # 2. pytorch training - if mask.size(2) > 0: # time2 > 0 - mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) - # For last chunk, time2 might be larger than scores.size(-1) - mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2) - scores = scores.masked_fill(mask, -float("inf")) - attn = torch.softmax(scores, dim=-1).masked_fill( - mask, 0.0 - ) # (batch, head, time1, time2) - # NOTE(xcsong): When will `if mask.size(2) > 0` be False? - # 1. onnx(16/-1, -1/-1, 16/0) - # 2. jit (16/-1, -1/-1, 16/0, 16/4) - else: - attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - - p_attn = self.dropout(attn) - x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) - - # n_feat!=h*d_k may be happened in GroupAttention - x = ( - x.transpose(1, 2).contiguous().view(n_batch, -1, self.n_feat) - ) # (batch, time1, d_model) - if padding_q is not None: - # for GroupedAttention in efficent conformer - x = x[:, : x.size(1) - padding_q] - - return self.linear_out(x) # (batch, time1, d_model) - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - pos_emb: torch.Tensor = torch.empty(0), - cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute 'Scaled Dot Product Attention' with rel. positional encoding. - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2). - pos_emb (torch.Tensor): Positional embedding tensor - (#batch, time2, size). - cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - """ - q = self.linear_q(query) - k = self.linear_k(key) # (#batch, time2, size) - v = self.linear_v(value) - p = self.linear_pos(pos_emb) # (#batch, time2, size) - - batch_size, seq_len_KV, _ = k.size() # seq_len_KV = time2 - - # (#batch, time2, size) -> (#batch, head, time2, size/head) - q = q.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) - k = k.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) - v = v.view(batch_size, -1, self.h, self.d_k).transpose(1, 2) - if cache.size(0) > 0: - # use attention cache - key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) - k = torch.cat([key_cache, k], dim=2) - v = torch.cat([value_cache, v], dim=2) - new_cache = torch.cat((k, v), dim=-1) - - # May be k and p does not match. eg. time2=18+18/2=27 > mask=36/2=18 - if mask is not None and mask.size(2) > 0: - time2 = mask.size(2) - k = k[:, :, -time2:, :] - v = v[:, :, -time2:, :] - - # q k v p: (batch, head, time1, d_k) - q, k, v, p, mask, padding_q = self.pad4group(q, k, v, p, mask, self.group_size) - - # q_with_bias_u & q_with_bias_v = (batch, head, time1, d_k) - q = q.transpose(1, 2) # (batch, time1, head, d_k) - q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) - q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) - - # compute attention score - # first compute matrix a and matrix c - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - # (batch, head, time1, time2) - matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) - - # compute matrix b and matrix d - # (batch, head, time1, time2) - matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) - # Remove rel_shift since it is useless in speech recognition, - # and it requires special attention for streaming. - # matrix_bd = self.rel_shift(matrix_bd) - - scores = (matrix_ac + matrix_bd) / math.sqrt( - self.d_k * self.group_size - ) # (batch, head, time1, time2) - - return self.forward_attention(v, scores, mask, padding_q), new_cache diff --git a/modules/wenet_extractor/efficient_conformer/convolution.py b/modules/wenet_extractor/efficient_conformer/convolution.py deleted file mode 100644 index 88ca82bffbd6ed5fd48a64d71f61bc281594efa2..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/efficient_conformer/convolution.py +++ /dev/null @@ -1,163 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""ConvolutionModule definition.""" -from typing import Tuple - -import torch -from torch import nn - - -class ConvolutionModule(nn.Module): - """ConvolutionModule in Conformer model.""" - - def __init__( - self, - channels: int, - kernel_size: int = 15, - activation: nn.Module = nn.ReLU(), - norm: str = "batch_norm", - causal: bool = False, - bias: bool = True, - stride: int = 1, - ): - """Construct an ConvolutionModule object. - Args: - channels (int): The number of channels of conv layers. - kernel_size (int): Kernel size of conv layers. - causal (int): Whether use causal convolution or not - stride (int): Stride Convolution, for efficient Conformer - """ - super().__init__() - - self.pointwise_conv1 = nn.Conv1d( - channels, - 2 * channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - # self.lorder is used to distinguish if it's a causal convolution, - # if self.lorder > 0: it's a causal convolution, the input will be - # padded with self.lorder frames on the left in forward. - # else: it's a symmetrical convolution - if causal: - padding = 0 - self.lorder = kernel_size - 1 - else: - # kernel_size should be an odd number for none causal convolution - assert (kernel_size - 1) % 2 == 0 - padding = (kernel_size - 1) // 2 - self.lorder = 0 - - self.depthwise_conv = nn.Conv1d( - channels, - channels, - kernel_size, - stride=stride, # for depthwise_conv in StrideConv - padding=padding, - groups=channels, - bias=bias, - ) - - assert norm in ["batch_norm", "layer_norm"] - if norm == "batch_norm": - self.use_layer_norm = False - self.norm = nn.BatchNorm1d(channels) - else: - self.use_layer_norm = True - self.norm = nn.LayerNorm(channels) - - self.pointwise_conv2 = nn.Conv1d( - channels, - channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - self.activation = activation - self.stride = stride - - def forward( - self, - x: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - cache: torch.Tensor = torch.zeros((0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute convolution module. - Args: - x (torch.Tensor): Input tensor (#batch, time, channels). - mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), - (0, 0, 0) means fake mask. - cache (torch.Tensor): left context cache, it is only - used in causal convolution (#batch, channels, cache_t), - (0, 0, 0) meas fake cache. - Returns: - torch.Tensor: Output tensor (#batch, time, channels). - """ - # exchange the temporal dimension and the feature dimension - x = x.transpose(1, 2) # (#batch, channels, time) - - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - if self.lorder > 0: - if cache.size(2) == 0: # cache_t == 0 - x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) - else: - # When export ONNX，the first cache is not None but all-zero, - # cause shape error in residual block, - # eg. cache14 + x9 = 23, 23-7+1=17 != 9 - cache = cache[:, :, -self.lorder :] - assert cache.size(0) == x.size(0) # equal batch - assert cache.size(1) == x.size(1) # equal channel - x = torch.cat((cache, x), dim=2) - assert x.size(2) > self.lorder - new_cache = x[:, :, -self.lorder :] - else: - # It's better we just return None if no cache is requried, - # However, for JIT export, here we just fake one tensor instead of - # None. - new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channel, dim) - x = nn.functional.glu(x, dim=1) # (batch, channel, dim) - - # 1D Depthwise Conv - x = self.depthwise_conv(x) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.activation(self.norm(x)) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.pointwise_conv2(x) - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - if mask_pad.size(2) != x.size(2): - mask_pad = mask_pad[:, :, :: self.stride] - x.masked_fill_(~mask_pad, 0.0) - - return x.transpose(1, 2), new_cache diff --git a/modules/wenet_extractor/efficient_conformer/encoder.py b/modules/wenet_extractor/efficient_conformer/encoder.py deleted file mode 100644 index e23c50763f4fddf610371e116e478fcf27f65ea8..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/efficient_conformer/encoder.py +++ /dev/null @@ -1,627 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Encoder definition.""" -from typing import Tuple, Optional, List, Union - -import torch -import logging -import torch.nn.functional as F - -from modules.wenet_extractor.transformer.positionwise_feed_forward import ( - PositionwiseFeedForward, -) -from modules.wenet_extractor.transformer.embedding import PositionalEncoding -from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding -from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding -from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4 -from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6 -from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8 -from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling -from modules.wenet_extractor.transformer.attention import MultiHeadedAttention -from modules.wenet_extractor.transformer.attention import ( - RelPositionMultiHeadedAttention, -) -from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer - -from modules.wenet_extractor.efficient_conformer.subsampling import Conv2dSubsampling2 -from modules.wenet_extractor.efficient_conformer.convolution import ConvolutionModule -from modules.wenet_extractor.efficient_conformer.attention import ( - GroupedRelPositionMultiHeadedAttention, -) -from modules.wenet_extractor.efficient_conformer.encoder_layer import ( - StrideConformerEncoderLayer, -) - -from modules.wenet_extractor.utils.common import get_activation -from modules.wenet_extractor.utils.mask import make_pad_mask -from modules.wenet_extractor.utils.mask import add_optional_chunk_mask - - -class EfficientConformerEncoder(torch.nn.Module): - """Conformer encoder module.""" - - def __init__( - self, - input_size: int, - output_size: int = 256, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.0, - input_layer: str = "conv2d", - pos_enc_layer_type: str = "rel_pos", - normalize_before: bool = True, - static_chunk_size: int = 0, - use_dynamic_chunk: bool = False, - global_cmvn: torch.nn.Module = None, - use_dynamic_left_chunk: bool = False, - macaron_style: bool = True, - activation_type: str = "swish", - use_cnn_module: bool = True, - cnn_module_kernel: int = 15, - causal: bool = False, - cnn_module_norm: str = "batch_norm", - stride_layer_idx: Optional[Union[int, List[int]]] = 3, - stride: Optional[Union[int, List[int]]] = 2, - group_layer_idx: Optional[Union[int, List[int], tuple]] = (0, 1, 2, 3), - group_size: int = 3, - stride_kernel: bool = True, - **kwargs, - ): - """Construct Efficient Conformer Encoder - - Args: - input_size to use_dynamic_chunk, see in BaseEncoder - macaron_style (bool): Whether to use macaron style for - positionwise layer. - activation_type (str): Encoder activation function type. - use_cnn_module (bool): Whether to use convolution module. - cnn_module_kernel (int): Kernel size of convolution module. - causal (bool): whether to use causal convolution or not. - stride_layer_idx (list): layer id with StrideConv, start from 0 - stride (list): stride size of each StrideConv in efficient conformer - group_layer_idx (list): layer id with GroupedAttention, start from 0 - group_size (int): group size of every GroupedAttention layer - stride_kernel (bool): default True. True: recompute cnn kernels with stride. - """ - super().__init__() - self._output_size = output_size - - if pos_enc_layer_type == "abs_pos": - pos_enc_class = PositionalEncoding - elif pos_enc_layer_type == "rel_pos": - pos_enc_class = RelPositionalEncoding - elif pos_enc_layer_type == "no_pos": - pos_enc_class = NoPositionalEncoding - else: - raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) - - if input_layer == "linear": - subsampling_class = LinearNoSubsampling - elif input_layer == "conv2d2": - subsampling_class = Conv2dSubsampling2 - elif input_layer == "conv2d": - subsampling_class = Conv2dSubsampling4 - elif input_layer == "conv2d6": - subsampling_class = Conv2dSubsampling6 - elif input_layer == "conv2d8": - subsampling_class = Conv2dSubsampling8 - else: - raise ValueError("unknown input_layer: " + input_layer) - - logging.info( - f"input_layer = {input_layer}, " f"subsampling_class = {subsampling_class}" - ) - - self.global_cmvn = global_cmvn - self.embed = subsampling_class( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - self.input_layer = input_layer - self.normalize_before = normalize_before - self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) - self.static_chunk_size = static_chunk_size - self.use_dynamic_chunk = use_dynamic_chunk - self.use_dynamic_left_chunk = use_dynamic_left_chunk - - activation = get_activation(activation_type) - self.num_blocks = num_blocks - self.attention_heads = attention_heads - self.cnn_module_kernel = cnn_module_kernel - self.global_chunk_size = 0 - self.chunk_feature_map = 0 - - # efficient conformer configs - self.stride_layer_idx = ( - [stride_layer_idx] if type(stride_layer_idx) == int else stride_layer_idx - ) - self.stride = [stride] if type(stride) == int else stride - self.group_layer_idx = ( - [group_layer_idx] if type(group_layer_idx) == int else group_layer_idx - ) - self.grouped_size = group_size # group size of every GroupedAttention layer - - assert len(self.stride) == len(self.stride_layer_idx) - self.cnn_module_kernels = [cnn_module_kernel] # kernel size of each StridedConv - for i in self.stride: - if stride_kernel: - self.cnn_module_kernels.append(self.cnn_module_kernels[-1] // i) - else: - self.cnn_module_kernels.append(self.cnn_module_kernels[-1]) - - logging.info( - f"stride_layer_idx= {self.stride_layer_idx}, " - f"stride = {self.stride}, " - f"cnn_module_kernel = {self.cnn_module_kernels}, " - f"group_layer_idx = {self.group_layer_idx}, " - f"grouped_size = {self.grouped_size}" - ) - - # feed-forward module definition - positionwise_layer = PositionwiseFeedForward - positionwise_layer_args = ( - output_size, - linear_units, - dropout_rate, - activation, - ) - # convolution module definition - convolution_layer = ConvolutionModule - - # encoder definition - index = 0 - layers = [] - for i in range(num_blocks): - # self-attention module definition - if i in self.group_layer_idx: - encoder_selfattn_layer = GroupedRelPositionMultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - self.grouped_size, - ) - else: - if pos_enc_layer_type == "no_pos": - encoder_selfattn_layer = MultiHeadedAttention - else: - encoder_selfattn_layer = RelPositionMultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - ) - - # conformer module definition - if i in self.stride_layer_idx: - # conformer block with downsampling - convolution_layer_args_stride = ( - output_size, - self.cnn_module_kernels[index], - activation, - cnn_module_norm, - causal, - True, - self.stride[index], - ) - layers.append( - StrideConformerEncoderLayer( - output_size, - encoder_selfattn_layer(*encoder_selfattn_layer_args), - positionwise_layer(*positionwise_layer_args), - positionwise_layer(*positionwise_layer_args) - if macaron_style - else None, - convolution_layer(*convolution_layer_args_stride) - if use_cnn_module - else None, - torch.nn.AvgPool1d( - kernel_size=self.stride[index], - stride=self.stride[index], - padding=0, - ceil_mode=True, - count_include_pad=False, - ), # pointwise_conv_layer - dropout_rate, - normalize_before, - ) - ) - index = index + 1 - else: - # conformer block - convolution_layer_args_normal = ( - output_size, - self.cnn_module_kernels[index], - activation, - cnn_module_norm, - causal, - ) - layers.append( - ConformerEncoderLayer( - output_size, - encoder_selfattn_layer(*encoder_selfattn_layer_args), - positionwise_layer(*positionwise_layer_args), - positionwise_layer(*positionwise_layer_args) - if macaron_style - else None, - convolution_layer(*convolution_layer_args_normal) - if use_cnn_module - else None, - dropout_rate, - normalize_before, - ) - ) - - self.encoders = torch.nn.ModuleList(layers) - - def set_global_chunk_size(self, chunk_size): - """Used in ONNX export.""" - logging.info(f"set global chunk size: {chunk_size}, default is 0.") - self.global_chunk_size = chunk_size - if self.embed.subsampling_rate == 2: - self.chunk_feature_map = 2 * self.global_chunk_size + 1 - elif self.embed.subsampling_rate == 6: - self.chunk_feature_map = 6 * self.global_chunk_size + 5 - elif self.embed.subsampling_rate == 8: - self.chunk_feature_map = 8 * self.global_chunk_size + 7 - else: - self.chunk_feature_map = 4 * self.global_chunk_size + 3 - - def output_size(self) -> int: - return self._output_size - - def calculate_downsampling_factor(self, i: int) -> int: - factor = 1 - for idx, stride_idx in enumerate(self.stride_layer_idx): - if i > stride_idx: - factor *= self.stride[idx] - return factor - - def forward( - self, - xs: torch.Tensor, - xs_lens: torch.Tensor, - decoding_chunk_size: int = 0, - num_decoding_left_chunks: int = -1, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Embed positions in tensor. - Args: - xs: padded input tensor (B, T, D) - xs_lens: input length (B) - decoding_chunk_size: decoding chunk size for dynamic chunk - 0: default for training, use random dynamic chunk. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - num_decoding_left_chunks: number of left chunks, this is for decoding, - the chunk size is decoding_chunk_size. - >=0: use num_decoding_left_chunks - <0: use all left chunks - Returns: - encoder output tensor xs, and subsampled masks - xs: padded output tensor (B, T' ~= T/subsample_rate, D) - masks: torch.Tensor batch padding mask after subsample - (B, 1, T' ~= T/subsample_rate) - """ - T = xs.size(1) - masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) - if self.global_cmvn is not None: - xs = self.global_cmvn(xs) - xs, pos_emb, masks = self.embed(xs, masks) - mask_pad = masks # (B, 1, T/subsample_rate) - chunk_masks = add_optional_chunk_mask( - xs, - masks, - self.use_dynamic_chunk, - self.use_dynamic_left_chunk, - decoding_chunk_size, - self.static_chunk_size, - num_decoding_left_chunks, - ) - index = 0 # traverse stride - for i, layer in enumerate(self.encoders): - # layer return : x, mask, new_att_cache, new_cnn_cache - xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) - if i in self.stride_layer_idx: - masks = masks[:, :, :: self.stride[index]] - chunk_masks = chunk_masks[ - :, :: self.stride[index], :: self.stride[index] - ] - mask_pad = masks - pos_emb = pos_emb[:, :: self.stride[index], :] - index = index + 1 - - if self.normalize_before: - xs = self.after_norm(xs) - # Here we assume the mask is not changed in encoder layers, so just - # return the masks before encoder layers, and the masks will be used - # for cross attention with decoder later - return xs, masks - - def forward_chunk( - self, - xs: torch.Tensor, - offset: int, - required_cache_size: int, - att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Forward just one chunk - - Args: - xs (torch.Tensor): chunk input - offset (int): current offset in encoder output time stamp - required_cache_size (int): cache size required for next chunk - compuation - >=0: actual cache size - <0: means all history cache is required - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (elayers, b=1, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - att_mask : mask matrix of self attention - - Returns: - torch.Tensor: output of current input xs - torch.Tensor: subsampling cache required for next chunk computation - List[torch.Tensor]: encoder layers output cache required for next - chunk computation - List[torch.Tensor]: conformer cnn cache - - """ - assert xs.size(0) == 1 - - # using downsampling factor to recover offset - offset *= self.calculate_downsampling_factor(self.num_blocks + 1) - - chunk_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) - chunk_masks = chunk_masks.unsqueeze(1) # (1, 1, xs-time) - - real_len = 0 - if self.global_chunk_size > 0: - # for ONNX decode simulation， padding xs to chunk_size - real_len = xs.size(1) - pad_len = self.chunk_feature_map - real_len - xs = F.pad(xs, (0, 0, 0, pad_len), value=0.0) - chunk_masks = F.pad(chunk_masks, (0, pad_len), value=0.0) - - if self.global_cmvn is not None: - xs = self.global_cmvn(xs) - - # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) - xs, pos_emb, chunk_masks = self.embed(xs, chunk_masks, offset) - elayers, cache_t1 = att_cache.size(0), att_cache.size(2) - chunk_size = xs.size(1) - attention_key_size = cache_t1 + chunk_size - # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) - # shape(pos_emb) = (b=1, chunk_size, emb_size=output_size=hidden-dim) - - if required_cache_size < 0: - next_cache_start = 0 - elif required_cache_size == 0: - next_cache_start = attention_key_size - else: - next_cache_start = max(attention_key_size - required_cache_size, 0) - - r_att_cache = [] - r_cnn_cache = [] - mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) - mask_pad = mask_pad.unsqueeze(1) # batchPad (b=1, 1, time=chunk_size) - - if self.global_chunk_size > 0: - # for ONNX decode simulation - pos_emb = self.embed.position_encoding( - offset=max(offset - cache_t1, 0), size=cache_t1 + self.global_chunk_size - ) - att_mask[:, :, -self.global_chunk_size :] = chunk_masks - mask_pad = chunk_masks.to(torch.bool) - else: - pos_emb = self.embed.position_encoding( - offset=offset - cache_t1, size=attention_key_size - ) - - max_att_len, max_cnn_len = 0, 0 # for repeat_interleave of new_att_cache - for i, layer in enumerate(self.encoders): - factor = self.calculate_downsampling_factor(i) - # NOTE(xcsong): Before layer.forward - # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), - # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) - # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ] - att_cache_trunc = 0 - if xs.size(1) + att_cache.size(2) / factor > pos_emb.size(1): - # The time step is not divisible by the downsampling multiple - att_cache_trunc = ( - xs.size(1) + att_cache.size(2) // factor - pos_emb.size(1) + 1 - ) - xs, _, new_att_cache, new_cnn_cache = layer( - xs, - att_mask, - pos_emb, - mask_pad=mask_pad, - att_cache=att_cache[i : i + 1, :, ::factor, :][ - :, :, att_cache_trunc:, : - ], - cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache, - ) - - if i in self.stride_layer_idx: - # compute time dimension for next block - efficient_index = self.stride_layer_idx.index(i) - att_mask = att_mask[ - :, :: self.stride[efficient_index], :: self.stride[efficient_index] - ] - mask_pad = mask_pad[ - :, :: self.stride[efficient_index], :: self.stride[efficient_index] - ] - pos_emb = pos_emb[:, :: self.stride[efficient_index], :] - - # shape(new_att_cache) = [batch, head, time2, outdim] - new_att_cache = new_att_cache[:, :, next_cache_start // factor :, :] - # shape(new_cnn_cache) = [1, batch, outdim, cache_t2] - new_cnn_cache = new_cnn_cache.unsqueeze(0) - - # use repeat_interleave to new_att_cache - new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2) - # padding new_cnn_cache to cnn.lorder for casual convolution - new_cnn_cache = F.pad( - new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0) - ) - - if i == 0: - # record length for the first block as max length - max_att_len = new_att_cache.size(2) - max_cnn_len = new_cnn_cache.size(3) - - # update real shape of att_cache and cnn_cache - r_att_cache.append(new_att_cache[:, :, -max_att_len:, :]) - r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:]) - - if self.normalize_before: - xs = self.after_norm(xs) - - # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), - # ? may be larger than cache_t1, it depends on required_cache_size - r_att_cache = torch.cat(r_att_cache, dim=0) - # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) - r_cnn_cache = torch.cat(r_cnn_cache, dim=0) - - if self.global_chunk_size > 0 and real_len: - chunk_real_len = ( - real_len - // self.embed.subsampling_rate - // self.calculate_downsampling_factor(self.num_blocks + 1) - ) - # Keeping 1 more timestep can mitigate information leakage - # from the encoder caused by the padding - xs = xs[:, : chunk_real_len + 1, :] - - return xs, r_att_cache, r_cnn_cache - - def forward_chunk_by_chunk( - self, - xs: torch.Tensor, - decoding_chunk_size: int, - num_decoding_left_chunks: int = -1, - use_onnx=False, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward input chunk by chunk with chunk_size like a streaming - fashion - - Here we should pay special attention to computation cache in the - streaming style forward chunk by chunk. Three things should be taken - into account for computation in the current network: - 1. transformer/conformer encoder layers output cache - 2. convolution in conformer - 3. convolution in subsampling - - However, we don't implement subsampling cache for: - 1. We can control subsampling module to output the right result by - overlapping input instead of cache left context, even though it - wastes some computation, but subsampling only takes a very - small fraction of computation in the whole model. - 2. Typically, there are several covolution layers with subsampling - in subsampling module, it is tricky and complicated to do cache - with different convolution layers with different subsampling - rate. - 3. Currently, nn.Sequential is used to stack all the convolution - layers in subsampling, we need to rewrite it to make it work - with cache, which is not prefered. - Args: - xs (torch.Tensor): (1, max_len, dim) - decoding_chunk_size (int): decoding chunk size - num_decoding_left_chunks (int): - use_onnx (bool): True for simulating ONNX model inference. - """ - assert decoding_chunk_size > 0 - # The model is trained by static or dynamic chunk - assert self.static_chunk_size > 0 or self.use_dynamic_chunk - subsampling = self.embed.subsampling_rate - context = self.embed.right_context + 1 # Add current frame - stride = subsampling * decoding_chunk_size - decoding_window = (decoding_chunk_size - 1) * subsampling + context - num_frames = xs.size(1) - - outputs = [] - offset = 0 - required_cache_size = decoding_chunk_size * num_decoding_left_chunks - if use_onnx: - logging.info("Simulating for ONNX runtime ...") - att_cache: torch.Tensor = torch.zeros( - ( - self.num_blocks, - self.attention_heads, - required_cache_size, - self.output_size() // self.attention_heads * 2, - ), - device=xs.device, - ) - cnn_cache: torch.Tensor = torch.zeros( - (self.num_blocks, 1, self.output_size(), self.cnn_module_kernel - 1), - device=xs.device, - ) - self.set_global_chunk_size(chunk_size=decoding_chunk_size) - else: - logging.info("Simulating for JIT runtime ...") - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) - - # Feed forward overlap input step by step - for cur in range(0, num_frames - context + 1, stride): - end = min(cur + decoding_window, num_frames) - logging.info( - f"-->> frame chunk msg: cur={cur}, " - f"end={end}, num_frames={end-cur}, " - f"decoding_window={decoding_window}" - ) - if use_onnx: - att_mask: torch.Tensor = torch.ones( - (1, 1, required_cache_size + decoding_chunk_size), - dtype=torch.bool, - device=xs.device, - ) - if cur == 0: - att_mask[:, :, :required_cache_size] = 0 - else: - att_mask: torch.Tensor = torch.ones( - (0, 0, 0), dtype=torch.bool, device=xs.device - ) - - chunk_xs = xs[:, cur:end, :] - (y, att_cache, cnn_cache) = self.forward_chunk( - chunk_xs, offset, required_cache_size, att_cache, cnn_cache, att_mask - ) - outputs.append(y) - offset += y.size(1) - - ys = torch.cat(outputs, 1) - masks = torch.ones(1, 1, ys.size(1), device=ys.device, dtype=torch.bool) - return ys, masks diff --git a/modules/wenet_extractor/efficient_conformer/encoder_layer.py b/modules/wenet_extractor/efficient_conformer/encoder_layer.py deleted file mode 100644 index 9156c0edf9f79df6425accd542369e139591ed42..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/efficient_conformer/encoder_layer.py +++ /dev/null @@ -1,172 +0,0 @@ -## This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Encoder self-attention layer definition.""" - -from typing import Optional, Tuple -import torch -from torch import nn - - -class StrideConformerEncoderLayer(nn.Module): - """Encoder layer module. - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` - instance can be used as the argument. - feed_forward (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward` instance can be used as the argument. - feed_forward_macaron (torch.nn.Module): Additional feed-forward module - instance. - `PositionwiseFeedForward` instance can be used as the argument. - conv_module (torch.nn.Module): Convolution module instance. - `ConvlutionModule` instance can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): - True: use layer_norm before each sub-block. - False: use layer_norm after each sub-block. - """ - - def __init__( - self, - size: int, - self_attn: torch.nn.Module, - feed_forward: Optional[nn.Module] = None, - feed_forward_macaron: Optional[nn.Module] = None, - conv_module: Optional[nn.Module] = None, - pointwise_conv_layer: Optional[nn.Module] = None, - dropout_rate: float = 0.1, - normalize_before: bool = True, - ): - """Construct an EncoderLayer object.""" - super().__init__() - self.self_attn = self_attn - self.feed_forward = feed_forward - self.feed_forward_macaron = feed_forward_macaron - self.conv_module = conv_module - self.pointwise_conv_layer = pointwise_conv_layer - self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module - self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module - if feed_forward_macaron is not None: - self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5) - self.ff_scale = 0.5 - else: - self.ff_scale = 1.0 - if self.conv_module is not None: - self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module - self.norm_final = nn.LayerNorm( - size, eps=1e-5 - ) # for the final output of the block - self.dropout = nn.Dropout(dropout_rate) - self.size = size - self.normalize_before = normalize_before - self.concat_linear = nn.Linear(size + size, size) - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute encoded features. - - Args: - x (torch.Tensor): (#batch, time, size) - mask (torch.Tensor): Mask tensor for the input (#batch, time，time), - (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): positional encoding, must not be None - for ConformerEncoderLayer. - mask_pad (torch.Tensor): batch padding mask used for conv module. - (#batch, 1，time), (0, 0, 0) means fake mask. - att_cache (torch.Tensor): Cache tensor of the KEY & VALUE - (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. - cnn_cache (torch.Tensor): Convolution cache in conformer layer - (#batch=1, size, cache_t2) - Returns: - torch.Tensor: Output tensor (#batch, time, size). - torch.Tensor: Mask tensor (#batch, time, time). - torch.Tensor: att_cache tensor, - (#batch=1, head, cache_t1 + time, d_k * 2). - torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). - """ - - # whether to use macaron style - if self.feed_forward_macaron is not None: - residual = x - if self.normalize_before: - x = self.norm_ff_macaron(x) - x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) - if not self.normalize_before: - x = self.norm_ff_macaron(x) - - # multi-headed self-attention module - residual = x - if self.normalize_before: - x = self.norm_mha(x) - - x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) - - x = residual + self.dropout(x_att) - if not self.normalize_before: - x = self.norm_mha(x) - - # convolution module - # Fake new cnn cache here, and then change it in conv_module - new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device) - if self.conv_module is not None: - residual = x - if self.normalize_before: - x = self.norm_conv(x) - x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) - - # add pointwise_conv for efficient conformer - # pointwise_conv_layer does not change shape - if self.pointwise_conv_layer is not None: - residual = residual.transpose(1, 2) - residual = self.pointwise_conv_layer(residual) - residual = residual.transpose(1, 2) - assert residual.size(0) == x.size(0) - assert residual.size(1) == x.size(1) - assert residual.size(2) == x.size(2) - - x = residual + self.dropout(x) - - if not self.normalize_before: - x = self.norm_conv(x) - - # feed forward module - residual = x - if self.normalize_before: - x = self.norm_ff(x) - - x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) - if not self.normalize_before: - x = self.norm_ff(x) - - if self.conv_module is not None: - x = self.norm_final(x) - - return x, mask, new_att_cache, new_cnn_cache diff --git a/modules/wenet_extractor/efficient_conformer/subsampling.py b/modules/wenet_extractor/efficient_conformer/subsampling.py deleted file mode 100644 index 6017084c2accb40c86be16d798e587b4f32c26d4..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/efficient_conformer/subsampling.py +++ /dev/null @@ -1,81 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - - -"""Subsampling layer definition.""" - -from typing import Tuple, Union - -import torch -from modules.wenet_extractor.transformer.subsampling import BaseSubsampling - - -class Conv2dSubsampling2(BaseSubsampling): - """Convolutional 2D subsampling (to 1/4 length). - - Args: - idim (int): Input dimension. - odim (int): Output dimension. - dropout_rate (float): Dropout rate. - - """ - - def __init__( - self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module - ): - """Construct an Conv2dSubsampling4 object.""" - super().__init__() - self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU()) - self.out = torch.nn.Sequential(torch.nn.Linear(odim * ((idim - 1) // 2), odim)) - self.pos_enc = pos_enc_class - # The right context for every conv layer is computed by: - # (kernel_size - 1) * frame_rate_of_this_layer - self.subsampling_rate = 2 - # 2 = (3 - 1) * 1 - self.right_context = 2 - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - offset: Union[int, torch.Tensor] = 0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Subsample x. - - Args: - x (torch.Tensor): Input tensor (#batch, time, idim). - x_mask (torch.Tensor): Input mask (#batch, 1, time). - - Returns: - torch.Tensor: Subsampled tensor (#batch, time', odim), - where time' = time // 2. - torch.Tensor: Subsampled mask (#batch, 1, time'), - where time' = time // 2. - torch.Tensor: positional encoding - - """ - x = x.unsqueeze(1) # (b, c=1, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) - x, pos_emb = self.pos_enc(x, offset) - return x, pos_emb, x_mask[:, :, :-2:2] diff --git a/modules/wenet_extractor/paraformer/paraformer.py b/modules/wenet_extractor/paraformer/paraformer.py deleted file mode 100644 index 8f144baa2a5df2292fa4616952338c8f937bfcba..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/paraformer/paraformer.py +++ /dev/null @@ -1,366 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from typing import Dict, Optional, Tuple - -import torch - -from modules.wenet_extractor.cif.predictor import MAELoss -from modules.wenet_extractor.paraformer.search.beam_search import Hypothesis -from modules.wenet_extractor.transformer.asr_model import ASRModel -from modules.wenet_extractor.transformer.ctc import CTC -from modules.wenet_extractor.transformer.decoder import TransformerDecoder -from modules.wenet_extractor.transformer.encoder import TransformerEncoder -from modules.wenet_extractor.utils.common import IGNORE_ID, add_sos_eos, th_accuracy -from modules.wenet_extractor.utils.mask import make_pad_mask - - -class Paraformer(ASRModel): - """Paraformer: Fast and Accurate Parallel Transformer for - Non-autoregressive End-to-End Speech Recognition - see https://arxiv.org/pdf/2206.08317.pdf - """ - - def __init__( - self, - vocab_size: int, - encoder: TransformerEncoder, - decoder: TransformerDecoder, - ctc: CTC, - predictor, - ctc_weight: float = 0.5, - predictor_weight: float = 1.0, - predictor_bias: int = 0, - ignore_id: int = IGNORE_ID, - reverse_weight: float = 0.0, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - ): - assert 0.0 <= ctc_weight <= 1.0, ctc_weight - assert 0.0 <= predictor_weight <= 1.0, predictor_weight - - super().__init__( - vocab_size, - encoder, - decoder, - ctc, - ctc_weight, - ignore_id, - reverse_weight, - lsm_weight, - length_normalized_loss, - ) - self.predictor = predictor - self.predictor_weight = predictor_weight - self.predictor_bias = predictor_bias - self.criterion_pre = MAELoss(normalize_length=length_normalized_loss) - - def forward( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - text: torch.Tensor, - text_lengths: torch.Tensor, - ) -> Dict[str, Optional[torch.Tensor]]: - """Frontend + Encoder + Decoder + Calc loss - - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - assert text_lengths.dim() == 1, text_lengths.shape - # Check that batch_size is unified - assert ( - speech.shape[0] - == speech_lengths.shape[0] - == text.shape[0] - == text_lengths.shape[0] - ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) - # 1. Encoder - encoder_out, encoder_mask = self.encoder(speech, speech_lengths) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - - # 2a. Attention-decoder branch - if self.ctc_weight != 1.0: - loss_att, acc_att, loss_pre = self._calc_att_loss( - encoder_out, encoder_mask, text, text_lengths - ) - else: - # loss_att = None - # loss_pre = None - loss_att: torch.Tensor = torch.tensor(0) - loss_pre: torch.Tensor = torch.tensor(0) - - # 2b. CTC branch - if self.ctc_weight != 0.0: - loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) - else: - loss_ctc = None - - if loss_ctc is None: - loss = loss_att + self.predictor_weight * loss_pre - # elif loss_att is None: - elif loss_att == torch.tensor(0): - loss = loss_ctc - else: - loss = ( - self.ctc_weight * loss_ctc - + (1 - self.ctc_weight) * loss_att - + self.predictor_weight * loss_pre - ) - return { - "loss": loss, - "loss_att": loss_att, - "loss_ctc": loss_ctc, - "loss_pre": loss_pre, - } - - def _calc_att_loss( - self, - encoder_out: torch.Tensor, - encoder_mask: torch.Tensor, - ys_pad: torch.Tensor, - ys_pad_lens: torch.Tensor, - ) -> Tuple[torch.Tensor, float, torch.Tensor]: - if self.predictor_bias == 1: - _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) - ys_pad_lens = ys_pad_lens + self.predictor_bias - pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor( - encoder_out, ys_pad, encoder_mask, ignore_id=self.ignore_id - ) - # 1. Forward decoder - decoder_out, _, _ = self.decoder( - encoder_out, encoder_mask, pre_acoustic_embeds, ys_pad_lens - ) - - # 2. Compute attention loss - loss_att = self.criterion_att(decoder_out, ys_pad) - acc_att = th_accuracy( - decoder_out.view(-1, self.vocab_size), - ys_pad, - ignore_label=self.ignore_id, - ) - loss_pre: torch.Tensor = self.criterion_pre( - ys_pad_lens.type_as(pre_token_length), pre_token_length - ) - - return loss_att, acc_att, loss_pre - - def calc_predictor(self, encoder_out, encoder_mask): - encoder_mask = ( - ~make_pad_mask(encoder_mask, max_len=encoder_out.size(1))[:, None, :] - ).to(encoder_out.device) - pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor( - encoder_out, None, encoder_mask, ignore_id=self.ignore_id - ) - return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index - - def cal_decoder_with_predictor( - self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens - ): - decoder_out, _, _ = self.decoder( - encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens - ) - decoder_out = torch.log_softmax(decoder_out, dim=-1) - return decoder_out, ys_pad_lens - - def recognize(self): - raise NotImplementedError - - def paraformer_greedy_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply beam search on attention decoder - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - - Returns: - torch.Tensor: decoding result, (batch, max_result_len) - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - device = speech.device - batch_size = speech.shape[0] - - # Let's assume B = batch_size and N = beam_size - # 1. Encoder - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - # 2. Predictor - predictor_outs = self.calc_predictor(encoder_out, encoder_mask) - pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( - predictor_outs[0], - predictor_outs[1], - predictor_outs[2], - predictor_outs[3], - ) - pre_token_length = pre_token_length.round().long() - if torch.max(pre_token_length) < 1: - return torch.tensor([]), torch.tensor([]) - # 2. Decoder forward - decoder_outs = self.cal_decoder_with_predictor( - encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length - ) - decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] - hyps = [] - b, n, d = decoder_out.size() - for i in range(b): - x = encoder_out[i, : encoder_out_lens[i], :] - am_scores = decoder_out[i, : pre_token_length[i], :] - yseq = am_scores.argmax(dim=-1) - score = am_scores.max(dim=-1)[0] - score = torch.sum(score, dim=-1) - # pad with mask tokens to ensure compatibility with sos/eos tokens - yseq = torch.tensor( - [self.sos] + yseq.tolist() + [self.eos], device=yseq.device - ) - nbest_hyps = [Hypothesis(yseq=yseq, score=score)] - - for hyp in nbest_hyps: - assert isinstance(hyp, (Hypothesis)), type(hyp) - - # remove sos/eos and get hyps - last_pos = -1 - if isinstance(hyp.yseq, list): - token_int = hyp.yseq[1:last_pos] - else: - token_int = hyp.yseq[1:last_pos].tolist() - - # remove blank symbol id and unk id, which is assumed to be 0 - # and 1 - token_int = list(filter(lambda x: x != 0 and x != 1, token_int)) - hyps.append(token_int) - return hyps - - def paraformer_beam_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - beam_search: torch.nn.Module = None, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Apply beam search on attention decoder - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_lengths (torch.Tensor): (batch, ) - beam_search (torch.nn.Moudle): beam search module - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - - Returns: - torch.Tensor: decoding result, (batch, max_result_len) - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - device = speech.device - batch_size = speech.shape[0] - - # Let's assume B = batch_size and N = beam_size - # 1. Encoder - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - # 2. Predictor - predictor_outs = self.calc_predictor(encoder_out, encoder_mask) - pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = ( - predictor_outs[0], - predictor_outs[1], - predictor_outs[2], - predictor_outs[3], - ) - pre_token_length = pre_token_length.round().long() - if torch.max(pre_token_length) < 1: - return torch.tensor([]), torch.tensor([]) - # 2. Decoder forward - decoder_outs = self.cal_decoder_with_predictor( - encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length - ) - decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] - hyps = [] - b, n, d = decoder_out.size() - for i in range(b): - x = encoder_out[i, : encoder_out_lens[i], :] - am_scores = decoder_out[i, : pre_token_length[i], :] - if beam_search is not None: - nbest_hyps = beam_search(x=x, am_scores=am_scores) - nbest_hyps = nbest_hyps[:1] - else: - yseq = am_scores.argmax(dim=-1) - score = am_scores.max(dim=-1)[0] - score = torch.sum(score, dim=-1) - # pad with mask tokens to ensure compatibility with sos/eos - # tokens - yseq = torch.tensor( - [self.sos] + yseq.tolist() + [self.eos], device=yseq.device - ) - nbest_hyps = [Hypothesis(yseq=yseq, score=score)] - - for hyp in nbest_hyps: - assert isinstance(hyp, (Hypothesis)), type(hyp) - - # remove sos/eos and get hyps - last_pos = -1 - if isinstance(hyp.yseq, list): - token_int = hyp.yseq[1:last_pos] - else: - token_int = hyp.yseq[1:last_pos].tolist() - - # remove blank symbol id and unk id, which is assumed to be 0 - # and 1 - token_int = list(filter(lambda x: x != 0 and x != 1, token_int)) - hyps.append(token_int) - return hyps diff --git a/modules/wenet_extractor/paraformer/search/beam_search.py b/modules/wenet_extractor/paraformer/search/beam_search.py deleted file mode 100644 index 5389ce4ac7f0026ab1dcf042c62b9c790bd61085..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/paraformer/search/beam_search.py +++ /dev/null @@ -1,479 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from itertools import chain -from typing import Any -from typing import Dict -from typing import List -from typing import Tuple -from typing import Union -from typing import NamedTuple - -import torch - -from modules.wenet_extractor.paraformer.utils import end_detect -from modules.wenet_extractor.paraformer.search.ctc import CTCPrefixScorer -from modules.wenet_extractor.paraformer.search.scorer_interface import ( - ScorerInterface, - PartialScorerInterface, -) - - -class Hypothesis(NamedTuple): - """Hypothesis data type.""" - - yseq: torch.Tensor - score: Union[float, torch.Tensor] = 0 - scores: Dict[str, Union[float, torch.Tensor]] = dict() - states: Dict[str, Any] = dict() - - def asdict(self) -> dict: - """Convert data to JSON-friendly dict.""" - return self._replace( - yseq=self.yseq.tolist(), - score=float(self.score), - scores={k: float(v) for k, v in self.scores.items()}, - )._asdict() - - -class BeamSearchCIF(torch.nn.Module): - """Beam search implementation.""" - - def __init__( - self, - scorers: Dict[str, ScorerInterface], - weights: Dict[str, float], - beam_size: int, - vocab_size: int, - sos: int, - eos: int, - pre_beam_ratio: float = 1.5, - pre_beam_score_key: str = None, - ): - """Initialize beam search. - - Args: - scorers (dict[str, ScorerInterface]): Dict of decoder modules - e.g., Decoder, CTCPrefixScorer, LM - The scorer will be ignored if it is `None` - weights (dict[str, float]): Dict of weights for each scorers - The scorer will be ignored if its weight is 0 - beam_size (int): The number of hypotheses kept during search - vocab_size (int): The number of vocabulary - sos (int): Start of sequence id - eos (int): End of sequence id - pre_beam_score_key (str): key of scores to perform pre-beam search - pre_beam_ratio (float): beam size in the pre-beam search - will be `int(pre_beam_ratio * beam_size)` - - """ - super().__init__() - # set scorers - self.weights = weights - self.scorers = dict() - self.full_scorers = dict() - self.part_scorers = dict() - # this module dict is required for recursive cast - # `self.to(device, dtype)` in `recog.py` - self.nn_dict = torch.nn.ModuleDict() - for k, v in scorers.items(): - w = weights.get(k, 0) - if w == 0 or v is None: - continue - assert isinstance( - v, ScorerInterface - ), f"{k} ({type(v)}) does not implement ScorerInterface" - self.scorers[k] = v - if isinstance(v, PartialScorerInterface): - self.part_scorers[k] = v - else: - self.full_scorers[k] = v - if isinstance(v, torch.nn.Module): - self.nn_dict[k] = v - - # set configurations - self.sos = sos - self.eos = eos - self.pre_beam_size = int(pre_beam_ratio * beam_size) - self.beam_size = beam_size - self.n_vocab = vocab_size - if ( - pre_beam_score_key is not None - and pre_beam_score_key != "full" - and pre_beam_score_key not in self.full_scorers - ): - raise KeyError( - f"{pre_beam_score_key} is not found in " f"{self.full_scorers}" - ) - self.pre_beam_score_key = pre_beam_score_key - self.do_pre_beam = ( - self.pre_beam_score_key is not None - and self.pre_beam_size < self.n_vocab - and len(self.part_scorers) > 0 - ) - - def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]: - """Get an initial hypothesis data. - - Args: - x (torch.Tensor): The encoder output feature - - Returns: - Hypothesis: The initial hypothesis. - - """ - init_states = dict() - init_scores = dict() - for k, d in self.scorers.items(): - init_states[k] = d.init_state(x) - init_scores[k] = 0.0 - return [ - Hypothesis( - score=0.0, - scores=init_scores, - states=init_states, - yseq=torch.tensor([self.sos], device=x.device), - ) - ] - - @staticmethod - def append_token(xs: torch.Tensor, x: int) -> torch.Tensor: - """Append new token to prefix tokens. - - Args: - xs (torch.Tensor): The prefix token - x (int): The new token to append - - Returns: - torch.Tensor: New tensor contains: xs + [x] with xs.dtype and - xs.device - - """ - x = torch.tensor([x], dtype=xs.dtype, device=xs.device) - return torch.cat((xs, x)) - - def score_full( - self, hyp: Hypothesis, x: torch.Tensor - ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: - """Score new hypothesis by `self.full_scorers`. - - Args: - hyp (Hypothesis): Hypothesis with prefix tokens to score - x (torch.Tensor): Corresponding input feature - - Returns: - Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of - score dict of `hyp` that has string keys of `self.full_scorers` - and tensor score values of shape: `(self.n_vocab,)`, - and state dict that has string keys - and state values of `self.full_scorers` - - """ - scores = dict() - states = dict() - for k, d in self.full_scorers.items(): - scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x) - return scores, states - - def score_partial( - self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor - ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: - """Score new hypothesis by `self.part_scorers`. - - Args: - hyp (Hypothesis): Hypothesis with prefix tokens to score - ids (torch.Tensor): 1D tensor of new partial tokens to score - x (torch.Tensor): Corresponding input feature - - Returns: - Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of - score dict of `hyp` that has string keys of `self.part_scorers` - and tensor score values of shape: `(len(ids),)`, - and state dict that has string keys - and state values of `self.part_scorers` - - """ - scores = dict() - states = dict() - for k, d in self.part_scorers.items(): - scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x) - return scores, states - - def beam( - self, weighted_scores: torch.Tensor, ids: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute topk full token ids and partial token ids. - - Args: - weighted_scores (torch.Tensor): The weighted sum scores for each - tokens. - Its shape is `(self.n_vocab,)`. - ids (torch.Tensor): The partial token ids to compute topk - - Returns: - Tuple[torch.Tensor, torch.Tensor]: - The topk full token ids and partial token ids. - Their shapes are `(self.beam_size,)` - - """ - # no pre beam performed - if weighted_scores.size(0) == ids.size(0): - top_ids = weighted_scores.topk(self.beam_size)[1] - return top_ids, top_ids - - # mask pruned in pre-beam not to select in topk - tmp = weighted_scores[ids] - weighted_scores[:] = -float("inf") - weighted_scores[ids] = tmp - top_ids = weighted_scores.topk(self.beam_size)[1] - local_ids = weighted_scores[ids].topk(self.beam_size)[1] - return top_ids, local_ids - - @staticmethod - def merge_scores( - prev_scores: Dict[str, float], - next_full_scores: Dict[str, torch.Tensor], - full_idx: int, - next_part_scores: Dict[str, torch.Tensor], - part_idx: int, - ) -> Dict[str, torch.Tensor]: - """Merge scores for new hypothesis. - - Args: - prev_scores (Dict[str, float]): - The previous hypothesis scores by `self.scorers` - next_full_scores (Dict[str, torch.Tensor]): scores by - `self.full_scorers` - full_idx (int): The next token id for `next_full_scores` - next_part_scores (Dict[str, torch.Tensor]): - scores of partial tokens by `self.part_scorers` - part_idx (int): The new token id for `next_part_scores` - - Returns: - Dict[str, torch.Tensor]: The new score dict. - Its keys are names of `self.full_scorers` and - `self.part_scorers`. - Its values are scalar tensors by the scorers. - - """ - new_scores = dict() - for k, v in next_full_scores.items(): - new_scores[k] = prev_scores[k] + v[full_idx] - for k, v in next_part_scores.items(): - new_scores[k] = prev_scores[k] + v[part_idx] - return new_scores - - def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any: - """Merge states for new hypothesis. - - Args: - states: states of `self.full_scorers` - part_states: states of `self.part_scorers` - part_idx (int): The new token id for `part_scores` - - Returns: - Dict[str, torch.Tensor]: The new score dict. - Its keys are names of `self.full_scorers` and - `self.part_scorers`. - Its values are states of the scorers. - - """ - new_states = dict() - for k, v in states.items(): - new_states[k] = v - for k, d in self.part_scorers.items(): - new_states[k] = d.select_state(part_states[k], part_idx) - return new_states - - def search( - self, running_hyps: List[Hypothesis], x: torch.Tensor, am_score: torch.Tensor - ) -> List[Hypothesis]: - """Search new tokens for running hypotheses and encoded speech x. - - Args: - running_hyps (List[Hypothesis]): Running hypotheses on beam - x (torch.Tensor): Encoded speech feature (T, D) - - Returns: - List[Hypotheses]: Best sorted hypotheses - - """ - best_hyps = [] - part_ids = torch.arange(self.n_vocab, device=x.device) # no pre-beam - for hyp in running_hyps: - # scoring - weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device) - weighted_scores += am_score - scores, states = self.score_full(hyp, x) - for k in self.full_scorers: - weighted_scores += self.weights[k] * scores[k] - # partial scoring - if self.do_pre_beam: - pre_beam_scores = ( - weighted_scores - if self.pre_beam_score_key == "full" - else scores[self.pre_beam_score_key] - ) - part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1] - part_scores, part_states = self.score_partial(hyp, part_ids, x) - for k in self.part_scorers: - weighted_scores[part_ids] += self.weights[k] * part_scores[k] - # add previous hyp score - weighted_scores += hyp.score - - # update hyps - for j, part_j in zip(*self.beam(weighted_scores, part_ids)): - # will be (2 x beam at most) - best_hyps.append( - Hypothesis( - score=weighted_scores[j], - yseq=self.append_token(hyp.yseq, j), - scores=self.merge_scores( - hyp.scores, scores, j, part_scores, part_j - ), - states=self.merge_states(states, part_states, part_j), - ) - ) - - # sort and prune 2 x beam -> beam - best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[ - : min(len(best_hyps), self.beam_size) - ] - return best_hyps - - def forward( - self, - x: torch.Tensor, - am_scores: torch.Tensor, - maxlenratio: float = 0.0, - minlenratio: float = 0.0, - ) -> List[Hypothesis]: - """Perform beam search. - - Args: - x (torch.Tensor): Encoded speech feature (T, D) - maxlenratio (float): Input length ratio to obtain max output length. - If maxlenratio=0.0 (default), it uses a end-detect function - to automatically find maximum hypothesis lengths - If maxlenratio<0.0, its absolute value is interpreted - as a constant max output length. - minlenratio (float): Input length ratio to obtain min output length. - - Returns: - list[Hypothesis]: N-best decoding results - - """ - # set length bounds - maxlen = am_scores.shape[0] - - # main loop of prefix search - running_hyps = self.init_hyp(x) - ended_hyps = [] - for i in range(maxlen): - best = self.search(running_hyps, x, am_scores[i]) - # post process of one iteration - running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps) - # end detection - if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i): - break - - nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True) - # check the number of hypotheses reaching to eos - if len(nbest_hyps) == 0: - return ( - [] - if minlenratio < 0.1 - else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1)) - ) - - best = nbest_hyps[0] - return nbest_hyps - - def post_process( - self, - i: int, - maxlen: int, - maxlenratio: float, - running_hyps: List[Hypothesis], - ended_hyps: List[Hypothesis], - ) -> List[Hypothesis]: - """Perform post-processing of beam search iterations. - - Args: - i (int): The length of hypothesis tokens. - maxlen (int): The maximum length of tokens in beam search. - maxlenratio (int): The maximum length ratio in beam search. - running_hyps (List[Hypothesis]): The running hypotheses in beam - search. - ended_hyps (List[Hypothesis]): The ended hypotheses in beam search. - - Returns: - List[Hypothesis]: The new running hypotheses. - - """ - - # add eos in the final loop to avoid that there are no ended hyps - if i == maxlen - 1: - # logging.info("adding in the last position in the loop") - running_hyps = [ - h._replace(yseq=self.append_token(h.yseq, self.eos)) - for h in running_hyps - ] - - # add ended hypotheses to a final list, and removed them from current - # hypotheses - # (this will be a problem, number of hyps < beam) - remained_hyps = [] - for hyp in running_hyps: - if hyp.yseq[-1] == self.eos: - # e.g., Word LM needs to add final score - for k, d in chain(self.full_scorers.items(), self.part_scorers.items()): - s = d.final_score(hyp.states[k]) - hyp.scores[k] += s - hyp = hyp._replace(score=hyp.score + self.weights[k] * s) - ended_hyps.append(hyp) - else: - remained_hyps.append(hyp) - return remained_hyps - - -def build_beam_search(model, args, device): - scorers = {} - if model.ctc is not None: - ctc = CTCPrefixScorer(ctc=model.ctc, eos=model.eos) - scorers.update(ctc=ctc) - weights = dict( - decoder=1.0 - args.ctc_weight, - ctc=args.ctc_weight, - length_bonus=args.penalty, - ) - beam_search = BeamSearchCIF( - beam_size=args.beam_size, - weights=weights, - scorers=scorers, - sos=model.sos, - eos=model.eos, - vocab_size=model.vocab_size, - pre_beam_score_key=None if args.ctc_weight == 1.0 else "full", - ) - beam_search.to(device=device, dtype=torch.float32).eval() - return beam_search diff --git a/modules/wenet_extractor/paraformer/search/ctc.py b/modules/wenet_extractor/paraformer/search/ctc.py deleted file mode 100644 index a40c337ed3ce69051292096a9648b6675f3b8049..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/paraformer/search/ctc.py +++ /dev/null @@ -1,181 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""ScorerInterface implementation for CTC.""" -import numpy as np -import torch - -from modules.wenet_extractor.paraformer.search.ctc_prefix_score import CTCPrefixScore -from modules.wenet_extractor.paraformer.search.ctc_prefix_score import CTCPrefixScoreTH -from modules.wenet_extractor.paraformer.search.scorer_interface import ( - BatchPartialScorerInterface, -) - - -class CTCPrefixScorer(BatchPartialScorerInterface): - """Decoder interface wrapper for CTCPrefixScore.""" - - def __init__(self, ctc: torch.nn.Module, eos: int): - """Initialize class. - - Args: - ctc (torch.nn.Module): The CTC implementation. - For example, :class:`espnet.nets.pytorch_backend.ctc.CTC` - eos (int): The end-of-sequence id. - - """ - self.ctc = ctc - self.eos = eos - self.impl = None - - def init_state(self, x: torch.Tensor): - """Get an initial state for decoding. - - Args: - x (torch.Tensor): The encoded feature tensor - - Returns: initial state - - """ - logp = self.ctc.log_softmax(x.unsqueeze(0)).detach().squeeze(0).cpu().numpy() - # TODO(karita): use CTCPrefixScoreTH - self.impl = CTCPrefixScore(logp, 0, self.eos, np) - return 0, self.impl.initial_state() - - def select_state(self, state, i, new_id=None): - """Select state with relative ids in the main beam search. - - Args: - state: Decoder state for prefix tokens - i (int): Index to select a state in the main beam search - new_id (int): New label id to select a state if necessary - - Returns: - state: pruned state - - """ - if type(state) == tuple: - if len(state) == 2: # for CTCPrefixScore - sc, st = state - return sc[i], st[i] - else: # for CTCPrefixScoreTH (need new_id > 0) - r, log_psi, f_min, f_max, scoring_idmap = state - s = log_psi[i, new_id].expand(log_psi.size(1)) - if scoring_idmap is not None: - return r[:, :, i, scoring_idmap[i, new_id]], s, f_min, f_max - else: - return r[:, :, i, new_id], s, f_min, f_max - return None if state is None else state[i] - - def score_partial(self, y, ids, state, x): - """Score new token. - - Args: - y (torch.Tensor): 1D prefix token - next_tokens (torch.Tensor): torch.int64 next token to score - state: decoder state for prefix tokens - x (torch.Tensor): 2D encoder feature that generates ys - - Returns: - tuple[torch.Tensor, Any]: - Tuple of a score tensor for y that has a shape - `(len(next_tokens),)` and next state for ys - - """ - prev_score, state = state - presub_score, new_st = self.impl(y.cpu(), ids.cpu(), state) - tscore = torch.as_tensor( - presub_score - prev_score, device=x.device, dtype=x.dtype - ) - return tscore, (presub_score, new_st) - - def batch_init_state(self, x: torch.Tensor): - """Get an initial state for decoding. - - Args: - x (torch.Tensor): The encoded feature tensor - - Returns: initial state - - """ - logp = self.ctc.log_softmax(x.unsqueeze(0)) # assuming batch_size = 1 - xlen = torch.tensor([logp.size(1)]) - self.impl = CTCPrefixScoreTH(logp, xlen, 0, self.eos) - return None - - def batch_score_partial(self, y, ids, state, x): - """Score new token. - - Args: - y (torch.Tensor): 1D prefix token - ids (torch.Tensor): torch.int64 next token to score - state: decoder state for prefix tokens - x (torch.Tensor): 2D encoder feature that generates ys - - Returns: - tuple[torch.Tensor, Any]: - Tuple of a score tensor for y that has a shape - `(len(next_tokens),)` and next state for ys - - """ - batch_state = ( - ( - torch.stack([s[0] for s in state], dim=2), - torch.stack([s[1] for s in state]), - state[0][2], - state[0][3], - ) - if state[0] is not None - else None - ) - return self.impl(y, batch_state, ids) - - def extend_prob(self, x: torch.Tensor): - """Extend probs for decoding. - - This extension is for streaming decoding - as in Eq (14) in https://arxiv.org/abs/2006.14941 - - Args: - x (torch.Tensor): The encoded feature tensor - - """ - logp = self.ctc.log_softmax(x.unsqueeze(0)) - self.impl.extend_prob(logp) - - def extend_state(self, state): - """Extend state for decoding. - - This extension is for streaming decoding - as in Eq (14) in https://arxiv.org/abs/2006.14941 - - Args: - state: The states of hyps - - Returns: exteded state - - """ - new_state = [] - for s in state: - new_state.append(self.impl.extend_state(s)) - - return new_state diff --git a/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py b/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py deleted file mode 100644 index b4a4eb50b7556f6a463aef5428dba3ebc4f49875..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py +++ /dev/null @@ -1,377 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import torch -import numpy as np - -import six - - -class CTCPrefixScore(object): - """Compute CTC label sequence scores - - which is based on Algorithm 2 in WATANABE et al. - "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION," - but extended to efficiently compute the probablities of multiple labels - simultaneously - """ - - def __init__(self, x, blank, eos, xp): - self.xp = xp - self.logzero = -10000000000.0 - self.blank = blank - self.eos = eos - self.input_length = len(x) - self.x = x - - def initial_state(self): - """Obtain an initial CTC state - - :return: CTC state - """ - # initial CTC state is made of a frame x 2 tensor that corresponds to - # r_t^n() and r_t^b(), where 0 and 1 of axis=1 represent - # superscripts n and b (non-blank and blank), respectively. - r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32) - r[0, 1] = self.x[0, self.blank] - for i in six.moves.range(1, self.input_length): - r[i, 1] = r[i - 1, 1] + self.x[i, self.blank] - return r - - def __call__(self, y, cs, r_prev): - """Compute CTC prefix scores for next labels - - :param y : prefix label sequence - :param cs : array of next labels - :param r_prev: previous CTC state - :return ctc_scores, ctc_states - """ - # initialize CTC states - output_length = len(y) - 1 # ignore sos - # new CTC states are prepared as a frame x (n or b) x n_labels tensor - # that corresponds to r_t^n(h) and r_t^b(h). - r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32) - xs = self.x[:, cs] - if output_length == 0: - r[0, 0] = xs[0] - r[0, 1] = self.logzero - else: - r[output_length - 1] = self.logzero - - # prepare forward probabilities for the last label - r_sum = self.xp.logaddexp( - r_prev[:, 0], r_prev[:, 1] - ) # log(r_t^n(g) + r_t^b(g)) - last = y[-1] - if output_length > 0 and last in cs: - log_phi = self.xp.ndarray((self.input_length, len(cs)), dtype=np.float32) - for i in six.moves.range(len(cs)): - log_phi[:, i] = r_sum if cs[i] != last else r_prev[:, 1] - else: - log_phi = r_sum - - # compute forward probabilities log(r_t^n(h)), log(r_t^b(h)), - # and log prefix probabilities log(psi) - start = max(output_length, 1) - log_psi = r[start - 1, 0] - for t in six.moves.range(start, self.input_length): - r[t, 0] = self.xp.logaddexp(r[t - 1, 0], log_phi[t - 1]) + xs[t] - r[t, 1] = ( - self.xp.logaddexp(r[t - 1, 0], r[t - 1, 1]) + self.x[t, self.blank] - ) - log_psi = self.xp.logaddexp(log_psi, log_phi[t - 1] + xs[t]) - - # get P(...eos|X) that ends with the prefix itself - eos_pos = self.xp.where(cs == self.eos)[0] - if len(eos_pos) > 0: - log_psi[eos_pos] = r_sum[-1] # log(r_T^n(g) + r_T^b(g)) - - # exclude blank probs - blank_pos = self.xp.where(cs == self.blank)[0] - if len(blank_pos) > 0: - log_psi[blank_pos] = self.logzero - - # return the log prefix probability and CTC states, where the label axis - # of the CTC states is moved to the first axis to slice it easily - return log_psi, self.xp.rollaxis(r, 2) - - -class CTCPrefixScoreTH(object): - """Batch processing of CTCPrefixScore - - which is based on Algorithm 2 in WATANABE et al. - "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION," - but extended to efficiently compute the label probablities for multiple - hypotheses simultaneously - See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based - Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019. - """ - - def __init__(self, x, xlens, blank, eos, margin=0): - """Construct CTC prefix scorer - - :param torch.Tensor x: input label posterior sequences (B, T, O) - :param torch.Tensor xlens: input lengths (B,) - :param int blank: blank label id - :param int eos: end-of-sequence id - :param int margin: margin parameter for windowing (0 means no windowing) - """ - # In the comment lines, - # we assume T: input_length, B: batch size, W: beam width, O: output dim - self.logzero = -10000000000.0 - self.blank = blank - self.eos = eos - self.batch = x.size(0) - self.input_length = x.size(1) - self.odim = x.size(2) - self.dtype = x.dtype - self.device = ( - torch.device("cuda:%d" % x.get_device()) - if x.is_cuda - else torch.device("cpu") - ) - # Pad the rest of posteriors in the batch - # TODO(takaaki-hori): need a better way without for-loops - for i, l in enumerate(xlens): - if l < self.input_length: - x[i, l:, :] = self.logzero - x[i, l:, blank] = 0 - # Reshape input x - xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O) - xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) - self.x = torch.stack([xn, xb]) # (2, T, B, O) - self.end_frames = torch.as_tensor(xlens) - 1 - - # Setup CTC windowing - self.margin = margin - if margin > 0: - self.frame_ids = torch.arange( - self.input_length, dtype=self.dtype, device=self.device - ) - # Base indices for index conversion - self.idx_bh = None - self.idx_b = torch.arange(self.batch, device=self.device) - self.idx_bo = (self.idx_b * self.odim).unsqueeze(1) - - def __call__(self, y, state, scoring_ids=None, att_w=None): - """Compute CTC prefix scores for next labels - - :param list y: prefix label sequences - :param tuple state: previous CTC state - :param torch.Tensor pre_scores: scores for pre-selection of hypotheses - (BW, O) - :param torch.Tensor att_w: attention weights to decide CTC window - :return new_state, ctc_local_scores (BW, O) - """ - output_length = len(y[0]) - 1 # ignore sos - last_ids = [yi[-1] for yi in y] # last output label ids - n_bh = len(last_ids) # batch * hyps - n_hyps = n_bh // self.batch # assuming each utterance has the same - self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0 - # prepare state info - if state is None: - r_prev = torch.full( - (self.input_length, 2, self.batch, n_hyps), - self.logzero, - dtype=self.dtype, - device=self.device, - ) - r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2) - r_prev = r_prev.view(-1, 2, n_bh) - s_prev = 0.0 - f_min_prev = 0 - f_max_prev = 1 - else: - r_prev, s_prev, f_min_prev, f_max_prev = state - - # select input dimensions for scoring - if self.scoring_num > 0: - scoring_idmap = torch.full( - (n_bh, self.odim), -1, dtype=torch.long, device=self.device - ) - snum = self.scoring_num - if self.idx_bh is None or n_bh > len(self.idx_bh): - self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1) - scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange( - snum, device=self.device - ) - scoring_idx = ( - scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1) - ).view(-1) - x_ = torch.index_select( - self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx - ).view(2, -1, n_bh, snum) - else: - scoring_ids = None - scoring_idmap = None - snum = self.odim - x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum) - - # new CTC forward probs are prepared as a (T x 2 x BW x S) tensor - # that corresponds to r_t^n(h) and r_t^b(h) in a batch. - r = torch.full( - (self.input_length, 2, n_bh, snum), - self.logzero, - dtype=self.dtype, - device=self.device, - ) - if output_length == 0: - r[0, 0] = x_[0, 0] - - r_sum = torch.logsumexp(r_prev, 1) - log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum) - if scoring_ids is not None: - for idx in range(n_bh): - pos = scoring_idmap[idx, last_ids[idx]] - if pos >= 0: - log_phi[:, idx, pos] = r_prev[:, 1, idx] - else: - for idx in range(n_bh): - log_phi[:, idx, last_ids[idx]] = r_prev[:, 1, idx] - - # decide start and end frames based on attention weights - if att_w is not None and self.margin > 0: - f_arg = torch.matmul(att_w, self.frame_ids) - f_min = max(int(f_arg.min().cpu()), f_min_prev) - f_max = max(int(f_arg.max().cpu()), f_max_prev) - start = min(f_max_prev, max(f_min - self.margin, output_length, 1)) - end = min(f_max + self.margin, self.input_length) - else: - f_min = f_max = 0 - start = max(output_length, 1) - end = self.input_length - - # compute forward probabilities log(r_t^n(h)) and log(r_t^b(h)) - for t in range(start, end): - rp = r[t - 1] - rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view( - 2, 2, n_bh, snum - ) - r[t] = torch.logsumexp(rr, 1) + x_[:, t] - - # compute log prefix probabilities log(psi) - log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0] - if scoring_ids is not None: - log_psi = torch.full( - (n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device - ) - log_psi_ = torch.logsumexp( - torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0), - dim=0, - ) - for si in range(n_bh): - log_psi[si, scoring_ids[si]] = log_psi_[si] - else: - log_psi = torch.logsumexp( - torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0), - dim=0, - ) - - for si in range(n_bh): - log_psi[si, self.eos] = r_sum[self.end_frames[si // n_hyps], si] - - # exclude blank probs - log_psi[:, self.blank] = self.logzero - - return (log_psi - s_prev), (r, log_psi, f_min, f_max, scoring_idmap) - - def index_select_state(self, state, best_ids): - """Select CTC states according to best ids - - :param state : CTC state - :param best_ids : index numbers selected by beam pruning (B, W) - :return selected_state - """ - r, s, f_min, f_max, scoring_idmap = state - # convert ids to BHO space - n_bh = len(s) - n_hyps = n_bh // self.batch - vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1) - # select hypothesis scores - s_new = torch.index_select(s.view(-1), 0, vidx) - s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim) - # convert ids to BHS space (S: scoring_num) - if scoring_idmap is not None: - snum = self.scoring_num - hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view( - -1 - ) - label_ids = torch.fmod(best_ids, self.odim).view(-1) - score_idx = scoring_idmap[hyp_idx, label_ids] - score_idx[score_idx == -1] = 0 - vidx = score_idx + hyp_idx * snum - else: - snum = self.odim - # select forward probabilities - r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view( - -1, 2, n_bh - ) - return r_new, s_new, f_min, f_max - - def extend_prob(self, x): - """Extend CTC prob. - - :param torch.Tensor x: input label posterior sequences (B, T, O) - """ - - if self.x.shape[1] < x.shape[1]: # self.x (2,T,B,O); x (B,T,O) - # Pad the rest of posteriors in the batch - # TODO(takaaki-hori): need a better way without for-loops - xlens = [x.size(1)] - for i, l in enumerate(xlens): - if l < self.input_length: - x[i, l:, :] = self.logzero - x[i, l:, self.blank] = 0 - tmp_x = self.x - xn = x.transpose(0, 1) # (B, T, O) -> (T, B, O) - xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim) - self.x = torch.stack([xn, xb]) # (2, T, B, O) - self.x[:, : tmp_x.shape[1], :, :] = tmp_x - self.input_length = x.size(1) - self.end_frames = torch.as_tensor(xlens) - 1 - - def extend_state(self, state): - """Compute CTC prefix state. - - - :param state : CTC state - :return ctc_state - """ - - if state is None: - # nothing to do - return state - else: - r_prev, s_prev, f_min_prev, f_max_prev = state - - r_prev_new = torch.full( - (self.input_length, 2), - self.logzero, - dtype=self.dtype, - device=self.device, - ) - start = max(r_prev.shape[0], 1) - r_prev_new[0:start] = r_prev - for t in six.moves.range(start, self.input_length): - r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank] - - return r_prev_new, s_prev, f_min_prev, f_max_prev diff --git a/modules/wenet_extractor/paraformer/search/scorer_interface.py b/modules/wenet_extractor/paraformer/search/scorer_interface.py deleted file mode 100644 index ed021726f3a93dddd9b00eab23bc6fbe7583a04b..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/paraformer/search/scorer_interface.py +++ /dev/null @@ -1,208 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Scorer interface module.""" -from abc import ABC -from typing import Any -from typing import List -from typing import Tuple - -import torch - - -class ScorerInterface: - """Scorer interface for beam search. - - The scorer performs scoring of the all tokens in vocabulary. - - Examples: - * Search heuristics - * :class:`espnet.nets.scorers.length_bonus.LengthBonus` - * Decoder networks of the sequence-to-sequence models - * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder - .Decoder` - * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder` - * Neural language models - * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM` - * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM` - * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM` - - """ - - def init_state(self, x: torch.Tensor) -> Any: - """Get an initial state for decoding (optional). - - Args: - x (torch.Tensor): The encoded feature tensor - - Returns: initial state - - """ - return None - - def select_state(self, state: Any, i: int, new_id: int = None) -> Any: - """Select state with relative ids in the main beam search. - - Args: - state: Decoder state for prefix tokens - i (int): Index to select a state in the main beam search - new_id (int): New label index to select a state if necessary - - Returns: - state: pruned state - - """ - return None if state is None else state[i] - - def score( - self, y: torch.Tensor, state: Any, x: torch.Tensor - ) -> Tuple[torch.Tensor, Any]: - """Score new token (required). - - Args: - y (torch.Tensor): 1D torch.int64 prefix tokens. - state: Scorer state for prefix tokens - x (torch.Tensor): The encoder feature that generates ys. - - Returns: - tuple[torch.Tensor, Any]: Tuple of - scores for next token that has a shape of `(n_vocab)` - and next state for ys - - """ - raise NotImplementedError - - def final_score(self, state: Any) -> float: - """Score eos (optional). - - Args: - state: Scorer state for prefix tokens - - Returns: - float: final score - - """ - return 0.0 - - -class BatchScorerInterface(ScorerInterface, ABC): - """Batch scorer interface.""" - - def batch_init_state(self, x: torch.Tensor) -> Any: - """Get an initial state for decoding (optional). - - Args: - x (torch.Tensor): The encoded feature tensor - - Returns: initial state - - """ - return self.init_state(x) - - def batch_score( - self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor - ) -> Tuple[torch.Tensor, List[Any]]: - """Score new token batch (required). - - Args: - ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). - states (List[Any]): Scorer states for prefix tokens. - xs (torch.Tensor): - The encoder feature that generates ys (n_batch, xlen, n_feat). - - Returns: - tuple[torch.Tensor, List[Any]]: Tuple of - batchfied scores for next token with shape of `(n_batch, - n_vocab)` - and next state list for ys. - - """ - scores = list() - outstates = list() - for i, (y, state, x) in enumerate(zip(ys, states, xs)): - score, outstate = self.score(y, state, x) - outstates.append(outstate) - scores.append(score) - scores = torch.cat(scores, 0).view(ys.shape[0], -1) - return scores, outstates - - -class PartialScorerInterface(ScorerInterface, ABC): - """Partial scorer interface for beam search. - - The partial scorer performs scoring when non-partial scorer finished scoring - and receives pre-pruned next tokens to score because it is too heavy to - score all the tokens. - - Examples: - * Prefix search for connectionist-temporal-classification models - * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer` - - """ - - def score_partial( - self, y: torch.Tensor, next_tokens: torch.Tensor, state: Any, x: torch.Tensor - ) -> Tuple[torch.Tensor, Any]: - """Score new token (required). - - Args: - y (torch.Tensor): 1D prefix token - next_tokens (torch.Tensor): torch.int64 next token to score - state: decoder state for prefix tokens - x (torch.Tensor): The encoder feature that generates ys - - Returns: - tuple[torch.Tensor, Any]: - Tuple of a score tensor for y that has a shape - `(len(next_tokens),)` and next state for ys - - """ - raise NotImplementedError - - -class BatchPartialScorerInterface(BatchScorerInterface, PartialScorerInterface, ABC): - """Batch partial scorer interface for beam search.""" - - def batch_score_partial( - self, - ys: torch.Tensor, - next_tokens: torch.Tensor, - states: List[Any], - xs: torch.Tensor, - ) -> Tuple[torch.Tensor, Any]: - """Score new token (required). - - Args: - ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). - next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch, - n_token). - states (List[Any]): Scorer states for prefix tokens. - xs (torch.Tensor): - The encoder feature that generates ys (n_batch, xlen, n_feat). - - Returns: - tuple[torch.Tensor, Any]: - Tuple of a score tensor for ys that has a shape `(n_batch, - n_vocab)` - and next states for ys - """ - raise NotImplementedError diff --git a/modules/wenet_extractor/paraformer/utils.py b/modules/wenet_extractor/paraformer/utils.py deleted file mode 100644 index 83d644f7643f3f0c213a54ab4b634e89e51a1c40..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/paraformer/utils.py +++ /dev/null @@ -1,76 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from typing import Optional - -import six -import torch -import numpy as np - - -def sequence_mask( - lengths, - maxlen: Optional[int] = None, - dtype: torch.dtype = torch.float32, - device: Optional[torch.device] = None, -) -> torch.Tensor: - if maxlen is None: - maxlen = lengths.max() - row_vector = torch.arange(0, maxlen, 1).to(lengths.device) - matrix = torch.unsqueeze(lengths, dim=-1) - mask = row_vector < matrix - mask = mask.detach() - - return mask.type(dtype).to(device) if device is not None else mask.type(dtype) - - -def end_detect(ended_hyps, i, M=3, d_end=np.log(1 * np.exp(-10))): - """End detection. - - described in Eq. (50) of S. Watanabe et al - "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition" - - :param ended_hyps: - :param i: - :param M: - :param d_end: - :return: - """ - if len(ended_hyps) == 0: - return False - count = 0 - best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0] - for m in six.moves.range(M): - # get ended_hyps with their length is i - m - hyp_length = i - m - hyps_same_length = [x for x in ended_hyps if len(x["yseq"]) == hyp_length] - if len(hyps_same_length) > 0: - best_hyp_same_length = sorted( - hyps_same_length, key=lambda x: x["score"], reverse=True - )[0] - if best_hyp_same_length["score"] - best_hyp["score"] < d_end: - count += 1 - - if count == M: - return True - else: - return False diff --git a/modules/wenet_extractor/squeezeformer/__init__.py b/modules/wenet_extractor/squeezeformer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/squeezeformer/attention.py b/modules/wenet_extractor/squeezeformer/attention.py deleted file mode 100644 index 830da3dccc0257afc36747c7f572455746ef652e..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/attention.py +++ /dev/null @@ -1,239 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Multi-Head Attention layer definition.""" - -import math -import torch -import torch.nn as nn -from modules.wenet_extractor.transformer.attention import MultiHeadedAttention -from typing import Tuple - - -class RelPositionMultiHeadedAttention(MultiHeadedAttention): - """Multi-Head Attention layer with relative position encoding. - Paper: https://arxiv.org/abs/1901.02860 - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - """ - - def __init__( - self, - n_head, - n_feat, - dropout_rate, - do_rel_shift=False, - adaptive_scale=False, - init_weights=False, - ): - """Construct an RelPositionMultiHeadedAttention object.""" - super().__init__(n_head, n_feat, dropout_rate) - # linear transformation for positional encoding - self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) - # these two learnable bias are used in matrix c and matrix d - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - self.do_rel_shift = do_rel_shift - self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) - self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) - torch.nn.init.xavier_uniform_(self.pos_bias_u) - torch.nn.init.xavier_uniform_(self.pos_bias_v) - self.adaptive_scale = adaptive_scale - self.ada_scale = nn.Parameter( - torch.ones([1, 1, n_feat]), requires_grad=adaptive_scale - ) - self.ada_bias = nn.Parameter( - torch.zeros([1, 1, n_feat]), requires_grad=adaptive_scale - ) - if init_weights: - self.init_weights() - - def init_weights(self): - input_max = (self.h * self.d_k) ** -0.5 - torch.nn.init.uniform_(self.linear_q.weight, -input_max, input_max) - torch.nn.init.uniform_(self.linear_q.bias, -input_max, input_max) - torch.nn.init.uniform_(self.linear_k.weight, -input_max, input_max) - torch.nn.init.uniform_(self.linear_k.bias, -input_max, input_max) - torch.nn.init.uniform_(self.linear_v.weight, -input_max, input_max) - torch.nn.init.uniform_(self.linear_v.bias, -input_max, input_max) - torch.nn.init.uniform_(self.linear_pos.weight, -input_max, input_max) - torch.nn.init.uniform_(self.linear_out.weight, -input_max, input_max) - torch.nn.init.uniform_(self.linear_out.bias, -input_max, input_max) - - def rel_shift(self, x, zero_triu: bool = False): - """Compute relative positinal encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, size). - zero_triu (bool): If true, return the lower triangular part of - the matrix. - Returns: - torch.Tensor: Output tensor. - """ - - zero_pad = torch.zeros( - (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype - ) - x_padded = torch.cat([zero_pad, x], dim=-1) - - x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) - x = x_padded[:, :, 1:].view_as(x) - - if zero_triu: - ones = torch.ones((x.size(2), x.size(3))) - x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] - - return x - - def forward_attention( - self, - value: torch.Tensor, - scores: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ) -> torch.Tensor: - """Compute attention context vector. - - Args: - value (torch.Tensor): Transformed value, size - (#batch, n_head, time2, d_k). - scores (torch.Tensor): Attention score, size - (#batch, n_head, time1, time2). - mask (torch.Tensor): Mask, size (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - - Returns: - torch.Tensor: Transformed value (#batch, time1, d_model) - weighted by the attention score (#batch, time1, time2). - - """ - n_batch = value.size(0) - # NOTE(xcsong): When will `if mask.size(2) > 0` be True? - # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the - # 1st chunk to ease the onnx export.] - # 2. pytorch training - if mask.size(2) > 0: # time2 > 0 - mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) - # For last chunk, time2 might be larger than scores.size(-1) - mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2) - scores = scores.masked_fill(mask, -float("inf")) - # (batch, head, time1, time2) - attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) - # NOTE(xcsong): When will `if mask.size(2) > 0` be False? - # 1. onnx(16/-1, -1/-1, 16/0) - # 2. jit (16/-1, -1/-1, 16/0, 16/4) - else: - attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - - p_attn = self.dropout(attn) - x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) - x = ( - x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) - ) # (batch, time1, d_model) - - return self.linear_out(x) # (batch, time1, d_model) - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - pos_emb: torch.Tensor = torch.empty(0), - cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute 'Scaled Dot Product Attention' with rel. positional encoding. - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): Positional embedding tensor - (#batch, time2, size). - cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - """ - if self.adaptive_scale: - query = self.ada_scale * query + self.ada_bias - key = self.ada_scale * key + self.ada_bias - value = self.ada_scale * value + self.ada_bias - q, k, v = self.forward_qkv(query, key, value) - q = q.transpose(1, 2) # (batch, time1, head, d_k) - - # NOTE(xcsong): - # when export onnx model, for 1st chunk, we feed - # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) - # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). - # In all modes, `if cache.size(0) > 0` will alwayse be `True` - # and we will always do splitting and - # concatnation(this will simplify onnx export). Note that - # it's OK to concat & split zero-shaped tensors(see code below). - # when export jit model, for 1st chunk, we always feed - # cache(0, 0, 0, 0) since jit supports dynamic if-branch. - # >>> a = torch.ones((1, 2, 0, 4)) - # >>> b = torch.ones((1, 2, 3, 4)) - # >>> c = torch.cat((a, b), dim=2) - # >>> torch.equal(b, c) # True - # >>> d = torch.split(a, 2, dim=-1) - # >>> torch.equal(d[0], d[1]) # True - if cache.size(0) > 0: - key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) - k = torch.cat([key_cache, k], dim=2) - v = torch.cat([value_cache, v], dim=2) - # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's - # non-trivial to calculate `next_cache_start` here. - new_cache = torch.cat((k, v), dim=-1) - - n_batch_pos = pos_emb.size(0) - p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) - p = p.transpose(1, 2) # (batch, head, time1, d_k) - - # (batch, head, time1, d_k) - q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) - # (batch, head, time1, d_k) - q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) - - # compute attention score - # first compute matrix a and matrix c - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - # (batch, head, time1, time2) - matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) - - # compute matrix b and matrix d - # (batch, head, time1, time2) - matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) - # Remove rel_shift since it is useless in speech recognition, - # and it requires special attention for streaming. - if self.do_rel_shift: - matrix_bd = self.rel_shift(matrix_bd) - - scores = (matrix_ac + matrix_bd) / math.sqrt( - self.d_k - ) # (batch, head, time1, time2) - - return self.forward_attention(v, scores, mask), new_cache diff --git a/modules/wenet_extractor/squeezeformer/conv2d.py b/modules/wenet_extractor/squeezeformer/conv2d.py deleted file mode 100644 index 01d2981e4b6853013ce1a629496e03b04517d0c0..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/conv2d.py +++ /dev/null @@ -1,93 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Conv2d Module with Valid Padding""" - -import torch.nn.functional as F -from torch.nn.modules.conv import _ConvNd, _size_2_t, Union, _pair, Tensor, Optional - - -class Conv2dValid(_ConvNd): - """ - Conv2d operator for VALID mode padding. - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: _size_2_t, - stride: _size_2_t = 1, - padding: Union[str, _size_2_t] = 0, - dilation: _size_2_t = 1, - groups: int = 1, - bias: bool = True, - padding_mode: str = "zeros", # TODO: refine this type - device=None, - dtype=None, - valid_trigx: bool = False, - valid_trigy: bool = False, - ) -> None: - factory_kwargs = {"device": device, "dtype": dtype} - kernel_size_ = _pair(kernel_size) - stride_ = _pair(stride) - padding_ = padding if isinstance(padding, str) else _pair(padding) - dilation_ = _pair(dilation) - super(Conv2dValid, self).__init__( - in_channels, - out_channels, - kernel_size_, - stride_, - padding_, - dilation_, - False, - _pair(0), - groups, - bias, - padding_mode, - **factory_kwargs, - ) - self.valid_trigx = valid_trigx - self.valid_trigy = valid_trigy - - def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]): - validx, validy = 0, 0 - if self.valid_trigx: - validx = ( - input.size(-2) * (self.stride[-2] - 1) - 1 + self.kernel_size[-2] - ) // 2 - if self.valid_trigy: - validy = ( - input.size(-1) * (self.stride[-1] - 1) - 1 + self.kernel_size[-1] - ) // 2 - return F.conv2d( - input, - weight, - bias, - self.stride, - (validx, validy), - self.dilation, - self.groups, - ) - - def forward(self, input: Tensor) -> Tensor: - return self._conv_forward(input, self.weight, self.bias) diff --git a/modules/wenet_extractor/squeezeformer/convolution.py b/modules/wenet_extractor/squeezeformer/convolution.py deleted file mode 100644 index f0ec43dadcd22aff53f79c459888344def57ef12..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/convolution.py +++ /dev/null @@ -1,182 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""ConvolutionModule definition.""" - -from typing import Tuple - -import torch -from torch import nn - - -class ConvolutionModule(nn.Module): - """ConvolutionModule in Conformer model.""" - - def __init__( - self, - channels: int, - kernel_size: int = 15, - activation: nn.Module = nn.ReLU(), - norm: str = "batch_norm", - causal: bool = False, - bias: bool = True, - adaptive_scale: bool = False, - init_weights: bool = False, - ): - """Construct an ConvolutionModule object. - Args: - channels (int): The number of channels of conv layers. - kernel_size (int): Kernel size of conv layers. - causal (int): Whether use causal convolution or not - """ - super().__init__() - self.bias = bias - self.channels = channels - self.kernel_size = kernel_size - self.adaptive_scale = adaptive_scale - self.ada_scale = torch.nn.Parameter( - torch.ones([1, 1, channels]), requires_grad=adaptive_scale - ) - self.ada_bias = torch.nn.Parameter( - torch.zeros([1, 1, channels]), requires_grad=adaptive_scale - ) - - self.pointwise_conv1 = nn.Conv1d( - channels, - 2 * channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - # self.lorder is used to distinguish if it's a causal convolution, - # if self.lorder > 0: it's a causal convolution, the input will be - # padded with self.lorder frames on the left in forward. - # else: it's a symmetrical convolution - if causal: - padding = 0 - self.lorder = kernel_size - 1 - else: - # kernel_size should be an odd number for none causal convolution - assert (kernel_size - 1) % 2 == 0 - padding = (kernel_size - 1) // 2 - self.lorder = 0 - self.depthwise_conv = nn.Conv1d( - channels, - channels, - kernel_size, - stride=1, - padding=padding, - groups=channels, - bias=bias, - ) - - assert norm in ["batch_norm", "layer_norm"] - if norm == "batch_norm": - self.use_layer_norm = False - self.norm = nn.BatchNorm1d(channels) - else: - self.use_layer_norm = True - self.norm = nn.LayerNorm(channels) - - self.pointwise_conv2 = nn.Conv1d( - channels, - channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - self.activation = activation - if init_weights: - self.init_weights() - - def init_weights(self): - pw_max = self.channels**-0.5 - dw_max = self.kernel_size**-0.5 - torch.nn.init.uniform_(self.pointwise_conv1.weight.data, -pw_max, pw_max) - if self.bias: - torch.nn.init.uniform_(self.pointwise_conv1.bias.data, -pw_max, pw_max) - torch.nn.init.uniform_(self.depthwise_conv.weight.data, -dw_max, dw_max) - if self.bias: - torch.nn.init.uniform_(self.depthwise_conv.bias.data, -dw_max, dw_max) - torch.nn.init.uniform_(self.pointwise_conv2.weight.data, -pw_max, pw_max) - if self.bias: - torch.nn.init.uniform_(self.pointwise_conv2.bias.data, -pw_max, pw_max) - - def forward( - self, - x: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - cache: torch.Tensor = torch.zeros((0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute convolution module. - Args: - x (torch.Tensor): Input tensor (#batch, time, channels). - mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), - (0, 0, 0) means fake mask. - cache (torch.Tensor): left context cache, it is only - used in causal convolution (#batch, channels, cache_t), - (0, 0, 0) meas fake cache. - Returns: - torch.Tensor: Output tensor (#batch, time, channels). - """ - if self.adaptive_scale: - x = self.ada_scale * x + self.ada_bias - # exchange the temporal dimension and the feature dimension - x = x.transpose(1, 2) # (#batch, channels, time) - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - if self.lorder > 0: - if cache.size(2) == 0: # cache_t == 0 - x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) - else: - assert cache.size(0) == x.size(0) # equal batch - assert cache.size(1) == x.size(1) # equal channel - x = torch.cat((cache, x), dim=2) - assert x.size(2) > self.lorder - new_cache = x[:, :, -self.lorder :] - else: - # It's better we just return None if no cache is required, - # However, for JIT export, here we just fake one tensor instead of - # None. - new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channel, dim) - x = nn.functional.glu(x, dim=1) # (batch, channel, dim) - - # 1D Depthwise Conv - x = self.depthwise_conv(x) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.activation(self.norm(x)) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.pointwise_conv2(x) - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - return x.transpose(1, 2), new_cache diff --git a/modules/wenet_extractor/squeezeformer/encoder.py b/modules/wenet_extractor/squeezeformer/encoder.py deleted file mode 100644 index 4d4fbe9f615c8d9889a6c2ea821afe392bfab5db..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/encoder.py +++ /dev/null @@ -1,514 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import torch -import torch.nn as nn -from typing import Tuple, Union, Optional, List -from modules.wenet_extractor.squeezeformer.subsampling import ( - DepthwiseConv2dSubsampling4, - TimeReductionLayer1D, - TimeReductionLayer2D, - TimeReductionLayerStream, -) -from modules.wenet_extractor.squeezeformer.encoder_layer import ( - SqueezeformerEncoderLayer, -) -from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding -from modules.wenet_extractor.transformer.attention import MultiHeadedAttention -from modules.wenet_extractor.squeezeformer.attention import ( - RelPositionMultiHeadedAttention, -) -from modules.wenet_extractor.squeezeformer.positionwise_feed_forward import ( - PositionwiseFeedForward, -) -from modules.wenet_extractor.squeezeformer.convolution import ConvolutionModule -from modules.wenet_extractor.utils.mask import make_pad_mask, add_optional_chunk_mask -from modules.wenet_extractor.utils.common import get_activation - - -class SqueezeformerEncoder(nn.Module): - def __init__( - self, - input_size: int = 80, - encoder_dim: int = 256, - output_size: int = 256, - attention_heads: int = 4, - num_blocks: int = 12, - reduce_idx: Optional[Union[int, List[int]]] = 5, - recover_idx: Optional[Union[int, List[int]]] = 11, - feed_forward_expansion_factor: int = 4, - dw_stride: bool = False, - input_dropout_rate: float = 0.1, - pos_enc_layer_type: str = "rel_pos", - time_reduction_layer_type: str = "conv1d", - do_rel_shift: bool = True, - feed_forward_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.1, - cnn_module_kernel: int = 31, - cnn_norm_type: str = "batch_norm", - dropout: float = 0.1, - causal: bool = False, - adaptive_scale: bool = True, - activation_type: str = "swish", - init_weights: bool = True, - global_cmvn: torch.nn.Module = None, - normalize_before: bool = False, - use_dynamic_chunk: bool = False, - concat_after: bool = False, - static_chunk_size: int = 0, - use_dynamic_left_chunk: bool = False, - ): - """Construct SqueezeformerEncoder - - Args: - input_size to use_dynamic_chunk, see in Transformer BaseEncoder. - encoder_dim (int): The hidden dimension of encoder layer. - output_size (int): The output dimension of final projection layer. - attention_heads (int): Num of attention head in attention module. - num_blocks (int): Num of encoder layers. - reduce_idx Optional[Union[int, List[int]]]: - reduce layer index, from 40ms to 80ms per frame. - recover_idx Optional[Union[int, List[int]]]: - recover layer index, from 80ms to 40ms per frame. - feed_forward_expansion_factor (int): Enlarge coefficient of FFN. - dw_stride (bool): Whether do depthwise convolution - on subsampling module. - input_dropout_rate (float): Dropout rate of input projection layer. - pos_enc_layer_type (str): Self attention type. - time_reduction_layer_type (str): Conv1d or Conv2d reduction layer. - do_rel_shift (bool): Whether to do relative shift - operation on rel-attention module. - cnn_module_kernel (int): Kernel size of CNN module. - activation_type (str): Encoder activation function type. - use_cnn_module (bool): Whether to use convolution module. - cnn_module_kernel (int): Kernel size of convolution module. - adaptive_scale (bool): Whether to use adaptive scale. - init_weights (bool): Whether to initialize weights. - causal (bool): whether to use causal convolution or not. - """ - super(SqueezeformerEncoder, self).__init__() - self.global_cmvn = global_cmvn - self.reduce_idx: Optional[Union[int, List[int]]] = ( - [reduce_idx] if type(reduce_idx) == int else reduce_idx - ) - self.recover_idx: Optional[Union[int, List[int]]] = ( - [recover_idx] if type(recover_idx) == int else recover_idx - ) - self.check_ascending_list() - if reduce_idx is None: - self.time_reduce = None - else: - if recover_idx is None: - self.time_reduce = "normal" # no recovery at the end - else: - self.time_reduce = "recover" # recovery at the end - assert len(self.reduce_idx) == len(self.recover_idx) - self.reduce_stride = 2 - self._output_size = output_size - self.normalize_before = normalize_before - self.static_chunk_size = static_chunk_size - self.use_dynamic_chunk = use_dynamic_chunk - self.use_dynamic_left_chunk = use_dynamic_left_chunk - self.pos_enc_layer_type = pos_enc_layer_type - activation = get_activation(activation_type) - - # self-attention module definition - if pos_enc_layer_type != "rel_pos": - encoder_selfattn_layer = MultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - ) - else: - encoder_selfattn_layer = RelPositionMultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - encoder_dim, - attention_dropout_rate, - do_rel_shift, - adaptive_scale, - init_weights, - ) - - # feed-forward module definition - positionwise_layer = PositionwiseFeedForward - positionwise_layer_args = ( - encoder_dim, - encoder_dim * feed_forward_expansion_factor, - feed_forward_dropout_rate, - activation, - adaptive_scale, - init_weights, - ) - - # convolution module definition - convolution_layer = ConvolutionModule - convolution_layer_args = ( - encoder_dim, - cnn_module_kernel, - activation, - cnn_norm_type, - causal, - True, - adaptive_scale, - init_weights, - ) - - self.embed = DepthwiseConv2dSubsampling4( - 1, - encoder_dim, - RelPositionalEncoding(encoder_dim, dropout_rate=0.1), - dw_stride, - input_size, - input_dropout_rate, - init_weights, - ) - - self.preln = nn.LayerNorm(encoder_dim) - self.encoders = torch.nn.ModuleList( - [ - SqueezeformerEncoderLayer( - encoder_dim, - encoder_selfattn_layer(*encoder_selfattn_layer_args), - positionwise_layer(*positionwise_layer_args), - convolution_layer(*convolution_layer_args), - positionwise_layer(*positionwise_layer_args), - normalize_before, - dropout, - concat_after, - ) - for _ in range(num_blocks) - ] - ) - if time_reduction_layer_type == "conv1d": - time_reduction_layer = TimeReductionLayer1D - time_reduction_layer_args = { - "channel": encoder_dim, - "out_dim": encoder_dim, - } - elif time_reduction_layer_type == "stream": - time_reduction_layer = TimeReductionLayerStream - time_reduction_layer_args = { - "channel": encoder_dim, - "out_dim": encoder_dim, - } - else: - time_reduction_layer = TimeReductionLayer2D - time_reduction_layer_args = {"encoder_dim": encoder_dim} - - self.time_reduction_layer = time_reduction_layer(**time_reduction_layer_args) - self.time_recover_layer = nn.Linear(encoder_dim, encoder_dim) - self.final_proj = None - if output_size != encoder_dim: - self.final_proj = nn.Linear(encoder_dim, output_size) - - def output_size(self) -> int: - return self._output_size - - def forward( - self, - xs: torch.Tensor, - xs_lens: torch.Tensor, - decoding_chunk_size: int = 0, - num_decoding_left_chunks: int = -1, - ) -> Tuple[torch.Tensor, torch.Tensor]: - T = xs.size(1) - masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) - if self.global_cmvn is not None: - xs = self.global_cmvn(xs) - xs, pos_emb, masks = self.embed(xs, masks) - mask_pad = masks # (B, 1, T/subsample_rate) - chunk_masks = add_optional_chunk_mask( - xs, - masks, - self.use_dynamic_chunk, - self.use_dynamic_left_chunk, - decoding_chunk_size, - self.static_chunk_size, - num_decoding_left_chunks, - ) - xs_lens = mask_pad.squeeze(1).sum(1) - xs = self.preln(xs) - recover_activations: List[ - Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] - ] = [] - index = 0 - for i, layer in enumerate(self.encoders): - if self.reduce_idx is not None: - if self.time_reduce is not None and i in self.reduce_idx: - recover_activations.append((xs, chunk_masks, pos_emb, mask_pad)) - xs, xs_lens, chunk_masks, mask_pad = self.time_reduction_layer( - xs, xs_lens, chunk_masks, mask_pad - ) - pos_emb = pos_emb[:, ::2, :] - index += 1 - - if self.recover_idx is not None: - if self.time_reduce == "recover" and i in self.recover_idx: - index -= 1 - ( - recover_tensor, - recover_chunk_masks, - recover_pos_emb, - recover_mask_pad, - ) = recover_activations[index] - # recover output length for ctc decode - xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2) - xs = self.time_recover_layer(xs) - recoverd_t = recover_tensor.size(1) - xs = recover_tensor + xs[:, :recoverd_t, :].contiguous() - chunk_masks = recover_chunk_masks - pos_emb = recover_pos_emb - mask_pad = recover_mask_pad - xs = xs.masked_fill(~mask_pad[:, 0, :].unsqueeze(-1), 0.0) - - xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) - - if self.final_proj is not None: - xs = self.final_proj(xs) - return xs, masks - - def check_ascending_list(self): - if self.reduce_idx is not None: - assert self.reduce_idx == sorted( - self.reduce_idx - ), "reduce_idx should be int or ascending list" - if self.recover_idx is not None: - assert self.recover_idx == sorted( - self.recover_idx - ), "recover_idx should be int or ascending list" - - def calculate_downsampling_factor(self, i: int) -> int: - if self.reduce_idx is None: - return 1 - else: - reduce_exp, recover_exp = 0, 0 - for exp, rd_idx in enumerate(self.reduce_idx): - if i >= rd_idx: - reduce_exp = exp + 1 - if self.recover_idx is not None: - for exp, rc_idx in enumerate(self.recover_idx): - if i >= rc_idx: - recover_exp = exp + 1 - return int(2 ** (reduce_exp - recover_exp)) - - def forward_chunk( - self, - xs: torch.Tensor, - offset: int, - required_cache_size: int, - att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ Forward just one chunk - - Args: - xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - offset (int): current offset in encoder output time stamp - required_cache_size (int): cache size required for next chunk - compuation - >=0: actual cache size - <0: means all history cache is required - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (elayers, b=1, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - - Returns: - torch.Tensor: output of current input xs, - with shape (b=1, chunk_size, hidden-dim). - torch.Tensor: new attention cache required for next chunk, with - dynamic shape (elayers, head, ?, d_k * 2) - depending on required_cache_size. - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - - """ - assert xs.size(0) == 1 - # tmp_masks is just for interface compatibility - tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) - tmp_masks = tmp_masks.unsqueeze(1) - if self.global_cmvn is not None: - xs = self.global_cmvn(xs) - # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) - xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) - # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) - elayers, cache_t1 = att_cache.size(0), att_cache.size(2) - chunk_size = xs.size(1) - attention_key_size = cache_t1 + chunk_size - pos_emb = self.embed.position_encoding( - offset=offset - cache_t1, size=attention_key_size - ) - if required_cache_size < 0: - next_cache_start = 0 - elif required_cache_size == 0: - next_cache_start = attention_key_size - else: - next_cache_start = max(attention_key_size - required_cache_size, 0) - - r_att_cache = [] - r_cnn_cache = [] - - mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) - mask_pad = mask_pad.unsqueeze(1) - max_att_len: int = 0 - recover_activations: List[ - Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor] - ] = [] - index = 0 - xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int) - xs = self.preln(xs) - for i, layer in enumerate(self.encoders): - # NOTE(xcsong): Before layer.forward - # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), - # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) - if self.reduce_idx is not None: - if self.time_reduce is not None and i in self.reduce_idx: - recover_activations.append((xs, att_mask, pos_emb, mask_pad)) - xs, xs_lens, att_mask, mask_pad = self.time_reduction_layer( - xs, xs_lens, att_mask, mask_pad - ) - pos_emb = pos_emb[:, ::2, :] - index += 1 - - if self.recover_idx is not None: - if self.time_reduce == "recover" and i in self.recover_idx: - index -= 1 - ( - recover_tensor, - recover_att_mask, - recover_pos_emb, - recover_mask_pad, - ) = recover_activations[index] - # recover output length for ctc decode - xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2) - xs = self.time_recover_layer(xs) - recoverd_t = recover_tensor.size(1) - xs = recover_tensor + xs[:, :recoverd_t, :].contiguous() - att_mask = recover_att_mask - pos_emb = recover_pos_emb - mask_pad = recover_mask_pad - if att_mask.size(1) != 0: - xs = xs.masked_fill(~att_mask[:, 0, :].unsqueeze(-1), 0.0) - - factor = self.calculate_downsampling_factor(i) - - xs, _, new_att_cache, new_cnn_cache = layer( - xs, - att_mask, - pos_emb, - att_cache=att_cache[i : i + 1][:, :, ::factor, :][ - :, :, : pos_emb.size(1) - xs.size(1), : - ] - if elayers > 0 - else att_cache[:, :, ::factor, :], - cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache, - ) - # NOTE(xcsong): After layer.forward - # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), - # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) - cached_att = new_att_cache[:, :, next_cache_start // factor :, :] - cached_cnn = new_cnn_cache.unsqueeze(0) - cached_att = ( - cached_att.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3) - ) - if i == 0: - # record length for the first block as max length - max_att_len = cached_att.size(2) - r_att_cache.append(cached_att[:, :, :max_att_len, :]) - r_cnn_cache.append(cached_cnn) - # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), - # ? may be larger than cache_t1, it depends on required_cache_size - r_att_cache = torch.cat(r_att_cache, dim=0) - # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) - r_cnn_cache = torch.cat(r_cnn_cache, dim=0) - - if self.final_proj is not None: - xs = self.final_proj(xs) - return (xs, r_att_cache, r_cnn_cache) - - def forward_chunk_by_chunk( - self, - xs: torch.Tensor, - decoding_chunk_size: int, - num_decoding_left_chunks: int = -1, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward input chunk by chunk with chunk_size like a streaming - fashion - - Here we should pay special attention to computation cache in the - streaming style forward chunk by chunk. Three things should be taken - into account for computation in the current network: - 1. transformer/conformer encoder layers output cache - 2. convolution in conformer - 3. convolution in subsampling - - However, we don't implement subsampling cache for: - 1. We can control subsampling module to output the right result by - overlapping input instead of cache left context, even though it - wastes some computation, but subsampling only takes a very - small fraction of computation in the whole model. - 2. Typically, there are several covolution layers with subsampling - in subsampling module, it is tricky and complicated to do cache - with different convolution layers with different subsampling - rate. - 3. Currently, nn.Sequential is used to stack all the convolution - layers in subsampling, we need to rewrite it to make it work - with cache, which is not prefered. - Args: - xs (torch.Tensor): (1, max_len, dim) - chunk_size (int): decoding chunk size - """ - assert decoding_chunk_size > 0 - # The model is trained by static or dynamic chunk - assert self.static_chunk_size > 0 or self.use_dynamic_chunk - subsampling = self.embed.subsampling_rate - context = self.embed.right_context + 1 # Add current frame - stride = subsampling * decoding_chunk_size - decoding_window = (decoding_chunk_size - 1) * subsampling + context - num_frames = xs.size(1) - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) - outputs = [] - offset = 0 - required_cache_size = decoding_chunk_size * num_decoding_left_chunks - - # Feed forward overlap input step by step - for cur in range(0, num_frames - context + 1, stride): - end = min(cur + decoding_window, num_frames) - chunk_xs = xs[:, cur:end, :] - (y, att_cache, cnn_cache) = self.forward_chunk( - chunk_xs, offset, required_cache_size, att_cache, cnn_cache - ) - outputs.append(y) - offset += y.size(1) - ys = torch.cat(outputs, 1) - masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool) - return ys, masks diff --git a/modules/wenet_extractor/squeezeformer/encoder_layer.py b/modules/wenet_extractor/squeezeformer/encoder_layer.py deleted file mode 100644 index 74e7c0fdeee6f7156468b14416d268702cf4d40a..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/encoder_layer.py +++ /dev/null @@ -1,129 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""SqueezeformerEncoderLayer definition.""" - -import torch -import torch.nn as nn -from typing import Optional, Tuple - - -class SqueezeformerEncoderLayer(nn.Module): - """Encoder layer module. - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` - instance can be used as the argument. - feed_forward1 (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward` instance can be used as the argument. - conv_module (torch.nn.Module): Convolution module instance. - `ConvlutionModule` instance can be used as the argument. - feed_forward2 (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward` instance can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): - True: use layer_norm before each sub-block. - False: use layer_norm after each sub-block. - """ - - def __init__( - self, - size: int, - self_attn: torch.nn.Module, - feed_forward1: Optional[nn.Module] = None, - conv_module: Optional[nn.Module] = None, - feed_forward2: Optional[nn.Module] = None, - normalize_before: bool = False, - dropout_rate: float = 0.1, - concat_after: bool = False, - ): - super(SqueezeformerEncoderLayer, self).__init__() - self.size = size - self.self_attn = self_attn - self.layer_norm1 = nn.LayerNorm(size) - self.ffn1 = feed_forward1 - self.layer_norm2 = nn.LayerNorm(size) - self.conv_module = conv_module - self.layer_norm3 = nn.LayerNorm(size) - self.ffn2 = feed_forward2 - self.layer_norm4 = nn.LayerNorm(size) - self.normalize_before = normalize_before - self.dropout = nn.Dropout(dropout_rate) - self.concat_after = concat_after - if concat_after: - self.concat_linear = nn.Linear(size + size, size) - else: - self.concat_linear = nn.Identity() - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - # self attention module - residual = x - if self.normalize_before: - x = self.layer_norm1(x) - x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) - if self.concat_after: - x_concat = torch.cat((x, x_att), dim=-1) - x = residual + self.concat_linear(x_concat) - else: - x = residual + self.dropout(x_att) - if not self.normalize_before: - x = self.layer_norm1(x) - - # ffn module - residual = x - if self.normalize_before: - x = self.layer_norm2(x) - x = self.ffn1(x) - x = residual + self.dropout(x) - if not self.normalize_before: - x = self.layer_norm2(x) - - # conv module - new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - residual = x - if self.normalize_before: - x = self.layer_norm3(x) - x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) - x = residual + self.dropout(x) - if not self.normalize_before: - x = self.layer_norm3(x) - - # ffn module - residual = x - if self.normalize_before: - x = self.layer_norm4(x) - x = self.ffn2(x) - # we do not use dropout here since it is inside feed forward function - x = residual + self.dropout(x) - if not self.normalize_before: - x = self.layer_norm4(x) - - return x, mask, new_att_cache, new_cnn_cache diff --git a/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py b/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py deleted file mode 100644 index bfd4aabf9a7e246243d9792616bbe995c2a8a6ba..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py +++ /dev/null @@ -1,88 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Positionwise feed forward layer definition.""" - -import torch - - -class PositionwiseFeedForward(torch.nn.Module): - """Positionwise feed forward layer. - - FeedForward are appied on each position of the sequence. - The output dim is same with the input dim. - - Args: - idim (int): Input dimenstion. - hidden_units (int): The number of hidden units. - dropout_rate (float): Dropout rate. - activation (torch.nn.Module): Activation function - """ - - def __init__( - self, - idim: int, - hidden_units: int, - dropout_rate: float, - activation: torch.nn.Module = torch.nn.ReLU(), - adaptive_scale: bool = False, - init_weights: bool = False, - ): - """Construct a PositionwiseFeedForward object.""" - super(PositionwiseFeedForward, self).__init__() - self.idim = idim - self.hidden_units = hidden_units - self.w_1 = torch.nn.Linear(idim, hidden_units) - self.activation = activation - self.dropout = torch.nn.Dropout(dropout_rate) - self.w_2 = torch.nn.Linear(hidden_units, idim) - self.ada_scale = None - self.ada_bias = None - self.adaptive_scale = adaptive_scale - self.ada_scale = torch.nn.Parameter( - torch.ones([1, 1, idim]), requires_grad=adaptive_scale - ) - self.ada_bias = torch.nn.Parameter( - torch.zeros([1, 1, idim]), requires_grad=adaptive_scale - ) - if init_weights: - self.init_weights() - - def init_weights(self): - ffn1_max = self.idim**-0.5 - ffn2_max = self.hidden_units**-0.5 - torch.nn.init.uniform_(self.w_1.weight.data, -ffn1_max, ffn1_max) - torch.nn.init.uniform_(self.w_1.bias.data, -ffn1_max, ffn1_max) - torch.nn.init.uniform_(self.w_2.weight.data, -ffn2_max, ffn2_max) - torch.nn.init.uniform_(self.w_2.bias.data, -ffn2_max, ffn2_max) - - def forward(self, xs: torch.Tensor) -> torch.Tensor: - """Forward function. - - Args: - xs: input tensor (B, L, D) - Returns: - output tensor, (B, L, D) - """ - if self.adaptive_scale: - xs = self.ada_scale * xs + self.ada_bias - return self.w_2(self.dropout(self.activation(self.w_1(xs)))) diff --git a/modules/wenet_extractor/squeezeformer/subsampling.py b/modules/wenet_extractor/squeezeformer/subsampling.py deleted file mode 100644 index 329dd6c51f1719d73307d636dd625cf98a34cd34..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/squeezeformer/subsampling.py +++ /dev/null @@ -1,321 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""DepthwiseConv2dSubsampling4 and TimeReductionLayer definition.""" - -import torch -import torch.nn as nn -import torch.nn.functional as F -from modules.wenet_extractor.transformer.subsampling import BaseSubsampling -from typing import Tuple -from modules.wenet_extractor.squeezeformer.conv2d import Conv2dValid - - -class DepthwiseConv2dSubsampling4(BaseSubsampling): - """Depthwise Convolutional 2D subsampling (to 1/4 length). - - Args: - idim (int): Input dimension. - odim (int): Output dimension. - pos_enc_class (nn.Module): position encoding class. - dw_stride (int): Whether do depthwise convolution. - input_size (int): filter bank dimension. - - """ - - def __init__( - self, - idim: int, - odim: int, - pos_enc_class: torch.nn.Module, - dw_stride: bool = False, - input_size: int = 80, - input_dropout_rate: float = 0.1, - init_weights: bool = True, - ): - super(DepthwiseConv2dSubsampling4, self).__init__() - self.idim = idim - self.odim = odim - self.pw_conv = nn.Conv2d( - in_channels=idim, out_channels=odim, kernel_size=3, stride=2 - ) - self.act1 = nn.ReLU() - self.dw_conv = nn.Conv2d( - in_channels=odim, - out_channels=odim, - kernel_size=3, - stride=2, - groups=odim if dw_stride else 1, - ) - self.act2 = nn.ReLU() - self.pos_enc = pos_enc_class - self.input_proj = nn.Sequential( - nn.Linear(odim * (((input_size - 1) // 2 - 1) // 2), odim), - nn.Dropout(p=input_dropout_rate), - ) - if init_weights: - linear_max = (odim * input_size / 4) ** -0.5 - torch.nn.init.uniform_( - self.input_proj.state_dict()["0.weight"], -linear_max, linear_max - ) - torch.nn.init.uniform_( - self.input_proj.state_dict()["0.bias"], -linear_max, linear_max - ) - self.subsampling_rate = 4 - # 6 = (3 - 1) * 1 + (3 - 1) * 2 - self.right_context = 6 - - def forward( - self, x: torch.Tensor, x_mask: torch.Tensor, offset: int = 0 - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - x = x.unsqueeze(1) # (b, c=1, t, f) - x = self.pw_conv(x) - x = self.act1(x) - x = self.dw_conv(x) - x = self.act2(x) - b, c, t, f = x.size() - x = x.permute(0, 2, 1, 3) - x = x.contiguous().view(b, t, c * f) - x, pos_emb = self.pos_enc(x, offset) - x = self.input_proj(x) - return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2] - - -class TimeReductionLayer1D(nn.Module): - """ - Modified NeMo, - Squeezeformer Time Reduction procedure. - Downsamples the audio by `stride` in the time dimension. - Args: - channel (int): input dimension of - MultiheadAttentionMechanism and PositionwiseFeedForward - out_dim (int): Output dimension of the module. - kernel_size (int): Conv kernel size for - depthwise convolution in convolution module - stride (int): Downsampling factor in time dimension. - """ - - def __init__( - self, channel: int, out_dim: int, kernel_size: int = 5, stride: int = 2 - ): - super(TimeReductionLayer1D, self).__init__() - - self.channel = channel - self.out_dim = out_dim - self.kernel_size = kernel_size - self.stride = stride - self.padding = max(0, self.kernel_size - self.stride) - - self.dw_conv = nn.Conv1d( - in_channels=channel, - out_channels=channel, - kernel_size=kernel_size, - stride=stride, - padding=self.padding, - groups=channel, - ) - - self.pw_conv = nn.Conv1d( - in_channels=channel, - out_channels=out_dim, - kernel_size=1, - stride=1, - padding=0, - groups=1, - ) - - self.init_weights() - - def init_weights(self): - dw_max = self.kernel_size**-0.5 - pw_max = self.channel**-0.5 - torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) - torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) - torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) - torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) - - def forward( - self, - xs, - xs_lens: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ): - xs = xs.transpose(1, 2) # [B, C, T] - xs = xs.masked_fill(mask_pad.eq(0), 0.0) - - xs = self.dw_conv(xs) - xs = self.pw_conv(xs) - - xs = xs.transpose(1, 2) # [B, T, C] - - B, T, D = xs.size() - mask = mask[:, :: self.stride, :: self.stride] - mask_pad = mask_pad[:, :, :: self.stride] - L = mask_pad.size(-1) - # For JIT exporting, we remove F.pad operator. - if L - T < 0: - xs = xs[:, : L - T, :].contiguous() - else: - dummy_pad = torch.zeros(B, L - T, D, device=xs.device) - xs = torch.cat([xs, dummy_pad], dim=1) - - xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc") - return xs, xs_lens, mask, mask_pad - - -class TimeReductionLayer2D(nn.Module): - def __init__(self, kernel_size: int = 5, stride: int = 2, encoder_dim: int = 256): - super(TimeReductionLayer2D, self).__init__() - self.encoder_dim = encoder_dim - self.kernel_size = kernel_size - self.dw_conv = Conv2dValid( - in_channels=encoder_dim, - out_channels=encoder_dim, - kernel_size=(kernel_size, 1), - stride=stride, - valid_trigy=True, - ) - self.pw_conv = Conv2dValid( - in_channels=encoder_dim, - out_channels=encoder_dim, - kernel_size=1, - stride=1, - valid_trigx=False, - valid_trigy=False, - ) - - self.kernel_size = kernel_size - self.stride = stride - self.init_weights() - - def init_weights(self): - dw_max = self.kernel_size**-0.5 - pw_max = self.encoder_dim**-0.5 - torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) - torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) - torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) - torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) - - def forward( - self, - xs: torch.Tensor, - xs_lens: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - xs = xs.masked_fill(mask_pad.transpose(1, 2).eq(0), 0.0) - xs = xs.unsqueeze(2) - padding1 = self.kernel_size - self.stride - xs = F.pad(xs, (0, 0, 0, 0, 0, padding1, 0, 0), mode="constant", value=0.0) - xs = self.dw_conv(xs.permute(0, 3, 1, 2)) - xs = self.pw_conv(xs).permute(0, 3, 2, 1).squeeze(1).contiguous() - tmp_length = xs.size(1) - xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc") - padding2 = max(0, (xs_lens.max() - tmp_length).data.item()) - batch_size, hidden = xs.size(0), xs.size(-1) - dummy_pad = torch.zeros(batch_size, padding2, hidden, device=xs.device) - xs = torch.cat([xs, dummy_pad], dim=1) - mask = mask[:, ::2, ::2] - mask_pad = mask_pad[:, :, ::2] - return xs, xs_lens, mask, mask_pad - - -class TimeReductionLayerStream(nn.Module): - """ - Squeezeformer Time Reduction procedure. - Downsamples the audio by `stride` in the time dimension. - Args: - channel (int): input dimension of - MultiheadAttentionMechanism and PositionwiseFeedForward - out_dim (int): Output dimension of the module. - kernel_size (int): Conv kernel size for - depthwise convolution in convolution module - stride (int): Downsampling factor in time dimension. - """ - - def __init__( - self, channel: int, out_dim: int, kernel_size: int = 1, stride: int = 2 - ): - super(TimeReductionLayerStream, self).__init__() - - self.channel = channel - self.out_dim = out_dim - self.kernel_size = kernel_size - self.stride = stride - - self.dw_conv = nn.Conv1d( - in_channels=channel, - out_channels=channel, - kernel_size=kernel_size, - stride=stride, - padding=0, - groups=channel, - ) - - self.pw_conv = nn.Conv1d( - in_channels=channel, - out_channels=out_dim, - kernel_size=1, - stride=1, - padding=0, - groups=1, - ) - - self.init_weights() - - def init_weights(self): - dw_max = self.kernel_size**-0.5 - pw_max = self.channel**-0.5 - torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max) - torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max) - torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max) - torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max) - - def forward( - self, - xs, - xs_lens: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ): - xs = xs.transpose(1, 2) # [B, C, T] - xs = xs.masked_fill(mask_pad.eq(0), 0.0) - - xs = self.dw_conv(xs) - xs = self.pw_conv(xs) - - xs = xs.transpose(1, 2) # [B, T, C] - - B, T, D = xs.size() - mask = mask[:, :: self.stride, :: self.stride] - mask_pad = mask_pad[:, :, :: self.stride] - L = mask_pad.size(-1) - # For JIT exporting, we remove F.pad operator. - if L - T < 0: - xs = xs[:, : L - T, :].contiguous() - else: - dummy_pad = torch.zeros(B, L - T, D, device=xs.device) - xs = torch.cat([xs, dummy_pad], dim=1) - - xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc") - return xs, xs_lens, mask, mask_pad diff --git a/modules/wenet_extractor/ssl/bestrq/bestqr_model.py b/modules/wenet_extractor/ssl/bestrq/bestqr_model.py deleted file mode 100644 index 04d065ee457f3c968932171c0b9cb53f21d43254..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/ssl/bestrq/bestqr_model.py +++ /dev/null @@ -1,156 +0,0 @@ -from typing import Optional, Tuple -import torch - -from wenet.ssl.bestrq.mask import compute_mask_indices -from wenet.utils.mask import make_pad_mask - - -class BestRQModel(torch.nn.Module): - def __init__( - self, - encoder: torch.nn.Module, - input_dim: int = 256, - embedding_dim: int = 256, - num_embeddings: int = 8192, - num_codebooks: int = 1, - dropout_rate: float = 0.1, - mask_prob: float = 0.01, - mask_length: int = 10, - min_masks: int = 2, - layer_norm_epsilon=1e-5, - ) -> None: - super().__init__() - - assert mask_prob > 0.0 - - self.mask_prob = mask_prob - # NOTE: should filter audio less than mask_length - self.mask_length = mask_length - self.min_masks = min_masks - - self.input_dropout = torch.nn.Dropout(dropout_rate) - - # [embedding_dim, num_embeddings] - random_embedding_weight = torch.empty( - num_codebooks, embedding_dim, num_embeddings, requires_grad=False - ) - self.embeddings = torch.nn.init.normal_(random_embedding_weight) - - random_projection_weight = torch.empty( - input_dim, embedding_dim, requires_grad=False - ) - self.projection = torch.nn.init.xavier_normal_(random_projection_weight) - - mask_emb_weight = torch.Tensor(input_dim) - mask_emb_weight.requires_grad = True - self.mask_emb = torch.nn.init.normal_(mask_emb_weight, mean=0, std=0.1) - - self.input_layer_norm = torch.nn.LayerNorm(input_dim, layer_norm_epsilon) - self.encoder = encoder - self.encoder_top_n_out = torch.nn.parameter.Parameter( - torch.Tensor(num_codebooks, self.encoder.output_size(), num_embeddings) - ) - - def forward( - self, - xs: torch.Tensor, - xs_lens: torch.Tensor, - text: Optional[torch.Tensor] = None, - text_length: Optional[torch.Tensor] = None, - ): - # should support nonstreamming and streamming - # TODO(Mddct): streamming future - # eg: full attenton and chunk or dynamic chunk training - # 1 forward subsampling - xs, pos_emb, masks = self._forward_subsampling(xs, xs_lens) - unmasked_xs = xs - # 2 mask features - # 2.0 apply mask - masked_xs, masked_masks = self._apply_mask(xs) - # 2.1 get nearest embedding - target_ids = self._nearest_embedding_idx(unmasked_xs) - # 3 forward xxx-formaer block - out, out_mask = self._forward_encoder_blocks(masked_xs, masks, pos_emb, masks) - # 4 get logits - out = out.unsqueeze(1) # [B, 1, T', dim] - top_n_out = self.encoder_top_n_out.unsqueeze( - 0 - ) # [num_codebooks, dim, num_embeddings] - out = torch.matmul(out, top_n_out) # [B, num_codebooks, T', num_embeddings] - - # 5 compute loss - loss = self._compute_loss(out, target_ids, out_mask.squeeze(1) * masked_masks) - return {"loss": loss} - - def _compute_loss( - self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor - ): - input = input.transpose(1, 3) # [B, num_embeddings, T' num_codebooks] - entropy = torch.nn.functional.cross_entropy( - input, target, reduction="none" - ) # [B, T', num_codebooks] - # stop gradient for non mask area - loss = entropy * mask.unsqueeze(2) - return loss.sum() / (mask.sum() * loss.size(2)) - - def _forward_encoder_blocks( - self, - xs: torch.Tensor, - xs_masks: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor, - ): - masks = xs_masks - for layer in self.encoder.encoders: - xs, masks, _, _ = layer(xs, xs_masks, pos_emb, mask_pad) - if self.encoder.normalize_before: - xs = self.encoder.after_norm(xs) - # Here we assume the mask is not changed in encoder layers, so just - # return the masks before encoder layers, and the masks will be used - # for cross attention with decoder later - return xs, masks - - def _nearest_embedding_idx(self, xs: torch.Tensor) -> torch.Tensor: - xs = self.input_layer_norm(xs) - xs = self.input_dropout(xs) - xs = torch.matmul(xs, self.projection.to(xs.device)) - - B, T, C = xs.size() - flattened_input = xs.view(-1, C) - embeddings = self.embeddings.to( - xs.device - ) # [num_codebooks, embedding_dim, num_embeddings] - # [num_codebooks, B*T, num_embeddings] - distance = ( - torch.sum(flattened_input**2, dim=1, keepdim=True).unsqueeze(0) - + torch.sum(embeddings**2, dim=1, keepdim=True) - - 2 * torch.matmul(flattened_input.unsqueeze(0), embeddings) - ) - - out = torch.argmin(distance, dim=-1) # [num_codebooks, B*T] - out = out.transpose(0, 1) # [B*T, num_codebooks] - return out.reshape(B, T, -1) # [B, T, num_codebooks] - - def _apply_mask(self, xs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - masks = compute_mask_indices( - xs.size()[:-1], - self.mask_prob, - self.mask_length, - self.min_masks, - device=xs.device, - ) - masks_expand = masks.unsqueeze(-1) # [B, T, 1] - - mask_emb = self.mask_emb.to(xs.device).view(1, 1, -1) - xs = torch.where(masks_expand, mask_emb, xs) - return xs, masks - - def _forward_subsampling( - self, xs: torch.Tensor, xs_lens: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - T = xs.size(1) - masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) - if self.encoder.global_cmvn is not None: - xs = self.encoder.global_cmvn(xs) - xs, pos_emb, masks = self.encoder.embed(xs, masks) - return xs, pos_emb, masks diff --git a/modules/wenet_extractor/ssl/bestrq/mask.py b/modules/wenet_extractor/ssl/bestrq/mask.py deleted file mode 100644 index 20bcdd35efa65a6685cba676cb1aef1ae076caa5..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/ssl/bestrq/mask.py +++ /dev/null @@ -1,55 +0,0 @@ -import torch - - -def _sampler( - pdf: torch.Tensor, num_samples: int, device=torch.device("cpu") -) -> torch.Tensor: - size = pdf.size() - z = -torch.log(torch.rand(size, device=device)) - _, indices = torch.topk(pdf + z, num_samples) - return indices - - -def compute_mask_indices( - size: torch.Size, - mask_prob: float, - mask_length: int, - min_masks: int = 0, - device=torch.device("cpu"), -) -> torch.Tensor: - assert len(size) == 2 - batch_size, seq_length = size - - # compute number of masked span in batch - num_masked_spans = ( - mask_prob * float(seq_length) / float(mask_length) + torch.rand(1)[0] - ) - num_masked_spans = int(num_masked_spans) - num_masked_spans = max(num_masked_spans, min_masks) - - # num_masked <= seq_length - if num_masked_spans * mask_length > seq_length: - num_masked_spans = seq_length // mask_length - - pdf = torch.ones(batch_size, seq_length - (mask_length - 1), device=device) - mask_idxs = _sampler(pdf, num_masked_spans, device=device) - - mask_idxs = ( - mask_idxs.unsqueeze(-1) - .repeat(1, 1, mask_length) - .view(batch_size, num_masked_spans * mask_length) - ) # [B,num_masked_spans*mask_length] - - offset = ( - torch.arange(mask_length, device=device) - .view(1, 1, -1) - .repeat(1, num_masked_spans, 1) - ) # [1,num_masked_spans,mask_length] - offset = offset.view(1, num_masked_spans * mask_length) - - mask_idxs = mask_idxs + offset # [B,num_masked_spans, mask_length] - - ones = torch.ones(batch_size, seq_length, dtype=torch.bool, device=mask_idxs.device) - # masks to fill - full_mask = torch.zeros_like(ones, dtype=torch.bool, device=mask_idxs.device) - return torch.scatter(full_mask, dim=1, index=mask_idxs, src=ones) diff --git a/modules/wenet_extractor/transducer/__init__.py b/modules/wenet_extractor/transducer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/transducer/joint.py b/modules/wenet_extractor/transducer/joint.py deleted file mode 100644 index 8592136c9f33902942fb01ee42af82761b7ad26b..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transducer/joint.py +++ /dev/null @@ -1,72 +0,0 @@ -from typing import Optional - -import torch -from torch import nn -from modules.wenet_extractor.utils.common import get_activation - - -class TransducerJoint(torch.nn.Module): - def __init__( - self, - voca_size: int, - enc_output_size: int, - pred_output_size: int, - join_dim: int, - prejoin_linear: bool = True, - postjoin_linear: bool = False, - joint_mode: str = "add", - activation: str = "tanh", - ): - # TODO(Mddct): concat in future - assert joint_mode in ["add"] - super().__init__() - - self.activatoin = get_activation(activation) - self.prejoin_linear = prejoin_linear - self.postjoin_linear = postjoin_linear - self.joint_mode = joint_mode - - if not self.prejoin_linear and not self.postjoin_linear: - assert enc_output_size == pred_output_size == join_dim - # torchscript compatibility - self.enc_ffn: Optional[nn.Linear] = None - self.pred_ffn: Optional[nn.Linear] = None - if self.prejoin_linear: - self.enc_ffn = nn.Linear(enc_output_size, join_dim) - self.pred_ffn = nn.Linear(pred_output_size, join_dim) - # torchscript compatibility - self.post_ffn: Optional[nn.Linear] = None - if self.postjoin_linear: - self.post_ffn = nn.Linear(join_dim, join_dim) - - self.ffn_out = nn.Linear(join_dim, voca_size) - - def forward(self, enc_out: torch.Tensor, pred_out: torch.Tensor): - """ - Args: - enc_out (torch.Tensor): [B, T, E] - pred_out (torch.Tensor): [B, T, P] - Return: - [B,T,U,V] - """ - if ( - self.prejoin_linear - and self.enc_ffn is not None - and self.pred_ffn is not None - ): - enc_out = self.enc_ffn(enc_out) # [B,T,E] -> [B,T,V] - pred_out = self.pred_ffn(pred_out) - - enc_out = enc_out.unsqueeze(2) # [B,T,V] -> [B,T,1,V] - pred_out = pred_out.unsqueeze(1) # [B,U,V] -> [B,1 U, V] - - # TODO(Mddct): concat joint - _ = self.joint_mode - out = enc_out + pred_out # [B,T,U,V] - - if self.postjoin_linear and self.post_ffn is not None: - out = self.post_ffn(out) - - out = self.activatoin(out) - out = self.ffn_out(out) - return out diff --git a/modules/wenet_extractor/transducer/predictor.py b/modules/wenet_extractor/transducer/predictor.py deleted file mode 100644 index 9c1ffdc7b30c23147ba80ae928eb3bd18f602932..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transducer/predictor.py +++ /dev/null @@ -1,477 +0,0 @@ -from typing import List, Optional, Tuple - -import torch -from torch import nn -from modules.wenet_extractor.utils.common import get_activation, get_rnn - - -def ApplyPadding(input, padding, pad_value) -> torch.Tensor: - """ - Args: - input: [bs, max_time_step, dim] - padding: [bs, max_time_step] - """ - return padding * pad_value + input * (1 - padding) - - -class PredictorBase(torch.nn.Module): - # NOTE(Mddct): We can use ABC abstract here, but - # keep this class simple enough for now - def __init__(self) -> None: - super().__init__() - - def init_state( - self, batch_size: int, device: torch.device, method: str = "zero" - ) -> List[torch.Tensor]: - _, _, _ = batch_size, method, device - raise NotImplementedError("this is a base precictor") - - def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: - _ = cache - raise NotImplementedError("this is a base precictor") - - def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: - _ = cache - raise NotImplementedError("this is a base precictor") - - def forward( - self, - input: torch.Tensor, - cache: Optional[List[torch.Tensor]] = None, - ): - ( - _, - _, - ) = ( - input, - cache, - ) - raise NotImplementedError("this is a base precictor") - - def forward_step( - self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor] - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - ( - _, - _, - _, - ) = ( - input, - padding, - cache, - ) - raise NotImplementedError("this is a base precictor") - - -class RNNPredictor(PredictorBase): - def __init__( - self, - voca_size: int, - embed_size: int, - output_size: int, - embed_dropout: float, - hidden_size: int, - num_layers: int, - bias: bool = True, - rnn_type: str = "lstm", - dropout: float = 0.1, - ) -> None: - super().__init__() - self.n_layers = num_layers - self.hidden_size = hidden_size - # disable rnn base out projection - self.embed = nn.Embedding(voca_size, embed_size) - self.dropout = nn.Dropout(embed_dropout) - # NOTE(Mddct): rnn base from torch not support layer norm - # will add layer norm and prune value in cell and layer - # ref: https://github.com/Mddct/neural-lm/blob/main/models/gru_cell.py - self.rnn = get_rnn(rnn_type=rnn_type)( - input_size=embed_size, - hidden_size=hidden_size, - num_layers=num_layers, - bias=bias, - batch_first=True, - dropout=dropout, - ) - self.projection = nn.Linear(hidden_size, output_size) - - def forward( - self, - input: torch.Tensor, - cache: Optional[List[torch.Tensor]] = None, - ) -> torch.Tensor: - """ - Args: - input (torch.Tensor): [batch, max_time). - padding (torch.Tensor): [batch, max_time] - cache : rnn predictor cache[0] == state_m - cache[1] == state_c - Returns: - output: [batch, max_time, output_size] - """ - - # NOTE(Mddct): we don't use pack input format - embed = self.embed(input) # [batch, max_time, emb_size] - embed = self.dropout(embed) - states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None - if cache is None: - state = self.init_state(batch_size=input.size(0), device=input.device) - states = (state[0], state[1]) - else: - assert len(cache) == 2 - states = (cache[0], cache[1]) - out, (m, c) = self.rnn(embed, states) - out = self.projection(out) - - # NOTE(Mddct): Although we don't use staate in transducer - # training forward, we need make it right for padding value - # so we create forward_step for infering, forward for training - _, _ = m, c - return out - - def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: - """ - Args: - cache: [state_m, state_c] - state_ms: [1*n_layers, bs, ...] - state_cs: [1*n_layers, bs, ...] - Returns: - new_cache: [[state_m_1, state_c_1], [state_m_2, state_c_2]...] - """ - assert len(cache) == 2 - state_ms = cache[0] - state_cs = cache[1] - - assert state_ms.size(1) == state_cs.size(1) - - new_cache: List[List[torch.Tensor]] = [] - for state_m, state_c in zip( - torch.split(state_ms, 1, dim=1), torch.split(state_cs, 1, dim=1) - ): - new_cache.append([state_m, state_c]) - return new_cache - - def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: - """ - Args: - cache : [[state_m_1, state_c_1], [state_m_1, state_c_1]...] - - Returns: - new_caceh: [state_ms, state_cs], - state_ms: [1*n_layers, bs, ...] - state_cs: [1*n_layers, bs, ...] - """ - state_ms = torch.cat([states[0] for states in cache], dim=1) - state_cs = torch.cat([states[1] for states in cache], dim=1) - return [state_ms, state_cs] - - def init_state( - self, - batch_size: int, - device: torch.device, - method: str = "zero", - ) -> List[torch.Tensor]: - assert batch_size > 0 - # TODO(Mddct): xavier init method - _ = method - return [ - torch.zeros(1 * self.n_layers, batch_size, self.hidden_size, device=device), - torch.zeros(1 * self.n_layers, batch_size, self.hidden_size, device=device), - ] - - def forward_step( - self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor] - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """ - Args: - input (torch.Tensor): [batch_size, time_step=1] - padding (torch.Tensor): [batch_size,1], 1 is padding value - cache : rnn predictor cache[0] == state_m - cache[1] == state_c - """ - assert len(cache) == 2 - state_m, state_c = cache[0], cache[1] - embed = self.embed(input) # [batch, 1, emb_size] - embed = self.dropout(embed) - out, (m, c) = self.rnn(embed, (state_m, state_c)) - - out = self.projection(out) - m = ApplyPadding(m, padding.unsqueeze(0), state_m) - c = ApplyPadding(c, padding.unsqueeze(0), state_c) - - return (out, [m, c]) - - -class EmbeddingPredictor(PredictorBase): - """Embedding predictor - - Described in: - https://arxiv.org/pdf/2109.07513.pdf - - embed-> proj -> layer norm -> swish - """ - - def __init__( - self, - voca_size: int, - embed_size: int, - embed_dropout: float, - n_head: int, - history_size: int = 2, - activation: str = "swish", - bias: bool = False, - layer_norm_epsilon: float = 1e-5, - ) -> None: - super().__init__() - # multi head - self.num_heads = n_head - self.embed_size = embed_size - self.context_size = history_size + 1 - self.pos_embed = torch.nn.Linear( - embed_size * self.context_size, self.num_heads, bias=bias - ) - self.embed = nn.Embedding(voca_size, self.embed_size) - self.embed_dropout = nn.Dropout(p=embed_dropout) - self.ffn = nn.Linear(self.embed_size, self.embed_size) - self.norm = nn.LayerNorm(self.embed_size, eps=layer_norm_epsilon) - self.activatoin = get_activation(activation) - - def init_state( - self, batch_size: int, device: torch.device, method: str = "zero" - ) -> List[torch.Tensor]: - assert batch_size > 0 - _ = method - return [ - torch.zeros( - batch_size, self.context_size - 1, self.embed_size, device=device - ), - ] - - def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: - """ - Args: - cache : [history] - history: [bs, ...] - Returns: - new_ache : [[history_1], [history_2], [history_3]...] - """ - assert len(cache) == 1 - cache_0 = cache[0] - history: List[List[torch.Tensor]] = [] - for h in torch.split(cache_0, 1, dim=0): - history.append([h]) - return history - - def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: - """ - Args: - cache : [[history_1], [history_2], [history3]...] - - Returns: - new_caceh: [history], - history: [bs, ...] - """ - history = torch.cat([h[0] for h in cache], dim=0) - return [history] - - def forward(self, input: torch.Tensor, cache: Optional[List[torch.Tensor]] = None): - """forward for training""" - input = self.embed(input) # [bs, seq_len, embed] - input = self.embed_dropout(input) - if cache is None: - zeros = self.init_state(input.size(0), device=input.device)[0] - else: - assert len(cache) == 1 - zeros = cache[0] - - input = torch.cat( - (zeros, input), dim=1 - ) # [bs, context_size-1 + seq_len, embed] - - input = input.unfold(1, self.context_size, 1).permute( - 0, 1, 3, 2 - ) # [bs, seq_len, context_size, embed] - # multi head pos: [n_head, embed, context_size] - multi_head_pos = self.pos_embed.weight.view( - self.num_heads, self.embed_size, self.context_size - ) - - # broadcast dot attenton - input_expand = input.unsqueeze(2) # [bs, seq_len, 1, context_size, embed] - multi_head_pos = multi_head_pos.permute( - 0, 2, 1 - ) # [num_heads, context_size, embed] - - # [bs, seq_len, num_heads, context_size, embed] - weight = input_expand * multi_head_pos - weight = weight.sum(dim=-1, keepdim=False).unsqueeze( - 3 - ) # [bs, seq_len, num_heads, 1, context_size] - output = weight.matmul(input_expand).squeeze( - dim=3 - ) # [bs, seq_len, num_heads, embed] - output = output.sum(dim=2) # [bs, seq_len, embed] - output = output / (self.num_heads * self.context_size) - - output = self.ffn(output) - output = self.norm(output) - output = self.activatoin(output) - return output - - def forward_step( - self, - input: torch.Tensor, - padding: torch.Tensor, - cache: List[torch.Tensor], - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """forward step for inference - Args: - input (torch.Tensor): [batch_size, time_step=1] - padding (torch.Tensor): [batch_size,1], 1 is padding value - cache: for embedding predictor, cache[0] == history - """ - assert input.size(1) == 1 - assert len(cache) == 1 - history = cache[0] - assert history.size(1) == self.context_size - 1 - input = self.embed(input) # [bs, 1, embed] - input = self.embed_dropout(input) - context_input = torch.cat((history, input), dim=1) - input_expand = context_input.unsqueeze(1).unsqueeze( - 2 - ) # [bs, 1, 1, context_size, embed] - - # multi head pos: [n_head, embed, context_size] - multi_head_pos = self.pos_embed.weight.view( - self.num_heads, self.embed_size, self.context_size - ) - - multi_head_pos = multi_head_pos.permute( - 0, 2, 1 - ) # [num_heads, context_size, embed] - # [bs, 1, num_heads, context_size, embed] - weight = input_expand * multi_head_pos - weight = weight.sum(dim=-1, keepdim=False).unsqueeze( - 3 - ) # [bs, 1, num_heads, 1, context_size] - output = weight.matmul(input_expand).squeeze(dim=3) # [bs, 1, num_heads, embed] - output = output.sum(dim=2) # [bs, 1, embed] - output = output / (self.num_heads * self.context_size) - - output = self.ffn(output) - output = self.norm(output) - output = self.activatoin(output) - new_cache = context_input[:, 1:, :] - # TODO(Mddct): we need padding new_cache in future - # new_cache = ApplyPadding(history, padding, new_cache) - return (output, [new_cache]) - - -class ConvPredictor(PredictorBase): - def __init__( - self, - voca_size: int, - embed_size: int, - embed_dropout: float, - history_size: int = 2, - activation: str = "relu", - bias: bool = False, - layer_norm_epsilon: float = 1e-5, - ) -> None: - super().__init__() - - assert history_size >= 0 - self.embed_size = embed_size - self.context_size = history_size + 1 - self.embed = nn.Embedding(voca_size, self.embed_size) - self.embed_dropout = nn.Dropout(p=embed_dropout) - self.conv = nn.Conv1d( - in_channels=embed_size, - out_channels=embed_size, - kernel_size=self.context_size, - padding=0, - groups=embed_size, - bias=bias, - ) - self.norm = nn.LayerNorm(embed_size, eps=layer_norm_epsilon) - self.activatoin = get_activation(activation) - - def init_state( - self, batch_size: int, device: torch.device, method: str = "zero" - ) -> List[torch.Tensor]: - assert batch_size > 0 - assert method == "zero" - return [ - torch.zeros( - batch_size, self.context_size - 1, self.embed_size, device=device - ) - ] - - def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]: - """ - Args: - cache : [[history_1], [history_2], [history3]...] - - Returns: - new_caceh: [history], - history: [bs, ...] - """ - history = torch.cat([h[0] for h in cache], dim=0) - return [history] - - def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]: - """ - Args: - cache : [history] - history: [bs, ...] - Returns: - new_ache : [[history_1], [history_2], [history_3]...] - """ - assert len(cache) == 1 - cache_0 = cache[0] - history: List[List[torch.Tensor]] = [] - for h in torch.split(cache_0, 1, dim=0): - history.append([h]) - return history - - def forward(self, input: torch.Tensor, cache: Optional[List[torch.Tensor]] = None): - """forward for training""" - input = self.embed(input) # [bs, seq_len, embed] - input = self.embed_dropout(input) - if cache is None: - zeros = self.init_state(input.size(0), device=input.device)[0] - else: - assert len(cache) == 1 - zeros = cache[0] - - input = torch.cat( - (zeros, input), dim=1 - ) # [bs, context_size-1 + seq_len, embed] - input = input.permute(0, 2, 1) - out = self.conv(input).permute(0, 2, 1) - out = self.activatoin(self.norm(out)) - return out - - def forward_step( - self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor] - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """forward step for inference - Args: - input (torch.Tensor): [batch_size, time_step=1] - padding (torch.Tensor): [batch_size,1], 1 is padding value - cache: for embedding predictor, cache[0] == history - """ - assert input.size(1) == 1 - assert len(cache) == 1 - history = cache[0] - assert history.size(1) == self.context_size - 1 - input = self.embed(input) # [bs, 1, embed] - input = self.embed_dropout(input) - context_input = torch.cat((history, input), dim=1) - input = context_input.permute(0, 2, 1) - out = self.conv(input).permute(0, 2, 1) - out = self.activatoin(self.norm(out)) - - new_cache = context_input[:, 1:, :] - # TODO(Mddct): apply padding in future - return (out, [new_cache]) diff --git a/modules/wenet_extractor/transducer/search/greedy_search.py b/modules/wenet_extractor/transducer/search/greedy_search.py deleted file mode 100644 index df4cfea28902aac0005b0b72e930c790a2d79de4..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transducer/search/greedy_search.py +++ /dev/null @@ -1,52 +0,0 @@ -from typing import List - -import torch - - -def basic_greedy_search( - model: torch.nn.Module, - encoder_out: torch.Tensor, - encoder_out_lens: torch.Tensor, - n_steps: int = 64, -) -> List[List[int]]: - # fake padding - padding = torch.zeros(1, 1).to(encoder_out.device) - # sos - pred_input_step = torch.tensor([model.blank]).reshape(1, 1) - cache = model.predictor.init_state(1, method="zero", device=encoder_out.device) - new_cache: List[torch.Tensor] = [] - t = 0 - hyps = [] - prev_out_nblk = True - pred_out_step = None - per_frame_max_noblk = n_steps - per_frame_noblk = 0 - while t < encoder_out_lens: - encoder_out_step = encoder_out[:, t : t + 1, :] # [1, 1, E] - if prev_out_nblk: - step_outs = model.predictor.forward_step( - pred_input_step, padding, cache - ) # [1, 1, P] - pred_out_step, new_cache = step_outs[0], step_outs[1] - - joint_out_step = model.joint(encoder_out_step, pred_out_step) # [1,1,v] - joint_out_probs = joint_out_step.log_softmax(dim=-1) - - joint_out_max = joint_out_probs.argmax(dim=-1).squeeze() # [] - if joint_out_max != model.blank: - hyps.append(joint_out_max.item()) - prev_out_nblk = True - per_frame_noblk = per_frame_noblk + 1 - pred_input_step = joint_out_max.reshape(1, 1) - # state_m, state_c = clstate_out_m, state_out_c - cache = new_cache - - if joint_out_max == model.blank or per_frame_noblk >= per_frame_max_noblk: - if joint_out_max == model.blank: - prev_out_nblk = False - # TODO(Mddct): make t in chunk for streamming - # or t should't be too lang to predict none blank - t = t + 1 - per_frame_noblk = 0 - - return [hyps] diff --git a/modules/wenet_extractor/transducer/search/prefix_beam_search.py b/modules/wenet_extractor/transducer/search/prefix_beam_search.py deleted file mode 100644 index b4b4f4ea46f24f75f43f7cef27d9a12b57a7452d..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transducer/search/prefix_beam_search.py +++ /dev/null @@ -1,149 +0,0 @@ -from typing import List, Tuple - -import torch -from modules.wenet_extractor.utils.common import log_add - - -class Sequence: - __slots__ = {"hyp", "score", "cache"} - - def __init__( - self, - hyp: List[torch.Tensor], - score, - cache: List[torch.Tensor], - ): - self.hyp = hyp - self.score = score - self.cache = cache - - -class PrefixBeamSearch: - def __init__(self, encoder, predictor, joint, ctc, blank): - self.encoder = encoder - self.predictor = predictor - self.joint = joint - self.ctc = ctc - self.blank = blank - - def forward_decoder_one_step( - self, encoder_x: torch.Tensor, pre_t: torch.Tensor, cache: List[torch.Tensor] - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - padding = torch.zeros(pre_t.size(0), 1, device=encoder_x.device) - pre_t, new_cache = self.predictor.forward_step( - pre_t.unsqueeze(-1), padding, cache - ) - x = self.joint(encoder_x, pre_t) # [beam, 1, 1, vocab] - x = x.log_softmax(dim=-1) - return x, new_cache - - def prefix_beam_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - beam_size: int = 5, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ctc_weight: float = 0.3, - transducer_weight: float = 0.7, - ): - """prefix beam search - also see wenet.transducer.transducer.beam_search - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - device = speech.device - batch_size = speech.shape[0] - assert batch_size == 1 - - # 1. Encoder - encoder_out, _ = self.encoder( - speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks - ) # (B, maxlen, encoder_dim) - maxlen = encoder_out.size(1) - - ctc_probs = self.ctc.log_softmax(encoder_out).squeeze(0) - beam_init: List[Sequence] = [] - - # 2. init beam using Sequence to save beam unit - cache = self.predictor.init_state(1, method="zero", device=device) - beam_init.append(Sequence(hyp=[self.blank], score=0.0, cache=cache)) - # 3. start decoding (notice: we use breathwise first searching) - # !!!! In this decoding method: one frame do not output multi units. !!!! - # !!!! Experiments show that this strategy has little impact !!!! - for i in range(maxlen): - # 3.1 building input - # decoder taking the last token to predict the next token - input_hyp = [s.hyp[-1] for s in beam_init] - input_hyp_tensor = torch.tensor(input_hyp, dtype=torch.int, device=device) - # building statement from beam - cache_batch = self.predictor.cache_to_batch([s.cache for s in beam_init]) - # build score tensor to do torch.add() function - scores = torch.tensor([s.score for s in beam_init]).to(device) - - # 3.2 forward decoder - logp, new_cache = self.forward_decoder_one_step( - encoder_out[:, i, :].unsqueeze(1), - input_hyp_tensor, - cache_batch, - ) # logp: (N, 1, 1, vocab_size) - logp = logp.squeeze(1).squeeze(1) # logp: (N, vocab_size) - new_cache = self.predictor.batch_to_cache(new_cache) - - # 3.3 shallow fusion for transducer score - # and ctc score where we can also add the LM score - logp = torch.log( - torch.add( - transducer_weight * torch.exp(logp), - ctc_weight * torch.exp(ctc_probs[i].unsqueeze(0)), - ) - ) - - # 3.4 first beam prune - top_k_logp, top_k_index = logp.topk(beam_size) # (N, N) - scores = torch.add(scores.unsqueeze(1), top_k_logp) - - # 3.5 generate new beam (N*N) - beam_A = [] - for j in range(len(beam_init)): - # update seq - base_seq = beam_init[j] - for t in range(beam_size): - # blank: only update the score - if top_k_index[j, t] == self.blank: - new_seq = Sequence( - hyp=base_seq.hyp.copy(), - score=scores[j, t].item(), - cache=base_seq.cache, - ) - - beam_A.append(new_seq) - # other unit: update hyp score statement and last - else: - hyp_new = base_seq.hyp.copy() - hyp_new.append(top_k_index[j, t].item()) - new_seq = Sequence( - hyp=hyp_new, score=scores[j, t].item(), cache=new_cache[j] - ) - beam_A.append(new_seq) - - # 3.6 prefix fusion - fusion_A = [beam_A[0]] - for j in range(1, len(beam_A)): - s1 = beam_A[j] - if_do_append = True - for t in range(len(fusion_A)): - # notice: A_ can not fusion with A - if s1.hyp == fusion_A[t].hyp: - fusion_A[t].score = log_add([fusion_A[t].score, s1.score]) - if_do_append = False - break - if if_do_append: - fusion_A.append(s1) - - # 4. second pruned - fusion_A.sort(key=lambda x: x.score, reverse=True) - beam_init = fusion_A[:beam_size] - - return beam_init, encoder_out diff --git a/modules/wenet_extractor/transducer/transducer.py b/modules/wenet_extractor/transducer/transducer.py deleted file mode 100644 index 3618eeb2e355955a606d59e3a91f6253872843e0..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transducer/transducer.py +++ /dev/null @@ -1,486 +0,0 @@ -from typing import Dict, List, Optional, Tuple, Union - -import torch -import torchaudio -from torch import nn -from torch.nn.utils.rnn import pad_sequence - -from modules.wenet_extractor.transducer.predictor import PredictorBase -from modules.wenet_extractor.transducer.search.greedy_search import basic_greedy_search -from modules.wenet_extractor.transducer.search.prefix_beam_search import ( - PrefixBeamSearch, -) -from modules.wenet_extractor.transformer.asr_model import ASRModel -from modules.wenet_extractor.transformer.ctc import CTC -from modules.wenet_extractor.transformer.decoder import ( - BiTransformerDecoder, - TransformerDecoder, -) -from modules.wenet_extractor.transformer.label_smoothing_loss import LabelSmoothingLoss -from modules.wenet_extractor.utils.common import ( - IGNORE_ID, - add_blank, - add_sos_eos, - reverse_pad_list, -) - - -class Transducer(ASRModel): - """Transducer-ctc-attention hybrid Encoder-Predictor-Decoder model""" - - def __init__( - self, - vocab_size: int, - blank: int, - encoder: nn.Module, - predictor: PredictorBase, - joint: nn.Module, - attention_decoder: Optional[ - Union[TransformerDecoder, BiTransformerDecoder] - ] = None, - ctc: Optional[CTC] = None, - ctc_weight: float = 0, - ignore_id: int = IGNORE_ID, - reverse_weight: float = 0.0, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - transducer_weight: float = 1.0, - attention_weight: float = 0.0, - ) -> None: - assert attention_weight + ctc_weight + transducer_weight == 1.0 - super().__init__( - vocab_size, - encoder, - attention_decoder, - ctc, - ctc_weight, - ignore_id, - reverse_weight, - lsm_weight, - length_normalized_loss, - ) - - self.blank = blank - self.transducer_weight = transducer_weight - self.attention_decoder_weight = 1 - self.transducer_weight - self.ctc_weight - - self.predictor = predictor - self.joint = joint - self.bs = None - - # Note(Mddct): decoder also means predictor in transducer, - # but here decoder is attention decoder - del self.criterion_att - if attention_decoder is not None: - self.criterion_att = LabelSmoothingLoss( - size=vocab_size, - padding_idx=ignore_id, - smoothing=lsm_weight, - normalize_length=length_normalized_loss, - ) - - def forward( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - text: torch.Tensor, - text_lengths: torch.Tensor, - ) -> Dict[str, Optional[torch.Tensor]]: - """Frontend + Encoder + predictor + joint + loss - - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - assert text_lengths.dim() == 1, text_lengths.shape - # Check that batch_size is unified - assert ( - speech.shape[0] - == speech_lengths.shape[0] - == text.shape[0] - == text_lengths.shape[0] - ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) - - # Encoder - encoder_out, encoder_mask = self.encoder(speech, speech_lengths) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - # predictor - ys_in_pad = add_blank(text, self.blank, self.ignore_id) - predictor_out = self.predictor(ys_in_pad) - # joint - joint_out = self.joint(encoder_out, predictor_out) - # NOTE(Mddct): some loss implementation require pad valid is zero - # torch.int32 rnnt_loss required - rnnt_text = text.to(torch.int64) - rnnt_text = torch.where(rnnt_text == self.ignore_id, 0, rnnt_text).to( - torch.int32 - ) - rnnt_text_lengths = text_lengths.to(torch.int32) - encoder_out_lens = encoder_out_lens.to(torch.int32) - loss = torchaudio.functional.rnnt_loss( - joint_out, - rnnt_text, - encoder_out_lens, - rnnt_text_lengths, - blank=self.blank, - reduction="mean", - ) - loss_rnnt = loss - - loss = self.transducer_weight * loss - # optional attention decoder - loss_att: Optional[torch.Tensor] = None - if self.attention_decoder_weight != 0.0 and self.decoder is not None: - loss_att, _ = self._calc_att_loss( - encoder_out, encoder_mask, text, text_lengths - ) - - # optional ctc - loss_ctc: Optional[torch.Tensor] = None - if self.ctc_weight != 0.0 and self.ctc is not None: - loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) - else: - loss_ctc = None - - if loss_ctc is not None: - loss = loss + self.ctc_weight * loss_ctc.sum() - if loss_att is not None: - loss = loss + self.attention_decoder_weight * loss_att.sum() - # NOTE: 'loss' must be in dict - return { - "loss": loss, - "loss_att": loss_att, - "loss_ctc": loss_ctc, - "loss_rnnt": loss_rnnt, - } - - def init_bs(self): - if self.bs is None: - self.bs = PrefixBeamSearch( - self.encoder, self.predictor, self.joint, self.ctc, self.blank - ) - - def _cal_transducer_score( - self, - encoder_out: torch.Tensor, - encoder_mask: torch.Tensor, - hyps_lens: torch.Tensor, - hyps_pad: torch.Tensor, - ): - # ignore id -> blank, add blank at head - hyps_pad_blank = add_blank(hyps_pad, self.blank, self.ignore_id) - xs_in_lens = encoder_mask.squeeze(1).sum(1).int() - - # 1. Forward predictor - predictor_out = self.predictor(hyps_pad_blank) - # 2. Forward joint - joint_out = self.joint(encoder_out, predictor_out) - rnnt_text = hyps_pad.to(torch.int64) - rnnt_text = torch.where(rnnt_text == self.ignore_id, 0, rnnt_text).to( - torch.int32 - ) - # 3. Compute transducer loss - loss_td = torchaudio.functional.rnnt_loss( - joint_out, - rnnt_text, - xs_in_lens, - hyps_lens.int(), - blank=self.blank, - reduction="none", - ) - return loss_td * -1 - - def _cal_attn_score( - self, - encoder_out: torch.Tensor, - encoder_mask: torch.Tensor, - hyps_pad: torch.Tensor, - hyps_lens: torch.Tensor, - ): - # (beam_size, max_hyps_len) - ori_hyps_pad = hyps_pad - - # td_score = loss_td * -1 - hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) - hyps_lens = hyps_lens + 1 # Add at begining - # used for right to left decoder - r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) - r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) - decoder_out, r_decoder_out, _ = self.decoder( - encoder_out, - encoder_mask, - hyps_pad, - hyps_lens, - r_hyps_pad, - self.reverse_weight, - ) # (beam_size, max_hyps_len, vocab_size) - decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) - decoder_out = decoder_out.cpu().numpy() - # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a - # conventional transformer decoder. - r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) - r_decoder_out = r_decoder_out.cpu().numpy() - return decoder_out, r_decoder_out - - def beam_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - beam_size: int = 5, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ctc_weight: float = 0.3, - transducer_weight: float = 0.7, - ): - """beam search - - Args: - speech (torch.Tensor): (batch=1, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - ctc_weight (float): ctc probability weight in transducer - prefix beam search. - final_prob = ctc_weight * ctc_prob + transducer_weight * transducer_prob - transducer_weight (float): transducer probability weight in - prefix beam search - Returns: - List[List[int]]: best path result - - """ - self.init_bs() - beam, _ = self.bs.prefix_beam_search( - speech, - speech_lengths, - decoding_chunk_size, - beam_size, - num_decoding_left_chunks, - simulate_streaming, - ctc_weight, - transducer_weight, - ) - return beam[0].hyp[1:], beam[0].score - - def transducer_attention_rescoring( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - beam_size: int, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - reverse_weight: float = 0.0, - ctc_weight: float = 0.0, - attn_weight: float = 0.0, - transducer_weight: float = 0.0, - search_ctc_weight: float = 1.0, - search_transducer_weight: float = 0.0, - beam_search_type: str = "transducer", - ) -> List[List[int]]: - """beam search - - Args: - speech (torch.Tensor): (batch=1, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - ctc_weight (float): ctc probability weight using in rescoring. - rescore_prob = ctc_weight * ctc_prob + - transducer_weight * (transducer_loss * -1) + - attn_weight * attn_prob - attn_weight (float): attn probability weight using in rescoring. - transducer_weight (float): transducer probability weight using in - rescoring - search_ctc_weight (float): ctc weight using - in rnnt beam search (seeing in self.beam_search) - search_transducer_weight (float): transducer weight using - in rnnt beam search (seeing in self.beam_search) - Returns: - List[List[int]]: best path result - - """ - - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - if reverse_weight > 0.0: - # decoder should be a bitransformer decoder if reverse_weight > 0.0 - assert hasattr(self.decoder, "right_decoder") - device = speech.device - batch_size = speech.shape[0] - # For attention rescoring we only support batch_size=1 - assert batch_size == 1 - # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size - self.init_bs() - if beam_search_type == "transducer": - beam, encoder_out = self.bs.prefix_beam_search( - speech, - speech_lengths, - decoding_chunk_size=decoding_chunk_size, - beam_size=beam_size, - num_decoding_left_chunks=num_decoding_left_chunks, - ctc_weight=search_ctc_weight, - transducer_weight=search_transducer_weight, - ) - beam_score = [s.score for s in beam] - hyps = [s.hyp[1:] for s in beam] - - elif beam_search_type == "ctc": - hyps, encoder_out = self._ctc_prefix_beam_search( - speech, - speech_lengths, - beam_size=beam_size, - decoding_chunk_size=decoding_chunk_size, - num_decoding_left_chunks=num_decoding_left_chunks, - simulate_streaming=simulate_streaming, - ) - beam_score = [hyp[1] for hyp in hyps] - hyps = [hyp[0] for hyp in hyps] - assert len(hyps) == beam_size - - # build hyps and encoder output - hyps_pad = pad_sequence( - [torch.tensor(hyp, device=device, dtype=torch.long) for hyp in hyps], - True, - self.ignore_id, - ) # (beam_size, max_hyps_len) - hyps_lens = torch.tensor( - [len(hyp) for hyp in hyps], device=device, dtype=torch.long - ) # (beam_size,) - - encoder_out = encoder_out.repeat(beam_size, 1, 1) - encoder_mask = torch.ones( - beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device - ) - - # 2.1 calculate transducer score - td_score = self._cal_transducer_score( - encoder_out, - encoder_mask, - hyps_lens, - hyps_pad, - ) - # 2.2 calculate attention score - decoder_out, r_decoder_out = self._cal_attn_score( - encoder_out, - encoder_mask, - hyps_pad, - hyps_lens, - ) - - # Only use decoder score for rescoring - best_score = -float("inf") - best_index = 0 - for i, hyp in enumerate(hyps): - score = 0.0 - for j, w in enumerate(hyp): - score += decoder_out[i][j][w] - score += decoder_out[i][len(hyp)][self.eos] - td_s = td_score[i] - # add right to left decoder score - if reverse_weight > 0: - r_score = 0.0 - for j, w in enumerate(hyp): - r_score += r_decoder_out[i][len(hyp) - j - 1][w] - r_score += r_decoder_out[i][len(hyp)][self.eos] - score = score * (1 - reverse_weight) + r_score * reverse_weight - # add ctc score - score = ( - score * attn_weight - + beam_score[i] * ctc_weight - + td_s * transducer_weight - ) - if score > best_score: - best_score = score - best_index = i - - return hyps[best_index], best_score - - def greedy_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - n_steps: int = 64, - ) -> List[List[int]]: - """greedy search - - Args: - speech (torch.Tensor): (batch=1, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - Returns: - List[List[int]]: best path result - """ - # TODO(Mddct): batch decode - assert speech.size(0) == 1 - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - # TODO(Mddct): forward chunk by chunk - _ = simulate_streaming - # Let's assume B = batch_size - encoder_out, encoder_mask = self.encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - ) - encoder_out_lens = encoder_mask.squeeze(1).sum() - hyps = basic_greedy_search(self, encoder_out, encoder_out_lens, n_steps=n_steps) - - return hyps - - @torch.jit.export - def forward_encoder_chunk( - self, - xs: torch.Tensor, - offset: int, - required_cache_size: int, - att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - return self.encoder.forward_chunk( - xs, offset, required_cache_size, att_cache, cnn_cache - ) - - @torch.jit.export - def forward_predictor_step( - self, xs: torch.Tensor, cache: List[torch.Tensor] - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - assert len(cache) == 2 - # fake padding - padding = torch.zeros(1, 1) - return self.predictor.forward_step(xs, padding, cache) - - @torch.jit.export - def forward_joint_step( - self, enc_out: torch.Tensor, pred_out: torch.Tensor - ) -> torch.Tensor: - return self.joint(enc_out, pred_out) - - @torch.jit.export - def forward_predictor_init_state(self) -> List[torch.Tensor]: - return self.predictor.init_state(1, device=torch.device("cpu")) diff --git a/modules/wenet_extractor/transformer/__init__.py b/modules/wenet_extractor/transformer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/transformer/asr_model.py b/modules/wenet_extractor/transformer/asr_model.py deleted file mode 100644 index 4ad7869679ee15fdcf6a759a7c39762d7f166ff2..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/asr_model.py +++ /dev/null @@ -1,1056 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from collections import defaultdict -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn.functional as F -from torch.nn.utils.rnn import pad_sequence - -from modules.wenet_extractor.transformer.ctc import CTC -from modules.wenet_extractor.transformer.decoder import TransformerDecoder -from modules.wenet_extractor.transformer.encoder import TransformerEncoder -from modules.wenet_extractor.transformer.label_smoothing_loss import LabelSmoothingLoss -from modules.wenet_extractor.utils.common import ( - IGNORE_ID, - add_sos_eos, - log_add, - remove_duplicates_and_blank, - th_accuracy, - reverse_pad_list, -) -from modules.wenet_extractor.utils.mask import ( - make_pad_mask, - mask_finished_preds, - mask_finished_scores, - subsequent_mask, -) - - -class ASRModel(torch.nn.Module): - """CTC-attention hybrid Encoder-Decoder model""" - - def __init__( - self, - vocab_size: int, - encoder: TransformerEncoder, - decoder: TransformerDecoder, - ctc: CTC, - ctc_weight: float = 0.5, - ignore_id: int = IGNORE_ID, - reverse_weight: float = 0.0, - lsm_weight: float = 0.0, - length_normalized_loss: bool = False, - lfmmi_dir: str = "", - ): - assert 0.0 <= ctc_weight <= 1.0, ctc_weight - - super().__init__() - # note that eos is the same as sos (equivalent ID) - self.sos = vocab_size - 1 - self.eos = vocab_size - 1 - self.vocab_size = vocab_size - self.ignore_id = ignore_id - self.ctc_weight = ctc_weight - self.reverse_weight = reverse_weight - - self.encoder = encoder - self.decoder = decoder - self.ctc = ctc - self.criterion_att = LabelSmoothingLoss( - size=vocab_size, - padding_idx=ignore_id, - smoothing=lsm_weight, - normalize_length=length_normalized_loss, - ) - self.lfmmi_dir = lfmmi_dir - if self.lfmmi_dir != "": - self.load_lfmmi_resource() - - def forward( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - text: torch.Tensor, - text_lengths: torch.Tensor, - ) -> Dict[str, Optional[torch.Tensor]]: - """Frontend + Encoder + Decoder + Calc loss - - Args: - speech: (Batch, Length, ...) - speech_lengths: (Batch, ) - text: (Batch, Length) - text_lengths: (Batch,) - """ - - assert text_lengths.dim() == 1, text_lengths.shape - # Check that batch_size is unified - assert ( - speech.shape[0] - == speech_lengths.shape[0] - == text.shape[0] - == text_lengths.shape[0] - ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) - # 1. Encoder - encoder_out, encoder_mask = self.encoder(speech, speech_lengths) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - - # 2a. Attention-decoder branch - if self.ctc_weight != 1.0: - loss_att, acc_att = self._calc_att_loss( - encoder_out, encoder_mask, text, text_lengths - ) - else: - loss_att = None - - # 2b. CTC branch or LF-MMI loss - if self.ctc_weight != 0.0: - if self.lfmmi_dir != "": - loss_ctc = self._calc_lfmmi_loss(encoder_out, encoder_mask, text) - else: - loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) - else: - loss_ctc = None - - if loss_ctc is None: - loss = loss_att - elif loss_att is None: - loss = loss_ctc - else: - loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att - return {"loss": loss, "loss_att": loss_att, "loss_ctc": loss_ctc} - - def _calc_att_loss( - self, - encoder_out: torch.Tensor, - encoder_mask: torch.Tensor, - ys_pad: torch.Tensor, - ys_pad_lens: torch.Tensor, - ) -> Tuple[torch.Tensor, float]: - ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) - ys_in_lens = ys_pad_lens + 1 - - # reverse the seq, used for right to left decoder - r_ys_pad = reverse_pad_list(ys_pad, ys_pad_lens, float(self.ignore_id)) - r_ys_in_pad, r_ys_out_pad = add_sos_eos( - r_ys_pad, self.sos, self.eos, self.ignore_id - ) - # 1. Forward decoder - decoder_out, r_decoder_out, _ = self.decoder( - encoder_out, - encoder_mask, - ys_in_pad, - ys_in_lens, - r_ys_in_pad, - self.reverse_weight, - ) - # 2. Compute attention loss - loss_att = self.criterion_att(decoder_out, ys_out_pad) - r_loss_att = torch.tensor(0.0) - if self.reverse_weight > 0.0: - r_loss_att = self.criterion_att(r_decoder_out, r_ys_out_pad) - loss_att = ( - loss_att * (1 - self.reverse_weight) + r_loss_att * self.reverse_weight - ) - acc_att = th_accuracy( - decoder_out.view(-1, self.vocab_size), - ys_out_pad, - ignore_label=self.ignore_id, - ) - return loss_att, acc_att - - def _forward_encoder( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> Tuple[torch.Tensor, torch.Tensor]: - # Let's assume B = batch_size - # 1. Encoder - if simulate_streaming and decoding_chunk_size > 0: - encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk( - speech, - decoding_chunk_size=decoding_chunk_size, - num_decoding_left_chunks=num_decoding_left_chunks, - ) # (B, maxlen, encoder_dim) - else: - encoder_out, encoder_mask = self.encoder( - speech, - speech_lengths, - decoding_chunk_size=decoding_chunk_size, - num_decoding_left_chunks=num_decoding_left_chunks, - ) # (B, maxlen, encoder_dim) - return encoder_out, encoder_mask - - def encoder_extractor( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> Tuple[torch.Tensor, torch.Tensor]: - # assert speech.shape[0] == speech_lengths[0] - assert decoding_chunk_size != 0 - batch_size = speech.shape[0] - - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - - return encoder_out - - def recognize( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - beam_size: int = 10, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> torch.Tensor: - """Apply beam search on attention decoder - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - - Returns: - torch.Tensor: decoding result, (batch, max_result_len) - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - device = speech.device - batch_size = speech.shape[0] - - # Let's assume B = batch_size and N = beam_size - # 1. Encoder - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - maxlen = encoder_out.size(1) - encoder_dim = encoder_out.size(2) - running_size = batch_size * beam_size - encoder_out = ( - encoder_out.unsqueeze(1) - .repeat(1, beam_size, 1, 1) - .view(running_size, maxlen, encoder_dim) - ) # (B*N, maxlen, encoder_dim) - encoder_mask = ( - encoder_mask.unsqueeze(1) - .repeat(1, beam_size, 1, 1) - .view(running_size, 1, maxlen) - ) # (B*N, 1, max_len) - - hyps = torch.ones([running_size, 1], dtype=torch.long, device=device).fill_( - self.sos - ) # (B*N, 1) - scores = torch.tensor( - [0.0] + [-float("inf")] * (beam_size - 1), dtype=torch.float - ) - scores = ( - scores.to(device).repeat([batch_size]).unsqueeze(1).to(device) - ) # (B*N, 1) - end_flag = torch.zeros_like(scores, dtype=torch.bool, device=device) - cache: Optional[List[torch.Tensor]] = None - # 2. Decoder forward step by step - for i in range(1, maxlen + 1): - # Stop if all batch and all beam produce eos - if end_flag.sum() == running_size: - break - # 2.1 Forward decoder step - hyps_mask = ( - subsequent_mask(i).unsqueeze(0).repeat(running_size, 1, 1).to(device) - ) # (B*N, i, i) - # logp: (B*N, vocab) - logp, cache = self.decoder.forward_one_step( - encoder_out, encoder_mask, hyps, hyps_mask, cache - ) - # 2.2 First beam prune: select topk best prob at current time - top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N) - top_k_logp = mask_finished_scores(top_k_logp, end_flag) - top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos) - # 2.3 Second beam prune: select topk score with history - scores = scores + top_k_logp # (B*N, N), broadcast add - scores = scores.view(batch_size, beam_size * beam_size) # (B, N*N) - scores, offset_k_index = scores.topk(k=beam_size) # (B, N) - # Update cache to be consistent with new topk scores / hyps - cache_index = (offset_k_index // beam_size).view(-1) # (B*N) - base_cache_index = ( - torch.arange(batch_size, device=device) - .view(-1, 1) - .repeat([1, beam_size]) - * beam_size - ).view( - -1 - ) # (B*N) - cache_index = base_cache_index + cache_index - cache = [torch.index_select(c, dim=0, index=cache_index) for c in cache] - scores = scores.view(-1, 1) # (B*N, 1) - # 2.4. Compute base index in top_k_index, - # regard top_k_index as (B*N*N),regard offset_k_index as (B*N), - # then find offset_k_index in top_k_index - base_k_index = ( - torch.arange(batch_size, device=device) - .view(-1, 1) - .repeat([1, beam_size]) - ) # (B, N) - base_k_index = base_k_index * beam_size * beam_size - best_k_index = base_k_index.view(-1) + offset_k_index.view(-1) # (B*N) - - # 2.5 Update best hyps - best_k_pred = torch.index_select( - top_k_index.view(-1), dim=-1, index=best_k_index - ) # (B*N) - best_hyps_index = best_k_index // beam_size - last_best_k_hyps = torch.index_select( - hyps, dim=0, index=best_hyps_index - ) # (B*N, i) - hyps = torch.cat( - (last_best_k_hyps, best_k_pred.view(-1, 1)), dim=1 - ) # (B*N, i+1) - - # 2.6 Update end flag - end_flag = torch.eq(hyps[:, -1], self.eos).view(-1, 1) - - # 3. Select best of best - scores = scores.view(batch_size, beam_size) - # TODO: length normalization - best_scores, best_index = scores.max(dim=-1) - best_hyps_index = ( - best_index - + torch.arange(batch_size, dtype=torch.long, device=device) * beam_size - ) - best_hyps = torch.index_select(hyps, dim=0, index=best_hyps_index) - best_hyps = best_hyps[:, 1:] - return best_hyps, best_scores - - def ctc_greedy_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> List[List[int]]: - """Apply CTC greedy search - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - Returns: - List[List[int]]: best path result - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - batch_size = speech.shape[0] - # Let's assume B = batch_size - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - maxlen = encoder_out.size(1) - encoder_out_lens = encoder_mask.squeeze(1).sum(1) - ctc_probs = self.ctc.log_softmax(encoder_out) # (B, maxlen, vocab_size) - topk_prob, topk_index = ctc_probs.topk(1, dim=2) # (B, maxlen, 1) - topk_index = topk_index.view(batch_size, maxlen) # (B, maxlen) - mask = make_pad_mask(encoder_out_lens, maxlen) # (B, maxlen) - topk_index = topk_index.masked_fill_(mask, self.eos) # (B, maxlen) - hyps = [hyp.tolist() for hyp in topk_index] - scores = topk_prob.max(1) - hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps] - return hyps, scores - - def _ctc_prefix_beam_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - beam_size: int, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> Tuple[List[List[int]], torch.Tensor]: - """CTC prefix beam search inner implementation - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - - Returns: - List[List[int]]: nbest results - torch.Tensor: encoder output, (1, max_len, encoder_dim), - it will be used for rescoring in attention rescoring mode - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - batch_size = speech.shape[0] - # For CTC prefix beam search, we only support batch_size=1 - assert batch_size == 1 - # Let's assume B = batch_size and N = beam_size - # 1. Encoder forward and get CTC score - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - maxlen = encoder_out.size(1) - ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) - ctc_probs = ctc_probs.squeeze(0) - # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score)) - cur_hyps = [(tuple(), (0.0, -float("inf")))] - # 2. CTC beam search step by step - for t in range(0, maxlen): - logp = ctc_probs[t] # (vocab_size,) - # key: prefix, value (pb, pnb), default value(-inf, -inf) - next_hyps = defaultdict(lambda: (-float("inf"), -float("inf"))) - # 2.1 First beam prune: select topk best - top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,) - for s in top_k_index: - s = s.item() - ps = logp[s].item() - for prefix, (pb, pnb) in cur_hyps: - last = prefix[-1] if len(prefix) > 0 else None - if s == 0: # blank - n_pb, n_pnb = next_hyps[prefix] - n_pb = log_add([n_pb, pb + ps, pnb + ps]) - next_hyps[prefix] = (n_pb, n_pnb) - elif s == last: - # Update *ss -> *s; - n_pb, n_pnb = next_hyps[prefix] - n_pnb = log_add([n_pnb, pnb + ps]) - next_hyps[prefix] = (n_pb, n_pnb) - # Update *s-s -> *ss, - is for blank - n_prefix = prefix + (s,) - n_pb, n_pnb = next_hyps[n_prefix] - n_pnb = log_add([n_pnb, pb + ps]) - next_hyps[n_prefix] = (n_pb, n_pnb) - else: - n_prefix = prefix + (s,) - n_pb, n_pnb = next_hyps[n_prefix] - n_pnb = log_add([n_pnb, pb + ps, pnb + ps]) - next_hyps[n_prefix] = (n_pb, n_pnb) - - # 2.2 Second beam prune - next_hyps = sorted( - next_hyps.items(), key=lambda x: log_add(list(x[1])), reverse=True - ) - cur_hyps = next_hyps[:beam_size] - hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps] - return hyps, encoder_out - - def ctc_prefix_beam_search( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - beam_size: int, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - ) -> List[int]: - """Apply CTC prefix beam search - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - - Returns: - List[int]: CTC prefix beam search nbest results - """ - hyps, _ = self._ctc_prefix_beam_search( - speech, - speech_lengths, - beam_size, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) - return hyps[0] - - def attention_rescoring( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - beam_size: int, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - ctc_weight: float = 0.0, - simulate_streaming: bool = False, - reverse_weight: float = 0.0, - ) -> List[int]: - """Apply attention rescoring decoding, CTC prefix beam search - is applied first to get nbest, then we resoring the nbest on - attention decoder with corresponding encoder out - - Args: - speech (torch.Tensor): (batch, max_len, feat_dim) - speech_length (torch.Tensor): (batch, ) - beam_size (int): beam size for beam search - decoding_chunk_size (int): decoding chunk for dynamic chunk - trained model. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - 0: used for training, it's prohibited here - simulate_streaming (bool): whether do encoder forward in a - streaming fashion - reverse_weight (float): right to left decoder weight - ctc_weight (float): ctc score weight - - Returns: - List[int]: Attention rescoring result - """ - assert speech.shape[0] == speech_lengths.shape[0] - assert decoding_chunk_size != 0 - if reverse_weight > 0.0: - # decoder should be a bitransformer decoder if reverse_weight > 0.0 - assert hasattr(self.decoder, "right_decoder") - device = speech.device - batch_size = speech.shape[0] - # For attention rescoring we only support batch_size=1 - assert batch_size == 1 - # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size - hyps, encoder_out = self._ctc_prefix_beam_search( - speech, - speech_lengths, - beam_size, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) - - assert len(hyps) == beam_size - hyps_pad = pad_sequence( - [torch.tensor(hyp[0], device=device, dtype=torch.long) for hyp in hyps], - True, - self.ignore_id, - ) # (beam_size, max_hyps_len) - ori_hyps_pad = hyps_pad - hyps_lens = torch.tensor( - [len(hyp[0]) for hyp in hyps], device=device, dtype=torch.long - ) # (beam_size,) - hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) - hyps_lens = hyps_lens + 1 # Add at begining - encoder_out = encoder_out.repeat(beam_size, 1, 1) - encoder_mask = torch.ones( - beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device - ) - # used for right to left decoder - r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) - r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) - decoder_out, r_decoder_out, _ = self.decoder( - encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight - ) # (beam_size, max_hyps_len, vocab_size) - decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) - decoder_out = decoder_out.cpu().numpy() - # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a - # conventional transformer decoder. - r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) - r_decoder_out = r_decoder_out.cpu().numpy() - # Only use decoder score for rescoring - best_score = -float("inf") - best_index = 0 - for i, hyp in enumerate(hyps): - score = 0.0 - for j, w in enumerate(hyp[0]): - score += decoder_out[i][j][w] - score += decoder_out[i][len(hyp[0])][self.eos] - # add right to left decoder score - if reverse_weight > 0: - r_score = 0.0 - for j, w in enumerate(hyp[0]): - r_score += r_decoder_out[i][len(hyp[0]) - j - 1][w] - r_score += r_decoder_out[i][len(hyp[0])][self.eos] - score = score * (1 - reverse_weight) + r_score * reverse_weight - # add ctc score - score += hyp[1] * ctc_weight - if score > best_score: - best_score = score - best_index = i - return hyps[best_index][0], best_score - - @torch.jit.unused - def load_lfmmi_resource(self): - with open("{}/tokens.txt".format(self.lfmmi_dir), "r") as fin: - for line in fin: - arr = line.strip().split() - if arr[0] == "": - self.sos_eos_id = int(arr[1]) - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.graph_compiler = MmiTrainingGraphCompiler( - self.lfmmi_dir, - device=device, - oov="", - sos_id=self.sos_eos_id, - eos_id=self.sos_eos_id, - ) - self.lfmmi = LFMMILoss( - graph_compiler=self.graph_compiler, - den_scale=1, - use_pruned_intersect=False, - ) - self.word_table = {} - with open("{}/words.txt".format(self.lfmmi_dir), "r") as fin: - for line in fin: - arr = line.strip().split() - assert len(arr) == 2 - self.word_table[int(arr[1])] = arr[0] - - @torch.jit.unused - def _calc_lfmmi_loss(self, encoder_out, encoder_mask, text): - ctc_probs = self.ctc.log_softmax(encoder_out) - supervision_segments = torch.stack( - ( - torch.arange(len(encoder_mask)), - torch.zeros(len(encoder_mask)), - encoder_mask.squeeze(dim=1).sum(dim=1).to("cpu"), - ), - 1, - ).to(torch.int32) - dense_fsa_vec = k2.DenseFsaVec( - ctc_probs, - supervision_segments, - allow_truncate=3, - ) - text = [ - " ".join([self.word_table[j.item()] for j in i if j != -1]) for i in text - ] - loss = self.lfmmi(dense_fsa_vec=dense_fsa_vec, texts=text) / len(text) - return loss - - def load_hlg_resource_if_necessary(self, hlg, word): - if not hasattr(self, "hlg"): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.hlg = k2.Fsa.from_dict(torch.load(hlg, map_location=device)) - if not hasattr(self.hlg, "lm_scores"): - self.hlg.lm_scores = self.hlg.scores.clone() - if not hasattr(self, "word_table"): - self.word_table = {} - with open(word, "r") as fin: - for line in fin: - arr = line.strip().split() - assert len(arr) == 2 - self.word_table[int(arr[1])] = arr[0] - - @torch.no_grad() - def hlg_onebest( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - hlg: str = "", - word: str = "", - symbol_table: Dict[str, int] = None, - ) -> List[int]: - self.load_hlg_resource_if_necessary(hlg, word) - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) - supervision_segments = torch.stack( - ( - torch.arange(len(encoder_mask)), - torch.zeros(len(encoder_mask)), - encoder_mask.squeeze(dim=1).sum(dim=1).cpu(), - ), - 1, - ).to(torch.int32) - lattice = get_lattice( - nnet_output=ctc_probs, - decoding_graph=self.hlg, - supervision_segments=supervision_segments, - search_beam=20, - output_beam=7, - min_active_states=30, - max_active_states=10000, - subsampling_factor=4, - ) - best_path = one_best_decoding(lattice=lattice, use_double_scores=True) - hyps = get_texts(best_path) - hyps = [[symbol_table[k] for j in i for k in self.word_table[j]] for i in hyps] - return hyps - - @torch.no_grad() - def hlg_rescore( - self, - speech: torch.Tensor, - speech_lengths: torch.Tensor, - decoding_chunk_size: int = -1, - num_decoding_left_chunks: int = -1, - simulate_streaming: bool = False, - lm_scale: float = 0, - decoder_scale: float = 0, - r_decoder_scale: float = 0, - hlg: str = "", - word: str = "", - symbol_table: Dict[str, int] = None, - ) -> List[int]: - self.load_hlg_resource_if_necessary(hlg, word) - device = speech.device - encoder_out, encoder_mask = self._forward_encoder( - speech, - speech_lengths, - decoding_chunk_size, - num_decoding_left_chunks, - simulate_streaming, - ) # (B, maxlen, encoder_dim) - ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) - supervision_segments = torch.stack( - ( - torch.arange(len(encoder_mask)), - torch.zeros(len(encoder_mask)), - encoder_mask.squeeze(dim=1).sum(dim=1).cpu(), - ), - 1, - ).to(torch.int32) - lattice = get_lattice( - nnet_output=ctc_probs, - decoding_graph=self.hlg, - supervision_segments=supervision_segments, - search_beam=20, - output_beam=7, - min_active_states=30, - max_active_states=10000, - subsampling_factor=4, - ) - nbest = Nbest.from_lattice( - lattice=lattice, - num_paths=100, - use_double_scores=True, - nbest_scale=0.5, - ) - nbest = nbest.intersect(lattice) - assert hasattr(nbest.fsa, "lm_scores") - assert hasattr(nbest.fsa, "tokens") - assert isinstance(nbest.fsa.tokens, torch.Tensor) - - tokens_shape = nbest.fsa.arcs.shape().remove_axis(1) - tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.tokens) - tokens = tokens.remove_values_leq(0) - hyps = tokens.tolist() - - # cal attention_score - hyps_pad = pad_sequence( - [torch.tensor(hyp, device=device, dtype=torch.long) for hyp in hyps], - True, - self.ignore_id, - ) # (beam_size, max_hyps_len) - ori_hyps_pad = hyps_pad - hyps_lens = torch.tensor( - [len(hyp) for hyp in hyps], device=device, dtype=torch.long - ) # (beam_size,) - hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) - hyps_lens = hyps_lens + 1 # Add at begining - encoder_out_repeat = [] - tot_scores = nbest.tot_scores() - repeats = [tot_scores[i].shape[0] for i in range(tot_scores.dim0)] - for i in range(len(encoder_out)): - encoder_out_repeat.append(encoder_out[i : i + 1].repeat(repeats[i], 1, 1)) - encoder_out = torch.concat(encoder_out_repeat, dim=0) - encoder_mask = torch.ones( - encoder_out.size(0), 1, encoder_out.size(1), dtype=torch.bool, device=device - ) - # used for right to left decoder - r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) - r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) - reverse_weight = 0.5 - decoder_out, r_decoder_out, _ = self.decoder( - encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight - ) # (beam_size, max_hyps_len, vocab_size) - decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) - decoder_out = decoder_out - # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a - # conventional transformer decoder. - r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) - r_decoder_out = r_decoder_out - - decoder_scores = torch.tensor( - [ - sum([decoder_out[i, j, hyps[i][j]] for j in range(len(hyps[i]))]) - for i in range(len(hyps)) - ], - device=device, - ) - r_decoder_scores = [] - for i in range(len(hyps)): - score = 0 - for j in range(len(hyps[i])): - score += r_decoder_out[i, len(hyps[i]) - j - 1, hyps[i][j]] - score += r_decoder_out[i, len(hyps[i]), self.eos] - r_decoder_scores.append(score) - r_decoder_scores = torch.tensor(r_decoder_scores, device=device) - - am_scores = nbest.compute_am_scores() - ngram_lm_scores = nbest.compute_lm_scores() - tot_scores = ( - am_scores.values - + lm_scale * ngram_lm_scores.values - + decoder_scale * decoder_scores - + r_decoder_scale * r_decoder_scores - ) - ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) - max_indexes = ragged_tot_scores.argmax() - best_path = k2.index_fsa(nbest.fsa, max_indexes) - hyps = get_texts(best_path) - hyps = [[symbol_table[k] for j in i for k in self.word_table[j]] for i in hyps] - return hyps - - @torch.jit.export - def subsampling_rate(self) -> int: - """Export interface for c++ call, return subsampling_rate of the - model - """ - return self.encoder.embed.subsampling_rate - - @torch.jit.export - def right_context(self) -> int: - """Export interface for c++ call, return right_context of the model""" - return self.encoder.embed.right_context - - @torch.jit.export - def sos_symbol(self) -> int: - """Export interface for c++ call, return sos symbol id of the model""" - return self.sos - - @torch.jit.export - def eos_symbol(self) -> int: - """Export interface for c++ call, return eos symbol id of the model""" - return self.eos - - @torch.jit.export - def forward_encoder_chunk( - self, - xs: torch.Tensor, - offset: int, - required_cache_size: int, - att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ Export interface for c++ call, give input chunk xs, and return - output from time 0 to current chunk. - - Args: - xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - offset (int): current offset in encoder output time stamp - required_cache_size (int): cache size required for next chunk - compuation - >=0: actual cache size - <0: means all history cache is required - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (elayers, b=1, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - - Returns: - torch.Tensor: output of current input xs, - with shape (b=1, chunk_size, hidden-dim). - torch.Tensor: new attention cache required for next chunk, with - dynamic shape (elayers, head, ?, d_k * 2) - depending on required_cache_size. - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - - """ - return self.encoder.forward_chunk( - xs, offset, required_cache_size, att_cache, cnn_cache - ) - - @torch.jit.export - def ctc_activation(self, xs: torch.Tensor) -> torch.Tensor: - """Export interface for c++ call, apply linear transform and log - softmax before ctc - Args: - xs (torch.Tensor): encoder output - - Returns: - torch.Tensor: activation before ctc - - """ - return self.ctc.log_softmax(xs) - - @torch.jit.export - def is_bidirectional_decoder(self) -> bool: - """ - Returns: - torch.Tensor: decoder output - """ - if hasattr(self.decoder, "right_decoder"): - return True - else: - return False - - @torch.jit.export - def forward_attention_decoder( - self, - hyps: torch.Tensor, - hyps_lens: torch.Tensor, - encoder_out: torch.Tensor, - reverse_weight: float = 0, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Export interface for c++ call, forward decoder with multiple - hypothesis from ctc prefix beam search and one encoder output - Args: - hyps (torch.Tensor): hyps from ctc prefix beam search, already - pad sos at the begining - hyps_lens (torch.Tensor): length of each hyp in hyps - encoder_out (torch.Tensor): corresponding encoder output - r_hyps (torch.Tensor): hyps from ctc prefix beam search, already - pad eos at the begining which is used fo right to left decoder - reverse_weight: used for verfing whether used right to left decoder, - > 0 will use. - - Returns: - torch.Tensor: decoder output - """ - assert encoder_out.size(0) == 1 - num_hyps = hyps.size(0) - assert hyps_lens.size(0) == num_hyps - encoder_out = encoder_out.repeat(num_hyps, 1, 1) - encoder_mask = torch.ones( - num_hyps, - 1, - encoder_out.size(1), - dtype=torch.bool, - device=encoder_out.device, - ) - - # input for right to left decoder - # this hyps_lens has count token, we need minus it. - r_hyps_lens = hyps_lens - 1 - # this hyps has included token, so it should be - # convert the original hyps. - r_hyps = hyps[:, 1:] - # >>> r_hyps - # >>> tensor([[ 1, 2, 3], - # >>> [ 9, 8, 4], - # >>> [ 2, -1, -1]]) - # >>> r_hyps_lens - # >>> tensor([3, 3, 1]) - - # NOTE(Mddct): `pad_sequence` is not supported by ONNX, it is used - # in `reverse_pad_list` thus we have to refine the below code. - # Issue: https://github.com/wenet-e2e/wenet/issues/1113 - # Equal to: - # >>> r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(self.ignore_id)) - # >>> r_hyps, _ = add_sos_eos(r_hyps, self.sos, self.eos, self.ignore_id) - max_len = torch.max(r_hyps_lens) - index_range = torch.arange(0, max_len, 1).to(encoder_out.device) - seq_len_expand = r_hyps_lens.unsqueeze(1) - seq_mask = seq_len_expand > index_range # (beam, max_len) - # >>> seq_mask - # >>> tensor([[ True, True, True], - # >>> [ True, True, True], - # >>> [ True, False, False]]) - index = (seq_len_expand - 1) - index_range # (beam, max_len) - # >>> index - # >>> tensor([[ 2, 1, 0], - # >>> [ 2, 1, 0], - # >>> [ 0, -1, -2]]) - index = index * seq_mask - # >>> index - # >>> tensor([[2, 1, 0], - # >>> [2, 1, 0], - # >>> [0, 0, 0]]) - r_hyps = torch.gather(r_hyps, 1, index) - # >>> r_hyps - # >>> tensor([[3, 2, 1], - # >>> [4, 8, 9], - # >>> [2, 2, 2]]) - r_hyps = torch.where(seq_mask, r_hyps, self.eos) - # >>> r_hyps - # >>> tensor([[3, 2, 1], - # >>> [4, 8, 9], - # >>> [2, eos, eos]]) - r_hyps = torch.cat([hyps[:, 0:1], r_hyps], dim=1) - # >>> r_hyps - # >>> tensor([[sos, 3, 2, 1], - # >>> [sos, 4, 8, 9], - # >>> [sos, 2, eos, eos]]) - - decoder_out, r_decoder_out, _ = self.decoder( - encoder_out, encoder_mask, hyps, hyps_lens, r_hyps, reverse_weight - ) # (num_hyps, max_hyps_len, vocab_size) - decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) - - # right to left decoder may be not used during decoding process, - # which depends on reverse_weight param. - # r_dccoder_out will be 0.0, if reverse_weight is 0.0 - r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) - return decoder_out, r_decoder_out diff --git a/modules/wenet_extractor/transformer/attention.py b/modules/wenet_extractor/transformer/attention.py deleted file mode 100644 index a13c0ce0aa408166a990fba119ba2e167f2ee868..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/attention.py +++ /dev/null @@ -1,326 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Multi-Head Attention layer definition.""" - -import math -from typing import Tuple - -import torch -from torch import nn - - -class MultiHeadedAttention(nn.Module): - """Multi-Head Attention layer. - - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - - """ - - def __init__(self, n_head: int, n_feat: int, dropout_rate: float): - """Construct an MultiHeadedAttention object.""" - super().__init__() - assert n_feat % n_head == 0 - # We assume d_v always equals d_k - self.d_k = n_feat // n_head - self.h = n_head - self.linear_q = nn.Linear(n_feat, n_feat) - self.linear_k = nn.Linear(n_feat, n_feat) - self.linear_v = nn.Linear(n_feat, n_feat) - self.linear_out = nn.Linear(n_feat, n_feat) - self.dropout = nn.Dropout(p=dropout_rate) - - def forward_qkv( - self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Transform query, key and value. - - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - - Returns: - torch.Tensor: Transformed query tensor, size - (#batch, n_head, time1, d_k). - torch.Tensor: Transformed key tensor, size - (#batch, n_head, time2, d_k). - torch.Tensor: Transformed value tensor, size - (#batch, n_head, time2, d_k). - - """ - n_batch = query.size(0) - q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) - k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) - v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) - q = q.transpose(1, 2) # (batch, head, time1, d_k) - k = k.transpose(1, 2) # (batch, head, time2, d_k) - v = v.transpose(1, 2) # (batch, head, time2, d_k) - - return q, k, v - - def forward_attention( - self, - value: torch.Tensor, - scores: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ) -> torch.Tensor: - """Compute attention context vector. - - Args: - value (torch.Tensor): Transformed value, size - (#batch, n_head, time2, d_k). - scores (torch.Tensor): Attention score, size - (#batch, n_head, time1, time2). - mask (torch.Tensor): Mask, size (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - - Returns: - torch.Tensor: Transformed value (#batch, time1, d_model) - weighted by the attention score (#batch, time1, time2). - - """ - n_batch = value.size(0) - # NOTE(xcsong): When will `if mask.size(2) > 0` be True? - # 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the - # 1st chunk to ease the onnx export.] - # 2. pytorch training - if mask.size(2) > 0: # time2 > 0 - mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) - # For last chunk, time2 might be larger than scores.size(-1) - mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2) - scores = scores.masked_fill(mask, -float("inf")) - attn = torch.softmax(scores, dim=-1).masked_fill( - mask, 0.0 - ) # (batch, head, time1, time2) - # NOTE(xcsong): When will `if mask.size(2) > 0` be False? - # 1. onnx(16/-1, -1/-1, 16/0) - # 2. jit (16/-1, -1/-1, 16/0, 16/4) - else: - attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - - p_attn = self.dropout(attn) - x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) - x = ( - x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k) - ) # (batch, time1, d_model) - - return self.linear_out(x) # (batch, time1, d_model) - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - pos_emb: torch.Tensor = torch.empty(0), - cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute scaled dot product attention. - - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2). - 1.When applying cross attention between decoder and encoder, - the batch padding mask for input is in (#batch, 1, T) shape. - 2.When applying self attention of encoder, - the mask is in (#batch, T, T) shape. - 3.When applying self attention of decoder, - the mask is in (#batch, L, L) shape. - 4.If the different position in decoder see different block - of the encoder, such as Mocha, the passed in mask could be - in (#batch, L, T) shape. But there is no such case in current - Wenet. - cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - - - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - - """ - q, k, v = self.forward_qkv(query, key, value) - - # NOTE(xcsong): - # when export onnx model, for 1st chunk, we feed - # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) - # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). - # In all modes, `if cache.size(0) > 0` will alwayse be `True` - # and we will always do splitting and - # concatnation(this will simplify onnx export). Note that - # it's OK to concat & split zero-shaped tensors(see code below). - # when export jit model, for 1st chunk, we always feed - # cache(0, 0, 0, 0) since jit supports dynamic if-branch. - # >>> a = torch.ones((1, 2, 0, 4)) - # >>> b = torch.ones((1, 2, 3, 4)) - # >>> c = torch.cat((a, b), dim=2) - # >>> torch.equal(b, c) # True - # >>> d = torch.split(a, 2, dim=-1) - # >>> torch.equal(d[0], d[1]) # True - if cache.size(0) > 0: - key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) - k = torch.cat([key_cache, k], dim=2) - v = torch.cat([value_cache, v], dim=2) - # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's - # non-trivial to calculate `next_cache_start` here. - new_cache = torch.cat((k, v), dim=-1) - - scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) - return self.forward_attention(v, scores, mask), new_cache - - -class RelPositionMultiHeadedAttention(MultiHeadedAttention): - """Multi-Head Attention layer with relative position encoding. - Paper: https://arxiv.org/abs/1901.02860 - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - """ - - def __init__(self, n_head, n_feat, dropout_rate): - """Construct an RelPositionMultiHeadedAttention object.""" - super().__init__(n_head, n_feat, dropout_rate) - # linear transformation for positional encoding - self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) - # these two learnable bias are used in matrix c and matrix d - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) - self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) - torch.nn.init.xavier_uniform_(self.pos_bias_u) - torch.nn.init.xavier_uniform_(self.pos_bias_v) - - def rel_shift(self, x, zero_triu: bool = False): - """Compute relative positinal encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, size). - zero_triu (bool): If true, return the lower triangular part of - the matrix. - Returns: - torch.Tensor: Output tensor. - """ - - zero_pad = torch.zeros( - (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype - ) - x_padded = torch.cat([zero_pad, x], dim=-1) - - x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2)) - x = x_padded[:, :, 1:].view_as(x) - - if zero_triu: - ones = torch.ones((x.size(2), x.size(3))) - x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] - - return x - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - pos_emb: torch.Tensor = torch.empty(0), - cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute 'Scaled Dot Product Attention' with rel. positional encoding. - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): Positional embedding tensor - (#batch, time2, size). - cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - """ - q, k, v = self.forward_qkv(query, key, value) - q = q.transpose(1, 2) # (batch, time1, head, d_k) - - # NOTE(xcsong): - # when export onnx model, for 1st chunk, we feed - # cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode) - # or cache(1, head, real_cache_t, d_k * 2) (16/4 mode). - # In all modes, `if cache.size(0) > 0` will alwayse be `True` - # and we will always do splitting and - # concatnation(this will simplify onnx export). Note that - # it's OK to concat & split zero-shaped tensors(see code below). - # when export jit model, for 1st chunk, we always feed - # cache(0, 0, 0, 0) since jit supports dynamic if-branch. - # >>> a = torch.ones((1, 2, 0, 4)) - # >>> b = torch.ones((1, 2, 3, 4)) - # >>> c = torch.cat((a, b), dim=2) - # >>> torch.equal(b, c) # True - # >>> d = torch.split(a, 2, dim=-1) - # >>> torch.equal(d[0], d[1]) # True - if cache.size(0) > 0: - key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1) - k = torch.cat([key_cache, k], dim=2) - v = torch.cat([value_cache, v], dim=2) - # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's - # non-trivial to calculate `next_cache_start` here. - new_cache = torch.cat((k, v), dim=-1) - - n_batch_pos = pos_emb.size(0) - p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) - p = p.transpose(1, 2) # (batch, head, time1, d_k) - - # (batch, head, time1, d_k) - q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) - # (batch, head, time1, d_k) - q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) - - # compute attention score - # first compute matrix a and matrix c - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - # (batch, head, time1, time2) - matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) - - # compute matrix b and matrix d - # (batch, head, time1, time2) - matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) - # Remove rel_shift since it is useless in speech recognition, - # and it requires special attention for streaming. - # matrix_bd = self.rel_shift(matrix_bd) - - scores = (matrix_ac + matrix_bd) / math.sqrt( - self.d_k - ) # (batch, head, time1, time2) - - return self.forward_attention(v, scores, mask), new_cache diff --git a/modules/wenet_extractor/transformer/cmvn.py b/modules/wenet_extractor/transformer/cmvn.py deleted file mode 100644 index d97f8924e7bf0db25d475be79256086682aa9f6a..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/cmvn.py +++ /dev/null @@ -1,51 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import torch - - -class GlobalCMVN(torch.nn.Module): - def __init__(self, mean: torch.Tensor, istd: torch.Tensor, norm_var: bool = True): - """ - Args: - mean (torch.Tensor): mean stats - istd (torch.Tensor): inverse std, std which is 1.0 / std - """ - super().__init__() - assert mean.shape == istd.shape - self.norm_var = norm_var - # The buffer can be accessed from this module using self.mean - self.register_buffer("mean", mean) - self.register_buffer("istd", istd) - - def forward(self, x: torch.Tensor): - """ - Args: - x (torch.Tensor): (batch, max_len, feat_dim) - - Returns: - (torch.Tensor): normalized feature - """ - x = x - self.mean - if self.norm_var: - x = x * self.istd - return x diff --git a/modules/wenet_extractor/transformer/convolution.py b/modules/wenet_extractor/transformer/convolution.py deleted file mode 100644 index be1148386f8aab6c1b01244f4a84c7e90aba7952..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/convolution.py +++ /dev/null @@ -1,154 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""ConvolutionModule definition.""" - -from typing import Tuple - -import torch -from torch import nn - - -class ConvolutionModule(nn.Module): - """ConvolutionModule in Conformer model.""" - - def __init__( - self, - channels: int, - kernel_size: int = 15, - activation: nn.Module = nn.ReLU(), - norm: str = "batch_norm", - causal: bool = False, - bias: bool = True, - ): - """Construct an ConvolutionModule object. - Args: - channels (int): The number of channels of conv layers. - kernel_size (int): Kernel size of conv layers. - causal (int): Whether use causal convolution or not - """ - super().__init__() - - self.pointwise_conv1 = nn.Conv1d( - channels, - 2 * channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - # self.lorder is used to distinguish if it's a causal convolution, - # if self.lorder > 0: it's a causal convolution, the input will be - # padded with self.lorder frames on the left in forward. - # else: it's a symmetrical convolution - if causal: - padding = 0 - self.lorder = kernel_size - 1 - else: - # kernel_size should be an odd number for none causal convolution - assert (kernel_size - 1) % 2 == 0 - padding = (kernel_size - 1) // 2 - self.lorder = 0 - self.depthwise_conv = nn.Conv1d( - channels, - channels, - kernel_size, - stride=1, - padding=padding, - groups=channels, - bias=bias, - ) - - assert norm in ["batch_norm", "layer_norm"] - if norm == "batch_norm": - self.use_layer_norm = False - self.norm = nn.BatchNorm1d(channels) - else: - self.use_layer_norm = True - self.norm = nn.LayerNorm(channels) - - self.pointwise_conv2 = nn.Conv1d( - channels, - channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - ) - self.activation = activation - - def forward( - self, - x: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - cache: torch.Tensor = torch.zeros((0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute convolution module. - Args: - x (torch.Tensor): Input tensor (#batch, time, channels). - mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), - (0, 0, 0) means fake mask. - cache (torch.Tensor): left context cache, it is only - used in causal convolution (#batch, channels, cache_t), - (0, 0, 0) meas fake cache. - Returns: - torch.Tensor: Output tensor (#batch, time, channels). - """ - # exchange the temporal dimension and the feature dimension - x = x.transpose(1, 2) # (#batch, channels, time) - - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - if self.lorder > 0: - if cache.size(2) == 0: # cache_t == 0 - x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0) - else: - assert cache.size(0) == x.size(0) # equal batch - assert cache.size(1) == x.size(1) # equal channel - x = torch.cat((cache, x), dim=2) - assert x.size(2) > self.lorder - new_cache = x[:, :, -self.lorder :] - else: - # It's better we just return None if no cache is required, - # However, for JIT export, here we just fake one tensor instead of - # None. - new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channel, dim) - x = nn.functional.glu(x, dim=1) # (batch, channel, dim) - - # 1D Depthwise Conv - x = self.depthwise_conv(x) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.activation(self.norm(x)) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.pointwise_conv2(x) - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - return x.transpose(1, 2), new_cache diff --git a/modules/wenet_extractor/transformer/ctc.py b/modules/wenet_extractor/transformer/ctc.py deleted file mode 100644 index 3cbf13a83137642a61b2c46033907ca22d9d6498..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/ctc.py +++ /dev/null @@ -1,95 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import torch -import torch.nn.functional as F - - -class CTC(torch.nn.Module): - """CTC module""" - - def __init__( - self, - odim: int, - encoder_output_size: int, - dropout_rate: float = 0.0, - reduce: bool = True, - ): - """Construct CTC module - Args: - odim: dimension of outputs - encoder_output_size: number of encoder projection units - dropout_rate: dropout rate (0.0 ~ 1.0) - reduce: reduce the CTC loss into a scalar - """ - super().__init__() - eprojs = encoder_output_size - self.dropout_rate = dropout_rate - self.ctc_lo = torch.nn.Linear(eprojs, odim) - - reduction_type = "sum" if reduce else "none" - self.ctc_loss = torch.nn.CTCLoss(reduction=reduction_type) - - def forward( - self, - hs_pad: torch.Tensor, - hlens: torch.Tensor, - ys_pad: torch.Tensor, - ys_lens: torch.Tensor, - ) -> torch.Tensor: - """Calculate CTC loss. - - Args: - hs_pad: batch of padded hidden state sequences (B, Tmax, D) - hlens: batch of lengths of hidden state sequences (B) - ys_pad: batch of padded character id sequence tensor (B, Lmax) - ys_lens: batch of lengths of character sequence (B) - """ - # hs_pad: (B, L, NProj) -> ys_hat: (B, L, Nvocab) - ys_hat = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate)) - # ys_hat: (B, L, D) -> (L, B, D) - ys_hat = ys_hat.transpose(0, 1) - ys_hat = ys_hat.log_softmax(2) - loss = self.ctc_loss(ys_hat, ys_pad, hlens, ys_lens) - # Batch-size average - loss = loss / ys_hat.size(1) - return loss - - def log_softmax(self, hs_pad: torch.Tensor) -> torch.Tensor: - """log_softmax of frame activations - - Args: - Tensor hs_pad: 3d tensor (B, Tmax, eprojs) - Returns: - torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim) - """ - return F.log_softmax(self.ctc_lo(hs_pad), dim=2) - - def argmax(self, hs_pad: torch.Tensor) -> torch.Tensor: - """argmax of frame activations - - Args: - torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs) - Returns: - torch.Tensor: argmax applied 2d tensor (B, Tmax) - """ - return torch.argmax(self.ctc_lo(hs_pad), dim=2) diff --git a/modules/wenet_extractor/transformer/decoder.py b/modules/wenet_extractor/transformer/decoder.py deleted file mode 100644 index 8312769ad80d5ff06132bc6c5f440ad63b2d53d4..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/decoder.py +++ /dev/null @@ -1,323 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# -"""Decoder definition.""" -from typing import Tuple, List, Optional - -import torch - -from modules.wenet_extractor.transformer.attention import MultiHeadedAttention -from modules.wenet_extractor.transformer.decoder_layer import DecoderLayer -from modules.wenet_extractor.transformer.embedding import PositionalEncoding -from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding -from modules.wenet_extractor.transformer.positionwise_feed_forward import ( - PositionwiseFeedForward, -) -from modules.wenet_extractor.utils.mask import subsequent_mask, make_pad_mask - - -class TransformerDecoder(torch.nn.Module): - """Base class of Transfomer decoder module. - Args: - vocab_size: output dim - encoder_output_size: dimension of attention - attention_heads: the number of heads of multi head attention - linear_units: the hidden units number of position-wise feedforward - num_blocks: the number of decoder blocks - dropout_rate: dropout rate - self_attention_dropout_rate: dropout rate for attention - input_layer: input layer type - use_output_layer: whether to use output layer - pos_enc_class: PositionalEncoding or ScaledPositionalEncoding - normalize_before: - True: use layer_norm before each sub-block of a layer. - False: use layer_norm after each sub-block of a layer. - src_attention: if false, encoder-decoder cross attention is not - applied, such as CIF model - """ - - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - normalize_before: bool = True, - src_attention: bool = True, - ): - super().__init__() - attention_dim = encoder_output_size - - if input_layer == "embed": - self.embed = torch.nn.Sequential( - torch.nn.Embedding(vocab_size, attention_dim), - PositionalEncoding(attention_dim, positional_dropout_rate), - ) - elif input_layer == "none": - self.embed = NoPositionalEncoding(attention_dim, positional_dropout_rate) - else: - raise ValueError(f"only 'embed' is supported: {input_layer}") - - self.normalize_before = normalize_before - self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5) - self.use_output_layer = use_output_layer - self.output_layer = torch.nn.Linear(attention_dim, vocab_size) - self.num_blocks = num_blocks - self.decoders = torch.nn.ModuleList( - [ - DecoderLayer( - attention_dim, - MultiHeadedAttention( - attention_heads, attention_dim, self_attention_dropout_rate - ), - MultiHeadedAttention( - attention_heads, attention_dim, src_attention_dropout_rate - ) - if src_attention - else None, - PositionwiseFeedForward(attention_dim, linear_units, dropout_rate), - dropout_rate, - normalize_before, - ) - for _ in range(self.num_blocks) - ] - ) - - def forward( - self, - memory: torch.Tensor, - memory_mask: torch.Tensor, - ys_in_pad: torch.Tensor, - ys_in_lens: torch.Tensor, - r_ys_in_pad: torch.Tensor = torch.empty(0), - reverse_weight: float = 0.0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Forward decoder. - Args: - memory: encoded memory, float32 (batch, maxlen_in, feat) - memory_mask: encoder memory mask, (batch, 1, maxlen_in) - ys_in_pad: padded input token ids, int64 (batch, maxlen_out) - ys_in_lens: input lengths of this batch (batch) - r_ys_in_pad: not used in transformer decoder, in order to unify api - with bidirectional decoder - reverse_weight: not used in transformer decoder, in order to unify - api with bidirectional decode - Returns: - (tuple): tuple containing: - x: decoded token score before softmax (batch, maxlen_out, - vocab_size) if use_output_layer is True, - torch.tensor(0.0), in order to unify api with bidirectional decoder - olens: (batch, ) - """ - tgt = ys_in_pad - maxlen = tgt.size(1) - # tgt_mask: (B, 1, L) - tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1) - tgt_mask = tgt_mask.to(tgt.device) - # m: (1, L, L) - m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0) - # tgt_mask: (B, L, L) - tgt_mask = tgt_mask & m - x, _ = self.embed(tgt) - for layer in self.decoders: - x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory, memory_mask) - if self.normalize_before: - x = self.after_norm(x) - if self.use_output_layer: - x = self.output_layer(x) - olens = tgt_mask.sum(1) - return x, torch.tensor(0.0), olens - - def forward_one_step( - self, - memory: torch.Tensor, - memory_mask: torch.Tensor, - tgt: torch.Tensor, - tgt_mask: torch.Tensor, - cache: Optional[List[torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """Forward one step. - This is only used for decoding. - Args: - memory: encoded memory, float32 (batch, maxlen_in, feat) - memory_mask: encoded memory mask, (batch, 1, maxlen_in) - tgt: input token ids, int64 (batch, maxlen_out) - tgt_mask: input token mask, (batch, maxlen_out) - dtype=torch.uint8 in PyTorch 1.2- - dtype=torch.bool in PyTorch 1.2+ (include 1.2) - cache: cached output list of (batch, max_time_out-1, size) - Returns: - y, cache: NN output value and cache per `self.decoders`. - y.shape` is (batch, maxlen_out, token) - """ - x, _ = self.embed(tgt) - new_cache = [] - for i, decoder in enumerate(self.decoders): - if cache is None: - c = None - else: - c = cache[i] - x, tgt_mask, memory, memory_mask = decoder( - x, tgt_mask, memory, memory_mask, cache=c - ) - new_cache.append(x) - if self.normalize_before: - y = self.after_norm(x[:, -1]) - else: - y = x[:, -1] - if self.use_output_layer: - y = torch.log_softmax(self.output_layer(y), dim=-1) - return y, new_cache - - -class BiTransformerDecoder(torch.nn.Module): - """Base class of Transfomer decoder module. - Args: - vocab_size: output dim - encoder_output_size: dimension of attention - attention_heads: the number of heads of multi head attention - linear_units: the hidden units number of position-wise feedforward - num_blocks: the number of decoder blocks - r_num_blocks: the number of right to left decoder blocks - dropout_rate: dropout rate - self_attention_dropout_rate: dropout rate for attention - input_layer: input layer type - use_output_layer: whether to use output layer - pos_enc_class: PositionalEncoding or ScaledPositionalEncoding - normalize_before: - True: use layer_norm before each sub-block of a layer. - False: use layer_norm after each sub-block of a layer. - """ - - def __init__( - self, - vocab_size: int, - encoder_output_size: int, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - r_num_blocks: int = 0, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - self_attention_dropout_rate: float = 0.0, - src_attention_dropout_rate: float = 0.0, - input_layer: str = "embed", - use_output_layer: bool = True, - normalize_before: bool = True, - ): - super().__init__() - self.left_decoder = TransformerDecoder( - vocab_size, - encoder_output_size, - attention_heads, - linear_units, - num_blocks, - dropout_rate, - positional_dropout_rate, - self_attention_dropout_rate, - src_attention_dropout_rate, - input_layer, - use_output_layer, - normalize_before, - ) - - self.right_decoder = TransformerDecoder( - vocab_size, - encoder_output_size, - attention_heads, - linear_units, - r_num_blocks, - dropout_rate, - positional_dropout_rate, - self_attention_dropout_rate, - src_attention_dropout_rate, - input_layer, - use_output_layer, - normalize_before, - ) - - def forward( - self, - memory: torch.Tensor, - memory_mask: torch.Tensor, - ys_in_pad: torch.Tensor, - ys_in_lens: torch.Tensor, - r_ys_in_pad: torch.Tensor, - reverse_weight: float = 0.0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Forward decoder. - Args: - memory: encoded memory, float32 (batch, maxlen_in, feat) - memory_mask: encoder memory mask, (batch, 1, maxlen_in) - ys_in_pad: padded input token ids, int64 (batch, maxlen_out) - ys_in_lens: input lengths of this batch (batch) - r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out), - used for right to left decoder - reverse_weight: used for right to left decoder - Returns: - (tuple): tuple containing: - x: decoded token score before softmax (batch, maxlen_out, - vocab_size) if use_output_layer is True, - r_x: x: decoded token score (right to left decoder) - before softmax (batch, maxlen_out, vocab_size) - if use_output_layer is True, - olens: (batch, ) - """ - l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad, ys_in_lens) - r_x = torch.tensor(0.0) - if reverse_weight > 0.0: - r_x, _, olens = self.right_decoder( - memory, memory_mask, r_ys_in_pad, ys_in_lens - ) - return l_x, r_x, olens - - def forward_one_step( - self, - memory: torch.Tensor, - memory_mask: torch.Tensor, - tgt: torch.Tensor, - tgt_mask: torch.Tensor, - cache: Optional[List[torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, List[torch.Tensor]]: - """Forward one step. - This is only used for decoding. - Args: - memory: encoded memory, float32 (batch, maxlen_in, feat) - memory_mask: encoded memory mask, (batch, 1, maxlen_in) - tgt: input token ids, int64 (batch, maxlen_out) - tgt_mask: input token mask, (batch, maxlen_out) - dtype=torch.uint8 in PyTorch 1.2- - dtype=torch.bool in PyTorch 1.2+ (include 1.2) - cache: cached output list of (batch, max_time_out-1, size) - Returns: - y, cache: NN output value and cache per `self.decoders`. - y.shape` is (batch, maxlen_out, token) - """ - return self.left_decoder.forward_one_step( - memory, memory_mask, tgt, tgt_mask, cache - ) diff --git a/modules/wenet_extractor/transformer/decoder_layer.py b/modules/wenet_extractor/transformer/decoder_layer.py deleted file mode 100644 index fc4171c47d4cccddade323f04ee1730b98feff76..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/decoder_layer.py +++ /dev/null @@ -1,140 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Decoder self-attention layer definition.""" -from typing import Optional, Tuple - -import torch -from torch import nn - - -class DecoderLayer(nn.Module): - """Single decoder layer module. - - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` instance can be used as the argument. - src_attn (torch.nn.Module): Inter-attention module instance. - `MultiHeadedAttention` instance can be used as the argument. - If `None` is passed, Inter-attention is not used, such as - CIF, GPT, and other decoder only model. - feed_forward (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward` instance can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): - True: use layer_norm before each sub-block. - False: to use layer_norm after each sub-block. - """ - - def __init__( - self, - size: int, - self_attn: nn.Module, - src_attn: Optional[nn.Module], - feed_forward: nn.Module, - dropout_rate: float, - normalize_before: bool = True, - ): - """Construct an DecoderLayer object.""" - super().__init__() - self.size = size - self.self_attn = self_attn - self.src_attn = src_attn - self.feed_forward = feed_forward - self.norm1 = nn.LayerNorm(size, eps=1e-5) - self.norm2 = nn.LayerNorm(size, eps=1e-5) - self.norm3 = nn.LayerNorm(size, eps=1e-5) - self.dropout = nn.Dropout(dropout_rate) - self.normalize_before = normalize_before - - def forward( - self, - tgt: torch.Tensor, - tgt_mask: torch.Tensor, - memory: torch.Tensor, - memory_mask: torch.Tensor, - cache: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute decoded features. - - Args: - tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size). - tgt_mask (torch.Tensor): Mask for input tensor - (#batch, maxlen_out). - memory (torch.Tensor): Encoded memory - (#batch, maxlen_in, size). - memory_mask (torch.Tensor): Encoded memory mask - (#batch, maxlen_in). - cache (torch.Tensor): cached tensors. - (#batch, maxlen_out - 1, size). - - Returns: - torch.Tensor: Output tensor (#batch, maxlen_out, size). - torch.Tensor: Mask for output tensor (#batch, maxlen_out). - torch.Tensor: Encoded memory (#batch, maxlen_in, size). - torch.Tensor: Encoded memory mask (#batch, maxlen_in). - - """ - residual = tgt - if self.normalize_before: - tgt = self.norm1(tgt) - - if cache is None: - tgt_q = tgt - tgt_q_mask = tgt_mask - else: - # compute only the last frame query keeping dim: max_time_out -> 1 - assert cache.shape == ( - tgt.shape[0], - tgt.shape[1] - 1, - self.size, - ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}" - tgt_q = tgt[:, -1:, :] - residual = residual[:, -1:, :] - tgt_q_mask = tgt_mask[:, -1:, :] - - x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]) - if not self.normalize_before: - x = self.norm1(x) - - if self.src_attn is not None: - residual = x - if self.normalize_before: - x = self.norm2(x) - x = residual + self.dropout( - self.src_attn(x, memory, memory, memory_mask)[0] - ) - if not self.normalize_before: - x = self.norm2(x) - - residual = x - if self.normalize_before: - x = self.norm3(x) - x = residual + self.dropout(self.feed_forward(x)) - if not self.normalize_before: - x = self.norm3(x) - - if cache is not None: - x = torch.cat([cache, x], dim=1) - - return x, tgt_mask, memory, memory_mask diff --git a/modules/wenet_extractor/transformer/embedding.py b/modules/wenet_extractor/transformer/embedding.py deleted file mode 100644 index 55ee113f8d267875f147a55326883c207cc0c646..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/embedding.py +++ /dev/null @@ -1,174 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Positonal Encoding Module.""" - -import math -from typing import Tuple, Union - -import torch -import torch.nn.functional as F - - -class PositionalEncoding(torch.nn.Module): - """Positional encoding. - - :param int d_model: embedding dim - :param float dropout_rate: dropout rate - :param int max_len: maximum input length - - PE(pos, 2i) = sin(pos/(10000^(2i/dmodel))) - PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel))) - """ - - def __init__( - self, - d_model: int, - dropout_rate: float, - max_len: int = 5000, - reverse: bool = False, - ): - """Construct an PositionalEncoding object.""" - super().__init__() - self.d_model = d_model - self.xscale = math.sqrt(self.d_model) - self.dropout = torch.nn.Dropout(p=dropout_rate) - self.max_len = max_len - - self.pe = torch.zeros(self.max_len, self.d_model) - position = torch.arange(0, self.max_len, dtype=torch.float32).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, self.d_model, 2, dtype=torch.float32) - * -(math.log(10000.0) / self.d_model) - ) - self.pe[:, 0::2] = torch.sin(position * div_term) - self.pe[:, 1::2] = torch.cos(position * div_term) - self.pe = self.pe.unsqueeze(0) - - def forward( - self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Add positional encoding. - - Args: - x (torch.Tensor): Input. Its shape is (batch, time, ...) - offset (int, torch.tensor): position offset - - Returns: - torch.Tensor: Encoded tensor. Its shape is (batch, time, ...) - torch.Tensor: for compatibility to RelPositionalEncoding - """ - - self.pe = self.pe.to(x.device) - pos_emb = self.position_encoding(offset, x.size(1), False) - x = x * self.xscale + pos_emb - return self.dropout(x), self.dropout(pos_emb) - - def position_encoding( - self, offset: Union[int, torch.Tensor], size: int, apply_dropout: bool = True - ) -> torch.Tensor: - """For getting encoding in a streaming fashion - - Attention!!!!! - we apply dropout only once at the whole utterance level in a none - streaming way, but will call this function several times with - increasing input size in a streaming scenario, so the dropout will - be applied several times. - - Args: - offset (int or torch.tensor): start offset - size (int): required size of position encoding - - Returns: - torch.Tensor: Corresponding encoding - """ - # How to subscript a Union type: - # https://github.com/pytorch/pytorch/issues/69434 - if isinstance(offset, int): - assert offset + size < self.max_len - pos_emb = self.pe[:, offset : offset + size] - elif isinstance(offset, torch.Tensor) and offset.dim() == 0: # scalar - assert offset + size < self.max_len - pos_emb = self.pe[:, offset : offset + size] - else: # for batched streaming decoding on GPU - assert torch.max(offset) + size < self.max_len - index = offset.unsqueeze(1) + torch.arange(0, size).to( - offset.device - ) # B X T - flag = index > 0 - # remove negative offset - index = index * flag - pos_emb = F.embedding(index, self.pe[0]) # B X T X d_model - - if apply_dropout: - pos_emb = self.dropout(pos_emb) - return pos_emb - - -class RelPositionalEncoding(PositionalEncoding): - """Relative positional encoding module. - See : Appendix B in https://arxiv.org/abs/1901.02860 - Args: - d_model (int): Embedding dimension. - dropout_rate (float): Dropout rate. - max_len (int): Maximum input length. - """ - - def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): - """Initialize class.""" - super().__init__(d_model, dropout_rate, max_len, reverse=True) - - def forward( - self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute positional encoding. - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - torch.Tensor: Positional embedding tensor (1, time, `*`). - """ - self.pe = self.pe.to(x.device) - x = x * self.xscale - pos_emb = self.position_encoding(offset, x.size(1), False) - return self.dropout(x), self.dropout(pos_emb) - - -class NoPositionalEncoding(torch.nn.Module): - """No position encoding""" - - def __init__(self, d_model: int, dropout_rate: float): - super().__init__() - self.d_model = d_model - self.dropout = torch.nn.Dropout(p=dropout_rate) - - def forward( - self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0 - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Just return zero vector for interface compatibility""" - pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device) - return self.dropout(x), pos_emb - - def position_encoding( - self, offset: Union[int, torch.Tensor], size: int - ) -> torch.Tensor: - return torch.zeros(1, size, self.d_model) diff --git a/modules/wenet_extractor/transformer/encoder.py b/modules/wenet_extractor/transformer/encoder.py deleted file mode 100644 index c0563262afec7d74e6a1d26a46c3f03376f290fd..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/encoder.py +++ /dev/null @@ -1,503 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Encoder definition.""" -from typing import Tuple - -import torch - -from modules.wenet_extractor.transformer.attention import MultiHeadedAttention -from modules.wenet_extractor.transformer.attention import ( - RelPositionMultiHeadedAttention, -) -from modules.wenet_extractor.transformer.convolution import ConvolutionModule -from modules.wenet_extractor.transformer.embedding import PositionalEncoding -from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding -from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding -from modules.wenet_extractor.transformer.encoder_layer import TransformerEncoderLayer -from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer -from modules.wenet_extractor.transformer.positionwise_feed_forward import ( - PositionwiseFeedForward, -) -from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4 -from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6 -from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8 -from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling -from modules.wenet_extractor.utils.common import get_activation -from modules.wenet_extractor.utils.mask import make_pad_mask -from modules.wenet_extractor.utils.mask import add_optional_chunk_mask - - -class BaseEncoder(torch.nn.Module): - def __init__( - self, - input_size: int, - output_size: int = 256, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.0, - input_layer: str = "conv2d", - pos_enc_layer_type: str = "abs_pos", - normalize_before: bool = True, - static_chunk_size: int = 0, - use_dynamic_chunk: bool = False, - global_cmvn: torch.nn.Module = None, - use_dynamic_left_chunk: bool = False, - ): - """ - Args: - input_size (int): input dim - output_size (int): dimension of attention - attention_heads (int): the number of heads of multi head attention - linear_units (int): the hidden units number of position-wise feed - forward - num_blocks (int): the number of decoder blocks - dropout_rate (float): dropout rate - attention_dropout_rate (float): dropout rate in attention - positional_dropout_rate (float): dropout rate after adding - positional encoding - input_layer (str): input layer type. - optional [linear, conv2d, conv2d6, conv2d8] - pos_enc_layer_type (str): Encoder positional encoding layer type. - opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos] - normalize_before (bool): - True: use layer_norm before each sub-block of a layer. - False: use layer_norm after each sub-block of a layer. - static_chunk_size (int): chunk size for static chunk training and - decoding - use_dynamic_chunk (bool): whether use dynamic chunk size for - training or not, You can only use fixed chunk(chunk_size > 0) - or dyanmic chunk size(use_dynamic_chunk = True) - global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module - use_dynamic_left_chunk (bool): whether use dynamic left chunk in - dynamic chunk training - """ - super().__init__() - self._output_size = output_size - - if pos_enc_layer_type == "abs_pos": - pos_enc_class = PositionalEncoding - elif pos_enc_layer_type == "rel_pos": - pos_enc_class = RelPositionalEncoding - elif pos_enc_layer_type == "no_pos": - pos_enc_class = NoPositionalEncoding - else: - raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) - - if input_layer == "linear": - subsampling_class = LinearNoSubsampling - elif input_layer == "conv2d": - subsampling_class = Conv2dSubsampling4 - elif input_layer == "conv2d6": - subsampling_class = Conv2dSubsampling6 - elif input_layer == "conv2d8": - subsampling_class = Conv2dSubsampling8 - else: - raise ValueError("unknown input_layer: " + input_layer) - - self.global_cmvn = global_cmvn - self.embed = subsampling_class( - input_size, - output_size, - dropout_rate, - pos_enc_class(output_size, positional_dropout_rate), - ) - - self.normalize_before = normalize_before - self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5) - self.static_chunk_size = static_chunk_size - self.use_dynamic_chunk = use_dynamic_chunk - self.use_dynamic_left_chunk = use_dynamic_left_chunk - - def output_size(self) -> int: - return self._output_size - - def forward( - self, - xs: torch.Tensor, - xs_lens: torch.Tensor, - decoding_chunk_size: int = 0, - num_decoding_left_chunks: int = -1, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Embed positions in tensor. - - Args: - xs: padded input tensor (B, T, D) - xs_lens: input length (B) - decoding_chunk_size: decoding chunk size for dynamic chunk - 0: default for training, use random dynamic chunk. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - num_decoding_left_chunks: number of left chunks, this is for decoding, - the chunk size is decoding_chunk_size. - >=0: use num_decoding_left_chunks - <0: use all left chunks - Returns: - encoder output tensor xs, and subsampled masks - xs: padded output tensor (B, T' ~= T/subsample_rate, D) - masks: torch.Tensor batch padding mask after subsample - (B, 1, T' ~= T/subsample_rate) - """ - T = xs.size(1) - masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T) - if self.global_cmvn is not None: - xs = self.global_cmvn(xs) - xs, pos_emb, masks = self.embed(xs, masks) - mask_pad = masks # (B, 1, T/subsample_rate) - chunk_masks = add_optional_chunk_mask( - xs, - masks, - self.use_dynamic_chunk, - self.use_dynamic_left_chunk, - decoding_chunk_size, - self.static_chunk_size, - num_decoding_left_chunks, - ) - for layer in self.encoders: - xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) - if self.normalize_before: - xs = self.after_norm(xs) - # Here we assume the mask is not changed in encoder layers, so just - # return the masks before encoder layers, and the masks will be used - # for cross attention with decoder later - return xs, masks - - def forward_chunk( - self, - xs: torch.Tensor, - offset: int, - required_cache_size: int, - att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), - att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ Forward just one chunk - - Args: - xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim), - where `time == (chunk_size - 1) * subsample_rate + \ - subsample.right_context + 1` - offset (int): current offset in encoder output time stamp - required_cache_size (int): cache size required for next chunk - compuation - >=0: actual cache size - <0: means all history cache is required - att_cache (torch.Tensor): cache tensor for KEY & VALUE in - transformer/conformer attention, with shape - (elayers, head, cache_t1, d_k * 2), where - `head * d_k == hidden-dim` and - `cache_t1 == chunk_size * num_decoding_left_chunks`. - cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer, - (elayers, b=1, hidden-dim, cache_t2), where - `cache_t2 == cnn.lorder - 1` - - Returns: - torch.Tensor: output of current input xs, - with shape (b=1, chunk_size, hidden-dim). - torch.Tensor: new attention cache required for next chunk, with - dynamic shape (elayers, head, ?, d_k * 2) - depending on required_cache_size. - torch.Tensor: new conformer cnn cache required for next chunk, with - same shape as the original cnn_cache. - - """ - assert xs.size(0) == 1 - # tmp_masks is just for interface compatibility - tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool) - tmp_masks = tmp_masks.unsqueeze(1) - if self.global_cmvn is not None: - xs = self.global_cmvn(xs) - # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim) - xs, pos_emb, _ = self.embed(xs, tmp_masks, offset) - # NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim) - elayers, cache_t1 = att_cache.size(0), att_cache.size(2) - chunk_size = xs.size(1) - attention_key_size = cache_t1 + chunk_size - pos_emb = self.embed.position_encoding( - offset=offset - cache_t1, size=attention_key_size - ) - if required_cache_size < 0: - next_cache_start = 0 - elif required_cache_size == 0: - next_cache_start = attention_key_size - else: - next_cache_start = max(attention_key_size - required_cache_size, 0) - r_att_cache = [] - r_cnn_cache = [] - for i, layer in enumerate(self.encoders): - # NOTE(xcsong): Before layer.forward - # shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2), - # shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2) - xs, _, new_att_cache, new_cnn_cache = layer( - xs, - att_mask, - pos_emb, - att_cache=att_cache[i : i + 1] if elayers > 0 else att_cache, - cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache, - ) - # NOTE(xcsong): After layer.forward - # shape(new_att_cache) is (1, head, attention_key_size, d_k * 2), - # shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2) - r_att_cache.append(new_att_cache[:, :, next_cache_start:, :]) - r_cnn_cache.append(new_cnn_cache.unsqueeze(0)) - if self.normalize_before: - xs = self.after_norm(xs) - - # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2), - # ? may be larger than cache_t1, it depends on required_cache_size - r_att_cache = torch.cat(r_att_cache, dim=0) - # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2) - r_cnn_cache = torch.cat(r_cnn_cache, dim=0) - - return (xs, r_att_cache, r_cnn_cache) - - def forward_chunk_by_chunk( - self, - xs: torch.Tensor, - decoding_chunk_size: int, - num_decoding_left_chunks: int = -1, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Forward input chunk by chunk with chunk_size like a streaming - fashion - - Here we should pay special attention to computation cache in the - streaming style forward chunk by chunk. Three things should be taken - into account for computation in the current network: - 1. transformer/conformer encoder layers output cache - 2. convolution in conformer - 3. convolution in subsampling - - However, we don't implement subsampling cache for: - 1. We can control subsampling module to output the right result by - overlapping input instead of cache left context, even though it - wastes some computation, but subsampling only takes a very - small fraction of computation in the whole model. - 2. Typically, there are several covolution layers with subsampling - in subsampling module, it is tricky and complicated to do cache - with different convolution layers with different subsampling - rate. - 3. Currently, nn.Sequential is used to stack all the convolution - layers in subsampling, we need to rewrite it to make it work - with cache, which is not prefered. - Args: - xs (torch.Tensor): (1, max_len, dim) - chunk_size (int): decoding chunk size - """ - assert decoding_chunk_size > 0 - # The model is trained by static or dynamic chunk - assert self.static_chunk_size > 0 or self.use_dynamic_chunk - subsampling = self.embed.subsampling_rate - context = self.embed.right_context + 1 # Add current frame - stride = subsampling * decoding_chunk_size - decoding_window = (decoding_chunk_size - 1) * subsampling + context - num_frames = xs.size(1) - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device) - outputs = [] - offset = 0 - required_cache_size = decoding_chunk_size * num_decoding_left_chunks - - # Feed forward overlap input step by step - for cur in range(0, num_frames - context + 1, stride): - end = min(cur + decoding_window, num_frames) - chunk_xs = xs[:, cur:end, :] - (y, att_cache, cnn_cache) = self.forward_chunk( - chunk_xs, offset, required_cache_size, att_cache, cnn_cache - ) - outputs.append(y) - offset += y.size(1) - ys = torch.cat(outputs, 1) - masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool) - return ys, masks - - -class TransformerEncoder(BaseEncoder): - """Transformer encoder module.""" - - def __init__( - self, - input_size: int, - output_size: int = 256, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.0, - input_layer: str = "conv2d", - pos_enc_layer_type: str = "abs_pos", - normalize_before: bool = True, - static_chunk_size: int = 0, - use_dynamic_chunk: bool = False, - global_cmvn: torch.nn.Module = None, - use_dynamic_left_chunk: bool = False, - ): - """Construct TransformerEncoder - - See Encoder for the meaning of each parameter. - """ - super().__init__( - input_size, - output_size, - attention_heads, - linear_units, - num_blocks, - dropout_rate, - positional_dropout_rate, - attention_dropout_rate, - input_layer, - pos_enc_layer_type, - normalize_before, - static_chunk_size, - use_dynamic_chunk, - global_cmvn, - use_dynamic_left_chunk, - ) - self.encoders = torch.nn.ModuleList( - [ - TransformerEncoderLayer( - output_size, - MultiHeadedAttention( - attention_heads, output_size, attention_dropout_rate - ), - PositionwiseFeedForward(output_size, linear_units, dropout_rate), - dropout_rate, - normalize_before, - ) - for _ in range(num_blocks) - ] - ) - - -class ConformerEncoder(BaseEncoder): - """Conformer encoder module.""" - - def __init__( - self, - input_size: int, - output_size: int = 256, - attention_heads: int = 4, - linear_units: int = 2048, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.0, - input_layer: str = "conv2d", - pos_enc_layer_type: str = "rel_pos", - normalize_before: bool = True, - static_chunk_size: int = 0, - use_dynamic_chunk: bool = False, - global_cmvn: torch.nn.Module = None, - use_dynamic_left_chunk: bool = False, - positionwise_conv_kernel_size: int = 1, - macaron_style: bool = True, - selfattention_layer_type: str = "rel_selfattn", - activation_type: str = "swish", - use_cnn_module: bool = True, - cnn_module_kernel: int = 15, - causal: bool = False, - cnn_module_norm: str = "batch_norm", - ): - """Construct ConformerEncoder - - Args: - input_size to use_dynamic_chunk, see in BaseEncoder - positionwise_conv_kernel_size (int): Kernel size of positionwise - conv1d layer. - macaron_style (bool): Whether to use macaron style for - positionwise layer. - selfattention_layer_type (str): Encoder attention layer type, - the parameter has no effect now, it's just for configure - compatibility. - activation_type (str): Encoder activation function type. - use_cnn_module (bool): Whether to use convolution module. - cnn_module_kernel (int): Kernel size of convolution module. - causal (bool): whether to use causal convolution or not. - """ - super().__init__( - input_size, - output_size, - attention_heads, - linear_units, - num_blocks, - dropout_rate, - positional_dropout_rate, - attention_dropout_rate, - input_layer, - pos_enc_layer_type, - normalize_before, - static_chunk_size, - use_dynamic_chunk, - global_cmvn, - use_dynamic_left_chunk, - ) - activation = get_activation(activation_type) - - # self-attention module definition - if pos_enc_layer_type != "rel_pos": - encoder_selfattn_layer = MultiHeadedAttention - else: - encoder_selfattn_layer = RelPositionMultiHeadedAttention - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - ) - # feed-forward module definition - positionwise_layer = PositionwiseFeedForward - positionwise_layer_args = ( - output_size, - linear_units, - dropout_rate, - activation, - ) - # convolution module definition - convolution_layer = ConvolutionModule - convolution_layer_args = ( - output_size, - cnn_module_kernel, - activation, - cnn_module_norm, - causal, - ) - - self.encoders = torch.nn.ModuleList( - [ - ConformerEncoderLayer( - output_size, - encoder_selfattn_layer(*encoder_selfattn_layer_args), - positionwise_layer(*positionwise_layer_args), - positionwise_layer(*positionwise_layer_args) - if macaron_style - else None, - convolution_layer(*convolution_layer_args) - if use_cnn_module - else None, - dropout_rate, - normalize_before, - ) - for _ in range(num_blocks) - ] - ) diff --git a/modules/wenet_extractor/transformer/encoder_layer.py b/modules/wenet_extractor/transformer/encoder_layer.py deleted file mode 100644 index 6b2ebb2f4fc9cdb5f6cd6c2401597638aa27fa4c..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/encoder_layer.py +++ /dev/null @@ -1,242 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Encoder self-attention layer definition.""" - -from typing import Optional, Tuple - -import torch -from torch import nn - - -class TransformerEncoderLayer(nn.Module): - """Encoder layer module. - - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` - instance can be used as the argument. - feed_forward (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward`, instance can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): - True: use layer_norm before each sub-block. - False: to use layer_norm after each sub-block. - """ - - def __init__( - self, - size: int, - self_attn: torch.nn.Module, - feed_forward: torch.nn.Module, - dropout_rate: float, - normalize_before: bool = True, - ): - """Construct an EncoderLayer object.""" - super().__init__() - self.self_attn = self_attn - self.feed_forward = feed_forward - self.norm1 = nn.LayerNorm(size, eps=1e-5) - self.norm2 = nn.LayerNorm(size, eps=1e-5) - self.dropout = nn.Dropout(dropout_rate) - self.size = size - self.normalize_before = normalize_before - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute encoded features. - - Args: - x (torch.Tensor): (#batch, time, size) - mask (torch.Tensor): Mask tensor for the input (#batch, time，time), - (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): just for interface compatibility - to ConformerEncoderLayer - mask_pad (torch.Tensor): does not used in transformer layer, - just for unified api with conformer. - att_cache (torch.Tensor): Cache tensor of the KEY & VALUE - (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. - cnn_cache (torch.Tensor): Convolution cache in conformer layer - (#batch=1, size, cache_t2), not used here, it's for interface - compatibility to ConformerEncoderLayer. - Returns: - torch.Tensor: Output tensor (#batch, time, size). - torch.Tensor: Mask tensor (#batch, time, time). - torch.Tensor: att_cache tensor, - (#batch=1, head, cache_t1 + time, d_k * 2). - torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2). - - """ - residual = x - if self.normalize_before: - x = self.norm1(x) - x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache) - x = residual + self.dropout(x_att) - if not self.normalize_before: - x = self.norm1(x) - - residual = x - if self.normalize_before: - x = self.norm2(x) - x = residual + self.dropout(self.feed_forward(x)) - if not self.normalize_before: - x = self.norm2(x) - - fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - return x, mask, new_att_cache, fake_cnn_cache - - -class ConformerEncoderLayer(nn.Module): - """Encoder layer module. - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` - instance can be used as the argument. - feed_forward (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward` instance can be used as the argument. - feed_forward_macaron (torch.nn.Module): Additional feed-forward module - instance. - `PositionwiseFeedForward` instance can be used as the argument. - conv_module (torch.nn.Module): Convolution module instance. - `ConvlutionModule` instance can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): - True: use layer_norm before each sub-block. - False: use layer_norm after each sub-block. - """ - - def __init__( - self, - size: int, - self_attn: torch.nn.Module, - feed_forward: Optional[nn.Module] = None, - feed_forward_macaron: Optional[nn.Module] = None, - conv_module: Optional[nn.Module] = None, - dropout_rate: float = 0.1, - normalize_before: bool = True, - ): - """Construct an EncoderLayer object.""" - super().__init__() - self.self_attn = self_attn - self.feed_forward = feed_forward - self.feed_forward_macaron = feed_forward_macaron - self.conv_module = conv_module - self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module - self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module - if feed_forward_macaron is not None: - self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5) - self.ff_scale = 0.5 - else: - self.ff_scale = 1.0 - if self.conv_module is not None: - self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module - self.norm_final = nn.LayerNorm( - size, eps=1e-5 - ) # for the final output of the block - self.dropout = nn.Dropout(dropout_rate) - self.size = size - self.normalize_before = normalize_before - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute encoded features. - - Args: - x (torch.Tensor): (#batch, time, size) - mask (torch.Tensor): Mask tensor for the input (#batch, time，time), - (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): positional encoding, must not be None - for ConformerEncoderLayer. - mask_pad (torch.Tensor): batch padding mask used for conv module. - (#batch, 1，time), (0, 0, 0) means fake mask. - att_cache (torch.Tensor): Cache tensor of the KEY & VALUE - (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. - cnn_cache (torch.Tensor): Convolution cache in conformer layer - (#batch=1, size, cache_t2) - Returns: - torch.Tensor: Output tensor (#batch, time, size). - torch.Tensor: Mask tensor (#batch, time, time). - torch.Tensor: att_cache tensor, - (#batch=1, head, cache_t1 + time, d_k * 2). - torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). - """ - - # whether to use macaron style - if self.feed_forward_macaron is not None: - residual = x - if self.normalize_before: - x = self.norm_ff_macaron(x) - x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x)) - if not self.normalize_before: - x = self.norm_ff_macaron(x) - - # multi-headed self-attention module - residual = x - if self.normalize_before: - x = self.norm_mha(x) - x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache) - x = residual + self.dropout(x_att) - if not self.normalize_before: - x = self.norm_mha(x) - - # convolution module - # Fake new cnn cache here, and then change it in conv_module - new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - if self.conv_module is not None: - residual = x - if self.normalize_before: - x = self.norm_conv(x) - x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) - x = residual + self.dropout(x) - - if not self.normalize_before: - x = self.norm_conv(x) - - # feed forward module - residual = x - if self.normalize_before: - x = self.norm_ff(x) - - x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) - if not self.normalize_before: - x = self.norm_ff(x) - - if self.conv_module is not None: - x = self.norm_final(x) - - return x, mask, new_att_cache, new_cnn_cache diff --git a/modules/wenet_extractor/transformer/label_smoothing_loss.py b/modules/wenet_extractor/transformer/label_smoothing_loss.py deleted file mode 100644 index 5c2b1cae5923fef66665adf9f84604af03c1ea9a..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/label_smoothing_loss.py +++ /dev/null @@ -1,106 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Label smoothing module.""" - -import torch -from torch import nn - - -class LabelSmoothingLoss(nn.Module): - """Label-smoothing loss. - - In a standard CE loss, the label's data distribution is: - [0,1,2] -> - [ - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - [0.0, 0.0, 1.0], - ] - - In the smoothing version CE Loss,some probabilities - are taken from the true label prob (1.0) and are divided - among other labels. - - e.g. - smoothing=0.1 - [0,1,2] -> - [ - [0.9, 0.05, 0.05], - [0.05, 0.9, 0.05], - [0.05, 0.05, 0.9], - ] - - Args: - size (int): the number of class - padding_idx (int): padding class id which will be ignored for loss - smoothing (float): smoothing rate (0.0 means the conventional CE) - normalize_length (bool): - normalize loss by sequence length if True - normalize loss by batch size if False - """ - - def __init__( - self, - size: int, - padding_idx: int, - smoothing: float, - normalize_length: bool = False, - ): - """Construct an LabelSmoothingLoss object.""" - super(LabelSmoothingLoss, self).__init__() - self.criterion = nn.KLDivLoss(reduction="none") - self.padding_idx = padding_idx - self.confidence = 1.0 - smoothing - self.smoothing = smoothing - self.size = size - self.normalize_length = normalize_length - - def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute loss between x and target. - - The model outputs and data labels tensors are flatten to - (batch*seqlen, class) shape and a mask is applied to the - padding part which should not be calculated for loss. - - Args: - x (torch.Tensor): prediction (batch, seqlen, class) - target (torch.Tensor): - target signal masked with self.padding_id (batch, seqlen) - Returns: - loss (torch.Tensor) : The KL loss, scalar float value - """ - assert x.size(2) == self.size - batch_size = x.size(0) - x = x.view(-1, self.size) - target = target.view(-1) - # use zeros_like instead of torch.no_grad() for true_dist, - # since no_grad() can not be exported by JIT - true_dist = torch.zeros_like(x) - true_dist.fill_(self.smoothing / (self.size - 1)) - ignore = target == self.padding_idx # (B,) - total = len(target) - ignore.sum().item() - target = target.masked_fill(ignore, 0) # avoid -1 index - true_dist.scatter_(1, target.unsqueeze(1), self.confidence) - kl = self.criterion(torch.log_softmax(x, dim=1), true_dist) - denom = total if self.normalize_length else batch_size - return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom diff --git a/modules/wenet_extractor/transformer/positionwise_feed_forward.py b/modules/wenet_extractor/transformer/positionwise_feed_forward.py deleted file mode 100644 index eaaefdc61d392d44e503458deb490690b3183a03..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/positionwise_feed_forward.py +++ /dev/null @@ -1,63 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Positionwise feed forward layer definition.""" - -import torch - - -class PositionwiseFeedForward(torch.nn.Module): - """Positionwise feed forward layer. - - FeedForward are appied on each position of the sequence. - The output dim is same with the input dim. - - Args: - idim (int): Input dimenstion. - hidden_units (int): The number of hidden units. - dropout_rate (float): Dropout rate. - activation (torch.nn.Module): Activation function - """ - - def __init__( - self, - idim: int, - hidden_units: int, - dropout_rate: float, - activation: torch.nn.Module = torch.nn.ReLU(), - ): - """Construct a PositionwiseFeedForward object.""" - super(PositionwiseFeedForward, self).__init__() - self.w_1 = torch.nn.Linear(idim, hidden_units) - self.activation = activation - self.dropout = torch.nn.Dropout(dropout_rate) - self.w_2 = torch.nn.Linear(hidden_units, idim) - - def forward(self, xs: torch.Tensor) -> torch.Tensor: - """Forward function. - - Args: - xs: input tensor (B, L, D) - Returns: - output tensor, (B, L, D) - """ - return self.w_2(self.dropout(self.activation(self.w_1(xs)))) diff --git a/modules/wenet_extractor/transformer/subsampling.py b/modules/wenet_extractor/transformer/subsampling.py deleted file mode 100644 index ffbb5dae0dd013c916cc4a4266cdc838ba48a84f..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/subsampling.py +++ /dev/null @@ -1,257 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - - -"""Subsampling layer definition.""" - -from typing import Tuple, Union - -import torch - - -class BaseSubsampling(torch.nn.Module): - def __init__(self): - super().__init__() - self.right_context = 0 - self.subsampling_rate = 1 - - def position_encoding( - self, offset: Union[int, torch.Tensor], size: int - ) -> torch.Tensor: - return self.pos_enc.position_encoding(offset, size) - - -class LinearNoSubsampling(BaseSubsampling): - """Linear transform the input without subsampling - - Args: - idim (int): Input dimension. - odim (int): Output dimension. - dropout_rate (float): Dropout rate. - - """ - - def __init__( - self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module - ): - """Construct an linear object.""" - super().__init__() - self.out = torch.nn.Sequential( - torch.nn.Linear(idim, odim), - torch.nn.LayerNorm(odim, eps=1e-5), - torch.nn.Dropout(dropout_rate), - ) - self.pos_enc = pos_enc_class - self.right_context = 0 - self.subsampling_rate = 1 - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - offset: Union[int, torch.Tensor] = 0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Input x. - - Args: - x (torch.Tensor): Input tensor (#batch, time, idim). - x_mask (torch.Tensor): Input mask (#batch, 1, time). - - Returns: - torch.Tensor: linear input tensor (#batch, time', odim), - where time' = time . - torch.Tensor: linear input mask (#batch, 1, time'), - where time' = time . - - """ - x = self.out(x) - x, pos_emb = self.pos_enc(x, offset) - return x, pos_emb, x_mask - - -class Conv2dSubsampling4(BaseSubsampling): - """Convolutional 2D subsampling (to 1/4 length). - - Args: - idim (int): Input dimension. - odim (int): Output dimension. - dropout_rate (float): Dropout rate. - - """ - - def __init__( - self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module - ): - """Construct an Conv2dSubsampling4 object.""" - super().__init__() - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 3, 2), - torch.nn.ReLU(), - ) - self.out = torch.nn.Sequential( - torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim) - ) - self.pos_enc = pos_enc_class - # The right context for every conv layer is computed by: - # (kernel_size - 1) * frame_rate_of_this_layer - self.subsampling_rate = 4 - # 6 = (3 - 1) * 1 + (3 - 1) * 2 - self.right_context = 6 - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - offset: Union[int, torch.Tensor] = 0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Subsample x. - - Args: - x (torch.Tensor): Input tensor (#batch, time, idim). - x_mask (torch.Tensor): Input mask (#batch, 1, time). - - Returns: - torch.Tensor: Subsampled tensor (#batch, time', odim), - where time' = time // 4. - torch.Tensor: Subsampled mask (#batch, 1, time'), - where time' = time // 4. - torch.Tensor: positional encoding - - """ - x = x.unsqueeze(1) # (b, c=1, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) - x, pos_emb = self.pos_enc(x, offset) - return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2] - - -class Conv2dSubsampling6(BaseSubsampling): - """Convolutional 2D subsampling (to 1/6 length). - Args: - idim (int): Input dimension. - odim (int): Output dimension. - dropout_rate (float): Dropout rate. - pos_enc (torch.nn.Module): Custom position encoding layer. - """ - - def __init__( - self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module - ): - """Construct an Conv2dSubsampling6 object.""" - super().__init__() - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 5, 3), - torch.nn.ReLU(), - ) - self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim) - self.pos_enc = pos_enc_class - # 10 = (3 - 1) * 1 + (5 - 1) * 2 - self.subsampling_rate = 6 - self.right_context = 10 - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - offset: Union[int, torch.Tensor] = 0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Subsample x. - Args: - x (torch.Tensor): Input tensor (#batch, time, idim). - x_mask (torch.Tensor): Input mask (#batch, 1, time). - - Returns: - torch.Tensor: Subsampled tensor (#batch, time', odim), - where time' = time // 6. - torch.Tensor: Subsampled mask (#batch, 1, time'), - where time' = time // 6. - torch.Tensor: positional encoding - """ - x = x.unsqueeze(1) # (b, c, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) - x, pos_emb = self.pos_enc(x, offset) - return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3] - - -class Conv2dSubsampling8(BaseSubsampling): - """Convolutional 2D subsampling (to 1/8 length). - - Args: - idim (int): Input dimension. - odim (int): Output dimension. - dropout_rate (float): Dropout rate. - - """ - - def __init__( - self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module - ): - """Construct an Conv2dSubsampling8 object.""" - super().__init__() - self.conv = torch.nn.Sequential( - torch.nn.Conv2d(1, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 3, 2), - torch.nn.ReLU(), - torch.nn.Conv2d(odim, odim, 3, 2), - torch.nn.ReLU(), - ) - self.linear = torch.nn.Linear( - odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim - ) - self.pos_enc = pos_enc_class - self.subsampling_rate = 8 - # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4 - self.right_context = 14 - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - offset: Union[int, torch.Tensor] = 0, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Subsample x. - - Args: - x (torch.Tensor): Input tensor (#batch, time, idim). - x_mask (torch.Tensor): Input mask (#batch, 1, time). - - Returns: - torch.Tensor: Subsampled tensor (#batch, time', odim), - where time' = time // 8. - torch.Tensor: Subsampled mask (#batch, 1, time'), - where time' = time // 8. - torch.Tensor: positional encoding - """ - x = x.unsqueeze(1) # (b, c, t, f) - x = self.conv(x) - b, c, t, f = x.size() - x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) - x, pos_emb = self.pos_enc(x, offset) - return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2] diff --git a/modules/wenet_extractor/transformer/swish.py b/modules/wenet_extractor/transformer/swish.py deleted file mode 100644 index 8728089430d0c20fa1575d12e3b6573417b4448c..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/transformer/swish.py +++ /dev/null @@ -1,33 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -"""Swish() activation function for Conformer.""" - -import torch - - -class Swish(torch.nn.Module): - """Construct an Swish object.""" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Return Swish activation function.""" - return x * torch.sigmoid(x) diff --git a/modules/wenet_extractor/utils/__init__.py b/modules/wenet_extractor/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/wenet_extractor/utils/checkpoint.py b/modules/wenet_extractor/utils/checkpoint.py deleted file mode 100644 index f17784b1e1b41284191b0caff7570dd2066f7e91..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/checkpoint.py +++ /dev/null @@ -1,113 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import logging -import os -import re - -import yaml -import torch -from collections import OrderedDict - -import datetime - - -def load_checkpoint(model: torch.nn.Module, path: str) -> dict: - if torch.cuda.is_available(): - logging.info("Checkpoint: loading from checkpoint %s for GPU" % path) - checkpoint = torch.load(path) - else: - logging.info("Checkpoint: loading from checkpoint %s for CPU" % path) - checkpoint = torch.load(path, map_location="cpu") - model.load_state_dict(checkpoint, strict=False) - info_path = re.sub(".pt$", ".yaml", path) - configs = {} - if os.path.exists(info_path): - with open(info_path, "r") as fin: - configs = yaml.load(fin, Loader=yaml.FullLoader) - return configs - - -def save_checkpoint(model: torch.nn.Module, path: str, infos=None): - """ - Args: - infos (dict or None): any info you want to save. - """ - logging.info("Checkpoint: save to checkpoint %s" % path) - if isinstance(model, torch.nn.DataParallel): - state_dict = model.module.state_dict() - elif isinstance(model, torch.nn.parallel.DistributedDataParallel): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save(state_dict, path) - info_path = re.sub(".pt$", ".yaml", path) - if infos is None: - infos = {} - infos["save_time"] = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S") - with open(info_path, "w") as fout: - data = yaml.dump(infos) - fout.write(data) - - -def filter_modules(model_state_dict, modules): - new_mods = [] - incorrect_mods = [] - mods_model = model_state_dict.keys() - for mod in modules: - if any(key.startswith(mod) for key in mods_model): - new_mods += [mod] - else: - incorrect_mods += [mod] - if incorrect_mods: - logging.warning( - "module(s) %s don't match or (partially match) " - "available modules in model.", - incorrect_mods, - ) - logging.warning("for information, the existing modules in model are:") - logging.warning("%s", mods_model) - - return new_mods - - -def load_trained_modules(model: torch.nn.Module, args: None): - # Load encoder modules with pre-trained model(s). - enc_model_path = args.enc_init - enc_modules = args.enc_init_mods - main_state_dict = model.state_dict() - logging.warning("model(s) found for pre-initialization") - if os.path.isfile(enc_model_path): - logging.info("Checkpoint: loading from checkpoint %s for CPU" % enc_model_path) - model_state_dict = torch.load(enc_model_path, map_location="cpu") - modules = filter_modules(model_state_dict, enc_modules) - partial_state_dict = OrderedDict() - for key, value in model_state_dict.items(): - if any(key.startswith(m) for m in modules): - partial_state_dict[key] = value - main_state_dict.update(partial_state_dict) - else: - logging.warning("model was not found : %s", enc_model_path) - - model.load_state_dict(main_state_dict) - configs = {} - return configs diff --git a/modules/wenet_extractor/utils/cmvn.py b/modules/wenet_extractor/utils/cmvn.py deleted file mode 100644 index 483adc1f709786ae83f570faf10ee1a1ded85e52..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/cmvn.py +++ /dev/null @@ -1,103 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import json -import math - -import numpy as np - - -def _load_json_cmvn(json_cmvn_file): - """Load the json format cmvn stats file and calculate cmvn - - Args: - json_cmvn_file: cmvn stats file in json format - - Returns: - a numpy array of [means, vars] - """ - with open(json_cmvn_file) as f: - cmvn_stats = json.load(f) - - means = cmvn_stats["mean_stat"] - variance = cmvn_stats["var_stat"] - count = cmvn_stats["frame_num"] - for i in range(len(means)): - means[i] /= count - variance[i] = variance[i] / count - means[i] * means[i] - if variance[i] < 1.0e-20: - variance[i] = 1.0e-20 - variance[i] = 1.0 / math.sqrt(variance[i]) - cmvn = np.array([means, variance]) - return cmvn - - -def _load_kaldi_cmvn(kaldi_cmvn_file): - """Load the kaldi format cmvn stats file and calculate cmvn - - Args: - kaldi_cmvn_file: kaldi text style global cmvn file, which - is generated by: - compute-cmvn-stats --binary=false scp:feats.scp global_cmvn - - Returns: - a numpy array of [means, vars] - """ - means = [] - variance = [] - with open(kaldi_cmvn_file, "r") as fid: - # kaldi binary file start with '\0B' - if fid.read(2) == "\0B": - logging.error( - "kaldi cmvn binary file is not supported, please " - "recompute it by: compute-cmvn-stats --binary=false " - " scp:feats.scp global_cmvn" - ) - sys.exit(1) - fid.seek(0) - arr = fid.read().split() - assert arr[0] == "[" - assert arr[-2] == "0" - assert arr[-1] == "]" - feat_dim = int((len(arr) - 2 - 2) / 2) - for i in range(1, feat_dim + 1): - means.append(float(arr[i])) - count = float(arr[feat_dim + 1]) - for i in range(feat_dim + 2, 2 * feat_dim + 2): - variance.append(float(arr[i])) - - for i in range(len(means)): - means[i] /= count - variance[i] = variance[i] / count - means[i] * means[i] - if variance[i] < 1.0e-20: - variance[i] = 1.0e-20 - variance[i] = 1.0 / math.sqrt(variance[i]) - cmvn = np.array([means, variance]) - return cmvn - - -def load_cmvn(cmvn_file, is_json): - if is_json: - cmvn = _load_json_cmvn(cmvn_file) - else: - cmvn = _load_kaldi_cmvn(cmvn_file) - return cmvn[0], cmvn[1] diff --git a/modules/wenet_extractor/utils/common.py b/modules/wenet_extractor/utils/common.py deleted file mode 100644 index 8900ce4cd279e87829807c14a14e2ca38841b89d..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/common.py +++ /dev/null @@ -1,266 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# -"""Unility functions for Transformer.""" - -import math -from typing import List, Tuple - -import torch -from torch.nn.utils.rnn import pad_sequence - -IGNORE_ID = -1 - - -def pad_list(xs: List[torch.Tensor], pad_value: int): - """Perform padding for the list of tensors. - - Args: - xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. - pad_value (float): Value for padding. - - Returns: - Tensor: Padded tensor (B, Tmax, `*`). - - Examples: - >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)] - >>> x - [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] - >>> pad_list(x, 0) - tensor([[1., 1., 1., 1.], - [1., 1., 0., 0.], - [1., 0., 0., 0.]]) - - """ - n_batch = len(xs) - max_len = max([x.size(0) for x in xs]) - pad = torch.zeros(n_batch, max_len, dtype=xs[0].dtype, device=xs[0].device) - pad = pad.fill_(pad_value) - for i in range(n_batch): - pad[i, : xs[i].size(0)] = xs[i] - - return pad - - -def add_blank(ys_pad: torch.Tensor, blank: int, ignore_id: int) -> torch.Tensor: - """Prepad blank for transducer predictor - - Args: - ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax) - blank (int): index of - - Returns: - ys_in (torch.Tensor) : (B, Lmax + 1) - - Examples: - >>> blank = 0 - >>> ignore_id = -1 - >>> ys_pad - tensor([[ 1, 2, 3, 4, 5], - [ 4, 5, 6, -1, -1], - [ 7, 8, 9, -1, -1]], dtype=torch.int32) - >>> ys_in = add_blank(ys_pad, 0, -1) - >>> ys_in - tensor([[0, 1, 2, 3, 4, 5], - [0, 4, 5, 6, 0, 0], - [0, 7, 8, 9, 0, 0]]) - """ - bs = ys_pad.size(0) - _blank = torch.tensor( - [blank], dtype=torch.long, requires_grad=False, device=ys_pad.device - ) - _blank = _blank.repeat(bs).unsqueeze(1) # [bs,1] - out = torch.cat([_blank, ys_pad], dim=1) # [bs, Lmax+1] - return torch.where(out == ignore_id, blank, out) - - -def add_sos_eos( - ys_pad: torch.Tensor, sos: int, eos: int, ignore_id: int -) -> Tuple[torch.Tensor, torch.Tensor]: - """Add and labels. - - Args: - ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax) - sos (int): index of - eos (int): index of - ignore_id (int): index of padding - - Returns: - ys_in (torch.Tensor) : (B, Lmax + 1) - ys_out (torch.Tensor) : (B, Lmax + 1) - - Examples: - >>> sos_id = 10 - >>> eos_id = 11 - >>> ignore_id = -1 - >>> ys_pad - tensor([[ 1, 2, 3, 4, 5], - [ 4, 5, 6, -1, -1], - [ 7, 8, 9, -1, -1]], dtype=torch.int32) - >>> ys_in,ys_out=add_sos_eos(ys_pad, sos_id , eos_id, ignore_id) - >>> ys_in - tensor([[10, 1, 2, 3, 4, 5], - [10, 4, 5, 6, 11, 11], - [10, 7, 8, 9, 11, 11]]) - >>> ys_out - tensor([[ 1, 2, 3, 4, 5, 11], - [ 4, 5, 6, 11, -1, -1], - [ 7, 8, 9, 11, -1, -1]]) - """ - _sos = torch.tensor( - [sos], dtype=torch.long, requires_grad=False, device=ys_pad.device - ) - _eos = torch.tensor( - [eos], dtype=torch.long, requires_grad=False, device=ys_pad.device - ) - ys = [y[y != ignore_id] for y in ys_pad] # parse padded ys - ys_in = [torch.cat([_sos, y], dim=0) for y in ys] - ys_out = [torch.cat([y, _eos], dim=0) for y in ys] - return pad_list(ys_in, eos), pad_list(ys_out, ignore_id) - - -def reverse_pad_list( - ys_pad: torch.Tensor, ys_lens: torch.Tensor, pad_value: float = -1.0 -) -> torch.Tensor: - """Reverse padding for the list of tensors. - - Args: - ys_pad (tensor): The padded tensor (B, Tokenmax). - ys_lens (tensor): The lens of token seqs (B) - pad_value (int): Value for padding. - - Returns: - Tensor: Padded tensor (B, Tokenmax). - - Examples: - >>> x - tensor([[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]]) - >>> pad_list(x, 0) - tensor([[4, 3, 2, 1], - [7, 6, 5, 0], - [9, 8, 0, 0]]) - - """ - r_ys_pad = pad_sequence( - [(torch.flip(y.int()[:i], [0])) for y, i in zip(ys_pad, ys_lens)], - True, - pad_value, - ) - return r_ys_pad - - -def th_accuracy( - pad_outputs: torch.Tensor, pad_targets: torch.Tensor, ignore_label: int -) -> float: - """Calculate accuracy. - - Args: - pad_outputs (Tensor): Prediction tensors (B * Lmax, D). - pad_targets (LongTensor): Target label tensors (B, Lmax). - ignore_label (int): Ignore label id. - - Returns: - float: Accuracy value (0.0 - 1.0). - - """ - pad_pred = pad_outputs.view( - pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1) - ).argmax(2) - mask = pad_targets != ignore_label - numerator = torch.sum( - pad_pred.masked_select(mask) == pad_targets.masked_select(mask) - ) - denominator = torch.sum(mask) - return float(numerator) / float(denominator) - - -def get_rnn(rnn_type: str) -> torch.nn.Module: - assert rnn_type in ["rnn", "lstm", "gru"] - if rnn_type == "rnn": - return torch.nn.RNN - elif rnn_type == "lstm": - return torch.nn.LSTM - else: - return torch.nn.GRU - - -def get_activation(act): - """Return activation function.""" - # Lazy load to avoid unused import - from modules.wenet_extractor.transformer.swish import Swish - - activation_funcs = { - "hardtanh": torch.nn.Hardtanh, - "tanh": torch.nn.Tanh, - "relu": torch.nn.ReLU, - "selu": torch.nn.SELU, - "swish": getattr(torch.nn, "SiLU", Swish), - "gelu": torch.nn.GELU, - } - - return activation_funcs[act]() - - -def get_subsample(config): - input_layer = config["encoder_conf"]["input_layer"] - assert input_layer in ["conv2d", "conv2d6", "conv2d8"] - if input_layer == "conv2d": - return 4 - elif input_layer == "conv2d6": - return 6 - elif input_layer == "conv2d8": - return 8 - - -def remove_duplicates_and_blank(hyp: List[int]) -> List[int]: - new_hyp: List[int] = [] - cur = 0 - while cur < len(hyp): - if hyp[cur] != 0: - new_hyp.append(hyp[cur]) - prev = cur - while cur < len(hyp) and hyp[cur] == hyp[prev]: - cur += 1 - return new_hyp - - -def replace_duplicates_with_blank(hyp: List[int]) -> List[int]: - new_hyp: List[int] = [] - cur = 0 - while cur < len(hyp): - new_hyp.append(hyp[cur]) - prev = cur - cur += 1 - while cur < len(hyp) and hyp[cur] == hyp[prev] and hyp[cur] != 0: - new_hyp.append(0) - cur += 1 - return new_hyp - - -def log_add(args: List[int]) -> float: - """ - Stable log add - """ - if all(a == -float("inf") for a in args): - return -float("inf") - a_max = max(args) - lsp = math.log(sum(math.exp(a - a_max) for a in args)) - return a_max + lsp diff --git a/modules/wenet_extractor/utils/config.py b/modules/wenet_extractor/utils/config.py deleted file mode 100644 index 5f7ab928eec9d18d1af46655f9f324a7ac1eb9dc..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/config.py +++ /dev/null @@ -1,48 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - - -import copy - - -def override_config(configs, override_list): - new_configs = copy.deepcopy(configs) - for item in override_list: - arr = item.split() - if len(arr) != 2: - print(f"the overrive {item} format not correct, skip it") - continue - keys = arr[0].split(".") - s_configs = new_configs - for i, key in enumerate(keys): - if key not in s_configs: - print(f"the overrive {item} format not correct, skip it") - if i == len(keys) - 1: - param_type = type(s_configs[key]) - if param_type != bool: - s_configs[key] = param_type(arr[1]) - else: - s_configs[key] = arr[1] in ["true", "True"] - print(f"override {arr[0]} with {arr[1]}") - else: - s_configs = s_configs[key] - return new_configs diff --git a/modules/wenet_extractor/utils/ctc_util.py b/modules/wenet_extractor/utils/ctc_util.py deleted file mode 100644 index 03154248bb087d930a9cc579e73d782756cd6ea0..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/ctc_util.py +++ /dev/null @@ -1,96 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import numpy as np -import torch - - -def insert_blank(label, blank_id=0): - """Insert blank token between every two label token.""" - label = np.expand_dims(label, 1) - blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id - label = np.concatenate([blanks, label], axis=1) - label = label.reshape(-1) - label = np.append(label, label[0]) - return label - - -def forced_align(ctc_probs: torch.Tensor, y: torch.Tensor, blank_id=0) -> list: - """ctc forced alignment. - - Args: - torch.Tensor ctc_probs: hidden state sequence, 2d tensor (T, D) - torch.Tensor y: id sequence tensor 1d tensor (L) - int blank_id: blank symbol index - Returns: - torch.Tensor: alignment result - """ - y_insert_blank = insert_blank(y, blank_id) - - log_alpha = torch.zeros((ctc_probs.size(0), len(y_insert_blank))) - log_alpha = log_alpha - float("inf") # log of zero - state_path = ( - torch.zeros((ctc_probs.size(0), len(y_insert_blank)), dtype=torch.int16) - 1 - ) # state path - - # init start state - log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]] - log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]] - - for t in range(1, ctc_probs.size(0)): - for s in range(len(y_insert_blank)): - if ( - y_insert_blank[s] == blank_id - or s < 2 - or y_insert_blank[s] == y_insert_blank[s - 2] - ): - candidates = torch.tensor( - [log_alpha[t - 1, s], log_alpha[t - 1, s - 1]] - ) - prev_state = [s, s - 1] - else: - candidates = torch.tensor( - [ - log_alpha[t - 1, s], - log_alpha[t - 1, s - 1], - log_alpha[t - 1, s - 2], - ] - ) - prev_state = [s, s - 1, s - 2] - log_alpha[t, s] = torch.max(candidates) + ctc_probs[t][y_insert_blank[s]] - state_path[t, s] = prev_state[torch.argmax(candidates)] - - state_seq = -1 * torch.ones((ctc_probs.size(0), 1), dtype=torch.int16) - - candidates = torch.tensor( - [log_alpha[-1, len(y_insert_blank) - 1], log_alpha[-1, len(y_insert_blank) - 2]] - ) - final_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2] - state_seq[-1] = final_state[torch.argmax(candidates)] - for t in range(ctc_probs.size(0) - 2, -1, -1): - state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]] - - output_alignment = [] - for t in range(0, ctc_probs.size(0)): - output_alignment.append(y_insert_blank[state_seq[t, 0]]) - - return output_alignment diff --git a/modules/wenet_extractor/utils/executor.py b/modules/wenet_extractor/utils/executor.py deleted file mode 100644 index 8d55549fec4f4d350e56093f3995ac93a31ff029..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/executor.py +++ /dev/null @@ -1,163 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import logging -from contextlib import nullcontext - -# if your python version < 3.7 use the below one -# from contextlib import suppress as nullcontext -import torch -from torch.nn.utils import clip_grad_norm_ - - -class Executor: - def __init__(self): - self.step = 0 - - def train( - self, model, optimizer, scheduler, data_loader, device, writer, args, scaler - ): - """Train one epoch""" - model.train() - clip = args.get("grad_clip", 50.0) - log_interval = args.get("log_interval", 10) - rank = args.get("rank", 0) - epoch = args.get("epoch", 0) - accum_grad = args.get("accum_grad", 1) - is_distributed = args.get("is_distributed", True) - use_amp = args.get("use_amp", False) - logging.info( - "using accumulate grad, new batch size is {} times" - " larger than before".format(accum_grad) - ) - if use_amp: - assert scaler is not None - # A context manager to be used in conjunction with an instance of - # torch.nn.parallel.DistributedDataParallel to be able to train - # with uneven inputs across participating processes. - if isinstance(model, torch.nn.parallel.DistributedDataParallel): - model_context = model.join - else: - model_context = nullcontext - num_seen_utts = 0 - with model_context(): - for batch_idx, batch in enumerate(data_loader): - key, feats, target, feats_lengths, target_lengths = batch - feats = feats.to(device) - target = target.to(device) - feats_lengths = feats_lengths.to(device) - target_lengths = target_lengths.to(device) - num_utts = target_lengths.size(0) - if num_utts == 0: - continue - context = None - # Disable gradient synchronizations across DDP processes. - # Within this context, gradients will be accumulated on module - # variables, which will later be synchronized. - if is_distributed and batch_idx % accum_grad != 0: - context = model.no_sync - # Used for single gpu training and DDP gradient synchronization - # processes. - else: - context = nullcontext - with context(): - # autocast context - # The more details about amp can be found in - # https://pytorch.org/docs/stable/notes/amp_examples.html - with torch.cuda.amp.autocast(scaler is not None): - loss_dict = model(feats, feats_lengths, target, target_lengths) - loss = loss_dict["loss"] / accum_grad - if use_amp: - scaler.scale(loss).backward() - else: - loss.backward() - - num_seen_utts += num_utts - if batch_idx % accum_grad == 0: - if rank == 0 and writer is not None: - writer.add_scalar("train_loss", loss, self.step) - # Use mixed precision training - if use_amp: - scaler.unscale_(optimizer) - grad_norm = clip_grad_norm_(model.parameters(), clip) - # Must invoke scaler.update() if unscale_() is used in - # the iteration to avoid the following error: - # RuntimeError: unscale_() has already been called - # on this optimizer since the last update(). - # We don't check grad here since that if the gradient - # has inf/nan values, scaler.step will skip - # optimizer.step(). - scaler.step(optimizer) - scaler.update() - else: - grad_norm = clip_grad_norm_(model.parameters(), clip) - if torch.isfinite(grad_norm): - optimizer.step() - optimizer.zero_grad() - scheduler.step() - self.step += 1 - if batch_idx % log_interval == 0: - lr = optimizer.param_groups[0]["lr"] - log_str = "TRAIN Batch {}/{} loss {:.6f} ".format( - epoch, batch_idx, loss.item() * accum_grad - ) - for name, value in loss_dict.items(): - if name != "loss" and value is not None: - log_str += "{} {:.6f} ".format(name, value.item()) - log_str += "lr {:.8f} rank {}".format(lr, rank) - logging.debug(log_str) - - def cv(self, model, data_loader, device, args): - """Cross validation on""" - model.eval() - rank = args.get("rank", 0) - epoch = args.get("epoch", 0) - log_interval = args.get("log_interval", 10) - # in order to avoid division by 0 - num_seen_utts = 1 - total_loss = 0.0 - with torch.no_grad(): - for batch_idx, batch in enumerate(data_loader): - key, feats, target, feats_lengths, target_lengths = batch - feats = feats.to(device) - target = target.to(device) - feats_lengths = feats_lengths.to(device) - target_lengths = target_lengths.to(device) - num_utts = target_lengths.size(0) - if num_utts == 0: - continue - loss_dict = model(feats, feats_lengths, target, target_lengths) - loss = loss_dict["loss"] - if torch.isfinite(loss): - num_seen_utts += num_utts - total_loss += loss.item() * num_utts - if batch_idx % log_interval == 0: - log_str = "CV Batch {}/{} loss {:.6f} ".format( - epoch, batch_idx, loss.item() - ) - for name, value in loss_dict.items(): - if name != "loss" and value is not None: - log_str += "{} {:.6f} ".format(name, value.item()) - log_str += "history loss {:.6f}".format(total_loss / num_seen_utts) - log_str += " rank {}".format(rank) - logging.debug(log_str) - return total_loss, num_seen_utts diff --git a/modules/wenet_extractor/utils/file_utils.py b/modules/wenet_extractor/utils/file_utils.py deleted file mode 100644 index 443759bd4da9d7f85d48f0534205f9e5cd7048df..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/file_utils.py +++ /dev/null @@ -1,77 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import re - - -def read_lists(list_file): - lists = [] - with open(list_file, "r", encoding="utf8") as fin: - for line in fin: - lists.append(line.strip()) - return lists - - -def read_non_lang_symbols(non_lang_sym_path): - """read non-linguistic symbol from file. - - The file format is like below: - - {NOISE}\n - {BRK}\n - ... - - - Args: - non_lang_sym_path: non-linguistic symbol file path, None means no any - syms. - - """ - if non_lang_sym_path is None: - return None - else: - syms = read_lists(non_lang_sym_path) - non_lang_syms_pattern = re.compile(r"(\[[^\[\]]+\]|<[^<>]+>|{[^{}]+})") - for sym in syms: - if non_lang_syms_pattern.fullmatch(sym) is None: - - class BadSymbolFormat(Exception): - pass - - raise BadSymbolFormat( - "Non-linguistic symbols should be " - "formatted in {xxx}//[xxx], consider" - " modify '%s' to meet the requirment. " - "More details can be found in discussions here : " - "https://github.com/wenet-e2e/wenet/pull/819" % (sym) - ) - return syms - - -def read_symbol_table(symbol_table_file): - symbol_table = {} - with open(symbol_table_file, "r", encoding="utf8") as fin: - for line in fin: - arr = line.strip().split() - assert len(arr) == 2 - symbol_table[arr[0]] = int(arr[1]) - return symbol_table diff --git a/modules/wenet_extractor/utils/init_model.py b/modules/wenet_extractor/utils/init_model.py deleted file mode 100644 index 82a391efec400163935c387e1134f179ea48e2cb..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/init_model.py +++ /dev/null @@ -1,152 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -import torch -from modules.wenet_extractor.transducer.joint import TransducerJoint -from modules.wenet_extractor.transducer.predictor import ( - ConvPredictor, - EmbeddingPredictor, - RNNPredictor, -) -from modules.wenet_extractor.transducer.transducer import Transducer -from modules.wenet_extractor.transformer.asr_model import ASRModel -from modules.wenet_extractor.transformer.cmvn import GlobalCMVN -from modules.wenet_extractor.transformer.ctc import CTC -from modules.wenet_extractor.transformer.decoder import ( - BiTransformerDecoder, - TransformerDecoder, -) -from modules.wenet_extractor.transformer.encoder import ( - ConformerEncoder, - TransformerEncoder, -) -from modules.wenet_extractor.squeezeformer.encoder import SqueezeformerEncoder -from modules.wenet_extractor.efficient_conformer.encoder import ( - EfficientConformerEncoder, -) -from modules.wenet_extractor.paraformer.paraformer import Paraformer -from modules.wenet_extractor.cif.predictor import Predictor -from modules.wenet_extractor.utils.cmvn import load_cmvn - - -def init_model(configs): - if configs["cmvn_file"] is not None: - mean, istd = load_cmvn(configs["cmvn_file"], configs["is_json_cmvn"]) - global_cmvn = GlobalCMVN( - torch.from_numpy(mean).float(), torch.from_numpy(istd).float() - ) - else: - global_cmvn = None - - input_dim = configs["input_dim"] - vocab_size = configs["output_dim"] - - encoder_type = configs.get("encoder", "conformer") - decoder_type = configs.get("decoder", "bitransformer") - - if encoder_type == "conformer": - encoder = ConformerEncoder( - input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"] - ) - elif encoder_type == "squeezeformer": - encoder = SqueezeformerEncoder( - input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"] - ) - elif encoder_type == "efficientConformer": - encoder = EfficientConformerEncoder( - input_dim, - global_cmvn=global_cmvn, - **configs["encoder_conf"], - **configs["encoder_conf"]["efficient_conf"] - if "efficient_conf" in configs["encoder_conf"] - else {}, - ) - else: - encoder = TransformerEncoder( - input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"] - ) - if decoder_type == "transformer": - decoder = TransformerDecoder( - vocab_size, encoder.output_size(), **configs["decoder_conf"] - ) - else: - assert 0.0 < configs["model_conf"]["reverse_weight"] < 1.0 - assert configs["decoder_conf"]["r_num_blocks"] > 0 - decoder = BiTransformerDecoder( - vocab_size, encoder.output_size(), **configs["decoder_conf"] - ) - ctc = CTC(vocab_size, encoder.output_size()) - - # Init joint CTC/Attention or Transducer model - if "predictor" in configs: - predictor_type = configs.get("predictor", "rnn") - if predictor_type == "rnn": - predictor = RNNPredictor(vocab_size, **configs["predictor_conf"]) - elif predictor_type == "embedding": - predictor = EmbeddingPredictor(vocab_size, **configs["predictor_conf"]) - configs["predictor_conf"]["output_size"] = configs["predictor_conf"][ - "embed_size" - ] - elif predictor_type == "conv": - predictor = ConvPredictor(vocab_size, **configs["predictor_conf"]) - configs["predictor_conf"]["output_size"] = configs["predictor_conf"][ - "embed_size" - ] - else: - raise NotImplementedError("only rnn, embedding and conv type support now") - configs["joint_conf"]["enc_output_size"] = configs["encoder_conf"][ - "output_size" - ] - configs["joint_conf"]["pred_output_size"] = configs["predictor_conf"][ - "output_size" - ] - joint = TransducerJoint(vocab_size, **configs["joint_conf"]) - model = Transducer( - vocab_size=vocab_size, - blank=0, - predictor=predictor, - encoder=encoder, - attention_decoder=decoder, - joint=joint, - ctc=ctc, - **configs["model_conf"], - ) - elif "paraformer" in configs: - predictor = Predictor(**configs["cif_predictor_conf"]) - model = Paraformer( - vocab_size=vocab_size, - encoder=encoder, - decoder=decoder, - ctc=ctc, - predictor=predictor, - **configs["model_conf"], - ) - else: - model = ASRModel( - vocab_size=vocab_size, - encoder=encoder, - decoder=decoder, - ctc=ctc, - lfmmi_dir=configs.get("lfmmi_dir", ""), - **configs["model_conf"], - ) - return model diff --git a/modules/wenet_extractor/utils/mask.py b/modules/wenet_extractor/utils/mask.py deleted file mode 100644 index 97b6149781893da3fb37e225ef47338a79ef601f..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/mask.py +++ /dev/null @@ -1,304 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - - -import torch - -''' -def subsequent_mask( - size: int, - device: torch.device = torch.device("cpu"), -) -> torch.Tensor: - """Create mask for subsequent steps (size, size). - - This mask is used only in decoder which works in an auto-regressive mode. - This means the current step could only do attention with its left steps. - - In encoder, fully attention is used when streaming is not necessary and - the sequence is not long. In this case, no attention mask is needed. - - When streaming is need, chunk-based attention is used in encoder. See - subsequent_chunk_mask for the chunk-based attention mask. - - Args: - size (int): size of mask - str device (str): "cpu" or "cuda" or torch.Tensor.device - dtype (torch.device): result dtype - - Returns: - torch.Tensor: mask - - Examples: - >>> subsequent_mask(3) - [[1, 0, 0], - [1, 1, 0], - [1, 1, 1]] - """ - ret = torch.ones(size, size, device=device, dtype=torch.bool) - return torch.tril(ret) -''' - - -def subsequent_mask( - size: int, - device: torch.device = torch.device("cpu"), -) -> torch.Tensor: - """Create mask for subsequent steps (size, size). - - This mask is used only in decoder which works in an auto-regressive mode. - This means the current step could only do attention with its left steps. - - In encoder, fully attention is used when streaming is not necessary and - the sequence is not long. In this case, no attention mask is needed. - - When streaming is need, chunk-based attention is used in encoder. See - subsequent_chunk_mask for the chunk-based attention mask. - - Args: - size (int): size of mask - str device (str): "cpu" or "cuda" or torch.Tensor.device - dtype (torch.device): result dtype - - Returns: - torch.Tensor: mask - - Examples: - >>> subsequent_mask(3) - [[1, 0, 0], - [1, 1, 0], - [1, 1, 1]] - """ - arange = torch.arange(size, device=device) - mask = arange.expand(size, size) - arange = arange.unsqueeze(-1) - mask = mask <= arange - return mask - - -def subsequent_chunk_mask( - size: int, - chunk_size: int, - num_left_chunks: int = -1, - device: torch.device = torch.device("cpu"), -) -> torch.Tensor: - """Create mask for subsequent steps (size, size) with chunk size, - this is for streaming encoder - - Args: - size (int): size of mask - chunk_size (int): size of chunk - num_left_chunks (int): number of left chunks - <0: use full chunk - >=0: use num_left_chunks - device (torch.device): "cpu" or "cuda" or torch.Tensor.device - - Returns: - torch.Tensor: mask - - Examples: - >>> subsequent_chunk_mask(4, 2) - [[1, 1, 0, 0], - [1, 1, 0, 0], - [1, 1, 1, 1], - [1, 1, 1, 1]] - """ - ret = torch.zeros(size, size, device=device, dtype=torch.bool) - for i in range(size): - if num_left_chunks < 0: - start = 0 - else: - start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) - ending = min((i // chunk_size + 1) * chunk_size, size) - ret[i, start:ending] = True - return ret - - -def add_optional_chunk_mask( - xs: torch.Tensor, - masks: torch.Tensor, - use_dynamic_chunk: bool, - use_dynamic_left_chunk: bool, - decoding_chunk_size: int, - static_chunk_size: int, - num_decoding_left_chunks: int, -): - """Apply optional mask for encoder. - - Args: - xs (torch.Tensor): padded input, (B, L, D), L for max length - mask (torch.Tensor): mask for xs, (B, 1, L) - use_dynamic_chunk (bool): whether to use dynamic chunk or not - use_dynamic_left_chunk (bool): whether to use dynamic left chunk for - training. - decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's - 0: default for training, use random dynamic chunk. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - static_chunk_size (int): chunk size for static chunk training/decoding - if it's greater than 0, if use_dynamic_chunk is true, - this parameter will be ignored - num_decoding_left_chunks: number of left chunks, this is for decoding, - the chunk size is decoding_chunk_size. - >=0: use num_decoding_left_chunks - <0: use all left chunks - - Returns: - torch.Tensor: chunk mask of the input xs. - """ - # Whether to use chunk mask or not - if use_dynamic_chunk: - max_len = xs.size(1) - if decoding_chunk_size < 0: - chunk_size = max_len - num_left_chunks = -1 - elif decoding_chunk_size > 0: - chunk_size = decoding_chunk_size - num_left_chunks = num_decoding_left_chunks - else: - # chunk size is either [1, 25] or full context(max_len). - # Since we use 4 times subsampling and allow up to 1s(100 frames) - # delay, the maximum frame is 100 / 4 = 25. - chunk_size = torch.randint(1, max_len, (1,)).item() - num_left_chunks = -1 - if chunk_size > max_len // 2: - chunk_size = max_len - else: - chunk_size = chunk_size % 25 + 1 - if use_dynamic_left_chunk: - max_left_chunks = (max_len - 1) // chunk_size - num_left_chunks = torch.randint(0, max_left_chunks, (1,)).item() - chunk_masks = subsequent_chunk_mask( - xs.size(1), chunk_size, num_left_chunks, xs.device - ) # (L, L) - chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) - chunk_masks = masks & chunk_masks # (B, L, L) - elif static_chunk_size > 0: - num_left_chunks = num_decoding_left_chunks - chunk_masks = subsequent_chunk_mask( - xs.size(1), static_chunk_size, num_left_chunks, xs.device - ) # (L, L) - chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) - chunk_masks = masks & chunk_masks # (B, L, L) - else: - chunk_masks = masks - return chunk_masks - - -def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: - """Make mask tensor containing indices of padded part. - - See description of make_non_pad_mask. - - Args: - lengths (torch.Tensor): Batch of lengths (B,). - Returns: - torch.Tensor: Mask tensor containing indices of padded part. - - Examples: - >>> lengths = [5, 3, 2] - >>> make_pad_mask(lengths) - masks = [[0, 0, 0, 0 ,0], - [0, 0, 0, 1, 1], - [0, 0, 1, 1, 1]] - """ - batch_size = lengths.size(0) - max_len = max_len if max_len > 0 else lengths.max().item() - seq_range = torch.arange(0, max_len, dtype=torch.int64, device=lengths.device) - seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) - seq_length_expand = lengths.unsqueeze(-1) - mask = seq_range_expand >= seq_length_expand - return mask - - -def make_non_pad_mask(lengths: torch.Tensor) -> torch.Tensor: - """Make mask tensor containing indices of non-padded part. - - The sequences in a batch may have different lengths. To enable - batch computing, padding is need to make all sequence in same - size. To avoid the padding part pass value to context dependent - block such as attention or convolution , this padding part is - masked. - - This pad_mask is used in both encoder and decoder. - - 1 for non-padded part and 0 for padded part. - - Args: - lengths (torch.Tensor): Batch of lengths (B,). - Returns: - torch.Tensor: mask tensor containing indices of padded part. - - Examples: - >>> lengths = [5, 3, 2] - >>> make_non_pad_mask(lengths) - masks = [[1, 1, 1, 1 ,1], - [1, 1, 1, 0, 0], - [1, 1, 0, 0, 0]] - """ - return ~make_pad_mask(lengths) - - -def mask_finished_scores(score: torch.Tensor, flag: torch.Tensor) -> torch.Tensor: - """ - If a sequence is finished, we only allow one alive branch. This function - aims to give one branch a zero score and the rest -inf score. - - Args: - score (torch.Tensor): A real value array with shape - (batch_size * beam_size, beam_size). - flag (torch.Tensor): A bool array with shape - (batch_size * beam_size, 1). - - Returns: - torch.Tensor: (batch_size * beam_size, beam_size). - """ - beam_size = score.size(-1) - zero_mask = torch.zeros_like(flag, dtype=torch.bool) - if beam_size > 1: - unfinished = torch.cat((zero_mask, flag.repeat([1, beam_size - 1])), dim=1) - finished = torch.cat((flag, zero_mask.repeat([1, beam_size - 1])), dim=1) - else: - unfinished = zero_mask - finished = flag - score.masked_fill_(unfinished, -float("inf")) - score.masked_fill_(finished, 0) - return score - - -def mask_finished_preds( - pred: torch.Tensor, flag: torch.Tensor, eos: int -) -> torch.Tensor: - """ - If a sequence is finished, all of its branch should be - - Args: - pred (torch.Tensor): A int array with shape - (batch_size * beam_size, beam_size). - flag (torch.Tensor): A bool array with shape - (batch_size * beam_size, 1). - - Returns: - torch.Tensor: (batch_size * beam_size). - """ - beam_size = pred.size(-1) - finished = flag.repeat([1, beam_size]) - return pred.masked_fill_(finished, eos) diff --git a/modules/wenet_extractor/utils/scheduler.py b/modules/wenet_extractor/utils/scheduler.py deleted file mode 100644 index a5885dbb1faca52272f8825df2da7aa7533d1540..0000000000000000000000000000000000000000 --- a/modules/wenet_extractor/utils/scheduler.py +++ /dev/null @@ -1,738 +0,0 @@ -# This module is from [WeNet](https://github.com/wenet-e2e/wenet). - -# ## Citations - -# ```bibtex -# @inproceedings{yao2021wenet, -# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, -# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, -# booktitle={Proc. Interspeech}, -# year={2021}, -# address={Brno, Czech Republic }, -# organization={IEEE} -# } - -# @article{zhang2022wenet, -# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, -# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, -# journal={arXiv preprint arXiv:2203.15455}, -# year={2022} -# } -# - -from typing import Union - -import math -import warnings -import torch -from torch.optim.lr_scheduler import _LRScheduler - - -class WarmupLR(_LRScheduler): - """The WarmupLR scheduler - - This scheduler is almost same as NoamLR Scheduler except for following - difference: - - NoamLR: - lr = optimizer.lr * model_size ** -0.5 - * min(step ** -0.5, step * warmup_step ** -1.5) - WarmupLR: - lr = optimizer.lr * warmup_step ** 0.5 - * min(step ** -0.5, step * warmup_step ** -1.5) - - Note that the maximum lr equals to optimizer.lr in this scheduler. - - """ - - def __init__( - self, - optimizer: torch.optim.Optimizer, - warmup_steps: Union[int, float] = 25000, - last_epoch: int = -1, - ): - self.warmup_steps = warmup_steps - - # __init__() must be invoked before setting field - # because step() is also invoked in __init__() - super().__init__(optimizer, last_epoch) - - def __repr__(self): - return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})" - - def get_lr(self): - step_num = self.last_epoch + 1 - if self.warmup_steps == 0: - return [lr * step_num**-0.5 for lr in self.base_lrs] - else: - return [ - lr - * self.warmup_steps**0.5 - * min(step_num**-0.5, step_num * self.warmup_steps**-1.5) - for lr in self.base_lrs - ] - - def set_step(self, step: int): - self.last_epoch = step - - -class WarmupPolicy(_LRScheduler): - """Adds warmup kwargs and warmup logic to lr policy. - All arguments should be passed as kwargs for clarity, - Args: - warmup_steps: Number of training steps in warmup stage - warmup_ratio: Ratio of warmup steps to total steps - max_steps: Total number of steps while training or `None` for - infinite training - """ - - def __init__( - self, - optimizer, - *, - warmup_steps=None, - warmup_ratio=None, - max_steps=None, - min_lr=0.0, - last_epoch=-1, - ): - assert not ( - warmup_steps is not None and warmup_ratio is not None - ), "Either use particular number of step or ratio" - assert ( - warmup_ratio is None or max_steps is not None - ), "If there is a ratio, there should be a total steps" - - # It is necessary to assign all attributes *before* __init__, - # as class is wrapped by an inner class. - self.max_steps = max_steps - if warmup_steps is not None: - self.warmup_steps = warmup_steps - elif warmup_ratio is not None: - self.warmup_steps = int(warmup_ratio * max_steps) - else: - self.warmup_steps = 0 - - self.min_lr = min_lr - super().__init__(optimizer, last_epoch) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn( - "To get the last learning rate computed " - "by the scheduler, please use `get_last_lr()`.", - UserWarning, - stacklevel=2, - ) - - step = self.last_epoch - - if step <= self.warmup_steps and self.warmup_steps > 0: - return self._get_warmup_lr(step) - - if step > self.max_steps: - return [self.min_lr for _ in self.base_lrs] - - return self._get_lr(step) - - def _get_warmup_lr(self, step): - lr_val = (step + 1) / (self.warmup_steps + 1) - return [initial_lr * lr_val for initial_lr in self.base_lrs] - - def _get_lr(self, step): - """Simple const lr policy""" - return self.base_lrs - - -class SquareRootConstantPolicy(_LRScheduler): - """Adds warmup kwargs and warmup logic to lr policy. - All arguments should be passed as kwargs for clarity, - Args: - warmup_steps: Number of training steps in warmup stage - warmup_ratio: Ratio of warmup steps to total steps - max_steps: Total number of steps while training or `None` for - infinite training - """ - - def __init__( - self, - optimizer, - *, - constant_steps=None, - constant_ratio=None, - max_steps=None, - min_lr=0.0, - last_epoch=-1, - ): - assert not ( - constant_steps is not None and constant_ratio is not None - ), "Either use particular number of step or ratio" - assert ( - constant_ratio is None or max_steps is not None - ), "If there is a ratio, there should be a total steps" - - # It is necessary to assign all attributes *before* __init__, - # as class is wrapped by an inner class. - self.max_steps = max_steps - if constant_steps is not None: - self.constant_steps = constant_steps - elif constant_ratio is not None: - self.constant_steps = int(constant_ratio * max_steps) - else: - self.constant_steps = 0 - - self.constant_lr = 1 / (constant_steps**0.5) - self.min_lr = min_lr - super().__init__(optimizer, last_epoch) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn( - "To get the last learning rate computed " - "by the scheduler, please use `get_last_lr()`.", - UserWarning, - stacklevel=2, - ) - - step = self.last_epoch - - if step <= self.constant_steps: - return [self.constant_lr for _ in self.base_lrs] - - if step > self.max_steps: - return [self.min_lr for _ in self.base_lrs] - - return self._get_lr(step) - - def _get_lr(self, step): - """Simple const lr policy""" - return self.base_lrs - - -class WarmupHoldPolicy(WarmupPolicy): - """Variant of WarmupPolicy which maintains high - learning rate for a defined number of steps. - All arguments should be passed as kwargs for clarity, - Args: - warmup_steps: Number of training steps in warmup stage - warmup_ratio: Ratio of warmup steps to total steps - hold_steps: Number of training steps to - hold the learning rate after warm up - hold_ratio: Ratio of hold steps to total steps - max_steps: Total number of steps while training or `None` for - infinite training - """ - - def __init__( - self, - optimizer, - *, - warmup_steps=None, - warmup_ratio=None, - hold_steps=None, - hold_ratio=None, - max_steps=None, - min_lr=0.0, - last_epoch=-1, - ): - assert not ( - hold_steps is not None and hold_ratio is not None - ), "Either use particular number of step or ratio" - assert ( - hold_ratio is None or max_steps is not None - ), "If there is a ratio, there should be a total steps" - - self.min_lr = min_lr - self._last_warmup_lr = 0.0 - - # Necessary to duplicate as class attributes are hidden in inner class - self.max_steps = max_steps - if warmup_steps is not None: - self.warmup_steps = warmup_steps - elif warmup_ratio is not None: - self.warmup_steps = int(warmup_ratio * max_steps) - else: - self.warmup_steps = 0 - - if hold_steps is not None: - self.hold_steps = hold_steps + self.warmup_steps - elif hold_ratio is not None: - self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps - else: - self.hold_steps = 0 - - super().__init__( - optimizer, - warmup_steps=warmup_steps, - warmup_ratio=warmup_ratio, - max_steps=max_steps, - last_epoch=last_epoch, - min_lr=min_lr, - ) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn( - "To get the last learning rate computed by the scheduler," - " " - "please use `get_last_lr()`.", - UserWarning, - stacklevel=2, - ) - - step = self.last_epoch - - # Warmup phase - if step <= self.warmup_steps and self.warmup_steps > 0: - return self._get_warmup_lr(step) - - # Hold phase - if (step >= self.warmup_steps) and (step < self.hold_steps): - return self.base_lrs - - if step > self.max_steps: - return [self.min_lr for _ in self.base_lrs] - - return self._get_lr(step) - - -class WarmupAnnealHoldPolicy(_LRScheduler): - """Adds warmup kwargs and warmup logic to lr policy. - All arguments should be passed as kwargs for clarity, - Args: - warmup_steps: Number of training steps in warmup stage - warmup_ratio: Ratio of warmup steps to total steps - max_steps: Total number of steps while training or `None` for - infinite training - min_lr: Minimum lr to hold the learning rate after decay at. - constant_steps: Number of steps to keep lr constant at. - constant_ratio: Ratio of steps to keep lr constant. - """ - - def __init__( - self, - optimizer, - *, - warmup_steps=None, - warmup_ratio=None, - constant_steps=None, - constant_ratio=None, - max_steps=None, - min_lr=0.0, - last_epoch=-1, - ): - assert not ( - warmup_steps is not None and warmup_ratio is not None - ), "Either use particular number of step or ratio" - assert not ( - constant_steps is not None and constant_ratio is not None - ), "Either use constant_steps or constant_ratio" - assert ( - warmup_ratio is None or max_steps is not None - ), "If there is a ratio, there should be a total steps" - - # It is necessary to assign all attributes *before* __init__, - # as class is wrapped by an inner class. - self.max_steps = max_steps - - if warmup_steps is not None: - self.warmup_steps = warmup_steps - elif warmup_ratio is not None: - self.warmup_steps = int(warmup_ratio * max_steps) - else: - self.warmup_steps = 0 - - if constant_steps is not None: - self.constant_steps = constant_steps - elif constant_ratio is not None: - self.constant_steps = int(constant_ratio * max_steps) - else: - self.constant_steps = 0 - - self.decay_steps = max_steps - (self.constant_steps + self.warmup_steps) - - self.min_lr = min_lr - super().__init__(optimizer, last_epoch) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn( - "To get the last learning rate computed " - "by the scheduler, please use `get_last_lr()`.", - UserWarning, - stacklevel=2, - ) - - step = self.last_epoch - - # Warmup steps - if self.warmup_steps > 0 and step <= self.warmup_steps: - return self._get_warmup_lr(step) - - # Constant steps after warmup and decay - if ( - self.constant_steps > 0 - and (self.warmup_steps + self.decay_steps) < step <= self.max_steps - ): - return self._get_constant_lr(step) - - # Min lr after max steps of updates - if step > self.max_steps: - return [self.min_lr for _ in self.base_lrs] - - return self._get_lr(step) - - def _get_warmup_lr(self, step): - lr_val = (step + 1) / (self.warmup_steps + 1) - return [initial_lr * lr_val for initial_lr in self.base_lrs] - - def _get_constant_lr(self, step): - return [self.min_lr for _ in self.base_lrs] - - def _get_lr(self, step): - """Simple const lr policy""" - return self.base_lrs - - -def _squareroot_annealing(initial_lr, step, max_steps, min_lr): - mult = ((max_steps - step) / max_steps) ** 0.5 - out_lr = initial_lr * mult - out_lr = max(out_lr, min_lr) - return out_lr - - -def _square_annealing(initial_lr, step, max_steps, min_lr): - mult = ((max_steps - step) / max_steps) ** 2 - out_lr = initial_lr * mult - out_lr = max(out_lr, min_lr) - return out_lr - - -def _cosine_annealing(initial_lr, step, max_steps, min_lr): - mult = 0.5 * (1 + math.cos(math.pi * step / max_steps)) - out_lr = (initial_lr - min_lr) * mult + min_lr - return out_lr - - -def _linear_warmup_with_cosine_annealing( - max_lr, warmup_steps, step, decay_steps, min_lr -): - assert max_lr > min_lr - # Use linear warmup for the initial part. - if warmup_steps > 0 and step <= warmup_steps: - return max_lr * float(step) / float(warmup_steps) - - # For any steps larger than `decay_steps`, use `min_lr`. - if step > warmup_steps + decay_steps: - return min_lr - - # If we are done with the warmup period, use the decay style. - num_steps_ = step - warmup_steps - decay_steps_ = decay_steps - decay_ratio = float(num_steps_) / float(decay_steps_) - assert decay_ratio >= 0.0 - assert decay_ratio <= 1.0 - delta_lr = max_lr - min_lr - - coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0) - - return min_lr + coeff * delta_lr - - -def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle): - if cycle: - multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps) - decay_steps *= multiplier - else: - step = min(step, decay_steps) - p = step / decay_steps - lr = (initial_lr - min_lr) * math.pow(1.0 - p, power) - lr += min_lr - return lr - - -def _noam_hold_annealing( - initial_lr, step, warmup_steps, hold_steps, decay_rate, min_lr -): - # hold_steps = total number of steps - # to hold the LR, not the warmup + hold steps. - T_warmup_decay = max(1, warmup_steps**decay_rate) - T_hold_decay = max(1, (step - hold_steps) ** decay_rate) - lr = (initial_lr * T_warmup_decay) / T_hold_decay - lr = max(lr, min_lr) - return lr - - -class SquareAnnealing(WarmupPolicy): - def __init__(self, optimizer, *, max_steps, min_lr=1e-5, last_epoch=-1, **kwargs): - super().__init__( - optimizer=optimizer, - max_steps=max_steps, - last_epoch=last_epoch, - min_lr=min_lr, - **kwargs, - ) - - def _get_lr(self, step): - new_lrs = [ - _square_annealing( - initial_lr=initial_lr, - step=step - self.warmup_steps, - max_steps=self.max_steps - self.warmup_steps, - min_lr=self.min_lr, - ) - for initial_lr in self.base_lrs - ] - return new_lrs - - -class SquareRootAnnealing(WarmupPolicy): - def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs): - super().__init__( - optimizer=optimizer, - max_steps=max_steps, - last_epoch=last_epoch, - min_lr=min_lr, - **kwargs, - ) - - def _get_lr(self, step): - new_lrs = [ - _squareroot_annealing( - initial_lr=initial_lr, - step=step, - max_steps=self.max_steps, - min_lr=self.min_lr, - ) - for initial_lr in self.base_lrs - ] - return new_lrs - - -class CosineAnnealing(WarmupAnnealHoldPolicy): - def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs): - super().__init__( - optimizer=optimizer, - max_steps=max_steps, - last_epoch=last_epoch, - min_lr=min_lr, - **kwargs, - ) - - def _get_lr(self, step): - for initial_lr in self.base_lrs: - if initial_lr < self.min_lr: - raise ValueError( - f"{self} received an initial learning rate " - f"that was lower than the minimum learning rate." - ) - - if self.constant_steps is None or self.constant_steps == 0: - new_lrs = [ - _cosine_annealing( - initial_lr=initial_lr, - step=step - self.warmup_steps, - max_steps=self.max_steps - self.warmup_steps, - min_lr=self.min_lr, - ) - for initial_lr in self.base_lrs - ] - else: - new_lrs = self._get_linear_warmup_with_cosine_annealing_lr(step) - return new_lrs - - def _get_warmup_lr(self, step): - if self.constant_steps is None or self.constant_steps == 0: - return super()._get_warmup_lr(step) - else: - # Use linear warmup for the initial part. - return self._get_linear_warmup_with_cosine_annealing_lr(step) - - def _get_constant_lr(self, step): - # Only called when `constant_steps` > 0. - return self._get_linear_warmup_with_cosine_annealing_lr(step) - - def _get_linear_warmup_with_cosine_annealing_lr(self, step): - # Cosine Schedule for Megatron LM, - # slightly different warmup schedule + constant LR at the end. - new_lrs = [ - _linear_warmup_with_cosine_annealing( - max_lr=self.base_lrs[0], - warmup_steps=self.warmup_steps, - step=step, - decay_steps=self.decay_steps, - min_lr=self.min_lr, - ) - for _ in self.base_lrs - ] - return new_lrs - - -class NoamAnnealing(_LRScheduler): - def __init__( - self, - optimizer, - *, - d_model, - warmup_steps=None, - warmup_ratio=None, - max_steps=None, - min_lr=0.0, - last_epoch=-1, - ): - self._normalize = d_model ** (-0.5) - assert not ( - warmup_steps is not None and warmup_ratio is not None - ), "Either use particular number of step or ratio" - assert ( - warmup_ratio is None or max_steps is not None - ), "If there is a ratio, there should be a total steps" - - # It is necessary to assign all attributes *before* __init__, - # as class is wrapped by an inner class. - self.max_steps = max_steps - if warmup_steps is not None: - self.warmup_steps = warmup_steps - elif warmup_ratio is not None: - self.warmup_steps = int(warmup_ratio * max_steps) - else: - self.warmup_steps = 0 - - self.min_lr = min_lr - super().__init__(optimizer, last_epoch) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn( - "To get the last learning rate computed " - "by the scheduler, please use `get_last_lr()`.", - UserWarning, - stacklevel=2, - ) - - step = max(1, self.last_epoch) - - for initial_lr in self.base_lrs: - if initial_lr < self.min_lr: - raise ValueError( - f"{self} received an initial learning rate " - f"that was lower than the minimum learning rate." - ) - - new_lrs = [ - self._noam_annealing(initial_lr=initial_lr, step=step) - for initial_lr in self.base_lrs - ] - return new_lrs - - def _noam_annealing(self, initial_lr, step): - if self.warmup_steps > 0: - mult = self._normalize * min( - step ** (-0.5), step * (self.warmup_steps ** (-1.5)) - ) - else: - mult = self._normalize * step ** (-0.5) - - out_lr = initial_lr * mult - if step > self.warmup_steps: - out_lr = max(out_lr, self.min_lr) - return out_lr - - -class NoamHoldAnnealing(WarmupHoldPolicy): - def __init__( - self, - optimizer, - *, - max_steps, - decay_rate=0.5, - min_lr=0.0, - last_epoch=-1, - **kwargs, - ): - """ - From Nemo: - Implementation of the Noam Hold Annealing policy - from the SqueezeFormer paper. - - Unlike NoamAnnealing, the peak learning rate - can be explicitly set for this scheduler. - The schedule first performs linear warmup, - then holds the peak LR, then decays with some schedule for - the remainder of the steps. - Therefore the min-lr is still dependent - on the hyper parameters selected. - - It's schedule is determined by three factors- - - Warmup Steps: Initial stage, where linear warmup - occurs uptil the peak LR is reached. Unlike NoamAnnealing, - the peak LR is explicitly stated here instead of a scaling factor. - - Hold Steps: Intermediate stage, where the peak LR - is maintained for some number of steps. In this region, - the high peak LR allows the model to converge faster - if training is stable. However the high LR - may also cause instability during training. - Should usually be a significant fraction of training - steps (around 30-40% of the entire training steps). - - Decay Steps: Final stage, where the LR rapidly decays - with some scaling rate (set by decay rate). - To attain Noam decay, use 0.5, - for Squeezeformer recommended decay, use 1.0. - The fast decay after prolonged high LR during - hold phase allows for rapid convergence. - - References: - - [Squeezeformer: - An Efficient Transformer for Automatic Speech Recognition] - (https://arxiv.org/abs/2206.00888) - - Args: - optimizer: Pytorch compatible Optimizer object. - warmup_steps: Number of training steps in warmup stage - warmup_ratio: Ratio of warmup steps to total steps - hold_steps: Number of training steps to - hold the learning rate after warm up - hold_ratio: Ratio of hold steps to total steps - max_steps: Total number of steps while training or `None` for - infinite training - decay_rate: Float value describing the polynomial decay - after the hold period. Default value - of 0.5 corresponds to Noam decay. - min_lr: Minimum learning rate. - """ - self.decay_rate = decay_rate - super().__init__( - optimizer=optimizer, - max_steps=max_steps, - last_epoch=last_epoch, - min_lr=min_lr, - **kwargs, - ) - - def _get_lr(self, step): - if self.warmup_steps is None or self.warmup_steps == 0: - raise ValueError("Noam scheduler cannot be used without warmup steps") - - if self.hold_steps > 0: - hold_steps = self.hold_steps - self.warmup_steps - else: - hold_steps = 0 - - new_lrs = [ - _noam_hold_annealing( - initial_lr, - step=step, - warmup_steps=self.warmup_steps, - hold_steps=hold_steps, - decay_rate=self.decay_rate, - min_lr=self.min_lr, - ) - for initial_lr in self.base_lrs - ] - return new_lrs - - def set_step(self, step: int): - self.last_epoch = step diff --git a/modules/whisper_extractor/README.md b/modules/whisper_extractor/README.md deleted file mode 100644 index bfcac7e44a45f52864deb42330d7572c1d6a3962..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/README.md +++ /dev/null @@ -1,23 +0,0 @@ -## Acknowledgement - -This module borrows some codes from [Whisper](https://github.com/openai/whisper.git). - -## Citations - -```bibtex -@inproceedings{openai-whisper, - author = {Alec Radford and - Jong Wook Kim and - Tao Xu and - Greg Brockman and - Christine McLeavey and - Ilya Sutskever}, - title = {Robust Speech Recognition via Large-Scale Weak Supervision}, - booktitle = {{ICML}}, - series = {Proceedings of Machine Learning Research}, - volume = {202}, - pages = {28492--28518}, - publisher = {{PMLR}}, - year = {2023} -} -``` \ No newline at end of file diff --git a/modules/whisper_extractor/__init__.py b/modules/whisper_extractor/__init__.py deleted file mode 100644 index 6b40d4c9101908f2bf4848bdfd438b1ad345aa35..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/__init__.py +++ /dev/null @@ -1,169 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -import hashlib -import io -import os -import urllib -import warnings -from typing import List, Optional, Union - -import torch -from tqdm import tqdm - -from .audio import load_audio, log_mel_spectrogram, pad_or_trim -from .decoding import DecodingOptions, DecodingResult, decode, detect_language -from .model import Whisper, ModelDimensions -from .transcribe import transcribe -from .version import __version__ - - -_MODELS = { - "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", - "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", - "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", - "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", - "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", - "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", - "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", - "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", - "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", - "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", - "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", -} - - -def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: - os.makedirs(root, exist_ok=True) - - expected_sha256 = url.split("/")[-2] - download_target = os.path.join(root, os.path.basename(url)) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError(f"{download_target} exists and is not a regular file") - - if os.path.isfile(download_target): - with open(download_target, "rb") as f: - model_bytes = f.read() - if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: - return model_bytes if in_memory else download_target - else: - warnings.warn( - f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" - ) - - with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm( - total=int(source.info().get("Content-Length")), - ncols=80, - unit="iB", - unit_scale=True, - unit_divisor=1024, - ) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - model_bytes = open(download_target, "rb").read() - if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: - raise RuntimeError( - "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." - ) - - return model_bytes if in_memory else download_target - - -def available_models() -> List[str]: - """Returns the names of available models""" - return list(_MODELS.keys()) - - -def load_model( - name: str, - device: Optional[Union[str, torch.device]] = None, - download_root: str = None, - in_memory: bool = False, - checkpoint_file=None, -) -> Whisper: - """ - Load a Whisper ASR model - - Parameters - ---------- - name : str - one of the official model names listed by `whisper.available_models()`, or - path to a model checkpoint containing the model dimensions and the model state_dict. - device : Union[str, torch.device] - the PyTorch device to put the model into - download_root: str - path to download the model files; by default, it uses "~/.cache/whisper" - in_memory: bool - whether to preload the model weights into host memory - - Returns - ------- - model : Whisper - The Whisper ASR model instance - """ - - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - if download_root is None: - download_root = os.getenv( - "XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache", "whisper") - ) - - if not os.path.exists(checkpoint_file): - if name in _MODELS: - checkpoint_file = _download(_MODELS[name], download_root, in_memory) - elif os.path.isfile(name): - checkpoint_file = open(name, "rb").read() if in_memory else name - else: - raise RuntimeError( - f"Model {name} not found; available models = {available_models()}" - ) - else: - checkpoint_file = ( - open(checkpoint_file, "rb").read() if in_memory else checkpoint_file - ) - - with ( - io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") - ) as fp: - checkpoint = torch.load(fp, map_location=device) - del checkpoint_file - - dims = ModelDimensions(**checkpoint["dims"]) - model = Whisper(dims) - model.load_state_dict(checkpoint["model_state_dict"]) - - return model.to(device) diff --git a/modules/whisper_extractor/__main__.py b/modules/whisper_extractor/__main__.py deleted file mode 100644 index a47432861819924eb1816fda046dc3dafbe0beb7..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/__main__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .transcribe import cli - - -cli() diff --git a/modules/whisper_extractor/assets/gpt2/merges.txt b/modules/whisper_extractor/assets/gpt2/merges.txt deleted file mode 100644 index 6636bda4a1fd7a63653dffb22683b8162c8de956..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/assets/gpt2/merges.txt +++ /dev/null @@ -1,50001 +0,0 @@ -#version: 0.2 - Trained by `huggingface/tokenizers` -Ġ t -Ġ a -h e -i n -r e -o n -Ġt he -e r -Ġ s -a t -Ġ w -Ġ o -e n -Ġ c -i t -i s -a n -o r -e s -Ġ b -e d -Ġ f -in g -Ġ p -o u -Ġa n -a l -a r -Ġt o -Ġ m -Ġo f -Ġ in -Ġ d -Ġ h -Ġan d -i c -a s -l e -Ġt h -i on -o m -l l -en t -Ġ n -Ġ l -s t -Ġ re -v e -Ġ e -r o -l y -Ġb e -Ġ g -Ġ T -c t -Ġ S -i d -o t -Ġ I -u t -e t -Ġ A -Ġ is -Ġ on -i m -a m -o w -a y -a d -s e -Ġth at -Ġ C -i g -Ġf or -a c -Ġ y -v er -u r -Ġ u -l d -Ġs t -Ġ M -' s -Ġ he -Ġ it -at ion -it h -i r -c e -Ġy ou -i l -Ġ B -Ġw h -o l -Ġ P -Ġw ith -Ġ 1 -t er -c h -Ġa s -Ġw e -Ġ ( -n d -i ll -Ġ D -i f -Ġ 2 -a g -er s -k e -Ġ " -Ġ H -e m -Ġc on -Ġ W -Ġ R -he r -Ġw as -Ġ r -o d -Ġ F -u l -at e -Ġa t -r i -p p -o re -ĠT he -Ġs e -u s -Ġp ro -Ġh a -u m -Ġa re -Ġd e -a in -an d -Ġo r -ig h -es t -is t -a b -r om -Ġ N -t h -Ġc om -Ġ G -u n -o p -0 0 -Ġ L -Ġn ot -es s -Ġe x -Ġ v -re s -Ġ E -e w -it y -an t -Ġb y -e l -o s -or t -o c -q u -Ġf rom -Ġha ve -Ġs u -i ve -ou ld -Ġs h -Ġth is -n t -r a -p e -igh t -ar t -m ent -Ġa l -u st -en d -- - -al l -Ġ O -ac k -Ġc h -Ġ le -i es -re d -ar d -â Ģ -ou t -Ġ J -Ġa b -e ar -i v -al ly -ou r -o st -g h -p t -Ġp l -as t -Ġc an -a k -om e -u d -T he -Ġh is -Ġd o -Ġg o -Ġh as -g e -' t -Ġ U -r ou -Ġs a -Ġ j -Ġb ut -Ġw or -Ġa ll -e ct -Ġ k -am e -Ġw ill -o k -Ġw he -Ġthe y -id e -0 1 -f f -ic h -p l -t her -Ġt r -. . -Ġin t -i e -u re -ag e -Ġn e -i al -a p -in e -ic e -Ġm e -Ġo ut -an s -on e -on g -ion s -Ġwh o -Ġ K -Ġu p -Ġthe ir -Ġa d -Ġ 3 -Ġu s -at ed -ou s -Ġm ore -u e -o g -ĠS t -in d -i ke -Ġs o -im e -p er -. " -b er -i z -a ct -Ġon e -Ġsa id -Ġ - -a re -Ġyou r -c c -ĠT h -Ġc l -e p -a ke -ab le -i p -Ġcon t -Ġwh ich -i a -Ġ im -Ġab out -Ġwe re -ver y -u b -Ġh ad -Ġ en -Ġcom p -, " -ĠI n -Ġu n -Ġa g -i re -ac e -a u -ar y -Ġw ould -as s -r y -Ġ âĢ -c l -o ok -e re -s o -Ġ V -ig n -i b -Ġof f -Ġt e -v en -Ġ Y -i le -o se -it e -or m -Ġ2 01 -Ġre s -Ġm an -Ġp er -Ġo ther -or d -ul t -Ġbe en -Ġl ike -as e -an ce -k s -ay s -ow n -en ce -Ġd is -ct ion -Ġan y -Ġa pp -Ġs p -in t -res s -ation s -a il -Ġ 4 -ic al -Ġthe m -Ġhe r -ou nt -ĠC h -Ġa r -Ġ if -Ġthe re -Ġp e -Ġy ear -a v -Ġm y -Ġs ome -Ġwhe n -ou gh -ac h -Ġth an -r u -on d -ic k -Ġo ver -ve l -Ġ qu -Ċ Ċ -Ġs c -re at -re e -ĠI t -ou nd -p ort -Ġal so -Ġp art -f ter -Ġk n -Ġbe c -Ġt ime -en s -Ġ 5 -op le -Ġwh at -Ġn o -d u -m er -an g -Ġn ew --- -- -Ġg et -or y -it ion -ing s -Ġj ust -Ġint o -Ġ 0 -ent s -o ve -t e -Ġpe ople -Ġp re -Ġit s -Ġre c -Ġt w -i an -ir st -ar k -or s -Ġwor k -ad e -o b -Ġs he -Ġo ur -w n -in k -l ic -Ġ1 9 -ĠH e -is 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ame -ou n -ĠA l -c i -Ġdiff ere -Ġwh ile ----- ---- -Ġg ame -ce pt -Ġs im -.. . -Ġin ter -e k -Ġre port -Ġpro du -Ġst ill -l ed -a h -Ġhe re -Ġwor ld -Ġth ough -Ġn um -ar ch -im es -al e -ĠS e -ĠI f -/ / -ĠL e -Ġre t -Ġre f -Ġtr ans -n er -ut ion -ter s -Ġt ake -ĠC l -Ġcon f -w ay -a ve -Ġgo ing -Ġs l -u g -ĠA meric -Ġspe c -Ġh and -Ġbet ween -ist s -ĠD e -o ot -I t -Ġe ar -Ġagain st -Ġh igh -g an -a z -at her -Ġex p -Ġo p -Ġin s -Ġg r -Ġhel p -Ġre qu -et s -in s -ĠP ro -is m -Ġf ound -l and -at a -us s -am es -Ġp erson -Ġg reat -p r -Ġs ign -ĠA n -' ve -Ġs omet -Ġs er -h ip -Ġr un -Ġ : -Ġt er -ire ct -Ġf ollow -Ġd et -ic es -Ġf ind -1 2 -Ġm em -Ġc r -e red -e x -Ġex t -ut h -en se -c o -Ġte am -v ing -ou se -as h -at t -v ed -Ġsy stem -ĠA s -d er -iv es -m in -Ġle ad -ĠB l -c ent -Ġa round -Ġgo vern -Ġc ur -vel op -an y -Ġc our -al th -ag es -iz e -Ġc ar -od e -Ġl aw -Ġre ad -' m -c on -Ġre al -Ġsupp ort -Ġ1 2 -.. .. -Ġre ally -n ess -Ġf act -Ġd ay -Ġb oth -y ing -Ġs erv -ĠF or -Ġth 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-ĠTyr ann -ĠSabb ath -ĠNich ols -Ġra pt -ih ar -Ġshe lling -Ġillum inate -Ġdent ist -ut or -ĠInteg ration -Ġwh ims -ĠLiter ary -Be aut -Ġp archment -ag ara -Br and -Ġder og -âĢ¦ ) -ĠNor se -Ġunw itting -Ġc uc -Ġborder line -Ġupset ting -Ġrec ourse -Ġd raped -ĠRad ar -Ġcold er -ĠPep si -im inary -], [ -65 8 -V i -ĠF rem -ĠP es -Ġveter inary -ĠT ED -ĠEp idem -n ova -k id -Ġdev out -o ct -j ad -M oh -ĠP AY -Ġge ometric -Ġ3 23 -Ġcircum ference -ich ick -19 75 -ĠY uri -ĠSh all -ĠH over -un in -S pr -Ġg raft -ĠHapp iness -Ġdisadvant ages -att acks -Ġhub s -ĠStar Craft -é ĸ -Ġgall eries -ĠKor ra -Ġgrocer ies -ĠGors uch -Ġrap ists -Ġfun gi -ĠTyph oon -V ector -ĠEm press -b attle -4 68 -Ġparas ite -ĠBom ber -S G -ex ist -ĠP f -Ġun se -Ġsurge ons -B irth -ĠUn sure -ĠPrint ed -ĠBehavior al -ĠA ster -Pak istan -Ġun ethical -Ġs v -ĠIo T -Ġlay outs -P ain -Ġconst ants -ĠL W -ĠB ake -Ġtow els -Ġdeterior ation -ĠBol ivia -Ġblind ed -ĠW arden -ĠMist ress -Ġon stage -Ġcl ans -ĠB EST -19 60 -Ġant ique 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ub -Or ange -Ġfingert ips -ĠWood en -ĠK idd -ĠJ PM -ĠDam n -C ow -c odes -48 2 -Ġiniti ating -ĠEl k -ĠCut ting -Ġabsent ee -ĠV ance -ĠLil ith -G UI -Ġobsc ured -Ġdwar ves -ĠCh op -ĠB oko -Val ues -Ġmult imedia -Ġbrew ed -Reg ular -CRIP TION -ĠMort al -Ġa pex -Ġtravel er -Ġbo ils -Ġspray ing -Rep resent -ĠStars hip -4 28 -Ġdisappro val -Ġshadow y -Ġlament ed -ĠRe place -ĠFran ç -67 7 -d or -Ġunst oppable -Ġcoh orts -gy n -ĠClass ics -ĠAm ph -Ġsl uggish -ĠAdd iction -ĠPad res -Ġins cription -Ġin human -min us -ĠJere miah -at ars -Ter ror -ĠT os -ĠSh arma -ast a -c atch -Ġpl umbing -ĠTim bers -Sh ar -H al -ĠO sc -Ġcou pling -hum ans -Ġsp onge -Ġid ols -ĠSp a -ĠAdv ocate -ĠBe ats -lu a -Ġtick ing -Ġload er -ĠG ron -8 10 -Ġstim ulated -Ġside bar -ĠManufact urer -ore And -19 73 -Ġpra ises -ĠFl ores -dis able -ĠElect rical -ra ise -E th -Ġmigr ated -Ġlect urer -K ids -ĠCa vern -Ġk ettle -Ġgly c -ĠMand ela -ĠF ully -å§ « -FIN EST -Ġsquee zing -ĠRy der -amp oo -oreAnd Online -Inst oreAndOnline 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-Ġreform ing -Ġfrust rations -ĠDead pool -ĠBel ichick -ract or -ĠRagnar ok -ĠD rupal -ĠApp roximately -19 20 -ĠHub ble -arm or -ĠSar as -ĠJon as -Ġnostalg ic -Ġfeas ibility -Sah aran -Ġorb iting -Ġ9 70 -R u -Ġsh in -ĠInvestig ators -Ġinconsist encies -ĠP AN -B G -Ġgraz ing -Ġdetect ors -ĠStart up -ĠFun ny -ĠNa omi -Consider ing -Ġh og -ut f -ce mic -Ġfort ified -ĠFun ctions -Ġcod ec -nut rition -H at -" ! -micro soft -55 8 -ĠTh in -ĠA CE -Al ias -ĠO PS -p apers -P K -ãĢ İ -Ġimpro bable -N orthern -equ al -Ġlook out -Ġty res -ĠMod ified -ĠK op -Abs olutely -Ġbuild up -sil ver -Ġaud i -Ġgro tesque -ĠSab er -ĠPres byter -ON Y -Ġglac iers -ĠSho als -ĠK ass -ĠH RC -ĠNic ol -ĠL unch -ĠF oss -âĸ Ĵ -AD RA -ĠOne Plus -o ing -ground s -Ġincident al -Ġdatas ets -68 9 -ĠClarks on -Ġassemb ling -ĠCorrect ions -Ġdrink ers -Ġqual ifiers -Ġle ash -Ġunf ounded -ĠH undred -Ġkick off -T i -Ġrecon cil -ĠGr ants -ĠCompl iance -ĠDexter ity -Ġ19 06 -w arn -D allas -Max imum -n ard -av ia -be aut -ens itivity -tr ace -Ġpione ers -ĠF ract -ãĢ ı -Ġpre cept -Ġgloss y -ĠI EEE -Ac ross -Ġ6 80 -S leep -che on -Ġsatir ical -ĠMin otaur -ĠCla ude -Ġr é -ape go -Ġcar rot -ĠSem in -ino a -Ġz o -Ind ependent -Ġdiagn oses -ĠC ue -M AR -Ġrend ition -ĠK ik -Ġpath ology -Ġselect s -Link edIn -Ġass ay -ĠD res -Ġtext ual -post ed -IT AL -ĠM aul -N eal -Ġinter connected -Ġerr atic -ĠVir us -Ġ5 30 -Ġenvironmental ists -ĠP helps -Ġeng agements -ĠIN ST -Ġeconom ical -nox ious -Ġg earing -izz y -Ġfavor ably -ĠMcG ill -T erm -Ġh anged -Ġball park -ĠRe yes -Ġbe ware -ĠP sal -ĠMass acre -q i -Ġin accessible -acly sm -Ġfr ay -ill ac -Ġbitter ly -ĠCert ification -Mich igan -Ġir respective -al ore -Em pty -Ġendorse ments -Ġund et -f g -equ ipped -Ġmerc iless -ĠC ust -Ġimm ature -Ġvou cher -ĠBlack well -Ñ ı -h awk -dis ciplinary -ile e -ĠMak oto -ĠD ude -ãĥĩ ãĤ£ -Y ears -Ġin ver -Ġsh aman -ĠY ong -ip el -ell en -ĠCath y -br ids -Ġs arc -65 1 -N ear -Ġground work -Ġam az -Ġ4 15 -ĠHunting ton -hew s -ĠB ung -Ġarbit rarily -ĠW it -ĠAl berto -Ġdis qualified -best os -46 1 -Ġp c -Ġ28 4 -ro bat -Rob in -Ġh ugs -ĠTrans ition -ĠOcc asionally -Ġ3 26 -ĠWh ilst -ĠLe y -Ġspaces hip -cs v -Ġun successfully -ĠA u -le ck -ĠWing ed -ĠGrizz lies -. � -Ġne arer -ĠSorce ress -ĠInd igo -El se -8 40 -let es -Co ach -Ġup bringing -ĠK es -Ġseparat ist -Ġrac ists -Ġch ained -Ġabst inence -lear ning -Ġrein stated -Ġsymm etry -Ġremind ers -ĠChe vy -Ġm ont -Ġexempl ary -ĠT OR -Z X -Ġqual itative -ĠSt amp -ĠSav annah -ĠRoss i -Ġp aed -Ġdispens aries -ĠWall s -ĠCh ronic -Ġcompliment ary -ĠBeir ut -Ġ+ --- -igs list -Ġcrypt ographic -mas ters -ĠCap itals -Ġmax imal -Ġent ropy -Point s -Ġcombat ants -l ip -ĠGl ob -ĠB MC -ph ase -th ank -HT TP -Ġcomm uter -Ġ\( \ -.. / -ĠReg ener -ĠDO I -ĠActiv ision -Ġsl it -os al -RE M -Ġch ants -Y u -Ke ys -Bre xit -ĠFor ced -Ari zona -Ġsquad ron -IS O -ĠMal one -Ġ3 38 -Ġcontrast ing -Ġt idal -Ġlib el -Ġimpl anted -Ġupro ar -ĠC ater -Ġpropos itions -M anchester -ĠEuro s -it amin -G il -ĠEl ven -ĠSe 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", -al on -K rist -Ġmeas urable -Ġimagin able -Ġapost les -Y N -7 60 -Ġster oid -Ġspecific ity -ĠL ocated -ĠBeck er -ĠE du -ĠDiet ary -uts ch -ĠMar ilyn -Ġbl ister -ĠM EP -ĠK oz -ĠC MS -y ahoo -ĠCar ney -Ġbo asting -ĠC aleb -By te -read s -ad en -Pro blem -ĠWood ward -S we -S up -ĠK GB -Set up -Ġtac it -Ġret ribution -Ġd ues -ĠM ü -. ? -ä¸ Ń -p ots -Ġcame o -ĠP AL -educ ation -A my -like ly -g ling -Ġconstitution ally -ĠHam m -ĠSpe ak -Ġwid gets -br ate -Ġcra ppy -ĠI ter -Ġanticip ating -ĠB out -P ixel -ĠY ep -ĠLaur ie -Ġh ut -Ġbullet in -ĠSal vation -Ġch ats -ear able -Honest ly -AL TH -onse qu -c ult -isco very -ovy ch -Ġse lves -ĠSat oshi -S ounds -Ġconver gence -ĠRosen berg -19 74 -Ġnas al -Ġfull est -Ġfer ocious -x us -ist e -AM S -Ġlobb ied -Ġso othing -ĠGun n -t oday -0 24 -Ġinspir ational -ĠN BN -p b -g ewater -or ah -all owed -ĠCol iseum -Ġspecial izing -Ġinsane ly -ĠT ape -del ay -Ġt arn -ĠP ound -Ġmel anch -Ġdeploy ments -il and -Ġless en -Ġfur ry -ĠUE FA -Ġblood shed -ĠMe ier -ither ing -Ġhe irs -ĠJ aw -ax ter -ĠPublic ations -Ġal ters -int ention -ĠWinc hester -d etermination -ĠLif etime -th in -Mon ster -7 80 -Ġapprox imation -Ġsuper markets -ĠSecond s -or os -h uge -Ġb ribe -ĠLIM ITED -un ed -Ġmis interpret -ĠIn jury -Ġ3 67 -Ġthreshold s -ĠCarn ival -Ġgastro intestinal -Ġguid eline -Ġde ceived -f eatures -Ġpurported ly -ĠRon nie -ĠNew t -Ġsp acious -as us -Ġsuperhero es -ĠCyn thia -le gged -k amp -ch io -Ġth umbnail -ĠShir ley -ill ation -Ġshe ds -ĠZ y -E PA -Ġdam s -Ġy awn -n ah -ĠPe ggy -ĠE rie -ĠJu ventus -ĠF ountain -r x -don ald -al bum -ĠComp rehensive -Ġc aching -ĠU z -ulner ability -ĠPrinc iple -ĠJ ian -ing ers -cast s -ĠOs iris -ch art -t ile -ĠTiff any -ĠPatt on -ĠWh ip -Ġovers ized -J e -ĠCind erella -ĠB orders -ĠDa esh -M ah -Ġdog ma -Ġcommun ists -v u -Coun cil -Ġfresh water -Ġw ounding -Ġdeb acle -Ġyoung ster -Ġthread ed -ĠB ots -ĠSav ings -ãģ Ĥ -ol ing -oh o -Ġillum ination -M RI -Ġlo osen -tr ump -ag ency -ur ion -Ġmoment arily -ĠCh 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-ĠInqu isition -ĠP oke -Ġforecast ing -Ġcompl ains -S ense -ĠHer mes -isc overed -Ġb ible -ĠMor ph -Ġg erm -78 5 -D ON -Ġcon gen -Ġcr ane -ĠD PR -Ġrespect fully -R oom -ĠN aw -ĠDal ai -re ason -ĠAng us -Educ ation -ĠTitan ic -Ë ľ -Ġo val -un ited -Ġthird s -Ġmoist ur -ĠC PC -M iami -Ġtent acles -ĠPol aris -ex c -ex clusive -ĠPra irie -Ġcol ossal -ĠBl end -sur prisingly -ÃŃ s -Ġindo ctr -Ġbas al -ĠMP EG -und o -Spl it -Develop ment -Ġlan tern -19 71 -Ġprov ocation -Ġang uish -ĠB ind -ĠLe ia -duc ers -ipp y -conserv ancy -Ġinitial ize -ĠTw ice -ĠSu k -Ġpred ic -Ġdi ploma -Ġsoc iop -Ing redients -Ġhamm ered -ĠIr ma -Q aida -Ġglim ps -ĠB ian -Ġst acking -Ġf end -gov track -Ġun n -dem ocratic -ig ree -Ġ5 80 -Ġ29 4 -Ġstraw berry -ID ER -Ġcher ished -ĠH ots -Ġinfer red -Ġ8 08 -ĠS ocrates -O regon -ĠR oses -ĠFO IA -Ġins ensitive -Ġ40 8 -Recomm end -ĠSh ine -Ġpain staking -UG E -ĠHell er -ĠEnter prises -I OR -ad j -N RS -L G -Ġalien ated -Ġacknowled gement -ĠA UD -ĠRen eg -Ġvou chers -Ġ9 60 -Ġm oot -ĠDim ensions -Ġc abbage -B right -g at -ĠK lu -Ġlat ent -Ġz e -ĠM eng -Ġdis perse -Ġpand emonium -H Q -Ġvirt uous -ĠLoc ations -ee per -prov ided -Ġse ams -ĠW T -iz o -PR OV -Ġtit anium -Ġrecol lection -Ġcr an -Ġ7 80 -ĠN F -49 1 -64 2 -p acking -59 8 -text ure -Sp ider -fre edom -cipl ed -ĠTAM ADRA -âĻ ¦ -aut hent -ĠW ANT -r ified -Ġr ites -Ġuter us -k iss -Ġâī ¤ -Ġsk illet -Ġdis enfranch -ĠGa al -Comp an -Ġage ing -gu ide -B alt -Ġiter ator -Ġdiscretion ary -t ips -Ġprim ates -ĠTechn ique -ĠPay ments -az el -ĠR OCK -stant ial -0 60 -Ġd mg -ĠJack ets -ĠPlay off -Ġnurs ery -ĠSy mb -art on -Ġannex ation -Color ado -Ġco ils -ĠSh oes -âĦ¢ : -ĠRo z -COM PLE -ĠEve rest -ĠTri umph -J oy -G rid -à ¼ -process or -ĠPros per -ĠSever us -ĠSelect ed -r g -ĠTay yip -St ra -Ġski ing -Ġ? ) -Ġpe g -Tes la -Ġtime frame -Ġmaster mind -ĠN B -scient ific -ĠSh it -gener ic -IN TER -N UM -Ġst roll -ĠEn ix -ĠM MR -ĠE MS -m ovie -Ĥ ª -Ġminim izing -idd ling -Ġilleg itimate -Ġprot otyp -Ġpremature ly 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100644 index afde9f4fa8bcf6d4762c5780977825666dc49eac..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/assets/multilingual/merges.txt +++ /dev/null @@ -1,50000 +0,0 @@ -Ġ t -Ġ a -Ġt h -i n -e r -Ġ w -Ġ s -o u -Ġth e -r e -o n -a t -e n -Ġ c -i t -i s -Ġ b -n d -Ġ d -Ġ m -Ġ h -Ġ o -in g -e s -Ġ p -Ġt o -a n -Ġ f -o r -l l -Ġ I -Ġ l -Ġ y -a r -Ġ g -Ġy ou -e d -Ġa nd -Ġ in -Ġo f -a s -Ġ n -o m -i c -Ġth at -u s -e t -v e -a l -o w -l e -Ġ is -Ġ e -Ġ it -o t -' s -Ġb e -i on -Ġ T -Ġw h -Ġ A -en t -Ġ S -Ġ re -a y -Ġw e -Ġ on -er e -Ġh a -u t -a c -i d -i g -o s -k e -v er -i m -Ġ Ð -ĠT h -a m -a ll -Ġf or -e l -c h -r o -Ġth is -Ġs t -Ġ W -Ġ u -a d -ou t -i r -l d -c t -Ġ k -i f -Ġg o -. . -Ð ¾ -it h -l y -h t -q u -Ġ - -Ġd o -Ġ j -Ġha ve -Ġ B -Ġa n -Ġw ith -Ġa re -Ġ r -Ġd e -Ġs e -Ġs o -Ġ v -s t -i ll -u r -Ġl i -Ġ M -es t -o d -all y -' t -us t -Ġa s -Ġ C -c e -Ġm e -Ð ° -Ð µ -i l -Ġ H -Ġw as -t er -t h -Ġc an -an t -Ġc om -ou r -ig ht -Ġ Y -at ion -ĠA nd -o l -Ġs h -Ñ Ĥ -o p -s e -Ġn ot -ĠS o -Ġn e -u n -Ġa b -Ġli ke -Ġa t -Ġ D -i e -Ġh e -Ġc on -Ġc h -o re -Ġa l -Ġo r -Ġ qu -Ġ O -om e -r a -u l -Ġ N -p p -Ġyou r -ou ld -Ġ P -Ġf r -g e -er s -' re -Ð ¸ -Ġthe y -Ġwh at -us e -Ġa ll -ĠTh e -Ġ L -es s -e m -Ġk n -Ġj ust -ar t -Ġp ro -ver y -u m -Ġl o -Ġ ì -Ġm y -o k -Ġe x -a b -Ġth ere -Ġb ut -Ġkn ow -Ġs u -Ġ G -Ñ ģ -Ġ E -Ġm a -о Ð -Ġ en -Ġab out -ĠI t -is t -Ġw or -r i -in d -Ġon e -at e -a nd -in k -Ġl e -or t -' m -Ġ F -ic h -Ñ Ģ -id e -Ġg et -Ġ out -.. . -Ġw ill -ã ģ -i ve -Ð ½ -Ġfr om -a in -ĠW e -Ġu p -p e -re s -c a -Ġ R -Ġ if -Ġp l -Ġd on -ac k -Ġ 1 -Ġ " -Ġt r -Ġ us -ĠW h -it y -Ġ J -ĠY ou -Ġh ere -h er -Ġs ome -ou g -a k -ar d -Ġgo ing -Ġu n -m ent -Ġth ink -Ġp e -en d -Ġ ( -ca use -Ġt im -as t -à © -Ġ our -Ġw ant -am e -i es -Ġ ë -u d -in e -Ġre ally -Ġt e -Ġse e -c i -Ġb y -s o -u re -os e -Ġ [ -a re -Ġm ore -a h -on e -c k -op le -а Ð -Ġthe n -Ġth ing -Ġthe m -v en -ou nd -os t -on g -e ct -Ġr ight -a g -Ġin t -Ġpe ople -Ġwh en -ou s -p l -Ġtim e -Ġ im -Ġwh o -Ġ 2 -a p -Ġbe cause -h ing -Ġn o -ic e -Ġlo ok -Ġh as -Ġw ould -Ġh ow -ac t -Ġf e -n t -oug h -Ġp r -ĠB ut -Ġs ay -Ñ ĥ -Ġn ow -Ġm an -Ġ very -Ġwor k -i z -Ġ K -i v -it t -Ġa r -e p -Ġc l -Ġwh ich -Ġc o -an s -' ve -Ġs a -f f -' ll -Ġan y -Ġa ct -Ġy e -b er -ac h -a ge -p er -Ġal so -f er -Ġthe se -Ġa d -е Ð -th er -ac e -ic k -a ke -re at -i re -u e -Ġa g -Ġ U -u ch -ion s -r y -0 0 -n a -Ġd id -Ġqu e -Ġha d -Ġe very -ĠH e -Ġl a -Ġw ay -Ġs p -b le -ĠTh is -as s -Ġthe ir -it e -Ġne ed -Ġp art -Ġw ere -Ġb ack -i p -ow n -om et -b e -as e -Ġma ke -ir st -i a -en ce -an g -an k -Ġg ot -Ġp re -Ġcon t -Ġo ther -p t -ĠTh at -o g -Ġgo od -Ġint o -al k -Ġbe en -Ġa m -Ġo ver -u ally -Ġ â -ì Ŀ -Ġu nd -h e -w ay -Ġg r -Ñ Į -Ġd if -Ġp er -Ñ ı -ĠI n -Ġt w -on d -ar s -in t -or m -Ġl ot -Ġwh ere -Ġ à -Ġ V -Ġs omet -Ð » -en s -Ġg u -Ġa c -u g -Ñ ĭ -Ä ± -Ġf irst -re e -Ġh is -itt le -Ġim p -Ġm o -a v -Ġl ittle -ĠWh at -Ġm uch -Ġ z -Ġ ê -ab le -ĠÐ ¿ -Ġp o -Ġcom p -n e -Ġd is 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17295, "ĠRadio": 17296, "à¸ŀ": 17297, "Dav": 17298, "Ġembarrassing": 17299, "ĠìŀĪìĸ´": 17300, "Ġcasting": 17301, "Ġcage": 17302, "ĠPsych": 17303, "ĠìĿ¼ëĭ¨": 17304, "Ġž": 17305, "imb": 17306, "Ġdirectors": 17307, "SH": 17308, "ĠÏĦην": 17309, "á»ģu": 17310, "ĠkonuÅŁ": 17311, "Ġoptional": 17312, "quarters": 17313, "iker": 17314, "ĠSant": 17315, "Ġverses": 17316, "ë¶Ģ": 17317, "Ġolar": 17318, "ĠÏĩ": 17319, "ãĥķ": 17320, "Ġγια": 17321, "ĠImm": 17322, "Ġcontroversial": 17323, "Ġersten": 17324, "Ġrecip": 17325, "ĠChristianity": 17326, "Ġê´ľ": 17327, "ordon": 17328, "×ķש": 17329, "Ġslash": 17330, "ĠPf": 17331, "ÑĥдÑĮ": 17332, "×ķ×Ŀ": 17333, "ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, "ports": 17845, "ĠcÅ": 17846, "Ġmonkey": 17847, "Ġbrac": 17848, "wei": 17849, "lung": 17850, "Ġartif": 17851, "Ġsyrup": 17852, "ĠÐĶав": 17853, "Ġlifted": 17854, "Ġchez": 17855, "ĠAdvent": 17856, "ĠStock": 17857, "Ġdol": 17858, "мен": 17859, "иÑĪÑĮ": 17860, "Ġyn": 17861, "gio": 17862, "det": 17863, "Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, "ìĨĮë": 17921, "Ġting": 17922, "Ġnasty": 17923, "Ġgauge": 17924, "ĠÑĢод": 17925, "ĠHans": 17926, "Your": 17927, "αν": 17928, "Ġprojet": 17929, "ĠHawaii": 17930, "Ġsuspicious": 17931, "Ġschw": 17932, "Ġremoval": 17933, "Ġintrig": 17934, "ĠMU": 17935, "Ġponto": 17936, "ा": 17937, "ĠобÑĢаз": 17938, "Ġguessing": 17939, "pace": 17940, "Ġmothers": 17941, "Ġmillimeter": 17942, "ление": 17943, "没æľī": 17944, "Ġavailability": 17945, "icz": 17946, "æѤ": 17947, "Ġfract": 17948, "Ġbases": 17949, "km": 17950, "ĠBTS": 17951, "ĠField": 17952, "Ġdzie": 17953, "Ġsegundo": 17954, "ĠëĤĺëĬĶ": 17955, "Ġlegitimate": 17956, "imas": 17957, "Ġвн": 17958, "Ġcorruption": 17959, "Ġsmash": 17960, "ĠValent": 17961, "Ġaligned": 17962, "ĠPennsylvania": 17963, "Ġgab": 17964, "ĠEun": 17965, "enth": 17966, "ĠMorning": 17967, "Ġcandle": 17968, "Ġbackpack": 17969, "ĠIslamic": 17970, "ações": 17971, "Ġencry": 17972, "Ġmushrooms": 17973, "íĮĮ": 17974, "dit": 17975, "Ġtransit": 17976, "ĠWisconsin": 17977, "Ġparticipated": 17978, "ĠIls": 17979, "Ġunfold": 17980, "¶Ģë": 17981, "Ġprofits": 17982, "Ġwarming": 17983, "ĠGang": 17984, "Ġnetworking": 17985, "Ġmega": 17986, "Ġthoroughly": 17987, "lements": 17988, "ĠHm": 17989, "Ġdeciding": 17990, "Ġemotionally": 17991, "Ġexhausted": 17992, "ĠÐŁÐ¾ÑĤ": 17993, "cido": 17994, "ĠHTML": 17995, "Ġcopyright": 17996, "Ġmelody": 17997, "yim": 17998, "Ġanders": 17999, "oshop": 18000, "Ġë³¼": 18001, "Ġathlete": 18002, "ĠGE": 18003, "Ġfrequent": 18004, "Ġdesires": 18005, "Ġneeding": 18006, "ĠYun": 18007, "Ġrifle": 18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåĪ°": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, "Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 18094, "©°": 18095, "ĠFish": 18096, "........": 18097, "Ġvessel": 18098, "Ġtrabajo": 18099, "ãģµ": 18100, "ĠOcean": 18101, "å§IJ": 18102, "yg": 18103, "Ġtowns": 18104, "del": 18105, "Ġterrifying": 18106, "ĠçalÄ±ÅŁ": 18107, "Ġsino": 18108, "Ġeats": 18109, "Ġgez": 18110, "Ġgeme": 18111, "ĠìĻĦ": 18112, "Ġcompart": 18113, "Ġimplementing": 18114, "ĠPotter": 18115, 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18296, "Ġvois": 18297, "ĠMoscow": 18298, "Ġchairs": 18299, "his": 18300, "Ġrefugees": 18301, "kg": 18302, "Ġkole": 18303, "į¨": 18304, "аÑģибо": 18305, "¦½": 18306, "ĠUniverse": 18307, "ĠDirect": 18308, "Ġcheating": 18309, "ĠCin": 18310, "Ġpatri": 18311, "Ġadvise": 18312, "ĠNether": 18313, "Ġprimeiro": 18314, "Ġmentioning": 18315, "nut": 18316, "56": 18317, "arı": 18318, "Ġpetite": 18319, "bled": 18320, "Ġpensar": 18321, "icio": 18322, "IND": 18323, "Ġveteran": 18324, "Ġladder": 18325, "Ġconsequence": 18326, "ожал": 18327, "ĠBurn": 18328, "Ġrug": 18329, "ĠMade": 18330, "Ġgit": 18331, "\"...": 18332, "Ġcompetitors": 18333, "Ġprzed": 18334, "Ġapparent": 18335, "ĠArgentina": 18336, "ĠWorking": 18337, "Ġcollaborate": 18338, "woman": 18339, "Ġretain": 18340, "Ġleurs": 18341, "Ġdashboard": 18342, "×Ļ×ĵ": 18343, "ĠEarly": 18344, "BM": 18345, "ĠеÑij": 18346, "олог": 18347, "Ġsatisfying": 18348, "Ġoftentimes": 18349, "Ġmapping": 18350, "ünkü": 18351, "arth": 18352, "fold": 18353, "Ġlaunching": 18354, "Ġaura": 18355, "Ġprecision": 18356, "works": 18357, "God": 18358, "Ġstrap": 18359, "ĠImper": 18360, "Ġrivers": 18361, "Ġ|": 18362, "Ġcuer": 18363, "regon": 18364, "Ġarrival": 18365, "каÑħ": 18366, "ĠMiami": 18367, "анÑĭ": 18368, "Ġsurvivors": 18369, "ĠSenior": 18370, "David": 18371, "Ġestado": 18372, "Ġsectors": 18373, "Ġpopping": 18374, "Ġchim": 18375, "ayı": 18376, "Ġkunnen": 18377, "Ġgallery": 18378, "Ġsunlight": 18379, "esehen": 18380, "Ġyelling": 18381, "ĠMein": 18382, "ĠPhoenix": 18383, "Ġmano": 18384, "Ġhistoria": 18385, "Ġoccurring": 18386, "欸": 18387, "ì¸": 18388, "ади": 18389, "å¾ħ": 18390, "Ġinstitutional": 18391, "ĠTut": 18392, "ç²": 18393, "Ġslaves": 18394, "ãģ©ãģĨ": 18395, "Ġforgiveness": 18396, "Ġtwin": 18397, "ĠHyun": 18398, "нÑĮ": 18399, "ĠKomm": 18400, "andra": 18401, "shot": 18402, "ssä": 18403, "ĠÑĨе": 18404, "atta": 18405, "Ġexpense": 18406, "ĠGPU": 18407, "ĠPast": 18408, "ribly": 18409, "ĠëŃIJìķ¼": 18410, "Ġгода": 18411, "Ġrespir": 18412, "æĿ±": 18413, "ĠQueens": 18414, "hops": 18415, "Ġsérie": 18416, "Ġpref": 18417, "Ġcomed": 18418, "Ġplut": 18419, "ĠOverall": 18420, "ĠãģĿ": 18421, "Ġcush": 18422, "Ġringing": 18423, "Ġincorrect": 18424, "ĠÑģÑĤÑĢ": 18425, "Ġgeometry": 18426, "Ġadvertis": 18427, "ĠШ": 18428, "Ġreviewed": 18429, "ãģĤãģĤ": 18430, "Ġdozens": 18431, "Ġdetermination": 18432, "ĠPhill": 18433, "Ġcontributed": 18434, "ĠCit": 18435, "Ġpassengers": 18436, "Ġcôté": 18437, "Ġrever": 18438, "Ġtechnological": 18439, "Ġallen": 18440, "Ġraining": 18441, "avi": 18442, "Ġsalty": 18443, "Ġtyping": 18444, "ĠÑĤе": 18445, "Ġtilt": 18446, "Ġì¹ĺ": 18447, "ĠоÑĢ": 18448, "ĠпÑĢÑıм": 18449, "Ġrou": 18450, "Ġarena": 18451, "arat": 18452, "åĪ«": 18453, "HHHH": 18454, "Ġmanufacturers": 18455, "ĠEdward": 18456, "Ġtuck": 18457, "Ġblows": 18458, "ingo": 18459, "ĠMarc": 18460, "ìķĦìĦľ": 18461, "Mich": 18462, "ĠClean": 18463, "è´": 18464, "esto": 18465, "ĠPack": 18466, "Ġshaft": 18467, "BRUNO": 18468, "Ġaven": 18469, "uur": 18470, "ÑģколÑĮко": 18471, 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"otz": 18530, ",": 21732, "Ġchords": 21733, "ĠSanders": 21734, "Ġë¶Ħë": 21735, "Ben": 21736, "Ġdarüber": 21737, "ilians": 21738, "Ġordering": 21739, "ĠManh": 21740, "Ġkilogram": 21741, "ĠkarÅŁ": 21742, "Ġgrasp": 21743, "Ġghosts": 21744, "alen": 21745, "ĠJedi": 21746, "Ġбли": 21747, "Ġdownloaded": 21748, "Ġconducting": 21749, "ĠHak": 21750, "Ġresearcher": 21751, "ilan": 21752, "good": 21753, "ĠHannah": 21754, "ĠdÃ¼ÅŁÃ¼n": 21755, "ĠMessiah": 21756, "uity": 21757, "iona": 21758, "Ġprobable": 21759, "ĠYE": 21760, "Ġindependently": 21761, "Ġbuffer": 21762, "burn": 21763, "ourd": 21764, "ĠMcK": 21765, "Ġlingu": 21766, "ujemy": 21767, "еÑĢÑĤ": 21768, "Ġintuitive": 21769, "Ġcracks": 21770, "appropri": 21771, "nty": 21772, "Ġgeen": 21773, "Ġlend": 21774, "Ġcertification": 21775, "IDS": 21776, "unter": 21777, "pees": 21778, "Ġtrump": 21779, "Ġbankrupt": 21780, "Ġfeas": 21781, "èĹ": 21782, "Ġduż": 21783, "æ¸ħ": 21784, "Ġviruses": 21785, "Ġ58": 21786, "god": 21787, "Ġжел": 21788, "Ġstalk": 21789, 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"Ġwounded": 21906, "ĠCuando": 21907, "Ġcushion": 21908, "Ġwhites": 21909, "Ġpreferences": 21910, "Ġì¤Ģë¹Ħ": 21911, "Ġkaż": 21912, "ĠGate": 21913, "ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 21923, "Ġging": 21924, "Ġumbrella": 21925, "IGHT": 21926, "Ġpension": 21927, "Ġcombining": 21928, "SS": 21929, "Ġrectangle": 21930, "á»ĩt": 21931, "Ġproxim": 21932, "ĠCow": 21933, "¸Į": 21934, "Ġintentional": 21935, "æķĻ": 21936, "Ġdecid": 21937, "ĠÑģкаж": 21938, "ĠUma": 21939, "iasm": 21940, "buz": 21941, "Ġdebris": 21942, "Ġcass": 21943, "ĠProp": 21944, "iska": 21945, "ëł¥": 21946, "esterol": 21947, "ussian": 21948, "ìĿ´ëŀij": 21949, "Ġunlimited": 21950, "Ġadmire": 21951, "Ġtightly": 21952, "Ġgenome": 21953, "ĠJunior": 21954, "venir": 21955, "gus": 21956, "ĠcÄĥ": 21957, "ĠVlad": 21958, "ĠíĤ": 21959, "Ġrelativ": 21960, "inci": 21961, "Ġaunque": 21962, "ĠBoys": 21963, "ÑĨион": 21964, 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"Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 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33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 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"ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, 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34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, 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"zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, "ĠпÑĢил": 34770, "ĠвÑĭпол": 34771, "Ġthôi": 34772, "ĠKENN": 34773, "Ġhanding": 34774, "actus": 34775, "Ġtacos": 34776, "Ġformerly": 34777, "ĠCorinthians": 34778, "ãģ«ãģ¯": 34779, "ÑĨÑĸÑĹ": 34780, "Ġpadre": 34781, "Ġcongregation": 34782, "æij": 34783, "fert": 34784, "Ġsubir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ĠCurry": 34789, "ĠìķĬëĬĶ": 34790, "елÑİ": 34791, "Ġfuss": 34792, "Ġbooty": 34793, "Ġlows": 34794, "Ġhommes": 34795, "ĠMH": 34796, "ĠDisneyland": 34797, "went": 34798, "Ġresidue": 34799, "Ġbeeping": 34800, "è¼ķ": 34801, "ätta": 34802, "Ġmould": 34803, "ĠProjekt": 34804, "stalk": 34805, "Ġartifact": 34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, "PO": 34885, "rators": 34886, "Ġlamps": 34887, "Ġterritorial": 34888, "Ġbackbone": 34889, "listed": 34890, "DY": 34891, "ĠاÙĦر": 34892, "Ġpursued": 34893, "ĠCommons": 34894, "Ġ곡": 34895, "locks": 34896, "edor": 34897, "Ġconceived": 34898, "gere": 34899, "Ġdisappearing": 34900, "ĠSull": 34901, "ĠìĹ°ë": 34902, "Ġhoffe": 34903, "Ġdetox": 34904, "íĶĮ": 34905, "Ġretir": 34906, "ĠëģĿëĤ": 34907, "Ġpergunta": 34908, "ĠBOY": 34909, "ç²¾": 34910, "Ġpenn": 34911, "æĿ¥äºĨ": 34912, "hés": 34913, "hon": 34914, "Ġcatastrophic": 34915, "Ġaust": 34916, "Ġtorso": 34917, "Ġìĸ´ëĬIJ": 34918, "ĠìĤ¬ëŀĮëĵ¤ìĿ´": 34919, "Ġmarvelous": 34920, "ĠHarley": 34921, "achine": 34922, "Ġtiế": 34923, "itto": 34924, "ĠIÃŃm": 34925, "ylon": 34926, "Ġshutdown": 34927, ".''": 34928, "Ġapologies": 34929, "ĠCommunication": 34930, "ĠговоÑĢÑİ": 34931, "ãģĤãĥ¼": 34932, "âĦ¢": 34933, "ÃŃveis": 34934, "acun": 34935, "Ġretaining": 34936, "Ġcontradiction": 34937, "ĠADAM": 34938, "COM": 34939, "Bryan": 34940, "ĠMonsieur": 34941, "Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 34999, "{\\": 35000, "ĠRück": 35001, "rite": 35002, "ĠZeus": 35003, "Ġcalorie": 35004, "Ġattractions": 35005, "ĠHinter": 35006, "Ġuhm": 35007, "ĠíĮIJ": 35008, "Ġrulers": 35009, "Ġdiscouraged": 35010, "Ġacontecer": 35011, "Ġaccents": 35012, "ĠOptim": 35013, "ĠAlg": 35014, "kids": 35015, "2021": 35016, "ĠLindsay": 35017, "Ġfilmmakers": 35018, "prowad": 35019, "Ġterug": 35020, "ëĭ´": 35021, "ĠSommer": 35022, "2018": 35023, "Ġborrowing": 35024, "ĠTransfer": 35025, "ноп": 35026, "arias": 35027, "Ġheadphone": 35028, "ì¼ľ": 35029, "Ġtranslating": 35030, "Ġaufge": 35031, "à®ªà®Ł": 35032, "weis": 35033, "avant": 35034, "paid": 35035, "baby": 35036, "Ġtoughest": 35037, "Ġrepeats": 35038, "ĠTeresa": 35039, "Lord": 35040, "Ġacabar": 35041, "ĠRide": 35042, "dir": 35043, "Ġleng": 35044, "Ġdwa": 35045, "Ġheadaches": 35046, "Ġnữa": 35047, "ĠнаÑģÑĤоÑıÑī": 35048, "Ġboils": 35049, "Ġlonging": 35050, "rias": 35051, "ório": 35052, "ĠParadise": 35053, "ĠSeñor": 35054, "erdem": 35055, "Ġreinst": 35056, "Ġsalaries": 35057, "Ġinsecurity": 35058, "ÅĤoÅĽci": 35059, "ĠабÑģолÑİÑĤно": 35060, "inken": 35061, "ĠEddy": 35062, "udos": 35063, "Ġdummy": 35064, "Ðļак": 35065, "six": 35066, "Ġinbox": 35067, "ẩ": 35068, "People": 35069, "á»ĵng": 35070, "Ġorganizers": 35071, "find": 35072, "Ġül": 35073, "ĠCOM": 35074, "ża": 35075, "weile": 35076, "Commentary": 35077, "íĬ¸ë¥¼": 35078, "ĠMittel": 35079, "kus": 35080, "èĽĭ": 35081, "न": 35082, "iral": 35083, "Ġgarment": 35084, "ικά": 35085, "Ġstool": 35086, "payers": 35087, "Ġshimmer": 35088, "ĠOllie": 35089, "ĠJeżeli": 35090, "è¿ĺæľī": 35091, "Ġ1977": 35092, "Ġjeux": 35093, "Ġextinct": 35094, "ĠTransportation": 35095, "ĠMaker": 35096, "Ġjohn": 35097, "Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, 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36184, "Ġê·¸ëŀ¬": 36185, "Ġlookin": 36186, "ĠTY": 36187, "ĠMint": 36188, "Add": 36189, "Ġmite": 36190, "ĠSmoke": 36191, "Ġnota": 36192, "Ġmoss": 36193, "ĠAbend": 36194, "Ġ컨": 36195, "Ġexaggerated": 36196, "fires": 36197, "Ġredist": 36198, "ffiti": 36199, "Ġopenness": 36200, "ê°IJìĿ´": 36201, "endeu": 36202, "енной": 36203, "Watch": 36204, "Ġavatar": 36205, "ĠPey": 36206, "urun": 36207, "Ġsenza": 36208, "Ġì§ĢìĹŃ": 36209, "ĠNatomiast": 36210, "Ġemergence": 36211, "rays": 36212, "Ġcrafted": 36213, "gary": 36214, "ãģłãģij": 36215, "üng": 36216, "-\"": 36217, "Ġhacked": 36218, "Ġstray": 36219, "encie": 36220, "emo": 36221, "Ġcomen": 36222, "ĠKız": 36223, "ĠJasmine": 36224, "ĠHindi": 36225, "manas": 36226, "Ġinfinitely": 36227, "emon": 36228, "ìĿ¸ëį°ìļĶ": 36229, "jak": 36230, "Ġroaring": 36231, "érique": 36232, "sweise": 36233, "ĠRolex": 36234, "åł±å°İ": 36235, "ĠStuart": 36236, "bnb": 36237, "Ġdiagnose": 36238, "Ġcoherent": 36239, "ĠMJ": 36240, "æºĸåĤĻ": 36241, "Ġpike": 36242, "lav": 36243, "Ġorchestral": 36244, "аÑģÑĤи": 36245, "Ġterminar": 36246, "Ġgatherings": 36247, "Ġcompliant": 36248, "Ġupgrading": 36249, "Ġregulator": 36250, "Ġlanç": 36251, "éĢ£": 36252, "Ġmerchants": 36253, "tawa": 36254, "Ġmonitored": 36255, "Ġrendre": 36256, "两": 36257, "Ġunterwegs": 36258, "anguard": 36259, "gard": 36260, "ĠBelow": 36261, "duino": 36262, "ĠЦе": 36263, "Ġimpedance": 36264, "ìľ¡": 36265, "份": 36266, "Ġaktuell": 36267, "ĠVatic": 36268, "åŃ©": 36269, "Ġstewards": 36270, "Ġbrightest": 36271, "Ġkenn": 36272, "Ġkau": 36273, "ĠMatrix": 36274, "ĠBark": 36275, "ĠðŁij": 36276, "Ġtaper": 36277, "Ġcasino": 36278, "ר×Ķ": 36279, "ysical": 36280, "Ġbuilders": 36281, "ĠczÅĤowie": 36282, "ĠNepal": 36283, "Ġ!\"": 36284, "Ġterme": 36285, "Ġinnych": 36286, "Ġmaths": 36287, "Ġdrafted": 36288, "ĠBalk": 36289, "Ġhesitant": 36290, "Ġvoltar": 36291, "Ġrevive": 36292, "ĠÑĦилÑĮма": 36293, "Ġassassin": 36294, "ĠSolutions": 36295, "Ġduel": 36296, "Ġbearings": 36297, "à¸Ħะ": 36298, "Ġrookie": 36299, "ikat": 36300, "Ġbiscuits": 36301, "Ġcords": 36302, "ÑĥваÑĤи": 36303, "ARIN": 36304, "Ġprogressing": 36305, "ĠGir": 36306, "Ġpenetrate": 36307, "ĠStorage": 36308, "eight": 36309, "ĠÑĤÑĢÑĥ": 36310, "ĠdonÃŃt": 36311, "Ġsizin": 36312, "Ġoutdated": 36313, "ĠнаÑĪи": 36314, "Ġaffir": 36315, "Ġspoons": 36316, "Ġoni": 36317, "Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 36360, "eka": 36361, "Ġcompletes": 36362, "ĠWells": 36363, "Ġroy": 36364, "ĠPray": 36365, "ĠKalau": 36366, "izin": 36367, "iaÅĤem": 36368, "Ġlocom": 36369, "ĠNashville": 36370, "ĠPentagon": 36371, "미": 36372, "ĠNEW": 36373, "ÄħÄĩ": 36374, "ÃŃss": 36375, "Ġmarrying": 36376, "Ġfeud": 36377, "íĻķ": 36378, "æĢ¥": 36379, ")!": 36380, "ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, "Ġdownloads": 36553, "Ġarmour": 36554, "Ġ×Ļ×ķתר": 36555, "Ġlongevity": 36556, "ĠTONER": 36557, "ĠкомменÑĤаÑĢ": 36558, "Ġczego": 36559, "Ġnotify": 36560, "Ġairports": 36561, "Ġenduring": 36562, "lette": 36563, "Ġapparat": 36564, "Ġhabil": 36565, "á»ĩc": 36566, "nad": 36567, "ICO": 36568, "ĠBrah": 36569, "Ġsegún": 36570, "Ġgovernors": 36571, "kaha": 36572, "ĠSchluss": 36573, "Ġodpowied": 36574, "irting": 36575, "Ġrempl": 36576, "ĠAboriginal": 36577, "identally": 36578, "Ġenhancing": 36579, "licting": 36580, "ĠHawaiian": 36581, "Ġstriving": 36582, "ĠNiet": 36583, "Ġznaczy": 36584, "Ġobedience": 36585, "ĠnÃ¥got": 36586, "Ġexpired": 36587, "Ġ1918": 36588, "presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, 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36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪا": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, "Ġmeist": 36894, "Ġimpatient": 36895, "Ġbehaves": 36896, "æĿ¾": 36897, "ĠëĤ´ìļ©": 36898, "Ġbackstory": 36899, "Ġagreeing": 36900, "ĠÅģ": 36901, "ihin": 36902, "Ġtemperatura": 36903, "ĠBackground": 36904, "Ġnutzen": 36905, "Ġëħ¹": 36906, "ĠMänner": 36907, "Ġcollaborations": 36908, "ĠKos": 36909, "éģİåİ»": 36910, "Ġnightmares": 36911, "ëĵ±": 36912, "ĠQueensland": 36913, "Ġassociates": 36914, "ĠKok": 36915, "Ġfactorial": 36916, "ĠHyung": 36917, "Ġê·¸ëĭ¤ìĿĮ": 36918, "Ġfilho": 36919, "Ġelét": 36920, "Ġíĸīë³µ": 36921, "°±": 36922, "Ġgefunden": 36923, "Ġsemicondu": 36924, "Ġcounselors": 36925, "ĠUpper": 36926, "ĠAub": 36927, "ickers": 36928, "Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, "Ġcurls": 36950, "ĠRhod": 36951, "End": 36952, "ĠET": 36953, "Ġacquaint": 36954, "ĠKelvin": 36955, "Ġtrif": 36956, "ĠAway": 36957, "ìŀIJëĬĶ": 36958, "vs": 36959, "Ġpágina": 36960, "Ġinlet": 36961, "ĠSantos": 36962, "Ġìļ°ìĻĢ": 36963, "Ġyapıyorsun": 36964, "theme": 36965, "Ġsouff": 36966, "Ġinjected": 36967, "Ġpóźniej": 36968, "iverso": 36969, "amped": 36970, "Ġdaher": 36971, "Ġdagger": 36972, "ĠлÑİбим": 36973, "Ġtummy": 36974, "Ġenlightened": 36975, "cents": 36976, "ĠDah": 36977, "Ġcuest": 36978, "ä¾Ĩ說": 36979, "ILY": 36980, "Ġ×ijר": 36981, "Ġbanging": 36982, "ĠEmil": 36983, "ĠCler": 36984, "ĠBorder": 36985, "ижÑĥ": 36986, "Ġpresenters": 36987, "ĠSTUD": 36988, "coins": 36989, "ĠíĻį": 36990, "Ġperks": 36991, "Ġparap": 36992, "Ġcertaines": 36993, "ĠLore": 36994, "öst": 36995, "ĠMARTIN": 36996, "Ġbios": 36997, "Ġwhereby": 36998, "verts": 36999, "ĠMiranda": 37000, "Ġstip": 37001, "澤": 37002, "andez": 37003, "׼׾": 37004, "ujin": 37005, "Ġê¾": 37006, "Ġallergies": 37007, "plate": 37008, "Ġyapıl": 37009, "Ġundertake": 37010, "ĠëĤĺê°Ģ": 37011, "Part": 37012, "Ġkızım": 37013, "hguru": 37014, "ãģĤãģ¨": 37015, "ĠJohns": 37016, "Ġeyelashes": 37017, "Ġdrained": 37018, "ĠstÃ¥r": 37019, "ãģĤãĤĬãģ¾ãģĻ": 37020, "ĠJade": 37021, "Ġcalend": 37022, "film": 37023, "Ġmesa": 37024, "Ġludzie": 37025, "Ġattracts": 37026, "Ġjuices": 37027, "Ġкил": 37028, "Ġnieuwe": 37029, "Ġmencion": 37030, "Ġignition": 37031, "Ġbladder": 37032, "andaag": 37033, "ĠExtension": 37034, "íĤ¨": 37035, "feed": 37036, "ĠÙĪÙĩ": 37037, "Ġspun": 37038, "Ġtät": 37039, "оÑĢоÑĤ": 37040, "tyard": 37041, "ronics": 37042, "ĠHuge": 37043, "Ñĥжд": 37044, "string": 37045, "Ġunjust": 37046, "Ġprawn": 37047, "Ġfrosting": 37048, "Ġdisappearance": 37049, "iosa": 37050, "Ġcardi": 37051, "ĠPriest": 37052, "ĠcientÃŃfic": 37053, "åĵªè£¡": 37054, "ĠÐĴаÑģ": 37055, "Ġë¶Ģíĥģ": 37056, "Ġthieves": 37057, "Ġphysique": 37058, "ĠEugene": 37059, "Ġблиз": 37060, "Ġmonopoly": 37061, "Ġbiography": 37062, "ĠhoÅŁ": 37063, "Ġtö": 37064, "mac": 37065, "Ġshocks": 37066, "ìĦ¸ë": 37067, "hit": 37068, "Ġsnug": 37069, "Ġincl": 37070, "Ġdedic": 37071, "Ġultras": 37072, "ĠизвеÑģÑĤ": 37073, "Ġutilization": 37074, "ĠÑģовеÑĢÑĪенно": 37075, "Ġservi": 37076, "stag": 37077, "180": 37078, "Ġsewer": 37079, "ĠChoice": 37080, "Ġdischarged": 37081, "ĠJD": 37082, "олеÑĤ": 37083, "ĠкваÑĢÑĤи": 37084, "Ġtelescop": 37085, "ĠJeÅĽli": 37086, "ĠNana": 37087, "cale": 37088, "ĠÑĤон": 37089, "mmm": 37090, "äºĨåIJ§": 37091, "Ġgehabt": 37092, "ëĤł": 37093, "æĬķ": 37094, "à¸Ļà¸Ļ": 37095, "Ġether": 37096, "Ġzen": 37097, "Ġresearched": 37098, "ĠCzyli": 37099, "å®Įåħ¨": 37100, "workers": 37101, "Ġ경찰": 37102, "Ġsheriff": 37103, "allo": 37104, "Ġtipos": 37105, "Ġprosecution": 37106, "Ġfrogs": 37107, "Ġfalt": 37108, "jd": 37109, "ĠíĮĶ": 37110, "Ġfiltered": 37111, "ĠOft": 37112, "Ġìį": 37113, "Ġdisfr": 37114, "ĠMustang": 37115, "Ġwoah": 37116, "ĠREALLY": 37117, "Ġмогли": 37118, "Ġentrada": 37119, "ĠигÑĢа": 37120, "Ġmixes": 37121, "ĠавÑĤомоб": 37122, "ÐĻ": 37123, "Ġshin": 37124, "Ġparanormal": 37125, "Ġsomeplace": 37126, "Ġdishon": 37127, "etaan": 37128, "Ġfuerte": 37129, "Ù¹": 37130, "Ġdoom": 37131, "ìĪľ": 37132, "Ġexistential": 37133, "Ġbuld": 37134, "ĠSDK": 37135, "ĠпÑĢавда": 37136, "Ġturnover": 37137, "ĠìĹ¬ê¸°ìĹIJ": 37138, "Ġह": 37139, "Ġmodeled": 37140, "Ġbugün": 37141, "Ġexperimentation": 37142, "Ġmornings": 37143, "Ġmedo": 37144, "Stevie": 37145, "Ġplayable": 37146, "Ġairlines": 37147, "gments": 37148, "Ġ기ë¶Ħ": 37149, "ĠTomb": 37150, "ĠMVP": 37151, "AUDIENCE": 37152, "Ġcheckout": 37153, "Ġpasst": 37154, "Ġbeispiel": 37155, "ĠLinks": 37156, "heavy": 37157, "Ġquestionable": 37158, "Ġìĵ°ë": 37159, "Ġsill": 37160, "Ġmanipulated": 37161, "ĠLoren": 37162, "Ġìľ¼": 37163, "Ġverge": 37164, "ák": 37165, "IES": 37166, "Ġsabot": 37167, "ĠCustomer": 37168, "ależy": 37169, "Ġnominee": 37170, "ĠGad": 37171, "Ġnouvelles": 37172, "ĠSPE": 37173, "istling": 37174, "Ġoval": 37175, "обÑĢаж": 37176, "ifty": 37177, "éĩİ": 37178, "Ġbezel": 37179, "yet": 37180, "Ġfreight": 37181, "ĠHanım": 37182, "rÃŃa": 37183, "Ġzoning": 37184, "Ġindem": 37185, "ĠBü": 37186, "Ġfeminism": 37187, "Ġvoix": 37188, "Ġoficial": 37189, "Ġdiyorum": 37190, "»IJ": 37191, "Ġarose": 37192, "Ġparar": 37193, "ìĿ¸ì§Ģ": 37194, "ĠMartine": 37195, "ĠLect": 37196, "Ġrester": 37197, "Ġdrowning": 37198, "uya": 37199, "cida": 37200, "ĠAriel": 37201, "Ġ02": 37202, "Ġ×Ķ×Ķ": 37203, "ç´ł": 37204, "ĠWert": 37205, "ТÑĭ": 37206, "Ġwidow": 37207, "Ġparchment": 37208, "Ġcottage": 37209, "ĠXL": 37210, "ĠSlack": 37211, "ĠNES": 37212, "Ġrobe": 37213, "Ġgimm": 37214, "Ġcaminho": 37215, "ĠHarper": 37216, "Ġcitrus": 37217, "Ġfirefighters": 37218, "Ġdopamine": 37219, "elets": 37220, "Ġdemocrat": 37221, "ìłľë¡ľ": 37222, "Ġplayback": 37223, "oj": 37224, "ĠпÑĢок": 37225, "ĠSullivan": 37226, "semble": 37227, "ĠWorth": 37228, "ĠMustafa": 37229, "าร": 37230, "Ġmets": 37231, "éĸĢ": 37232, "лоÑģÑĮ": 37233, "Ġinertia": 37234, "Ġuniforms": 37235, "足": 37236, "ério": 37237, "×ķר×Ķ": 37238, "ént": 37239, "Ġà®Ĵ": 37240, "ĠÑģамÑĭÑħ": 37241, "Ġvoulais": 37242, "ĠZimmer": 37243, "ê²łë": 37244, "ĠноÑģ": 37245, "encias": 37246, "Ġrelación": 37247, "Ġ걸ë": 37248, "Ġfaction": 37249, "Ġgosp": 37250, "полож": 37251, "nap": 37252, "hak": 37253, "Ġproceedings": 37254, "ĠìĨĶ": 37255, "ìķĦëĭĪ": 37256, "ĠìŀIJ기": 37257, "Ġwerd": 37258, "Ġsof": 37259, "Ġschlim": 37260, "Ġflavored": 37261, "Ġquadratic": 37262, "ĠBoot": 37263, "Ġpublicity": 37264, "ĠCaro": 37265, "Ġ?\"": 37266, "ниÑĨа": 37267, "mania": 37268, "ĠSUR": 37269, "ĠBUR": 37270, "lance": 37271, "ética": 37272, "Ġzobaczy": 37273, "Ġtrio": 37274, "sama": 37275, "ĠtaÅŁ": 37276, "Ġasymm": 37277, "resser": 37278, "Ġتع": 37279, "ĠпеÑģ": 37280, "Ġbeginnings": 37281, "ladım": 37282, "ĠбÑĭÑģÑĤÑĢ": 37283, "Ġmoo": 37284, "ĠGeneva": 37285, "Ġåľ¨": 37286, "erus": 37287, "borah": 37288, "Ġrefusing": 37289, "bull": 37290, "ĠWaiting": 37291, "ĠIndividual": 37292, "Ġanonym": 37293, "imens": 37294, "Ġmedidas": 37295, "Ġfragrant": 37296, "Ġdirectement": 37297, "ĠìķĦë§Ī": 37298, "uria": 37299, "Ġspherical": 37300, "Ġabge": 37301, "ĠVictorian": 37302, "Ġspectacle": 37303, "ĠRodriguez": 37304, "Ġocup": 37305, "ĠNär": 37306, "marks": 37307, "ngulo": 37308, "ĠLuci": 37309, "Ġshouted": 37310, "Ġregulators": 37311, "ÄŁini": 37312, "Ġdisent": 37313, "ĠÑĢÑĭн": 37314, "ëĤ¨": 37315, "ĠìĤ´ë": 37316, "Ġproblèmes": 37317, "ĠFinger": 37318, "assemble": 37319, "Ġpear": 37320, "Ġdroite": 37321, "ĠEverywhere": 37322, "tam": 37323, "оÑĤив": 37324, "вой": 37325, "ordinate": 37326, "ĠLak": 37327, "ĠmỼi": 37328, "ĠTelevision": 37329, "Ġexponentially": 37330, "avas": 37331, "Ġblev": 37332, "ĠMT": 37333, "俺": 37334, "Connell": 37335, "ĠêµŃ민": 37336, "ĠÑģвоим": 37337, "Ġacha": 37338, "ĠDynasty": 37339, "Jin": 37340, "Ġtore": 37341, "Ġflor": 37342, "Ġмногие": 37343, "æ²Ĵäºĭ": 37344, "owan": 37345, "bah": 37346, "Ġì£Ħ": 37347, "ĠCela": 37348, "Ġìµľê·¼": 37349, "Ġpermettre": 37350, "Ġabras": 37351, "Ġverstehen": 37352, "Ġescort": 37353, "ĠThem": 37354, "ärke": 37355, "porter": 37356, "Ġkahkaha": 37357, "Ġhect": 37358, "Ġdau": 37359, "wah": 37360, "olve": 37361, "ĠAges": 37362, "schaft": 37363, "ĠStell": 37364, "nelle": 37365, "ĠEnsuite": 37366, "ĠÐĴÑģем": 37367, "Ġcréd": 37368, "ĠPP": 37369, "lords": 37370, "grunting": 37371, "Ġcontraction": 37372, "Got": 37373, "Ġacquiring": 37374, "Ġsopr": 37375, "Ġpoisonous": 37376, "RNA": 37377, "Ġanar": 37378, "ĠHof": 37379, "')": 37380, "Ġremarkably": 37381, "Ġinternacional": 37382, "ücke": 37383, "inqu": 37384, "Ġduy": 37385, "Ġbeasts": 37386, "ĠLAN": 37387, "Ġprecedent": 37388, "ĠRPM": 37389, "åij¨": 37390, "Ġselon": 37391, "Ġmorte": 37392, "Ġcomeçou": 37393, "Ñıла": 37394, "Ġinterpreting": 37395, "ĠBurke": 37396, "ÑĤÑĢа": 37397, "ĠìĿ´ëŁ¬": 37398, "Ġpessim": 37399, "ĠNok": 37400, "íĮĿ": 37401, "Female": 37402, "Ġìĭ¤í": 37403, "ĻĢ": 37404, "Ġstimulation": 37405, "Ġslick": 37406, "Ġê°ĢëĬĶ": 37407, "Ġказ": 37408, "ĠHBO": 37409, "Ġpapier": 37410, "Ġkönnten": 37411, "Ñĥбли": 37412, "ĠConstant": 37413, "SPEAKING": 37414, "ĠktórÄħ": 37415, "Ġcosmetics": 37416, "ĠTrend": 37417, "Ġrobbery": 37418, "Ġtitt": 37419, "Ġgjort": 37420, "Ġdietary": 37421, "łĮ": 37422, "ĠKirby": 37423, "ĠпÑĢимеÑĢно": 37424, "Ġqualification": 37425, "Ġìķī": 37426, "Ġcabinets": 37427, "Ġhttp": 37428, "ĠErica": 37429, "義": 37430, "Ġdisadvantages": 37431, "Ġchattering": 37432, "yz": 37433, "feit": 37434, "Ġguild": 37435, "ĠETF": 37436, "ĠDragons": 37437, "ĠHERE": 37438, "venth": 37439, "ÙĦاÙħ": 37440, "Ġmarché": 37441, "Dam": 37442, "Ġphoton": 37443, "Ġestable": 37444, "Mag": 37445, "Ġolhar": 37446, "Ġcoupling": 37447, "ĠHilfe": 37448, "ĠWizard": 37449, "Ġмало": 37450, "help": 37451, "ĠlÃŃnea": 37452, "Ġì«": 37453, "Ġstandalone": 37454, "Ġmorale": 37455, "Ġzweite": 37456, "ãĤĪãĤįãģĹãģı": 37457, "ährt": 37458, "Ġdotted": 37459, "Ġdripping": 37460, "ĠFlag": 37461, "éĿĴ": 37462, "rocket": 37463, "rategy": 37464, "irim": 37465, "Ġíķĺë©´ìĦľ": 37466, "Ġsogenan": 37467, "ĠUno": 37468, "ĠSchutz": 37469, "Ġestilo": 37470, "ĠSubs": 37471, "ĠDaisy": 37472, "ÐĿеÑĤ": 37473, "'...": 37474, "Ġplatinum": 37475, "Ġbirl": 37476, "ĠSovi": 37477, "Ġviolate": 37478, "ÑĥеÑĤÑģÑı": 37479, "rill": 37480, "Ġtraz": 37481, "Ġsnip": 37482, "Ġcumpl": 37483, "à¸Ńà¸ģ": 37484, "Ġcuk": 37485, "éħĴ": 37486, "ĠParlament": 37487, "Ġhypert": 37488, "Ġpulp": 37489, "Ġtongues": 37490, "atto": 37491, "Ġbusca": 37492, "ihn": 37493, "ERO": 37494, "ĠÙĬع": 37495, "Ġvarias": 37496, "ĠMarian": 37497, "Ġbounded": 37498, "Ġpitching": 37499, "Ġdeficiency": 37500, "ĠBlessed": 37501, "ĠExerc": 37502, "uchs": 37503, "ĠnhÆ°ng": 37504, "æľ¬å½ĵ": 37505, "Ġraped": 37506, "hales": 37507, "Ġmala": 37508, "pic": 37509, "Ġ401": 37510, "ÅĽniej": 37511, "arina": 37512, "ëĵ¤ìĿĦ": 37513, "otti": 37514, "Ġдолго": 37515, "Ġtracker": 37516, "ĠShelby": 37517, "Ġvanished": 37518, "Ġbakery": 37519, "Kapı": 37520, "Jesus": 37521, "ĠKR": 37522, "JO": 37523, "ħ¸": 37524, "Ġdiscs": 37525, "ìĦ¯": 37526, "ì§Ģë": 37527, "×Ļצ": 37528, "emary": 37529, "Kendra": 37530, "Ġyük": 37531, "ückt": 37532, "Ġvaz": 37533, "Ġkup": 37534, "aktu": 37535, "ĠÑģпаÑģибо": 37536, "Ġaik": 37537, "Ġnursery": 37538, "Ġendangered": 37539, "êmement": 37540, "ematics": 37541, "Ġresponders": 37542, "ĠRepresentatives": 37543, "Ġsculptures": 37544, "igkeiten": 37545, "Ġdepl": 37546, "Ġinterpretations": 37547, "Ġdeadlines": 37548, "Ġ1942": 37549, "ÃĹ": 37550, "Ġsugars": 37551, "emu": 37552, "lively": 37553, "Ġrecreational": 37554, "Ġdistort": 37555, "Ġunderscore": 37556, "Ġunquote": 37557, "Ġsafest": 37558, "Ġswollen": 37559, "Ġanalyses": 37560, "Ġcommencé": 37561, "妹": 37562, "andin": 37563, "ĠХоÑĢоÑĪо": 37564, "Ġdiarr": 37565, "ãģ¾ãģģ": 37566, "ziest": 37567, "Ġtoothbrush": 37568, "éł»éģĵ": 37569, "uations": 37570, "Ġcade": 37571, "Ġbacklash": 37572, "hind": 37573, "Ġrisque": 37574, "zess": 37575, "ĠìĿ´ìķ¼ê¸°": 37576, "Ġesperar": 37577, "Ġtranslations": 37578, "ioned": 37579, "groans": 37580, "ĠпÑĥÑĤ": 37581, "Ġgenetically": 37582, "éĢł": 37583, "Ġhappiest": 37584, "Ġwerk": 37585, "atoon": 37586, "Ġmusi": 37587, "Ġfunção": 37588, "ĠìŀħëĭĪëĭ¤": 37589, "ĠÑĢай": 37590, "Ġbevor": 37591, "BLANK": 37592, "Ġrepentance": 37593, "Put": 37594, "Ġpotrzeb": 37595, "Ġsala": 37596, "Ġcampa": 37597, "WER": 37598, "ĠdecÃŃa": 37599, "Ġsécurité": 37600, "ĠAppreciate": 37601, "Ñĩи": 37602, "ĠRandom": 37603, "ë³Ħ": 37604, "kah": 37605, "Ġmöj": 37606, "Ġsäger": 37607, "Ġ×Ļ׼×ķ׾": 37608, "Ġ190": 37609, "xtures": 37610, "Eu": 37611, "Ġgä": 37612, "Ġ×ijת": 37613, "ĠCroat": 37614, "apo": 37615, "PLE": 37616, "Ġpersistence": 37617, "åĬ©": 37618, "Ġblends": 37619, "Ġtreffen": 37620, "ĠSantiago": 37621, "ydia": 37622, "aldo": 37623, "ĠTensorFlow": 37624, "ĠDual": 37625, "ãĥľ": 37626, "Ġchiff": 37627, "ìĹ´": 37628, "Ġcontracted": 37629, "Ġsegreg": 37630, "ĠFairy": 37631, "Ġwisely": 37632, "Ġvulnerabilities": 37633, "Ġhandheld": 37634, "Ġgadgets": 37635, "ĠboÅŁ": 37636, "ĠPopular": 37637, "Ġcurvature": 37638, "문": 37639, "ĠMARY": 37640, "ìĿ´ìĬ": 37641, "Ġformulation": 37642, "Ġcelery": 37643, "Ġblurry": 37644, "ĠTS": 37645, "alez": 37646, "Ġws": 37647, "Ġprogramm": 37648, "ĠStack": 37649, "ĠJIM": 37650, "овали": 37651, "ıll": 37652, "Ġpère": 37653, "ĠKanye": 37654, "ĠDelaware": 37655, "Ġãģł": 37656, "Ġdaunting": 37657, "ĠбеÑģ": 37658, "ĠStupid": 37659, "big": 37660, "fficial": 37661, "Ġprecipitation": 37662, "Ġplung": 37663, "ục": 37664, "burse": 37665, "Ġdarle": 37666, "Ġcripp": 37667, "Ġpioneer": 37668, "Ġdisput": 37669, "Ġsean": 37670, "ãģĵãĤĵãģª": 37671, "Ġresistor": 37672, "Ġallein": 37673, "ipples": 37674, "arel": 37675, "Ġendors": 37676, "zust": 37677, "ĠÑĢебÑıÑĤа": 37678, "eded": 37679, "Ġì¹´ë©Ķë": 37680, "Ġlleva": 37681, "Ġkennt": 37682, "Ġбал": 37683, "ĠDocument": 37684, "ĠKnights": 37685, "Ġbuckle": 37686, "Ġìī¬": 37687, "Ġalk": 37688, "ĠEveryday": 37689, "atters": 37690, "Ġtoilets": 37691, "Ġjugar": 37692, "ĠìŀĪì§Ģ": 37693, "Ġgenauso": 37694, "ĠLandesregierung": 37695, "ãģ£ãģ±": 37696, "ije": 37697, "Ġtrailers": 37698, "ĠTigers": 37699, "Ġgitti": 37700, "Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 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39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, 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39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 39245, "edel": 39246, "Ġìĸ´ìłľ": 39247, "Ġjogar": 39248, "Ġör": 39249, "Ġundertaking": 39250, "ĠStrength": 39251, "Ġmilhões": 39252, "ĠWine": 39253, "ĠMolt": 39254, "讲": 39255, "ãģijãĤĮ": 39256, "Ġundermine": 39257, "ĠArchives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "Ġcaste": 39262, "пÑĢ": 39263, "Ġlegislators": 39264, "ulators": 39265, "ênio": 39266, "Ġëį°ë": 39267, "ĠÑħоÑĤиÑĤе": 39268, "Ġнек": 39269, "Ġsurn": 39270, "Ġconsci": 39271, "ĠPOW": 39272, "Ġculinary": 39273, "ĠKAT": 39274, "ĠFolks": 39275, "Ñĭваем": 39276, "Ġвок": 39277, "ãģijãĤĭ": 39278, "service": 39279, "pts": 39280, "Ġпобед": 39281, "æĺ¯åķĬ": 39282, "Ġtents": 39283, "Ġnord": 39284, "STE": 39285, "Ġrepublican": 39286, "Ġwyk": 39287, "Ġminions": 39288, "èĻķ": 39289, "Ġmemang": 39290, "jest": 39291, "Ġcomparative": 39292, "Ġtyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "Ġnegativity": 39297, "Ġsjälv": 39298, "Ġdú": 39299, "æīĢæľī": 39300, "Ġrecalled": 39301, "cra": 39302, "ĠTada": 39303, "ĠÑĢÑĥки": 39304, "ĠопÑĢедел": 39305, "Ġprocrast": 39306, "Ġjogos": 39307, "ĠOo": 39308, "ĠHearts": 39309, "Ġéch": 39310, "ĠksiÄħż": 39311, "Ġcoarse": 39312, "ĠTube": 39313, "ĠGreens": 39314, "Ġén": 39315, "Ġdumbbell": 39316, "ĠÑĤи": 39317, "Ġquerer": 39318, "اØŃ": 39319, "Ïĥει": 39320, "ĠпÑĢавилÑĮно": 39321, "Ġпап": 39322, "Ġcompra": 39323, "Ġtér": 39324, "ĠAntes": 39325, "Ġoptimum": 39326, "Ġbiscuit": 39327, "κι": 39328, "aczego": 39329, "Ġìĭľê°ĦìĿ´": 39330, "ĠMarines": 39331, "vero": 39332, "Ġvaccinations": 39333, "Ġpetty": 39334, "riters": 39335, "Ġал": 39336, "country": 39337, "Ġcounters": 39338, "Ġattendant": 39339, "ĠHui": 39340, "ãģ¨ãģĦãģĨãģĵãģ¨ãģ§": 39341, "cka": 39342, "ÑģÑĤвеннÑĭй": 39343, "guy": 39344, "Ġtricked": 39345, "ĠRED": 39346, "Ġthrilling": 39347, "ÏĢοι": 39348, "Ġpiggy": 39349, "Ġanunci": 39350, "ORTER": 39351, "ĠValue": 39352, "Ġrond": 39353, "ĠADA": 39354, "Ġposer": 39355, "hores": 39356, "ĠRoland": 39357, "ĵ¯": 39358, "Ġnoir": 39359, "Ġש×IJ×": 39360, "ë°ľ": 39361, "iemand": 39362, "ĠпоÑĤеÑĢ": 39363, "ê³³": 39364, "Ġê±±": 39365, "Ġformatting": 39366, "ĠLed": 39367, "è§Ģçľ¾": 39368, "Ġkillers": 39369, "ĠÄijấy": 39370, "Ġhaar": 39371, "again": 39372, "!>[": 45687, "minster": 45688, "Ġвли": 45689, "Ġidentifier": 45690, "ĠLambda": 45691, "Ġtros": 45692, "Ġflawless": 45693, "Ġdetrimental": 45694, "Ġbunları": 45695, "War": 45696, "Ġregião": 45697, "羣çļĦæĺ¯": 45698, "ĠBike": 45699, "cessors": 45700, "Ġcùng": 45701, "ĠRN": 45702, "Ġê½ĥ": 45703, "Ġküçük": 45704, "ĠBeginning": 45705, "íĺ¸ë": 45706, "Ġgewe": 45707, "Ġdenote": 45708, "ĠAlberto": 45709, "Ġprobiot": 45710, "Ġode": 45711, "Ġmolar": 45712, 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45768, "éĻ³": 45769, "Ġretiring": 45770, "Ġvampires": 45771, "Ġmerde": 45772, "ĠChanging": 45773, "Ġtame": 45774, "Ġspokesperson": 45775, "Ġcay": 45776, "Ġflirting": 45777, "ĠGrö": 45778, "Ġwär": 45779, "Ġwyb": 45780, "Ġcoeur": 45781, "ạnh": 45782, "ĠìĻĢìĦľ": 45783, "Ġconnais": 45784, "ĠHundreds": 45785, "ĠBea": 45786, "ĠαÏĢ": 45787, "pruch": 45788, "Ġsociedade": 45789, "ĠWhilst": 45790, "ĠKait": 45791, "espace": 45792, "Ġchia": 45793, "ĠErm": 45794, "Ġë°Ķê¿": 45795, "Ġfences": 45796, "ĠMortal": 45797, "ê²ģ": 45798, "ĠгÑĢаÑĦ": 45799, "ĠHomeland": 45800, "ĠJUN": 45801, "isst": 45802, "Ġparlar": 45803, "Ġsporty": 45804, "éo": 45805, "Ġdeepen": 45806, "ĠBehavior": 45807, "éĢı": 45808, "åĵĪåĵĪåĵĪ": 45809, "Ġerrand": 45810, "Ġrotary": 45811, "ĠWellington": 45812, "Wind": 45813, "Ġmesela": 45814, "ảng": 45815, "iende": 45816, "Ġexcell": 45817, "ĠGenius": 45818, "ĠEduardo": 45819, "æľī人": 45820, "ĠÅŁunu": 45821, "ĠÄ°stanbul": 45822, "Ġproduto": 45823, "Ġãħİãħİ": 45824, "OFF": 45825, "Ġwollt": 45826, "çĪĨ": 45827, "Ġëī´ìĬ¤": 45828, "Ġlass": 45829, "Ġhertz": 45830, "Ġaromatic": 45831, "Ġзвон": 45832, "Ġautoc": 45833, "ĠLust": 45834, "Ġ112": 45835, "ĠÎĹ": 45836, "Ġreviewers": 45837, "Ġreceptive": 45838, "å°įäºĨ": 45839, "ând": 45840, "oglo": 45841, "ĠìķĦëĭĻ": 45842, "Ġngo": 45843, "ÑĸÑĤи": 45844, "Ã¥t": 45845, "cono": 45846, "Ġtekrar": 45847, "Ġì£¼ê³ł": 45848, "ĠgelmiÅŁ": 45849, "Ġbedtime": 45850, "ĠArgh": 45851, "ADA": 45852, "ĠгоÑĢода": 45853, "ĠÄĩ": 45854, "Ġalliances": 45855, "giggling": 45856, "Ġyerde": 45857, "Ġspies": 45858, "Ġgutes": 45859, "çi": 45860, "Ġalltid": 45861, "ĠLah": 45862, "ŀIJë": 45863, "ĠdokÅĤad": 45864, "ÙĪÙĬ": 45865, "Ġtoxicity": 45866, "Ġcancellation": 45867, "Ġ1958": 45868, "dro": 45869, "ĠìŀijìĿĢ": 45870, "ĠMotorola": 45871, "Ġmultin": 45872, "Ġenthusiasts": 45873, "ĠMighty": 45874, "ĠCoconut": 45875, ":ãĢĮ": 45876, "ĠPictures": 45877, "Ġsangre": 45878, "Ġblinking": 45879, "olesome": 45880, "ĠìĬ¤íĥĢìĿ¼": 45881, "FP": 45882, "Ġbooming": 45883, "ĠдеÑģÑıÑĤ": 45884, "Ġratchet": 45885, "Ġtimelines": 45886, "leness": 45887, "Ġcages": 45888, "ĠGoodnight": 45889, "ometimes": 45890, "Ġcunning": 45891, "ĠRisk": 45892, "uled": 45893, "dade": 45894, "Ġprata": 45895, "ĠgustarÃŃa": 45896, "amus": 45897, "ĠJinping": 45898, "Ġestrut": 45899, "Ġdescobrir": 45900, "ĠMÄģ": 45901, "ĠAllan": 45902, "ĠåĪĨ": 45903, "Ġ׾ק": 45904, "Ġpreserv": 45905, "ĠStrawberry": 45906, "Äı": 45907, "Lu": 45908, "Ġkro": 45909, "ĠReports": 45910, "ìħĶìķ¼": 45911, "Ġvalt": 45912, "Ġpouvait": 45913, "Ġappar": 45914, "ĠBone": 45915, "Ġpreferably": 45916, "ĠRepública": 45917, "å°±åĪ°": 45918, "Ġherzlich": 45919, "Ġchimney": 45920, "Ġçev": 45921, "Ġvisas": 45922, "Ġverr": 45923, "Ġcultivation": 45924, "ĠArmenia": 45925, "ĠвдÑĢÑĥг": 45926, "Ġcockro": 45927, "retched": 45928, "artz": 45929, "ĠлÑİдÑıм": 45930, "ĠpolÃŃticas": 45931, "ĠPanz": 45932, "ĠAKA": 45933, "ĠëĪĮ룬": 45934, "Ġerro": 45935, "Ġcamper": 45936, "Ġ102": 45937, "स": 45938, "done": 45939, "Ġhoard": 45940, "ĠÐŁÐ¾ÑĤом": 45941, "jeong": 45942, "Ġdesta": 45943, "pak": 45944, "Ġinim": 45945, "Ġgrowers": 45946, "ĠMessage": 45947, "Ġelector": 45948, "engage": 45949, "ĠForbes": 45950, "ĠCincinnati": 45951, "Ġdifférence": 45952, "df": 45953, "Ġspar": 45954, "Ġawaits": 45955, "ĠUSSR": 45956, "ĠRising": 45957, "ĠHoÅŁ": 45958, "Ġfooting": 45959, "Ġcondiciones": 45960, "ÑĤоÑĢов": 45961, "Ġclinician": 45962, "ĠDiskuss": 45963, "å£ĵ": 45964, "ר×Ĵ": 45965, "×¥": 45966, "iteit": 45967, "gren": 45968, "Ġcharisma": 45969, "Ġleuke": 45970, "Ġirritating": 45971, "Ġcirca": 45972, "ĠRhodes": 45973, "Ġpior": 45974, "Ġhandicap": 45975, "royable": 45976, "Ġvull": 45977, "OG": 45978, "ĠinÃŃcio": 45979, "ieri": 45980, "Ġsplashing": 45981, "Ġdemise": 45982, "Ġassistir": 45983, "ÑĩÑĤо": 45984, "Ġcovert": 45985, "ĠGud": 45986, "à¸ī": 45987, "klär": 45988, "ĠìŀIJ꾸": 45989, "Ġverändert": 45990, "ĠREM": 45991, "ĠConven": 45992, "atge": 45993, "Ġpierwsze": 45994, "Ġclergy": 45995, "lington": 45996, "liv": 45997, "VPN": 45998, "ĠÑģожал": 45999, "ĠHate": 46000, "ãģ¨ãģĵãĤį": 46001, "ÏĨο": 46002, "ĠRespons": 46003, "озд": 46004, "Ġetmek": 46005, "Ġchemin": 46006, "ÙħØ©": 46007, "Ġê°Ģ족": 46008, "Tre": 46009, "Ġumas": 46010, "ĠBurton": 46011, "Ġpatriarch": 46012, "ĠSmithsonian": 46013, "¥ĺ": 46014, "Moon": 46015, "Air": 46016, "Ġmedios": 46017, "Ġeraser": 46018, "Ġwollten": 46019, "Ġpareil": 46020, "ĠBillie": 46021, "æĬ½": 46022, "еÑĢÑĤв": 46023, "Ġparlament": 46024, "Ġagony": 46025, "ĠQUE": 46026, "sequently": 46027, "Another": 46028, "ĠWhew": 46029, "ĠAnnual": 46030, "Ġseben": 46031, "ìĥģìĿĦ": 46032, "values": 46033, "ŀľë§Į": 46034, "Ġsinon": 46035, "ereal": 46036, "ĠEnlight": 46037, "ĠChemistry": 46038, "ĠCatalunya": 46039, "Ġdoctr": 46040, "anton": 46041, "Ġstuk": 46042, "ĠPlate": 46043, "ĠKardashian": 46044, "Ġfilos": 46045, "ĠWet": 46046, "ĠпопÑĭÑĤ": 46047, "Ġunknowns": 46048, "ĠSchon": 46049, "ĠBaldwin": 46050, "Ġtelescopes": 46051, "ĠGucci": 46052, "oxide": 46053, "ĠConservative": 46054, "ìĦ±ìĿĦ": 46055, "Ġhinaus": 46056, "Power": 46057, "Ġê±´ê°ķ": 46058, "Ġprevail": 46059, "orman": 46060, "machine": 46061, "Ġ1946": 46062, "Ġunbel": 46063, "Ġschaut": 46064, "Ġpiel": 46065, "eenth": 46066, "Ġobjectively": 46067, "Ġchakra": 46068, "audio": 46069, "Ġchicos": 46070, "ĠVault": 46071, "å°Ī": 46072, "Ġmedicinal": 46073, "ĠTail": 46074, "While": 46075, "Ġasphalt": 46076, "Ġfroze": 46077, "ĠEK": 46078, "unching": 46079, "nosis": 46080, "2015": 46081, "ĠGri": 46082, "Ġoddly": 46083, "ĠMär": 46084, "ĠAeg": 46085, "colo": 46086, "Par": 46087, "Ġëĵ¤ìĸ´ë": 46088, "Ġvinden": 46089, "ĠOVER": 46090, "Ġiced": 46091, "Ġscorp": 46092, "Ġhac": 46093, "qualified": 46094, "ĠÑĥвидеÑĤÑĮ": 46095, "ermo": 46096, "HEN": 46097, "Ġsoi": 46098, "Ġmultiples": 46099, "Ġlayouts": 46100, "Ġblindness": 46101, "ĠBowser": 46102, "ĠподÑĤ": 46103, "ĠÃİ": 46104, "ventional": 46105, "Ġmata": 46106, "madı": 46107, "Ġgeez": 46108, "Ġcadence": 46109, "Ġważne": 46110, "ĠChristie": 46111, "venge": 46112, "Call": 46113, 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46170, "Ġréussi": 46171, "Ġhypertension": 46172, "ĠManual": 46173, "Ġprophetic": 46174, "Ġhandc": 46175, "ÑĮе": 46176, "Ġrefrain": 46177, "ĠSquid": 46178, "ìŀ¡": 46179, "Ġкоман": 46180, "ällen": 46181, "Ġllegó": 46182, "Ġbash": 46183, "iony": 46184, "ĠÑģклад": 46185, "Ġкаб": 46186, "Ġcareless": 46187, "ĠPool": 46188, "Ġtrás": 46189, "Ġfils": 46190, "ĠSchr": 46191, "Ġsprawd": 46192, "ĠMonaten": 46193, "Ġunforgettable": 46194, "ĠCotton": 46195, "Ġinconvenient": 46196, "ĠRX": 46197, "oris": 46198, "Ġhumbled": 46199, "ת×Ĺ": 46200, "Ġآپ": 46201, "ĠincreÃŃ": 46202, "ĠKommentare": 46203, "èĪĴ": 46204, "ración": 46205, "Ġvantage": 46206, "ĠSeal": 46207, "ĠìĿ´ê±°ë¥¼": 46208, "Ġjoue": 46209, "ãģĿãģĨãģ§ãģĻãģŃ": 46210, "Ġìĺ¤ëŀĺ": 46211, "ĠиÑģпÑĭÑĤ": 46212, "oben": 46213, "Ġgrate": 46214, "Ġcontrole": 46215, "ĠPercy": 46216, "ÅĤada": 46217, "Ġsimultaneous": 46218, "Ġprototy": 46219, "ĠgroÃŁer": 46220, "Ġbewusst": 46221, "inizi": 46222, "Ġpassieren": 46223, "ĠHappiness": 46224, "åīĩ": 46225, "shi": 46226, "geht": 46227, "Ġstationed": 46228, "ĠErgebnis": 46229, "Ġdirectamente": 46230, "Ġsurvives": 46231, "Ġpersones": 46232, "BERG": 46233, "Ġvomiting": 46234, "Ġconhecer": 46235, "Ġadjour": 46236, "ĠCivic": 46237, "pei": 46238, "burst": 46239, "Ġëĭ¤ëĭĪ": 46240, "éı": 46241, "Ġsled": 46242, "Ġplataforma": 46243, "ĠSect": 46244, "ĠDefin": 46245, "çĻ»éĮ²": 46246, "énom": 46247, "chnet": 46248, "Ġprofitability": 46249, "Ġerreicht": 46250, "á»ıi": 46251, "cation": 46252, "Ġì§Ģê¸": 46253, "Ġperdre": 46254, "Ġfelony": 46255, "Ġ1957": 46256, "æĪijå¾Ī": 46257, "Ġunsuccessful": 46258, "Ġnagyon": 46259, "Ġelasticity": 46260, "Ġfacade": 46261, "Ġearthly": 46262, "ĠамеÑĢикан": 46263, "Ġconn": 46264, "cla": 46265, "Du": 46266, "Ġpolitiques": 46267, "Ġhalo": 46268, "iantes": 46269, "Ġмоей": 46270, "ãĥ³ãĥī": 46271, "tones": 46272, "elier": 46273, "è®ļ": 46274, "htaking": 46275, "Ġwichtige": 46276, "Ġanno": 46277, "ĠLok": 46278, "illions": 46279, "Ġviver": 46280, "Ġsolchen": 46281, "Ġsuf": 46282, "ĠSalz": 46283, "ĠNvidia": 46284, "zuge": 46285, "ĠSpike": 46286, "Video": 46287, "Ġtwor": 46288, "ĠAla": 46289, "èijī": 46290, "Ġhanya": 46291, "ĠAdm": 46292, "ìĿµ": 46293, "ĠPatienten": 46294, "ĠOnion": 46295, "ĠKobe": 46296, "ĠScene": 46297, "ĠRash": 46298, "æ¨Ļ": 46299, "ÑĢаÑģÑĤ": 46300, "istani": 46301, "General": 46302, "leye": 46303, "imbap": 46304, "Ġconcealed": 46305, "ĠFridays": 46306, "ĠWool": 46307, "ĠновÑĭÑħ": 46308, "شر": 46309, "Ġê²°ê³¼": 46310, "Ġjedoch": 46311, "´ìĭľ": 46312, "ĵ¤ëıĦ": 46313, "Ġìŀ¥ëĤľ": 46314, "ukt": 46315, "Lou": 46316, "Ġ먹ìĸ´": 46317, "ĠExpect": 46318, "Ġдомой": 46319, "Ġirresponsible": 46320, "Ġacerca": 46321, "ĠZust": 46322, "ר×ĺ": 46323, "UI": 46324, "Ġyoutubers": 46325, "ĠPositive": 46326, "Ġsocioe": 46327, "Ġsnatch": 46328, "èĥĮ": 46329, "Ġrefreshed": 46330, "Ġnominations": 46331, "ĠPatt": 46332, "Ġobsolete": 46333, "ĠdemiÅŁ": 46334, "åı¤": 46335, "ormuÅŁ": 46336, "ĠìĨĶì§ģíŀĪ": 46337, "Ġfla": 46338, "Ġcraziest": 46339, "ĠZie": 46340, "ĠTú": 46341, "zep": 46342, "icem": 46343, "Ġë©ĭìŀĪ": 46344, "Ġcynical": 46345, "ãģĿãĤĵãģª": 46346, "Ġtresp": 46347, "Ġcraz": 46348, "Õ¥Õ": 46349, "Ġnelle": 46350, "Ġmph": 46351, "ĠNered": 46352, "ĠKob": 46353, "ĠEck": 46354, "¨¸ëĭĪ": 46355, "Jan": 46356, "ĠТогда": 46357, "Ġdeci": 46358, "ĠVog": 46359, "Ġbubbling": 46360, "éĢĢ": 46361, "úa": 46362, "Ġproductos": 46363, "iberal": 46364, "Ġreplicated": 46365, "ĠImprove": 46366, "illary": 46367, "Cha": 46368, "Ġrédu": 46369, "ĥIJíķĺë©´": 46370, "Ġconnot": 46371, "ĠKrit": 46372, "ĠдÑĥÑħов": 46373, "Ġtreadmill": 46374, "ĠPW": 46375, "ĠзовÑĥÑĤ": 46376, "Ġclams": 46377, "Ġdrafting": 46378, "Ġ1956": 46379, "unta": 46380, "Ġexpenditures": 46381, "ĠHoover": 46382, "WOO": 46383, "ÑĪее": 46384, "Ġdeduction": 46385, "monary": 46386, "Ġrecib": 46387, "Ġpovo": 46388, "ĠëįĶë": 46389, "ĠPAL": 46390, "ĠBlow": 46391, "Ġwyp": 46392, "Ġdestac": 46393, "deal": 46394, "Graeme": 46395, "Ġnécessaire": 46396, "Ġdamned": 46397, "Ġ1938": 46398, "Ġìĭ¤ìłľë¡ľ": 46399, "Ġtroop": 46400, "Ġinsightful": 46401, "ĠTJ": 46402, "ĠоÑģв": 46403, "Ġfidelity": 46404, "ĠSkip": 46405, "ĠMayo": 46406, "ë§Ŀ": 46407, "appe": 46408, "Ġblas": 46409, "ĠWY": 46410, "ĠGN": 46411, "ctar": 46412, "Su": 46413, "Ġcuent": 46414, "hews": 46415, "Ġcorpses": 46416, "Abs": 46417, "Ġwastewater": 46418, "Ġciek": 46419, "ĠOnu": 46420, "Ġexplosives": 46421, "Ġarma": 46422, "ĠSTEPHAN": 46423, "politik": 46424, "ĠOsaka": 46425, "taÅĤ": 46426, "Ġyapıyor": 46427, "Ġizquier": 46428, "Ġbeleza": 46429, "ĠWyatt": 46430, "åIJ¸": 46431, "Ġsuk": 46432, "Ġspecjal": 46433, "Ġdanke": 46434, "whistle": 46435, "ĠfÃŃsica": 46436, "ĠHarriet": 46437, "ĠìķĦíĮĮ": 46438, "Ġwillkommen": 46439, "iping": 46440, "ĠÑģмоÑĤÑĢиÑĤе": 46441, "ĠможеÑĪÑĮ": 46442, "Ġinaccurate": 46443, "Ġarrogance": 46444, "ĠRemo": 46445, "γά": 46446, "assed": 46447, "Ġdeliveries": 46448, "Ġstinky": 46449, "ĠпеÑĢеж": 46450, "jay": 46451, "Ġtransitional": 46452, "Ġrere": 46453, "ĠNGOs": 46454, "ĠATM": 46455, "خت": 46456, "iology": 46457, "Ġвлад": 46458, "Ġschme": 46459, "ĠShine": 46460, "ìķ¡": 46461, "pants": 46462, "Ġserge": 46463, "Ġsenhor": 46464, "Ġabduct": 46465, "ĠBryant": 46466, "VES": 46467, "Ġawakened": 46468, "ĠLaz": 46469, "ropolis": 46470, "ĠLao": 46471, "è¾Ľèĭ¦": 46472, "Ġvilla": 46473, "Ġsummers": 46474, "Ġenthal": 46475, "Ġ1949": 46476, "Via": 46477, "Ġìĸ´ì¨": 46478, "Ġtendon": 46479, "Ġviolet": 46480, "Ġintellectually": 46481, "Ġbounced": 46482, "araus": 46483, "Ġ1919": 46484, "Ġvraag": 46485, "Ġspel": 46486, "ĠSchwar": 46487, "Scott": 46488, "ĠIndo": 46489, "Ġë§Ŀ": 46490, "Ġcanonical": 46491, "ĠIKE": 46492, "ĠthatÃŃs": 46493, "Ġmellan": 46494, "æ¯Ĵ": 46495, "igmat": 46496, "Could": 46497, "...?)": 46498, "Ġfoarte": 46499, "ĠKumar": 46500, "rendo": 46501, "Ġélé": 46502, "à´": 46503, "valuation": 46504, "cases": 46505, "Ġintuitively": 46506, "hong": 46507, "etted": 46508, "Ġsouven": 46509, "Ġmorb": 46510, "Ġcors": 46511, "ĠNV": 46512, "ĠHasan": 46513, "æĥħåĨµ": 46514, "ieved": 46515, "Ġì§Ģê¸ĪìĿĢ": 46516, "Ġdumpling": 46517, "Ġcontrôle": 46518, "Ġambiguity": 46519, "æ©Łæľĥ": 46520, "Ġcog": 46521, "ĠScriptures": 46522, "Ġcai": 46523, "Ġbever": 46524, "大家éĥ½": 46525, "Ġhuis": 46526, "Ġaime": 46527, "Ġerklären": 46528, "ĠLM": 46529, "ĠFey": 46530, "éļ¾": 46531, "றத": 46532, "Ġsupervised": 46533, "Ġjewe": 46534, "spl": 46535, "ĠÑĨенÑĤÑĢ": 46536, "Ġcollisions": 46537, "ÙĦÙģ": 46538, "ĠHogwarts": 46539, "ĠDurham": 46540, "×ķ×£": 46541, "Ġphosphate": 46542, "Ġoversee": 46543, "Ġinspections": 46544, "Ġbrinc": 46545, "ĠZak": 46546, "Ġpayoff": 46547, "Ġchaud": 46548, "ĠHunger": 46549, "ãos": 46550, "vir": 46551, "Ġfiance": 46552, "Ġboug": 46553, "lived": 46554, "cry": 46555, "åĽŀä¾Ĩ": 46556, "Ġjointly": 46557, "Ġgirlfriends": 46558, "ĠNexus": 46559, "¦¬ê²łìĬµëĭĪëĭ¤": 46560, "ĠKwang": 46561, "åĵĪåĽī": 46562, "å§ij": 46563, "ÅĤÄĻ": 46564, "ĠNeden": 46565, "iece": 46566, "Ġinserting": 46567, "æŁĵ": 46568, "ĠMummy": 46569, "ĠGlobe": 46570, "Ġlee": 46571, "Ġgerman": 46572, "Ġcreams": 46573, 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"Ġchuckling": 48167, "ĠMih": 48168, "Ġrallies": 48169, "Ġmanter": 48170, "Ġearnest": 48171, "super": 48172, "Ġgece": 48173, "ĠRend": 48174, "ĠGerade": 48175, "jenigen": 48176, "ĠVall": 48177, "ĠìŀĪëĤĺ": 48178, "ĠÑģказала": 48179, "Ġtrabalh": 48180, "ĠнаÑĪем": 48181, "ĠмеÑħ": 48182, "ikit": 48183, "Ġnouns": 48184, "Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, "ĠопиÑģании": 48303, "Ġamber": 48304, "Ġgoosebumps": 48305, "ĠfrÃ¥gor": 48306, "ĠVital": 48307, "ĠIsraelites": 48308, "wasser": 48309, "Isn": 48310, "Ġcommits": 48311, "ĠSTEVEN": 48312, "ĠBevölker": 48313, "uitive": 48314, "Ġlegen": 48315, "Ġbruk": 48316, "иÑĢован": 48317, "ynen": 48318, "helm": 48319, "Ġgenerational": 48320, "ĠLändern": 48321, "οιÏĢÏĮν": 48322, "uzu": 48323, "Ġcaller": 48324, "онÑĮ": 48325, "ümü": 48326, "Ġbesar": 48327, "Ġplats": 48328, "Ġmigrated": 48329, "Ġjap": 48330, "ĠWAR": 48331, "Ġdissect": 48332, "ĠZusch": 48333, "ĠZeiten": 48334, "ĠLions": 48335, "ĠDF": 48336, "âĶ": 48337, "кив": 48338, "Ġpedestrians": 48339, "ĠMarilyn": 48340, "dock": 48341, "Ġyht": 48342, "Ġreincarn": 48343, "ĠSono": 48344, "ĠGrowth": 48345, "ÑĥÑģов": 48346, "Ġdungeons": 48347, "Ġbagus": 48348, "kich": 48349, "ĠÑĥкÑĢаÑĹ": 48350, "éĨ«": 48351, "ĠKeller": 48352, "chemistry": 48353, "Japanese": 48354, "Ġwillst": 48355, "Ġdecomposition": 48356, "ĠÑģÑĤен": 48357, "Ġrevived": 48358, "íķĻêµIJ": 48359, "ĠÅĵ": 48360, "ä½IJ": 48361, "ìĭ¸": 48362, "ippy": 48363, "Ġhourly": 48364, "jän": 48365, "ĠWorkshop": 48366, "Ŀ¼ìĦľ": 48367, "Ġcuarto": 48368, "Ġpatrim": 48369, "ĠBurch": 48370, "ĠìŀĪ기": 48371, "Ġhepat": 48372, "ĠhÃłng": 48373, "ĠëĮĢíķ´": 48374, "ĠваÑĪи": 48375, "Ġrework": 48376, "Ġparse": 48377, "Ġçıktı": 48378, "ĠSax": 48379, "ĠMongo": 48380, "ĠAaah": 48381, "ramble": 48382, "DJ": 48383, "Ġstabilized": 48384, "ĠSpeech": 48385, "Books": 48386, "Ġhurdles": 48387, "ĠWO": 48388, "ĠLamborg": 48389, "Ġ1933": 48390, "Ġvorbere": 48391, "Ġclinically": 48392, "Ġbreathtaking": 48393, "ĠGateway": 48394, "пеÑĢвÑĭÑħ": 48395, "uters": 48396, "Ġë¹µ": 48397, "Ġyeter": 48398, "Ġpulley": 48399, "Ġmuffin": 48400, "ĠPrefer": 48401, "ĠPence": 48402, "Ġinformação": 48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "Ī¬ë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, "Ġgymnastics": 48461, "пÑĢи": 48462, "ifiques": 48463, "Ġkarate": 48464, "Ġdomu": 48465, "ãģĿãĤĮãģ§": 48466, "OTHER": 48467, "Ġdemandé": 48468, "Ġbooklet": 48469, "ĠKyoto": 48470, "Ġwoh": 48471, "ĠMarÃŃa": 48472, "violent": 48473, "JE": 48474, "Ġlóg": 48475, "Ġbrutally": 48476, "cot": 48477, "ĠÙħÛĮ": 48478, "ĠWarsz": 48479, "å®Ī": 48480, "wol": 48481, "Ġmikä": 48482, "ĠPronounce": 48483, "ĠBrendan": 48484, "Ġroup": 48485, "Ġitaliano": 48486, "å¦ĤæѤ": 48487, "ĠкомпÑĮÑİÑĤ": 48488, "Ġurging": 48489, "edes": 48490, "Ġcarbono": 48491, "ĠRichardson": 48492, "ĠÐĿаÑĩ": 48493, "ĠTrainer": 48494, "ĠCrimea": 48495, "Ġdiapers": 48496, "Ġcovet": 48497, "ĠMahar": 48498, "ĠHutch": 48499, "ĠAusw": 48500, "berty": 48501, "Ġindifferent": 48502, "кÑĢеÑĤ": 48503, "uldade": 48504, "Ġharms": 48505, "¢ÙĨ": 48506, "lesia": 48507, "Ġgio": 48508, "ĠMistress": 48509, "ĠKnox": 48510, "ĠFREE": 48511, "Ġ루ë": 48512, "ĠнаÑĪа": 48513, "Ġinvincible": 48514, "Ġmaiden": 48515, "ĠJeez": 48516, "Ġbreve": 48517, "pole": 48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, "Ġilluminated": 48577, "Ġsiglo": 48578, "ĠVacc": 48579, "ией": 48580, "alis": 48581, "ĠÑĥÑģÑĤÑĢой": 48582, "Ġdona": 48583, "ÅĤos": 48584, "üman": 48585, "Ġproducción": 48586, "Ġclot": 48587, "ĠMango": 48588, "Ġuneasy": 48589, "Ġshuts": 48590, "ĠExamples": 48591, "vell": 48592, "ebe": 48593, "Ġpromptly": 48594, "ĠTeles": 48595, "ĠпÑĢоÑĪл": 48596, "Ġpuerta": 48597, "Ġüberzeug": 48598, "Ġcoch": 48599, "social": 48600, "ĠBenson": 48601, "ĠMeth": 48602, "ĠExped": 48603, "Ġsupplemental": 48604, "Ġconceive": 48605, "Ġ×ĺ×ķ×ij": 48606, "Ġcaptivity": 48607, "ıĻìķĪ": 48608, "ĠÑħÑĥд": 48609, "forming": 48610, "Ġuploads": 48611, "Ġturbulence": 48612, "joint": 48613, "Ġsatisfactory": 48614, "ĠAnime": 48615, "Ġwashes": 48616, "Ġliberals": 48617, "ĠSunshine": 48618, "ĠREAL": 48619, "ublik": 48620, "binary": 48621, "Tony": 48622, "Ġpolarized": 48623, "Ġenriched": 48624, "taking": 48625, "ĠëģĿëĤĺ": 48626, "Ġpleasures": 48627, "Ġextermin": 48628, "inese": 48629, "atl": 48630, "vär": 48631, "аÑĢÑĭ": 48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æݧ": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "> length: - array = array.index_select( - dim=axis, index=torch.arange(length, device=array.device) - ) - - if array.shape[axis] < length: - pad_widths = [(0, 0)] * array.ndim - pad_widths[axis] = (0, length - array.shape[axis]) - array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) - else: - if array.shape[axis] > length: - array = array.take(indices=range(length), axis=axis) - - if array.shape[axis] < length: - pad_widths = [(0, 0)] * array.ndim - pad_widths[axis] = (0, length - array.shape[axis]) - array = np.pad(array, pad_widths) - - return array - - -@lru_cache(maxsize=None) -def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: - """ - load the mel filterbank matrix for projecting STFT into a Mel spectrogram. - Allows decoupling librosa dependency; saved using: - - np.savez_compressed( - "mel_filters.npz", - mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), - ) - """ - assert n_mels == 80, f"Unsupported n_mels: {n_mels}" - with np.load( - os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") - ) as f: - return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) - - -def log_mel_spectrogram( - audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS -): - """ - Compute the log-Mel spectrogram of - - Parameters - ---------- - audio: Union[str, np.ndarray, torch.Tensor], shape = (*) - The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz - - n_mels: int - The number of Mel-frequency filters, only 80 is supported - - Returns - ------- - torch.Tensor, shape = (80, n_frames) - A Tensor that contains the Mel spectrogram - """ - if not torch.is_tensor(audio): - if isinstance(audio, str): - audio = load_audio(audio) - audio = torch.from_numpy(audio) - - window = torch.hann_window(N_FFT).to(audio.device) - stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) - magnitudes = stft[..., :-1].abs() ** 2 - - filters = mel_filters(audio.device, n_mels) - mel_spec = filters @ magnitudes - - log_spec = torch.clamp(mel_spec, min=1e-10).log10() - log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) - log_spec = (log_spec + 4.0) / 4.0 - return log_spec diff --git a/modules/whisper_extractor/decoding.py b/modules/whisper_extractor/decoding.py deleted file mode 100644 index 82fb9e32418110b69c1aa736dde60a5bb6588459..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/decoding.py +++ /dev/null @@ -1,839 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -from dataclasses import dataclass, field -from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING - -import numpy as np -import torch -import torch.nn.functional as F -from torch import Tensor -from torch.distributions import Categorical - -from .audio import CHUNK_LENGTH -from .tokenizer import Tokenizer, get_tokenizer -from .utils import compression_ratio - -if TYPE_CHECKING: - from .model import Whisper - - -@torch.no_grad() -def detect_language( - model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None -) -> Tuple[Tensor, List[dict]]: - """ - Detect the spoken language in the audio, and return them as list of strings, along with the ids - of the most probable language tokens and the probability distribution over all language tokens. - This is performed outside the main decode loop in order to not interfere with kv-caching. - - Returns - ------- - language_tokens : Tensor, shape = (n_audio,) - ids of the most probable language tokens, which appears after the startoftranscript token. - language_probs : List[Dict[str, float]], length = n_audio - list of dictionaries containing the probability distribution over all languages. - """ - if tokenizer is None: - tokenizer = get_tokenizer(model.is_multilingual) - if ( - tokenizer.language is None - or tokenizer.language_token not in tokenizer.sot_sequence - ): - raise ValueError( - f"This model doesn't have language tokens so it can't perform lang id" - ) - - single = mel.ndim == 2 - if single: - mel = mel.unsqueeze(0) - - # skip encoder forward pass if already-encoded audio features were given - if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): - mel = model.encoder(mel) - - # forward pass using a single token, startoftranscript - n_audio = mel.shape[0] - x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] - logits = model.logits(x, mel)[:, 0] - - # collect detected languages; suppress all non-language tokens - mask = torch.ones(logits.shape[-1], dtype=torch.bool) - mask[list(tokenizer.all_language_tokens)] = False - logits[:, mask] = -np.inf - language_tokens = logits.argmax(dim=-1) - language_token_probs = logits.softmax(dim=-1).cpu() - language_probs = [ - { - c: language_token_probs[i, j].item() - for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) - } - for i in range(n_audio) - ] - - if single: - language_tokens = language_tokens[0] - language_probs = language_probs[0] - - return language_tokens, language_probs - - -@dataclass(frozen=True) -class DecodingOptions: - task: str = ( - "transcribe" # whether to perform X->X "transcribe" or X->English "translate" - ) - language: Optional[ - str - ] = None # language that the audio is in; uses detected language if None - - # sampling-related options - temperature: float = 0.0 - sample_len: Optional[int] = None # maximum number of tokens to sample - best_of: Optional[ - int - ] = None # number of independent samples to collect, when t > 0 - beam_size: Optional[int] = None # number of beams in beam search, when t == 0 - patience: Optional[ - float - ] = None # patience in beam search (https://arxiv.org/abs/2204.05424) - - # options for ranking generations (either beams or best-of-N samples) - length_penalty: Optional[ - float - ] = None # "alpha" in Google NMT, None defaults to length norm - - # prompt, prefix, and token suppression - prompt: Optional[ - Union[str, List[int]] - ] = None # text or tokens for the previous context - prefix: Optional[ - Union[str, List[int]] - ] = None # text or tokens to prefix the current context - suppress_blank: bool = True # this will suppress blank outputs - - # list of tokens ids (or comma-separated token ids) to suppress - # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` - suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" - - # timestamp sampling options - without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only - max_initial_timestamp: Optional[ - float - ] = 1.0 # the initial timestamp cannot be later than this - - # implementation details - fp16: bool = True # use fp16 for most of the calculation - - -@dataclass(frozen=True) -class DecodingResult: - audio_features: Tensor - language: str - language_probs: Optional[Dict[str, float]] = None - tokens: List[int] = field(default_factory=list) - text: str = "" - avg_logprob: float = np.nan - no_speech_prob: float = np.nan - temperature: float = np.nan - compression_ratio: float = np.nan - - -class Inference: - def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: - """Perform a forward pass on the decoder and return per-token logits""" - raise NotImplementedError - - def rearrange_kv_cache(self, source_indices) -> None: - """Update the key-value cache according to the updated beams""" - raise NotImplementedError - - def cleanup_caching(self) -> None: - """Clean up any resources or hooks after decoding is finished""" - pass - - -class PyTorchInference(Inference): - def __init__(self, model: "Whisper", initial_token_length: int): - self.model: "Whisper" = model - self.initial_token_length = initial_token_length - self.kv_cache = {} - self.hooks = [] - - def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: - if not self.kv_cache: - self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() - - if tokens.shape[-1] > self.initial_token_length: - # only need to use the last token except in the first forward pass - tokens = tokens[:, -1:] - - return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) - - def cleanup_caching(self): - for hook in self.hooks: - hook.remove() - - self.kv_cache = {} - self.hooks = [] - - def rearrange_kv_cache(self, source_indices): - for module, tensor in self.kv_cache.items(): - # update the key/value cache to contain the selected sequences - self.kv_cache[module] = tensor[source_indices].detach() - - -class SequenceRanker: - def rank( - self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]] - ) -> List[int]: - """ - Given a list of groups of samples and their cumulative log probabilities, - return the indices of the samples in each group to select as the final result - """ - raise NotImplementedError - - -class MaximumLikelihoodRanker(SequenceRanker): - """ - Select the sample with the highest log probabilities, penalized using either - a simple length normalization or Google NMT paper's length penalty - """ - - def __init__(self, length_penalty: Optional[float]): - self.length_penalty = length_penalty - - def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): - def scores(logprobs, lengths): - result = [] - for logprob, length in zip(logprobs, lengths): - if self.length_penalty is None: - penalty = length - else: - # from the Google NMT paper - penalty = ((5 + length) / 6) ** self.length_penalty - result.append(logprob / penalty) - return result - - # get the sequence with the highest score - lengths = [[len(t) for t in s] for s in tokens] - return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] - - -class TokenDecoder: - def reset(self): - """Initialize any stateful variables for decoding a new sequence""" - - def update( - self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor - ) -> Tuple[Tensor, bool]: - """Specify how to select the next token, based on the current trace and logits - - Parameters - ---------- - tokens : Tensor, shape = (n_batch, current_sequence_length) - all tokens in the context so far, including the prefix and sot_sequence tokens - - logits : Tensor, shape = (n_batch, vocab_size) - per-token logits of the probability distribution at the current step - - sum_logprobs : Tensor, shape = (n_batch) - cumulative log probabilities for each sequence - - Returns - ------- - tokens : Tensor, shape = (n_batch, current_sequence_length + 1) - the tokens, appended with the selected next token - - completed : bool - True if all sequences has reached the end of text - - """ - raise NotImplementedError - - def finalize( - self, tokens: Tensor, sum_logprobs: Tensor - ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: - """Finalize search and return the final candidate sequences - - Parameters - ---------- - tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) - all tokens in the context so far, including the prefix and sot_sequence - - sum_logprobs : Tensor, shape = (n_audio, n_group) - cumulative log probabilities for each sequence - - Returns - ------- - tokens : Sequence[Sequence[Tensor]], length = n_audio - sequence of Tensors containing candidate token sequences, for each audio input - - sum_logprobs : List[List[float]], length = n_audio - sequence of cumulative log probabilities corresponding to the above - - """ - raise NotImplementedError - - -class GreedyDecoder(TokenDecoder): - def __init__(self, temperature: float, eot: int): - self.temperature = temperature - self.eot = eot - - def update( - self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor - ) -> Tuple[Tensor, bool]: - temperature = self.temperature - if temperature == 0: - next_tokens = logits.argmax(dim=-1) - else: - next_tokens = Categorical(logits=logits / temperature).sample() - - logprobs = F.log_softmax(logits.float(), dim=-1) - current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] - sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) - - next_tokens[tokens[:, -1] == self.eot] = self.eot - tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) - - completed = (tokens[:, -1] == self.eot).all() - return tokens, completed - - def finalize(self, tokens: Tensor, sum_logprobs: Tensor): - # make sure each sequence has at least one EOT token at the end - tokens = F.pad(tokens, (0, 1), value=self.eot) - return tokens, sum_logprobs.tolist() - - -class BeamSearchDecoder(TokenDecoder): - def __init__( - self, - beam_size: int, - eot: int, - inference: Inference, - patience: Optional[float] = None, - ): - self.beam_size = beam_size - self.eot = eot - self.inference = inference - self.patience = patience or 1.0 - self.max_candidates: int = round(beam_size * self.patience) - self.finished_sequences = None - - assert ( - self.max_candidates > 0 - ), f"Invalid beam size ({beam_size}) or patience ({patience})" - - def reset(self): - self.finished_sequences = None - - def update( - self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor - ) -> Tuple[Tensor, bool]: - if tokens.shape[0] % self.beam_size != 0: - raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") - - n_audio = tokens.shape[0] // self.beam_size - if self.finished_sequences is None: # for the first update - self.finished_sequences = [{} for _ in range(n_audio)] - - logprobs = F.log_softmax(logits.float(), dim=-1) - next_tokens, source_indices, finished_sequences = [], [], [] - for i in range(n_audio): - scores, sources, finished = {}, {}, {} - - # STEP 1: calculate the cumulative log probabilities for possible candidates - for j in range(self.beam_size): - idx = i * self.beam_size + j - prefix = tokens[idx].tolist() - for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): - new_logprob = (sum_logprobs[idx] + logprob).item() - sequence = tuple(prefix + [token.item()]) - scores[sequence] = new_logprob - sources[sequence] = idx - - # STEP 2: rank the candidates and keep the top beam_size sequences for each audio - saved = 0 - for sequence in sorted(scores, key=scores.get, reverse=True): - if sequence[-1] == self.eot: - finished[sequence] = scores[sequence] - else: - sum_logprobs[len(next_tokens)] = scores[sequence] - next_tokens.append(sequence) - source_indices.append(sources[sequence]) - - saved += 1 - if saved == self.beam_size: - break - - finished_sequences.append(finished) - - tokens = torch.tensor(next_tokens, device=tokens.device) - self.inference.rearrange_kv_cache(source_indices) - - # add newly finished sequences to self.finished_sequences - assert len(self.finished_sequences) == len(finished_sequences) - for previously_finished, newly_finished in zip( - self.finished_sequences, finished_sequences - ): - for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): - if len(previously_finished) >= self.max_candidates: - break # the candidate list is full - previously_finished[seq] = newly_finished[seq] - - # mark as completed if all audio has enough number of samples - completed = all( - len(sequences) >= self.max_candidates - for sequences in self.finished_sequences - ) - return tokens, completed - - def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): - # collect all finished sequences, including patience, and add unfinished ones if not enough - sum_logprobs = sum_logprobs.cpu() - for i, sequences in enumerate(self.finished_sequences): - if ( - len(sequences) < self.beam_size - ): # when not enough sequences are finished - for j in list(np.argsort(sum_logprobs[i]))[::-1]: - sequence = preceding_tokens[i, j].tolist() + [self.eot] - sequences[tuple(sequence)] = sum_logprobs[i][j].item() - if len(sequences) >= self.beam_size: - break - - tokens: List[List[Tensor]] = [ - [torch.tensor(seq) for seq in sequences.keys()] - for sequences in self.finished_sequences - ] - sum_logprobs: List[List[float]] = [ - list(sequences.values()) for sequences in self.finished_sequences - ] - return tokens, sum_logprobs - - -class LogitFilter: - def apply(self, logits: Tensor, tokens: Tensor) -> None: - """Apply any filtering or masking to logits in-place - - Parameters - ---------- - logits : Tensor, shape = (n_batch, vocab_size) - per-token logits of the probability distribution at the current step - - tokens : Tensor, shape = (n_batch, current_sequence_length) - all tokens in the context so far, including the prefix and sot_sequence tokens - - """ - raise NotImplementedError - - -class SuppressBlank(LogitFilter): - def __init__(self, tokenizer: Tokenizer, sample_begin: int): - self.tokenizer = tokenizer - self.sample_begin = sample_begin - - def apply(self, logits: Tensor, tokens: Tensor): - if tokens.shape[1] == self.sample_begin: - logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf - - -class SuppressTokens(LogitFilter): - def __init__(self, suppress_tokens: Sequence[int]): - self.suppress_tokens = list(suppress_tokens) - - def apply(self, logits: Tensor, tokens: Tensor): - logits[:, self.suppress_tokens] = -np.inf - - -class ApplyTimestampRules(LogitFilter): - def __init__( - self, - tokenizer: Tokenizer, - sample_begin: int, - max_initial_timestamp_index: Optional[int], - ): - self.tokenizer = tokenizer - self.sample_begin = sample_begin - self.max_initial_timestamp_index = max_initial_timestamp_index - - def apply(self, logits: Tensor, tokens: Tensor): - # suppress <|notimestamps|> which is handled by without_timestamps - if self.tokenizer.no_timestamps is not None: - logits[:, self.tokenizer.no_timestamps] = -np.inf - - # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly - for k in range(tokens.shape[0]): - seq = [t for t in tokens[k, self.sample_begin :].tolist()] - last_was_timestamp = ( - len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin - ) - penultimate_was_timestamp = ( - len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin - ) - - if last_was_timestamp: - if penultimate_was_timestamp: # has to be non-timestamp - logits[k, self.tokenizer.timestamp_begin :] = -np.inf - else: # cannot be normal text tokens - logits[k, : self.tokenizer.eot] = -np.inf - - if tokens.shape[1] == self.sample_begin: - # suppress generating non-timestamp tokens at the beginning - logits[:, : self.tokenizer.timestamp_begin] = -np.inf - - # apply the `max_initial_timestamp` option - if self.max_initial_timestamp_index is not None: - last_allowed = ( - self.tokenizer.timestamp_begin + self.max_initial_timestamp_index - ) - logits[:, last_allowed + 1 :] = -np.inf - - # if sum of probability over timestamps is above any other token, sample timestamp - logprobs = F.log_softmax(logits.float(), dim=-1) - for k in range(tokens.shape[0]): - timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp( - dim=-1 - ) - max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() - if timestamp_logprob > max_text_token_logprob: - logits[k, : self.tokenizer.timestamp_begin] = -np.inf - - -class DecodingTask: - inference: Inference - sequence_ranker: SequenceRanker - decoder: TokenDecoder - logit_filters: List[LogitFilter] - - def __init__(self, model: "Whisper", options: DecodingOptions): - self.model = model - - language = options.language or "en" - tokenizer = get_tokenizer( - model.is_multilingual, language=language, task=options.task - ) - self.tokenizer: Tokenizer = tokenizer - self.options: DecodingOptions = self._verify_options(options) - - self.n_group: int = options.beam_size or options.best_of or 1 - self.n_ctx: int = model.dims.n_text_ctx - self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 - - self.sot_sequence: Tuple[int] = tokenizer.sot_sequence - if self.options.without_timestamps: - self.sot_sequence = tokenizer.sot_sequence_including_notimestamps - - self.initial_tokens: Tuple[int] = self._get_initial_tokens() - self.sample_begin: int = len(self.initial_tokens) - self.sot_index: int = self.initial_tokens.index(tokenizer.sot) - - # inference: implements the forward pass through the decoder, including kv caching - self.inference = PyTorchInference(model, len(self.initial_tokens)) - - # sequence ranker: implements how to rank a group of sampled sequences - self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) - - # decoder: implements how to select the next tokens, given the autoregressive distribution - if options.beam_size is not None: - self.decoder = BeamSearchDecoder( - options.beam_size, tokenizer.eot, self.inference, options.patience - ) - else: - self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) - - # logit filters: applies various rules to suppress or penalize certain tokens - self.logit_filters = [] - if self.options.suppress_blank: - self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) - if self.options.suppress_tokens: - self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) - if not options.without_timestamps: - precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds - max_initial_timestamp_index = None - if options.max_initial_timestamp: - max_initial_timestamp_index = round( - self.options.max_initial_timestamp / precision - ) - self.logit_filters.append( - ApplyTimestampRules( - tokenizer, self.sample_begin, max_initial_timestamp_index - ) - ) - - def _verify_options(self, options: DecodingOptions) -> DecodingOptions: - if options.beam_size is not None and options.best_of is not None: - raise ValueError("beam_size and best_of can't be given together") - if options.temperature == 0: - if options.best_of is not None: - raise ValueError("best_of with greedy sampling (T=0) is not compatible") - if options.patience is not None and options.beam_size is None: - raise ValueError("patience requires beam_size to be given") - if options.length_penalty is not None and not ( - 0 <= options.length_penalty <= 1 - ): - raise ValueError("length_penalty (alpha) should be a value between 0 and 1") - - return options - - def _get_initial_tokens(self) -> Tuple[int]: - tokens = list(self.sot_sequence) - prefix = self.options.prefix - prompt = self.options.prompt - - if prefix: - prefix_tokens = ( - self.tokenizer.encode(" " + prefix.strip()) - if isinstance(prefix, str) - else prefix - ) - if self.sample_len is not None: - max_prefix_len = self.n_ctx // 2 - self.sample_len - prefix_tokens = prefix_tokens[-max_prefix_len:] - tokens = tokens + prefix_tokens - - if prompt: - prompt_tokens = ( - self.tokenizer.encode(" " + prompt.strip()) - if isinstance(prompt, str) - else prompt - ) - tokens = ( - [self.tokenizer.sot_prev] - + prompt_tokens[-(self.n_ctx // 2 - 1) :] - + tokens - ) - - return tuple(tokens) - - def _get_suppress_tokens(self) -> Tuple[int]: - suppress_tokens = self.options.suppress_tokens - - if isinstance(suppress_tokens, str): - suppress_tokens = [int(t) for t in suppress_tokens.split(",")] - - if -1 in suppress_tokens: - suppress_tokens = [t for t in suppress_tokens if t >= 0] - suppress_tokens.extend(self.tokenizer.non_speech_tokens) - elif suppress_tokens is None or len(suppress_tokens) == 0: - suppress_tokens = [] # interpret empty string as an empty list - else: - assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" - - suppress_tokens.extend( - [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] - ) - if self.tokenizer.no_speech is not None: - # no-speech probability is collected separately - suppress_tokens.append(self.tokenizer.no_speech) - - return tuple(sorted(set(suppress_tokens))) - - def _get_audio_features(self, mel: Tensor): - if self.options.fp16: - mel = mel.half() - - if mel.shape[-2:] == ( - self.model.dims.n_audio_ctx, - self.model.dims.n_audio_state, - ): - # encoded audio features are given; skip audio encoding - audio_features = mel - else: - audio_features = self.model.encoder(mel) - - if audio_features.dtype != ( - torch.float16 if self.options.fp16 else torch.float32 - ): - return TypeError( - f"audio_features has an incorrect dtype: {audio_features.dtype}" - ) - - return audio_features - - def _detect_language(self, audio_features: Tensor, tokens: Tensor): - languages = [self.options.language] * audio_features.shape[0] - lang_probs = None - - if self.options.language is None or self.options.task == "lang_id": - lang_tokens, lang_probs = self.model.detect_language( - audio_features, self.tokenizer - ) - languages = [max(probs, key=probs.get) for probs in lang_probs] - if self.options.language is None: - tokens[:, self.sot_index + 1] = lang_tokens # write language tokens - - return languages, lang_probs - - def _main_loop(self, audio_features: Tensor, tokens: Tensor): - assert audio_features.shape[0] == tokens.shape[0] - n_batch = tokens.shape[0] - sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) - no_speech_probs = [np.nan] * n_batch - - try: - for i in range(self.sample_len): - logits = self.inference.logits(tokens, audio_features) - - if ( - i == 0 and self.tokenizer.no_speech is not None - ): # save no_speech_probs - probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) - no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() - - # now we need to consider the logits at the last token only - logits = logits[:, -1] - - # apply the logit filters, e.g. for suppressing or applying penalty to - for logit_filter in self.logit_filters: - logit_filter.apply(logits, tokens) - - # expand the tokens tensor with the selected next tokens - tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) - - if completed or tokens.shape[-1] > self.n_ctx: - break - finally: - self.inference.cleanup_caching() - - return tokens, sum_logprobs, no_speech_probs - - @torch.no_grad() - def run(self, mel: Tensor) -> List[DecodingResult]: - self.decoder.reset() - tokenizer: Tokenizer = self.tokenizer - n_audio: int = mel.shape[0] - - audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass - tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) - - # detect language if requested, overwriting the language token - languages, language_probs = self._detect_language(audio_features, tokens) - if self.options.task == "lang_id": - return [ - DecodingResult( - audio_features=features, language=language, language_probs=probs - ) - for features, language, probs in zip( - audio_features, languages, language_probs - ) - ] - - # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling - audio_features = audio_features.repeat_interleave(self.n_group, dim=0) - tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) - - # call the main sampling loop - tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) - - # reshape the tensors to have (n_audio, n_group) as the first two dimensions - audio_features = audio_features[:: self.n_group] - no_speech_probs = no_speech_probs[:: self.n_group] - assert audio_features.shape[0] == len(no_speech_probs) == n_audio - - tokens = tokens.reshape(n_audio, self.n_group, -1) - sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) - - # get the final candidates for each group, and slice between the first sampled token and EOT - tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) - tokens: List[List[Tensor]] = [ - [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] - for s in tokens - ] - - # select the top-ranked sample in each group - selected = self.sequence_ranker.rank(tokens, sum_logprobs) - tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] - texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] - - sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] - avg_logprobs: List[float] = [ - lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) - ] - - fields = ( - texts, - languages, - tokens, - audio_features, - avg_logprobs, - no_speech_probs, - ) - if len(set(map(len, fields))) != 1: - raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") - - return [ - DecodingResult( - audio_features=features, - language=language, - tokens=tokens, - text=text, - avg_logprob=avg_logprob, - no_speech_prob=no_speech_prob, - temperature=self.options.temperature, - compression_ratio=compression_ratio(text), - ) - for text, language, tokens, features, avg_logprob, no_speech_prob in zip( - *fields - ) - ] - - -@torch.no_grad() -def decode( - model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions() -) -> Union[DecodingResult, List[DecodingResult]]: - """ - Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). - - Parameters - ---------- - model: Whisper - the Whisper model instance - - mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) - A tensor containing the Mel spectrogram(s) - - options: DecodingOptions - A dataclass that contains all necessary options for decoding 30-second segments - - Returns - ------- - result: Union[DecodingResult, List[DecodingResult]] - The result(s) of decoding contained in `DecodingResult` dataclass instance(s) - """ - single = mel.ndim == 2 - if single: - mel = mel.unsqueeze(0) - - result = DecodingTask(model, options).run(mel) - - if single: - result = result[0] - - return result diff --git a/modules/whisper_extractor/model.py b/modules/whisper_extractor/model.py deleted file mode 100644 index 6f0265c9924575d77ebbfd054cbdde6c35df242a..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/model.py +++ /dev/null @@ -1,328 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -from dataclasses import dataclass -from typing import Dict -from typing import Iterable, Optional - -import numpy as np -import torch -import torch.nn.functional as F -from torch import Tensor -from torch import nn - -from .transcribe import transcribe as transcribe_function -from .decoding import ( - detect_language as detect_language_function, - decode as decode_function, -) - - -@dataclass -class ModelDimensions: - n_mels: int - n_audio_ctx: int - n_audio_state: int - n_audio_head: int - n_audio_layer: int - n_vocab: int - n_text_ctx: int - n_text_state: int - n_text_head: int - n_text_layer: int - - -class LayerNorm(nn.LayerNorm): - def forward(self, x: Tensor) -> Tensor: - return super().forward(x.float()).type(x.dtype) - - -class Linear(nn.Linear): - def forward(self, x: Tensor) -> Tensor: - return F.linear( - x, - self.weight.to(x.dtype), - None if self.bias is None else self.bias.to(x.dtype), - ) - - -class Conv1d(nn.Conv1d): - def _conv_forward( - self, x: Tensor, weight: Tensor, bias: Optional[Tensor] - ) -> Tensor: - return super()._conv_forward( - x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) - ) - - -def sinusoids(length, channels, max_timescale=10000): - """Returns sinusoids for positional embedding""" - assert channels % 2 == 0 - log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) - inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) - scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] - return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) - - -class MultiHeadAttention(nn.Module): - def __init__(self, n_state: int, n_head: int): - super().__init__() - self.n_head = n_head - self.query = Linear(n_state, n_state) - self.key = Linear(n_state, n_state, bias=False) - self.value = Linear(n_state, n_state) - self.out = Linear(n_state, n_state) - - def forward( - self, - x: Tensor, - xa: Optional[Tensor] = None, - mask: Optional[Tensor] = None, - kv_cache: Optional[dict] = None, - ): - q = self.query(x) - - if kv_cache is None or xa is None or self.key not in kv_cache: - # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; - # otherwise, perform key/value projections for self- or cross-attention as usual. - k = self.key(x if xa is None else xa) - v = self.value(x if xa is None else xa) - else: - # for cross-attention, calculate keys and values once and reuse in subsequent calls. - k = kv_cache[self.key] - v = kv_cache[self.value] - - wv, qk = self.qkv_attention(q, k, v, mask) - return self.out(wv), qk - - def qkv_attention( - self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None - ): - n_batch, n_ctx, n_state = q.shape - scale = (n_state // self.n_head) ** -0.25 - q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale - k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale - v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) - - qk = q @ k - if mask is not None: - qk = qk + mask[:n_ctx, :n_ctx] - qk = qk.float() - - w = F.softmax(qk, dim=-1).to(q.dtype) - return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() - - -class ResidualAttentionBlock(nn.Module): - def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): - super().__init__() - - self.attn = MultiHeadAttention(n_state, n_head) - self.attn_ln = LayerNorm(n_state) - - self.cross_attn = ( - MultiHeadAttention(n_state, n_head) if cross_attention else None - ) - self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None - - n_mlp = n_state * 4 - self.mlp = nn.Sequential( - Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) - ) - self.mlp_ln = LayerNorm(n_state) - - def forward( - self, - x: Tensor, - xa: Optional[Tensor] = None, - mask: Optional[Tensor] = None, - kv_cache: Optional[dict] = None, - ): - x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] - if self.cross_attn: - x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] - x = x + self.mlp(self.mlp_ln(x)) - return x - - -class AudioEncoder(nn.Module): - def __init__( - self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int - ): - super().__init__() - self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) - self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) - self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] - ) - self.ln_post = LayerNorm(n_state) - - def forward(self, x: Tensor): - """ - x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) - the mel spectrogram of the audio - """ - x = F.gelu(self.conv1(x)) - x = F.gelu(self.conv2(x)) - x = x.permute(0, 2, 1) - - assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" - x = (x + self.positional_embedding).to(x.dtype) - - for block in self.blocks: - x = block(x) - - x = self.ln_post(x) - return x - - -class TextDecoder(nn.Module): - def __init__( - self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int - ): - super().__init__() - - self.token_embedding = nn.Embedding(n_vocab, n_state) - self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ - ResidualAttentionBlock(n_state, n_head, cross_attention=True) - for _ in range(n_layer) - ] - ) - self.ln = LayerNorm(n_state) - - mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) - self.register_buffer("mask", mask, persistent=False) - - def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): - """ - x : torch.LongTensor, shape = (batch_size, <= n_ctx) - the text tokens - xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) - the encoded audio features to be attended on - """ - offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 - x = ( - self.token_embedding(x) - + self.positional_embedding[offset : offset + x.shape[-1]] - ) - x = x.to(xa.dtype) - - for block in self.blocks: - x = block(x, xa, mask=self.mask, kv_cache=kv_cache) - - x = self.ln(x) - logits = ( - x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) - ).float() - - return logits - - -class Whisper(nn.Module): - def __init__(self, dims: ModelDimensions): - super().__init__() - self.dims = dims - self.encoder = AudioEncoder( - self.dims.n_mels, - self.dims.n_audio_ctx, - self.dims.n_audio_state, - self.dims.n_audio_head, - self.dims.n_audio_layer, - ) - self.decoder = TextDecoder( - self.dims.n_vocab, - self.dims.n_text_ctx, - self.dims.n_text_state, - self.dims.n_text_head, - self.dims.n_text_layer, - ) - - def embed_audio(self, mel: torch.Tensor): - return self.encoder(mel) - - def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): - return self.decoder(tokens, audio_features) - - def forward( - self, mel: torch.Tensor, tokens: torch.Tensor - ) -> Dict[str, torch.Tensor]: - return self.decoder(tokens, self.encoder(mel)) - - @property - def device(self): - return next(self.parameters()).device - - @property - def is_multilingual(self): - return self.dims.n_vocab == 51865 - - def install_kv_cache_hooks(self, cache: Optional[dict] = None): - """ - The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value - tensors calculated for the previous positions. This method returns a dictionary that stores - all caches, and the necessary hooks for the key and value projection modules that save the - intermediate tensors to be reused during later calculations. - - Returns - ------- - cache : Dict[nn.Module, torch.Tensor] - A dictionary object mapping the key/value projection modules to its cache - hooks : List[RemovableHandle] - List of PyTorch RemovableHandle objects to stop the hooks to be called - """ - cache = {**cache} if cache is not None else {} - hooks = [] - - def save_to_cache(module, _, output): - if ( - module not in cache - or output.shape[1] > self.decoder.positional_embedding.shape[0] - ): - cache[ - module - ] = output # save as-is, for the first token or cross attention - else: - cache[module] = torch.cat([cache[module], output], dim=1).detach() - return cache[module] - - def install_hooks(layer: nn.Module): - if isinstance(layer, MultiHeadAttention): - hooks.append(layer.key.register_forward_hook(save_to_cache)) - hooks.append(layer.value.register_forward_hook(save_to_cache)) - - self.decoder.apply(install_hooks) - return cache, hooks - - detect_language = detect_language_function - transcribe = transcribe_function - decode = decode_function diff --git a/modules/whisper_extractor/normalizers/__init__.py b/modules/whisper_extractor/normalizers/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/modules/whisper_extractor/normalizers/basic.py b/modules/whisper_extractor/normalizers/basic.py deleted file mode 100644 index 5b58315beaf001649386cb4db37027255bbd66e4..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/normalizers/basic.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -import re -import unicodedata - -import regex - -# non-ASCII letters that are not separated by "NFKD" normalization -ADDITIONAL_DIACRITICS = { - "œ": "oe", - "Œ": "OE", - "ø": "o", - "Ø": "O", - "æ": "ae", - "Æ": "AE", - "ß": "ss", - "ẞ": "SS", - "đ": "d", - "Đ": "D", - "ð": "d", - "Ð": "D", - "þ": "th", - "Þ": "th", - "ł": "l", - "Ł": "L", -} - - -def remove_symbols_and_diacritics(s: str, keep=""): - """ - Replace any other markers, symbols, and punctuations with a space, - and drop any diacritics (category 'Mn' and some manual mappings) - """ - return "".join( - c - if c in keep - else ADDITIONAL_DIACRITICS[c] - if c in ADDITIONAL_DIACRITICS - else "" - if unicodedata.category(c) == "Mn" - else " " - if unicodedata.category(c)[0] in "MSP" - else c - for c in unicodedata.normalize("NFKD", s) - ) - - -def remove_symbols(s: str): - """ - Replace any other markers, symbols, punctuations with a space, keeping diacritics - """ - return "".join( - " " if unicodedata.category(c)[0] in "MSP" else c - for c in unicodedata.normalize("NFKC", s) - ) - - -class BasicTextNormalizer: - def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): - self.clean = ( - remove_symbols_and_diacritics if remove_diacritics else remove_symbols - ) - self.split_letters = split_letters - - def __call__(self, s: str): - s = s.lower() - s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets - s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis - s = self.clean(s).lower() - - if self.split_letters: - s = " ".join(regex.findall(r"\X", s, regex.U)) - - s = re.sub( - r"\s+", " ", s - ) # replace any successive whitespace characters with a space - - return s diff --git a/modules/whisper_extractor/normalizers/english.json b/modules/whisper_extractor/normalizers/english.json deleted file mode 100644 index 74a1c3521d9af4a84a0aa65d95b0859138640799..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/normalizers/english.json +++ /dev/null @@ -1,1741 +0,0 @@ -{ - "accessorise": "accessorize", - "accessorised": "accessorized", - "accessorises": "accessorizes", - "accessorising": "accessorizing", - "acclimatisation": "acclimatization", - "acclimatise": "acclimatize", - "acclimatised": "acclimatized", - "acclimatises": "acclimatizes", - "acclimatising": "acclimatizing", - "accoutrements": "accouterments", - "aeon": "eon", - "aeons": "eons", - "aerogramme": "aerogram", - "aerogrammes": "aerograms", - "aeroplane": "airplane", - "aeroplanes": "airplanes", - "aesthete": "esthete", - "aesthetes": "esthetes", - "aesthetic": "esthetic", - "aesthetically": "esthetically", - "aesthetics": "esthetics", - "aetiology": "etiology", - "ageing": "aging", - "aggrandisement": "aggrandizement", - "agonise": "agonize", - "agonised": "agonized", - "agonises": "agonizes", - "agonising": "agonizing", - "agonisingly": "agonizingly", - "almanack": "almanac", - "almanacks": "almanacs", - "aluminium": "aluminum", - "amortisable": "amortizable", - "amortisation": "amortization", - "amortisations": "amortizations", - "amortise": "amortize", - "amortised": "amortized", - "amortises": "amortizes", - "amortising": "amortizing", - "amphitheatre": "amphitheater", - "amphitheatres": "amphitheaters", - "anaemia": "anemia", - "anaemic": "anemic", - "anaesthesia": "anesthesia", - "anaesthetic": "anesthetic", - "anaesthetics": "anesthetics", - "anaesthetise": "anesthetize", - "anaesthetised": "anesthetized", - "anaesthetises": "anesthetizes", - "anaesthetising": "anesthetizing", - "anaesthetist": "anesthetist", - "anaesthetists": "anesthetists", - "anaesthetize": "anesthetize", - "anaesthetized": "anesthetized", - "anaesthetizes": "anesthetizes", - "anaesthetizing": "anesthetizing", - "analogue": "analog", - "analogues": "analogs", - "analyse": "analyze", - "analysed": "analyzed", - "analyses": "analyzes", - "analysing": "analyzing", - "anglicise": "anglicize", - "anglicised": "anglicized", - "anglicises": "anglicizes", - "anglicising": "anglicizing", - "annualised": "annualized", - "antagonise": "antagonize", - "antagonised": "antagonized", - "antagonises": "antagonizes", - "antagonising": "antagonizing", - "apologise": "apologize", - "apologised": "apologized", - "apologises": "apologizes", - "apologising": "apologizing", - "appal": "appall", - "appals": "appalls", - "appetiser": "appetizer", - "appetisers": "appetizers", - "appetising": "appetizing", - "appetisingly": "appetizingly", - "arbour": "arbor", - "arbours": "arbors", - "archeological": "archaeological", - "archaeologically": "archeologically", - "archaeologist": "archeologist", - "archaeologists": "archeologists", - "archaeology": "archeology", - "ardour": "ardor", - "armour": "armor", - "armoured": "armored", - "armourer": "armorer", - "armourers": "armorers", - "armouries": "armories", - "armoury": "armory", - "artefact": "artifact", - "artefacts": "artifacts", - "authorise": "authorize", - "authorised": "authorized", - "authorises": "authorizes", - "authorising": "authorizing", - "axe": "ax", - "backpedalled": "backpedaled", - "backpedalling": "backpedaling", - "bannister": "banister", - "bannisters": "banisters", - "baptise": "baptize", - "baptised": "baptized", - "baptises": "baptizes", - "baptising": "baptizing", - "bastardise": "bastardize", - "bastardised": "bastardized", - "bastardises": "bastardizes", - "bastardising": "bastardizing", - "battleax": "battleaxe", - "baulk": "balk", - "baulked": "balked", - "baulking": "balking", - "baulks": "balks", - "bedevilled": "bedeviled", - "bedevilling": "bedeviling", - "behaviour": "behavior", - "behavioural": "behavioral", - "behaviourism": "behaviorism", - "behaviourist": "behaviorist", - "behaviourists": "behaviorists", - "behaviours": "behaviors", - "behove": "behoove", - "behoved": "behooved", - "behoves": "behooves", - "bejewelled": "bejeweled", - "belabour": "belabor", - "belaboured": "belabored", - "belabouring": "belaboring", - "belabours": "belabors", - "bevelled": "beveled", - "bevvies": "bevies", - "bevvy": "bevy", - "biassed": "biased", - "biassing": "biasing", - "bingeing": "binging", - "bougainvillaea": "bougainvillea", - "bougainvillaeas": "bougainvilleas", - "bowdlerise": "bowdlerize", - "bowdlerised": "bowdlerized", - "bowdlerises": "bowdlerizes", - "bowdlerising": "bowdlerizing", - "breathalyse": "breathalyze", - "breathalysed": "breathalyzed", - "breathalyser": "breathalyzer", - "breathalysers": "breathalyzers", - "breathalyses": "breathalyzes", - "breathalysing": "breathalyzing", - "brutalise": "brutalize", - "brutalised": "brutalized", - "brutalises": "brutalizes", - "brutalising": "brutalizing", - "busses": "buses", - "bussing": "busing", - "caesarean": "cesarean", - "caesareans": "cesareans", - "calibre": "caliber", - "calibres": "calibers", - "calliper": "caliper", - "callipers": "calipers", - "callisthenics": "calisthenics", - "canalise": "canalize", - "canalised": "canalized", - "canalises": "canalizes", - "canalising": "canalizing", - "cancelation": "cancellation", - "cancelations": "cancellations", - "cancelled": "canceled", - "cancelling": "canceling", - "candour": "candor", - "cannibalise": "cannibalize", - "cannibalised": "cannibalized", - "cannibalises": "cannibalizes", - "cannibalising": "cannibalizing", - "canonise": "canonize", - "canonised": "canonized", - "canonises": "canonizes", - "canonising": "canonizing", - "capitalise": "capitalize", - "capitalised": "capitalized", - "capitalises": "capitalizes", - "capitalising": "capitalizing", - "caramelise": "caramelize", - "caramelised": "caramelized", - "caramelises": "caramelizes", - "caramelising": "caramelizing", - "carbonise": "carbonize", - "carbonised": "carbonized", - "carbonises": "carbonizes", - "carbonising": "carbonizing", - "carolled": "caroled", - "carolling": "caroling", - "catalogue": "catalog", - "catalogued": "cataloged", - "catalogues": "catalogs", - "cataloguing": "cataloging", - "catalyse": "catalyze", - "catalysed": "catalyzed", - "catalyses": "catalyzes", - "catalysing": "catalyzing", - "categorise": "categorize", - "categorised": "categorized", - "categorises": "categorizes", - "categorising": "categorizing", - "cauterise": "cauterize", - "cauterised": "cauterized", - "cauterises": "cauterizes", - "cauterising": "cauterizing", - "cavilled": "caviled", - "cavilling": "caviling", - "centigramme": "centigram", - "centigrammes": "centigrams", - "centilitre": "centiliter", - "centilitres": "centiliters", - "centimetre": "centimeter", - "centimetres": "centimeters", - "centralise": "centralize", - "centralised": "centralized", - "centralises": "centralizes", - "centralising": "centralizing", - "centre": "center", - "centred": "centered", - "centrefold": "centerfold", - "centrefolds": "centerfolds", - "centrepiece": "centerpiece", - "centrepieces": "centerpieces", - "centres": "centers", - "channelled": "channeled", - "channelling": "channeling", - "characterise": "characterize", - "characterised": "characterized", - "characterises": "characterizes", - "characterising": "characterizing", - "cheque": "check", - "chequebook": "checkbook", - "chequebooks": "checkbooks", - "chequered": "checkered", - "cheques": "checks", - "chilli": "chili", - "chimaera": "chimera", - "chimaeras": "chimeras", - "chiselled": "chiseled", - "chiselling": "chiseling", - "circularise": "circularize", - "circularised": "circularized", - "circularises": "circularizes", - "circularising": "circularizing", - "civilise": "civilize", - "civilised": "civilized", - "civilises": "civilizes", - "civilising": "civilizing", - "clamour": "clamor", - "clamoured": "clamored", - "clamouring": "clamoring", - "clamours": "clamors", - "clangour": "clangor", - "clarinettist": "clarinetist", - "clarinettists": "clarinetists", - "collectivise": "collectivize", - "collectivised": "collectivized", - "collectivises": "collectivizes", - "collectivising": "collectivizing", - "colonisation": "colonization", - "colonise": "colonize", - "colonised": "colonized", - "coloniser": "colonizer", - "colonisers": "colonizers", - "colonises": "colonizes", - "colonising": "colonizing", - "colour": "color", - "colourant": "colorant", - "colourants": "colorants", - "coloured": "colored", - "coloureds": "coloreds", - "colourful": "colorful", - "colourfully": "colorfully", - "colouring": "coloring", - "colourize": "colorize", - "colourized": "colorized", - "colourizes": "colorizes", - "colourizing": "colorizing", - "colourless": "colorless", - "colours": "colors", - "commercialise": "commercialize", - "commercialised": "commercialized", - "commercialises": "commercializes", - "commercialising": "commercializing", - "compartmentalise": "compartmentalize", - "compartmentalised": "compartmentalized", - "compartmentalises": "compartmentalizes", - "compartmentalising": "compartmentalizing", - "computerise": "computerize", - "computerised": "computerized", - "computerises": "computerizes", - "computerising": "computerizing", - "conceptualise": "conceptualize", - "conceptualised": "conceptualized", - "conceptualises": "conceptualizes", - "conceptualising": "conceptualizing", - "connexion": "connection", - "connexions": "connections", - "contextualise": "contextualize", - "contextualised": "contextualized", - "contextualises": "contextualizes", - "contextualising": "contextualizing", - "cosier": "cozier", - "cosies": "cozies", - "cosiest": "coziest", - "cosily": "cozily", - "cosiness": "coziness", - "cosy": "cozy", - "councillor": "councilor", - "councillors": "councilors", - "counselled": "counseled", - "counselling": "counseling", - "counsellor": "counselor", - "counsellors": "counselors", - "crenelated": "crenellated", - "criminalise": "criminalize", - "criminalised": "criminalized", - "criminalises": "criminalizes", - "criminalising": "criminalizing", - "criticise": "criticize", - "criticised": "criticized", - "criticises": "criticizes", - "criticising": "criticizing", - "crueller": "crueler", - "cruellest": "cruelest", - "crystallisation": "crystallization", - "crystallise": "crystallize", - "crystallised": "crystallized", - "crystallises": "crystallizes", - "crystallising": "crystallizing", - "cudgelled": "cudgeled", - "cudgelling": "cudgeling", - "customise": "customize", - "customised": "customized", - "customises": "customizes", - "customising": "customizing", - "cypher": "cipher", - "cyphers": "ciphers", - "decentralisation": "decentralization", - "decentralise": "decentralize", - "decentralised": "decentralized", - "decentralises": "decentralizes", - "decentralising": "decentralizing", - "decriminalisation": "decriminalization", - "decriminalise": "decriminalize", - "decriminalised": "decriminalized", - "decriminalises": "decriminalizes", - "decriminalising": "decriminalizing", - "defence": "defense", - "defenceless": "defenseless", - "defences": "defenses", - "dehumanisation": "dehumanization", - "dehumanise": "dehumanize", - "dehumanised": "dehumanized", - "dehumanises": "dehumanizes", - "dehumanising": "dehumanizing", - "demeanour": "demeanor", - "demilitarisation": "demilitarization", - "demilitarise": "demilitarize", - "demilitarised": "demilitarized", - "demilitarises": "demilitarizes", - "demilitarising": "demilitarizing", - "demobilisation": "demobilization", - "demobilise": "demobilize", - "demobilised": "demobilized", - "demobilises": "demobilizes", - "demobilising": "demobilizing", - "democratisation": "democratization", - "democratise": "democratize", - "democratised": "democratized", - "democratises": "democratizes", - "democratising": "democratizing", - "demonise": "demonize", - "demonised": "demonized", - "demonises": "demonizes", - "demonising": "demonizing", - "demoralisation": "demoralization", - "demoralise": "demoralize", - "demoralised": "demoralized", - "demoralises": "demoralizes", - "demoralising": "demoralizing", - "denationalisation": "denationalization", - "denationalise": "denationalize", - "denationalised": "denationalized", - "denationalises": "denationalizes", - "denationalising": "denationalizing", - "deodorise": "deodorize", - "deodorised": "deodorized", - "deodorises": "deodorizes", - "deodorising": "deodorizing", - "depersonalise": "depersonalize", - "depersonalised": "depersonalized", - "depersonalises": "depersonalizes", - "depersonalising": "depersonalizing", - "deputise": "deputize", - "deputised": "deputized", - "deputises": "deputizes", - "deputising": "deputizing", - "desensitisation": "desensitization", - "desensitise": "desensitize", - "desensitised": "desensitized", - "desensitises": "desensitizes", - "desensitising": "desensitizing", - "destabilisation": "destabilization", - "destabilise": "destabilize", - "destabilised": "destabilized", - "destabilises": "destabilizes", - "destabilising": "destabilizing", - "dialled": "dialed", - "dialling": "dialing", - "dialogue": "dialog", - "dialogues": "dialogs", - "diarrhoea": "diarrhea", - "digitise": "digitize", - "digitised": "digitized", - "digitises": "digitizes", - "digitising": "digitizing", - "disc": "disk", - "discolour": "discolor", - "discoloured": "discolored", - "discolouring": "discoloring", - "discolours": "discolors", - "discs": "disks", - "disembowelled": "disemboweled", - "disembowelling": "disemboweling", - "disfavour": "disfavor", - "dishevelled": "disheveled", - "dishonour": "dishonor", - "dishonourable": "dishonorable", - "dishonourably": "dishonorably", - "dishonoured": "dishonored", - "dishonouring": "dishonoring", - "dishonours": "dishonors", - "disorganisation": "disorganization", - "disorganised": "disorganized", - "distil": "distill", - "distils": "distills", - "dramatisation": "dramatization", - "dramatisations": "dramatizations", - "dramatise": "dramatize", - "dramatised": "dramatized", - "dramatises": "dramatizes", - "dramatising": "dramatizing", - "draught": "draft", - "draughtboard": "draftboard", - "draughtboards": "draftboards", - "draughtier": "draftier", - "draughtiest": "draftiest", - "draughts": "drafts", - "draughtsman": "draftsman", - "draughtsmanship": "draftsmanship", - "draughtsmen": "draftsmen", - "draughtswoman": "draftswoman", - "draughtswomen": "draftswomen", - "draughty": "drafty", - "drivelled": "driveled", - "drivelling": "driveling", - "duelled": "dueled", - "duelling": "dueling", - "economise": "economize", - "economised": "economized", - "economises": "economizes", - "economising": "economizing", - "edoema": "edema", - "editorialise": "editorialize", - "editorialised": "editorialized", - "editorialises": "editorializes", - "editorialising": "editorializing", - "empathise": "empathize", - "empathised": "empathized", - "empathises": "empathizes", - "empathising": "empathizing", - "emphasise": "emphasize", - "emphasised": "emphasized", - "emphasises": "emphasizes", - "emphasising": "emphasizing", - "enamelled": "enameled", - "enamelling": "enameling", - "enamoured": "enamored", - "encyclopaedia": "encyclopedia", - "encyclopaedias": "encyclopedias", - "encyclopaedic": "encyclopedic", - "endeavour": "endeavor", - "endeavoured": "endeavored", - "endeavouring": "endeavoring", - "endeavours": "endeavors", - "energise": "energize", - "energised": "energized", - "energises": "energizes", - "energising": "energizing", - "enrol": "enroll", - "enrols": "enrolls", - "enthral": "enthrall", - "enthrals": "enthralls", - "epaulette": "epaulet", - "epaulettes": "epaulets", - "epicentre": "epicenter", - "epicentres": "epicenters", - "epilogue": "epilog", - "epilogues": "epilogs", - "epitomise": "epitomize", - "epitomised": "epitomized", - "epitomises": "epitomizes", - "epitomising": "epitomizing", - "equalisation": "equalization", - "equalise": "equalize", - "equalised": "equalized", - "equaliser": "equalizer", - "equalisers": "equalizers", - "equalises": "equalizes", - "equalising": "equalizing", - "eulogise": "eulogize", - "eulogised": "eulogized", - "eulogises": "eulogizes", - "eulogising": "eulogizing", - "evangelise": "evangelize", - "evangelised": "evangelized", - "evangelises": "evangelizes", - "evangelising": "evangelizing", - "exorcise": "exorcize", - "exorcised": "exorcized", - "exorcises": "exorcizes", - "exorcising": "exorcizing", - "extemporisation": "extemporization", - "extemporise": "extemporize", - "extemporised": "extemporized", - "extemporises": "extemporizes", - "extemporising": "extemporizing", - "externalisation": "externalization", - "externalisations": "externalizations", - "externalise": "externalize", - "externalised": "externalized", - "externalises": "externalizes", - "externalising": "externalizing", - "factorise": "factorize", - "factorised": "factorized", - "factorises": "factorizes", - "factorising": "factorizing", - "faecal": "fecal", - "faeces": "feces", - "familiarisation": "familiarization", - "familiarise": "familiarize", - "familiarised": "familiarized", - "familiarises": "familiarizes", - "familiarising": "familiarizing", - "fantasise": "fantasize", - "fantasised": "fantasized", - "fantasises": "fantasizes", - "fantasising": "fantasizing", - "favour": "favor", - "favourable": "favorable", - "favourably": "favorably", - "favoured": "favored", - "favouring": "favoring", - "favourite": "favorite", - "favourites": "favorites", - "favouritism": "favoritism", - "favours": "favors", - "feminise": "feminize", - "feminised": "feminized", - "feminises": "feminizes", - "feminising": "feminizing", - "fertilisation": "fertilization", - "fertilise": "fertilize", - "fertilised": "fertilized", - "fertiliser": "fertilizer", - "fertilisers": "fertilizers", - "fertilises": "fertilizes", - "fertilising": "fertilizing", - "fervour": "fervor", - "fibre": "fiber", - "fibreglass": "fiberglass", - "fibres": "fibers", - "fictionalisation": "fictionalization", - "fictionalisations": "fictionalizations", - "fictionalise": "fictionalize", - "fictionalised": "fictionalized", - "fictionalises": "fictionalizes", - "fictionalising": "fictionalizing", - "fillet": "filet", - "filleted": "fileted", - "filleting": "fileting", - "fillets": "filets", - "finalisation": "finalization", - "finalise": "finalize", - "finalised": "finalized", - "finalises": "finalizes", - "finalising": "finalizing", - "flautist": "flutist", - "flautists": "flutists", - "flavour": "flavor", - "flavoured": "flavored", - "flavouring": "flavoring", - "flavourings": "flavorings", - "flavourless": "flavorless", - "flavours": "flavors", - "flavoursome": "flavorsome", - "flyer / flier": "flier / flyer", - "foetal": "fetal", - "foetid": "fetid", - "foetus": "fetus", - "foetuses": "fetuses", - "formalisation": "formalization", - "formalise": "formalize", - "formalised": "formalized", - "formalises": "formalizes", - "formalising": "formalizing", - "fossilisation": "fossilization", - "fossilise": "fossilize", - "fossilised": "fossilized", - "fossilises": "fossilizes", - "fossilising": "fossilizing", - "fraternisation": "fraternization", - "fraternise": "fraternize", - "fraternised": "fraternized", - "fraternises": "fraternizes", - "fraternising": "fraternizing", - "fulfil": "fulfill", - "fulfilment": "fulfillment", - "fulfils": "fulfills", - "funnelled": "funneled", - "funnelling": "funneling", - "galvanise": "galvanize", - "galvanised": "galvanized", - "galvanises": "galvanizes", - "galvanising": "galvanizing", - "gambolled": "gamboled", - "gambolling": "gamboling", - "gaol": "jail", - "gaolbird": "jailbird", - "gaolbirds": "jailbirds", - "gaolbreak": "jailbreak", - "gaolbreaks": "jailbreaks", - "gaoled": "jailed", - "gaoler": "jailer", - "gaolers": "jailers", - "gaoling": "jailing", - "gaols": "jails", - "gasses": "gases", - "gage": "gauge", - "gaged": "gauged", - "gages": "gauges", - "gaging": "gauging", - "generalisation": "generalization", - "generalisations": "generalizations", - "generalise": "generalize", - "generalised": "generalized", - "generalises": "generalizes", - "generalising": "generalizing", - "ghettoise": "ghettoize", - "ghettoised": "ghettoized", - "ghettoises": "ghettoizes", - "ghettoising": "ghettoizing", - "gipsies": "gypsies", - "glamorise": "glamorize", - "glamorised": "glamorized", - "glamorises": "glamorizes", - "glamorising": "glamorizing", - "glamor": "glamour", - "globalisation": "globalization", - "globalise": "globalize", - "globalised": "globalized", - "globalises": "globalizes", - "globalising": "globalizing", - "glueing": "gluing", - "goitre": "goiter", - "goitres": "goiters", - "gonorrhoea": "gonorrhea", - "gramme": "gram", - "grammes": "grams", - "gravelled": "graveled", - "grey": "gray", - "greyed": "grayed", - "greying": "graying", - "greyish": "grayish", - "greyness": "grayness", - "greys": "grays", - "grovelled": "groveled", - "grovelling": "groveling", - "groyne": "groin", - "groynes": "groins", - "gruelling": "grueling", - "gruellingly": "gruelingly", - "gryphon": "griffin", - "gryphons": "griffins", - "gynaecological": "gynecological", - "gynaecologist": "gynecologist", - "gynaecologists": "gynecologists", - "gynaecology": "gynecology", - "haematological": "hematological", - "haematologist": "hematologist", - "haematologists": "hematologists", - "haematology": "hematology", - "haemoglobin": "hemoglobin", - "haemophilia": "hemophilia", - "haemophiliac": "hemophiliac", - "haemophiliacs": "hemophiliacs", - "haemorrhage": "hemorrhage", - "haemorrhaged": "hemorrhaged", - "haemorrhages": "hemorrhages", - "haemorrhaging": "hemorrhaging", - "haemorrhoids": "hemorrhoids", - "harbour": "harbor", - "harboured": "harbored", - "harbouring": "harboring", - "harbours": "harbors", - "harmonisation": "harmonization", - "harmonise": "harmonize", - "harmonised": "harmonized", - "harmonises": "harmonizes", - "harmonising": "harmonizing", - "homoeopath": "homeopath", - "homoeopathic": "homeopathic", - "homoeopaths": "homeopaths", - "homoeopathy": "homeopathy", - "homogenise": "homogenize", - "homogenised": "homogenized", - "homogenises": "homogenizes", - "homogenising": "homogenizing", - "honour": "honor", - "honourable": "honorable", - "honourably": "honorably", - "honoured": "honored", - "honouring": "honoring", - "honours": "honors", - "hospitalisation": "hospitalization", - "hospitalise": "hospitalize", - "hospitalised": "hospitalized", - "hospitalises": "hospitalizes", - "hospitalising": "hospitalizing", - "humanise": "humanize", - "humanised": "humanized", - "humanises": "humanizes", - "humanising": "humanizing", - "humour": "humor", - "humoured": "humored", - "humouring": "humoring", - "humourless": "humorless", - "humours": "humors", - "hybridise": "hybridize", - "hybridised": "hybridized", - "hybridises": "hybridizes", - "hybridising": "hybridizing", - "hypnotise": "hypnotize", - "hypnotised": "hypnotized", - "hypnotises": "hypnotizes", - "hypnotising": "hypnotizing", - "hypothesise": "hypothesize", - "hypothesised": "hypothesized", - "hypothesises": "hypothesizes", - "hypothesising": "hypothesizing", - "idealisation": "idealization", - "idealise": "idealize", - "idealised": "idealized", - "idealises": "idealizes", - "idealising": "idealizing", - "idolise": "idolize", - "idolised": "idolized", - "idolises": "idolizes", - "idolising": "idolizing", - "immobilisation": "immobilization", - "immobilise": "immobilize", - "immobilised": "immobilized", - "immobiliser": "immobilizer", - "immobilisers": "immobilizers", - "immobilises": "immobilizes", - "immobilising": "immobilizing", - "immortalise": "immortalize", - "immortalised": "immortalized", - "immortalises": "immortalizes", - "immortalising": "immortalizing", - "immunisation": "immunization", - "immunise": "immunize", - "immunised": "immunized", - "immunises": "immunizes", - "immunising": "immunizing", - "impanelled": "impaneled", - "impanelling": "impaneling", - "imperilled": "imperiled", - "imperilling": "imperiling", - "individualise": "individualize", - "individualised": "individualized", - "individualises": "individualizes", - "individualising": "individualizing", - "industrialise": "industrialize", - "industrialised": "industrialized", - "industrialises": "industrializes", - "industrialising": "industrializing", - "inflexion": "inflection", - "inflexions": "inflections", - "initialise": "initialize", - "initialised": "initialized", - "initialises": "initializes", - "initialising": "initializing", - "initialled": "initialed", - "initialling": "initialing", - "instal": "install", - "instalment": "installment", - "instalments": "installments", - "instals": "installs", - "instil": "instill", - "instils": "instills", - "institutionalisation": "institutionalization", - "institutionalise": "institutionalize", - "institutionalised": "institutionalized", - "institutionalises": "institutionalizes", - "institutionalising": "institutionalizing", - "intellectualise": "intellectualize", - "intellectualised": "intellectualized", - "intellectualises": "intellectualizes", - "intellectualising": "intellectualizing", - "internalisation": "internalization", - "internalise": "internalize", - "internalised": "internalized", - "internalises": "internalizes", - "internalising": "internalizing", - "internationalisation": "internationalization", - "internationalise": "internationalize", - "internationalised": "internationalized", - "internationalises": "internationalizes", - "internationalising": "internationalizing", - "ionisation": "ionization", - "ionise": "ionize", - "ionised": "ionized", - "ioniser": "ionizer", - "ionisers": "ionizers", - "ionises": "ionizes", - "ionising": "ionizing", - "italicise": "italicize", - "italicised": "italicized", - "italicises": "italicizes", - "italicising": "italicizing", - "itemise": "itemize", - "itemised": "itemized", - "itemises": "itemizes", - "itemising": "itemizing", - "jeopardise": "jeopardize", - "jeopardised": "jeopardized", - "jeopardises": "jeopardizes", - "jeopardising": "jeopardizing", - "jewelled": "jeweled", - "jeweller": "jeweler", - "jewellers": "jewelers", - "jewellery": "jewelry", - "judgement": "judgment", - "kilogramme": "kilogram", - "kilogrammes": "kilograms", - "kilometre": "kilometer", - "kilometres": "kilometers", - "labelled": "labeled", - "labelling": "labeling", - "labour": "labor", - "laboured": "labored", - "labourer": "laborer", - "labourers": "laborers", - "labouring": "laboring", - "labours": "labors", - "lacklustre": "lackluster", - "legalisation": "legalization", - "legalise": "legalize", - "legalised": "legalized", - "legalises": "legalizes", - "legalising": "legalizing", - "legitimise": "legitimize", - "legitimised": "legitimized", - "legitimises": "legitimizes", - "legitimising": "legitimizing", - "leukaemia": "leukemia", - "levelled": "leveled", - "leveller": "leveler", - "levellers": "levelers", - "levelling": "leveling", - "libelled": "libeled", - "libelling": "libeling", - "libellous": "libelous", - "liberalisation": "liberalization", - "liberalise": "liberalize", - "liberalised": "liberalized", - "liberalises": "liberalizes", - "liberalising": "liberalizing", - "licence": "license", - "licenced": "licensed", - "licences": "licenses", - "licencing": "licensing", - "likeable": "likable", - "lionisation": "lionization", - "lionise": "lionize", - "lionised": "lionized", - "lionises": "lionizes", - "lionising": "lionizing", - "liquidise": "liquidize", - "liquidised": "liquidized", - "liquidiser": "liquidizer", - "liquidisers": "liquidizers", - "liquidises": "liquidizes", - "liquidising": "liquidizing", - "litre": "liter", - "litres": "liters", - "localise": "localize", - "localised": "localized", - "localises": "localizes", - "localising": "localizing", - "louvre": "louver", - "louvred": "louvered", - "louvres": "louvers", - "lustre": "luster", - "magnetise": "magnetize", - "magnetised": "magnetized", - "magnetises": "magnetizes", - "magnetising": "magnetizing", - "manoeuvrability": "maneuverability", - "manoeuvrable": "maneuverable", - "manoeuvre": "maneuver", - "manoeuvred": "maneuvered", - "manoeuvres": "maneuvers", - "manoeuvring": "maneuvering", - "manoeuvrings": "maneuverings", - "marginalisation": "marginalization", - "marginalise": "marginalize", - "marginalised": "marginalized", - "marginalises": "marginalizes", - "marginalising": "marginalizing", - "marshalled": "marshaled", - "marshalling": "marshaling", - "marvelled": "marveled", - "marvelling": "marveling", - "marvellous": "marvelous", - "marvellously": "marvelously", - "materialisation": "materialization", - "materialise": "materialize", - "materialised": "materialized", - "materialises": "materializes", - "materialising": "materializing", - "maximisation": "maximization", - "maximise": "maximize", - "maximised": "maximized", - "maximises": "maximizes", - "maximising": "maximizing", - "meagre": "meager", - "mechanisation": "mechanization", - "mechanise": "mechanize", - "mechanised": "mechanized", - "mechanises": "mechanizes", - "mechanising": "mechanizing", - "mediaeval": "medieval", - "memorialise": "memorialize", - "memorialised": "memorialized", - "memorialises": "memorializes", - "memorialising": "memorializing", - "memorise": "memorize", - "memorised": "memorized", - "memorises": "memorizes", - "memorising": "memorizing", - "mesmerise": "mesmerize", - "mesmerised": "mesmerized", - "mesmerises": "mesmerizes", - "mesmerising": "mesmerizing", - "metabolise": "metabolize", - "metabolised": "metabolized", - "metabolises": "metabolizes", - "metabolising": "metabolizing", - "metre": "meter", - "metres": "meters", - "micrometre": "micrometer", - "micrometres": "micrometers", - "militarise": "militarize", - "militarised": "militarized", - "militarises": "militarizes", - "militarising": "militarizing", - "milligramme": "milligram", - "milligrammes": "milligrams", - "millilitre": "milliliter", - "millilitres": "milliliters", - "millimetre": "millimeter", - "millimetres": "millimeters", - "miniaturisation": "miniaturization", - "miniaturise": "miniaturize", - "miniaturised": "miniaturized", - "miniaturises": "miniaturizes", - "miniaturising": "miniaturizing", - "minibusses": "minibuses", - "minimise": "minimize", - "minimised": "minimized", - "minimises": "minimizes", - "minimising": "minimizing", - "misbehaviour": "misbehavior", - "misdemeanour": "misdemeanor", - "misdemeanours": "misdemeanors", - "misspelt": "misspelled", - "mitre": "miter", - "mitres": "miters", - "mobilisation": "mobilization", - "mobilise": "mobilize", - "mobilised": "mobilized", - "mobilises": "mobilizes", - "mobilising": "mobilizing", - "modelled": "modeled", - "modeller": "modeler", - "modellers": "modelers", - "modelling": "modeling", - "modernise": "modernize", - "modernised": "modernized", - "modernises": "modernizes", - "modernising": "modernizing", - "moisturise": "moisturize", - "moisturised": "moisturized", - "moisturiser": "moisturizer", - "moisturisers": "moisturizers", - "moisturises": "moisturizes", - "moisturising": "moisturizing", - "monologue": "monolog", - "monologues": "monologs", - "monopolisation": "monopolization", - "monopolise": "monopolize", - "monopolised": "monopolized", - "monopolises": "monopolizes", - "monopolising": "monopolizing", - "moralise": "moralize", - "moralised": "moralized", - "moralises": "moralizes", - "moralising": "moralizing", - "motorised": "motorized", - "mould": "mold", - "moulded": "molded", - "moulder": "molder", - "mouldered": "moldered", - "mouldering": "moldering", - "moulders": "molders", - "mouldier": "moldier", - "mouldiest": "moldiest", - "moulding": "molding", - "mouldings": "moldings", - "moulds": "molds", - "mouldy": "moldy", - "moult": "molt", - "moulted": "molted", - "moulting": "molting", - "moults": "molts", - "moustache": "mustache", - "moustached": "mustached", - "moustaches": "mustaches", - "moustachioed": "mustachioed", - "multicoloured": "multicolored", - "nationalisation": "nationalization", - "nationalisations": "nationalizations", - "nationalise": "nationalize", - "nationalised": "nationalized", - "nationalises": "nationalizes", - "nationalising": "nationalizing", - "naturalisation": "naturalization", - "naturalise": "naturalize", - "naturalised": "naturalized", - "naturalises": "naturalizes", - "naturalising": "naturalizing", - "neighbour": "neighbor", - "neighbourhood": "neighborhood", - "neighbourhoods": "neighborhoods", - "neighbouring": "neighboring", - "neighbourliness": "neighborliness", - "neighbourly": "neighborly", - "neighbours": "neighbors", - "neutralisation": "neutralization", - "neutralise": "neutralize", - "neutralised": "neutralized", - "neutralises": "neutralizes", - "neutralising": "neutralizing", - "normalisation": "normalization", - "normalise": "normalize", - "normalised": "normalized", - "normalises": "normalizes", - "normalising": "normalizing", - "odour": "odor", - "odourless": "odorless", - "odours": "odors", - "oesophagus": "esophagus", - "oesophaguses": "esophaguses", - "oestrogen": "estrogen", - "offence": "offense", - "offences": "offenses", - "omelette": "omelet", - "omelettes": "omelets", - "optimise": "optimize", - "optimised": "optimized", - "optimises": "optimizes", - "optimising": "optimizing", - "organisation": "organization", - "organisational": "organizational", - "organisations": "organizations", - "organise": "organize", - "organised": "organized", - "organiser": "organizer", - "organisers": "organizers", - "organises": "organizes", - "organising": "organizing", - "orthopaedic": "orthopedic", - "orthopaedics": "orthopedics", - "ostracise": "ostracize", - "ostracised": "ostracized", - "ostracises": "ostracizes", - "ostracising": "ostracizing", - "outmanoeuvre": "outmaneuver", - "outmanoeuvred": "outmaneuvered", - "outmanoeuvres": "outmaneuvers", - "outmanoeuvring": "outmaneuvering", - "overemphasise": "overemphasize", - "overemphasised": "overemphasized", - "overemphasises": "overemphasizes", - "overemphasising": "overemphasizing", - "oxidisation": "oxidization", - "oxidise": "oxidize", - "oxidised": "oxidized", - "oxidises": "oxidizes", - "oxidising": "oxidizing", - "paederast": "pederast", - "paederasts": "pederasts", - "paediatric": "pediatric", - "paediatrician": "pediatrician", - "paediatricians": "pediatricians", - "paediatrics": "pediatrics", - "paedophile": "pedophile", - "paedophiles": "pedophiles", - "paedophilia": "pedophilia", - "palaeolithic": "paleolithic", - "palaeontologist": "paleontologist", - "palaeontologists": "paleontologists", - "palaeontology": "paleontology", - "panelled": "paneled", - "panelling": "paneling", - "panellist": "panelist", - "panellists": "panelists", - "paralyse": "paralyze", - "paralysed": "paralyzed", - "paralyses": "paralyzes", - "paralysing": "paralyzing", - "parcelled": "parceled", - "parcelling": "parceling", - "parlour": "parlor", - "parlours": "parlors", - "particularise": "particularize", - "particularised": "particularized", - "particularises": "particularizes", - "particularising": "particularizing", - "passivisation": "passivization", - "passivise": "passivize", - "passivised": "passivized", - "passivises": "passivizes", - "passivising": "passivizing", - "pasteurisation": "pasteurization", - "pasteurise": "pasteurize", - "pasteurised": "pasteurized", - "pasteurises": "pasteurizes", - "pasteurising": "pasteurizing", - "patronise": "patronize", - "patronised": "patronized", - "patronises": "patronizes", - "patronising": "patronizing", - "patronisingly": "patronizingly", - "pedalled": "pedaled", - "pedalling": "pedaling", - "pedestrianisation": "pedestrianization", - "pedestrianise": "pedestrianize", - "pedestrianised": "pedestrianized", - "pedestrianises": "pedestrianizes", - "pedestrianising": "pedestrianizing", - "penalise": "penalize", - "penalised": "penalized", - "penalises": "penalizes", - "penalising": "penalizing", - "pencilled": "penciled", - "pencilling": "penciling", - "personalise": "personalize", - "personalised": "personalized", - "personalises": "personalizes", - "personalising": "personalizing", - "pharmacopoeia": "pharmacopeia", - "pharmacopoeias": "pharmacopeias", - "philosophise": "philosophize", - "philosophised": "philosophized", - "philosophises": "philosophizes", - "philosophising": "philosophizing", - "philtre": "filter", - "philtres": "filters", - "phoney": "phony", - "plagiarise": "plagiarize", - "plagiarised": "plagiarized", - "plagiarises": "plagiarizes", - "plagiarising": "plagiarizing", - "plough": "plow", - "ploughed": "plowed", - "ploughing": "plowing", - "ploughman": "plowman", - "ploughmen": "plowmen", - "ploughs": "plows", - "ploughshare": "plowshare", - "ploughshares": "plowshares", - "polarisation": "polarization", - "polarise": "polarize", - "polarised": "polarized", - "polarises": "polarizes", - "polarising": "polarizing", - "politicisation": "politicization", - "politicise": "politicize", - "politicised": "politicized", - "politicises": "politicizes", - "politicising": "politicizing", - "popularisation": "popularization", - "popularise": "popularize", - "popularised": "popularized", - "popularises": "popularizes", - "popularising": "popularizing", - "pouffe": "pouf", - "pouffes": "poufs", - "practise": "practice", - "practised": "practiced", - "practises": "practices", - "practising": "practicing", - "praesidium": "presidium", - "praesidiums": "presidiums", - "pressurisation": "pressurization", - "pressurise": "pressurize", - "pressurised": "pressurized", - "pressurises": "pressurizes", - "pressurising": "pressurizing", - "pretence": "pretense", - "pretences": "pretenses", - "primaeval": "primeval", - "prioritisation": "prioritization", - "prioritise": "prioritize", - "prioritised": "prioritized", - "prioritises": "prioritizes", - "prioritising": "prioritizing", - "privatisation": "privatization", - "privatisations": "privatizations", - "privatise": "privatize", - "privatised": "privatized", - "privatises": "privatizes", - "privatising": "privatizing", - "professionalisation": "professionalization", - "professionalise": "professionalize", - "professionalised": "professionalized", - "professionalises": "professionalizes", - "professionalising": "professionalizing", - "programme": "program", - "programmes": "programs", - "prologue": "prolog", - "prologues": "prologs", - "propagandise": "propagandize", - "propagandised": "propagandized", - "propagandises": "propagandizes", - "propagandising": "propagandizing", - "proselytise": "proselytize", - "proselytised": "proselytized", - "proselytiser": "proselytizer", - "proselytisers": "proselytizers", - "proselytises": "proselytizes", - "proselytising": "proselytizing", - "psychoanalyse": "psychoanalyze", - "psychoanalysed": "psychoanalyzed", - "psychoanalyses": "psychoanalyzes", - "psychoanalysing": "psychoanalyzing", - "publicise": "publicize", - "publicised": "publicized", - "publicises": "publicizes", - "publicising": "publicizing", - "pulverisation": "pulverization", - "pulverise": "pulverize", - "pulverised": "pulverized", - "pulverises": "pulverizes", - "pulverising": "pulverizing", - "pummelled": "pummel", - "pummelling": "pummeled", - "pyjama": "pajama", - "pyjamas": "pajamas", - "pzazz": "pizzazz", - "quarrelled": "quarreled", - "quarrelling": "quarreling", - "radicalise": "radicalize", - "radicalised": "radicalized", - "radicalises": "radicalizes", - "radicalising": "radicalizing", - "rancour": "rancor", - "randomise": "randomize", - "randomised": "randomized", - "randomises": "randomizes", - "randomising": "randomizing", - "rationalisation": "rationalization", - "rationalisations": "rationalizations", - "rationalise": "rationalize", - "rationalised": "rationalized", - "rationalises": "rationalizes", - "rationalising": "rationalizing", - "ravelled": "raveled", - "ravelling": "raveling", - "realisable": "realizable", - "realisation": "realization", - "realisations": "realizations", - "realise": "realize", - "realised": "realized", - "realises": "realizes", - "realising": "realizing", - "recognisable": "recognizable", - "recognisably": "recognizably", - "recognisance": "recognizance", - "recognise": "recognize", - "recognised": "recognized", - "recognises": "recognizes", - "recognising": "recognizing", - "reconnoitre": "reconnoiter", - "reconnoitred": "reconnoitered", - "reconnoitres": "reconnoiters", - "reconnoitring": "reconnoitering", - "refuelled": "refueled", - "refuelling": "refueling", - "regularisation": "regularization", - "regularise": "regularize", - "regularised": "regularized", - "regularises": "regularizes", - "regularising": "regularizing", - "remodelled": "remodeled", - "remodelling": "remodeling", - "remould": "remold", - "remoulded": "remolded", - "remoulding": "remolding", - "remoulds": "remolds", - "reorganisation": "reorganization", - "reorganisations": "reorganizations", - "reorganise": "reorganize", - "reorganised": "reorganized", - "reorganises": "reorganizes", - "reorganising": "reorganizing", - "revelled": "reveled", - "reveller": "reveler", - "revellers": "revelers", - "revelling": "reveling", - "revitalise": "revitalize", - "revitalised": "revitalized", - "revitalises": "revitalizes", - "revitalising": "revitalizing", - "revolutionise": "revolutionize", - "revolutionised": "revolutionized", - "revolutionises": "revolutionizes", - "revolutionising": "revolutionizing", - "rhapsodise": "rhapsodize", - "rhapsodised": "rhapsodized", - "rhapsodises": "rhapsodizes", - "rhapsodising": "rhapsodizing", - "rigour": "rigor", - "rigours": "rigors", - "ritualised": "ritualized", - "rivalled": "rivaled", - "rivalling": "rivaling", - "romanticise": "romanticize", - "romanticised": "romanticized", - "romanticises": "romanticizes", - "romanticising": "romanticizing", - "rumour": "rumor", - "rumoured": "rumored", - "rumours": "rumors", - "sabre": "saber", - "sabres": "sabers", - "saltpetre": "saltpeter", - "sanitise": "sanitize", - "sanitised": "sanitized", - "sanitises": "sanitizes", - "sanitising": "sanitizing", - "satirise": "satirize", - "satirised": "satirized", - "satirises": "satirizes", - "satirising": "satirizing", - "saviour": "savior", - "saviours": "saviors", - "savour": "savor", - "savoured": "savored", - "savouries": "savories", - "savouring": "savoring", - "savours": "savors", - "savoury": "savory", - "scandalise": "scandalize", - "scandalised": "scandalized", - "scandalises": "scandalizes", - "scandalising": "scandalizing", - "sceptic": "skeptic", - "sceptical": "skeptical", - "sceptically": "skeptically", - "scepticism": "skepticism", - "sceptics": "skeptics", - "sceptre": "scepter", - "sceptres": "scepters", - "scrutinise": "scrutinize", - "scrutinised": "scrutinized", - "scrutinises": "scrutinizes", - "scrutinising": "scrutinizing", - "secularisation": "secularization", - "secularise": "secularize", - "secularised": "secularized", - "secularises": "secularizes", - "secularising": "secularizing", - "sensationalise": "sensationalize", - "sensationalised": "sensationalized", - "sensationalises": "sensationalizes", - "sensationalising": "sensationalizing", - "sensitise": "sensitize", - "sensitised": "sensitized", - "sensitises": "sensitizes", - "sensitising": "sensitizing", - "sentimentalise": "sentimentalize", - "sentimentalised": "sentimentalized", - "sentimentalises": "sentimentalizes", - "sentimentalising": "sentimentalizing", - "sepulchre": "sepulcher", - "sepulchres": "sepulchers", - "serialisation": "serialization", - "serialisations": "serializations", - "serialise": "serialize", - "serialised": "serialized", - "serialises": "serializes", - "serialising": "serializing", - "sermonise": "sermonize", - "sermonised": "sermonized", - "sermonises": "sermonizes", - "sermonising": "sermonizing", - "sheikh": "sheik", - "shovelled": "shoveled", - "shovelling": "shoveling", - "shrivelled": "shriveled", - "shrivelling": "shriveling", - "signalise": "signalize", - "signalised": "signalized", - "signalises": "signalizes", - "signalising": "signalizing", - "signalled": "signaled", - "signalling": "signaling", - "smoulder": "smolder", - "smouldered": "smoldered", - "smouldering": "smoldering", - "smoulders": "smolders", - "snivelled": "sniveled", - "snivelling": "sniveling", - "snorkelled": "snorkeled", - "snorkelling": "snorkeling", - "snowplough": "snowplow", - "snowploughs": "snowplow", - "socialisation": "socialization", - "socialise": "socialize", - "socialised": "socialized", - "socialises": "socializes", - "socialising": "socializing", - "sodomise": "sodomize", - "sodomised": "sodomized", - "sodomises": "sodomizes", - "sodomising": "sodomizing", - "solemnise": "solemnize", - "solemnised": "solemnized", - "solemnises": "solemnizes", - "solemnising": "solemnizing", - "sombre": "somber", - "specialisation": "specialization", - "specialisations": "specializations", - "specialise": "specialize", - "specialised": "specialized", - "specialises": "specializes", - "specialising": "specializing", - "spectre": "specter", - "spectres": "specters", - "spiralled": "spiraled", - "spiralling": "spiraling", - "splendour": "splendor", - "splendours": "splendors", - "squirrelled": "squirreled", - "squirrelling": "squirreling", - "stabilisation": "stabilization", - "stabilise": "stabilize", - "stabilised": "stabilized", - "stabiliser": "stabilizer", - "stabilisers": "stabilizers", - "stabilises": "stabilizes", - "stabilising": "stabilizing", - "standardisation": "standardization", - "standardise": "standardize", - "standardised": "standardized", - "standardises": "standardizes", - "standardising": "standardizing", - "stencilled": "stenciled", - "stencilling": "stenciling", - "sterilisation": "sterilization", - "sterilisations": "sterilizations", - "sterilise": "sterilize", - "sterilised": "sterilized", - "steriliser": "sterilizer", - "sterilisers": "sterilizers", - "sterilises": "sterilizes", - "sterilising": "sterilizing", - "stigmatisation": "stigmatization", - "stigmatise": "stigmatize", - "stigmatised": "stigmatized", - "stigmatises": "stigmatizes", - "stigmatising": "stigmatizing", - "storey": "story", - "storeys": "stories", - "subsidisation": "subsidization", - "subsidise": "subsidize", - "subsidised": "subsidized", - "subsidiser": "subsidizer", - "subsidisers": "subsidizers", - "subsidises": "subsidizes", - "subsidising": "subsidizing", - "succour": "succor", - "succoured": "succored", - "succouring": "succoring", - "succours": "succors", - "sulphate": "sulfate", - "sulphates": "sulfates", - "sulphide": "sulfide", - "sulphides": "sulfides", - "sulphur": "sulfur", - "sulphurous": "sulfurous", - "summarise": "summarize", - "summarised": "summarized", - "summarises": "summarizes", - "summarising": "summarizing", - "swivelled": "swiveled", - "swivelling": "swiveling", - "symbolise": "symbolize", - "symbolised": "symbolized", - "symbolises": "symbolizes", - "symbolising": "symbolizing", - "sympathise": "sympathize", - "sympathised": "sympathized", - "sympathiser": "sympathizer", - "sympathisers": "sympathizers", - "sympathises": "sympathizes", - "sympathising": "sympathizing", - "synchronisation": "synchronization", - "synchronise": "synchronize", - "synchronised": "synchronized", - "synchronises": "synchronizes", - "synchronising": "synchronizing", - "synthesise": "synthesize", - "synthesised": "synthesized", - "synthesiser": "synthesizer", - "synthesisers": "synthesizers", - "synthesises": "synthesizes", - "synthesising": "synthesizing", - "syphon": "siphon", - "syphoned": "siphoned", - "syphoning": "siphoning", - "syphons": "siphons", - "systematisation": "systematization", - "systematise": "systematize", - "systematised": "systematized", - "systematises": "systematizes", - "systematising": "systematizing", - "tantalise": "tantalize", - "tantalised": "tantalized", - "tantalises": "tantalizes", - "tantalising": "tantalizing", - "tantalisingly": "tantalizingly", - "tasselled": "tasseled", - "technicolour": "technicolor", - "temporise": "temporize", - "temporised": "temporized", - "temporises": "temporizes", - "temporising": "temporizing", - "tenderise": "tenderize", - "tenderised": "tenderized", - "tenderises": "tenderizes", - "tenderising": "tenderizing", - "terrorise": "terrorize", - "terrorised": "terrorized", - "terrorises": "terrorizes", - "terrorising": "terrorizing", - "theatre": "theater", - "theatregoer": "theatergoer", - "theatregoers": "theatergoers", - "theatres": "theaters", - "theorise": "theorize", - "theorised": "theorized", - "theorises": "theorizes", - "theorising": "theorizing", - "tonne": "ton", - "tonnes": "tons", - "towelled": "toweled", - "towelling": "toweling", - "toxaemia": "toxemia", - "tranquillise": "tranquilize", - "tranquillised": "tranquilized", - "tranquilliser": "tranquilizer", - "tranquillisers": "tranquilizers", - "tranquillises": "tranquilizes", - "tranquillising": "tranquilizing", - "tranquillity": "tranquility", - "tranquillize": "tranquilize", - "tranquillized": "tranquilized", - "tranquillizer": "tranquilizer", - "tranquillizers": "tranquilizers", - "tranquillizes": "tranquilizes", - "tranquillizing": "tranquilizing", - "tranquilly": "tranquility", - "transistorised": "transistorized", - "traumatise": "traumatize", - "traumatised": "traumatized", - "traumatises": "traumatizes", - "traumatising": "traumatizing", - "travelled": "traveled", - "traveller": "traveler", - "travellers": "travelers", - "travelling": "traveling", - "travelog": "travelogue", - "travelogs": "travelogues", - "trialled": "trialed", - "trialling": "trialing", - "tricolour": "tricolor", - "tricolours": "tricolors", - "trivialise": "trivialize", - "trivialised": "trivialized", - "trivialises": "trivializes", - "trivialising": "trivializing", - "tumour": "tumor", - "tumours": "tumors", - "tunnelled": "tunneled", - "tunnelling": "tunneling", - "tyrannise": "tyrannize", - "tyrannised": "tyrannized", - "tyrannises": "tyrannizes", - "tyrannising": "tyrannizing", - "tyre": "tire", - "tyres": "tires", - "unauthorised": "unauthorized", - "uncivilised": "uncivilized", - "underutilised": "underutilized", - "unequalled": "unequaled", - "unfavourable": "unfavorable", - "unfavourably": "unfavorably", - "unionisation": "unionization", - "unionise": "unionize", - "unionised": "unionized", - "unionises": "unionizes", - "unionising": "unionizing", - "unorganised": "unorganized", - "unravelled": "unraveled", - "unravelling": "unraveling", - "unrecognisable": "unrecognizable", - "unrecognised": "unrecognized", - "unrivalled": "unrivaled", - "unsavoury": "unsavory", - "untrammelled": "untrammeled", - "urbanisation": "urbanization", - "urbanise": "urbanize", - "urbanised": "urbanized", - "urbanises": "urbanizes", - "urbanising": "urbanizing", - "utilisable": "utilizable", - "utilisation": "utilization", - "utilise": "utilize", - "utilised": "utilized", - "utilises": "utilizes", - "utilising": "utilizing", - "valour": "valor", - "vandalise": "vandalize", - "vandalised": "vandalized", - "vandalises": "vandalizes", - "vandalising": "vandalizing", - "vaporisation": "vaporization", - "vaporise": "vaporize", - "vaporised": "vaporized", - "vaporises": "vaporizes", - "vaporising": "vaporizing", - "vapour": "vapor", - "vapours": "vapors", - "verbalise": "verbalize", - "verbalised": "verbalized", - "verbalises": "verbalizes", - "verbalising": "verbalizing", - "victimisation": "victimization", - "victimise": "victimize", - "victimised": "victimized", - "victimises": "victimizes", - "victimising": "victimizing", - "videodisc": "videodisk", - "videodiscs": "videodisks", - "vigour": "vigor", - "visualisation": "visualization", - "visualisations": "visualizations", - "visualise": "visualize", - "visualised": "visualized", - "visualises": "visualizes", - "visualising": "visualizing", - "vocalisation": "vocalization", - "vocalisations": "vocalizations", - "vocalise": "vocalize", - "vocalised": "vocalized", - "vocalises": "vocalizes", - "vocalising": "vocalizing", - "vulcanised": "vulcanized", - "vulgarisation": "vulgarization", - "vulgarise": "vulgarize", - "vulgarised": "vulgarized", - "vulgarises": "vulgarizes", - "vulgarising": "vulgarizing", - "waggon": "wagon", - "waggons": "wagons", - "watercolour": "watercolor", - "watercolours": "watercolors", - "weaselled": "weaseled", - "weaselling": "weaseling", - "westernisation": "westernization", - "westernise": "westernize", - "westernised": "westernized", - "westernises": "westernizes", - "westernising": "westernizing", - "womanise": "womanize", - "womanised": "womanized", - "womaniser": "womanizer", - "womanisers": "womanizers", - "womanises": "womanizes", - "womanising": "womanizing", - "woollen": "woolen", - "woollens": "woolens", - "woollies": "woolies", - "woolly": "wooly", - "worshipped": "worshiped", - "worshipping": "worshiping", - "worshipper": "worshiper", - "yodelled": "yodeled", - "yodelling": "yodeling", - "yoghourt": "yogurt", - "yoghourts": "yogurts", - "yoghurt": "yogurt", - "yoghurts": "yogurts", - "mhm": "hmm", - "mmm": "hmm" -} \ No newline at end of file diff --git a/modules/whisper_extractor/normalizers/english.py b/modules/whisper_extractor/normalizers/english.py deleted file mode 100644 index c602d3bc87d2c198d07e325489714751718b983e..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/normalizers/english.py +++ /dev/null @@ -1,561 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os -import re -from fractions import Fraction -from typing import Iterator, List, Match, Optional, Union - -from more_itertools import windowed - -from .basic import remove_symbols_and_diacritics - - -class EnglishNumberNormalizer: - """ - Convert any spelled-out numbers into arabic numbers, while handling: - - - remove any commas - - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. - - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` - - spell out `one` and `ones` - - interpret successive single-digit numbers as nominal: `one oh one` -> `101` - """ - - def __init__(self): - super().__init__() - - self.zeros = {"o", "oh", "zero"} - self.ones = { - name: i - for i, name in enumerate( - [ - "one", - "two", - "three", - "four", - "five", - "six", - "seven", - "eight", - "nine", - "ten", - "eleven", - "twelve", - "thirteen", - "fourteen", - "fifteen", - "sixteen", - "seventeen", - "eighteen", - "nineteen", - ], - start=1, - ) - } - self.ones_plural = { - "sixes" if name == "six" else name + "s": (value, "s") - for name, value in self.ones.items() - } - self.ones_ordinal = { - "zeroth": (0, "th"), - "first": (1, "st"), - "second": (2, "nd"), - "third": (3, "rd"), - "fifth": (5, "th"), - "twelfth": (12, "th"), - **{ - name + ("h" if name.endswith("t") else "th"): (value, "th") - for name, value in self.ones.items() - if value > 3 and value != 5 and value != 12 - }, - } - self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} - - self.tens = { - "twenty": 20, - "thirty": 30, - "forty": 40, - "fifty": 50, - "sixty": 60, - "seventy": 70, - "eighty": 80, - "ninety": 90, - } - self.tens_plural = { - name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() - } - self.tens_ordinal = { - name.replace("y", "ieth"): (value, "th") - for name, value in self.tens.items() - } - self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} - - self.multipliers = { - "hundred": 100, - "thousand": 1_000, - "million": 1_000_000, - "billion": 1_000_000_000, - "trillion": 1_000_000_000_000, - "quadrillion": 1_000_000_000_000_000, - "quintillion": 1_000_000_000_000_000_000, - "sextillion": 1_000_000_000_000_000_000_000, - "septillion": 1_000_000_000_000_000_000_000_000, - "octillion": 1_000_000_000_000_000_000_000_000_000, - "nonillion": 1_000_000_000_000_000_000_000_000_000_000, - "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, - } - self.multipliers_plural = { - name + "s": (value, "s") for name, value in self.multipliers.items() - } - self.multipliers_ordinal = { - name + "th": (value, "th") for name, value in self.multipliers.items() - } - self.multipliers_suffixed = { - **self.multipliers_plural, - **self.multipliers_ordinal, - } - self.decimals = {*self.ones, *self.tens, *self.zeros} - - self.preceding_prefixers = { - "minus": "-", - "negative": "-", - "plus": "+", - "positive": "+", - } - self.following_prefixers = { - "pound": "£", - "pounds": "£", - "euro": "€", - "euros": "€", - "dollar": "$", - "dollars": "$", - "cent": "¢", - "cents": "¢", - } - self.prefixes = set( - list(self.preceding_prefixers.values()) - + list(self.following_prefixers.values()) - ) - self.suffixers = { - "per": {"cent": "%"}, - "percent": "%", - } - self.specials = {"and", "double", "triple", "point"} - - self.words = set( - [ - key - for mapping in [ - self.zeros, - self.ones, - self.ones_suffixed, - self.tens, - self.tens_suffixed, - self.multipliers, - self.multipliers_suffixed, - self.preceding_prefixers, - self.following_prefixers, - self.suffixers, - self.specials, - ] - for key in mapping - ] - ) - self.literal_words = {"one", "ones"} - - def process_words(self, words: List[str]) -> Iterator[str]: - prefix: Optional[str] = None - value: Optional[Union[str, int]] = None - skip = False - - def to_fraction(s: str): - try: - return Fraction(s) - except ValueError: - return None - - def output(result: Union[str, int]): - nonlocal prefix, value - result = str(result) - if prefix is not None: - result = prefix + result - value = None - prefix = None - return result - - if len(words) == 0: - return - - for prev, current, next in windowed([None] + words + [None], 3): - if skip: - skip = False - continue - - next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) - has_prefix = current[0] in self.prefixes - current_without_prefix = current[1:] if has_prefix else current - if re.match(r"^\d+(\.\d+)?$", current_without_prefix): - # arabic numbers (potentially with signs and fractions) - f = to_fraction(current_without_prefix) - assert f is not None - if value is not None: - if isinstance(value, str) and value.endswith("."): - # concatenate decimals / ip address components - value = str(value) + str(current) - continue - else: - yield output(value) - - prefix = current[0] if has_prefix else prefix - if f.denominator == 1: - value = f.numerator # store integers as int - else: - value = current_without_prefix - elif current not in self.words: - # non-numeric words - if value is not None: - yield output(value) - yield output(current) - elif current in self.zeros: - value = str(value or "") + "0" - elif current in self.ones: - ones = self.ones[current] - - if value is None: - value = ones - elif isinstance(value, str) or prev in self.ones: - if ( - prev in self.tens and ones < 10 - ): # replace the last zero with the digit - assert value[-1] == "0" - value = value[:-1] + str(ones) - else: - value = str(value) + str(ones) - elif ones < 10: - if value % 10 == 0: - value += ones - else: - value = str(value) + str(ones) - else: # eleven to nineteen - if value % 100 == 0: - value += ones - else: - value = str(value) + str(ones) - elif current in self.ones_suffixed: - # ordinal or cardinal; yield the number right away - ones, suffix = self.ones_suffixed[current] - if value is None: - yield output(str(ones) + suffix) - elif isinstance(value, str) or prev in self.ones: - if prev in self.tens and ones < 10: - assert value[-1] == "0" - yield output(value[:-1] + str(ones) + suffix) - else: - yield output(str(value) + str(ones) + suffix) - elif ones < 10: - if value % 10 == 0: - yield output(str(value + ones) + suffix) - else: - yield output(str(value) + str(ones) + suffix) - else: # eleven to nineteen - if value % 100 == 0: - yield output(str(value + ones) + suffix) - else: - yield output(str(value) + str(ones) + suffix) - value = None - elif current in self.tens: - tens = self.tens[current] - if value is None: - value = tens - elif isinstance(value, str): - value = str(value) + str(tens) - else: - if value % 100 == 0: - value += tens - else: - value = str(value) + str(tens) - elif current in self.tens_suffixed: - # ordinal or cardinal; yield the number right away - tens, suffix = self.tens_suffixed[current] - if value is None: - yield output(str(tens) + suffix) - elif isinstance(value, str): - yield output(str(value) + str(tens) + suffix) - else: - if value % 100 == 0: - yield output(str(value + tens) + suffix) - else: - yield output(str(value) + str(tens) + suffix) - elif current in self.multipliers: - multiplier = self.multipliers[current] - if value is None: - value = multiplier - elif isinstance(value, str) or value == 0: - f = to_fraction(value) - p = f * multiplier if f is not None else None - if f is not None and p.denominator == 1: - value = p.numerator - else: - yield output(value) - value = multiplier - else: - before = value // 1000 * 1000 - residual = value % 1000 - value = before + residual * multiplier - elif current in self.multipliers_suffixed: - multiplier, suffix = self.multipliers_suffixed[current] - if value is None: - yield output(str(multiplier) + suffix) - elif isinstance(value, str): - f = to_fraction(value) - p = f * multiplier if f is not None else None - if f is not None and p.denominator == 1: - yield output(str(p.numerator) + suffix) - else: - yield output(value) - yield output(str(multiplier) + suffix) - else: # int - before = value // 1000 * 1000 - residual = value % 1000 - value = before + residual * multiplier - yield output(str(value) + suffix) - value = None - elif current in self.preceding_prefixers: - # apply prefix (positive, minus, etc.) if it precedes a number - if value is not None: - yield output(value) - - if next in self.words or next_is_numeric: - prefix = self.preceding_prefixers[current] - else: - yield output(current) - elif current in self.following_prefixers: - # apply prefix (dollars, cents, etc.) only after a number - if value is not None: - prefix = self.following_prefixers[current] - yield output(value) - else: - yield output(current) - elif current in self.suffixers: - # apply suffix symbols (percent -> '%') - if value is not None: - suffix = self.suffixers[current] - if isinstance(suffix, dict): - if next in suffix: - yield output(str(value) + suffix[next]) - skip = True - else: - yield output(value) - yield output(current) - else: - yield output(str(value) + suffix) - else: - yield output(current) - elif current in self.specials: - if next not in self.words and not next_is_numeric: - # apply special handling only if the next word can be numeric - if value is not None: - yield output(value) - yield output(current) - elif current == "and": - # ignore "and" after hundreds, thousands, etc. - if prev not in self.multipliers: - if value is not None: - yield output(value) - yield output(current) - elif current == "double" or current == "triple": - if next in self.ones or next in self.zeros: - repeats = 2 if current == "double" else 3 - ones = self.ones.get(next, 0) - value = str(value or "") + str(ones) * repeats - skip = True - else: - if value is not None: - yield output(value) - yield output(current) - elif current == "point": - if next in self.decimals or next_is_numeric: - value = str(value or "") + "." - else: - # should all have been covered at this point - raise ValueError(f"Unexpected token: {current}") - else: - # all should have been covered at this point - raise ValueError(f"Unexpected token: {current}") - - if value is not None: - yield output(value) - - def preprocess(self, s: str): - # replace " and a half" with " point five" - results = [] - - segments = re.split(r"\band\s+a\s+half\b", s) - for i, segment in enumerate(segments): - if len(segment.strip()) == 0: - continue - if i == len(segments) - 1: - results.append(segment) - else: - results.append(segment) - last_word = segment.rsplit(maxsplit=2)[-1] - if last_word in self.decimals or last_word in self.multipliers: - results.append("point five") - else: - results.append("and a half") - - s = " ".join(results) - - # put a space at number/letter boundary - s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) - s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) - - # but remove spaces which could be a suffix - s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) - - return s - - def postprocess(self, s: str): - def combine_cents(m: Match): - try: - currency = m.group(1) - integer = m.group(2) - cents = int(m.group(3)) - return f"{currency}{integer}.{cents:02d}" - except ValueError: - return m.string - - def extract_cents(m: Match): - try: - return f"¢{int(m.group(1))}" - except ValueError: - return m.string - - # apply currency postprocessing; "$2 and ¢7" -> "$2.07" - s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) - s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) - - # write "one(s)" instead of "1(s)", just for the readability - s = re.sub(r"\b1(s?)\b", r"one\1", s) - - return s - - def __call__(self, s: str): - s = self.preprocess(s) - s = " ".join(word for word in self.process_words(s.split()) if word is not None) - s = self.postprocess(s) - - return s - - -class EnglishSpellingNormalizer: - """ - Applies British-American spelling mappings as listed in [1]. - - [1] https://www.tysto.com/uk-us-spelling-list.html - """ - - def __init__(self): - mapping_path = os.path.join(os.path.dirname(__file__), "english.json") - self.mapping = json.load(open(mapping_path)) - - def __call__(self, s: str): - return " ".join(self.mapping.get(word, word) for word in s.split()) - - -class EnglishTextNormalizer: - def __init__(self): - self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" - self.replacers = { - # common contractions - r"\bwon't\b": "will not", - r"\bcan't\b": "can not", - r"\blet's\b": "let us", - r"\bain't\b": "aint", - r"\by'all\b": "you all", - r"\bwanna\b": "want to", - r"\bgotta\b": "got to", - r"\bgonna\b": "going to", - r"\bi'ma\b": "i am going to", - r"\bimma\b": "i am going to", - r"\bwoulda\b": "would have", - r"\bcoulda\b": "could have", - r"\bshoulda\b": "should have", - r"\bma'am\b": "madam", - # contractions in titles/prefixes - r"\bmr\b": "mister ", - r"\bmrs\b": "missus ", - r"\bst\b": "saint ", - r"\bdr\b": "doctor ", - r"\bprof\b": "professor ", - r"\bcapt\b": "captain ", - r"\bgov\b": "governor ", - r"\bald\b": "alderman ", - r"\bgen\b": "general ", - r"\bsen\b": "senator ", - r"\brep\b": "representative ", - r"\bpres\b": "president ", - r"\brev\b": "reverend ", - r"\bhon\b": "honorable ", - r"\basst\b": "assistant ", - r"\bassoc\b": "associate ", - r"\blt\b": "lieutenant ", - r"\bcol\b": "colonel ", - r"\bjr\b": "junior ", - r"\bsr\b": "senior ", - r"\besq\b": "esquire ", - # prefect tenses, ideally it should be any past participles, but it's harder.. - r"'d been\b": " had been", - r"'s been\b": " has been", - r"'d gone\b": " had gone", - r"'s gone\b": " has gone", - r"'d done\b": " had done", # "'s done" is ambiguous - r"'s got\b": " has got", - # general contractions - r"n't\b": " not", - r"'re\b": " are", - r"'s\b": " is", - r"'d\b": " would", - r"'ll\b": " will", - r"'t\b": " not", - r"'ve\b": " have", - r"'m\b": " am", - } - self.standardize_numbers = EnglishNumberNormalizer() - self.standardize_spellings = EnglishSpellingNormalizer() - - def __call__(self, s: str): - s = s.lower() - - s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets - s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis - s = re.sub(self.ignore_patterns, "", s) - s = re.sub( - r"\s+'", "'", s - ) # standardize when there's a space before an apostrophe - - for pattern, replacement in self.replacers.items(): - s = re.sub(pattern, replacement, s) - - s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits - s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers - s = remove_symbols_and_diacritics( - s, keep=".%$¢€£" - ) # keep some symbols for numerics - - s = self.standardize_numbers(s) - s = self.standardize_spellings(s) - - # now remove prefix/suffix symbols that are not preceded/followed by numbers - s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) - s = re.sub(r"([^0-9])%", r"\1 ", s) - - s = re.sub( - r"\s+", " ", s - ) # replace any successive whitespace characters with a space - - return s diff --git a/modules/whisper_extractor/tokenizer.py b/modules/whisper_extractor/tokenizer.py deleted file mode 100644 index ba851f686d9a70fb924e480c20678ef978a5482b..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/tokenizer.py +++ /dev/null @@ -1,370 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -import os -from dataclasses import dataclass -from functools import lru_cache -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch -from transformers import GPT2TokenizerFast - -LANGUAGES = { - "en": "english", - "zh": "chinese", - "de": "german", - "es": "spanish", - "ru": "russian", - "ko": "korean", - "fr": "french", - "ja": "japanese", - "pt": "portuguese", - "tr": "turkish", - "pl": "polish", - "ca": "catalan", - "nl": "dutch", - "ar": "arabic", - "sv": "swedish", - "it": "italian", - "id": "indonesian", - "hi": "hindi", - "fi": "finnish", - "vi": "vietnamese", - "he": "hebrew", - "uk": "ukrainian", - "el": "greek", - "ms": "malay", - "cs": "czech", - "ro": "romanian", - "da": "danish", - "hu": "hungarian", - "ta": "tamil", - "no": "norwegian", - "th": "thai", - "ur": "urdu", - "hr": "croatian", - "bg": "bulgarian", - "lt": "lithuanian", - "la": "latin", - "mi": "maori", - "ml": "malayalam", - "cy": "welsh", - "sk": "slovak", - "te": "telugu", - "fa": "persian", - "lv": "latvian", - "bn": "bengali", - "sr": "serbian", - "az": "azerbaijani", - "sl": "slovenian", - "kn": "kannada", - "et": "estonian", - "mk": "macedonian", - "br": "breton", - "eu": "basque", - "is": "icelandic", - "hy": "armenian", - "ne": "nepali", - "mn": "mongolian", - "bs": "bosnian", - "kk": "kazakh", - "sq": "albanian", - "sw": "swahili", - "gl": "galician", - "mr": "marathi", - "pa": "punjabi", - "si": "sinhala", - "km": "khmer", - "sn": "shona", - "yo": "yoruba", - "so": "somali", - "af": "afrikaans", - "oc": "occitan", - "ka": "georgian", - "be": "belarusian", - "tg": "tajik", - "sd": "sindhi", - "gu": "gujarati", - "am": "amharic", - "yi": "yiddish", - "lo": "lao", - "uz": "uzbek", - "fo": "faroese", - "ht": "haitian creole", - "ps": "pashto", - "tk": "turkmen", - "nn": "nynorsk", - "mt": "maltese", - "sa": "sanskrit", - "lb": "luxembourgish", - "my": "myanmar", - "bo": "tibetan", - "tl": "tagalog", - "mg": "malagasy", - "as": "assamese", - "tt": "tatar", - "haw": "hawaiian", - "ln": "lingala", - "ha": "hausa", - "ba": "bashkir", - "jw": "javanese", - "su": "sundanese", -} - -# language code lookup by name, with a few language aliases -TO_LANGUAGE_CODE = { - **{language: code for code, language in LANGUAGES.items()}, - "burmese": "my", - "valencian": "ca", - "flemish": "nl", - "haitian": "ht", - "letzeburgesch": "lb", - "pushto": "ps", - "panjabi": "pa", - "moldavian": "ro", - "moldovan": "ro", - "sinhalese": "si", - "castilian": "es", -} - - -@dataclass(frozen=True) -class Tokenizer: - """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" - - tokenizer: "GPT2TokenizerFast" - language: Optional[str] - sot_sequence: Tuple[int] - - def encode(self, text, **kwargs): - return self.tokenizer.encode(text, **kwargs) - - def decode( - self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs - ): - return self.tokenizer.decode(token_ids, **kwargs) - - def decode_with_timestamps(self, tokens) -> str: - """ - Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. - This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". - """ - outputs = [[]] - for token in tokens: - if token >= self.timestamp_begin: - timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" - outputs.append(timestamp) - outputs.append([]) - else: - outputs[-1].append(token) - outputs = [ - s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs - ] - return "".join(outputs) - - @property - @lru_cache() - def eot(self) -> int: - return self.tokenizer.eos_token_id - - @property - @lru_cache() - def sot(self) -> int: - return self._get_single_token_id("<|startoftranscript|>") - - @property - @lru_cache() - def sot_lm(self) -> int: - return self._get_single_token_id("<|startoflm|>") - - @property - @lru_cache() - def sot_prev(self) -> int: - return self._get_single_token_id("<|startofprev|>") - - @property - @lru_cache() - def no_speech(self) -> int: - return self._get_single_token_id("<|nospeech|>") - - @property - @lru_cache() - def no_timestamps(self) -> int: - return self._get_single_token_id("<|notimestamps|>") - - @property - @lru_cache() - def timestamp_begin(self) -> int: - return self.tokenizer.all_special_ids[-1] + 1 - - @property - @lru_cache() - def language_token(self) -> int: - """Returns the token id corresponding to the value of the `language` field""" - if self.language is None: - raise ValueError(f"This tokenizer does not have language token configured") - - additional_tokens = dict( - zip( - self.tokenizer.additional_special_tokens, - self.tokenizer.additional_special_tokens_ids, - ) - ) - candidate = f"<|{self.language}|>" - if candidate in additional_tokens: - return additional_tokens[candidate] - - raise KeyError(f"Language {self.language} not found in tokenizer.") - - @property - @lru_cache() - def all_language_tokens(self) -> Tuple[int]: - result = [] - for token, token_id in zip( - self.tokenizer.additional_special_tokens, - self.tokenizer.additional_special_tokens_ids, - ): - if token.strip("<|>") in LANGUAGES: - result.append(token_id) - return tuple(result) - - @property - @lru_cache() - def all_language_codes(self) -> Tuple[str]: - return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) - - @property - @lru_cache() - def sot_sequence_including_notimestamps(self) -> Tuple[int]: - return tuple(list(self.sot_sequence) + [self.no_timestamps]) - - @property - @lru_cache() - def non_speech_tokens(self) -> Tuple[int]: - """ - Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech - annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. - - - ♪♪♪ - - ( SPEAKING FOREIGN LANGUAGE ) - - [DAVID] Hey there, - - keeping basic punctuations like commas, periods, question marks, exclamation points, etc. - """ - symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') - symbols += ( - "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() - ) - - # symbols that may be a single token or multiple tokens depending on the tokenizer. - # In case they're multiple tokens, suppress the first token, which is safe because: - # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress - # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. - miscellaneous = set("♩♪♫♬♭♮♯") - assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) - - # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word - result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} - for symbol in symbols + list(miscellaneous): - for tokens in [ - self.tokenizer.encode(symbol), - self.tokenizer.encode(" " + symbol), - ]: - if len(tokens) == 1 or symbol in miscellaneous: - result.add(tokens[0]) - - return tuple(sorted(result)) - - def _get_single_token_id(self, text) -> int: - tokens = self.tokenizer.encode(text) - assert len(tokens) == 1, f"{text} is not encoded as a single token" - return tokens[0] - - -@lru_cache(maxsize=None) -def build_tokenizer(name: str = "gpt2"): - os.environ["TOKENIZERS_PARALLELISM"] = "false" - path = os.path.join(os.path.dirname(__file__), "assets", name) - tokenizer = GPT2TokenizerFast.from_pretrained(path) - - specials = [ - "<|startoftranscript|>", - *[f"<|{lang}|>" for lang in LANGUAGES.keys()], - "<|translate|>", - "<|transcribe|>", - "<|startoflm|>", - "<|startofprev|>", - "<|nospeech|>", - "<|notimestamps|>", - ] - - tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) - return tokenizer - - -@lru_cache(maxsize=None) -def get_tokenizer( - multilingual: bool, - *, - task: Optional[str] = None, # Literal["transcribe", "translate", None] - language: Optional[str] = None, -) -> Tokenizer: - if language is not None: - language = language.lower() - if language not in LANGUAGES: - if language in TO_LANGUAGE_CODE: - language = TO_LANGUAGE_CODE[language] - else: - raise ValueError(f"Unsupported language: {language}") - - if multilingual: - tokenizer_name = "multilingual" - task = task or "transcribe" - language = language or "en" - else: - tokenizer_name = "gpt2" - task = None - language = None - - tokenizer = build_tokenizer(name=tokenizer_name) - all_special_ids: List[int] = tokenizer.all_special_ids - sot: int = all_special_ids[1] - translate: int = all_special_ids[-6] - transcribe: int = all_special_ids[-5] - - langs = tuple(LANGUAGES.keys()) - sot_sequence = [sot] - if language is not None: - sot_sequence.append(sot + 1 + langs.index(language)) - if task is not None: - sot_sequence.append(transcribe if task == "transcribe" else translate) - - return Tokenizer( - tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence) - ) diff --git a/modules/whisper_extractor/transcribe.py b/modules/whisper_extractor/transcribe.py deleted file mode 100644 index b00e909bdb5e0abdec24a80b09ce45a198a72d6c..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/transcribe.py +++ /dev/null @@ -1,524 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -import argparse -import os -import sys -import warnings -from typing import List, Optional, Tuple, Union, TYPE_CHECKING - -import numpy as np -import torch -import tqdm - -from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram -from .decoding import DecodingOptions, DecodingResult -from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer -from .utils import ( - exact_div, - format_timestamp, - optional_int, - optional_float, - str2bool, - write_txt, - write_vtt, - write_srt, -) - -if TYPE_CHECKING: - from .model import Whisper - - -def transcribe( - model: "Whisper", - audio: Union[str, np.ndarray, torch.Tensor], - *, - verbose: Optional[bool] = None, - temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), - compression_ratio_threshold: Optional[float] = 2.4, - logprob_threshold: Optional[float] = -1.0, - no_speech_threshold: Optional[float] = 0.6, - condition_on_previous_text: bool = True, - **decode_options, -): - """ - Transcribe an audio file using Whisper - - Parameters - ---------- - model: Whisper - The Whisper model instance - - audio: Union[str, np.ndarray, torch.Tensor] - The path to the audio file to open, or the audio waveform - - verbose: bool - Whether to display the text being decoded to the console. If True, displays all the details, - If False, displays minimal details. If None, does not display anything - - temperature: Union[float, Tuple[float, ...]] - Temperature for sampling. It can be a tuple of temperatures, which will be successively used - upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. - - compression_ratio_threshold: float - If the gzip compression ratio is above this value, treat as failed - - logprob_threshold: float - If the average log probability over sampled tokens is below this value, treat as failed - - no_speech_threshold: float - If the no_speech probability is higher than this value AND the average log probability - over sampled tokens is below `logprob_threshold`, consider the segment as silent - - condition_on_previous_text: bool - if True, the previous output of the model is provided as a prompt for the next window; - disabling may make the text inconsistent across windows, but the model becomes less prone to - getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. - - decode_options: dict - Keyword arguments to construct `DecodingOptions` instances - - Returns - ------- - A dictionary containing the resulting text ("text") and segment-level details ("segments"), and - the spoken language ("language"), which is detected when `decode_options["language"]` is None. - """ - dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 - if model.device == torch.device("cpu"): - if torch.cuda.is_available(): - warnings.warn("Performing inference on CPU when CUDA is available") - if dtype == torch.float16: - warnings.warn("FP16 is not supported on CPU; using FP32 instead") - dtype = torch.float32 - - if dtype == torch.float32: - decode_options["fp16"] = False - - mel = log_mel_spectrogram(audio) - - if decode_options.get("language", None) is None: - if not model.is_multilingual: - decode_options["language"] = "en" - else: - if verbose: - print( - "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" - ) - segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) - _, probs = model.detect_language(segment) - decode_options["language"] = max(probs, key=probs.get) - if verbose is not None: - print( - f"Detected language: {LANGUAGES[decode_options['language']].title()}" - ) - - language = decode_options["language"] - task = decode_options.get("task", "transcribe") - tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) - - def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: - temperatures = ( - [temperature] if isinstance(temperature, (int, float)) else temperature - ) - decode_result = None - - for t in temperatures: - kwargs = {**decode_options} - if t > 0: - # disable beam_size and patience when t > 0 - kwargs.pop("beam_size", None) - kwargs.pop("patience", None) - else: - # disable best_of when t == 0 - kwargs.pop("best_of", None) - - options = DecodingOptions(**kwargs, temperature=t) - decode_result = model.decode(segment, options) - - needs_fallback = False - if ( - compression_ratio_threshold is not None - and decode_result.compression_ratio > compression_ratio_threshold - ): - needs_fallback = True # too repetitive - if ( - logprob_threshold is not None - and decode_result.avg_logprob < logprob_threshold - ): - needs_fallback = True # average log probability is too low - - if not needs_fallback: - break - - return decode_result - - seek = 0 - input_stride = exact_div( - N_FRAMES, model.dims.n_audio_ctx - ) # mel frames per output token: 2 - time_precision = ( - input_stride * HOP_LENGTH / SAMPLE_RATE - ) # time per output token: 0.02 (seconds) - all_tokens = [] - all_segments = [] - prompt_reset_since = 0 - - initial_prompt = decode_options.pop("initial_prompt", None) or [] - if initial_prompt: - initial_prompt = tokenizer.encode(" " + initial_prompt.strip()) - all_tokens.extend(initial_prompt) - - def add_segment( - *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult - ): - text = tokenizer.decode( - [token for token in text_tokens if token < tokenizer.eot] - ) - if len(text.strip()) == 0: # skip empty text output - return - - all_segments.append( - { - "id": len(all_segments), - "seek": seek, - "start": start, - "end": end, - "text": text, - "tokens": text_tokens.tolist(), - "temperature": result.temperature, - "avg_logprob": result.avg_logprob, - "compression_ratio": result.compression_ratio, - "no_speech_prob": result.no_speech_prob, - } - ) - if verbose: - line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}\n" - # compared to just `print(line)`, this replaces any character not representable using - # the system default encoding with an '?', avoiding UnicodeEncodeError. - sys.stdout.buffer.write( - line.encode(sys.getdefaultencoding(), errors="replace") - ) - sys.stdout.flush() - - # show the progress bar when verbose is False (otherwise the transcribed text will be printed) - num_frames = mel.shape[-1] - previous_seek_value = seek - - with tqdm.tqdm( - total=num_frames, unit="frames", disable=verbose is not False - ) as pbar: - while seek < num_frames: - timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) - segment = pad_or_trim(mel[:, seek:], N_FRAMES).to(model.device).to(dtype) - segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE - - decode_options["prompt"] = all_tokens[prompt_reset_since:] - result: DecodingResult = decode_with_fallback(segment) - tokens = torch.tensor(result.tokens) - - if no_speech_threshold is not None: - # no voice activity check - should_skip = result.no_speech_prob > no_speech_threshold - if ( - logprob_threshold is not None - and result.avg_logprob > logprob_threshold - ): - # don't skip if the logprob is high enough, despite the no_speech_prob - should_skip = False - - if should_skip: - seek += segment.shape[ - -1 - ] # fast-forward to the next segment boundary - continue - - timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) - consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[ - 0 - ].add_(1) - if ( - len(consecutive) > 0 - ): # if the output contains two consecutive timestamp tokens - last_slice = 0 - for current_slice in consecutive: - sliced_tokens = tokens[last_slice:current_slice] - start_timestamp_position = ( - sliced_tokens[0].item() - tokenizer.timestamp_begin - ) - end_timestamp_position = ( - sliced_tokens[-1].item() - tokenizer.timestamp_begin - ) - add_segment( - start=timestamp_offset - + start_timestamp_position * time_precision, - end=timestamp_offset + end_timestamp_position * time_precision, - text_tokens=sliced_tokens[1:-1], - result=result, - ) - last_slice = current_slice - last_timestamp_position = ( - tokens[last_slice - 1].item() - tokenizer.timestamp_begin - ) - seek += last_timestamp_position * input_stride - all_tokens.extend(tokens[: last_slice + 1].tolist()) - else: - duration = segment_duration - timestamps = tokens[timestamp_tokens.nonzero().flatten()] - if ( - len(timestamps) > 0 - and timestamps[-1].item() != tokenizer.timestamp_begin - ): - # no consecutive timestamps but it has a timestamp; use the last one. - # single timestamp at the end means no speech after the last timestamp. - last_timestamp_position = ( - timestamps[-1].item() - tokenizer.timestamp_begin - ) - duration = last_timestamp_position * time_precision - - add_segment( - start=timestamp_offset, - end=timestamp_offset + duration, - text_tokens=tokens, - result=result, - ) - - seek += segment.shape[-1] - all_tokens.extend(tokens.tolist()) - - if not condition_on_previous_text or result.temperature > 0.5: - # do not feed the prompt tokens if a high temperature was used - prompt_reset_since = len(all_tokens) - - # update progress bar - pbar.update(min(num_frames, seek) - previous_seek_value) - previous_seek_value = seek - - return dict( - text=tokenizer.decode(all_tokens[len(initial_prompt) :]), - segments=all_segments, - language=language, - ) - - -def cli(): - from . import available_models - - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - parser.add_argument( - "audio", nargs="+", type=str, help="audio file(s) to transcribe" - ) - parser.add_argument( - "--model", - default="small", - choices=available_models(), - help="name of the Whisper model to use", - ) - parser.add_argument( - "--model_dir", - type=str, - default=None, - help="the path to save model files; uses ~/.cache/whisper by default", - ) - parser.add_argument( - "--device", - default="cuda" if torch.cuda.is_available() else "cpu", - help="device to use for PyTorch inference", - ) - parser.add_argument( - "--output_dir", - "-o", - type=str, - default=".", - help="directory to save the outputs", - ) - parser.add_argument( - "--verbose", - type=str2bool, - default=True, - help="whether to print out the progress and debug messages", - ) - - parser.add_argument( - "--task", - type=str, - default="transcribe", - choices=["transcribe", "translate"], - help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')", - ) - parser.add_argument( - "--language", - type=str, - default=None, - choices=sorted(LANGUAGES.keys()) - + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), - help="language spoken in the audio, specify None to perform language detection", - ) - - parser.add_argument( - "--temperature", type=float, default=0, help="temperature to use for sampling" - ) - parser.add_argument( - "--best_of", - type=optional_int, - default=5, - help="number of candidates when sampling with non-zero temperature", - ) - parser.add_argument( - "--beam_size", - type=optional_int, - default=5, - help="number of beams in beam search, only applicable when temperature is zero", - ) - parser.add_argument( - "--patience", - type=float, - default=None, - help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search", - ) - parser.add_argument( - "--length_penalty", - type=float, - default=None, - help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default", - ) - - parser.add_argument( - "--suppress_tokens", - type=str, - default="-1", - help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations", - ) - parser.add_argument( - "--initial_prompt", - type=str, - default=None, - help="optional text to provide as a prompt for the first window.", - ) - parser.add_argument( - "--condition_on_previous_text", - type=str2bool, - default=True, - help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop", - ) - parser.add_argument( - "--fp16", - type=str2bool, - default=True, - help="whether to perform inference in fp16; True by default", - ) - - parser.add_argument( - "--temperature_increment_on_fallback", - type=optional_float, - default=0.2, - help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below", - ) - parser.add_argument( - "--compression_ratio_threshold", - type=optional_float, - default=2.4, - help="if the gzip compression ratio is higher than this value, treat the decoding as failed", - ) - parser.add_argument( - "--logprob_threshold", - type=optional_float, - default=-1.0, - help="if the average log probability is lower than this value, treat the decoding as failed", - ) - parser.add_argument( - "--no_speech_threshold", - type=optional_float, - default=0.6, - help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence", - ) - parser.add_argument( - "--threads", - type=optional_int, - default=0, - help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS", - ) - - args = parser.parse_args().__dict__ - model_name: str = args.pop("model") - model_dir: str = args.pop("model_dir") - output_dir: str = args.pop("output_dir") - device: str = args.pop("device") - os.makedirs(output_dir, exist_ok=True) - - if model_name.endswith(".en") and args["language"] not in {"en", "English"}: - if args["language"] is not None: - warnings.warn( - f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead." - ) - args["language"] = "en" - - temperature = args.pop("temperature") - temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") - if temperature_increment_on_fallback is not None: - temperature = tuple( - np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback) - ) - else: - temperature = [temperature] - - threads = args.pop("threads") - if threads > 0: - torch.set_num_threads(threads) - - from . import load_model - - model = load_model(model_name, device=device, download_root=model_dir) - - for audio_path in args.pop("audio"): - result = transcribe(model, audio_path, temperature=temperature, **args) - - audio_basename = os.path.basename(audio_path) - - # save TXT - with open( - os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8" - ) as txt: - write_txt(result["segments"], file=txt) - - # save VTT - with open( - os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8" - ) as vtt: - write_vtt(result["segments"], file=vtt) - - # save SRT - with open( - os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8" - ) as srt: - write_srt(result["segments"], file=srt) - - -if __name__ == "__main__": - cli() diff --git a/modules/whisper_extractor/utils.py b/modules/whisper_extractor/utils.py deleted file mode 100644 index 760e5fffb7cfe4e30988ec9b5b943f90989b9a9f..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/utils.py +++ /dev/null @@ -1,120 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This module is modified from [Whisper](https://github.com/openai/whisper.git). - -# ## Citations - -# ```bibtex -# @inproceedings{openai-whisper, -# author = {Alec Radford and -# Jong Wook Kim and -# Tao Xu and -# Greg Brockman and -# Christine McLeavey and -# Ilya Sutskever}, -# title = {Robust Speech Recognition via Large-Scale Weak Supervision}, -# booktitle = {{ICML}}, -# series = {Proceedings of Machine Learning Research}, -# volume = {202}, -# pages = {28492--28518}, -# publisher = {{PMLR}}, -# year = {2023} -# } -# ``` -# - -import zlib -from typing import Iterator, TextIO - - -def exact_div(x, y): - assert x % y == 0 - return x // y - - -def str2bool(string): - str2val = {"True": True, "False": False} - if string in str2val: - return str2val[string] - else: - raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") - - -def optional_int(string): - return None if string == "None" else int(string) - - -def optional_float(string): - return None if string == "None" else float(string) - - -def compression_ratio(text) -> float: - text_bytes = text.encode("utf-8") - return len(text_bytes) / len(zlib.compress(text_bytes)) - - -def format_timestamp( - seconds: float, always_include_hours: bool = False, decimal_marker: str = "." -): - assert seconds >= 0, "non-negative timestamp expected" - milliseconds = round(seconds * 1000.0) - - hours = milliseconds // 3_600_000 - milliseconds -= hours * 3_600_000 - - minutes = milliseconds // 60_000 - milliseconds -= minutes * 60_000 - - seconds = milliseconds // 1_000 - milliseconds -= seconds * 1_000 - - hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" - return ( - f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" - ) - - -def write_txt(transcript: Iterator[dict], file: TextIO): - for segment in transcript: - print(segment["text"].strip(), file=file, flush=True) - - -def write_vtt(transcript: Iterator[dict], file: TextIO): - print("WEBVTT\n", file=file) - for segment in transcript: - print( - f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) - - -def write_srt(transcript: Iterator[dict], file: TextIO): - """ - Write a transcript to a file in SRT format. - - Example usage: - from pathlib import Path - from whisper.utils import write_srt - - result = transcribe(model, audio_path, temperature=temperature, **args) - - # save SRT - audio_basename = Path(audio_path).stem - with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: - write_srt(result["segments"], file=srt) - """ - for i, segment in enumerate(transcript, start=1): - # write srt lines - print( - f"{i}\n" - f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " - f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" - f"{segment['text'].strip().replace('-->', '->')}\n", - file=file, - flush=True, - ) diff --git a/modules/whisper_extractor/version.py b/modules/whisper_extractor/version.py deleted file mode 100644 index 91fba4bb5e6621010b41d23ab9db419bdb7aeb1e..0000000000000000000000000000000000000000 --- a/modules/whisper_extractor/version.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -__version__ = "20230117" diff --git a/optimizer/__init__.py b/optimizer/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/optimizer/optimizers.py b/optimizer/optimizers.py deleted file mode 100644 index b47420a7c9cc621c16cf29060e9f5542afad8691..0000000000000000000000000000000000000000 --- a/optimizer/optimizers.py +++ /dev/null @@ -1,780 +0,0 @@ -# This module is modified from https://github.com/Plachtaa/VALL-E-X/blob/3faaf8ccadb154d63b38070caf518ce9309ea0f4/modules/optim.py#L836 - -import logging -import contextlib -import torch -from torch import Tensor -from torch.optim.lr_scheduler import _LRScheduler -from torch.optim import Optimizer -from typing import List, Tuple -from collections import defaultdict - - -class NoamLR(_LRScheduler): - """ - Implements the Noam Learning rate schedule. This corresponds to increasing the learning rate - linearly for the first ``num_warmup`` training steps, and decreasing it thereafter proportionally - to the inverse square root of the step number, scaled by the inverse square root of the - dimensionality of the model. Time will tell if this is just madness or it's actually important. - Parameters - ---------- - num_warmup: ``int``, required. - The number of steps to linearly increase the learning rate. - """ - - def __init__(self, optimizer, num_warmup): - self.num_warmup = num_warmup - self.base_lr = optimizer.param_groups[0]["lr"] - super().__init__(optimizer) - - def get_lr(self): - last_epoch = max(1, self.last_epoch) - scale = min(last_epoch ** (-0.5), last_epoch * self.num_warmup ** (-1.5)) - return [scale * self.base_lr] - -class Eve(Optimizer): - """ - Implements Eve algorithm. This is a modified version of AdamW with a special - way of setting the weight-decay / shrinkage-factor, which is designed to make the - rms of the parameters approach a particular target_rms (default: 0.1). This is - for use with networks with 'scaled' versions of modules (see scaling.py), which - will be close to invariant to the absolute scale on the parameter matrix. - - The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. - The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_. - Eve is unpublished so far. - - Arguments: - params (iterable): iterable of parameters to optimize or dicts defining - parameter groups - lr (float, optional): learning rate (default: 1e-3) - betas (Tuple[float, float], optional): coefficients used for computing - running averages of gradient and its square (default: (0.9, 0.999)) - eps (float, optional): term added to the denominator to improve - numerical stability (default: 1e-8) - weight_decay (float, optional): weight decay coefficient (default: 3e-4; - this value means that the weight would decay significantly after - about 3k minibatches. Is not multiplied by learning rate, but - is conditional on RMS-value of parameter being > target_rms. - target_rms (float, optional): target root-mean-square value of - parameters, if they fall below this we will stop applying weight decay. - - - .. _Adam: A Method for Stochastic Optimization: - https://arxiv.org/abs/1412.6980 - .. _Decoupled Weight Decay Regularization: - https://arxiv.org/abs/1711.05101 - .. _On the Convergence of Adam and Beyond: - https://openreview.net/forum?id=ryQu7f-RZ - """ - - def __init__( - self, - params, - lr=1e-3, - betas=(0.9, 0.98), - eps=1e-8, - weight_decay=1e-3, - target_rms=0.1, - ): - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - if not 0 <= weight_decay <= 0.1: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - if not 0 < target_rms <= 10.0: - raise ValueError("Invalid target_rms value: {}".format(target_rms)) - defaults = dict( - lr=lr, - betas=betas, - eps=eps, - weight_decay=weight_decay, - target_rms=target_rms, - ) - super(Eve, self).__init__(params, defaults) - - def __setstate__(self, state): - super(Eve, self).__setstate__(state) - - @torch.no_grad() - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - - # Perform optimization step - grad = p.grad - if grad.is_sparse: - raise RuntimeError("AdamW does not support sparse gradients") - - state = self.state[p] - - # State initialization - if len(state) == 0: - state["step"] = 0 - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like( - p, memory_format=torch.preserve_format - ) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like( - p, memory_format=torch.preserve_format - ) - - exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] - - beta1, beta2 = group["betas"] - - state["step"] += 1 - bias_correction1 = 1 - beta1 ** state["step"] - bias_correction2 = 1 - beta2 ** state["step"] - - # Decay the first and second moment running average coefficient - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - denom = (exp_avg_sq.sqrt() * (bias_correction2**-0.5)).add_( - group["eps"] - ) - - step_size = group["lr"] / bias_correction1 - target_rms = group["target_rms"] - weight_decay = group["weight_decay"] - - if p.numel() > 1: - # avoid applying this weight-decay on "scaling factors" - # (which are scalar). - is_above_target_rms = p.norm() > (target_rms * (p.numel() ** 0.5)) - p.mul_(1 - (weight_decay * is_above_target_rms)) - - p.addcdiv_(exp_avg, denom, value=-step_size) - - # if random.random() < 0.0005: - # step = (exp_avg / denom) * step_size - # logging.info( - # f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}" - # ) - - return loss - -class BatchedOptimizer(Optimizer): - """ - This class adds to class Optimizer the capability to optimize parameters in batches: - it will stack the parameters and their grads for you so the optimizer can work - on tensors with an extra leading dimension. This is intended for speed with GPUs, - as it reduces the number of kernels launched in the optimizer. - - Args: - params: - """ - - def __init__(self, params, defaults): - super(BatchedOptimizer, self).__init__(params, defaults) - - @contextlib.contextmanager - def batched_params(self, param_group, group_params_names): - """ - This function returns (technically, yields) a list of - of tuples (p, state), where - p is a `fake` parameter that is stacked (over axis 0) from real parameters - that share the same shape, and its gradient is also stacked; - `state` is the state corresponding to this batch of parameters - (it will be physically located in the "state" for one of the real - parameters, the last one that has any particular shape and dtype). - - This function is decorated as a context manager so that it can - write parameters back to their "real" locations. - - The idea is, instead of doing: - - for p in group["params"]: - state = self.state[p] - ... - - you can do: - - with self.batched_params(group["params"]) as batches: - for p, state, p_names in batches: - ... - - - Args: - group: a parameter group, which is a list of parameters; should be - one of self.param_groups. - group_params_names: name for each parameter in group, - which is List[str]. - """ - batches = defaultdict( - list - ) # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter - batches_names = defaultdict( - list - ) # `batches` maps from tuple (dtype_as_str,*shape) to list of str - - assert len(param_group) == len(group_params_names) - for p, named_p in zip(param_group, group_params_names): - key = (str(p.dtype), *p.shape) - batches[key].append(p) - batches_names[key].append(named_p) - - batches_names_keys = list(batches_names.keys()) - sorted_idx = sorted( - range(len(batches_names)), key=lambda i: batches_names_keys[i] - ) - batches_names = [batches_names[batches_names_keys[idx]] for idx in sorted_idx] - batches = [batches[batches_names_keys[idx]] for idx in sorted_idx] - - stacked_params_dict = dict() - - # turn batches into a list, in deterministic order. - # tuples will contain tuples of (stacked_param, state, stacked_params_names), - # one for each batch in `batches`. - tuples = [] - - for batch, batch_names in zip(batches, batches_names): - p = batch[0] - # we arbitrarily store the state in the - # state corresponding to the 1st parameter in the - # group. class Optimizer will take care of saving/loading state. - state = self.state[p] - p_stacked = torch.stack(batch) - grad = torch.stack( - [torch.zeros_like(p) if p.grad is None else p.grad for p in batch] - ) - p_stacked.grad = grad - stacked_params_dict[key] = p_stacked - tuples.append((p_stacked, state, batch_names)) - - yield tuples - - for ((stacked_params, _state, _names), batch) in zip(tuples, batches): - for i, p in enumerate(batch): - p.copy_(stacked_params[i]) - -class ScaledAdam(BatchedOptimizer): - """ - Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update - proportional to the norm of that parameter; and also learn the scale of the parameter, - in log space, subject to upper and lower limits (as if we had factored each parameter as - param = underlying_param * log_scale.exp()) - - - Args: - params: The parameters or param_groups to optimize (like other Optimizer subclasses) - lr: The learning rate. We will typically use a learning rate schedule that starts - at 0.03 and decreases over time, i.e. much higher than other common - optimizers. - clipping_scale: (e.g. 2.0) - A scale for gradient-clipping: if specified, the normalized gradients - over the whole model will be clipped to have 2-norm equal to - `clipping_scale` times the median 2-norm over the most recent period - of `clipping_update_period` minibatches. By "normalized gradients", - we mean after multiplying by the rms parameter value for this tensor - [for non-scalars]; this is appropriate because our update is scaled - by this quantity. - betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad. - Must satisfy 0 < beta <= beta2 < 1. - scalar_lr_scale: A scaling factor on the learning rate, that we use to update the - scale of each parameter tensor and scalar parameters of the mode.. - If each parameter were decomposed - as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale - would be a the scaling factor on the learning rate of p_scale. - eps: A general-purpose epsilon to prevent division by zero - param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of - learning the scale on the parameters (we'll constrain the rms of each non-scalar - parameter tensor to be >= this value) - param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of - learning the scale on the parameters (we'll constrain the rms of each non-scalar - parameter tensor to be <= this value) - scalar_max: Maximum absolute value for scalar parameters (applicable if your - model has any parameters with numel() == 1). - size_update_period: The periodicity, in steps, with which we update the size (scale) - of the parameter tensor. This is provided to save a little time - in the update. - clipping_update_period: if clipping_scale is specified, this is the period - """ - - def __init__( - self, - params, - lr=3e-02, - clipping_scale=None, - betas=(0.9, 0.98), - scalar_lr_scale=0.1, - eps=1.0e-08, - param_min_rms=1.0e-05, - param_max_rms=3.0, - scalar_max=10.0, - size_update_period=4, - clipping_update_period=100, - parameters_names=None, - show_dominant_parameters=True, - ): - - assert parameters_names is not None, ( - "Please prepare parameters_names," - "which is a List[List[str]]. Each List[str] is for a group" - "and each str is for a parameter" - ) - defaults = dict( - lr=lr, - clipping_scale=clipping_scale, - betas=betas, - scalar_lr_scale=scalar_lr_scale, - eps=eps, - param_min_rms=param_min_rms, - param_max_rms=param_max_rms, - scalar_max=scalar_max, - size_update_period=size_update_period, - clipping_update_period=clipping_update_period, - ) - - super(ScaledAdam, self).__init__(params, defaults) - assert len(self.param_groups) == len(parameters_names) - self.parameters_names = parameters_names - self.show_dominant_parameters = show_dominant_parameters - - def __setstate__(self, state): - super(ScaledAdam, self).__setstate__(state) - - @torch.no_grad() - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - batch = True - - for group, group_params_names in zip(self.param_groups, self.parameters_names): - - with self.batched_params(group["params"], group_params_names) as batches: - - # batches is list of pairs (stacked_param, state). stacked_param is like - # a regular parameter, and will have a .grad, but the 1st dim corresponds to - # a stacking dim, it is not a real dim. - - if ( - len(batches[0][1]) == 0 - ): - clipping_scale = 1 - else: - clipping_scale = self._get_clipping_scale(group, batches) - - for p, state, _ in batches: - # Perform optimization step. - # grad is not going to be None, we handled that when creating the batches. - grad = p.grad - if grad.is_sparse: - raise RuntimeError( - "ScaledAdam optimizer does not support sparse gradients" - ) - # State initialization - if len(state) == 0: - self._init_state(group, p, state) - - self._step_one_batch(group, p, state, clipping_scale) - - return loss - - def _init_state(self, group: dict, p: Tensor, state: dict): - """ - Initializes state dict for parameter 'p'. Assumes that dim 0 of tensor p - is actually the batch dimension, corresponding to batched-together - parameters of a given shape. - - - Args: - group: Dict to look up configuration values. - p: The parameter that we are initializing the state for - state: Dict from string to whatever state we are initializing - """ - size_update_period = group["size_update_period"] - - state["step"] = 0 - - kwargs = {"device": p.device, "dtype": p.dtype} - - # 'delta' implements conventional momentum. There are - # several different kinds of update going on, so rather than - # compute "exp_avg" like in Adam, we store and decay a - # parameter-change "delta", which combines all forms of - # update. this is equivalent to how it's done in Adam, - # except for the first few steps. - state["delta"] = torch.zeros_like(p, memory_format=torch.preserve_format) - - batch_size = p.shape[0] - numel = p.numel() // batch_size - numel = p.numel() - - if numel > 1: - # "param_rms" just periodically records the scalar root-mean-square value of - # the parameter tensor. - # it has a shape like (batch_size, 1, 1, 1, 1) - param_rms = (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt() - state["param_rms"] = param_rms - - state["scale_exp_avg_sq"] = torch.zeros_like(param_rms) - state["scale_grads"] = torch.zeros( - size_update_period, *param_rms.shape, **kwargs - ) - - # exp_avg_sq is the weighted sum of scaled gradients. as in Adam. - state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format) - - def _get_clipping_scale( - self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]] - ) -> float: - """ - Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients - by this amount before applying the rest of the update. - - Args: - group: the parameter group, an item in self.param_groups - tuples: a list of tuples of (param, state, param_names) - where param is a batched set of parameters, - with a .grad (1st dim is batch dim) - and state is the state-dict where optimization parameters are kept. - param_names is a List[str] while each str is name for a parameter - in batched set of parameters "param". - """ - assert len(tuples) >= 1 - clipping_scale = group["clipping_scale"] - (first_p, first_state, _) = tuples[0] - step = first_state["step"] - if clipping_scale is None or step == 0: - # no clipping. return early on step == 0 because the other - # parameters' state won't have been initialized yet. - return 1.0 - clipping_update_period = group["clipping_update_period"] - - tot_sumsq = torch.tensor(0.0, device=first_p.device) - for (p, state, param_names) in tuples: - grad = p.grad - if grad.is_sparse: - raise RuntimeError( - "ScaledAdam optimizer does not support sparse gradients" - ) - if p.numel() == p.shape[0]: # a batch of scalars - tot_sumsq += (grad**2).sum() # sum() to change shape [1] to [] - else: - tot_sumsq += ((grad * state["param_rms"]) ** 2).sum() - - tot_norm = tot_sumsq.sqrt() - if "model_norms" not in first_state: - first_state["model_norms"] = torch.zeros( - clipping_update_period, device=p.device - ) - first_state["model_norms"][step % clipping_update_period] = tot_norm - - if step % clipping_update_period == 0: - # Print some stats. - # We don't reach here if step == 0 because we would have returned - # above. - sorted_norms = first_state["model_norms"].sort()[0].to("cpu") - quartiles = [] - for n in range(0, 5): - index = min( - clipping_update_period - 1, - (clipping_update_period // 4) * n, - ) - quartiles.append(sorted_norms[index].item()) - - median = quartiles[2] - threshold = clipping_scale * median - first_state["model_norm_threshold"] = threshold - percent_clipped = ( - first_state["num_clipped"] * 100.0 / clipping_update_period - if "num_clipped" in first_state - else 0.0 - ) - first_state["num_clipped"] = 0 - quartiles = " ".join(["%.3e" % x for x in quartiles]) - logging.info( - f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, " - f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}" - ) - - if step < clipping_update_period: - return 1.0 # We have not yet estimated a norm to clip to. - else: - try: - model_norm_threshold = first_state["model_norm_threshold"] - except KeyError: - logging.info( - "Warning: model_norm_threshold not in state: possibly " - "you changed config when restarting, adding clipping_scale option?" - ) - return 1.0 - ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item()) - if ans < 1.0: - first_state["num_clipped"] += 1 - if ans < 0.1: - logging.warn( - f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}" - ) - if self.show_dominant_parameters: - assert p.shape[0] == len(param_names) - self._show_gradient_dominating_parameter(tuples, tot_sumsq) - return ans - - def _show_gradient_dominating_parameter( - self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor - ): - """ - Show information of parameter wihch dominanting tot_sumsq. - - Args: - tuples: a list of tuples of (param, state, param_names) - where param is a batched set of parameters, - with a .grad (1st dim is batch dim) - and state is the state-dict where optimization parameters are kept. - param_names is a List[str] while each str is name for a parameter - in batched set of parameters "param". - tot_sumsq: sumsq of all parameters. Though it's could be calculated - from tuples, we still pass it to save some time. - """ - all_sumsq_orig = {} - for (p, state, batch_param_names) in tuples: - # p is a stacked batch parameters. - batch_grad = p.grad - if p.numel() == p.shape[0]: # a batch of scalars - batch_sumsq_orig = batch_grad**2 - # Dummpy values used by following `zip` statement. - batch_rms_orig = torch.ones(p.shape[0]) - else: - batch_rms_orig = state["param_rms"] - batch_sumsq_orig = ((batch_grad * batch_rms_orig) ** 2).sum( - dim=list(range(1, batch_grad.ndim)) - ) - - for name, sumsq_orig, rms, grad in zip( - batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad - ): - - proportion_orig = sumsq_orig / tot_sumsq - all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad) - - assert torch.isclose( - sum([value[0] for value in all_sumsq_orig.values()]).cpu(), - torch.tensor(1.0), - ) - sorted_by_proportion = { - k: v - for k, v in sorted( - all_sumsq_orig.items(), - key=lambda item: item[1][0], - reverse=True, - ) - } - dominant_param_name = next(iter(sorted_by_proportion)) - ( - dominant_proportion, - dominant_sumsq, - dominant_rms, - dominant_grad, - ) = sorted_by_proportion[dominant_param_name] - logging.info( - f"Parameter Dominanting tot_sumsq {dominant_param_name}" - f" with proportion {dominant_proportion:.2f}," - f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)" - f"={dominant_sumsq:.3e}," - f" grad_sumsq = {(dominant_grad**2).sum():.3e}," - f" orig_rms_sq={(dominant_rms**2).item():.3e}" - ) - - def _step_one_batch( - self, group: dict, p: Tensor, state: dict, clipping_scale: float - ): - """ - Do the step for one parameter, which is actually going to be a batch of - `real` parameters, with dim 0 as the batch dim. - Args: - group: dict to look up configuration values - p: parameter to update (actually multiple parameters stacked together - as a batch) - state: state-dict for p, to look up the optimizer state - """ - lr = group["lr"] - size_update_period = group["size_update_period"] - beta1 = group["betas"][0] - - grad = p.grad - if clipping_scale != 1.0: - grad = grad * clipping_scale - step = state["step"] - delta = state["delta"] - - delta.mul_(beta1) - batch_size = p.shape[0] - numel = p.numel() // batch_size - if numel > 1: - # Update the size/scale of p, and set param_rms - scale_grads = state["scale_grads"] - scale_grads[step % size_update_period] = (p * grad).sum( - dim=list(range(1, p.ndim)), keepdim=True - ) - if step % size_update_period == size_update_period - 1: - param_rms = state["param_rms"] # shape: (batch_size, 1, 1, ..) - param_rms.copy_( - (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt() - ) - if step > 0: - # self._size_update() learns the overall scale on the - # parameter, by shrinking or expanding it. - self._size_update(group, scale_grads, p, state) - - if numel == 1: - # For parameters with 1 element we just use regular Adam. - # Updates delta. - self._step_scalar(group, p, state) - else: - self._step(group, p, state) - - state["step"] = step + 1 - - def _size_update( - self, group: dict, scale_grads: Tensor, p: Tensor, state: dict - ) -> None: - """ - Called only where p.numel() > 1, this updates the scale of the parameter. - If we imagine: p = underlying_param * scale.exp(), and we are doing - gradient descent on underlying param and on scale, this function does the update - on `scale`. - - Args: - group: dict to look up configuration values - scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing - grads w.r.t. the scales. - p: The parameter to update - state: The state-dict of p - """ - - param_rms = state["param_rms"] - beta1, beta2 = group["betas"] - size_lr = group["lr"] * group["scalar_lr_scale"] - param_min_rms = group["param_min_rms"] - param_max_rms = group["param_max_rms"] - eps = group["eps"] - step = state["step"] - batch_size = p.shape[0] - - size_update_period = scale_grads.shape[0] - # correct beta2 for the size update period: we will have - # faster decay at this level. - beta2_corr = beta2**size_update_period - - scale_exp_avg_sq = state["scale_exp_avg_sq"] # shape: (batch_size, 1, 1, ..) - scale_exp_avg_sq.mul_(beta2_corr).add_( - (scale_grads**2).mean(dim=0), # mean over dim `size_update_period` - alpha=1 - beta2_corr, - ) # shape is (batch_size, 1, 1, ...) - - # The 1st time we reach here is when size_step == 1. - size_step = (step + 1) // size_update_period - bias_correction2 = 1 - beta2_corr**size_step - # we don't bother with bias_correction1; this will help prevent divergence - # at the start of training. - - denom = scale_exp_avg_sq.sqrt() + eps - - scale_step = ( - -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom - ) - - is_too_small = param_rms < param_min_rms - is_too_large = param_rms > param_max_rms - - # when the param gets too small, just don't shrink it any further. - scale_step.masked_fill_(is_too_small, 0.0) - # when it gets too large, stop it from getting any larger. - scale_step.masked_fill_(is_too_large, -size_lr * size_update_period) - delta = state["delta"] - # the factor of (1-beta1) relates to momentum. - delta.add_(p * scale_step, alpha=(1 - beta1)) - - def _step(self, group: dict, p: Tensor, state: dict): - """ - This function does the core update of self.step(), in the case where the members of - the batch have more than 1 element. - - Args: - group: A dict which will be used to look up configuration values - p: The parameter to be updated - grad: The grad of p - state: The state-dict corresponding to parameter p - - This function modifies p. - """ - grad = p.grad - lr = group["lr"] - beta1, beta2 = group["betas"] - eps = group["eps"] - param_min_rms = group["param_min_rms"] - step = state["step"] - - exp_avg_sq = state["exp_avg_sq"] - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2)) - - this_step = state["step"] - (state["zero_step"] if "zero_step" in state else 0) - bias_correction2 = 1 - beta2 ** (this_step + 1) - if bias_correction2 < 0.99: - # note: not in-place. - exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2) - - denom = exp_avg_sq.sqrt() - denom += eps - grad = grad / denom - - alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms) - - delta = state["delta"] - delta.add_(grad * alpha) - p.add_(delta) - - def _step_scalar(self, group: dict, p: Tensor, state: dict): - """ - A simplified form of the core update for scalar tensors, where we cannot get a good - estimate of the parameter rms. - """ - beta1, beta2 = group["betas"] - scalar_max = group["scalar_max"] - eps = group["eps"] - lr = group["lr"] * group["scalar_lr_scale"] - grad = p.grad - - exp_avg_sq = state["exp_avg_sq"] # shape: (batch_size,) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) - - # bias_correction2 is like in Adam. Don't bother with bias_correction1; - # slower update at the start will help stability anyway. - bias_correction2 = 1 - beta2 ** (state["step"] + 1) - denom = (exp_avg_sq / bias_correction2).sqrt() + eps - - delta = state["delta"] - delta.add_(grad / denom, alpha=-lr * (1 - beta1)) - p.clamp_(min=-scalar_max, max=scalar_max) - p.add_(delta) - diff --git a/preprocessors/__init__.py b/preprocessors/__init__.py deleted file mode 100644 index 92514cbbafea25f61bcc4f4bbdce13bfa58b5fd8..0000000000000000000000000000000000000000 --- a/preprocessors/__init__.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -For source datasets' standard samples -""" - -from collections import defaultdict -import os -import json - -SPEECH_DATASETS = ["vctk", "vctksample"] - -GOLDEN_TEST_SAMPLES = defaultdict(list) -GOLDEN_TEST_SAMPLES["m4singer"] = [ - "Alto-1_美错_0014", - "Bass-1_十年_0008", - "Soprano-2_同桌的你_0018", - "Tenor-5_爱笑的眼睛_0010", -] -GOLDEN_TEST_SAMPLES["svcc"] = [ - # IDF1 - "IDF1_10030", - "IDF1_10120", - "IDF1_10140", - # IDM1 - "IDM1_10001", - "IDM1_10030", - "IDM1_10120", - # CDF1 - "CDF1_10030", - "CDF1_10120", - "CDF1_10140", - # CDM1 - "CDM1_10001", - "CDM1_10030", - "CDM1_10120", -] -GOLDEN_TEST_SAMPLES["svcceval"] = [ - # SF1 - "SF1_30001", - "SF1_30002", - "SF1_30003", - # SM1 - "SM1_30001", - "SM1_30002", - "SM1_30003", -] -GOLDEN_TEST_SAMPLES["popbutfy"] = [ - "Female1#you_are_my_sunshine_Professional#0", - "Female4#Someone_Like_You_Professional#10", - "Male2#Lemon_Tree_Professional#12", - "Male5#can_you_feel_the_love_tonight_Professional#20", -] -GOLDEN_TEST_SAMPLES["opensinger"] = [ - "Man_0_大鱼_10", - "Man_21_丑八怪_14", - "Woman_39_mojito_22", - "Woman_40_易燃易爆炸_12", -] -GOLDEN_TEST_SAMPLES["nus48e"] = [ - "ADIZ_read#01#0000", - "MCUR_sing#10#0000", - "JLEE_read#08#0001", - "SAMF_sing#18#0001", -] -GOLDEN_TEST_SAMPLES["popcs"] = [ - "明天会更好_0004", - "欧若拉_0005", - "虫儿飞_0006", - "隐形的翅膀_0008", -] -GOLDEN_TEST_SAMPLES["kising"] = [ - "421_0040", - "424_0013", - "431_0026", -] -GOLDEN_TEST_SAMPLES["csd"] = [ - "en_004a_0001", - "en_042b_0006", - "kr_013a_0006", - "kr_045b_0004", -] -GOLDEN_TEST_SAMPLES["opera"] = [ - "fem_01#neg_1#0000", - "fem_12#pos_3#0003", - "male_02#neg_1#0002", - "male_11#pos_2#0001", -] -GOLDEN_TEST_SAMPLES["lijian"] = [ - "058矜持_0000", - "079绒花_0000", - "120遥远的天空底下_0000", -] -GOLDEN_TEST_SAMPLES["cdmusiceval"] = ["陶喆_普通朋友", "蔡琴_给电影人的情书"] - -GOLDEN_TRAIN_SAMPLES = defaultdict(list) - - -def get_golden_samples_indexes( - dataset_name, - dataset_dir=None, - cfg=None, - split=None, - min_samples=5, -): - """ - # Get Standard samples' indexes - """ - if dataset_dir is None: - assert cfg is not None - dataset_dir = os.path.join( - cfg.OUTPUT_PATH, - "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), - dataset_name, - ) - - assert split is not None - utt_file = os.path.join(dataset_dir, "{}.json".format(split)) - with open(utt_file, "r", encoding="utf-8") as f: - samples = json.load(f) - - if "train" in split: - golden_samples = GOLDEN_TRAIN_SAMPLES[dataset_name] - if "test" in split: - golden_samples = GOLDEN_TEST_SAMPLES[dataset_name] - - res = [] - for idx, utt in enumerate(samples): - if utt["Uid"] in golden_samples: - res.append(idx) - - if dataset_name == "cdmusiceval": - if "_".join(utt["Uid"].split("_")[:2]) in golden_samples: - res.append(idx) - - if len(res) == 0: - res = [i for i in range(min_samples)] - - return res - - -def get_specific_singer_indexes(dataset_dir, singer_name, split): - utt_file = os.path.join(dataset_dir, "{}.json".format(split)) - with open(utt_file, "r", encoding="utf-8") as f: - samples = json.load(f) - - res = [] - for idx, utt in enumerate(samples): - if utt["Singer"] == singer_name: - res.append(idx) - - assert len(res) != 0 - return res - - -def get_uids_and_wav_paths( - cfg, dataset, dataset_type="train", only_specific_singer=None, return_singers=False -): - dataset_dir = os.path.join( - cfg.OUTPUT_PATH, "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), dataset - ) - dataset_file = os.path.join( - dataset_dir, "{}.json".format(dataset_type.split("_")[-1]) - ) - with open(dataset_file, "r") as f: - utterances = json.load(f) - - indexes = range(len(utterances)) - if "golden" in dataset_type: - # golden_train or golden_test - indexes = get_golden_samples_indexes( - dataset, dataset_dir, split=dataset_type.split("_")[-1] - ) - if only_specific_singer is not None: - indexes = get_specific_singer_indexes( - dataset_dir, only_specific_singer, dataset_type - ) - - uids = [utterances[i]["Uid"] for i in indexes] - wav_paths = [utterances[i]["Path"] for i in indexes] - singers = [utterances[i]["Singer"] for i in indexes] - - if not return_singers: - return uids, wav_paths - else: - return uids, wav_paths, singers diff --git a/preprocessors/bigdata.py b/preprocessors/bigdata.py deleted file mode 100644 index da541191259e337933ce469503c0d057808ebb85..0000000000000000000000000000000000000000 --- a/preprocessors/bigdata.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import os -from collections import defaultdict -from tqdm import tqdm - - -def get_uids_and_wav_paths(cfg, dataset, dataset_type): - assert dataset == "bigdata" - dataset_dir = os.path.join( - cfg.OUTPUT_PATH, - "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), - "bigdata/{}".format(cfg.BIGDATA_VERSION), - ) - dataset_file = os.path.join( - dataset_dir, "{}.json".format(dataset_type.split("_")[-1]) - ) - with open(dataset_file, "r") as f: - utterances = json.load(f) - - # Uids - uids = [u["Uid"] for u in utterances] - - # Wav paths - wav_paths = [u["Path"] for u in utterances] - - return uids, wav_paths - - -def take_duration(utt): - return utt["Duration"] - - -def main(output_path, cfg): - datasets = cfg.dataset - - print("-" * 10) - print("Preparing samples for bigdata...") - print("Including: \n{}\n".format("\n".join(datasets))) - - datasets.sort() - bigdata_version = "_".join(datasets) - - save_dir = os.path.join(output_path, bigdata_version) - os.makedirs(save_dir, exist_ok=True) - - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id) - utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk) - utt2singer = open(utt2singer_file, "a+") - # We select songs of standard samples as test songs - train = [] - test = [] - - train_total_duration = 0 - test_total_duration = 0 - - # Singer unique names - singer_names = set() - - for dataset in datasets: - dataset_path = os.path.join(output_path, dataset) - train_json = os.path.join(dataset_path, "train.json") - test_json = os.path.join(dataset_path, "test.json") - - with open(train_json, "r", encoding="utf-8") as f: - train_utterances = json.load(f) - - with open(test_json, "r", encoding="utf-8") as f: - test_utterances = json.load(f) - - for utt in tqdm(train_utterances): - train.append(utt) - train_total_duration += utt["Duration"] - singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"])) - utt2singer.write( - "{}_{}\t{}_{}\n".format( - utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"] - ) - ) - - for utt in test_utterances: - test.append(utt) - test_total_duration += utt["Duration"] - singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"])) - utt2singer.write( - "{}_{}\t{}_{}\n".format( - utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"] - ) - ) - - utt2singer.close() - - train.sort(key=take_duration) - test.sort(key=take_duration) - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Singer Look Up Table - singer_names = list(singer_names) - singer_names.sort() - singer_lut = {name: i for i, name in enumerate(singer_names)} - print("#Singers: {}\n".format(len(singer_lut))) - - # Save - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) - - # Save meta info - meta_info = { - "datasets": datasets, - "train": {"size": len(train), "hours": round(train_total_duration / 3600, 4)}, - "test": {"size": len(test), "hours": round(test_total_duration / 3600, 4)}, - "singers": {"size": len(singer_lut)}, - } - singer2mins = defaultdict(float) - for utt in train: - dataset, singer, duration = utt["Dataset"], utt["Singer"], utt["Duration"] - singer2mins["{}_{}".format(dataset, singer)] += duration / 60 - singer2mins = sorted(singer2mins.items(), key=lambda x: x[1], reverse=True) - singer2mins = dict( - zip([i[0] for i in singer2mins], [round(i[1], 2) for i in singer2mins]) - ) - meta_info["singers"]["training_minutes"] = singer2mins - - with open(os.path.join(save_dir, "meta_info.json"), "w") as f: - json.dump(meta_info, f, indent=4, ensure_ascii=False) - - for singer, min in singer2mins.items(): - print("Singer {}: {} mins".format(singer, min)) - print("-" * 10, "\n") diff --git a/preprocessors/cdmusiceval.py b/preprocessors/cdmusiceval.py deleted file mode 100644 index 1c4af97c17153196501200811f4e96cfcfa2f4f1..0000000000000000000000000000000000000000 --- a/preprocessors/cdmusiceval.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from glob import glob -import os -import json -import torchaudio -from tqdm import tqdm -from collections import defaultdict - -from utils.util import has_existed, remove_and_create -from utils.audio_slicer import split_utterances_from_audio - - -def split_to_utterances(input_dir, output_dir): - print("Splitting to utterances for {}...".format(input_dir)) - - files_list = glob("*", root_dir=input_dir) - files_list.sort() - for wav_file in tqdm(files_list): - # # Load waveform - # waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file)) - - # Singer name, Song name - song_name, singer_name = wav_file.split("_")[2].split("-") - save_dir = os.path.join(output_dir, singer_name, song_name) - - split_utterances_from_audio( - os.path.join(input_dir, wav_file), save_dir, max_duration_of_utterance=10 - ) - - # # Split - # slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000, min_interval=1000) - # chunks = slicer.slice(waveform) - - # for i, chunk in enumerate(chunks): - # save_dir = os.path.join(output_dir, singer_name, song_name) - # os.makedirs(save_dir, exist_ok=True) - - # output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) - # save_audio(output_file, chunk, fs) - - -def _main(dataset_path): - """ - Split to utterances - """ - utterance_dir = os.path.join(dataset_path, "utterances") - remove_and_create(utterance_dir) - split_to_utterances(os.path.join(dataset_path, "vocal"), utterance_dir) - - -def statistics(utterance_dir): - singers = [] - songs = [] - singers2songs = defaultdict(lambda: defaultdict(list)) - - singer_infos = glob(utterance_dir + "/*") - - for singer_info in singer_infos: - singer = singer_info.split("/")[-1] - - song_infos = glob(singer_info + "/*") - - for song_info in song_infos: - song = song_info.split("/")[-1] - - singers.append(singer) - songs.append(song) - - utts = glob(song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - singers2songs[singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "Statistics: {} singers, {} utterances ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singers2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing samples for CD Music Eval...\n") - - if not os.path.exists(os.path.join(dataset_path, "utterances")): - print("Spliting into utterances...\n") - _main(dataset_path) - - save_dir = os.path.join(output_path, "cdmusiceval") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - utt_path = os.path.join(dataset_path, "utterances") - singers2songs, unique_singers = statistics(utt_path) - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for singer, songs in tqdm(singers2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in song_names: - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "cdmusiceval", - "Singer": singer, - "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) - res["Path"] = os.path.join(utt_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if duration <= 1e-8: - continue - - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/coco.py b/preprocessors/coco.py deleted file mode 100644 index 9ac462f32d7a58ed245f58d13a44e02b4efef06b..0000000000000000000000000000000000000000 --- a/preprocessors/coco.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def get_test_songs(): - return ["007Di Da Di"] - - -def coco_statistics(data_dir): - song2utts = defaultdict(list) - - song_infos = glob(data_dir + "/*") - - for song in song_infos: - song_name = song.split("/")[-1] - utts = glob(song + "/*.wav") - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - song2utts[song_name].append(uid) - - print("Coco: {} songs".format(len(song_infos))) - return song2utts - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing datasets for Coco...\n") - - save_dir = os.path.join(output_path, "coco") - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - if has_existed(test_output_file): - return - - # Load - song2utts = coco_statistics(dataset_path) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for song_name, uids in tqdm(song2utts.items()): - for chosen_uid in uids: - res = { - "Dataset": "coco", - "Singer": "coco", - "Song": song_name, - "Uid": "{}_{}".format(song_name, chosen_uid), - } - res["Path"] = "{}/{}.wav".format(song_name, chosen_uid) - res["Path"] = os.path.join(dataset_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if song_name in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/cocoeval.py b/preprocessors/cocoeval.py deleted file mode 100644 index 3cb3604c9d540d4e8f166f459a1ac992a1bd7a17..0000000000000000000000000000000000000000 --- a/preprocessors/cocoeval.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import random -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from utils.audio_slicer import split_utterances_from_audio -from preprocessors import GOLDEN_TEST_SAMPLES - - -def _split_utts(): - raw_dir = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/李玟/cocoeval/raw" - output_root = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/李玟/cocoeval/utterances" - - if os.path.exists(output_root): - os.system("rm -rf {}".format(output_root)) - - vocal_files = glob(os.path.join(raw_dir, "*/vocal.wav")) - for vocal_f in tqdm(vocal_files): - song_name = vocal_f.split("/")[-2] - - output_dir = os.path.join(output_root, song_name) - os.makedirs(output_dir, exist_ok=True) - - split_utterances_from_audio(vocal_f, output_dir, min_interval=300) - - -def cocoeval_statistics(data_dir): - song2utts = defaultdict(list) - - song_infos = glob(data_dir + "/*") - - for song in song_infos: - song_name = song.split("/")[-1] - utts = glob(song + "/*.wav") - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - song2utts[song_name].append(uid) - - print("Cocoeval: {} songs".format(len(song_infos))) - return song2utts - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing datasets for Cocoeval...\n") - - save_dir = os.path.join(output_path, "cocoeval") - test_output_file = os.path.join(save_dir, "test.json") - if has_existed(test_output_file): - return - - # Load - song2utts = cocoeval_statistics(dataset_path) - - train, test = [], [] - train_index_count, test_index_count = 0, 0 - train_total_duration, test_total_duration = 0.0, 0.0 - - for song_name, uids in tqdm(song2utts.items()): - for chosen_uid in uids: - res = { - "Dataset": "cocoeval", - "Singer": "TBD", - "Song": song_name, - "Uid": "{}_{}".format(song_name, chosen_uid), - } - res["Path"] = "{}/{}.wav".format(song_name, chosen_uid) - res["Path"] = os.path.join(dataset_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/csd.py b/preprocessors/csd.py deleted file mode 100644 index 645a8b3d1e2bc556ffa7ee36d9b889422e500619..0000000000000000000000000000000000000000 --- a/preprocessors/csd.py +++ /dev/null @@ -1,202 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import os -import glob -from tqdm import tqdm -import torchaudio -import pandas as pd -from glob import glob -from collections import defaultdict - -from utils.io import save_audio -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def save_utterance(output_file, waveform, fs, start, end, overlap=0.1): - """ - waveform: [#channel, audio_len] - start, end, overlap: seconds - """ - start = int((start - overlap) * fs) - end = int((end + overlap) * fs) - utterance = waveform[:, start:end] - save_audio(output_file, utterance, fs) - - -def split_to_utterances(language_dir, output_dir): - print("Splitting to utterances for {}...".format(language_dir)) - wav_dir = os.path.join(language_dir, "wav") - phoneme_dir = os.path.join(language_dir, "txt") - annot_dir = os.path.join(language_dir, "csv") - - pitches = set() - for wav_file in tqdm(glob("{}/*.wav".format(wav_dir))): - # Load waveform - song_name = wav_file.split("/")[-1].split(".")[0] - waveform, fs = torchaudio.load(wav_file) - - # Load utterances - phoneme_file = os.path.join(phoneme_dir, "{}.txt".format(song_name)) - with open(phoneme_file, "r") as f: - lines = f.readlines() - utterances = [l.strip().split() for l in lines] - utterances = [utt for utt in utterances if len(utt) > 0] - - # Load annotation - annot_file = os.path.join(annot_dir, "{}.csv".format(song_name)) - annot_df = pd.read_csv(annot_file) - pitches = pitches.union(set(annot_df["pitch"])) - starts = annot_df["start"].tolist() - ends = annot_df["end"].tolist() - syllables = annot_df["syllable"].tolist() - - # Split - curr = 0 - for i, phones in enumerate(utterances): - sz = len(phones) - assert phones[0] == syllables[curr] - assert phones[-1] == syllables[curr + sz - 1] - - s = starts[curr] - e = ends[curr + sz - 1] - curr += sz - - save_dir = os.path.join(output_dir, song_name) - os.makedirs(save_dir, exist_ok=True) - - output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) - save_utterance(output_file, waveform, fs, start=s, end=e) - - -def _main(dataset_path): - """ - Split to utterances - """ - utterance_dir = os.path.join(dataset_path, "utterances") - - for lang in ["english", "korean"]: - split_to_utterances(os.path.join(dataset_path, lang), utterance_dir) - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["csd"] - # every item is a tuple (language, song) - golden_songs = [s.split("_")[:2] for s in golden_samples] - # language_song, eg: en_001a - return golden_songs - - -def csd_statistics(data_dir): - languages = [] - songs = [] - languages2songs = defaultdict(lambda: defaultdict(list)) - - folder_infos = glob(data_dir + "/*") - - for folder_info in folder_infos: - folder_info_split = folder_info.split("/")[-1] - - language = folder_info_split[:2] - song = folder_info_split[2:] - - languages.append(language) - songs.append(song) - - utts = glob(folder_info + "/*") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - languages2songs[language][song].append(uid) - - unique_languages = list(set(languages)) - unique_songs = list(set(songs)) - unique_languages.sort() - unique_songs.sort() - - print( - "csd: {} languages, {} utterances ({} unique songs)".format( - len(unique_languages), len(songs), len(unique_songs) - ) - ) - print("Languages: \n{}".format("\t".join(unique_languages))) - return languages2songs - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for csd...\n") - - if not os.path.exists(os.path.join(dataset_path, "utterances")): - print("Spliting into utterances...\n") - _main(dataset_path) - - save_dir = os.path.join(output_path, "csd") - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - if has_existed(test_output_file): - return - - # Load - csd_path = os.path.join(dataset_path, "utterances") - - language2songs = csd_statistics(csd_path) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for language, songs in tqdm(language2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in song_names: - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "csd", - "Singer": "Female1_{}".format(language), - "Uid": "{}_{}_{}".format(language, chosen_song, chosen_uid), - } - res["Path"] = "{}{}/{}.wav".format(language, chosen_song, chosen_uid) - res["Path"] = os.path.join(csd_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if [language, chosen_song] in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/custom.py b/preprocessors/custom.py deleted file mode 100644 index daa457bb84b7aca70fcd631f25a1678e6e84a333..0000000000000000000000000000000000000000 --- a/preprocessors/custom.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from glob import glob -import os -import json -import torchaudio -from tqdm import tqdm -from collections import defaultdict - -from utils.util import has_existed - - -def statistics(utterance_dir): - singers = [] - songs = [] - singers2songs = defaultdict(lambda: defaultdict(list)) - - singer_infos = glob(utterance_dir + "/*") - - for singer_info in singer_infos: - singer = singer_info.split("/")[-1] - - song_infos = glob(singer_info + "/*") - - for song_info in song_infos: - song = song_info.split("/")[-1] - - singers.append(singer) - songs.append(song) - - utts = glob(song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - singers2songs[singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "Statistics: {} singers, {} utterances ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singers2songs, unique_singers - - -def main(output_path, dataset_path, dataset_name): - print("-" * 10) - print("Preparing samples for {}...\n".format(dataset_name)) - - save_dir = os.path.join(output_path, dataset_name) - os.makedirs(save_dir, exist_ok=True) - - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - singers2songs, unique_singers = statistics(dataset_path) - - # We select songs of standard samples as test songs - train = [] - test = [] - test_songs = set() - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for singer, songs in singers2songs.items(): - song_names = list(songs.keys()) - - print("Singer {}...".format(singer)) - for chosen_song in tqdm(song_names): - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": dataset_name, - "Singer": singer, - "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) - res["Path"] = os.path.join(dataset_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - # Remove the utterance whose duration is shorter than 0.1s - if duration <= 1e-2: - continue - - # Place into train or test - if "{}_{}".format(singer, chosen_song) not in test_songs: - test_songs.add("{}_{}".format(singer, chosen_song)) - - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/kising.py b/preprocessors/kising.py deleted file mode 100644 index f81b852aeb05a5d30fe22df07af4c4ab9defda2a..0000000000000000000000000000000000000000 --- a/preprocessors/kising.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import random -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def get_test_folders(): - golden_samples = GOLDEN_TEST_SAMPLES["kising"] - # every item is a string - golden_folders = [s.split("_")[:1] for s in golden_samples] - # folder, eg: 422 - return golden_folders - - -def KiSing_statistics(data_dir): - folders = [] - folders2utts = defaultdict(list) - - folder_infos = glob(data_dir + "/*") - - for folder_info in folder_infos: - folder = folder_info.split("/")[-1] - - folders.append(folder) - - utts = glob(folder_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - folders2utts[folder].append(uid) - - unique_folders = list(set(folders)) - unique_folders.sort() - - print("KiSing: {} unique songs".format(len(unique_folders))) - return folders2utts - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for KiSing...\n") - - save_dir = os.path.join(output_path, "kising") - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - if has_existed(test_output_file): - return - - # Load - KiSing_dir = dataset_path - - folders2utts = KiSing_statistics(KiSing_dir) - test_folders = get_test_folders() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - folder_names = list(folders2utts.keys()) - - for chosen_folder in folder_names: - for chosen_uid in folders2utts[chosen_folder]: - res = { - "Dataset": "kising", - "Singer": "female1", - "Uid": "{}_{}".format(chosen_folder, chosen_uid), - } - res["Path"] = "{}/{}.wav".format(chosen_folder, chosen_uid) - res["Path"] = os.path.join(KiSing_dir, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if ([chosen_folder]) in test_folders: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/libritts.py b/preprocessors/libritts.py deleted file mode 100644 index 090a81121c8db19a03e6d3fe668903567a8da0b3..0000000000000000000000000000000000000000 --- a/preprocessors/libritts.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed - - -def libritts_statistics(data_dir): - speakers = [] - distribution2speakers2pharases2utts = defaultdict( - lambda: defaultdict(lambda: defaultdict(list)) - ) - - distribution_infos = glob(data_dir + "/*") - - for distribution_info in distribution_infos: - distribution = distribution_info.split("/")[-1] - print(distribution) - - speaker_infos = glob(distribution_info + "/*") - - if len(speaker_infos) == 0: - continue - - for speaker_info in speaker_infos: - speaker = speaker_info.split("/")[-1] - - speakers.append(speaker) - - pharase_infos = glob(speaker_info + "/*") - - for pharase_info in pharase_infos: - pharase = pharase_info.split("/")[-1] - - utts = glob(pharase_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - distribution2speakers2pharases2utts[distribution][speaker][ - pharase - ].append(uid) - - unique_speakers = list(set(speakers)) - unique_speakers.sort() - - print("Speakers: \n{}".format("\t".join(unique_speakers))) - return distribution2speakers2pharases2utts, unique_speakers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing samples for libritts...\n") - - save_dir = os.path.join(output_path, "libritts") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if has_existed(train_output_file): - return - utt2singer = open(utt2singer_file, "w") - - # Load - libritts_path = dataset_path - - distribution2speakers2pharases2utts, unique_speakers = libritts_statistics( - libritts_path - ) - - # We select pharases of standard spekaer as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for distribution, speakers2pharases2utts in tqdm( - distribution2speakers2pharases2utts.items() - ): - for speaker, pharases2utts in tqdm(speakers2pharases2utts.items()): - pharase_names = list(pharases2utts.keys()) - - for chosen_pharase in pharase_names: - for chosen_uid in pharases2utts[chosen_pharase]: - res = { - "Dataset": "libritts", - "Singer": speaker, - "Uid": "{}#{}#{}#{}".format( - distribution, speaker, chosen_pharase, chosen_uid - ), - } - res["Path"] = "{}/{}/{}/{}.wav".format( - distribution, speaker, chosen_pharase, chosen_uid - ) - res["Path"] = os.path.join(libritts_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if not "train" in distribution: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_speakers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/lijian.py b/preprocessors/lijian.py deleted file mode 100644 index 459986a8d68a5b0dc0811f4f8197cd818fdc263f..0000000000000000000000000000000000000000 --- a/preprocessors/lijian.py +++ /dev/null @@ -1,151 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import glob -import os -import json -import torchaudio -from tqdm import tqdm -from collections import defaultdict - - -from utils.io import save_audio -from utils.util import has_existed, remove_and_create -from utils.audio_slicer import Slicer -from preprocessors import GOLDEN_TEST_SAMPLES - - -def split_to_utterances(input_dir, output_dir): - print("Splitting to utterances for {}...".format(input_dir)) - - files_list = glob.glob("*.flac", root_dir=input_dir) - files_list.sort() - for wav_file in tqdm(files_list): - # Load waveform - waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file)) - - # Song name - filename = wav_file.replace(" ", "") - filename = filename.replace("(Live)", "") - song_id, filename = filename.split("李健-") - - song_id = song_id.split("_")[0] - song_name = "{:03d}".format(int(song_id)) + filename.split("_")[0].split("-")[0] - - # Split - slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000) - chunks = slicer.slice(waveform) - - save_dir = os.path.join(output_dir, song_name) - remove_and_create(save_dir) - - for i, chunk in enumerate(chunks): - output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) - save_audio(output_file, chunk, fs) - - -def _main(dataset_path): - """ - Split to utterances - """ - utterance_dir = os.path.join(dataset_path, "utterances") - split_to_utterances(os.path.join(dataset_path, "vocal_v2"), utterance_dir) - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["lijian"] - golden_songs = [s.split("_")[0] for s in golden_samples] - return golden_songs - - -def statistics(utt_dir): - song2utts = defaultdict(list) - - song_infos = glob.glob(utt_dir + "/*") - song_infos.sort() - for song in song_infos: - song_name = song.split("/")[-1] - utt_infos = glob.glob(song + "/*.wav") - utt_infos.sort() - for utt in utt_infos: - uid = utt.split("/")[-1].split(".")[0] - song2utts[song_name].append(uid) - - utt_sum = sum([len(utts) for utts in song2utts.values()]) - print("Li Jian: {} unique songs, {} utterances".format(len(song2utts), utt_sum)) - return song2utts - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for Li Jian...\n") - - if not os.path.exists(os.path.join(dataset_path, "utterances")): - print("Spliting into utterances...\n") - _main(dataset_path) - - save_dir = os.path.join(output_path, "lijian") - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - if has_existed(test_output_file): - return - - # Load - lijian_path = os.path.join(dataset_path, "utterances") - song2utts = statistics(lijian_path) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for chosen_song, utts in tqdm(song2utts.items()): - for chosen_uid in song2utts[chosen_song]: - res = { - "Dataset": "lijian", - "Singer": "lijian", - "Uid": "{}_{}".format(chosen_song, chosen_uid), - } - res["Path"] = "{}/{}.wav".format(chosen_song, chosen_uid) - res["Path"] = os.path.join(lijian_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if duration <= 1e-8: - continue - - if chosen_song in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/ljspeech.py b/preprocessors/ljspeech.py deleted file mode 100644 index 9f4bd4ca386031a7f77e5283eab0129b75b85d54..0000000000000000000000000000000000000000 --- a/preprocessors/ljspeech.py +++ /dev/null @@ -1,197 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -from tqdm import tqdm -import os -import torchaudio -from utils import audio -import csv -import random - -from utils.util import has_existed -from text import _clean_text -import librosa -import soundfile as sf -from scipy.io import wavfile - -from pathlib import Path -import numpy as np - - -def textgird_extract( - corpus_directory, - output_directory, - mfa_path=os.path.join("mfa", "montreal-forced-aligner", "bin", "mfa_align"), - lexicon=os.path.join("mfa", "lexicon", "librispeech-lexicon.txt"), - acoustic_model_path=os.path.join( - "mfa", "montreal-forced-aligner", "pretrained_models", "english.zip" - ), - jobs="8", -): - assert os.path.exists( - corpus_directory - ), "Please check the directionary contains *.wav, *.lab" - assert ( - os.path.exists(mfa_path) - and os.path.exists(lexicon) - and os.path.exists(acoustic_model_path) - ), f"Please download the MFA tools to {mfa_path} firstly" - Path(output_directory).mkdir(parents=True, exist_ok=True) - print(f"MFA results are save in {output_directory}") - os.system( - f".{os.path.sep}{mfa_path} {corpus_directory} {lexicon} {acoustic_model_path} {output_directory} -j {jobs} --clean" - ) - - -def get_lines(file): - lines = [] - with open(file, encoding="utf-8") as f: - for line in tqdm(f): - lines.append(line.strip()) - return lines - - -def get_uid2utt(ljspeech_path, dataset, cfg): - index_count = 0 - total_duration = 0 - - uid2utt = [] - for l in tqdm(dataset): - items = l.split("|") - uid = items[0] - text = items[2] - - res = { - "Dataset": "LJSpeech", - "index": index_count, - "Singer": "LJSpeech", - "Uid": uid, - "Text": text, - } - - # Duration in wav files - audio_file = os.path.join(ljspeech_path, "wavs/{}.wav".format(uid)) - - res["Path"] = audio_file - - waveform, sample_rate = torchaudio.load(audio_file) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - uid2utt.append(res) - - index_count = index_count + 1 - total_duration += duration - - return uid2utt, total_duration / 3600 - - -def split_dataset(lines, test_rate=0.05, test_size=None): - if test_size == None: - test_size = int(len(lines) * test_rate) - random.shuffle(lines) - - train_set = [] - test_set = [] - - for line in lines[:test_size]: - test_set.append(line) - for line in lines[test_size:]: - train_set.append(line) - return train_set, test_set - - -max_wav_value = 32768.0 - - -def prepare_align(dataset, dataset_path, cfg, output_path): - in_dir = dataset_path - out_dir = os.path.join(output_path, dataset, cfg.raw_data) - sampling_rate = cfg.sample_rate - cleaners = cfg.text_cleaners - speaker = "LJSpeech" - with open(os.path.join(dataset_path, "metadata.csv"), encoding="utf-8") as f: - for line in tqdm(f): - parts = line.strip().split("|") - base_name = parts[0] - text = parts[2] - text = _clean_text(text, cleaners) - - output_wav_path = os.path.join(out_dir, speaker, "{}.wav".format(base_name)) - output_lab_path = os.path.join(out_dir, speaker, "{}.lab".format(base_name)) - - if os.path.exists(output_wav_path) and os.path.exists(output_lab_path): - continue - - wav_path = os.path.join(in_dir, "wavs", "{}.wav".format(base_name)) - if os.path.exists(wav_path): - os.makedirs(os.path.join(out_dir, speaker), exist_ok=True) - wav, _ = librosa.load(wav_path, sampling_rate) - wav = wav / max(abs(wav)) * max_wav_value - - wavfile.write( - os.path.join(out_dir, speaker, "{}.wav".format(base_name)), - sampling_rate, - wav.astype(np.int16), - ) - - with open( - os.path.join(out_dir, speaker, "{}.lab".format(base_name)), - "w", - ) as f1: - f1.write(text) - # Extract textgird with MFA - textgird_extract( - corpus_directory=out_dir, - output_directory=os.path.join(output_path, dataset, "TextGrid"), - ) - - -def main(output_path, dataset_path, cfg): - print("-" * 10) - print("Dataset splits for {}...\n".format("LJSpeech")) - - dataset = "LJSpeech" - - save_dir = os.path.join(output_path, dataset) - os.makedirs(save_dir, exist_ok=True) - ljspeech_path = dataset_path - - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - - speaker = "LJSpeech" - speakers = [dataset + "_" + speaker] - singer_lut = {name: i for i, name in enumerate(sorted(speakers))} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) - - if has_existed(train_output_file) and has_existed(test_output_file): - return - - meta_file = os.path.join(ljspeech_path, "metadata.csv") - lines = get_lines(meta_file) - - train_set, test_set = split_dataset(lines) - - res, hours = get_uid2utt(ljspeech_path, train_set, cfg) - - # Save train - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(res, f, indent=4, ensure_ascii=False) - - print("Train_hours= {}".format(hours)) - - res, hours = get_uid2utt(ljspeech_path, test_set, cfg) - - # Save test - os.makedirs(save_dir, exist_ok=True) - with open(test_output_file, "w") as f: - json.dump(res, f, indent=4, ensure_ascii=False) - - print("Test_hours= {}".format(hours)) diff --git a/preprocessors/ljspeech_vocoder.py b/preprocessors/ljspeech_vocoder.py deleted file mode 100644 index b6ef4fd2c88d85454a939e9fe3329e02b0be949b..0000000000000000000000000000000000000000 --- a/preprocessors/ljspeech_vocoder.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob - -from utils.util import has_existed - - -def main(output_path, dataset_path): - print("-" * 10) - print("Dataset splits for ljspeech...\n") - - save_dir = os.path.join(output_path, "ljspeech") - ljspeech_path = dataset_path - - wave_files = glob(ljspeech_path + "/wavs/*.wav") - - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - - if has_existed(train_output_file): - return - - utts = [] - - for wave_file in tqdm(wave_files): - res = { - "Dataset": "ljspeech", - "Singer": "female1", - "Uid": "{}".format(wave_file.split("/")[-1].split(".")[0]), - } - res["Path"] = wave_file - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if duration <= 1e-8: - continue - - utts.append(res) - - test_length = len(utts) // 20 - - train_utts = [] - train_index_count = 0 - train_total_duration = 0 - - for i in tqdm(range(len(utts) - test_length)): - tmp = utts[i] - tmp["index"] = train_index_count - train_index_count += 1 - train_total_duration += tmp["Duration"] - train_utts.append(tmp) - - test_utts = [] - test_index_count = 0 - test_total_duration = 0 - - for i in tqdm(range(len(utts) - test_length, len(utts))): - tmp = utts[i] - tmp["index"] = test_index_count - test_index_count += 1 - test_total_duration += tmp["Duration"] - test_utts.append(tmp) - - print("#Train = {}, #Test = {}".format(len(train_utts), len(test_utts))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(train_utts, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test_utts, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/m4singer.py b/preprocessors/m4singer.py deleted file mode 100644 index eb8eb1715ce2af6d712efd9d93c5983dd58ba257..0000000000000000000000000000000000000000 --- a/preprocessors/m4singer.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import librosa -from tqdm import tqdm -from collections import defaultdict - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["m4singer"] - # every item is a tuple (singer, song) - golden_songs = [s.split("_")[:2] for s in golden_samples] - # singer_song, eg: Alto-1_美错 - golden_songs = ["_".join(t) for t in golden_songs] - return golden_songs - - -def m4singer_statistics(meta): - singers = [] - songs = [] - singer2songs = defaultdict(lambda: defaultdict(list)) - for utt in meta: - p, s, uid = utt["item_name"].split("#") - singers.append(p) - songs.append(s) - singer2songs[p][s].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "M4Singer: {} singers, {} utterances ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singer2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for m4singer...\n") - - save_dir = os.path.join(output_path, "m4singer") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - m4singer_dir = dataset_path - meta_file = os.path.join(m4singer_dir, "meta.json") - with open(meta_file, "r", encoding="utf-8") as f: - meta = json.load(f) - - singer2songs, unique_singers = m4singer_statistics(meta) - - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for singer, songs in tqdm(singer2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in song_names: - chosen_song = chosen_song.replace(" ", "-") - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "m4singer", - "Singer": singer, - "Song": chosen_song, - "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), - } - - res["Path"] = os.path.join( - m4singer_dir, "{}#{}/{}.wav".format(singer, chosen_song, chosen_uid) - ) - assert os.path.exists(res["Path"]) - - duration = librosa.get_duration(filename=res["Path"]) - res["Duration"] = duration - - if "_".join([singer, chosen_song]) in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/metadata.py b/preprocessors/metadata.py deleted file mode 100644 index ffa5c3e8128856da084307389bcab8f432831985..0000000000000000000000000000000000000000 --- a/preprocessors/metadata.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -from tqdm import tqdm - - -def cal_metadata(cfg): - """ - Dump metadata (singers.json, meta_info.json, utt2singer) for singer dataset or multi-datasets. - """ - from collections import Counter - - datasets = cfg.dataset - - print("-" * 10) - print("Preparing metadata...") - print("Including: \n{}\n".format("\n".join(datasets))) - - datasets.sort() - - for dataset in tqdm(datasets): - save_dir = os.path.join(cfg.preprocess.processed_dir, dataset) - assert os.path.exists(save_dir) - - # 'train.json' and 'test.json' of target dataset - train_metadata = os.path.join(save_dir, "train.json") - test_metadata = os.path.join(save_dir, "test.json") - - # Sort the metadata as the duration order - with open(train_metadata, "r", encoding="utf-8") as f: - train_utterances = json.load(f) - with open(test_metadata, "r", encoding="utf-8") as f: - test_utterances = json.load(f) - - train_utterances = sorted(train_utterances, key=lambda x: x["Duration"]) - test_utterances = sorted(test_utterances, key=lambda x: x["Duration"]) - - # Write back the sorted metadata - with open(train_metadata, "w") as f: - json.dump(train_utterances, f, indent=4, ensure_ascii=False) - with open(test_metadata, "w") as f: - json.dump(test_utterances, f, indent=4, ensure_ascii=False) - - # Paths of metadata needed to be generated - singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id) - utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk) - - # Get the total duration and singer names for train and test utterances - train_total_duration = sum(utt["Duration"] for utt in train_utterances) - test_total_duration = sum(utt["Duration"] for utt in test_utterances) - - singer_names = set( - f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" - for utt in train_utterances + test_utterances - ) - - # Write the utt2singer file and sort the singer names - with open(utt2singer_file, "w", encoding="utf-8") as f: - for utt in train_utterances + test_utterances: - f.write( - f"{utt['Dataset']}_{utt['Uid']}\t{replace_augment_name(utt['Dataset'])}_{utt['Singer']}\n" - ) - - singer_names = sorted(singer_names) - singer_lut = {name: i for i, name in enumerate(singer_names)} - - # dump singers.json - with open(singer_dict_file, "w", encoding="utf-8") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) - - meta_info = { - "dataset": dataset, - "statistics": { - "size": len(train_utterances) + len(test_utterances), - "hours": round(train_total_duration / 3600, 4) - + round(test_total_duration / 3600, 4), - }, - "train": { - "size": len(train_utterances), - "hours": round(train_total_duration / 3600, 4), - }, - "test": { - "size": len(test_utterances), - "hours": round(test_total_duration / 3600, 4), - }, - "singers": {"size": len(singer_lut)}, - } - # Use Counter to count the minutes for each singer - total_singer2mins = Counter() - training_singer2mins = Counter() - for utt in train_utterances: - k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" - training_singer2mins[k] += utt["Duration"] / 60 - total_singer2mins[k] += utt["Duration"] / 60 - for utt in test_utterances: - k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}" - total_singer2mins[k] += utt["Duration"] / 60 - - training_singer2mins = dict( - sorted(training_singer2mins.items(), key=lambda x: x[1], reverse=True) - ) - training_singer2mins = {k: round(v, 2) for k, v in training_singer2mins.items()} - meta_info["singers"]["training_minutes"] = training_singer2mins - - total_singer2mins = dict( - sorted(total_singer2mins.items(), key=lambda x: x[1], reverse=True) - ) - total_singer2mins = {k: round(v, 2) for k, v in total_singer2mins.items()} - meta_info["singers"]["minutes"] = total_singer2mins - - with open(os.path.join(save_dir, "meta_info.json"), "w") as f: - json.dump(meta_info, f, indent=4, ensure_ascii=False) - - for singer, min in training_singer2mins.items(): - print(f"Singer {singer}: {min} mins for training") - print("-" * 10, "\n") - - -def replace_augment_name(dataset: str) -> str: - """Replace the augmented dataset name with the original dataset name. - >>> print(replace_augment_name("dataset_equalizer")) - dataset - """ - if "equalizer" in dataset: - dataset = dataset.replace("_equalizer", "") - elif "formant_shift" in dataset: - dataset = dataset.replace("_formant_shift", "") - elif "pitch_shift" in dataset: - dataset = dataset.replace("_pitch_shift", "") - elif "time_stretch" in dataset: - dataset = dataset.replace("_time_stretch", "") - else: - pass - return dataset diff --git a/preprocessors/nus48e.py b/preprocessors/nus48e.py deleted file mode 100644 index e780b3265b3921f21ee0efb5fcd9162e5555c529..0000000000000000000000000000000000000000 --- a/preprocessors/nus48e.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob -from collections import defaultdict - - -from utils.io import save_audio -from utils.util import has_existed -from utils.audio_slicer import Slicer -from preprocessors import GOLDEN_TEST_SAMPLES - - -def split_to_utterances(dataset_path, singer, style, output_dir): - data_dir = os.path.join(dataset_path, singer, style) - - print("Splitting to utterances for {}...".format(data_dir)) - - wave_files = glob(data_dir + "/*.wav") - - for wav_file in tqdm(wave_files): - # Load waveform - song_name = wav_file.split("/")[-1].split(".")[0] - waveform, fs = torchaudio.load(wav_file) - - # Split - slicer = Slicer(sr=fs, threshold=-40.0, max_sil_kept=4000) - chunks = slicer.slice(waveform) - - for i, chunk in enumerate(chunks): - save_dir = os.path.join(output_dir, singer, style, song_name) - os.makedirs(save_dir, exist_ok=True) - - output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) - save_audio(output_file, chunk, fs) - - -def _main(dataset_path): - """ - Split to utterances - """ - utterance_dir = os.path.join(dataset_path, "utterances") - - singer_infos = glob(dataset_path + "/*") - - for singer_info in singer_infos: - singer = singer_info.split("/")[-1] - - for style in ["read", "sing"]: - split_to_utterances(dataset_path, singer, style, utterance_dir) - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["nus48e"] - # every item is a tuple (singer, song) - golden_songs = [s.split("#")[:2] for s in golden_samples] - # singer_song, eg: Female1#Almost_lover_Amateur - return golden_songs - - -def nus48e_statistics(data_dir): - singers = [] - songs = [] - singer2songs = defaultdict(lambda: defaultdict(list)) - - singer_infos = glob(data_dir + "/*") - - for singer_info in singer_infos: - singer_info_split = singer_info.split("/")[-1] - - style_infos = glob(singer_info + "/*") - - for style_info in style_infos: - style_info_split = style_info.split("/")[-1] - - singer = singer_info_split + "_" + style_info_split - singers.append(singer) - - song_infos = glob(style_info + "/*") - - for song_info in song_infos: - song = song_info.split("/")[-1] - - songs.append(song) - - utts = glob(song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - singer2songs[singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "nus_48_e: {} singers, {} utterances ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singer2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for nus48e...\n") - - if not os.path.exists(os.path.join(dataset_path, "utterances")): - print("Spliting into utterances...\n") - _main(dataset_path) - - save_dir = os.path.join(output_path, "nus48e") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - nus48e_path = os.path.join(dataset_path, "utterances") - - singer2songs, unique_singers = nus48e_statistics(nus48e_path) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for singer, songs in singer2songs.items(): - song_names = list(songs.keys()) - - for chosen_song in song_names: - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "nus48e", - "Singer": singer, - "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}/{}/{}/{}.wav".format( - singer.split("_")[0], singer.split("_")[-1], chosen_song, chosen_uid - ) - res["Path"] = os.path.join(nus48e_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if duration <= 1e-8: - continue - - if ([singer, chosen_song]) in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/opencpop.py b/preprocessors/opencpop.py deleted file mode 100644 index d1da6e303cbb06370f3215ce5c2cb4dd22e611b5..0000000000000000000000000000000000000000 --- a/preprocessors/opencpop.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -from tqdm import tqdm -import os -import librosa - -from utils.util import has_existed - - -def get_lines(file): - with open(file, "r") as f: - lines = f.readlines() - lines = [l.strip() for l in lines] - return lines - - -def get_uid2utt(opencpop_path, dataset, dataset_type): - index_count = 0 - total_duration = 0 - - file = os.path.join(opencpop_path, "segments", "{}.txt".format(dataset_type)) - lines = get_lines(file) - - uid2utt = [] - for l in tqdm(lines): - items = l.split("|") - uid = items[0] - - res = { - "Dataset": dataset, - "index": index_count, - "Singer": "female1", - "Uid": uid, - } - - # Duration in wav files - audio_file = os.path.join(opencpop_path, "segments/wavs/{}.wav".format(uid)) - res["Path"] = audio_file - - duration = librosa.get_duration(filename=res["Path"]) - res["Duration"] = duration - - uid2utt.append(res) - - index_count = index_count + 1 - total_duration += duration - - return uid2utt, total_duration / 3600 - - -def main(dataset, output_path, dataset_path): - print("-" * 10) - print("Dataset splits for {}...\n".format(dataset)) - - save_dir = os.path.join(output_path, dataset) - opencpop_path = dataset_path - for dataset_type in ["train", "test"]: - output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) - if has_existed(output_file): - continue - - res, hours = get_uid2utt(opencpop_path, dataset, dataset_type) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(output_file, "w") as f: - json.dump(res, f, indent=4, ensure_ascii=False) - - print("{}_{}_hours= {}".format(dataset, dataset_type, hours)) diff --git a/preprocessors/opensinger.py b/preprocessors/opensinger.py deleted file mode 100644 index 93fc3f648df11a866f6c7017d7c6964c9b36cee6..0000000000000000000000000000000000000000 --- a/preprocessors/opensinger.py +++ /dev/null @@ -1,169 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import random -import os -import json -import librosa -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["opensinger"] - # every item is a tuple (singer, song) - golden_songs = [s.split("_")[:3] for s in golden_samples] - # singer_song, eg: Female1#Almost_lover_Amateur - return golden_songs - - -def opensinger_statistics(data_dir): - singers = [] - songs = [] - singer2songs = defaultdict(lambda: defaultdict(list)) - - gender_infos = glob(data_dir + "/*") - - for gender_info in gender_infos: - gender_info_split = gender_info.split("/")[-1][:-3] - - singer_and_song_infos = glob(gender_info + "/*") - - for singer_and_song_info in singer_and_song_infos: - singer_and_song_info_split = singer_and_song_info.split("/")[-1].split("_") - singer_id, song = ( - singer_and_song_info_split[0], - singer_and_song_info_split[1], - ) - singer = gender_info_split + "_" + singer_id - singers.append(singer) - songs.append(song) - - utts = glob(singer_and_song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split("_")[-1].split(".")[0] - singer2songs[singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "opensinger: {} singers, {} songs ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singer2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for opensinger...\n") - - save_dir = os.path.join(output_path, "opensinger") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - opensinger_path = dataset_path - - singer2songs, unique_singers = opensinger_statistics(opensinger_path) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for i, (singer, songs) in enumerate(singer2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in tqdm( - song_names, desc="Singer {}/{}".format(i, len(singer2songs)) - ): - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "opensinger", - "Singer": singer, - "Song": chosen_song, - "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}Raw/{}_{}/{}_{}_{}.wav".format( - singer.split("_")[0], - singer.split("_")[1], - chosen_song, - singer.split("_")[1], - chosen_song, - chosen_uid, - ) - res["Path"] = os.path.join(opensinger_path, res["Path"]) - assert os.path.exists(res["Path"]) - - duration = librosa.get_duration(filename=res["Path"]) - res["Duration"] = duration - - if duration > 30: - print( - "Wav file: {}, the duration = {:.2f}s > 30s, which has been abandoned.".format( - res["Path"], duration - ) - ) - continue - - if ( - [singer.split("_")[0], singer.split("_")[1], chosen_song] - ) in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/opera.py b/preprocessors/opera.py deleted file mode 100644 index c421fbb8346945c5dec40b1c872cebcb49f1f606..0000000000000000000000000000000000000000 --- a/preprocessors/opera.py +++ /dev/null @@ -1,186 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import os -from tqdm import tqdm -import torchaudio -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from utils.io import save_audio -from utils.audio_slicer import Slicer -from preprocessors import GOLDEN_TEST_SAMPLES - - -def split_to_utterances(language_dir, output_dir): - print("Splitting to utterances for {}...".format(language_dir)) - - for wav_file in tqdm(glob("{}/*/*".format(language_dir))): - # Load waveform - singer_name, song_name = wav_file.split("/")[-2:] - song_name = song_name.split(".")[0] - waveform, fs = torchaudio.load(wav_file) - - # Split - slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000) - chunks = slicer.slice(waveform) - - for i, chunk in enumerate(chunks): - save_dir = os.path.join(output_dir, singer_name, song_name) - os.makedirs(save_dir, exist_ok=True) - - output_file = os.path.join(save_dir, "{:04d}.wav".format(i)) - save_audio(output_file, chunk, fs) - - -def _main(dataset_path): - """ - Split to utterances - """ - utterance_dir = os.path.join(dataset_path, "utterances") - - for lang in ["chinese", "western"]: - split_to_utterances(os.path.join(dataset_path, lang), utterance_dir) - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["opera"] - # every item is a tuple (singer, song) - golden_songs = [s.split("#")[:2] for s in golden_samples] - # singer#song, eg:fem_01#neg_01 - return golden_songs - - -def opera_statistics(data_dir): - singers = [] - songs = [] - singers2songs = defaultdict(lambda: defaultdict(list)) - - singer_infos = glob(data_dir + "/*") - - for singer_info in singer_infos: - singer = singer_info.split("/")[-1] - - song_infos = glob(singer_info + "/*") - - for song_info in song_infos: - song = song_info.split("/")[-1] - - singers.append(singer) - songs.append(song) - - utts = glob(song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - singers2songs[singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "opera: {} singers, {} utterances ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singers2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for opera...\n") - - if not os.path.exists(os.path.join(dataset_path, "utterances")): - print("Spliting into utterances...\n") - _main(dataset_path) - - save_dir = os.path.join(output_path, "opera") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - opera_path = os.path.join(dataset_path, "utterances") - - singers2songs, unique_singers = opera_statistics(opera_path) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for singer, songs in tqdm(singers2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in song_names: - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "opera", - "Singer": singer, - "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid) - res["Path"] = os.path.join(opera_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if duration <= 1e-8: - continue - - if ([singer, chosen_song]) in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/pjs.py b/preprocessors/pjs.py deleted file mode 100644 index 78d69bc56cae59b3bf512078e365853ccad053ff..0000000000000000000000000000000000000000 --- a/preprocessors/pjs.py +++ /dev/null @@ -1,135 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -from tqdm import tqdm -import glob -import json -import torchaudio - -from utils.util import has_existed -from utils.io import save_audio - - -def get_splitted_utterances( - raw_wav_dir, trimed_wav_dir, n_utterance_splits, overlapping -): - res = [] - raw_song_files = glob.glob( - os.path.join(raw_wav_dir, "**/pjs*_song.wav"), recursive=True - ) - trimed_song_files = glob.glob( - os.path.join(trimed_wav_dir, "**/*.wav"), recursive=True - ) - - if len(raw_song_files) * n_utterance_splits == len(trimed_song_files): - print("Splitted done...") - for wav_file in tqdm(trimed_song_files): - uid = wav_file.split("/")[-1].split(".")[0] - utt = {"Dataset": "pjs", "Singer": "male1", "Uid": uid, "Path": wav_file} - - waveform, sample_rate = torchaudio.load(wav_file) - duration = waveform.size(-1) / sample_rate - utt["Duration"] = duration - - res.append(utt) - - else: - for wav_file in tqdm(raw_song_files): - song_id = wav_file.split("/")[-1].split(".")[0] - - waveform, sample_rate = torchaudio.load(wav_file) - trimed_waveform = torchaudio.functional.vad(waveform, sample_rate) - trimed_waveform = torchaudio.functional.vad( - trimed_waveform.flip(dims=[1]), sample_rate - ).flip(dims=[1]) - - audio_len = trimed_waveform.size(-1) - lapping_len = overlapping * sample_rate - - for i in range(n_utterance_splits): - start = i * audio_len // 3 - end = start + audio_len // 3 + lapping_len - splitted_waveform = trimed_waveform[:, start:end] - - utt = { - "Dataset": "pjs", - "Singer": "male1", - "Uid": "{}_{}".format(song_id, i), - } - - # Duration - duration = splitted_waveform.size(-1) / sample_rate - utt["Duration"] = duration - - # Save trimed wav - splitted_waveform_file = os.path.join( - trimed_wav_dir, "{}.wav".format(utt["Uid"]) - ) - save_audio(splitted_waveform_file, splitted_waveform, sample_rate) - - # Path - utt["Path"] = splitted_waveform_file - - res.append(utt) - - res = sorted(res, key=lambda x: x["Uid"]) - return res - - -def main(output_path, dataset_path, n_utterance_splits=3, overlapping=1): - """ - 1. Split one raw utterance to three splits (since some samples are too long) - 2. Overlapping of ajacent splits is 1 s - """ - print("-" * 10) - print("Preparing training dataset for PJS...") - - save_dir = os.path.join(output_path, "pjs") - raw_wav_dir = os.path.join(dataset_path, "PJS_corpus_ver1.1") - - # Trim for silence - trimed_wav_dir = os.path.join(dataset_path, "trim") - os.makedirs(trimed_wav_dir, exist_ok=True) - - # Total utterances - utterances = get_splitted_utterances( - raw_wav_dir, trimed_wav_dir, n_utterance_splits, overlapping - ) - total_uids = [utt["Uid"] for utt in utterances] - - # Test uids - n_test_songs = 3 - test_uids = [] - for i in range(1, n_test_songs + 1): - test_uids += [ - "pjs00{}_song_{}".format(i, split_id) - for split_id in range(n_utterance_splits) - ] - - # Train uids - train_uids = [uid for uid in total_uids if uid not in test_uids] - - for dataset_type in ["train", "test"]: - output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) - if has_existed(output_file): - continue - - uids = eval("{}_uids".format(dataset_type)) - res = [utt for utt in utterances if utt["Uid"] in uids] - for i in range(len(res)): - res[i]["index"] = i - - time = sum([utt["Duration"] for utt in res]) - print( - "{}, Total size: {}, Total Duraions = {} s = {:.2f} hour\n".format( - dataset_type, len(res), time, time / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(output_file, "w") as f: - json.dump(res, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/popbutfy.py b/preprocessors/popbutfy.py deleted file mode 100644 index 72ba7bdd60f6d370a7ff39bbb90d695f1157a4b4..0000000000000000000000000000000000000000 --- a/preprocessors/popbutfy.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -import librosa -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["popbutfy"] - # every item is a tuple (singer, song) - golden_songs = [s.split("#")[:2] for s in golden_samples] - # singer#song, eg: Female1#Almost_lover_Amateur - return golden_songs - - -def popbutfy_statistics(data_dir): - singers = [] - songs = [] - singer2songs = defaultdict(lambda: defaultdict(list)) - - data_infos = glob(data_dir + "/*") - - for data_info in data_infos: - data_info_split = data_info.split("/")[-1].split("#") - - singer, song = data_info_split[0], data_info_split[-1] - singers.append(singer) - songs.append(song) - - utts = glob(data_info + "/*") - - for utt in utts: - uid = utt.split("/")[-1].split("_")[-1].split(".")[0] - singer2songs[singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "PopBuTFy: {} singers, {} utterances ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return singer2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for popbutfy...\n") - - save_dir = os.path.join(output_path, "popbutfy") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - popbutfy_dir = dataset_path - - singer2songs, unique_singers = popbutfy_statistics(popbutfy_dir) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for singer, songs in tqdm(singer2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in song_names: - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "popbutfy", - "Singer": singer, - "Song": chosen_song, - "Uid": "{}#{}#".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}#singing#{}/{}#singing#{}_{}.mp3".format( - singer, chosen_song, singer, chosen_song, chosen_uid - ) - if not os.path.exists(os.path.join(popbutfy_dir, res["Path"])): - res["Path"] = "{}#singing#{}/{}#singing#{}_{}.wav".format( - singer, chosen_song, singer, chosen_song, chosen_uid - ) - res["Path"] = os.path.join(popbutfy_dir, res["Path"]) - assert os.path.exists(res["Path"]) - - if res["Path"].split("/")[-1].split(".")[-1] == "wav": - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - else: - waveform, sample_rate = librosa.load(res["Path"]) - duration = waveform.shape[-1] / sample_rate - res["Duration"] = duration - - if ([singer, chosen_song]) in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/popcs.py b/preprocessors/popcs.py deleted file mode 100644 index 9acf2127ec27f679d715108495f580a30f75d01a..0000000000000000000000000000000000000000 --- a/preprocessors/popcs.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -from glob import glob -from collections import defaultdict - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def get_test_songs(): - golden_samples = GOLDEN_TEST_SAMPLES["popcs"] - # every item is a string - golden_songs = [s.split("_")[:1] for s in golden_samples] - # song, eg: 万有引力 - return golden_songs - - -def popcs_statistics(data_dir): - songs = [] - songs2utts = defaultdict(list) - - song_infos = glob(data_dir + "/*") - - for song_info in song_infos: - song_info_split = song_info.split("/")[-1].split("-")[-1] - - songs.append(song_info_split) - - utts = glob(song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split("_")[0] - songs2utts[song_info_split].append(uid) - - unique_songs = list(set(songs)) - unique_songs.sort() - - print( - "popcs: {} utterances ({} unique songs)".format(len(songs), len(unique_songs)) - ) - print("Songs: \n{}".format("\t".join(unique_songs))) - return songs2utts - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for popcs...\n") - - save_dir = os.path.join(output_path, "popcs") - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - if has_existed(test_output_file): - return - - # Load - popcs_dir = dataset_path - - songs2utts = popcs_statistics(popcs_dir) - test_songs = get_test_songs() - - # We select songs of standard samples as test songs - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - song_names = list(songs2utts.keys()) - - for chosen_song in song_names: - for chosen_uid in songs2utts[chosen_song]: - res = { - "Dataset": "popcs", - "Singer": "female1", - "Song": chosen_song, - "Uid": "{}_{}".format(chosen_song, chosen_uid), - } - res["Path"] = "popcs-{}/{}_wf0.wav".format(chosen_song, chosen_uid) - res["Path"] = os.path.join(popcs_dir, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - if ([chosen_song]) in test_songs: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save - os.makedirs(save_dir, exist_ok=True) - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/processor.py b/preprocessors/processor.py deleted file mode 100644 index 5661d74f0d27f9c3c949d82a41b010e6760b3802..0000000000000000000000000000000000000000 --- a/preprocessors/processor.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import re -from preprocessors import ( - m4singer, - opencpop, - svcc, - pjs, - popbutfy, - opensinger, - popcs, - kising, - csd, - opera, - nus48e, - svcceval, - vctk, - vctksample, - libritts, - lijian, - cdmusiceval, - ljspeech, - coco, - cocoeval, - custom, - vocalist, - ljspeech_vocoder, -) - - -def preprocess_dataset( - dataset, dataset_path, output_path, cfg, is_custom_dataset=False -): - """Call specific function to handle specific dataset - Args: - dataset (str): name of a dataset, e.g. opencpop, m4singer - dataset_path (str): path to dataset - output_path (str): path to store preprocessing result files - """ - if is_custom_dataset: - custom.main(output_path, dataset_path, dataset_name=dataset) - return - - if re.match("opencpop*", dataset): - opencpop.main(dataset, output_path, dataset_path) - if dataset == "m4singer": - m4singer.main(output_path, dataset_path) - if dataset == "svcc": - svcc.main(output_path, dataset_path) - if dataset == "pjs": - pjs.main(output_path, dataset_path) - if dataset == "popbutfy": - popbutfy.main(output_path, dataset_path) - if dataset == "opensinger": - opensinger.main(output_path, dataset_path) - if dataset == "popcs": - popcs.main(output_path, dataset_path) - if dataset == "kising": - kising.main(output_path, dataset_path) - if dataset == "csd": - csd.main(output_path, dataset_path) - if dataset == "opera": - opera.main(output_path, dataset_path) - if dataset == "nus48e": - nus48e.main(output_path, dataset_path) - if dataset == "vctk": - vctk.main(output_path, dataset_path) - if dataset == "svcceval": - svcceval.main(output_path, dataset_path) - if dataset == "libritts": - libritts.main(output_path, dataset_path) - if dataset == "lijian": - lijian.main(output_path, dataset_path) - if dataset == "cdmusiceval": - cdmusiceval.main(output_path, dataset_path) - if dataset == "LJSpeech": - ljspeech.main(output_path, dataset_path, cfg) - if dataset == "ljspeech": - ljspeech_vocoder.main(output_path, dataset_path) - if dataset == "coco": - coco.main(output_path, dataset_path) - if dataset == "cocoeval": - cocoeval.main(output_path, dataset_path) - if dataset == "vocalist": - vocalist.main(output_path, dataset_path) - - -def prepare_align(dataset, dataset_path, cfg, output_path): - """Call specific function to handle specific dataset - - Args: - dataset (str): name of a dataset, e.g. ljspeech - dataset_path (str): path to dataset - output_path (str): path to store preprocessing result files - """ - if dataset == "LJSpeech": - ljspeech.prepare_align(dataset, dataset_path, cfg, output_path) diff --git a/preprocessors/svcc.py b/preprocessors/svcc.py deleted file mode 100644 index 6afee6197146eff9464839ea7844edbb64e76211..0000000000000000000000000000000000000000 --- a/preprocessors/svcc.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import glob -import librosa -import json - -from utils.util import has_existed -from preprocessors import GOLDEN_TEST_SAMPLES - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing training dataset for svcc...") - - data_dir = os.path.join(dataset_path, "Data") - save_dir = os.path.join(output_path, "svcc") - os.makedirs(save_dir, exist_ok=True) - - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - utt2singer = open(utt2singer_file, "w") - - # Load utterances - train = [] - test = [] - singers = [] - - for wav_file in glob.glob(os.path.join(data_dir, "*/*.wav")): - singer, filename = wav_file.split("/")[-2:] - uid = filename.split(".")[0] - utt = { - "Dataset": "svcc", - "Singer": singer, - "Uid": "{}_{}".format(singer, uid), - "Path": wav_file, - } - - # Duration - duration = librosa.get_duration(filename=wav_file) - utt["Duration"] = duration - - if utt["Uid"] in GOLDEN_TEST_SAMPLES["svcc"]: - test.append(utt) - else: - train.append(utt) - - singers.append(singer) - utt2singer.write("{}\t{}\n".format(utt["Uid"], utt["Singer"])) - - # Save singers.json - unique_singers = list(set(singers)) - unique_singers.sort() - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) - - train_total_duration = sum([utt["Duration"] for utt in train]) - test_total_duration = sum([utt["Duration"] for utt in test]) - - for dataset_type in ["train", "test"]: - output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) - if has_existed(output_file): - continue - - utterances = eval(dataset_type) - utterances = sorted(utterances, key=lambda x: x["Uid"]) - - for i in range(len(utterances)): - utterances[i]["index"] = i - - print("{}: Total size: {}\n".format(dataset_type, len(utterances))) - - # Save - with open(output_file, "w") as f: - json.dump(utterances, f, indent=4, ensure_ascii=False) - - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) diff --git a/preprocessors/svcceval.py b/preprocessors/svcceval.py deleted file mode 100644 index 871e78956d8e9d323125aaa79770f02176e2426c..0000000000000000000000000000000000000000 --- a/preprocessors/svcceval.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import glob -import librosa -import json - -from utils.util import has_existed - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing training dataset for svcceval...") - - data_dir = os.path.join(dataset_path, "Data") - save_dir = os.path.join(output_path, "svcceval") - os.makedirs(save_dir, exist_ok=True) - - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - utt2singer = open(utt2singer_file, "w") - - # Load utterances - train = [] - test = [] - singers = [] - for wav_file in glob.glob(os.path.join(data_dir, "*/*.wav")): - singer, filename = wav_file.split("/")[-2:] - uid = filename.split(".")[0] - utt = { - "Dataset": "svcceval", - "Singer": singer, - "Uid": "{}_{}".format(singer, uid), - "Path": wav_file, - } - - # Duration - duration = librosa.get_duration(filename=wav_file) - utt["Duration"] = duration - - test.append(utt) - - singers.append(singer) - utt2singer.write("{}\t{}\n".format(utt["Uid"], utt["Singer"])) - - # Save singers.json - unique_singers = list(set(singers)) - unique_singers.sort() - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) - - train_total_duration = sum([utt["Duration"] for utt in train]) - test_total_duration = sum([utt["Duration"] for utt in test]) - - for dataset_type in ["train", "test"]: - output_file = os.path.join(save_dir, "{}.json".format(dataset_type)) - if has_existed(output_file): - continue - - utterances = eval(dataset_type) - utterances = sorted(utterances, key=lambda x: x["Uid"]) - - for i in range(len(utterances)): - utterances[i]["index"] = i - - print("{}: Total size: {}\n".format(dataset_type, len(utterances))) - - # Save - with open(output_file, "w") as f: - json.dump(utterances, f, indent=4, ensure_ascii=False) - - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) diff --git a/preprocessors/vctk.py b/preprocessors/vctk.py deleted file mode 100644 index aa4c1c69414561f6e40c69acc653630ba783abd1..0000000000000000000000000000000000000000 --- a/preprocessors/vctk.py +++ /dev/null @@ -1,163 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import librosa -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed - - -def get_lines(file): - with open(file, "r") as f: - lines = f.readlines() - lines = [l.strip() for l in lines] - return lines - - -def vctk_statistics(data_dir): - speakers = [] - speakers2utts = defaultdict(list) - - speaker_infos = glob(data_dir + "/wav48_silence_trimmed" + "/*") - - for speaker_info in speaker_infos: - speaker = speaker_info.split("/")[-1] - - if speaker == "log.txt": - continue - - speakers.append(speaker) - - utts = glob(speaker_info + "/*") - - for utt in utts: - uid = ( - utt.split("/")[-1].split("_")[1] - + "_" - + utt.split("/")[-1].split("_")[2].split(".")[0] - ) - speakers2utts[speaker].append(uid) - - unique_speakers = list(set(speakers)) - unique_speakers.sort() - - print("Speakers: \n{}".format("\t".join(unique_speakers))) - return speakers2utts, unique_speakers - - -def vctk_speaker_infos(data_dir): - file = os.path.join(data_dir, "speaker-info.txt") - lines = get_lines(file) - - ID2speakers = defaultdict() - for l in tqdm(lines): - items = l.replace(" ", "") - - if items[:2] == "ID": - # The header line - continue - - if items[0] == "p": - id = items[:4] - gender = items[6] - elif items[0] == "s": - id = items[:2] - gender = items[4] - - if gender == "F": - speaker = "female_{}".format(id) - elif gender == "M": - speaker = "male_{}".format(id) - - ID2speakers[id] = speaker - - return ID2speakers - - -def main(output_path, dataset_path, TEST_NUM_OF_EVERY_SPEAKER=3): - print("-" * 10) - print("Preparing test samples for vctk...") - - save_dir = os.path.join(output_path, "vctk") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if has_existed(train_output_file): - return - utt2singer = open(utt2singer_file, "w") - - # Load - vctk_dir = dataset_path - - ID2speakers = vctk_speaker_infos(vctk_dir) - speaker2utts, unique_speakers = vctk_statistics(vctk_dir) - - # We select speakers of standard samples as test utts - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - test_speaker_count = defaultdict(int) - - train_total_duration = 0 - test_total_duration = 0 - - for i, speaker in enumerate(speaker2utts.keys()): - for chosen_uid in tqdm( - speaker2utts[speaker], - desc="Speaker {}/{}, #Train = {}, #Test = {}".format( - i + 1, len(speaker2utts), train_index_count, test_index_count - ), - ): - res = { - "Dataset": "vctk", - "Singer": ID2speakers[speaker], - "Uid": "{}#{}".format(ID2speakers[speaker], chosen_uid), - } - res["Path"] = "{}/{}_{}.flac".format(speaker, speaker, chosen_uid) - res["Path"] = os.path.join(vctk_dir, "wav48_silence_trimmed", res["Path"]) - assert os.path.exists(res["Path"]) - - duration = librosa.get_duration(filename=res["Path"]) - res["Duration"] = duration - - if test_speaker_count[speaker] < TEST_NUM_OF_EVERY_SPEAKER: - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - test_speaker_count[speaker] += 1 - else: - res["index"] = train_index_count - train_total_duration += duration - train.append(res) - train_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_speakers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/preprocessors/vctkfewsinger.py b/preprocessors/vctkfewsinger.py deleted file mode 100644 index e4b6adc76597b18ccbbf065ef339ccff77032a52..0000000000000000000000000000000000000000 --- a/preprocessors/vctkfewsinger.py +++ /dev/null @@ -1,175 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import pickle -import glob -from collections import defaultdict -from tqdm import tqdm - - -# Train: male 20 hours, female 10 hours -TRAIN_MALE_MAX_SECONDS = 20 * 3600 -TRAIN_FEMALE_MAX_SECONDS = 10 * 3600 -TEST_MAX_NUM_EVERY_PERSON = 5 - - -def select_sample_idxs(): - chosen_speakers = get_chosen_speakers() - - with open(os.path.join(vctk_dir, "train.json"), "r") as f: - raw_train = json.load(f) - with open(os.path.join(vctk_dir, "test.json"), "r") as f: - raw_test = json.load(f) - - train_idxs, test_idxs = [], [] - - # =========== Test =========== - test_nums = defaultdict(int) - for utt in tqdm(raw_train): - idx = utt["index"] - singer = utt["Singer"] - - if singer in chosen_speakers and test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON: - test_nums[singer] += 1 - test_idxs.append("train_{}".format(idx)) - - for utt in tqdm(raw_test): - idx = utt["index"] - singer = utt["Singer"] - - if singer in chosen_speakers and test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON: - test_nums[singer] += 1 - test_idxs.append("test_{}".format(idx)) - - # =========== Train =========== - for utt in tqdm(raw_train): - idx = utt["index"] - singer = utt["Singer"] - - if singer in chosen_speakers and "train_{}".format(idx) not in test_idxs: - train_idxs.append("train_{}".format(idx)) - - for utt in tqdm(raw_test): - idx = utt["index"] - singer = utt["Singer"] - - if singer in chosen_speakers and "test_{}".format(idx) not in test_idxs: - train_idxs.append("test_{}".format(idx)) - - train_idxs.sort() - test_idxs.sort() - return train_idxs, test_idxs, raw_train, raw_test - - -def statistics_of_speakers(): - speaker2time = defaultdict(float) - sex2time = defaultdict(float) - - with open(os.path.join(vctk_dir, "train.json"), "r") as f: - train = json.load(f) - with open(os.path.join(vctk_dir, "test.json"), "r") as f: - test = json.load(f) - - for utt in train + test: - # minutes - speaker2time[utt["Singer"]] += utt["Duration"] - # hours - sex2time[utt["Singer"].split("_")[0]] += utt["Duration"] - - print( - "Female: {:.2f} hours, Male: {:.2f} hours.\n".format( - sex2time["female"] / 3600, sex2time["male"] / 3600 - ) - ) - - speaker2time = sorted(speaker2time.items(), key=lambda x: x[-1], reverse=True) - for singer, seconds in speaker2time: - print("{}\t{:.2f} mins".format(singer, seconds / 60)) - - return speaker2time - - -def get_chosen_speakers(): - speaker2time = statistics_of_speakers() - - chosen_time = defaultdict(float) - chosen_speaker = defaultdict(list) - train_constrait = { - "male": TRAIN_MALE_MAX_SECONDS, - "female": TRAIN_FEMALE_MAX_SECONDS, - } - - for speaker, seconds in speaker2time: - sex = speaker.split("_")[0] - if chosen_time[sex] < train_constrait[sex]: - chosen_time[sex] += seconds - chosen_speaker[sex].append(speaker) - - speaker2time = dict(speaker2time) - chosen_speaker = chosen_speaker["male"] + chosen_speaker["female"] - print("\n#Chosen speakers = {}".format(len(chosen_speaker))) - for spk in chosen_speaker: - print("{}\t{:.2f} mins".format(spk, speaker2time[spk] / 60)) - - return chosen_speaker - - -if __name__ == "__main__": - root_path = "" - vctk_dir = os.path.join(root_path, "vctk") - fewspeaker_dir = os.path.join(root_path, "vctkfewspeaker") - os.makedirs(fewspeaker_dir, exist_ok=True) - - train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs() - print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs))) - - # There are no data leakage - assert len(set(train_idxs).intersection(set(test_idxs))) == 0 - for idx in train_idxs + test_idxs: - # No test data of raw vctk - assert "test_" not in idx - - for split, chosen_idxs in zip(["train", "test"], [train_idxs, test_idxs]): - print("{}: #chosen idx = {}\n".format(split, len(chosen_idxs))) - - # Select features - feat_files = glob.glob("**/train.pkl", root_dir=vctk_dir, recursive=True) - for file in tqdm(feat_files): - raw_file = os.path.join(vctk_dir, file) - new_file = os.path.join( - fewspeaker_dir, file.replace("train.pkl", "{}.pkl".format(split)) - ) - - new_dir = "/".join(new_file.split("/")[:-1]) - os.makedirs(new_dir, exist_ok=True) - - if "mel_min" in file or "mel_max" in file: - os.system("cp {} {}".format(raw_file, new_file)) - continue - - with open(raw_file, "rb") as f: - raw_feats = pickle.load(f) - - print("file: {}, #raw_feats = {}".format(file, len(raw_feats))) - new_feats = [] - for idx in chosen_idxs: - chosen_split_is_train, raw_idx = idx.split("_") - assert chosen_split_is_train == "train" - new_feats.append(raw_feats[int(raw_idx)]) - - with open(new_file, "wb") as f: - pickle.dump(new_feats, f) - print("New file: {}, #new_feats = {}".format(new_file, len(new_feats))) - - # Utterance re-index - news_utts = [raw_train[int(idx.split("_")[-1])] for idx in chosen_idxs] - for i, utt in enumerate(news_utts): - utt["Dataset"] = "vctkfewsinger" - utt["index"] = i - - with open(os.path.join(fewspeaker_dir, "{}.json".format(split)), "w") as f: - json.dump(news_utts, f, indent=4) diff --git a/preprocessors/vctksample.py b/preprocessors/vctksample.py deleted file mode 100644 index 476790f3941eb07cfe6ed9051293004b4e588f24..0000000000000000000000000000000000000000 --- a/preprocessors/vctksample.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import pickle -import glob -from collections import defaultdict -from tqdm import tqdm -from preprocessors import get_golden_samples_indexes - - -TRAIN_MAX_NUM_EVERY_PERSON = 250 -TEST_MAX_NUM_EVERY_PERSON = 25 - - -def select_sample_idxs(): - # =========== Train =========== - with open(os.path.join(vctk_dir, "train.json"), "r") as f: - raw_train = json.load(f) - - train_idxs = [] - train_nums = defaultdict(int) - for utt in tqdm(raw_train): - idx = utt["index"] - singer = utt["Singer"] - - if train_nums[singer] < TRAIN_MAX_NUM_EVERY_PERSON: - train_idxs.append(idx) - train_nums[singer] += 1 - - # =========== Test =========== - with open(os.path.join(vctk_dir, "test.json"), "r") as f: - raw_test = json.load(f) - - # golden test - test_idxs = get_golden_samples_indexes( - dataset_name="vctk", split="test", dataset_dir=vctk_dir - ) - test_nums = defaultdict(int) - for idx in test_idxs: - singer = raw_test[idx]["Singer"] - test_nums[singer] += 1 - - for utt in tqdm(raw_test): - idx = utt["index"] - singer = utt["Singer"] - - if test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON: - test_idxs.append(idx) - test_nums[singer] += 1 - - train_idxs.sort() - test_idxs.sort() - return train_idxs, test_idxs, raw_train, raw_test - - -if __name__ == "__main__": - root_path = "" - vctk_dir = os.path.join(root_path, "vctk") - sample_dir = os.path.join(root_path, "vctksample") - os.makedirs(sample_dir, exist_ok=True) - - train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs() - print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs))) - - for split, chosen_idxs, utterances in zip( - ["train", "test"], [train_idxs, test_idxs], [raw_train, raw_test] - ): - print( - "#{} = {}, #chosen idx = {}\n".format( - split, len(utterances), len(chosen_idxs) - ) - ) - - # Select features - feat_files = glob.glob( - "**/{}.pkl".format(split), root_dir=vctk_dir, recursive=True - ) - for file in tqdm(feat_files): - raw_file = os.path.join(vctk_dir, file) - new_file = os.path.join(sample_dir, file) - - new_dir = "/".join(new_file.split("/")[:-1]) - os.makedirs(new_dir, exist_ok=True) - - if "mel_min" in file or "mel_max" in file: - os.system("cp {} {}".format(raw_file, new_file)) - continue - - with open(raw_file, "rb") as f: - raw_feats = pickle.load(f) - - print("file: {}, #raw_feats = {}".format(file, len(raw_feats))) - new_feats = [raw_feats[idx] for idx in chosen_idxs] - with open(new_file, "wb") as f: - pickle.dump(new_feats, f) - - # Utterance re-index - news_utts = [utterances[idx] for idx in chosen_idxs] - for i, utt in enumerate(news_utts): - utt["Dataset"] = "vctksample" - utt["index"] = i - - with open(os.path.join(sample_dir, "{}.json".format(split)), "w") as f: - json.dump(news_utts, f, indent=4) diff --git a/preprocessors/vocalist.py b/preprocessors/vocalist.py deleted file mode 100644 index 44de1ac00ebcf10d65e6af37a98afcfe6ae85b89..0000000000000000000000000000000000000000 --- a/preprocessors/vocalist.py +++ /dev/null @@ -1,137 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import torchaudio -from tqdm import tqdm -from glob import glob -from collections import defaultdict - -from utils.util import has_existed - - -def vocalist_statistics(data_dir): - singers = [] - songs = [] - global2singer2songs = defaultdict(lambda: defaultdict(lambda: defaultdict(list))) - - global_infos = glob(data_dir + "/*") - - for global_info in global_infos: - global_split = global_info.split("/")[-1] - - singer_infos = glob(global_info + "/*") - - for singer_info in singer_infos: - singer = singer_info.split("/")[-1] - - singers.append(singer) - - song_infos = glob(singer_info + "/*") - for song_info in song_infos: - song = song_info.split("/")[-1] - - songs.append(song) - - utts = glob(song_info + "/*.wav") - - for utt in utts: - uid = utt.split("/")[-1].split(".")[0] - global2singer2songs[global_split][singer][song].append(uid) - - unique_singers = list(set(singers)) - unique_songs = list(set(songs)) - unique_singers.sort() - unique_songs.sort() - - print( - "vocalist: {} singers, {} songs ({} unique songs)".format( - len(unique_singers), len(songs), len(unique_songs) - ) - ) - print("Singers: \n{}".format("\t".join(unique_singers))) - return global2singer2songs, unique_singers - - -def main(output_path, dataset_path): - print("-" * 10) - print("Preparing test samples for vocalist...\n") - - save_dir = os.path.join(output_path, "vocalist") - os.makedirs(save_dir, exist_ok=True) - train_output_file = os.path.join(save_dir, "train.json") - test_output_file = os.path.join(save_dir, "test.json") - singer_dict_file = os.path.join(save_dir, "singers.json") - utt2singer_file = os.path.join(save_dir, "utt2singer") - if ( - has_existed(train_output_file) - and has_existed(test_output_file) - and has_existed(singer_dict_file) - and has_existed(utt2singer_file) - ): - return - utt2singer = open(utt2singer_file, "w") - - # Load - vocalist_path = dataset_path - - global2singer2songs, unique_singers = vocalist_statistics(vocalist_path) - - train = [] - test = [] - - train_index_count = 0 - test_index_count = 0 - - train_total_duration = 0 - test_total_duration = 0 - - for global_info, singer2songs in tqdm(global2singer2songs.items()): - for singer, songs in tqdm(singer2songs.items()): - song_names = list(songs.keys()) - - for chosen_song in song_names: - for chosen_uid in songs[chosen_song]: - res = { - "Dataset": "opensinger", - "Singer": singer, - "Song": chosen_song, - "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid), - } - res["Path"] = "{}/{}/{}/{}.wav".format( - global_info, singer, chosen_song, chosen_uid - ) - res["Path"] = os.path.join(vocalist_path, res["Path"]) - assert os.path.exists(res["Path"]) - - waveform, sample_rate = torchaudio.load(res["Path"]) - duration = waveform.size(-1) / sample_rate - res["Duration"] = duration - - res["index"] = test_index_count - test_total_duration += duration - test.append(res) - test_index_count += 1 - - utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"])) - - print("#Train = {}, #Test = {}".format(len(train), len(test))) - print( - "#Train hours= {}, #Test hours= {}".format( - train_total_duration / 3600, test_total_duration / 3600 - ) - ) - - # Save train.json and test.json - with open(train_output_file, "w") as f: - json.dump(train, f, indent=4, ensure_ascii=False) - with open(test_output_file, "w") as f: - json.dump(test, f, indent=4, ensure_ascii=False) - - # Save singers.json - singer_lut = {name: i for i, name in enumerate(unique_singers)} - with open(singer_dict_file, "w") as f: - json.dump(singer_lut, f, indent=4, ensure_ascii=False) diff --git a/pretrained/bigvgan/args.json b/pretrained/bigvgan/args.json deleted file mode 100644 index 06878c8cc6e7c667b107836a51c1d577c15fa7b1..0000000000000000000000000000000000000000 --- a/pretrained/bigvgan/args.json +++ /dev/null @@ -1,235 +0,0 @@ -{ - "base_config": "egs/vocoder/gan/exp_config_base.json", - "exp_name": "bigvgan_large", - "inference": { - "batch_size": 1, - }, - "model": { - "bigvgan": { - "activation": "snakebeta", - "resblock": "1", - "resblock_dilation_sizes": [ - [ - 1, - 3, - 5, - ], - [ - 1, - 3, - 5, - ], - [ - 1, - 3, - 5, - ], - ], - "resblock_kernel_sizes": [ - 3, - 7, - 11, - ], - "snake_logscale": true, - "upsample_initial_channel": 1536, - "upsample_kernel_sizes": [ - 8, - 8, - 4, - 4, - 4, - 4, - ], - "upsample_rates": [ - 4, - 4, - 2, - 2, - 2, - 2, - ], - }, - "discriminators": [ - "mpd", - "msstftd", - ], - "generator": "bigvgan", - "mpd": { - "discriminator_channel_multi": 1, - "mpd_reshapes": [ - 2, - 3, - 5, - 7, - 11, - ], - "use_spectral_norm": false, - }, - "mrd": { - "discriminator_channel_multi": 1, - "mrd_override": false, - "resolutions": [ - [ - 1024, - 120, - 600, - ], - [ - 2048, - 240, - 1200, - ], - [ - 512, - 50, - 240, - ], - ], - "use_spectral_norm": false, - }, - "msstftd": { - "filters": 32, - }, - }, - "model_type": "GANVocoder", - "preprocess": { - "audio_dir": "audios", - "bits": 8, - "contentvec_dir": "contentvec", - "cut_mel_frame": 32, - "data_augment": false, - "dur_dir": "durs", - "duration_dir": "duration", - "emo2id": "emo2id.json", - "energy_dir": "energys", - "energy_extract_mode": "from_mel", - "energy_norm": false, - "extract_audio": true, - "extract_contentvec_feature": false, - "extract_duration": false, - "extract_energy": false, - "extract_label": false, - "extract_mcep": false, - "extract_mel": true, - "extract_mert_feature": false, - "extract_one_hot": false, - "extract_pitch": false, - "extract_uv": false, - "extract_wenet_feature": false, - "extract_whisper_feature": false, - "f0_max": 1100, - "f0_min": 50, - "file_lst": "file.lst", - "fmax": 12000, - "fmin": 0, - "hop_size": 256, - "is_mu_law": false, - "lab_dir": "labs", - "label_dir": "labels", - "mcep_dir": "mcep", - "mel_dir": "mels", - "mel_min_max_norm": false, - "min_level_db": -115, - "n_fft": 1024, - "n_mel": 100, - "num_silent_frames": 8, - "phone_seq_file": "phone_seq_file", - "pitch_bin": 256, - "pitch_dir": "pitches", - "pitch_extractor": "parselmouth", - "pitch_max": 1100.0, - "pitch_min": 50.0, - "pitch_norm": false, - "processed_dir": "processed_data", - "ref_level_db": 20, - "sample_rate": 24000, - "spk2id": "singers.json", - "train_file": "train.json", - "trim_fft_size": 512, - "trim_hop_size": 128, - "trim_silence": false, - "trim_top_db": 30, - "trimmed_wav_dir": "trimmed_wavs", - "use_audio": true, - "use_dur": false, - "use_emoid": false, - "use_frame_duration": false, - "use_frame_energy": false, - "use_frame_pitch": false, - "use_lab": false, - "use_label": false, - "use_log_scale_energy": false, - "use_log_scale_pitch": false, - "use_mel": true, - "use_one_hot": false, - "use_phn_seq": false, - "use_phone_duration": false, - "use_phone_energy": false, - "use_phone_pitch": false, - "use_spkid": false, - "use_uv": false, - "use_wav": false, - "use_wenet": false, - "utt2emo": "utt2emo", - "utt2spk": "utt2spk", - "uv_dir": "uvs", - "valid_file": "test.json", - "wav_dir": "wavs", - "wenet_dir": "wenet", - "win_size": 1024, - }, - "supported_model_type": [ - "GANVocoder", - "Fastspeech2", - "DiffSVC", - "Transformer", - "EDM", - "CD", - ], - "train": { - "adamw": { - "adam_b1": 0.8, - "adam_b2": 0.99, - "lr": 0.0002, - }, - "batch_size": 4, - "criterions": [ - "feature", - "discriminator", - "generator", - "mel", - ], - "dataloader": { - "num_worker": 4, - "pin_memory": true, - }, - "ddp": true, - "epochs": 50000, - "exponential_lr": { - "lr_decay": 0.999, - }, - "gradient_accumulation_step": 1, - "keep_checkpoint_max": 5, - "max_epoch": 1000000, - "max_steps": 1000000, - "multi_speaker_training": false, - "random_seed": 114514, - "run_eval": [ - true, - ], - "sampler": { - "drop_last": true, - "holistic_shuffle": true, - }, - "save_checkpoint_stride": [ - 200, - ], - "save_checkpoints_steps": 10000, - "save_summary_steps": 500, - "total_training_steps": 50000, - "tracker": [ - "tensorboard", - ], - "valid_interval": 10000, - }, -} \ No newline at end of file diff --git a/pretrained/contentvec/README.md b/pretrained/contentvec/README.md deleted file mode 100644 index 6ea10938244c7282355be035c9489efa5bf08bdd..0000000000000000000000000000000000000000 --- a/pretrained/contentvec/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Download - -- [Link](https://github.com/auspicious3000/contentvec) -- Model: `ContentVec_legacy` -- Classes: 500 diff --git a/processors/__init__.py b/processors/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/processors/acoustic_extractor.py b/processors/acoustic_extractor.py deleted file mode 100644 index 24d096bd2b651d6cd9ae733daf3c501d0ca28bf3..0000000000000000000000000000000000000000 --- a/processors/acoustic_extractor.py +++ /dev/null @@ -1,903 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import torch -import numpy as np - -import json -from tqdm import tqdm -from sklearn.preprocessing import StandardScaler -from utils.io import save_feature, save_txt -from utils.util import has_existed -from utils.tokenizer import extract_encodec_token -from utils.stft import TacotronSTFT -from utils.dsp import compress, audio_to_label -from utils.data_utils import remove_outlier -from preprocessors.metadata import replace_augment_name -from scipy.interpolate import interp1d - -ZERO = 1e-12 - - -def extract_utt_acoustic_features_parallel(metadata, dataset_output, cfg, n_workers=1): - """Extract acoustic features from utterances using muliprocess - - Args: - metadata (dict): dictionary that stores data in train.json and test.json files - dataset_output (str): directory to store acoustic features - cfg (dict): dictionary that stores configurations - n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. - - Returns: - list: acoustic features - """ - for utt in tqdm(metadata): - if cfg.task_type == "tts": - extract_utt_acoustic_features_tts(dataset_output, cfg, utt) - if cfg.task_type == "svc": - extract_utt_acoustic_features_svc(dataset_output, cfg, utt) - if cfg.task_type == "vocoder": - extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt) - if cfg.task_type == "tta": - extract_utt_acoustic_features_tta(dataset_output, cfg, utt) - - -def avg_phone_feature(feature, duration, interpolation=False): - feature = feature[: sum(duration)] - if interpolation: - nonzero_ids = np.where(feature != 0)[0] - interp_fn = interp1d( - nonzero_ids, - feature[nonzero_ids], - fill_value=(feature[nonzero_ids[0]], feature[nonzero_ids[-1]]), - bounds_error=False, - ) - feature = interp_fn(np.arange(0, len(feature))) - - # Phoneme-level average - pos = 0 - for i, d in enumerate(duration): - if d > 0: - feature[i] = np.mean(feature[pos : pos + d]) - else: - feature[i] = 0 - pos += d - feature = feature[: len(duration)] - return feature - - -def extract_utt_acoustic_features_serial(metadata, dataset_output, cfg): - """Extract acoustic features from utterances (in single process) - - Args: - metadata (dict): dictionary that stores data in train.json and test.json files - dataset_output (str): directory to store acoustic features - cfg (dict): dictionary that stores configurations - - """ - for utt in tqdm(metadata): - if cfg.task_type == "tts": - extract_utt_acoustic_features_tts(dataset_output, cfg, utt) - if cfg.task_type == "svc": - extract_utt_acoustic_features_svc(dataset_output, cfg, utt) - if cfg.task_type == "vocoder": - extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt) - if cfg.task_type == "tta": - extract_utt_acoustic_features_tta(dataset_output, cfg, utt) - - -def __extract_utt_acoustic_features(dataset_output, cfg, utt): - """Extract acoustic features from utterances (in single process) - - Args: - dataset_output (str): directory to store acoustic features - cfg (dict): dictionary that stores configurations - utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, - path to utternace, duration, utternace index - - """ - from utils import audio, f0, world, duration - - uid = utt["Uid"] - wav_path = utt["Path"] - if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)): - wav_path = os.path.join( - dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav" - ) - - with torch.no_grad(): - # Load audio data into tensor with sample rate of the config file - wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) - wav = wav_torch.cpu().numpy() - - # extract features - if cfg.preprocess.extract_duration: - durations, phones, start, end = duration.get_duration( - utt, wav, cfg.preprocess - ) - save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations) - save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones) - wav = wav[start:end].astype(np.float32) - wav_torch = torch.from_numpy(wav).to(wav_torch.device) - - if cfg.preprocess.extract_linear_spec: - from utils.mel import extract_linear_features - - linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess) - save_feature( - dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy() - ) - - if cfg.preprocess.extract_mel: - from utils.mel import extract_mel_features - - if cfg.preprocess.mel_extract_mode == "taco": - _stft = TacotronSTFT( - sampling_rate=cfg.preprocess.sample_rate, - win_length=cfg.preprocess.win_size, - hop_length=cfg.preprocess.hop_size, - filter_length=cfg.preprocess.n_fft, - n_mel_channels=cfg.preprocess.n_mel, - mel_fmin=cfg.preprocess.fmin, - mel_fmax=cfg.preprocess.fmax, - ) - mel = extract_mel_features( - wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft - ) - if cfg.preprocess.extract_duration: - mel = mel[:, : sum(durations)] - else: - mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) - save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) - - if cfg.preprocess.extract_energy: - if ( - cfg.preprocess.energy_extract_mode == "from_mel" - and cfg.preprocess.extract_mel - ): - energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() - elif cfg.preprocess.energy_extract_mode == "from_waveform": - energy = audio.energy(wav, cfg.preprocess) - elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft": - _stft = TacotronSTFT( - sampling_rate=cfg.preprocess.sample_rate, - win_length=cfg.preprocess.win_size, - hop_length=cfg.preprocess.hop_size, - filter_length=cfg.preprocess.n_fft, - n_mel_channels=cfg.preprocess.n_mel, - mel_fmin=cfg.preprocess.fmin, - mel_fmax=cfg.preprocess.fmax, - ) - _, energy = audio.get_energy_from_tacotron(wav, _stft) - else: - assert cfg.preprocess.energy_extract_mode in [ - "from_mel", - "from_waveform", - "from_tacotron_stft", - ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" - if cfg.preprocess.extract_duration: - energy = energy[: sum(durations)] - phone_energy = avg_phone_feature(energy, durations) - save_feature( - dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy - ) - - save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) - - if cfg.preprocess.extract_pitch: - pitch = f0.get_f0(wav, cfg.preprocess) - if cfg.preprocess.extract_duration: - pitch = pitch[: sum(durations)] - phone_pitch = avg_phone_feature(pitch, durations, interpolation=True) - save_feature( - dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch - ) - save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) - - if cfg.preprocess.extract_uv: - assert isinstance(pitch, np.ndarray) - uv = pitch != 0 - save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) - - if cfg.preprocess.extract_audio: - save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav) - - if cfg.preprocess.extract_label: - if cfg.preprocess.is_mu_law: - # compress audio - wav = compress(wav, cfg.preprocess.bits) - label = audio_to_label(wav, cfg.preprocess.bits) - save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) - - if cfg.preprocess.extract_acoustic_token: - if cfg.preprocess.acoustic_token_extractor == "Encodec": - codes = extract_encodec_token(wav_path) - save_feature( - dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes - ) - - -def extract_utt_acoustic_features_tts(dataset_output, cfg, utt): - __extract_utt_acoustic_features(dataset_output, cfg, utt) - - -def extract_utt_acoustic_features_svc(dataset_output, cfg, utt): - """Extract acoustic features from utterances (in single process) - - Args: - dataset_output (str): directory to store acoustic features - cfg (dict): dictionary that stores configurations - utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, - path to utternace, duration, utternace index - - """ - from utils import audio, f0, world, duration - - uid = utt["Uid"] - wav_path = utt["Path"] - - with torch.no_grad(): - # Load audio data into tensor with sample rate of the config file - wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) - wav = wav_torch.cpu().numpy() - - # extract features - if cfg.preprocess.extract_mel: - from utils.mel import extract_mel_features - - mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) - save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) - - if cfg.preprocess.extract_energy: - energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() - save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) - - if cfg.preprocess.extract_pitch: - pitch = f0.get_f0(wav, cfg.preprocess) - save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) - - if cfg.preprocess.extract_uv: - assert isinstance(pitch, np.ndarray) - uv = pitch != 0 - save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) - - -def extract_utt_acoustic_features_tta(dataset_output, cfg, utt): - __extract_utt_acoustic_features(dataset_output, cfg, utt) - - -def extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt): - """Extract acoustic features from utterances (in single process) - - Args: - dataset_output (str): directory to store acoustic features - cfg (dict): dictionary that stores configurations - utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, - path to utternace, duration, utternace index - - """ - from utils import audio, f0, world, duration - - uid = utt["Uid"] - wav_path = utt["Path"] - - with torch.no_grad(): - # Load audio data into tensor with sample rate of the config file - wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) - wav = wav_torch.cpu().numpy() - - # extract features - if cfg.preprocess.extract_mel: - from utils.mel import extract_mel_features - - mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) - save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) - - if cfg.preprocess.extract_energy: - if ( - cfg.preprocess.energy_extract_mode == "from_mel" - and cfg.preprocess.extract_mel - ): - energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() - elif cfg.preprocess.energy_extract_mode == "from_waveform": - energy = audio.energy(wav, cfg.preprocess) - else: - assert cfg.preprocess.energy_extract_mode in [ - "from_mel", - "from_waveform", - ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" - - save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) - - if cfg.preprocess.extract_pitch: - pitch = f0.get_f0(wav, cfg.preprocess) - save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) - - if cfg.preprocess.extract_uv: - assert isinstance(pitch, np.ndarray) - uv = pitch != 0 - save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) - - if cfg.preprocess.extract_audio: - save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav) - - if cfg.preprocess.extract_label: - if cfg.preprocess.is_mu_law: - # compress audio - wav = compress(wav, cfg.preprocess.bits) - label = audio_to_label(wav, cfg.preprocess.bits) - save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) - - -def cal_normalized_mel(mel, dataset_name, cfg): - mel_min, mel_max = load_mel_extrema(cfg, dataset_name) - mel_norm = normalize_mel_channel(mel, mel_min, mel_max) - return mel_norm - - -def cal_mel_min_max(dataset, output_path, cfg, metadata=None): - dataset_output = os.path.join(output_path, dataset) - - if metadata is None: - metadata = [] - for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: - dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) - with open(dataset_file, "r") as f: - metadata.extend(json.load(f)) - - tmp_mel_min = [] - tmp_mel_max = [] - for item in metadata: - mel_path = os.path.join( - dataset_output, cfg.preprocess.mel_dir, item["Uid"] + ".npy" - ) - if not os.path.exists(mel_path): - continue - mel = np.load(mel_path) - if mel.shape[0] != cfg.preprocess.n_mel: - mel = mel.T - # mel: (n_mels, T) - assert mel.shape[0] == cfg.preprocess.n_mel - - tmp_mel_min.append(np.min(mel, axis=-1)) - tmp_mel_max.append(np.max(mel, axis=-1)) - - mel_min = np.min(tmp_mel_min, axis=0) - mel_max = np.max(tmp_mel_max, axis=0) - - ## save mel min max data - mel_min_max_dir = os.path.join(dataset_output, cfg.preprocess.mel_min_max_stats_dir) - os.makedirs(mel_min_max_dir, exist_ok=True) - - mel_min_path = os.path.join(mel_min_max_dir, "mel_min.npy") - mel_max_path = os.path.join(mel_min_max_dir, "mel_max.npy") - np.save(mel_min_path, mel_min) - np.save(mel_max_path, mel_max) - - -def denorm_for_pred_mels(cfg, dataset_name, split, pred): - """ - Args: - pred: a list whose every element is (frame_len, n_mels) - Return: - similar like pred - """ - mel_min, mel_max = load_mel_extrema(cfg.preprocess, dataset_name) - recovered_mels = [ - denormalize_mel_channel(mel.T, mel_min, mel_max).T for mel in pred - ] - - return recovered_mels - - -def load_mel_extrema(cfg, dataset_name): - data_dir = os.path.join(cfg.processed_dir, dataset_name, cfg.mel_min_max_stats_dir) - - min_file = os.path.join(data_dir, "mel_min.npy") - max_file = os.path.join(data_dir, "mel_max.npy") - - mel_min = np.load(min_file) - mel_max = np.load(max_file) - - return mel_min, mel_max - - -def denormalize_mel_channel(mel, mel_min, mel_max): - mel_min = np.expand_dims(mel_min, -1) - mel_max = np.expand_dims(mel_max, -1) - return (mel + 1) / 2 * (mel_max - mel_min + ZERO) + mel_min - - -def normalize_mel_channel(mel, mel_min, mel_max): - mel_min = np.expand_dims(mel_min, -1) - mel_max = np.expand_dims(mel_max, -1) - return (mel - mel_min) / (mel_max - mel_min + ZERO) * 2 - 1 - - -def normalize(dataset, feat_dir, cfg): - dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset) - print(f"normalize {feat_dir}") - - max_value = np.finfo(np.float64).min - min_value = np.finfo(np.float64).max - - scaler = StandardScaler() - feat_files = os.listdir(os.path.join(dataset_output, feat_dir)) - - for feat_file in tqdm(feat_files): - feat_file = os.path.join(dataset_output, feat_dir, feat_file) - if not feat_file.endswith(".npy"): - continue - feat = np.load(feat_file) - max_value = max(max_value, max(feat)) - min_value = min(min_value, min(feat)) - scaler.partial_fit(feat.reshape((-1, 1))) - mean = scaler.mean_[0] - std = scaler.scale_[0] - stat = np.array([min_value, max_value, mean, std]) - stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy") - np.save(stat_npy, stat) - return mean, std, min_value, max_value - - -def load_normalized(feat_dir, dataset_name, cfg): - dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset_name) - stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy") - min_value, max_value, mean, std = np.load(stat_npy) - return mean, std, min_value, max_value - - -def cal_pitch_statistics_svc(dataset, output_path, cfg, metadata=None): - # path of dataset - dataset_dir = os.path.join(output_path, dataset) - save_dir = os.path.join(dataset_dir, cfg.preprocess.pitch_dir) - os.makedirs(save_dir, exist_ok=True) - if has_existed(os.path.join(save_dir, "statistics.json")): - return - - if metadata is None: - # load singers and ids - singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) - - # combine train and test metadata - metadata = [] - for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: - dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) - with open(dataset_file, "r") as f: - metadata.extend(json.load(f)) - else: - singers = list(set([item["Singer"] for item in metadata])) - singers = { - "{}_{}".format(dataset, name): idx for idx, name in enumerate(singers) - } - - # use different scalers for each singer - pitch_scalers = [[] for _ in range(len(singers))] - total_pitch_scalers = [[] for _ in range(len(singers))] - - for utt_info in tqdm(metadata, desc="Loading F0..."): - # utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' - singer = utt_info["Singer"] - pitch_path = os.path.join( - dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" - ) - # total_pitch contains all pitch including unvoiced frames - if not os.path.exists(pitch_path): - continue - total_pitch = np.load(pitch_path) - assert len(total_pitch) > 0 - # pitch contains only voiced frames - pitch = total_pitch[total_pitch != 0] - spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] - - # update pitch scalers - pitch_scalers[spkid].extend(pitch.tolist()) - # update total pitch scalers - total_pitch_scalers[spkid].extend(total_pitch.tolist()) - - # save pitch statistics for each singer in dict - sta_dict = {} - for singer in tqdm(singers, desc="Singers statistics"): - spkid = singers[singer] - # voiced pitch statistics - mean, std, min, max, median = ( - np.mean(pitch_scalers[spkid]), - np.std(pitch_scalers[spkid]), - np.min(pitch_scalers[spkid]), - np.max(pitch_scalers[spkid]), - np.median(pitch_scalers[spkid]), - ) - - # total pitch statistics - mean_t, std_t, min_t, max_t, median_t = ( - np.mean(total_pitch_scalers[spkid]), - np.std(total_pitch_scalers[spkid]), - np.min(total_pitch_scalers[spkid]), - np.max(total_pitch_scalers[spkid]), - np.median(total_pitch_scalers[spkid]), - ) - sta_dict[singer] = { - "voiced_positions": { - "mean": mean, - "std": std, - "median": median, - "min": min, - "max": max, - }, - "total_positions": { - "mean": mean_t, - "std": std_t, - "median": median_t, - "min": min_t, - "max": max_t, - }, - } - - # save statistics - with open(os.path.join(save_dir, "statistics.json"), "w") as f: - json.dump(sta_dict, f, indent=4, ensure_ascii=False) - - -def cal_pitch_statistics(dataset, output_path, cfg): - # path of dataset - dataset_dir = os.path.join(output_path, dataset) - if cfg.preprocess.use_phone_pitch: - pitch_dir = cfg.preprocess.phone_pitch_dir - else: - pitch_dir = cfg.preprocess.pitch_dir - save_dir = os.path.join(dataset_dir, pitch_dir) - - os.makedirs(save_dir, exist_ok=True) - if has_existed(os.path.join(save_dir, "statistics.json")): - return - # load singers and ids - singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) - - # combine train and test metadata - metadata = [] - for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: - dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) - with open(dataset_file, "r") as f: - metadata.extend(json.load(f)) - - # use different scalers for each singer - pitch_scalers = [[] for _ in range(len(singers))] - total_pitch_scalers = [[] for _ in range(len(singers))] - - for utt_info in metadata: - utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' - singer = utt_info["Singer"] - pitch_path = os.path.join(dataset_dir, pitch_dir, utt_info["Uid"] + ".npy") - # total_pitch contains all pitch including unvoiced frames - if not os.path.exists(pitch_path): - continue - total_pitch = np.load(pitch_path) - assert len(total_pitch) > 0 - # pitch contains only voiced frames - # pitch = total_pitch[total_pitch != 0] - if cfg.preprocess.pitch_remove_outlier: - pitch = remove_outlier(total_pitch) - spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] - - # update pitch scalers - pitch_scalers[spkid].extend(pitch.tolist()) - # update total pitch scalers - total_pitch_scalers[spkid].extend(total_pitch.tolist()) - - # save pitch statistics for each singer in dict - sta_dict = {} - for singer in singers: - spkid = singers[singer] - # voiced pitch statistics - mean, std, min, max, median = ( - np.mean(pitch_scalers[spkid]), - np.std(pitch_scalers[spkid]), - np.min(pitch_scalers[spkid]), - np.max(pitch_scalers[spkid]), - np.median(pitch_scalers[spkid]), - ) - - # total pitch statistics - mean_t, std_t, min_t, max_t, median_t = ( - np.mean(total_pitch_scalers[spkid]), - np.std(total_pitch_scalers[spkid]), - np.min(total_pitch_scalers[spkid]), - np.max(total_pitch_scalers[spkid]), - np.median(total_pitch_scalers[spkid]), - ) - sta_dict[singer] = { - "voiced_positions": { - "mean": mean, - "std": std, - "median": median, - "min": min, - "max": max, - }, - "total_positions": { - "mean": mean_t, - "std": std_t, - "median": median_t, - "min": min_t, - "max": max_t, - }, - } - - # save statistics - with open(os.path.join(save_dir, "statistics.json"), "w") as f: - json.dump(sta_dict, f, indent=4, ensure_ascii=False) - - -def cal_energy_statistics(dataset, output_path, cfg): - # path of dataset - dataset_dir = os.path.join(output_path, dataset) - if cfg.preprocess.use_phone_energy: - energy_dir = cfg.preprocess.phone_energy_dir - else: - energy_dir = cfg.preprocess.energy_dir - save_dir = os.path.join(dataset_dir, energy_dir) - os.makedirs(save_dir, exist_ok=True) - print(os.path.join(save_dir, "statistics.json")) - if has_existed(os.path.join(save_dir, "statistics.json")): - return - # load singers and ids - singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) - - # combine train and test metadata - metadata = [] - for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: - dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) - with open(dataset_file, "r") as f: - metadata.extend(json.load(f)) - - # use different scalers for each singer - energy_scalers = [[] for _ in range(len(singers))] - total_energy_scalers = [[] for _ in range(len(singers))] - - for utt_info in metadata: - utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' - singer = utt_info["Singer"] - energy_path = os.path.join(dataset_dir, energy_dir, utt_info["Uid"] + ".npy") - # total_energy contains all energy including unvoiced frames - if not os.path.exists(energy_path): - continue - total_energy = np.load(energy_path) - assert len(total_energy) > 0 - # energy contains only voiced frames - # energy = total_energy[total_energy != 0] - if cfg.preprocess.energy_remove_outlier: - energy = remove_outlier(total_energy) - spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] - - # update energy scalers - energy_scalers[spkid].extend(energy.tolist()) - # update total energyscalers - total_energy_scalers[spkid].extend(total_energy.tolist()) - - # save energy statistics for each singer in dict - sta_dict = {} - for singer in singers: - spkid = singers[singer] - # voiced energy statistics - mean, std, min, max, median = ( - np.mean(energy_scalers[spkid]), - np.std(energy_scalers[spkid]), - np.min(energy_scalers[spkid]), - np.max(energy_scalers[spkid]), - np.median(energy_scalers[spkid]), - ) - - # total energy statistics - mean_t, std_t, min_t, max_t, median_t = ( - np.mean(total_energy_scalers[spkid]), - np.std(total_energy_scalers[spkid]), - np.min(total_energy_scalers[spkid]), - np.max(total_energy_scalers[spkid]), - np.median(total_energy_scalers[spkid]), - ) - sta_dict[singer] = { - "voiced_positions": { - "mean": mean, - "std": std, - "median": median, - "min": min, - "max": max, - }, - "total_positions": { - "mean": mean_t, - "std": std_t, - "median": median_t, - "min": min_t, - "max": max_t, - }, - } - - # save statistics - with open(os.path.join(save_dir, "statistics.json"), "w") as f: - json.dump(sta_dict, f, indent=4, ensure_ascii=False) - - -def copy_acoustic_features(metadata, dataset_dir, src_dataset_dir, cfg): - """Copy acoustic features from src_dataset_dir to dataset_dir - - Args: - metadata (dict): dictionary that stores data in train.json and test.json files - dataset_dir (str): directory to store acoustic features - src_dataset_dir (str): directory to store acoustic features - cfg (dict): dictionary that stores configurations - - """ - - if cfg.preprocess.extract_mel: - if not has_existed(os.path.join(dataset_dir, cfg.preprocess.mel_dir)): - os.makedirs( - os.path.join(dataset_dir, cfg.preprocess.mel_dir), exist_ok=True - ) - print( - "Copying mel features from {} to {}...".format( - src_dataset_dir, dataset_dir - ) - ) - for utt_info in tqdm(metadata): - src_mel_path = os.path.join( - src_dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy" - ) - dst_mel_path = os.path.join( - dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy" - ) - # create soft-links - if not os.path.exists(dst_mel_path): - os.symlink(src_mel_path, dst_mel_path) - if cfg.preprocess.extract_energy: - if not has_existed(os.path.join(dataset_dir, cfg.preprocess.energy_dir)): - os.makedirs( - os.path.join(dataset_dir, cfg.preprocess.energy_dir), exist_ok=True - ) - print( - "Copying energy features from {} to {}...".format( - src_dataset_dir, dataset_dir - ) - ) - for utt_info in tqdm(metadata): - src_energy_path = os.path.join( - src_dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy" - ) - dst_energy_path = os.path.join( - dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy" - ) - # create soft-links - if not os.path.exists(dst_energy_path): - os.symlink(src_energy_path, dst_energy_path) - if cfg.preprocess.extract_pitch: - if not has_existed(os.path.join(dataset_dir, cfg.preprocess.pitch_dir)): - os.makedirs( - os.path.join(dataset_dir, cfg.preprocess.pitch_dir), exist_ok=True - ) - print( - "Copying pitch features from {} to {}...".format( - src_dataset_dir, dataset_dir - ) - ) - for utt_info in tqdm(metadata): - src_pitch_path = os.path.join( - src_dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" - ) - dst_pitch_path = os.path.join( - dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" - ) - # create soft-links - if not os.path.exists(dst_pitch_path): - os.symlink(src_pitch_path, dst_pitch_path) - if cfg.preprocess.extract_uv: - if not has_existed(os.path.join(dataset_dir, cfg.preprocess.uv_dir)): - os.makedirs( - os.path.join(dataset_dir, cfg.preprocess.uv_dir), exist_ok=True - ) - print( - "Copying uv features from {} to {}...".format( - src_dataset_dir, dataset_dir - ) - ) - for utt_info in tqdm(metadata): - src_uv_path = os.path.join( - src_dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy" - ) - dst_uv_path = os.path.join( - dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy" - ) - # create soft-links - if not os.path.exists(dst_uv_path): - os.symlink(src_uv_path, dst_uv_path) - if cfg.preprocess.extract_audio: - if not has_existed(os.path.join(dataset_dir, cfg.preprocess.audio_dir)): - os.makedirs( - os.path.join(dataset_dir, cfg.preprocess.audio_dir), exist_ok=True - ) - print( - "Copying audio features from {} to {}...".format( - src_dataset_dir, dataset_dir - ) - ) - for utt_info in tqdm(metadata): - src_audio_path = os.path.join( - src_dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".npy" - ) - dst_audio_path = os.path.join( - dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".npy" - ) - # create soft-links - if not os.path.exists(dst_audio_path): - os.symlink(src_audio_path, dst_audio_path) - if cfg.preprocess.extract_label: - if not has_existed(os.path.join(dataset_dir, cfg.preprocess.label_dir)): - os.makedirs( - os.path.join(dataset_dir, cfg.preprocess.label_dir), exist_ok=True - ) - print( - "Copying label features from {} to {}...".format( - src_dataset_dir, dataset_dir - ) - ) - for utt_info in tqdm(metadata): - src_label_path = os.path.join( - src_dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy" - ) - dst_label_path = os.path.join( - dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy" - ) - # create soft-links - if not os.path.exists(dst_label_path): - os.symlink(src_label_path, dst_label_path) - - -def align_duration_mel(dataset, output_path, cfg): - print("align the duration and mel") - - dataset_dir = os.path.join(output_path, dataset) - metadata = [] - for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: - dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) - with open(dataset_file, "r") as f: - metadata.extend(json.load(f)) - - utt2dur = {} - for index in tqdm(range(len(metadata))): - utt_info = metadata[index] - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - mel_path = os.path.join(dataset_dir, cfg.preprocess.mel_dir, uid + ".npy") - mel = np.load(mel_path).transpose(1, 0) - duration_path = os.path.join( - dataset_dir, cfg.preprocess.duration_dir, uid + ".npy" - ) - duration = np.load(duration_path) - if sum(duration) != mel.shape[0]: - duration_sum = sum(duration) - mel_len = mel.shape[0] - mismatch = abs(duration_sum - mel_len) - assert mismatch <= 5, "duration and mel length mismatch!" - cloned = np.array(duration, copy=True) - if duration_sum > mel_len: - for j in range(1, len(duration) - 1): - if mismatch == 0: - break - dur_val = cloned[-j] - if dur_val >= mismatch: - cloned[-j] -= mismatch - mismatch -= dur_val - break - else: - cloned[-j] = 0 - mismatch -= dur_val - - elif duration_sum < mel_len: - cloned[-1] += mismatch - duration = cloned - utt2dur[utt] = duration - np.save(duration_path, duration) - - return utt2dur diff --git a/processors/content_extractor.py b/processors/content_extractor.py deleted file mode 100644 index c034b6bf6aac19eb3ab912661110c8066aa3119b..0000000000000000000000000000000000000000 --- a/processors/content_extractor.py +++ /dev/null @@ -1,540 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import torch -import numpy as np -import yaml -import copy -from tqdm import tqdm -from torchaudio.compliance import kaldi -from torch.nn.utils.rnn import pad_sequence -from torch.utils.data import DataLoader -from fairseq import checkpoint_utils -from transformers import AutoModel, Wav2Vec2FeatureExtractor - -from utils.io_optim import ( - TorchaudioDataset, - LibrosaDataset, - FFmpegDataset, - collate_batch, -) -from modules import whisper_extractor as whisper -from modules.wenet_extractor.utils.init_model import init_model -from modules.wenet_extractor.utils.checkpoint import load_checkpoint - -""" - Extractor for content features - 1. whisper - 2. contentvec - 3. wenet - 4. mert - - Pipeline: - in preprocess.py: - call extract_utt_content_features() to extract content features for each utterance - extract_utt_content_features() envelopes the following steps: - 1. load the model (whisper, contentvec, wenet) - 2. extract the content features - 3. save the content features into files - in svc_dataset.py: - call offline_align() to align the content features to the given target length - -""" - -""" - Extractor Usage: - 1. initialize an instance of extractor - extractor = WhisperExtractor(cfg) - 2. load the specified model - extractor.load_model() - 3. extract the content features - extractor.extract_content(utt) for single utterance - extractor.extract_content_batch(utts) for batch utterances - 4. save the content features - extractor.save_feature(utt, content_feature) for single utterance -""" - - -class BaseExtractor: - def __init__(self, cfg): - self.cfg = cfg - self.extractor_type = None - self.model = None - - def offline_align(self, content, target_len): - """ - args: - content: (source_len, dim) - target_len: target length - return: - mapped_feature: (target_len, dim) - """ - target_hop = self.cfg.preprocess.hop_size - - assert self.extractor_type in ["whisper", "contentvec", "wenet"] - if self.extractor_type == "whisper": - source_hop = ( - self.cfg.preprocess.whisper_frameshift - * self.cfg.preprocess.whisper_downsample_rate - * self.cfg.preprocess.sample_rate - ) - elif self.extractor_type == "contentvec": - source_hop = ( - self.cfg.preprocess.contentvec_frameshift - * self.cfg.preprocess.sample_rate - ) - elif self.extractor_type == "wenet": - source_hop = ( - self.cfg.preprocess.wenet_frameshift - * self.cfg.preprocess.wenet_downsample_rate - * self.cfg.preprocess.sample_rate - ) - source_hop = int(source_hop) - factor = np.gcd(source_hop, target_hop) - source_hop //= factor - target_hop //= factor - - # (source_len, 256) - _, width = content.shape - # slice the content from padded feature - source_len = min(target_len * target_hop // source_hop + 1, len(content)) - - # const ~= target_len * target_hop - const = source_len * source_hop // target_hop * target_hop - - # (source_len * source_hop, dim) - up_sampling_feats = np.repeat(content, source_hop, axis=0) - # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) - down_sampling_feats = np.average( - up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 - ) - - err = abs(target_len - len(down_sampling_feats)) - if err > 8: - # err_log_dir is indeterminate - err_log_dir = os.path.join( - self.cfg.preprocess.processed_dir, "align_max_err.log" - ) - try: - with open(err_log_dir, "r") as f: - err_num = int(f.read()) - except: - with open(err_log_dir, "w") as f: - f.write("0") - err_num = 0 - if err > err_num: - with open(err_log_dir, "w") as f: - f.write(str(err)) - - if len(down_sampling_feats) < target_len: - # (1, dim) -> (err, dim) - end = down_sampling_feats[-1][None, :].repeat(err, axis=0) - down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) - - # (target_len, dim) - mapped_feature = down_sampling_feats[:target_len] - - return mapped_feature - - def save_feature(self, utt, content_feature): - """Save a single utternace to path {cfg.preprocess.processed_dir} - - Args: - utt (dict): one item in metadata, containing information for one utterance - content_feature (tensor): content feature of one utterance - """ - uid = utt["Uid"] - assert self.extractor_type != None - out_dir = os.path.join( - self.cfg.preprocess.processed_dir, utt["Dataset"], self.extractor_type - ) - os.makedirs(out_dir, exist_ok=True) - save_path = os.path.join(out_dir, uid + ".npy") - # only keep effective parts - duration = utt["Duration"] - if self.extractor_type == "whisper": - frameshift = ( - self.cfg.preprocess.whisper_frameshift - * self.cfg.preprocess.whisper_downsample_rate - ) # 20ms - elif self.extractor_type == "contentvec": - frameshift = self.cfg.preprocess.contentvec_frameshift # 20ms - elif self.extractor_type == "wenet": - frameshift = ( - self.cfg.preprocess.wenet_frameshift - * self.cfg.preprocess.wenet_downsample_rate - ) # 40ms - elif self.extractor_type == "mert": - frameshift = self.cfg.preprocess.mert_frameshift - else: - raise NotImplementedError - # calculate the number of valid frames - num_frames = int(np.ceil((duration - frameshift) / frameshift)) + 1 - # (num_frames, dim) -> (valid_frames, dim) - assert ( - len(content_feature.shape) == 2 - ), "content feature shape error, it should be (num_frames, dim)" - content_feature = content_feature[:num_frames, :] - np.save(save_path, content_feature.cpu().detach().numpy()) - - -class WhisperExtractor(BaseExtractor): - def __init__(self, config): - super(WhisperExtractor, self).__init__(config) - self.extractor_type = "whisper" - - def load_model(self): - # load whisper checkpoint - print("Loading Whisper Model...") - - checkpoint_file = ( - self.cfg.preprocess.whisper_model_path - if "whisper_model_path" in self.cfg.preprocess - else None - ) - model = whisper.load_model( - self.cfg.preprocess.whisper_model, checkpoint_file=checkpoint_file - ) - if torch.cuda.is_available(): - print("Using GPU...\n") - model = model.cuda() - else: - print("Using CPU...\n") - - self.model = model.eval() - - def extract_content_features(self, wavs, lens): - """extract content features from a batch of dataloader - Args: - wavs: tensor (batch_size, T) - lens: list - """ - # wavs: (batch, max_len) - wavs = whisper.pad_or_trim(wavs) - # batch_mel: (batch, 80, 3000) - batch_mel = whisper.log_mel_spectrogram(wavs).to(self.model.device) - with torch.no_grad(): - # (batch, 1500, 1024) - features = self.model.embed_audio(batch_mel) - return features - - -class ContentvecExtractor(BaseExtractor): - def __init__(self, cfg): - super(ContentvecExtractor, self).__init__(cfg) - self.extractor_type = "contentvec" - - def load_model(self): - assert self.model == None - # Load model - ckpt_path = self.cfg.preprocess.contentvec_file - print("Load Contentvec Model...") - - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - [ckpt_path], - suffix="", - ) - model = models[0] - model.eval() - - if torch.cuda.is_available(): - # print("Using GPU...\n") - model = model.cuda() - - self.model = model - - def extract_content_features(self, wavs, lens): - """extract content features from a batch of dataloader - Args: - wavs: tensor (batch, T) - lens: list - """ - device = next(self.model.parameters()).device - wavs = wavs.to(device) # (batch, max_len) - padding_mask = torch.eq(wavs, torch.zeros_like(wavs)).to(device) - with torch.no_grad(): - logits = self.model.extract_features( - source=wavs, padding_mask=padding_mask, output_layer=12 - ) - # feats: (batch, T, 256) - feats = self.model.final_proj(logits[0]) - return feats - - -class WenetExtractor(BaseExtractor): - def __init__(self, config): - super(WenetExtractor, self).__init__(config) - self.extractor_type = "wenet" - - def load_model(self): - wenet_cfg = self.cfg.preprocess.wenet_config - wenet_model_path = self.cfg.preprocess.wenet_model_path - # load Wenet config - with open(wenet_cfg, "r") as w: - wenet_configs = yaml.load(w, Loader=yaml.FullLoader) - self.extract_conf = copy.deepcopy(wenet_configs["dataset_conf"]) - print("Loading Wenet Model...") - self.model = init_model(wenet_configs) - load_checkpoint(self.model, wenet_model_path) - - if torch.cuda.is_available(): - print("Using GPU...\n") - self.model = self.model.cuda() - else: - print("Using CPU...\n") - - self.model = self.model.eval() - - def extract_content_features(self, wavs, lens): - """extract content features from a batch of dataloader - Args: - wavs: tensor - lens: list - """ - feats_list = [] - lengths_list = [] - - device = next(self.model.parameters()).device - # Extract fbank/mfcc features by kaldi - assert self.extract_conf is not None, "load model first!" - feats_type = self.extract_conf.get("feats_type", "fbank") - assert feats_type in ["fbank", "mfcc"] - - for idx, wav in enumerate(wavs): - # wav: (T) - wav = wav[: lens[idx]].to(device) - - # pad one frame to compensate for the frame cut off after feature extraction - pad_tensor = torch.zeros(160, device=wav.device) - wav = torch.cat((wav, pad_tensor), dim=-1) - wav *= 1 << 15 - - wav = wav.unsqueeze(0) # (T) -> (1, T) - if feats_type == "fbank": - fbank_conf = self.extract_conf.get("fbank_conf", {}) - feat = kaldi.fbank( - wav, - sample_frequency=16000, - num_mel_bins=fbank_conf["num_mel_bins"], - frame_length=fbank_conf["frame_length"], - frame_shift=fbank_conf["frame_shift"], - dither=fbank_conf["dither"], - ) - elif feats_type == "mfcc": - mfcc_conf = self.extract_conf.get("mfcc", {}) - feat = kaldi.mfcc( - wav, - sample_frequency=16000, - num_mel_bins=mfcc_conf["num_mel_bins"], - frame_length=mfcc_conf["frame_length"], - frame_shift=mfcc_conf["frame_shift"], - dither=mfcc_conf["dither"], - num_ceps=mfcc_conf.get("num_ceps", 40), - high_freq=mfcc_conf.get("high_freq", 0.0), - low_freq=mfcc_conf.get("low_freq", 20.0), - ) - feats_list.append(feat) - lengths_list.append(feat.shape[0]) - - feats_lengths = torch.tensor(lengths_list, dtype=torch.int32).to(device) - feats_tensor = pad_sequence(feats_list, batch_first=True).to( - device - ) # (batch, len, 80) - - features = self.model.encoder_extractor( - feats_tensor, - feats_lengths, - decoding_chunk_size=-1, - num_decoding_left_chunks=-1, - simulate_streaming=False, - ) - return features - - -class MertExtractor(BaseExtractor): - def __init__(self, cfg): - super(MertExtractor, self).__init__(cfg) - self.extractor_type = "mert" - self.preprocessor = None - - def load_model(self): - assert self.model == None - assert self.preprocessor == None - - print("Loading MERT Model: ...", self.cfg.preprocess.mert_model) - - local_mert_path = "/mnt/workspace/fangzihao/acce/Amphion/pretrained/MERT" - - model_name = self.cfg.preprocess.mert_model - model = AutoModel.from_pretrained(local_mert_path, trust_remote_code=True) - - if torch.cuda.is_available(): - model = model.cuda() - preprocessor = Wav2Vec2FeatureExtractor.from_pretrained( - local_mert_path, trust_remote_code=True - ) - - self.model = model - self.preprocessor = preprocessor - - def extract_content_features(self, wavs, lens): - """extract content features from a batch of dataloader - Args: - wavs: tensor (batch, T) - lens: list - """ - with torch.no_grad(): - sample_rate = self.preprocessor.sampling_rate - device = next(self.model.parameters()).device - assert ( - sample_rate == self.cfg.preprocess.mert_sample_rate - ), "mert sample rate mismatch, expected {}, got {}".format( - self.cfg.preprocess.mert_sample_rate, sample_rate - ) - mert_features = [] - # wav: (len) - for wav in wavs: - # {input_values: tensor, attention_mask: tensor} - inputs = self.preprocessor( - wavs, sampling_rate=sample_rate, return_tensors="pt" - ).to(device) - - outputs = self.model(**inputs, output_hidden_states=True) - # (25 layers, time steps, 1024 feature_dim) - all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() - # (1, frame_len, 1024) -> (frame_len, 1024) - feature = outputs.hidden_states[ - self.cfg.preprocess.mert_feature_layer - ].squeeze(0) - mert_features.append(feature) - - return mert_features - - -def extract_utt_content_features_dataloader(cfg, metadata, num_workers): - dataset_name = metadata[0]["Dataset"] - - if cfg.preprocess.extract_whisper_feature: - feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "whisper") - os.makedirs(feat_dir, exist_ok=True) - feat_files_num = len(os.listdir(feat_dir)) - - if feat_files_num != len(metadata): - whisper_waveforms = FFmpegDataset( - cfg, dataset_name, cfg.preprocess.whisper_sample_rate, metadata=metadata - ) - data_loader = DataLoader( - whisper_waveforms, - num_workers=num_workers, - shuffle=False, - pin_memory=cfg.preprocess.pin_memory, - batch_size=cfg.preprocess.content_feature_batch_size, - collate_fn=collate_batch, - drop_last=False, - ) - extractor = WhisperExtractor(cfg) - extractor.load_model() - for batch_idx, items in enumerate(tqdm(data_loader)): - _metadata, wavs, lens = items - - batch_content_features = extractor.extract_content_features( - wavs, - lens, - ) - for index, utt in enumerate(_metadata): - extractor.save_feature(utt, batch_content_features[index]) - - if cfg.preprocess.extract_contentvec_feature: - feat_dir = os.path.join( - cfg.preprocess.processed_dir, dataset_name, "contentvec" - ) - os.makedirs(feat_dir, exist_ok=True) - feat_files_num = len(os.listdir(feat_dir)) - - if feat_files_num != len(metadata): - contentvec_waveforms = LibrosaDataset( - cfg, - dataset_name, - cfg.preprocess.contentvec_sample_rate, - metadata=metadata, - ) - data_loader = DataLoader( - contentvec_waveforms, - num_workers=num_workers, - shuffle=False, - pin_memory=cfg.preprocess.pin_memory, - batch_size=cfg.preprocess.content_feature_batch_size, - collate_fn=collate_batch, - drop_last=False, - ) - extractor = ContentvecExtractor(cfg) - extractor.load_model() - for batch_idx, items in enumerate(tqdm(data_loader)): - _metadata, wavs, lens = items - - batch_content_features = extractor.extract_content_features(wavs, lens) - for index, utt in enumerate(_metadata): - extractor.save_feature(utt, batch_content_features[index]) - - if cfg.preprocess.extract_wenet_feature: - feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "wenet") - os.makedirs(feat_dir, exist_ok=True) - feat_files_num = len(os.listdir(feat_dir)) - - if feat_files_num != len(metadata): - wenet_waveforms = TorchaudioDataset( - cfg, dataset_name, cfg.preprocess.wenet_sample_rate, metadata=metadata - ) - data_loader = DataLoader( - wenet_waveforms, - num_workers=num_workers, - shuffle=False, - pin_memory=cfg.preprocess.pin_memory, - batch_size=cfg.preprocess.content_feature_batch_size, - collate_fn=collate_batch, - drop_last=False, - ) - extractor = WenetExtractor(cfg) - extractor.load_model() - for batch_idx, items in enumerate(tqdm(data_loader)): - _metadata, wavs, lens = items - - batch_content_features = extractor.extract_content_features( - wavs, - lens, - ) - for index, utt in enumerate(_metadata): - extractor.save_feature(utt, batch_content_features[index]) - - if cfg.preprocess.extract_mert_feature: - feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "mert") - os.makedirs(feat_dir, exist_ok=True) - feat_files_num = len(os.listdir(feat_dir)) - - if feat_files_num != len(metadata): - mert_waveforms = TorchaudioDataset( - cfg, dataset_name, cfg.preprocess.mert_sample_rate, metadata=metadata - ) - data_loader = DataLoader( - mert_waveforms, - num_workers=num_workers, - shuffle=False, - pin_memory=cfg.preprocess.pin_memory, - batch_size=cfg.preprocess.content_feature_batch_size, - collate_fn=collate_batch, - drop_last=False, - ) - extractor = MertExtractor(cfg) - extractor.load_model() - for batch_idx, items in enumerate(tqdm(data_loader)): - _metadata, wavs, lens = items - - batch_content_features = extractor.extract_content_features( - wavs, - lens, - ) - for index, utt in enumerate(_metadata): - extractor.save_feature(utt, batch_content_features[index]) diff --git a/processors/data_augment.py b/processors/data_augment.py deleted file mode 100644 index 2fc183361d4bcfd454693ee0b7ffdd9758c09312..0000000000000000000000000000000000000000 --- a/processors/data_augment.py +++ /dev/null @@ -1,378 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -import random -import os -import json - -import numpy as np -import parselmouth -import torch -import torchaudio -from tqdm import tqdm - -from audiomentations import TimeStretch - -from pedalboard import ( - Pedalboard, - HighShelfFilter, - LowShelfFilter, - PeakFilter, - PitchShift, -) - -from utils.util import has_existed - -PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT = 0.0 -PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT = 1.0 -PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT = 1.0 -PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT = 1.0 -PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT = 1.0 - - -def wav_to_Sound(wav, sr: int) -> parselmouth.Sound: - """Convert a waveform to a parselmouth.Sound object - - Args: - wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples) - sr (int, optional): sampling rate. - - Returns: - parselmouth.Sound: a parselmouth.Sound object - """ - assert wav.shape == (1, len(wav[0])), "wav must be of shape (1, n_samples)" - sound = None - if isinstance(wav, np.ndarray): - sound = parselmouth.Sound(wav[0], sampling_frequency=sr) - elif isinstance(wav, torch.Tensor): - sound = parselmouth.Sound(wav[0].numpy(), sampling_frequency=sr) - assert sound is not None, "wav must be either np.ndarray or torch.Tensor" - return sound - - -def get_pitch_median(wav, sr: int): - """Get the median pitch of a waveform - - Args: - wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples) - sr (int, optional): sampling rate. - - Returns: - parselmouth.Pitch, float: a parselmouth.Pitch object and the median pitch - """ - if not isinstance(wav, parselmouth.Sound): - sound = wav_to_Sound(wav, sr) - else: - sound = wav - pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT - - # To Pitch: Time step(s)(standard value: 0.0), Pitch floor (Hz)(standard value: 75), Pitch ceiling (Hz)(standard value: 600.0) - pitch = parselmouth.praat.call(sound, "To Pitch", 0.8 / 75, 75, 600) - # Get quantile: From time (s), To time (s), Quantile(0.5 is then the 50% quantile, i.e., the median), Units (Hertz or Bark) - pitch_median = parselmouth.praat.call(pitch, "Get quantile", 0.0, 0.0, 0.5, "Hertz") - - return pitch, pitch_median - - -def change_gender( - sound, - pitch=None, - formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT, - new_pitch_median: float = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT, - pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT, - duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT, -) -> parselmouth.Sound: - """Invoke change gender function in praat - - Args: - sound (parselmouth.Sound): a parselmouth.Sound object - pitch (parselmouth.Pitch, optional): a parselmouth.Pitch object. Defaults to None. - formant_shift_ratio (float, optional): formant shift ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch. Less than 1.0 means lower pitch. - new_pitch_median (float, optional): new pitch median. - pitch_range_ratio (float, optional): pitch range ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch range. Less than 1.0 means lower pitch range. - duration_factor (float, optional): duration factor. A value of 1.0 means no change. Greater than 1.0 means longer duration. Less than 1.0 means shorter duration. - - Returns: - parselmouth.Sound: a parselmouth.Sound object - """ - if pitch is None: - new_sound = parselmouth.praat.call( - sound, - "Change gender", - 75, - 600, - formant_shift_ratio, - new_pitch_median, - pitch_range_ratio, - duration_factor, - ) - else: - new_sound = parselmouth.praat.call( - (sound, pitch), - "Change gender", - formant_shift_ratio, - new_pitch_median, - pitch_range_ratio, - duration_factor, - ) - return new_sound - - -def apply_formant_and_pitch_shift( - sound: parselmouth.Sound, - formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT, - pitch_shift_ratio: float = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT, - pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT, - duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT, -) -> parselmouth.Sound: - """use Praat "Changer gender" command to manipulate pitch and formant - "Change gender": Praat -> Sound Object -> Convert -> Change gender - refer to Help of Praat for more details - # https://github.com/YannickJadoul/Parselmouth/issues/25#issuecomment-608632887 might help - """ - pitch = None - new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT - if pitch_shift_ratio != 1.0: - pitch, pitch_median = get_pitch_median(sound, sound.sampling_frequency) - new_pitch_median = pitch_median * pitch_shift_ratio - - # refer to https://github.com/praat/praat/issues/1926#issuecomment-974909408 - pitch_minimum = parselmouth.praat.call( - pitch, "Get minimum", 0.0, 0.0, "Hertz", "Parabolic" - ) - new_median = pitch_median * pitch_shift_ratio - scaled_minimum = pitch_minimum * pitch_shift_ratio - result_minimum = new_median + (scaled_minimum - new_median) * pitch_range_ratio - if result_minimum < 0: - new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT - pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT - - if math.isnan(new_pitch_median): - new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT - pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT - - new_sound = change_gender( - sound, - pitch, - formant_shift_ratio, - new_pitch_median, - pitch_range_ratio, - duration_factor, - ) - return new_sound - - -# Function used in EQ -def pedalboard_equalizer(wav: np.ndarray, sr: int) -> np.ndarray: - """Use pedalboard to do equalizer""" - board = Pedalboard() - - cutoff_low_freq = 60 - cutoff_high_freq = 10000 - - q_min = 2 - q_max = 5 - - random_all_freq = True - num_filters = 10 - if random_all_freq: - key_freqs = [random.uniform(1, 12000) for _ in range(num_filters)] - else: - key_freqs = [ - power_ratio(float(z) / (num_filters - 1), cutoff_low_freq, cutoff_high_freq) - for z in range(num_filters) - ] - q_values = [ - power_ratio(random.uniform(0, 1), q_min, q_max) for _ in range(num_filters) - ] - gains = [random.uniform(-12, 12) for _ in range(num_filters)] - # low-shelving filter - board.append( - LowShelfFilter( - cutoff_frequency_hz=key_freqs[0], gain_db=gains[0], q=q_values[0] - ) - ) - # peaking filters - for i in range(1, 9): - board.append( - PeakFilter( - cutoff_frequency_hz=key_freqs[i], gain_db=gains[i], q=q_values[i] - ) - ) - # high-shelving filter - board.append( - HighShelfFilter( - cutoff_frequency_hz=key_freqs[9], gain_db=gains[9], q=q_values[9] - ) - ) - - # Apply the pedalboard to the audio - processed_audio = board(wav, sr) - return processed_audio - - -def power_ratio(r: float, a: float, b: float): - return a * math.pow((b / a), r) - - -def audiomentations_time_stretch(wav: np.ndarray, sr: int) -> np.ndarray: - """Use audiomentations to do time stretch""" - transform = TimeStretch( - min_rate=0.8, max_rate=1.25, leave_length_unchanged=False, p=1.0 - ) - augmented_wav = transform(wav, sample_rate=sr) - return augmented_wav - - -def formant_and_pitch_shift( - sound: parselmouth.Sound, fs: bool, ps: bool -) -> parselmouth.Sound: - """ """ - formant_shift_ratio = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT - pitch_shift_ratio = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT - pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT - - assert fs != ps, "fs, ps are mutually exclusive" - - if fs: - formant_shift_ratio = random.uniform(1.0, 1.4) - use_reciprocal = random.uniform(-1, 1) > 0 - if use_reciprocal: - formant_shift_ratio = 1.0 / formant_shift_ratio - # only use praat to change formant - new_sound = apply_formant_and_pitch_shift( - sound, - formant_shift_ratio=formant_shift_ratio, - ) - return new_sound - - if ps: - board = Pedalboard() - board.append(PitchShift(random.uniform(-12, 12))) - wav_numpy = sound.values - wav_numpy = board(wav_numpy, sound.sampling_frequency) - # use pedalboard to change pitch - new_sound = parselmouth.Sound( - wav_numpy, sampling_frequency=sound.sampling_frequency - ) - return new_sound - - -def wav_manipulation( - wav: torch.Tensor, - sr: int, - aug_type: str = "None", - formant_shift: bool = False, - pitch_shift: bool = False, - time_stretch: bool = False, - equalizer: bool = False, -) -> torch.Tensor: - assert aug_type == "None" or aug_type in [ - "formant_shift", - "pitch_shift", - "time_stretch", - "equalizer", - ], "aug_type must be one of formant_shift, pitch_shift, time_stretch, equalizer" - - assert aug_type == "None" or ( - formant_shift == False - and pitch_shift == False - and time_stretch == False - and equalizer == False - ), "if aug_type is specified, other argument must be False" - - if aug_type != "None": - if aug_type == "formant_shift": - formant_shift = True - if aug_type == "pitch_shift": - pitch_shift = True - if aug_type == "equalizer": - equalizer = True - if aug_type == "time_stretch": - time_stretch = True - - wav_numpy = wav.numpy() - - if equalizer: - wav_numpy = pedalboard_equalizer(wav_numpy, sr) - - if time_stretch: - wav_numpy = audiomentations_time_stretch(wav_numpy, sr) - - sound = wav_to_Sound(wav_numpy, sr) - - if formant_shift or pitch_shift: - sound = formant_and_pitch_shift(sound, formant_shift, pitch_shift) - - wav = torch.from_numpy(sound.values).float() - # shape (1, n_samples) - return wav - - -def augment_dataset(cfg, dataset) -> list: - """Augment dataset with formant_shift, pitch_shift, time_stretch, equalizer - - Args: - cfg (dict): configuration - dataset (str): dataset name - - Returns: - list: augmented dataset names - """ - # load metadata - dataset_path = os.path.join(cfg.preprocess.processed_dir, dataset) - split = ["train", "test"] if "eval" not in dataset else ["test"] - augment_datasets = [] - aug_types = [ - "formant_shift" if cfg.preprocess.use_formant_shift else None, - "pitch_shift" if cfg.preprocess.use_pitch_shift else None, - "time_stretch" if cfg.preprocess.use_time_stretch else None, - "equalizer" if cfg.preprocess.use_equalizer else None, - ] - aug_types = filter(None, aug_types) - for aug_type in aug_types: - print("Augmenting {} with {}...".format(dataset, aug_type)) - new_dataset = dataset + "_" + aug_type - augment_datasets.append(new_dataset) - new_dataset_path = os.path.join(cfg.preprocess.processed_dir, new_dataset) - - for dataset_type in split: - metadata_path = os.path.join(dataset_path, "{}.json".format(dataset_type)) - augmented_metadata = [] - new_metadata_path = os.path.join( - new_dataset_path, "{}.json".format(dataset_type) - ) - os.makedirs(new_dataset_path, exist_ok=True) - new_dataset_wav_dir = os.path.join(new_dataset_path, "wav") - os.makedirs(new_dataset_wav_dir, exist_ok=True) - - if has_existed(new_metadata_path): - continue - - with open(metadata_path, "r") as f: - metadata = json.load(f) - - for utt in tqdm(metadata): - original_wav_path = utt["Path"] - original_wav, sr = torchaudio.load(original_wav_path) - new_wav = wav_manipulation(original_wav, sr, aug_type=aug_type) - new_wav_path = os.path.join(new_dataset_wav_dir, utt["Uid"] + ".wav") - torchaudio.save(new_wav_path, new_wav, sr) - new_utt = { - "Dataset": utt["Dataset"] + "_" + aug_type, - "index": utt["index"], - "Singer": utt["Singer"], - "Uid": utt["Uid"], - "Path": new_wav_path, - "Duration": utt["Duration"], - } - augmented_metadata.append(new_utt) - new_metadata_path = os.path.join( - new_dataset_path, "{}.json".format(dataset_type) - ) - with open(new_metadata_path, "w") as f: - json.dump(augmented_metadata, f, indent=4, ensure_ascii=False) - return augment_datasets diff --git a/processors/phone_extractor.py b/processors/phone_extractor.py deleted file mode 100644 index b0c53a79decf6c8c8e5ee68c5d8e05d878564f6a..0000000000000000000000000000000000000000 --- a/processors/phone_extractor.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -from tqdm import tqdm -from text.g2p_module import G2PModule, LexiconModule -from text.symbol_table import SymbolTable - -''' - phoneExtractor: extract phone from text -''' -class phoneExtractor: - def __init__(self, cfg, dataset_name=None, phone_symbol_file=None): - ''' - Args: - cfg: config - dataset_name: name of dataset - ''' - self.cfg = cfg - - # phone symbols dict - self.phone_symbols = set() - - # phone symbols dict file - if phone_symbol_file is not None: - self.phone_symbols_file = phone_symbol_file - elif dataset_name is not None: - self.dataset_name = dataset_name - self.phone_symbols_file = os.path.join(cfg.preprocess.processed_dir, - dataset_name, - cfg.preprocess.symbols_dict) - - - # initialize g2p module - if cfg.preprocess.phone_extractor in ["espeak", "pypinyin", "pypinyin_initials_finals"]: - self.g2p_module = G2PModule(backend=cfg.preprocess.phone_extractor) - elif cfg.preprocess.phone_extractor == 'lexicon': - assert cfg.preprocess.lexicon_path != "" - self.g2p_module = LexiconModule(cfg.preprocess.lexicon_path) - else: - print('No suppert to', cfg.preprocess.phone_extractor) - raise - - - def extract_phone(self, text): - ''' - Extract phone from text - Args: - - text: text of utterance - - Returns: - phone_symbols: set of phone symbols - phone_seq: list of phone sequence of each utterance - ''' - - if self.cfg.preprocess.phone_extractor in ["espeak", "pypinyin", "pypinyin_initials_finals"]: - text = text.replace("”", '"').replace("“", '"') - phone = self.g2p_module.g2p_conversion(text=text) - self.phone_symbols.update(phone) - phone_seq = [phn for phn in phone] - - elif self.cfg.preprocess.phone_extractor == 'lexicon': - phone_seq = self.g2p_module.g2p_conversion(text) - phone = phone_seq - if not isinstance(phone_seq, list): - phone_seq = phone_seq.split() - - return phone_seq - - def save_dataset_phone_symbols_to_table(self): - # load and merge saved phone symbols - if os.path.exists(self.phone_symbols_file): - phone_symbol_dict_saved = SymbolTable.from_file(self.phone_symbols_file)._sym2id.keys() - self.phone_symbols.update(set(phone_symbol_dict_saved)) - - # save phone symbols - phone_symbol_dict = SymbolTable() - for s in sorted(list(self.phone_symbols)): - phone_symbol_dict.add(s) - phone_symbol_dict.to_file(self.phone_symbols_file) - - -def extract_utt_phone_sequence(cfg, metadata): - ''' - Extract phone sequence from text - Args: - cfg: config - metadata: list of dict, each dict contains "Uid", "Text" - - ''' - - dataset_name = cfg.dataset[0] - - # output path - out_path = os.path.join(cfg.preprocess.processed_dir, dataset_name, cfg.preprocess.phone_dir) - os.makedirs(out_path, exist_ok=True) - - phone_extractor = phoneExtractor(cfg, dataset_name) - - for utt in tqdm(metadata): - uid = utt["Uid"] - text = utt["Text"] - - phone_seq = phone_extractor.extract_phone(text) - - phone_path = os.path.join(out_path, uid+'.phone') - with open(phone_path, 'w') as fin: - fin.write(' '.join(phone_seq)) - - if cfg.preprocess.phone_extractor != 'lexicon': - phone_extractor.save_dataset_phone_symbols_to_table() - - - -def save_all_dataset_phone_symbols_to_table(self, cfg, dataset): - # phone symbols dict - phone_symbols = set() - - for dataset_name in dataset: - phone_symbols_file = os.path.join(cfg.preprocess.processed_dir, - dataset_name, - cfg.preprocess.symbols_dict) - - # load and merge saved phone symbols - assert os.path.exists(phone_symbols_file) - phone_symbol_dict_saved = SymbolTable.from_file(phone_symbols_file)._sym2id.keys() - phone_symbols.update(set(phone_symbol_dict_saved)) - - # save all phone symbols to each dataset - phone_symbol_dict = SymbolTable() - for s in sorted(list(phone_symbols)): - phone_symbol_dict.add(s) - for dataset_name in dataset: - phone_symbols_file = os.path.join(cfg.preprocess.processed_dir, - dataset_name, - cfg.preprocess.symbols_dict) - phone_symbol_dict.to_file(phone_symbols_file) - - \ No newline at end of file diff --git a/text/__init__.py b/text/__init__.py deleted file mode 100644 index a4db9a6a2c8dbb20fb4188c322a7cc32afe722e2..0000000000000000000000000000000000000000 --- a/text/__init__.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" This code is modified from https://github.com/keithito/tacotron """ -import re -from text import cleaners -from text.symbols import symbols - - -# Mappings from symbol to numeric ID and vice versa: -_symbol_to_id = {s: i for i, s in enumerate(symbols)} -_id_to_symbol = {i: s for i, s in enumerate(symbols)} - -# Regular expression matching text enclosed in curly braces: -_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)") - - -def text_to_sequence(text, cleaner_names): - """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. - - The text can optionally have ARPAbet sequences enclosed in curly braces embedded - in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street." - - Args: - text: string to convert to a sequence - cleaner_names: names of the cleaner functions to run the text through - - Returns: - List of integers corresponding to the symbols in the text - """ - sequence = [] - - # Check for curly braces and treat their contents as ARPAbet: - while len(text): - m = _curly_re.match(text) - - if not m: - sequence += _symbols_to_sequence(_clean_text(text, cleaner_names)) - break - sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names)) - sequence += _arpabet_to_sequence(m.group(2)) - text = m.group(3) - return sequence - - -def sequence_to_text(sequence): - """Converts a sequence of IDs back to a string""" - result = "" - for symbol_id in sequence: - if symbol_id in _id_to_symbol: - s = _id_to_symbol[symbol_id] - # Enclose ARPAbet back in curly braces: - if len(s) > 1 and s[0] == "@": - s = "{%s}" % s[1:] - result += s - return result.replace("}{", " ") - - -def _clean_text(text, cleaner_names): - for name in cleaner_names: - cleaner = getattr(cleaners, name) - if not cleaner: - raise Exception("Unknown cleaner: %s" % name) - text = cleaner(text) - return text - - -def _symbols_to_sequence(symbols): - return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] - - -def _arpabet_to_sequence(text): - return _symbols_to_sequence(["@" + s for s in text.split()]) - - -def _should_keep_symbol(s): - return s in _symbol_to_id and s != "_" and s != "~" diff --git a/text/cleaners.py b/text/cleaners.py deleted file mode 100644 index 10df96dd400efa0cceb9283cbab0371196ff3c29..0000000000000000000000000000000000000000 --- a/text/cleaners.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" This code is modified from https://github.com/keithito/tacotron """ - -""" -Cleaners are transformations that run over the input text at both training and eval time. - -Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" -hyperparameter. Some cleaners are English-specific. You'll typically want to use: - 1. "english_cleaners" for English text - 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using - the Unidecode library (https://pypi.python.org/pypi/Unidecode) - 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update - the symbols in symbols.py to match your data). -""" - - -# Regular expression matching whitespace: -import re -from unidecode import unidecode -from .numbers import normalize_numbers - -_whitespace_re = re.compile(r"\s+") - -# List of (regular expression, replacement) pairs for abbreviations: -_abbreviations = [ - (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) - for x in [ - ("mrs", "misess"), - ("mr", "mister"), - ("dr", "doctor"), - ("st", "saint"), - ("co", "company"), - ("jr", "junior"), - ("maj", "major"), - ("gen", "general"), - ("drs", "doctors"), - ("rev", "reverend"), - ("lt", "lieutenant"), - ("hon", "honorable"), - ("sgt", "sergeant"), - ("capt", "captain"), - ("esq", "esquire"), - ("ltd", "limited"), - ("col", "colonel"), - ("ft", "fort"), - ] -] - - -def expand_abbreviations(text): - for regex, replacement in _abbreviations: - text = re.sub(regex, replacement, text) - return text - - -def expand_numbers(text): - return normalize_numbers(text) - - -def lowercase(text): - return text.lower() - - -def collapse_whitespace(text): - return re.sub(_whitespace_re, " ", text) - - -def convert_to_ascii(text): - return unidecode(text) - - -def basic_cleaners(text): - """Basic pipeline that lowercases and collapses whitespace without transliteration.""" - text = lowercase(text) - text = collapse_whitespace(text) - return text - - -def transliteration_cleaners(text): - """Pipeline for non-English text that transliterates to ASCII.""" - text = convert_to_ascii(text) - text = lowercase(text) - text = collapse_whitespace(text) - return text - - -def english_cleaners(text): - """Pipeline for English text, including number and abbreviation expansion.""" - text = convert_to_ascii(text) - text = lowercase(text) - text = expand_numbers(text) - text = expand_abbreviations(text) - text = collapse_whitespace(text) - return text diff --git a/text/cmudict.py b/text/cmudict.py deleted file mode 100644 index 684e63a52f84c4942d5be2a6fac80b6abc004eda..0000000000000000000000000000000000000000 --- a/text/cmudict.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" This code is modified from https://github.com/keithito/tacotron """ - -import re - - -valid_symbols = [ - "AA", - "AA0", - "AA1", - "AA2", - "AE", - "AE0", - "AE1", - "AE2", - "AH", - "AH0", - "AH1", - "AH2", - "AO", - "AO0", - "AO1", - "AO2", - "AW", - "AW0", - "AW1", - "AW2", - "AY", - "AY0", - "AY1", - "AY2", - "B", - "CH", - "D", - "DH", - "EH", - "EH0", - "EH1", - "EH2", - "ER", - "ER0", - "ER1", - "ER2", - "EY", - "EY0", - "EY1", - "EY2", - "F", - "G", - "HH", - "IH", - "IH0", - "IH1", - "IH2", - "IY", - "IY0", - "IY1", - "IY2", - "JH", - "K", - "L", - "M", - "N", - "NG", - "OW", - "OW0", - "OW1", - "OW2", - "OY", - "OY0", - "OY1", - "OY2", - "P", - "R", - "S", - "SH", - "T", - "TH", - "UH", - "UH0", - "UH1", - "UH2", - "UW", - "UW0", - "UW1", - "UW2", - "V", - "W", - "Y", - "Z", - "ZH", -] - -_valid_symbol_set = set(valid_symbols) - - -class CMUDict: - """Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict""" - - def __init__(self, file_or_path, keep_ambiguous=True): - if isinstance(file_or_path, str): - with open(file_or_path, encoding="latin-1") as f: - entries = _parse_cmudict(f) - else: - entries = _parse_cmudict(file_or_path) - if not keep_ambiguous: - entries = {word: pron for word, pron in entries.items() if len(pron) == 1} - self._entries = entries - - def __len__(self): - return len(self._entries) - - def lookup(self, word): - """Returns list of ARPAbet pronunciations of the given word.""" - return self._entries.get(word.upper()) - - -_alt_re = re.compile(r"\([0-9]+\)") - - -def _parse_cmudict(file): - cmudict = {} - for line in file: - if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"): - parts = line.split(" ") - word = re.sub(_alt_re, "", parts[0]) - pronunciation = _get_pronunciation(parts[1]) - if pronunciation: - if word in cmudict: - cmudict[word].append(pronunciation) - else: - cmudict[word] = [pronunciation] - return cmudict - - -def _get_pronunciation(s): - parts = s.strip().split(" ") - for part in parts: - if part not in _valid_symbol_set: - return None - return " ".join(parts) diff --git a/text/g2p.py b/text/g2p.py deleted file mode 100644 index 2368c1e97b4018abde395b190a9bbec76ba8ede0..0000000000000000000000000000000000000000 --- a/text/g2p.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import re -from g2p_en import G2p -from string import punctuation - - -def read_lexicon(lex_path): - lexicon = {} - with open(lex_path) as f: - for line in f: - temp = re.split(r"\s+", line.strip("\n")) - word = temp[0] - phones = temp[1:] - if word.lower() not in lexicon: - lexicon[word.lower()] = phones - return lexicon - - -def preprocess_english(text, lexicon): - text = text.rstrip(punctuation) - - g2p = G2p() - phones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.lower() in lexicon: - phones += lexicon[w.lower()] - else: - phones += list(filter(lambda p: p != " ", g2p(w))) - phones = "}{".join(phones) - phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones) - phones = phones.replace("}{", " ") - - return phones diff --git a/text/g2p_module.py b/text/g2p_module.py deleted file mode 100644 index 39a1ab3c701bbfd20cccca0047fcad4caa09c41d..0000000000000000000000000000000000000000 --- a/text/g2p_module.py +++ /dev/null @@ -1,221 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import re -from g2p_en import G2p -from string import punctuation -from typing import Any, Dict, List, Optional, Pattern, Union - -from phonemizer.backend import EspeakBackend -from phonemizer.backend.espeak.language_switch import LanguageSwitch -from phonemizer.backend.espeak.words_mismatch import WordMismatch -from phonemizer.punctuation import Punctuation -from phonemizer.separator import Separator - -try: - from pypinyin import Style, pinyin - from pypinyin.style._utils import get_finals, get_initials -except Exception: - pass - - -# This code is modified from -# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py - -class PypinyinBackend: - """PypinyinBackend for Chinese. Most codes is referenced from espnet. - There are two types pinyin or initials_finals, one is - just like "ni1 hao3", the other is like "n i1 h ao3". - """ - - def __init__( - self, - backend="initials_finals", - punctuation_marks: Union[str, Pattern] = Punctuation.default_marks(), - ) -> None: - self.backend = backend - self.punctuation_marks = punctuation_marks - - def phonemize( - self, text: List[str], separator: Separator, strip=True, njobs=1 - ) -> List[str]: - assert isinstance(text, List) - phonemized = [] - for _text in text: - _text = re.sub(" +", " ", _text.strip()) - _text = _text.replace(" ", separator.word) - phones = [] - if self.backend == "pypinyin": - for n, py in enumerate( - pinyin( - _text, style=Style.TONE3, neutral_tone_with_five=True - ) - ): - if all([c in self.punctuation_marks for c in py[0]]): - if len(phones): - assert phones[-1] == separator.syllable - phones.pop(-1) - - phones.extend(list(py[0])) - else: - phones.extend([py[0], separator.syllable]) - elif self.backend == "pypinyin_initials_finals": - for n, py in enumerate( - pinyin( - _text, style=Style.TONE3, neutral_tone_with_five=True - ) - ): - if all([c in self.punctuation_marks for c in py[0]]): - if len(phones): - assert phones[-1] == separator.syllable - phones.pop(-1) - phones.extend(list(py[0])) - else: - if py[0][-1].isalnum(): - initial = get_initials(py[0], strict=False) - if py[0][-1].isdigit(): - final = ( - get_finals(py[0][:-1], strict=False) - + py[0][-1] - ) - else: - final = get_finals(py[0], strict=False) - phones.extend( - [ - initial, - separator.phone, - final, - separator.syllable, - ] - ) - else: - assert ValueError - else: - raise NotImplementedError - phonemized.append( - "".join(phones).rstrip(f"{separator.word}{separator.syllable}") - ) - return phonemized - - -class G2PModule: - """Phonemize Text.""" - - def __init__( - self, - language="en-us", - backend="espeak", - separator=Separator(word="_", syllable="-", phone="|"), - preserve_punctuation=True, - punctuation_marks: Union[str, Pattern] = Punctuation.default_marks(), - with_stress: bool = False, - tie: Union[bool, str] = False, - language_switch: LanguageSwitch = "keep-flags", - words_mismatch: WordMismatch = "ignore", - ) -> None: - - self.backend = self._initialize_backend( - backend, language, punctuation_marks, preserve_punctuation, - with_stress, tie, language_switch, words_mismatch - ) - self.separator = separator - - def _initialize_backend( - self, backend, language, punctuation_marks, preserve_punctuation, - with_stress, tie, language_switch, words_mismatch - ): - if backend == "espeak": - return EspeakBackend( - language, - punctuation_marks=punctuation_marks, - preserve_punctuation=preserve_punctuation, - with_stress=with_stress, - tie=tie, - language_switch=language_switch, - words_mismatch=words_mismatch, - ) - elif backend in ["pypinyin", "pypinyin_initials_finals"]: - return PypinyinBackend( - backend=backend, - punctuation_marks=punctuation_marks + self.separator.word, - ) - else: - raise NotImplementedError(f"{backend}") - - def to_list(self, phonemized: str) -> List[str]: - fields = [] - for word in phonemized.split(self.separator.word): - pp = re.findall(r"\w+|[^\w\s]", word, re.UNICODE) - fields.extend( - [p for p in pp if p != self.separator.phone] - + [self.separator.word] - ) - assert len("".join(fields[:-1])) == len(phonemized) - phonemized.count( - self.separator.phone - ) - return fields[:-1] - - - def phonemization(self, text, strip=True) -> List[List[str]]: - if isinstance(text, str): - text = [text] - - phonemized = self.backend.phonemize( - text, separator=self.separator, strip=strip, njobs=1 - ) - phonemes = [self.to_list(p) for p in phonemized] - return phonemes - - def g2p_conversion(self, text: str) -> List[str]: - phonemes = self.phonemization([text.strip()]) - return phonemes[0] - - -class LexiconModule: - def __init__(self, lex_path, language="en-us") -> None: - - # todo: check lexicon derivation, merge with G2PModule? - lexicon = {} - with open(lex_path) as f: - for line in f: - temp = re.split(r"\s+", line.strip("\n")) - word = temp[0] - phones = temp[1:] - if word.lower() not in lexicon: - lexicon[word.lower()] = phones - self.lexicon = lexicon - self.language = language - - def g2p_conversion(self, text): - phone = None - - # todo: preprocess with other languages - if self.language == 'en-us': - phone = self.preprocess_english(text) - else: - print('No support to', self.language) - raise - - return phone - - - def preprocess_english(self, text): - text = text.rstrip(punctuation) - - g2p = G2p() - phones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.lower() in self.lexicon: - phones += self.lexicon[w.lower()] - else: - phones += list(filter(lambda p: p != " ", g2p(w))) - phones = "}{".join(phones) - phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones) - phones = phones.replace("}{", " ") - - - return phones diff --git a/text/lexicon/librispeech-lexicon.txt b/text/lexicon/librispeech-lexicon.txt deleted file mode 100644 index 1091e15b860e3ce6dd9c785ba62076fa0ea20b6f..0000000000000000000000000000000000000000 --- a/text/lexicon/librispeech-lexicon.txt +++ /dev/null @@ -1,206508 +0,0 @@ -A AH0 -A EY1 -A''S EY1 Z -A'BODY EY1 B AA2 D IY0 -A'COURT EY1 K AO2 R T -A'D EY1 D -A'GHA EY1 G AH0 -A'GOIN EY1 G OY1 N -A'LL EY1 L -A'M EY1 M -A'MIGHTY EY1 M AY1 T IY0 -A'MIGHTY'S EY1 M AY1 T IY0 Z -A'MOST EY1 M OW2 S T -A'N'T EY1 AH0 N T -A'PENNY EY1 P EH2 N IY0 -A'READY EY1 R IY1 D IY0 -A'RIGHT EY1 R AY2 T -A'RONY EY1 R OW1 N IY0 -A'S EY1 Z -A'TER EY1 T ER0 -A'TERNOON EY1 T ER0 N UW1 N -A'TERWARDS EY1 T ER0 W ER0 D Z -A'THEGITHER EY1 DH AH0 JH IH1 DH ER0 -A'THING EY1 DH IH0 NG -A'TIM EY1 T IH2 M -A'VE AH0 V -AA AA1 -AAANTHOR T R IH2 P AH0 L EY1 N TH ER0 -AACHEN AA1 K AH0 N -AAD AA1 D -AAGE AA1 ZH -AAGE'S IH0 JH IH0 Z -AAGOT AA1 G AH0 T -AAGOT'S AA1 G AH0 T S -AAH AA1 -AAHMES AA1 M Z -AAKRE AA1 K ER0 -AAL AA1 L -AALBOM AA1 L B AO2 M -AALST AA1 L S T 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IH0 N -BANGING B AE1 NG IH0 NG -BANGINGS B AE1 NG IH0 NG Z -BANGKOK B AE0 NG K AA1 K -BANGKOK B AE1 NG K AA0 K -BANGLE B AE1 NG G AH0 L -BANGLED B AE1 NG G AH0 L D -BANGLES B AE1 NG G AH0 L Z -BANGLETOP B AE1 NG G AH0 L T AA2 P -BANGLETOPS B AE1 NG G AH0 L T AA2 P S -BANGO B AA1 NG G OW0 -BANGOR B AE1 NG G AO2 R -BANGOR B AE1 NG G ER0 -BANGS B AE1 NG Z -BANGS'S B AE1 NG Z IH0 Z -BANGU B AE1 NG UW1 -BANGWAN B AE1 NG G W AA0 N -BANI B AA1 N IY0 -BANIAN B AE1 N IY0 AH0 N -BANIANS B AE1 N IY0 AH0 N Z -BANIM B AE1 N IH0 M -BANION B AE1 N Y AH0 N -BANION'S B AE1 N Y AH0 N Z -BANISH B AE1 N IH0 SH -BANISH'D B AE1 N IH0 SH D -BANISHED B AE1 N IH0 SH T -BANISHES B AE1 N IH0 SH IH0 Z -BANISHING B AE1 N IH0 SH IH0 NG -BANISHMENT B AE1 N IH0 SH M AH0 N T -BANISHMENTS B AE1 N IH0 SH M AH0 N T S -BANISSAT B AE1 N IH0 S AE0 T -BANISTER B AE1 N IH0 S T ER0 -BANISTERS B AE1 N IH0 S T ER0 Z -BANIYA B AE1 N IH0 Y AH0 -BANJARA B AA1 N Y AA1 R AH0 -BANJER B AE1 N JH ER0 -BANJO B AE1 N JH OW2 -BANJO'S B AE1 N JH OW2 Z -BANJOES B AE1 N JH OW2 Z -BANJOIST B AE1 N JH OW2 AH0 S T -BANJOS B AE1 N JH OW2 Z -BANK B AE1 NG K -BANK'S B AE1 NG K S -BANKA B AE1 NG K AH0 -BANKBILLS B AE1 NG K B IH2 L Z -BANKBOOK B AE1 NG K B UH2 K -BANKBOYS B AE1 NG K B OY2 Z -BANKCLERK B AE1 NG K L ER1 K -BANKCLERKS B AE1 NG K L ER1 K S -BANKED B AE1 NG K T -BANKEI B AE1 NG K IY0 -BANKER B AE1 NG K ER0 -BANKER'S B AE1 NG K ER0 Z -BANKERS B AE1 NG K ER0 Z -BANKES B AE1 NG K S -BANKES'S B AE1 NG K S IH0 Z -BANKET B AE1 NG K AH0 T -BANKHEAD B AE1 NG K HH EH2 D -BANKIM B AE0 NG K IY1 M -BANKIN B AE1 NG K IH0 N -BANKING B AE1 NG K IH0 NG -BANKNOTE B AE1 NG K N OW2 T -BANKNOTES B AE1 NG K N OW2 T S -BANKOK B AE1 NG K AH0 K -BANKROLL B AE1 NG K R OW2 L -BANKRUPT B AE1 NG K R AH0 P T -BANKRUPT'S B AE1 NG K R AH0 P T S -BANKRUPTCIES B AE1 NG K R AH0 P T S IY0 Z -BANKRUPTCY B AE1 NG K R AH0 P S IY0 -BANKRUPTCY B AE1 NG K R AH0 P T S IY0 -BANKRUPTED B AE1 NG K R AH0 P T IH0 D -BANKRUPTING B AE1 NG K R AH2 P T IH0 NG -BANKRUPTS B AE1 NG K R AH0 P S -BANKS B AE1 NG K S -BANKS'S B AE1 NG K S IH0 Z -BANKSIA B AE1 NG K S IY0 AH0 -BANKSIAN B AE1 NG ZH IH0 N -BANKSIAS B AE1 NG K S IY1 AH0 Z -BANKSIDE B AE1 NG K S AY2 D -BANLIEUE B AE0 N L UW2 -BANMANN B AE1 N M AH0 N -BANN B AE1 N -BANN'D B AE1 N D -BANNACK B AE1 N AH0 K -BANNACKS B AE1 N AE2 K S -BANNADONNA B AE1 N AH0 D AA1 N AH0 -BANNALEC B AE1 N AH0 L EH2 K -BANNATINE B AE1 N AH0 T AY2 N -BANNATYNE B AE1 N AH0 T AY2 N -BANNE B AE1 N -BANNED B AE1 N D -BANNEKER B AE1 N IH0 K ER0 -BANNER B AE1 N ER0 -BANNER'S B AE1 N ER0 Z -BANNERBRIDGE B AE1 N ER0 B R IH2 JH -BANNERDALE B AE1 N ER0 D EY2 L -BANNERED B AE1 N ER0 D -BANNERET B AE1 N ER0 EY1 -BANNERETS B AE1 N ER0 EY1 Z -BANNERHALL B AE1 N ER0 HH AO2 L -BANNERMAN B AE1 N ER0 M AH0 N -BANNERMAN'S B AE1 N ER0 M AH0 N Z -BANNERS B AE1 N ER0 Z -BANNERT B AE1 N ER0 T -BANNERTAIL B AE1 N ER0 T EY1 L -BANNET B AE1 N IH0 T -BANNIER B AE1 N IY0 ER0 -BANNING B AE1 N IH0 NG -BANNING'S B AE1 N IH0 NG Z -BANNINGTON'S B AE1 N IH0 NG T AH0 N Z -BANNISDALE B AE1 N IH0 S D EY2 L -BANNISTER B AE1 N AH0 S T ER0 -BANNISTER B AE1 N IH0 S T ER0 -BANNISTER'S B AE1 N IH0 S T ER0 Z -BANNISTERS B AE1 N IH0 S T ER0 Z -BANNOCK B AE1 N AH0 K -BANNOCKBURN B AE1 N AH0 K B ER0 N -BANNOCKS B AE1 N AH0 K S -BANNON B AE1 N AH0 N -BANNON'S B AE1 N AH0 N Z -BANNS B AE1 N Z -BANNU B AE1 N UW0 -BANO B AA1 N OW0 -BANOS B AA1 N OW0 Z -BANOU B AH0 N UW1 -BANQUE B AE1 NG K -BANQUET B AE1 NG K W AH0 T -BANQUET'S B AE1 NG K W AH0 T S -BANQUETA B AE1 NG K W EY1 T AH0 -BANQUETED B AE1 NG K W AH0 T IH0 D -BANQUETER B AE1 NG K W AH0 T ER0 -BANQUETERS B AE1 NG K W AH0 T ER0 Z -BANQUETING B AE1 NG K W AH0 T IH0 NG -BANQUETINGS B AE1 NG K W AH0 T IH0 NG Z -BANQUETS B AE1 NG K W AH0 T S -BANQUETTE B AE1 NG K EH1 T -BANQUETTES B AE1 NG K EH1 T S -BANQUETTING B AE1 NG K EH1 T IH0 NG -BANQUIER B AE1 NG K W AY1 ER0 -BANQUO B AE1 NG K W OW0 -BANQUO'S B AE1 NG K W OW0 Z -BANS B AE1 N Z -BANSHEE B AE0 N SH IY1 -BANSHEE B AE1 N SH IY0 -BANSHEE'S B AE0 N SH IY0 Z -BANSHEES B AE0 N SH IY0 Z -BANSKIAN B AE1 N S K IY0 AH0 N -BANSTEAD B AE1 N S T EH2 D -BANT B AE1 N T -BANTA B AE1 N T AH0 -BANTAM B AE1 N T AH0 M -BANTAM'S B AE1 N T AH0 M Z -BANTAMS B AE1 N T AH0 M Z -BANTEM B AE1 N T AH0 M -BANTER B AE1 N T ER0 -BANTERED B AE1 N T ER0 D -BANTERER B AE1 N T ER0 ER0 -BANTERING B AE1 N T ER0 IH0 NG -BANTERINGLY B AE1 N T ER0 IH0 NG L IY0 -BANTERINGS B AE1 N T ER0 IH0 NG Z -BANTERS B AE1 N T ER0 Z -BANTH B AE1 N TH -BANTHS B AE1 N TH S -BANTING B AE1 N T IH0 NG -BANTISON B AE1 N T IH0 S AH0 N -BANTLING B AE1 N T L IH0 NG -BANTLINGS B AE1 N T L IH0 NG Z -BANTO B AE1 N T OW0 -BANTOC B AE1 N T AH0 K -BANTOM B AE1 N T AH0 M -BANTOOM B AE1 N T UW2 M -BANTORNYI B AE1 N T ER0 N Y IY0 -BANTORNYI'S B AE1 N T ER0 N Y IY1 Z -BANTRY B AE1 N T R IY0 -BANTU B AE1 N T UW0 -BANTUS B AE1 N T AH0 S -BANTY B AE1 N T IY0 -BANU B AA1 N UW0 -BANVILLE B AA1 N V IH0 L -BANVILLE'S B AA1 N V IH0 L Z -BANYA B AA1 N Y AH0 -BANYA B AE1 N Y AH0 -BANYAN B AE1 N Y AH0 N -BANYANS B AE1 N Y AH0 N Z -BANYARD B AA1 N Y AA2 R D -BANZAI B AE1 N Z AY1 -BANZE B AE1 N Z -BAOBAB B AW1 B AE0 B -BAOBABS B AW1 B AE0 B Z -BAOUND B EY1 AW2 N D -BAOUT B EY1 AW2 T -BAP B AE0 P -BAPAUME B AE1 P UW1 M -BAPEDI B AE1 P AH0 D IY0 -BAPHOMET B AE1 F OW0 M EH1 T -BAPHOMETIC B AE1 F OW0 M EH1 T IH0 K -BAPONGQOLO B AH0 P AO1 NG K OW1 L OW0 -BAPPA B AE1 P AH0 -BAPS B AE1 P S -BAPTEME B AE1 P T IY2 M -BAPTIS B AE1 P T AH0 S -BAPTISE B AE1 P T AH0 S -BAPTISED B AE0 P T AY1 Z D -BAPTISING B AE1 P T AY2 Z IH0 NG -BAPTISM B AE1 P T IH0 Z AH0 M -BAPTISMAL B AE0 P T IH1 Z M AH0 L -BAPTISMS B AE1 P T IH2 Z AH0 M Z -BAPTIST B AE1 P T AH0 S T -BAPTIST B AE1 P T IH0 S T -BAPTIST'S B AE1 P T AH0 S T S -BAPTISTA B AE2 P T IH1 S T AH0 -BAPTISTA'S B AE0 P T IH1 S T AA0 Z -BAPTISTE B AH0 P T IH1 S T -BAPTISTE'S B AE1 P T AH0 S T S -BAPTISTERY B AE1 P T AH0 S T R IY0 -BAPTISTRY B AE1 P T AH0 S T R IY0 -BAPTISTS B AE1 P T AH0 S -BAPTISTS B AE1 P T AH0 S T S -BAPTIZE B AE0 P T AY1 Z -BAPTIZED B AE0 P T AY1 Z D -BAPTIZED B AE1 P T AY2 Z D -BAPTIZES B AE1 P T AY2 Z IH0 Z -BAPTIZIN B AE0 P T AY1 Z IH0 N -BAPTIZING B AE1 P T AY2 Z IH0 NG -BAPUTI B AA0 P UW1 T IY0 -BAR B AA1 R -BAR'D B ER0 D -BAR'L B ER0 AH0 L -BAR'LS B ER0 L Z -BAR'S B AA1 R Z -BARA B AA1 R AH0 -BARABA B AA0 R AA1 B AH0 -BARABBAS B AE1 R AH0 B AA1 S -BARACH B AH0 R AA1 K -BARACH B ER0 AA1 K -BARADLAY B AA0 R AA1 D L IY0 -BARAGAR B AE1 R AH0 G AA1 R -BARAGAT B AH0 R AA1 G AH0 T -BARAGUAN B ER0 AA1 G W AH0 N -BARAK B AA1 R AH0 K -BARAKA B AA1 R AH0 K AH0 -BARAKA'S B AA1 R AH0 K AH0 Z -BARAKOFF B AA1 R AH0 K AO2 F -BARAL B AE1 R AH0 L -BARALONG B ER0 AO1 L AO0 NG -BARALONGS B AE1 R AH0 L AO1 NG Z -BARAMULA B EH1 R AE0 M Y AH0 L AH0 -BARAN B AA0 R AA1 N -BARANCY B ER0 AH0 N S IY0 -BARANG B AA0 R AA1 NG -BARANG'S B AA0 R AA1 NG Z -BARANGAY B AA0 R AA1 NG G EY1 -BARANHOV B EH1 R AE0 N HH AH1 V -BARANOFF B ER0 AH0 N AO0 F -BARANOV B ER0 AH0 N AA0 V -BARANTE B AA0 R AA1 N T IY0 -BARAQUAN B AA0 R AA1 K W AH0 N -BARAT B ER0 AA1 -BARATARIA B AE1 R AH0 T AA1 R IY0 AH0 -BARATARIAN B ER0 AA1 T EH1 R IY0 AH0 N -BARATARIANS B ER0 AA1 T EH1 R IY0 AH0 N Z -BARATHRUM B AE1 R AH0 TH R AH0 M -BARATOWSKI B AE1 R AH0 T AO1 F S K IY0 -BARB B AA1 R B -BARB'ROUS B AA1 R B R AH0 S -BARB'RY B AA1 R B R IY0 -BARBA B AA1 R B AH0 -BARBACAN B AA1 R B AH0 K AA2 N -BARBACZY B AA1 R B AH0 CH IY0 -BARBADIAN B AA0 R B AA1 D IY0 AH0 N -BARBADIANS B AA0 R B AA1 D IY0 AH0 N Z -BARBADOES B AA0 R B AA1 D OW0 Z -BARBADOS B AA0 R B EY1 D OW0 S -BARBAIK B AA1 R B EY1 K -BARBAJA B AA0 R B AA0 Y AH0 -BARBANCON B AA0 R B AE1 NG K AH0 N -BARBAR B AA1 R B ER0 -BARBARA B AA1 R B ER0 AH0 -BARBARA B AA1 R B R AH0 -BARBARA'S B AA1 R B ER0 AH0 Z -BARBARE B AA0 R B EH1 R -BARBAREE B AA1 R B ER0 IY1 -BARBARELLA B AA1 R B ER0 EH1 L AH0 -BARBARES B AA0 R B EH1 R Z -BARBARI B AA0 R B AA1 R IY0 -BARBARIAN B AA0 R B EH1 R IY0 AH0 N -BARBARIAN'S B AA0 R B EH1 R IY0 AH0 N Z -BARBARIANS B AA0 R B EH1 R IY0 AH0 N Z -BARBARIC B AA0 R B AE1 R IH0 K -BARBARIC B AA0 R B EH1 R IH0 K -BARBARICALLY B AA0 R B AE1 R IH0 K L IY0 -BARBARICO B AA0 R B AA0 R IY1 K OW0 -BARBARIE B AA0 R B EH1 R IY0 -BARBARIGO B AA0 R B AA0 R IY1 G OW0 -BARBARISI B AA0 R B AA0 R IY1 S IY0 -BARBARISM B AA1 R B ER0 IH2 Z AH0 M -BARBARISMS B AA1 R B ER0 IH2 Z AH0 M Z -BARBARITIES B AA0 R B EH1 R AH0 T IY0 Z -BARBARITY B AA0 R B AE1 R AH0 T IY0 -BARBARITY B AA0 R B EH1 R AH0 T IY0 -BARBARIZED B AA1 R B ER0 AY2 Z D -BARBARO B AA0 R B AA1 R OW0 -BARBAROS B AA0 R B AA1 R OW0 Z -BARBAROSSA B AA1 R B ER0 AA1 S AH0 -BARBAROSSA'S B AA1 R B ER0 AA1 S AH0 Z -BARBAROUS B AA1 R B ER0 AH0 S -BARBAROUSLY B AA1 R B ER0 AH0 S L IY0 -BARBAROUX B AA1 R B ER0 UW1 -BARBARUS B AA1 R B ER0 AH0 S -BARBARY B AA1 R B ER0 IY0 -BARBARY'S B AA1 R B ER0 IY0 Z -BARBASCO B AA0 R B AA1 S K OW0 -BARBASSOU B AA1 R B AH0 S UW0 -BARBATO B AA0 R B AA1 T OW0 -BARBATUS B AA0 R B AE1 T AH0 S -BARBAULD B AA0 R B AA0 L D -BARBAULD'S B AA1 R B AO1 L D Z -BARBAY B AA0 R B EY2 -BARBAZURE B AA0 R B AA1 ZH ER0 -BARBE B AA1 R B -BARBEAU B AA0 R B OW1 -BARBECUE B AA1 R B IH0 K Y UW2 -BARBECUED B AA1 R B IH0 K Y UW2 D -BARBECUES B AA1 R B IH0 K Y UW2 Z -BARBED B AA1 R B D -BARBEE B AA1 R B IY1 -BARBEE'S B AA1 R B IY0 Z -BARBEL B AA1 R B AH0 L -BARBELL B AA1 R B EH2 L -BARBELLE B AA1 R B EH2 L -BARBELS B AA1 R B AH0 L Z -BARBEMOUCHE B AA1 R B IH0 M UW1 SH -BARBEMUCHE B AA1 R B IH0 M AH1 CH -BARBER B AA1 R B ER0 -BARBER'S B AA1 R B ER0 Z -BARBERED B AA1 R B ER0 D -BARBERIE B AA0 R B EH1 R IY0 -BARBERIN B AA0 R B EH1 R IH0 N -BARBERIN'S B AA0 R B EH1 R AH0 N Z -BARBERING B AA1 R B ER0 IH0 NG -BARBERINI B AA1 R B ER0 IY1 N IY0 -BARBERRIES B AA1 R B EH2 R IY0 Z -BARBERRY B AA1 R B EH2 R IY0 -BARBERS B AA1 R B ER0 Z -BARBERSHOP B AA1 R B ER0 SH AA2 P -BARBERTON B AA1 R B ER0 T AH0 N -BARBES B AA1 R B Z -BARBET B AA1 R B IH0 T -BARBETS B AA1 R B IH0 T S -BARBETTE B AA0 R B EH1 T -BARBEY B AA1 R B IY0 -BARBEZIEUX B AA0 R B EH1 Z IY0 OW0 -BARBI B AA1 R B IY0 -BARBICAN B AA1 R B IY0 K AH0 N -BARBICANE B AA0 R B AH0 K EY2 N -BARBICANE'S B AA1 R B IY0 K AH0 N Z -BARBICANS B AA1 R B IY0 K AH0 N Z -BARBIE B AA1 R B IY0 -BARBIER B AA1 R B IY0 ER0 -BARBIER'S B AA0 R B IH1 R Z -BARBIERE B AA0 R B IH1 R -BARBILLE B AA0 R B IH2 L -BARBILLES B AA0 R B IH1 L IY0 Z -BARBILLON B AA0 R B IH1 L AH0 N -BARBIN B AA1 R B IH0 N -BARBITON B AA0 R B IH1 T AH0 N -BARBIZON B AA0 R B IH1 Z AH0 N -BARBLESS B AA1 R B L IH0 S -BARBO B AA1 R B OW0 -BARBORIN B AA0 R B AO1 R IH0 N -BARBOROUGH B AA1 R B ER2 OW0 -BARBOUR B AA1 R B ER0 -BARBOUR'S B AA1 R B ER0 Z -BARBOUX B AA1 R B UW1 -BARBOX B AA1 R B AA2 K S -BARBOZA B AA0 R B OW1 Z AH0 -BARBRA B AA1 R B R AH0 -BARBRAN B AA1 R B R AH0 N -BARBRECK B AA1 R B R IH0 K -BARBRO B AA1 R B R OW0 -BARBRO'S B AA1 R B R OW0 Z -BARBS B AA1 R B Z -BARBUL B AA0 R B UW1 L -BARBURY B AA1 R B EH2 R IY0 -BARBUSSE B AA1 R B AH0 S -BARBUZZI B AA0 R B UW1 T S IY0 -BARBY B AA1 R B IY0 -BARBY'S B AA1 R B IY0 Z -BARCA B AA1 R K AH0 -BARCAROLE B AA1 R K EH1 R AH0 L -BARCAROLLE B AA1 R K AA0 R OW1 L -BARCAS B AA1 R K AH0 S -BARCE B AA2 R S -BARCELLO B AA2 R S EH1 L OW0 -BARCELONA B AA2 R S IH0 L OW1 N AH0 -BARCH B AA1 R K -BARCHESTER B AA1 R CH EH2 S T ER0 -BARCHON B AA0 CH AH0 N -BARCINE B AA2 R S AH0 N -BARCLAY B AA1 R K L EY2 -BARCLAY B AA1 R K L IY2 -BARCLAY'S B AA1 R K L EY0 Z -BARCLAY'S B AA1 R K L IY0 Z -BARCLAYS B AA1 R K L EY0 Z -BARCLAYS B AA1 R K L IY0 Z -BARCOE B AA1 R K OW0 -BARCOMBE B AA1 R K AH0 M -BARCOO B AA1 R K UW1 -BARCREEK B AA1 R K R IY2 K -BARCROFT B AA1 R K R AO2 F T -BARCROFT'S B AA1 R K R AO2 F T S -BARCUS B AA1 R K AH0 S -BARCY B AA1 R S IY0 -BARD B AA1 R D -BARD'S B AA1 R D Z -BARDAS B AA1 R D AH0 S -BARDD B AA1 R D -BARDEEN B AA1 R D IY1 N -BARDEL B AA1 R D EH2 L -BARDELL B AA0 R D EH1 L -BARDELL'S B AA2 R D EH1 L Z -BARDELYS B AA0 R D EH1 L IY0 Z -BARDEN B AA1 R D AH0 N -BARDI B AA1 R D IY0 -BARDIANNA B AA1 R D IY0 AE1 N AH0 -BARDIC B AA1 R D IH0 K -BARDIYA B ER0 D IY2 AH0 -BARDO B AA1 R D OW0 -BARDO'S B AA1 R D OW0 Z -BARDOLF B AA1 R D OW2 L F 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EH2 D IH0 D -BAREILLY B EH1 R AY1 L IY0 -BARELA B AA0 R EH1 L AH0 -BARELEGGED B EH1 R AH0 L EH1 G D -BARELLAN B AA0 R EH1 L AH0 N -BARELY B EH1 R L IY0 -BARENESS B EH1 R N AH0 S -BARENNA B AA0 R EH1 N AH0 -BARENT B EH1 R AH0 N T -BARENTZ B EH1 R AH0 N T S -BAREPOLES B EH1 R P OW2 L Z -BARER B EH1 R ER0 -BARERE B AA0 R EH0 R -BARES B EH1 R Z -BARESARK B AE1 R IH0 S AA1 R K -BARESHANKS B EH1 R SH AE1 NG K S -BAREST B EH1 R AH0 S T -BARETTA B ER0 EH1 T AH0 -BARETTI B AA0 R EH1 T IY0 -BARETTI'S B AA0 R EH1 T IY0 Z -BAREY B EH1 R IY0 -BAREZZI B AA0 R EH0 Z IY0 -BARFIELD B AA1 R F IY2 L D -BARFLEUR B AA1 R F L ER1 -BARFOOT B AA1 R F UH2 T -BARFOOT'S B AA1 R F UH2 T S -BARFORD B AA1 R F ER0 D -BARFORDSHIRE B AA1 R F ER0 D SH AY2 ER0 -BARFORTH B AA1 R F AO2 R TH -BARGA B AA1 R G AH0 -BARGAIN B AA1 R G AH0 N -BARGAIN B AA1 R G IH0 N -BARGAIN'S B AA1 R G AH0 N Z -BARGAINED B AA1 R G AH0 N D -BARGAINER B AA1 R G IH0 N ER0 -BARGAINERS B AA1 R G IH0 N ER0 Z -BARGAINING B AA1 R G IH0 N IH0 NG -BARGAININGS B AA1 R G IH0 N IH0 NG Z -BARGAINS B AA1 R G AH0 N Z -BARGAINS B AA1 R G IH0 N Z -BARGE B AA1 R JH -BARGE'S B AA1 R JH AH0 Z -BARGED B AA1 R JH D -BARGEE B AA1 R JH IY0 -BARGEE'S B AA1 R JH IY0 Z -BARGEES B AA1 R JH IY0 Z -BARGELLO B AA1 R JH EH1 L OW0 -BARGEMAN B AA1 R JH M AH0 N -BARGEMAN'S B AA1 R JH M AH0 N Z -BARGEMEN B AA1 R JH M AH0 N -BARGER B AA1 R JH ER0 -BARGES B AA1 R JH AH0 Z -BARGES B AA1 R JH IH0 Z -BARGETON B AA1 R G AH0 T AH0 N -BARGETON'S B AA1 R G AH0 T AH0 N Z -BARGIN B AA1 R JH AH0 N -BARGING B AA1 R JH IH0 NG -BARGLE B AA1 R G AH0 L -BARGO B AA1 R G OW2 -BARGRAVE B AA1 R G R EY2 V -BARGRAVE'S B AA1 R G R EY2 V Z -BARGROVE B AA1 R G R OW2 V -BARHA B AA1 R HH AH0 -BARHAM B AA1 R HH AH0 M -BARHOLM B AA1 R HH OW0 L M -BARHOP B AA1 R HH AO0 P -BARHWI B AA1 R HH Y UW1 AY1 -BARI B AA1 R IY0 -BARILLERIE B EH2 R IH0 L ER0 IY0 -BARILLON B EH2 R IH0 L AA0 N -BARILLOT B ER0 IH1 L AH0 T -BARIN B AE1 R IH0 N -BARINE B AA0 R IY1 N IY0 -BARINE'S B ER0 IY1 N Z -BARING B EH1 R IH0 NG -BARING'S B EH1 R IH0 NG Z -BARINGO B ER0 IH1 NG G OW0 -BARINGS B EH1 R IH0 NG Z -BARIS B AA1 R IY0 Z -BARITONE B EH1 R AH0 T OW2 N -BARITONES B EH1 R AH0 T OW2 N Z -BARIUM B EH1 R IY0 AH0 M -BARIZY B ER0 IH1 Z IY0 -BARJOLS B AA1 R JH AH0 L Z -BARJONA B AA1 R JH OW1 N AH0 -BARJONAS B AA1 R JH OW1 N AH0 S -BARK B AA1 R K -BARK'D B AA1 R K D -BARK'S B AA1 R K S -BARKA B AA1 R K AH0 -BARKAL B AA1 R K AH0 L -BARKDALE B AA1 R K D EY2 L -BARKE B AA1 R K -BARKED B AA1 R K T -BARKEEP B AA1 R K IY2 P -BARKEEPER B AA1 R K IY2 P ER0 -BARKEEPER'S B AA1 R K IY2 P ER0 Z -BARKEEPERS B AA1 R K IY2 P ER0 Z -BARKENTINE B AA1 R K EH0 N T IY1 N IY0 -BARKER B AA1 R K ER0 -BARKER'S B AA1 R K ER0 Z -BARKERS B AA1 R K ER0 Z -BARKET B AA1 R K IH0 T -BARKEWORTH B AA1 R K W ER2 TH -BARKHAM B AA1 R K AH0 M -BARKHOUSE B AA1 R K HH AW2 S -BARKIE B AA1 R K IY0 -BARKIN B AA1 R K IH0 N -BARKING B AA1 R K IH0 NG -BARKINGHAM B AA1 R K IH0 NG HH AE2 M -BARKINGS B AA1 R K IH0 NG Z -BARKINGTON B AA1 R K IH0 NG T AH0 N -BARKIS B AA1 R K IH0 S -BARKLAND B AA1 R K L AH0 N D -BARKLAY B AA1 R K L EY0 -BARKLESS B AA1 R K L AH0 S -BARKLEY B AA1 R K L IY0 -BARKLEY'S B AA1 R K L IY0 Z -BARKLY B AA1 R K L IY0 -BARKMAN B AA1 R K M AH0 N -BARKOO B AA1 R K UW2 -BARKOVIS B AA1 R K AH0 V IH0 S -BARKS B AA1 R K S -BARKSDALE B AA1 R K S D EY2 L -BARKSDALE'S B AA1 R K S D EY2 L Z -BARKWELL B AA1 R K W EH2 L -BARKWORTH B AA1 R K W ER2 TH -BARKWORTH'S B AA1 R K W ER2 TH S -BARKY B AA1 R K IY0 -BARL B AA1 R L -BARLAAM B AA1 R L AA1 M -BARLACCHIA B AA1 R L AA1 K IY0 AH0 -BARLAIMONT B AA1 R L EY2 M AA2 N T -BARLASCH B AA1 R L AE1 SH -BARLCOMB B AA1 R L K AH0 M -BARLEY B AA1 R L IY0 -BARLEY'S B AA1 R L IY0 Z -BARLEYCORN B AA1 R L IY0 K AO2 R N -BARLEYCORN'S B AA1 R L IY1 K AO0 R N Z -BARLEYFIELD B AA1 R L IY0 F IY2 L D -BARLIGHT B AA1 R L AY2 T -BARLING B AA1 R L IH0 NG -BARLINGFORD B AA1 R L IH0 NG F ER0 D -BARLOW B AA1 R L OW2 -BARLOW'S B AA1 R L OW2 Z -BARLOWE B AA1 R L OW2 -BARLOWE'S B AA1 R L OW2 Z -BARLOWS B AA1 R L OW2 Z -BARLY B AA1 R L IY0 -BARM B AA1 R M -BARMAID B AA1 R M EY2 D -BARMAID'S B AA1 R M EY2 D Z -BARMAIDS B AA1 R M EY2 D Z -BARMAN B AA1 R M AH0 N -BARMBERRY B AA1 R M B EH2 R IY0 -BARMBY B AA1 R M B IY0 -BARMBY'S B AA1 R M B IY0 Z -BARMBYS B AA1 R M B IY0 Z -BARMECIDE B AA1 R M AH0 S AY2 D -BARMEN B AA1 R M AH0 N -BARMESYDE B AA1 R M AH0 Z AY1 D -BARMINSTER B AA1 R M IH2 N S T ER0 -BARMITZVAH B AA1 R M IH0 T S V AH0 -BARMONT B AA1 R M AA2 N T -BARMOUTH B AA1 R M AH0 TH -BARMSTON B AA1 R M S T AH0 N -BARMY B AA1 R M IY0 -BARN B AA1 R N -BARN'S B AA1 R N Z -BARNABAS B AA1 R N AH0 B AH0 S -BARNABAS'S B AA1 R N AH0 B AH0 S IH0 Z -BARNABETTA B AA1 R N AH0 B EH1 T AH0 -BARNABITE B AA1 R N AH0 B AY2 T -BARNABITES B AA1 R N AH0 B AY2 T S -BARNABO B AA1 R N AH0 B OW0 -BARNABY B AA1 R N AH0 B IY0 -BARNABY'S B AA1 R N AH0 B IY0 Z -BARNACLE B AA1 R N AH0 K AH0 L -BARNACLED B AA1 R N AH0 K AH0 L D -BARNACLES B AA1 R N AH0 K AH0 L Z -BARNARD B AA1 R N AA0 R D -BARNARD'S B AA1 R N AA0 R D Z -BARNARDINE B AA1 R N AA0 R D IY1 N IY0 -BARNARDO B AA1 R N AA0 R D OW0 -BARNARDO'S B AA1 R N AA0 R D OW0 Z -BARNARDS B AA1 R N ER0 D Z -BARNATO B AA1 R N AA1 T OW0 -BARNAVE B AA1 R N AH0 V -BARNBY B AA1 R N B IY0 -BARNCASTLE B AA1 R N K AE2 S AH0 L -BARNCASTLE'S B AA1 R N K AE2 S AH0 L Z -BARNDALE B AA1 R N D EY2 L -BARNDALE'S B AA1 R N D EY2 L Z -BARNDOLLAR B AA1 R N D AA2 L ER0 -BARNDOOR B AA1 R N D AO2 R -BARNE B AA1 R N -BARNEA B AA1 R N AH0 -BARNEGAT B AA1 R N IH0 G AE0 T -BARNES B AA1 R N Z -BARNES'S B AA1 R N Z IH0 Z -BARNESS B AA1 R N AH0 S -BARNESVILLE B AA1 R N Z V IH2 L -BARNET B AA1 R N IH0 T -BARNET'S B AA1 R N AH0 T S -BARNETT B AA0 R N EH1 T -BARNETT'S B AA0 R N EH1 T S -BARNETTE B AA1 R N EH1 T -BARNEVELD B AA1 R N IH0 V IH0 L D -BARNEVELD'S B AA1 R N IH0 V IH0 L D Z -BARNEVELDT B AA1 R N AH0 V EH2 L T -BARNEWITZ B AA1 R N IH0 W IH0 T S -BARNEY B AA1 R N IY0 -BARNEY'S B AA1 R N IY0 Z -BARNHAM B AA1 R N HH AH0 M -BARNHARDT B AA1 R N HH AA2 R T -BARNHART B AA1 R N HH 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T ER0 Z -BAROMETERS B ER0 AA1 M IH0 T ER0 Z -BAROMETRIC B AE2 R AH0 M EH1 T R IH0 K -BAROMETRICAL B ER0 AA1 M AH0 T R IH0 K AH0 L -BARON B AE1 R AH0 N -BARON B EH1 R AH0 N -BARON'S B AE1 R AH0 N Z -BARON'S B EH1 R AH0 N Z -BARONAGE B EH1 R AH0 N IH0 JH -BARONCELLI B ER0 AA1 N CH EH1 L IY0 -BARONE B ER0 OW1 N -BARONESS B EH1 R AH0 N IH0 S -BARONESS'S B EH1 R AH0 N IH0 S IH0 Z -BARONESSA B EH2 R AH0 N EH1 S AH0 -BARONESSES B EH1 R AH0 N IH0 S IH0 Z -BARONET B EH1 R AH0 N AH0 T -BARONET B EH2 R AH0 N EH1 T -BARONET'S B EH1 R AH0 N AH0 T S -BARONETAGE B EH2 R AH0 N EH1 T IH0 JH -BARONETCIES B EH2 R AH0 N EH1 T S IY0 Z -BARONETCY B EH2 R AH0 N EH1 T S IY0 -BARONETS B EH1 R AH0 N AH0 T S -BARONETS B EH2 R AH0 N EH1 T S -BARONEY B ER0 OW1 N IY0 -BARONG B ER0 AO1 NG -BARONI B AA0 R OW1 N IY0 -BARONI'S B AA0 R OW1 N IY0 Z -BARONIAL B AA0 R OW1 N IY0 AH0 L -BARONIES B EH1 R AH0 N IY0 Z -BARONIN B AA0 R OW1 N IH0 N -BARONITE B EH1 R AH0 N AY2 T -BARONIUS B AA0 R OW1 N IY0 AH0 S -BARONMEAD B EH1 R AH0 N M IY2 D -BARONNE B ER0 AA1 N -BARONS B AE1 R AH0 N Z -BARONS B EH1 R AH0 N Z -BARONY B EH1 R AH0 N IY0 -BAROP B ER0 AA1 P -BAROQUE B ER0 OW1 K -BAROTSE B ER0 AA1 T S -BAROUCHE B AA0 R UW1 SH -BAROUCHE'S B AA0 R UW1 SH IH0 Z -BAROUCHES B AE2 R AW1 CH IH0 Z -BAROUDI B ER0 UW1 D IY0 -BAROUDI'S B ER0 AW1 D IY0 Z -BAROZZO B ER0 AA1 Z OW0 -BARQUE B AA0 R K -BARQUE'S B AA0 R K S -BARQUENTINE B AA1 R K W AH0 N T AY2 N -BARQUES B AA0 R K S -BARQUESIMETO B AA1 R K W EH2 S IY0 M EY1 T OW0 -BARQUEY B AA0 R K EY0 -BARR B AA1 R -BARR'D B ER0 D -BARR'S B AA1 R Z -BARRA B AA1 R AH0 -BARRABAS B AA0 R AA1 B AH0 Z -BARRACA B AA0 R AA1 K AH0 -BARRACAN B AE1 R AH0 K AA2 N -BARRACE B EH1 R AH0 S -BARRACK B AE1 R AH0 K -BARRACK B EH1 R AH0 K -BARRACKED B AE1 R AH0 K T -BARRACKPORE B AE1 R AH0 K P AO2 R -BARRACKS B AE1 R AH0 K S -BARRACKS B EH1 R AH0 K S -BARRACLOUGH B AE1 R AH0 K L AW0 -BARRACLOUGH'S B AE1 R AH0 K L AW0 Z -BARRACOMBE B AE1 R AH0 K OW1 M B IY0 -BARRACONDA B AE1 R AH0 K AA1 N D AH0 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-BJORNSON'S B Y AO1 R N S AH0 N Z -BJORNSTAM B Y AO1 R N S T AE0 M -BJORNSTJERNE B Y AO1 R N S T Y ER1 N -BJRN B Y R N -BL B L -BLA B L AH0 -BLAAUWILDEBEESTEFONTEIN B L AA2 V IH1 L D IH0 B IY0 S T EH0 F OW1 N T AY2 N -BLAB B L AE1 B -BLABBED B L AE1 B D -BLABBER B L AE1 B ER0 -BLABBIN B L AE1 B IH0 N -BLABBING B L AE1 B IH0 NG -BLABS B L AE1 B Z -BLACAS B L AE1 K AH0 Z -BLACE B L AA1 CH IY0 -BLACHERNAE B L AA1 CH ER0 N EY2 -BLACHERNE B L AA1 CH ER0 N -BLACHINGTON B L AE1 CH IH0 NG T AH0 N -BLACK B L AE1 K -BLACK'S B L AE1 K S -BLACKADDER B L AE1 K AE2 D ER0 -BLACKADDER'S B L AE1 K AE2 D ER0 Z -BLACKAMOOR B L AE1 K AH0 M UH2 R -BLACKAMOORS B L AE1 K AH0 M UW1 R Z -BLACKAMORE B L AE1 K AH0 M AO2 R -BLACKANA B L AE1 K AA1 N AH0 -BLACKBALL B L AE1 K B AO2 L -BLACKBALLED B L AE1 K B AO2 L D -BLACKBEAR B L AE1 K B EH2 R -BLACKBEARD B L AE1 K B ER0 D -BLACKBEARD'S B L AE1 K B IH1 R D Z -BLACKBEETLE B L AE1 K B IY1 T AH0 L -BLACKBEETLES B L AE1 K B IY1 T AH0 L Z -BLACKBERRIES B L AE1 K B EH2 R IY0 Z -BLACKBERRY B L AE1 K B EH2 R IY0 -BLACKBERRYING B L AE1 K B EH2 R IY0 IH0 NG -BLACKBIRD B L AE1 K B ER0 D -BLACKBIRD'S B L AE1 K B ER0 D Z -BLACKBIRDER B L AE1 K B ER0 D ER0 -BLACKBIRDS B L AE1 K B ER0 D Z -BLACKBOARD B L AE1 K B AO2 R D -BLACKBOARDS B L AE1 K B AO2 R D Z -BLACKBOROUGH B L AE1 K B ER0 OW0 -BLACKBOROUGH'S B L AE1 K B ER0 OW0 Z -BLACKBOURNE B L AE1 K B ER0 N -BLACKBOY B L AE1 K B OY2 -BLACKBURN B L AE1 K B ER0 N -BLACKBURN'S B L AE1 K B ER0 N Z -BLACKBURNE B L AE1 K B ER0 N -BLACKBURNIAN B L AE1 K B ER0 N IY0 AH0 N -BLACKBURNS B L AE1 K B ER1 N Z -BLACKCAP B L AE1 K K AE2 P -BLACKCAPS B L AE1 K K AE2 P S -BLACKCOCK B L AE1 K K AA2 K -BLACKDEED B L AE1 K D IY1 D -BLACKDEEP B L AE1 K D IY1 P -BLACKDOWN B L AE1 K D AW2 N -BLACKE B L AE1 K -BLACKED B L AE1 K T -BLACKEE B L AE1 K IY0 -BLACKEN B L AE1 K AH0 N -BLACKEN'D B L AE1 K AH0 N D -BLACKENED B L AE1 K AH0 N D -BLACKENING B L AE1 K AH0 N IH0 NG -BLACKENING B L AE1 K N IH0 NG -BLACKENS B L AE1 K AH0 N Z -BLACKER B L AE1 K ER0 -BLACKEST B L AE1 K AH0 S T -BLACKETT B L AE1 K IH0 T -BLACKETT'S B L AE1 K IH0 T S -BLACKEY B L AE1 K IY0 -BLACKFACED B L AE1 K F AH0 S T -BLACKFEATHER B L AE1 K F EH1 DH ER0 -BLACKFEET B L AE1 K F IY1 T -BLACKFELLOW B L AE1 K F EH2 L OW0 -BLACKFELLOWS B L AE1 K F EH2 L OW0 Z -BLACKFISH B L AE1 K F IH2 SH -BLACKFOOT B L AE1 K F UH2 T -BLACKFORD B L AE1 K F ER0 D -BLACKFORD'S B L AE1 K F ER0 D Z -BLACKFRIARS B L AE1 K F R AY1 ER0 Z -BLACKGUARD B L AE1 K G AA2 R D -BLACKGUARD'S B L AE1 K G AA2 R D Z -BLACKGUARDED B L AE1 K G AA1 R D IH0 D -BLACKGUARDING B L AE1 K G AA1 R D IH0 NG -BLACKGUARDISM B L AE1 K G AA2 R D IH0 Z AH0 M -BLACKGUARDLY B L AE1 K G AA1 R D L IY0 -BLACKGUARDS B L AE1 K G AA2 R D Z -BLACKHAMPTON B L AE1 K HH AE1 M P T AH0 N -BLACKHAWK B L AE1 K HH AO2 K -BLACKHEAD B L AE1 K HH EH2 D -BLACKHEADS B L AE1 K HH EH2 D Z -BLACKHEARTED B L AE1 K HH AA2 R T IH0 D -BLACKHEATH B L AE1 K HH IY2 TH -BLACKHOUSE B L AE1 K HH AW2 S -BLACKHURST B L AE1 K HH ER0 S T -BLACKIE B L AE1 K IY0 -BLACKIE'S B L AE1 K IY0 Z -BLACKIES B L AE1 K IY0 Z -BLACKIN B L AE1 K IH0 N -BLACKING B L AE1 K IH0 NG -BLACKISH B L AE1 K IH0 SH -BLACKJACK B L AE1 K JH AE2 K -BLACKJACKS B L AE1 K JH AE2 K S -BLACKLADIES B L AE1 K L AE2 D IY0 Z -BLACKLAND B L AE1 K L AH0 N D -BLACKLEAD B L AE1 K L EH0 D -BLACKLEADED B L AE1 K L EH1 D AH0 D -BLACKLEG B L AE1 K L EH2 G -BLACKLEGS B L AE1 K L EH2 G Z -BLACKLER B L AE1 K L ER0 -BLACKLETTER B L AE1 K L EH2 T ER0 -BLACKLIST B L AE1 K L IH2 S T -BLACKLISTED B L AE1 K L IH2 S T IH0 D -BLACKLISTING B L AE1 K L IH2 S T IH0 NG -BLACKLOCK B L AE1 K L AA2 K -BLACKLY B L AE1 K L IY0 -BLACKMAIL B L AE1 K M EY2 L -BLACKMAILED B L AE1 K M EY2 L D -BLACKMAILER B L AE1 K M EY2 L ER0 -BLACKMAILER'S B L AE1 K M EY2 L ER0 Z -BLACKMAILERS B L AE1 K M EY2 L ER0 Z -BLACKMAILING B L AE1 K M EY2 L IH0 NG -BLACKMAN B L AE1 K M AH0 N -BLACKMAN'S B L AE1 K M AH0 N Z -BLACKMOOR B L AE1 K M UH2 R -BLACKMORE B L AE1 K M AO0 R -BLACKMORE'S B L AE1 K M AO0 R Z -BLACKNESS B L AE1 K N AH0 S -BLACKNESSES B L AE1 K N AH0 S IH0 Z -BLACKOUT B L AE1 K AW2 T -BLACKPOOL B L AE1 K P UW2 L -BLACKROCK B L AE1 K R AA2 K -BLACKS B L AE1 K S -BLACKSHEEP B L AE1 K SH IY1 P -BLACKSMITH B L AE1 K S M IH2 TH -BLACKSMITH'S B L AE1 K S M IH2 TH S -BLACKSMITHING B L AE1 K S M IH2 DH IH0 NG -BLACKSMITHS B L AE1 K S M IH2 TH S -BLACKSNAKE B L AE1 K S N EY2 K -BLACKSNAKES B L AE1 K S N EY2 K S -BLACKSTABLE B L AE1 K S T AH0 B AH0 L -BLACKSTAFFE B L AE1 K S T AE2 F -BLACKSTEAD B L AE1 K S T EH2 D -BLACKSTICK B L AE1 K S T IH2 K -BLACKSTOCK B L AE1 K S T AA2 K -BLACKSTOCK'S B L AE1 K S T AA2 K S -BLACKSTONE B L AE1 K S T OW2 N -BLACKSTONE'S B L AE1 K S T OW2 N Z -BLACKSTRAP B L AE1 K S T R AE2 P -BLACKTAIL B L AE1 K T EY2 L -BLACKTHORN B L AE1 K TH AO2 R N -BLACKTHORNE B L AE1 K TH AO2 R N -BLACKTHORNS B L AE1 K TH AO2 R N Z -BLACKTON B L AE1 K T AH0 N -BLACKTON'S B L AE1 K T AH0 N Z -BLACKTONS B L AE1 K T AH0 N Z -BLACKTOOTH B L AE1 K T UW2 TH -BLACKWALL B L AE1 K W AO2 L -BLACKWATER B L AE1 K W AO2 T ER0 -BLACKWELL B L AE1 K W EH2 L -BLACKWELL'S B L AE1 K W EH2 L Z -BLACKWINGS B L AE1 K W IH0 NG Z -BLACKWOOD B L AE1 K W UH2 D -BLACKWOOD'S B L AE1 K W UH2 D Z -BLACKWOODS B L AE1 K W UH2 D Z -BLACKY B L AE1 K IY0 -BLACKY'S B L AE1 K IY0 Z -BLACQUAIRE B L AH0 K EH1 R -BLAD B L AE1 D -BLADDER B L AE1 D ER0 -BLADDERS B L AE1 D ER0 Z -BLADDERWORT B L AE1 D ER0 W ER0 T -BLADDERY B L AE1 D ER0 IY0 -BLADE B L EY1 D -BLADE'S B L EY1 D Z -BLADED B L EY1 D IH0 D -BLADEN B L EY1 D AH0 N -BLADENSBURG B L EY1 D AH0 N Z B ER0 G -BLADERGRASS B L EY1 D ER0 G R AE2 S -BLADES B L EY1 D Z -BLADESOVER B L EY1 D IH0 S OW2 V ER0 -BLADON B L AA0 D AO1 N -BLADUD B L EY1 D AH0 D -BLAENAU B L AE1 N AW0 -BLAESKAEK B L EH1 S K IY1 K -BLAETTER B L EH1 T ER0 -BLAGDEN B L AE1 G D AH0 N -BLAGDEN'S B L AE1 G D AH0 N Z -BLAGDON B L AE1 G D AH0 N -BLAGG B L AE1 G -BLAGGARD B L AE1 G ER0 D -BLAGGARDS B L AE1 G ER0 D Z -BLAGOVO B L AA0 G OW1 V OW0 -BLAGUARD B L AH0 G W AA1 R D -BLAGUE B L EY1 G -BLAGUEUR B L EY1 G ER0 -BLAH B L AA1 -BLAIKIE B L EY1 K IY0 -BLAIN B L EY1 N -BLAINE B L EY1 N -BLAINE'S B L EY1 N Z -BLAINES B L EY1 N Z -BLAINEY B L EY1 N IY0 -BLAINS B L EY1 N Z -BLAINVILLE B L EY1 N V IH2 L -BLAIR B L EH1 R -BLAIR'S B L EH1 R Z -BLAIRE B L EH1 R -BLAIRS B L EH1 R Z -BLAIRSDALE B L EH1 R S D EY2 L -BLAIRTOWN B L EH1 R T AW2 N -BLAISDELL B L EY1 S D AH0 L -BLAISDELL'S B L EY1 S D AH0 L Z -BLAISDELLS B L EY1 S D AH0 L Z -BLAISE B L EY1 Z -BLAISE'S B L EY1 Z IH0 Z -BLAISETTE B L EY1 Z EH1 T -BLAISOIS B L EY1 S W AA2 -BLAITIERE B L EY1 T Y EH1 R -BLAIZE B L EY1 Z -BLAK B L AE1 K -BLAKE B L EY1 K -BLAKE'S B L EY1 K S -BLAKEFORD B L EY1 K F AO0 R D -BLAKEFORD'S B L EY1 K F ER0 D Z -BLAKELEY B L EY1 K L IY0 -BLAKELEY'S B L EY1 K L IY0 Z -BLAKELY B L EY1 K L IY0 -BLAKELY'S B L EY1 K L IY0 Z -BLAKEMAN B L EY1 K M AH0 N -BLAKEMORE B L EY1 K M AO0 R -BLAKEN B L EY1 K AH0 N -BLAKENEY B L EY1 K N IY0 -BLAKENEY'S B L EY1 K N IY0 Z -BLAKENEYS B L EY1 K N IY0 Z -BLAKES B L EY1 K S -BLAKESLEE B L EY1 K S L IY0 -BLAKESLEY B L EY1 K S L IY0 -BLAKESLY B L EY1 K S L IY0 -BLAKESON B L EY1 K S AH0 N -BLAKESTON B L EY1 K S T AH0 N -BLAKEVILLE B L EY1 K V IH0 L -BLAKEY B L EY1 K IY0 -BLAKISTON B L AE1 K IH0 S T AA0 N -BLALOK B L AE1 L AA0 K -BLAM B L AE1 M -BLAM'D B L EY1 M D -BLAMABLE B L AE1 M AH0 B AH0 L -BLAME B L EY1 M -BLAMEABLE B L EY1 M AH0 B AH0 L -BLAMED B L EY1 M D -BLAMEDEST B L EY1 M D AH0 S T -BLAMEFUL B L EY1 M F AH0 L -BLAMELESS B L EY1 M L AH0 S -BLAMELESSLY B L EY1 M L AH0 S L IY0 -BLAMELESSNESS B L EY1 M L AH0 S N AH0 S -BLAMES B L EY1 M Z -BLAMEWORTHY B L EY1 M W ER2 DH IY0 -BLAMIN B L AE1 M IH0 N -BLAMING B L EY1 M IH0 NG -BLAMIRE B L AA0 M IH1 R IY0 -BLAMONT B L EY1 M AH0 N T -BLAMOR B L AE1 M ER0 -BLAMORE B L AA1 M AO0 R -BLAN B L AE1 N -BLANC B L AE1 NG K -BLANC'S B L AE1 NG K S -BLANCA B L AA1 NG K AH0 -BLANCA'S B L AE1 NG K AH0 Z -BLANCANDRIN B L AE1 NG K AH0 N D R IH0 N -BLANCAS B L AE1 NG K AH0 Z -BLANCH B L AE1 N CH -BLANCH'D B L AE1 N CH D -BLANCHARD B L AE1 N CH ER0 D -BLANCHARD'S B L AE1 N CH ER0 D Z -BLANCHARDS B L AE1 N CH ER0 D Z -BLANCHE B L AE1 N CH -BLANCHE'S B L AE1 N CH IY0 Z -BLANCHED B L AE1 N CH T -BLANCHEFLEUR B L AE1 N CH IH0 F L ER1 -BLANCHELANDE B L AE1 N CH EH0 L AA1 N D IY0 -BLANCHES B L AE1 N CH IH0 Z -BLANCHET B L AE1 N K IH0 T -BLANCHETTE B L AH0 N SH EH1 T -BLANCHFLOWER B L AE1 N CH F L AW2 ER0 -BLANCHFLOWER'S B L AE1 N CH F L AW2 ER0 Z -BLANCHIDINE B L AE1 N CH IH0 D AY2 N -BLANCHING B L AE1 N CH IH0 NG -BLANCHISSEUSE B L AE1 N CH AY2 Z S UW1 S -BLANCHISSEUSES B L AE1 N CH AY2 Z S IY0 AH0 S IH0 Z -BLANCHITA B L AE1 N CH IY1 T AH0 -BLANCHMINSTER B L AE1 N CH M IH2 N S T ER0 -BLANCMANGE B L AE1 NG K M EY1 N JH -BLANCMANGES B L AE1 NG K M EY1 N JH IH0 Z -BLANCMESNIL B L AE1 NG K M EH0 S N IH1 L -BLANCO B L AE1 NG K OW0 -BLANCO'S B L AE1 NG K OW0 Z -BLANCOS B L AE1 NG K OW0 Z -BLANCS B L AE1 NG K S -BLAND B L AE1 N D -BLAND'S B L AE1 N D Z -BLANDA B L AE1 N D AH0 -BLANDAMER B L AE1 N D AH0 M ER0 -BLANDAMER'S B L AE1 N D AE2 M ER0 Z -BLANDAMERS B L AE1 N D AE1 M ER0 Z -BLANDANO B L AA0 N D AA1 N OW0 -BLANDEN B L AE1 N D AH0 N -BLANDEN'S B L AE1 N D AH0 N Z -BLANDER B L AE1 N D ER0 -BLANDEROCKS B L AE1 N D ER0 AH0 K S -BLANDEST B L AE1 N D AH0 S T -BLANDEVILLE B L AE1 N D AH0 V IH2 L -BLANDFIELD B L AE1 N D F IY2 L D -BLANDFORD B L AE1 N D F AO0 R D -BLANDINA B L AA0 N D IY1 N AH0 -BLANDING B L AE1 N D IH0 NG -BLANDINGS B L AE1 N D IH0 NG Z -BLANDISH B L AE1 N D IH0 SH -BLANDISHING B L AE1 N D IH0 SH IH0 NG -BLANDISHMENT B L AE1 N D IH0 SH M AH0 N T -BLANDISHMENTS B L AE1 N D IH0 SH M AH0 N T S -BLANDLY B L AE1 N D L IY0 -BLANDNESS B L AE1 N D N AH0 S -BLANDOIS B L AE1 N D OW2 IH0 Z -BLANDULA B L AE1 N D UW1 L AH0 -BLANDY B L AE1 N D IY0 -BLANE B L EY1 N -BLANEY B L EY1 N IY0 -BLANEY'S B L EY1 N IY0 Z -BLANEYS B L EY1 N IY0 Z -BLANG B L AE1 NG -BLANGIN B L AE1 NG G IH0 N -BLANGY B L AE1 N JH IY0 -BLANID B L AE1 N IH0 D -BLANK B L AE1 NG K -BLANK'S B L AE1 NG K S -BLANKA B L AE1 NG K AH0 -BLANKA'S B L AE1 NG K AH0 Z -BLANKED B L AE1 NG K T -BLANKENBERG B L AE1 NG K AH0 N B ER0 G -BLANKENBERGHE B L AE1 NG K AH0 N B ER0 G -BLANKENBURG B L AE1 NG K AH0 N B ER0 G -BLANKENSHIP B L AE1 NG K AH0 N SH IH2 P -BLANKER B L AE1 NG K ER0 -BLANKEST B L AE1 NG K IH0 S T -BLANKET B L AE1 NG K AH0 T -BLANKET B L AE1 NG K IH0 T -BLANKETED B L AE1 NG K AH0 T IH0 D -BLANKETING B L AE1 NG K AH0 T IH0 NG -BLANKETS B L AE1 NG K AH0 T S -BLANKETY B L AE1 NG K AH0 T IY0 -BLANKINSHIP B L AE1 NG K IH0 N SH IH0 P -BLANKLEY'S B L AE1 NG K L IY0 Z -BLANKLY B L AE1 NG K L IY0 -BLANKNESS B L AE1 NG K N AH0 S -BLANKS B L AE1 NG K S -BLANKSHIRE B L AE1 NG K SH AY2 R -BLANKTON B L AE1 NG K T AH0 N -BLANKY B L AE1 NG K IY0 -BLANLEY B L AE1 N L IY0 -BLANQUET B L AE1 NG K W AH0 T -BLANQUETTE B L AE1 NG K EH1 T -BLANQUETTE'S B L AE1 NG K EH1 T S -BLANQUI B L AE1 NG K W IY0 -BLANSAC B L AE1 N S AE0 K -BLANSTON B L AE1 N S T AH0 N -BLANT B L AE1 N T -BLANT'S B L AE1 N T S -BLANTOCK B L AE1 N T AH0 K -BLANTON B L AE1 N T AH0 N -BLANTYRE B L AE1 N T AY2 R -BLARCUM B L AA1 R K AH0 M -BLARDEN B L AA1 R D AH0 N -BLARDEN'S B L AA1 R D AH0 N Z -BLARE B L EH1 R -BLARED B L EH1 R D -BLARES B L EH1 R Z -BLARING B L EH1 R IH0 NG -BLARNEY B L AA1 R N IY0 -BLARNEYED B L AA1 R N IY0 D -BLARNEYING B L AA1 R N IY0 IH0 NG -BLARST B L AA1 R S T -BLARSTED B L AA1 R S T AH0 D -BLAS B L AA1 S -BLASCO B L AA1 S K OW0 -BLASDELL B L AE1 S D AH0 L -BLASE B L EY1 Z -BLASEE B L EY1 Z IY1 -BLASENGAME B L EY1 S AH0 N G EY2 M -BLASES B L EY1 S IH0 Z -BLASH B L AA1 SH -BLASHFIELD B L AA1 SH F IY0 L D -BLASI B L EY1 Z IY0 -BLASIUS B L EY1 S IY0 IH0 S -BLASPHEME B L AE1 S F AH0 M -BLASPHEMED B L AE1 S F AH0 M D -BLASPHEMER B L AE1 S F AH0 M ER0 -BLASPHEMERS B L AE1 S F AH0 M ER0 Z -BLASPHEMES B L AE1 S F AH0 M Z -BLASPHEMETH B L AE1 S F AH0 M IH0 TH -BLASPHEMIES B L AE1 S F AH0 M IY0 Z -BLASPHEMIN B L AE1 S F AH0 M IH2 N -BLASPHEMING B L AE1 S F AH0 M IH0 NG -BLASPHEMOUS B L AE1 S F AH0 M AH0 S -BLASPHEMOUSLY B L AE1 S F AH0 M AH0 S L IY0 -BLASPHEMY B L AE1 S F AH0 M IY0 -BLASQUITO B L AE1 S K IY1 T OW0 -BLASS B L AE1 S -BLASSEMARE B L AE1 S AH0 M EH1 R -BLAST B L AE1 S T -BLASTBLOW B L AE1 S T B L OW2 -BLASTED B L AE1 S T AH0 D -BLASTED B L AE1 S T IH0 D -BLASTER B L AE1 S T ER0 -BLASTERS B L AE1 S T ER0 Z -BLASTIN B L AE1 S T IH0 N -BLASTING B L AE1 S T IH0 NG -BLASTODERM B L AE1 S T AH0 D ER0 M -BLASTOFF B L AE1 S T AO2 F -BLASTS B L AE1 S T S -BLAT B L AE1 T -BLATANCY B L EY1 T AH0 N S IY0 -BLATANT B L EY1 T AH0 N T -BLATANTLY B L EY1 T AH0 N T L IY0 -BLATCH B L AE1 CH -BLATCH'S B L AE1 CH IH0 Z -BLATCHFORD B L AE1 CH F ER0 D -BLATCHFORD'S B L AE1 CH F ER0 D Z -BLATCHLEY B L AE1 CH L IY0 -BLATE B L EY1 T -BLATHENOY B L AE1 TH IH0 N OY0 -BLATHER B L AE1 DH ER0 -BLATHERIN B L AE1 DH ER0 IH0 N -BLATHERING B L AE1 DH ER0 IH0 NG -BLATHERS B L AE1 DH ER0 Z -BLATHERSKITE B L AE1 DH ER0 S K AY2 T -BLATHERSKITES B L AE1 DH ER0 S K AY2 T S -BLATHERUMSKITE B L AE1 DH ER0 AH1 M S K AY2 T -BLATHERWICK B L AE1 DH ER0 W IH2 K -BLATHERWYCKE B L AE1 DH ER0 W IH1 K -BLATHWAYT B L AE1 TH W EY1 T -BLATT B L AE1 T -BLATTED B L AE1 T AH0 D -BLATTER B L AE1 T ER0 -BLATTERGOWL B L AE1 T ER0 G AW1 L -BLATTING B L AE1 T IH0 NG -BLAU B L AW1 -BLAUBEUREN B L AW1 B OY0 R AH0 N -BLAUE B L AW1 IY0 -BLAUNCH B L AO1 N CH -BLAUNCHEFLOUR B L AO1 N CH EH1 F L AW1 ER0 -BLAUSSER B L AW1 S ER0 -BLAUVELT B L AW1 V IH0 L T -BLAUW B L AW1 -BLAVATSKY B L AA0 V AA1 T S K IY0 -BLAVATSKY'S B L AA0 V AA1 T S K IY0 Z -BLAW B L AO1 -BLAWIN B L AW1 IH0 N -BLAWING B L AO1 IH0 NG -BLAWN B L AO2 N -BLAWS B L AO2 Z -BLAWSTED B L AW1 S T IH0 D -BLAY B L EY1 -BLAYDES B L EY1 D Z -BLAYE B L EY1 -BLAYLOCK B L EY1 L AH0 K -BLAYNEY B L EY1 N IY0 -BLAYTHWAITE B L EY1 TH W EY2 T -BLAZ'D B L EY1 Z AH0 D -BLAZE B L EY1 Z -BLAZED B L EY1 Z D -BLAZER B L EY1 Z ER0 -BLAZER'S B L EY1 Z ER0 Z -BLAZERS B L EY1 Z ER0 Z -BLAZES B L EY1 Z IH0 Z -BLAZIN B L AA0 Z IY1 N -BLAZING B L EY1 Z IH0 NG -BLAZINGLY B L EY1 Z IH0 NG L IY0 -BLAZIUS B L EY1 Z IY0 IH0 S -BLAZON B L EY1 Z AH0 N -BLAZON'D B L EY1 Z AH0 N D -BLAZONED B L EY1 Z AH0 N D -BLAZONING B L EY1 Z AH0 N IH0 NG -BLAZONINGS B L EY1 Z AH0 N IH0 NG Z -BLAZONRIES B L EY1 Z AH0 N R IY0 Z -BLAZONRY B L EY1 Z AH0 N R IY0 -BLAZONS B L EY1 Z AH0 N Z -BLE B AH0 L -BLEACH B L IY1 CH -BLEACH'D B L IY1 CH AH0 D -BLEACHED B L IY1 CH T -BLEACHER B L IY1 CH ER0 -BLEACHERS B L IY1 CH ER0 Z -BLEACHES B L IY1 CH IH0 Z -BLEACHIN B L IY1 CH IH0 N -BLEACHING B L IY1 CH IH0 NG -BLEACRES B L IY1 K ER0 Z -BLEAD B L IY1 D -BLEAK B L IY1 K -BLEAKER B L IY1 K ER0 -BLEAKEST B L IY1 K AH0 S T -BLEAKIRK B L IY1 K ER0 K -BLEAKLY B L IY1 K L IY0 -BLEAKNESS B L IY1 K N AH0 S -BLEAKRIDGE B L IY1 K R IH2 JH -BLEAKS B L IY1 K S -BLEAR B L IH1 R -BLEARED B L IH1 R D -BLEARILY B L IY1 R AH0 L IY0 -BLEARY B L IH1 R IY0 -BLEASE B L IY1 S -BLEAT B L IY1 T -BLEATED B L IY1 T IH0 D -BLEATIN B L IY1 T IH0 N -BLEATING B L IY1 T IH0 NG -BLEATINGS B L IY1 T IH0 NG Z -BLEATS B L IY1 T S -BLECKER B L EH1 K ER0 -BLED B L EH1 D -BLEDSHAW B L EH1 D SH AO2 -BLEDSOE B L EH1 D S OW0 -BLEDSOE'S B L EH1 D S OW0 Z -BLEE B L IY1 -BLEECKER B L IY1 K ER0 -BLEED B L IY1 D -BLEED'N B L IY1 D AH0 N -BLEEDEM B L IY1 D AH0 M -BLEEDER B L IY1 D ER0 -BLEEDIN B L IY1 D IH0 N -BLEEDING B L IY1 D IH0 NG -BLEEDINGS B L IY1 D IH0 NG Z -BLEEDS B L IY1 D Z -BLEEGED B L IY1 JH D -BLEEKER B L IY1 K ER0 -BLEEVE B L IY1 V -BLEEZE B L IY1 Z -BLEEZING B L IY1 Z IH0 NG -BLEFUSCU B L EH1 F AH0 S K Y UW2 -BLEIBEN B L AY1 B AH0 N -BLEIBT B L AY1 B IY1 T IY1 -BLEIBTREU B L AY1 B T R UW1 -BLEIS B L AY1 Z -BLEKE B L IY1 K -BLEKEKE B L EH1 K AH0 K IY0 -BLEKINGE B L EH1 K IH1 N JH -BLEMISH B L EH1 M IH0 SH -BLEMISHED B L EH1 M IH0 SH T -BLEMISHES B L EH1 M IH0 SH IH0 Z -BLEMISHLESS B L EH1 M IH0 SH L AH0 S -BLEMMYES B L EH1 M IY0 Z -BLEMONT B L EH1 M AH0 N T -BLENAU B L EH1 N AW0 -BLENAU'S B L IH0 N OW0 Z -BLENAVON B L EH1 N AH0 V AA0 N -BLENCATHRA B L EH1 N S EH2 TH R AH0 -BLENCH B L EH1 N CH -BLENCHED B L EH1 N CH T -BLENCHES B L EH1 N CH IH0 Z -BLENCHING B L EH1 N CH IH0 NG -BLEND B L EH1 N D -BLENDE B L EH1 N D -BLENDED B L EH1 N D AH0 D -BLENDED B L EH1 N D IH0 D -BLENDERSHIN B L EH1 N D ER0 SH IH0 N -BLENDING B L EH1 N D IH0 NG -BLENDINGS B L EH1 N D IH0 NG Z -BLENDS B L EH1 N D Z -BLENHAM B L EH1 N HH AH0 M -BLENHAM'S B L EH1 N AH0 M Z -BLENHEIM B L EH1 N HH AY2 M -BLENKER B L EH1 NG K ER0 -BLENKERS B L EH1 NG K ER0 Z -BLENKINS B L EH1 NG K IH0 N Z -BLENKINSOP B L EH1 NG K IH0 N S AH0 P -BLENKINSOPP B L EH1 NG K IH0 N S AH0 P -BLENKIRON B L EH1 NG K AY2 ER0 N -BLENKIRON'S B L EH1 N K AY1 R AH0 N Z -BLENNERHASSET B L EH1 N ER0 HH AE1 S EH0 T -BLENNERHASSETT B L EH1 N ER0 HH AE1 S IH0 T -BLENNERHASSETT'S B L EH1 N ER0 HH AE1 S IH0 T S -BLENNERHASSETTS B L EH1 N ER0 HH AE1 S IH0 T S -BLENNY B L EH1 N IY0 -BLENSOP B L EH1 N S AA2 P -BLENT B L EH1 N T -BLENTY B L EH1 N T IY0 -BLENTZ B L EH1 N T S -BLEOBERIS B L IY1 AH0 B ER0 IH0 S -BLERIOT B L IY1 R IY0 AH0 T -BLES B L Z -BLESBOK B L Z B AA0 K -BLESBOKS B L Z B AA0 K S -BLESH B L EH1 SH -BLESHUGH B L EH1 SH -BLESS B L EH1 S -BLESS'D B L EH1 S AH0 D -BLESSAC B L EH1 S AE0 K -BLESSE B L EH1 S -BLESSED B L EH1 S T -BLESSEDEST B L EH1 S AH0 D AH0 S T -BLESSEDLY B L EH1 S AH0 D L IY0 -BLESSEDNESS B L EH1 S AH0 D N AH0 S -BLESSES B L EH1 S IH0 Z -BLESSEST B L EH1 S AH0 S T -BLESSETH B L EH1 S IH0 TH -BLESSID B L EH1 S IH0 D -BLESSIN B L EH1 S IH0 N -BLESSIN'S B L EH1 S IH0 N Z -BLESSING B L EH1 S IH0 NG -BLESSINGS B L EH1 S IH0 NG Z -BLESSINGTON B L EH1 S IH0 NG T AH0 N -BLESSINGTON'S B L EH1 S IH0 NG T AH0 N Z -BLESSOM B L EH1 S AH0 M -BLEST B L EH1 S T -BLESUS B L EH1 S AH0 S -BLETCHLEY B L EH1 CH L IY0 -BLETHER B L EH1 TH ER0 -BLETHERIN B L EH1 TH ER0 AH0 N -BLETHERING B L EH1 TH ER0 IH0 NG -BLETHERS B L EH1 TH ER0 Z -BLETSO B L EH1 T S OW0 -BLETSOE B L EH1 T S OW0 -BLETSON B L EH1 T S AH0 N -BLEU B L UW1 -BLEUE B L UW1 -BLEURY B L UW1 R IY0 -BLEUS B L UW1 S -BLEVENS B L IY1 V AH0 N Z -BLEVINS B L EH1 V IH0 N Z -BLEW B L UW1 -BLEWET B L UW1 IH0 T -BLEWETT B L UW1 IH0 T -BLEWFORTH B L UW1 F AO2 R TH -BLEWITT B L UW1 IH0 T -BLEYER B L EY1 ER0 -BLEYS B L EY1 Z -BLI B L AY1 -BLI'ME B L AY1 M -BLICK B L IH1 K -BLICK'S B L IH1 K S -BLICKLEY B L IH1 K L IY0 -BLICKY B L IH1 K IY0 -BLID B L IH1 D -BLIDAH B L AY1 D AH0 -BLIEVE B L IY1 V -BLIFIL B L IH1 F AH0 L -BLIFIL'S B L IH1 F AH0 L Z -BLIGE B L AY1 JH -BLIGED B L AY1 JH D -BLIGH B L AY1 -BLIGH'S B L AY1 Z -BLIGHT B L AY1 T -BLIGHT'S B L AY1 T S -BLIGHTED B L AY1 T IH0 D -BLIGHTER B L AY1 T ER0 -BLIGHTER'S B L AY1 T ER0 Z -BLIGHTERS B L AY1 T ER0 Z -BLIGHTING B L AY1 T IH0 NG -BLIGHTS B L AY1 T S -BLIGHTY B L AY1 T IY0 -BLIGNY B L IH1 G N IY0 -BLIKSEM B L IH1 K S IH0 M -BLIMBER B L IH1 M B ER0 -BLIMBER'S B L IH1 M B ER0 Z -BLIME B L AY1 M -BLIMEY B L AY1 M IY0 -BLIMP B L IH1 M P -BLIMY B L AY1 M IY0 -BLIN B L IH0 N -BLIND B L AY1 N D -BLINDBECK B L AY1 N D B EH0 K -BLINDE B L AY1 N D -BLINDED B L AY1 N D IH0 D -BLINDER B L AY1 N D ER0 -BLINDER'S B L AY1 N D ER0 Z -BLINDERS B L AY1 N D ER0 Z -BLINDEST B L AY1 N D AH0 S T -BLINDETH B L AY1 N D IH0 TH -BLINDEYE B L AY1 N D AY0 -BLINDFOLD B L AY1 N D F OW2 L D -BLINDFOLDED B L AY1 N D F OW2 L D IH0 D -BLINDFOLDING B L AY1 N D F OW2 L D IH0 NG -BLINDIN B L AY1 N D IH0 N -BLINDING B L AY1 N D IH0 NG -BLINDINGLY B L AY1 N D IH0 NG L IY0 -BLINDISH B L AY1 N D IH0 SH -BLINDLESS B L AY1 N D L AH0 S -BLINDLY B L AY1 N D L IY0 -BLINDMAN B L AY1 N D M AH0 N -BLINDMAN'S B L AY1 N D M AH0 N Z -BLINDNESS B L AY1 N D N AH0 S -BLINDS B L AY1 N D Z -BLINDWAY B L AY1 N D W EY2 -BLING B L IH0 NG -BLINK B L IH1 NG K -BLINK'S B L IH1 NG K S -BLINKDALE B L IH1 NG K D EY2 L -BLINKED B L IH1 NG K T -BLINKER B L IH1 NG K ER0 -BLINKERS B L IH1 NG K ER0 Z -BLINKEY B L IH1 NG K IY0 -BLINKHORN B L IH1 NG K HH ER0 N -BLINKIE B L IH1 NG K IY0 -BLINKIN B L IH1 NG K IH0 N -BLINKING B L IH1 NG K IH0 NG -BLINKINGLY B L IH1 NG K IH0 NG L IY0 -BLINKS B L IH1 NG K S -BLINKY B L IH1 NG K IY0 -BLINKY'S B L IH1 NG K IY0 Z -BLINT B L IH1 N T -BLIP B L IH1 P -BLIPPER B L IH1 P ER0 -BLIPS B L IH1 P S -BLIROS B L IH1 R OW0 Z -BLISH B L IH1 SH -BLISLAND B L AY1 L AH0 N D -BLISS B L IH1 S -BLISS'S B L IH1 S IH0 Z -BLISSAM B L IH1 S AH0 M -BLISSE B L IH1 S -BLISSED B L IH1 S T -BLISSES B L IH1 S IH0 Z -BLISSFUL B L IH1 S F AH0 L -BLISSFULLY B L IH1 S F AH0 L IY0 -BLISSFULNESS B L IH1 S F AH0 L N AH0 S -BLISSID B L IH1 S AH0 D -BLISSINGTON B L IH1 S IH0 NG T AH0 N -BLISTER B L IH1 S T ER0 -BLISTER'D B L IH1 S T ER0 D -BLISTER'S B L IH1 S T ER0 Z -BLISTERED B L IH1 S T ER0 D -BLISTERING B L IH1 S T ER0 IH0 NG -BLISTERINGLY B L IH1 S T ER0 IH0 NG L IY0 -BLISTERS B L IH1 S T ER0 Z -BLITH B L AY1 TH -BLITHE B L AY1 DH -BLITHEDALE B L AY1 TH D EY2 L -BLITHELY B L AY1 TH L IY0 -BLITHENESS B L AY1 TH N AH0 S -BLITHER B L AY1 DH ER0 -BLITHERIN B L AY1 DH ER0 IH0 N -BLITHERING B L AY1 DH ER0 IH0 NG -BLITHERS B L AY1 DH ER0 Z -BLITHESOME B L AY1 TH S AH0 M -BLITHESOMENESS B L AY1 TH S AH0 M N AH0 S -BLITHEST B L AY1 DH AH0 S T -BLITZ B L IH1 T S -BLITZEN B L IH1 T S AH0 N -BLITZENBERG B L IH1 T Z AH0 N B ER0 G -BLIVEN B L AY1 V AH0 N -BLIVENS B L AY1 V AH0 N Z -BLIX B L IH1 K S -BLIXIE B L IH1 K S IY0 -BLIZZARD B L IH1 Z ER0 D -BLIZZARD'S B L IH1 Z ER0 D Z -BLIZZARDS B L IH1 Z ER0 D Z -BLIZZARDY B L IH1 Z ER0 D IY0 -BLO B L OW0 -BLOAT B L OW1 T -BLOATED B L OW1 T IH0 D -BLOATER B L OW1 T ER0 -BLOATERS B L OW1 T ER0 Z -BLOATING B L OW1 T IH0 NG -BLOB B L AA1 B -BLOB'S B L AA1 B Z -BLOBBER B L AA1 B ER0 -BLOBBS B L AA1 B Z -BLOBBY B L AA1 B IY0 -BLOBS B L AA1 B Z -BLOC B L AA1 K -BLOCH B L AA1 K -BLOCH'S B L AA1 K S -BLOCK B L AA1 K -BLOCK'D B L AA1 K D -BLOCK'S B L AA1 K S -BLOCKADE B L AA2 K EY1 D -BLOCKADED B L AA2 K EY1 D IH0 D -BLOCKADER B L AA2 K EY1 D ER0 -BLOCKADERS B L AA2 K EY1 D ER0 Z -BLOCKADES B L AA2 K EY1 D Z -BLOCKADING B L AA2 K EY1 D IH0 NG -BLOCKED B L AA1 K T -BLOCKER B L AA1 K ER0 -BLOCKHEAD B L AA1 K HH EH2 D -BLOCKHEADS B L AA1 K HH EH2 D Z -BLOCKHOUSE B L AA1 K HH AW2 S -BLOCKHOUSES B L AA1 K HH AW2 S AH0 Z -BLOCKIN B L AA1 K IH0 N -BLOCKING B L AA1 K IH0 NG -BLOCKISH B L AA1 K IH0 SH -BLOCKS B L AA1 K S -BLOCKSBERG B L AA1 K S B ER0 G -BLOCKULA B L AA1 K UW1 L AH0 -BLOCKY B L AA1 K IY0 -BLOCS B L AA1 K S -BLODGETT B L AA1 JH IH0 T -BLODGETT'S B L AA1 JH IH0 T S -BLODSOMBRE B L AA1 D S AH0 M B ER0 -BLODWELL B L AA1 D W EH2 L -BLOECKMAN B L OW1 K M AH0 N -BLOEDEL B L OW1 D AH0 L -BLOEMFONTEIN B L OW1 M F AO0 N T AY2 N -BLOFELD B L AA1 F EH2 L D -BLOG B L AO1 G -BLOGG B L AO1 G -BLOIS B L W AA1 -BLOK B L AA1 K -BLOKE B L OW1 K -BLOKE'S B L OW1 K S -BLOKES B L OW1 K S -BLOM B L AA1 M -BLOMBERG B L AA1 M B ER0 G -BLOME B L OW1 M -BLOME'S B L OW1 M Z -BLOMFIELD B L AA1 M F IY2 L D -BLOMIDON B L AA1 M IH0 D IH0 N -BLOMMERS B L AA1 M ER0 Z -BLOMSBERRY B L AA1 M S B EH2 R IY0 -BLONAY B L AA1 N EY0 -BLOND B L AA1 N D -BLONDE B L AA1 N D -BLONDE'S B L AA1 N D Z -BLONDEL B L AA1 N D AH0 L -BLONDEL'S B L AA1 N D AH0 L Z -BLONDER B L AA1 N D ER0 -BLONDES B L AA1 N D Z -BLONDET B L AA1 N D EH1 T -BLONDET'S B L AA1 N D AH0 T S -BLONDHEIM B L AA1 N D HH AY2 M -BLONDIE B L AA1 N D IY0 -BLONDIN B L AA1 N D IH0 N -BLONDINA B L AA1 N D IH0 N AH0 -BLONDINE B L AA1 N D AY2 N -BLONDINE'S B L AA1 N D AY2 N Z -BLONDNESS B L AA1 N D N AH0 S -BLONDS B L AA1 N D Z -BLONDY B L AA1 N D IY0 -BLONDY'S B L AA1 N D IY0 Z -BLONG B L AO1 NG -BLOO B L UW1 -BLOOD B L AH1 D -BLOOD' B L AH1 D -BLOOD'S B L AH1 D Z -BLOODCURDLING B L AH1 D K ER1 D AH0 L IH0 NG -BLOODED B L AH1 D IH0 D -BLOODEDLY B L AH1 D IH0 D L IY0 -BLOODEDNESS B L AH1 D AH0 D N AH0 S -BLOODGOOD B L AH1 D G UH2 D -BLOODGOOD'S B L AH1 D G UH2 D Z -BLOODGUILTINESS B L AH1 D G W IH1 T IY0 N AH0 S -BLOODHOUND B L AH1 D HH AW0 N D -BLOODHOUND'S B L AH1 D HH AW0 N D Z -BLOODHOUNDS B L AH1 D HH AW0 N D Z -BLOODHOUNDS B L AH1 D HH AW0 N Z -BLOODIED B L AH1 D IY0 D -BLOODIER B L AH1 D IY0 ER0 -BLOODIEST B L AH1 D IY0 AH0 S T -BLOODILY B L AH1 D AH0 L IY0 -BLOODING B L AH1 D IH0 NG -BLOODLESS B L AH1 D L AH0 S -BLOODLESSLY B L AH1 D L AH0 S L IY0 -BLOODLESSNESS B L AH1 D L AH0 S N AH0 S -BLOODLETTING B L AH1 D L EH2 T IH0 NG -BLOODROOT B L AH1 D R UW2 T -BLOODS B L AH1 D Z -BLOODSHED B L AH1 D SH EH2 D -BLOODSHEDDING B L AH1 D SH EH1 D IH0 NG -BLOODSHOT B L AH1 D SH AA2 T -BLOODSON B L AH1 D S AH0 N -BLOODSTAIN B L AH1 D S T EY2 N -BLOODSTAINED B L AH1 D S T EY2 N D -BLOODSTAINS B L AH1 D S T EY2 N Z -BLOODSTONE B L AH1 D S T OW2 N -BLOODSTREAM B L AH1 D S T R IY2 M -BLOODSUCK B L AH1 D S AH2 K -BLOODSUCKER B L AH1 D S AH2 K ER0 -BLOODSUCKERS B L AH1 D S AH2 K ER0 Z -BLOODSUCKING B L AH1 D S AH2 K IH0 NG -BLOODTHIRSTINESS B L AH1 D TH ER2 S T IY0 N AH0 S -BLOODTHIRSTY B L AH1 D TH ER2 S T IY0 -BLOODVESSEL B L AH1 D V EH1 S AH0 L -BLOODVESSELS B L AH1 D V EH1 S AH0 L Z -BLOODWOOD B L AH1 D W UH2 D -BLOODWOODS B L AH1 D W UH2 D Z -BLOODWORTH B L AH1 D W ER2 TH -BLOODY B L AH1 D IY0 -BLOOEY B L UW1 IY0 -BLOOIE B L UW1 IY0 -BLOOKER B L UH1 K ER0 -BLOOM B L UW1 M -BLOOM'D B L UW1 M D -BLOOM'S B L UW1 M Z -BLOOMDALE B L UW1 M D EY2 L -BLOOME B L UW1 M -BLOOMED B L UW1 M D -BLOOMER B L UW1 M ER0 -BLOOMER'S B L UW1 M ER0 Z -BLOOMERS B L UW1 M ER0 Z -BLOOMETH B L UW1 M IH0 TH -BLOOMFIELD B L UW1 M F IY2 L D -BLOOMFIELD'S B L UW1 M F IY2 L D Z -BLOOMIN B L UW1 M IH0 N -BLOOMING B L UW1 M IH0 NG -BLOOMINGDALE B L UW1 M IH0 NG D EY2 L -BLOOMINGSBURG B L UW1 M IH0 NG Z B ER0 G -BLOOMINGTON B L UW1 M IH0 NG T AH0 N -BLOOMLESS B L UW1 M L AH0 S -BLOOMS B L UW1 M Z -BLOOMSBURY B L UW1 M Z B EH2 R IY0 -BLOOMY B L UW1 M IY0 -BLOOR B L UH1 R -BLOOTCH B L UW1 CH -BLORE B L AO1 R -BLORENGE B L ER0 EH1 N JH -BLOS B L OW0 S -BLOSBERG B L AA1 S B ER0 G -BLOSS B L AO1 S -BLOSSER B L AO1 S ER0 -BLOSSETT B L AA1 S IH0 T -BLOSSOM B L AA1 S AH0 M -BLOSSOM'D B L AA1 S AH0 M D -BLOSSOM'S B L AA1 S AH0 M Z -BLOSSOMED B L AA1 S AH0 M D -BLOSSOMING B L AA1 S AH0 M IH0 NG -BLOSSOMINGS B L AA1 S AH0 M IH0 NG Z -BLOSSOMLESS B L AA1 S AH0 M L AH0 S -BLOSSOMNOSE B L AA1 S AH0 M N OW2 Z -BLOSSOMS B L AA1 S AH0 M Z -BLOSSOMY B L AA1 S AH0 M IY0 -BLOT B L AA1 T -BLOTCH B L AA1 T CH -BLOTCHED B L AA1 T CH T -BLOTCHES B L AA1 T CH AH0 Z -BLOTCHING B L AA1 T CH IH0 NG -BLOTCHY B L AA1 T CH IY0 -BLOTS B L AA1 T S -BLOTT B L AA1 T -BLOTTED B L AA1 T AH0 D -BLOTTER B L AA1 T ER0 -BLOTTERS B L AA1 T ER0 Z -BLOTTING B L AA1 T IH0 NG -BLOTTO B L AA1 T OW0 -BLOTTON B L AA1 T AH0 N -BLOUD B L AW1 D -BLOUDY B L AW1 D IY0 -BLOUGRAM B L AW1 G R AE2 M -BLOUGRAM'S B L AW1 G R AE2 M Z -BLOUNDELL B L AW1 N D AH0 L -BLOUNT B L AW1 N T -BLOUNT'S B L AW1 N T S -BLOUNTS B L AW1 N T S -BLOUSE B L AW1 S -BLOUSED B L AW1 S T -BLOUSES B L AW1 S AH0 Z -BLOUSES B L AW1 S IH0 Z -BLOUSY B L AW1 S IY0 -BLOW B L OW1 -BLOW'D B L OW1 D -BLOW'S B L OW1 Z -BLOWED B L OW1 D -BLOWELL B L OW1 AH0 L -BLOWEN B L OW1 AH0 N -BLOWER B L OW1 ER0 -BLOWER'S B L OW1 ER0 Z -BLOWERS B L OW1 ER0 Z -BLOWES B L OW1 Z -BLOWETH B L OW1 IH0 TH -BLOWFEN B L OW1 F AH0 N -BLOWFIELD B L OW1 F IY2 L D -BLOWGUN B L OW1 G AH2 N -BLOWGUNS B L OW1 G AH2 N Z -BLOWHARD B L OW1 HH AA2 R D 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B R AE1 G -BRAG'S B R AE1 G Z -BRAGA B R AA1 G AH0 -BRAGADINO B R AE1 G AH0 D IY1 N OW0 -BRAGANZA B R AH0 G AA1 N Z AH0 -BRAGANZA B R AH0 G AE1 N Z AH0 -BRAGDON B R AE1 G D AH0 N -BRAGDON'S B R AE1 G D AH0 N Z -BRAGE B R EY1 JH -BRAGELONNE B R EY1 G AH0 L AH1 N -BRAGG B R AE1 G -BRAGG'S B R AE1 G Z -BRAGGADOCIO B R AE2 G AH0 D OW1 SH IY0 OW2 -BRAGGADOCIOS B R AE2 G AH0 D OW1 SH IY0 OW2 Z -BRAGGART B R AE1 G ER0 T -BRAGGART'S B R AE1 G ER0 T S -BRAGGARTS B R AE1 G ER0 T S -BRAGGE B R AE1 G -BRAGGED B R AE1 G D -BRAGGER B R AE1 G ER0 -BRAGGIN B R AE1 G IH0 N -BRAGGING B R AE1 G IH0 NG -BRAGH B R AE1 G -BRAGI B R AA1 G IY0 -BRAGIN B R AE1 JH IH0 N -BRAGLEY B R AE1 G L IY0 -BRAGLEY'S B R AE1 G L IY0 Z -BRAGS B R AE1 G Z -BRAGTON B R AE1 G T AH0 N -BRAGWAINE B R AE1 G W EY0 N -BRAGWELL B R AE1 G W EH0 L -BRAGWELL'S B R AE1 G W EH2 L Z -BRAHAM B R AE1 HH AH0 M -BRAHAN B R AE1 HH AH0 N -BRAHE B R EY1 HH IY0 -BRAHIM B R AA0 HH IY1 M -BRAHM B R AA1 M -BRAHMA B R AA1 M AH0 -BRAHMA'S B R AA1 M AH0 Z -BRAHMACHARYA B R AA1 M AA0 K EH0 R Y AH0 -BRAHMADAITYA B R AA1 M AA0 D EY1 T Y AH0 -BRAHMAN B R AA1 M AH0 N -BRAHMAN'S B R AA1 M AH0 N Z -BRAHMANA B R AA1 M AA1 N AH0 -BRAHMANAS B R AA1 M AA1 N AH0 Z -BRAHMANI B R AA1 M AA1 N IY0 -BRAHMANIC B R AA1 M AE1 N IH0 K -BRAHMANICAL B R AA1 M AE1 N IH0 K AH0 L -BRAHMANISM B R AA1 M AH0 N IH2 Z AH0 M -BRAHMANS B R AA1 M AH0 N Z -BRAHMAPUTRA B R AA1 M AH0 P UW1 T R AH0 -BRAHMAS B R AA1 M AH0 Z -BRAHMIN B R AA1 M IH0 N -BRAHMIN'S B R AA1 M IH0 N Z -BRAHMINICAL B R AA1 M IH0 N IH0 K AH0 L -BRAHMINISM B R AA1 M IH0 N IH2 Z AH0 M -BRAHMINS B R AA1 M IH0 N Z -BRAHMO B R AA1 M OW0 -BRAHMS B R AA1 M Z -BRAID B R EY1 D -BRAID'S B R EY1 D Z -BRAIDED B R EY1 D IH0 D -BRAIDING B R EY1 D IH0 NG -BRAIDINGS B R EY1 D IH0 NG Z -BRAIDS B R EY1 D Z -BRAIDWOOD B R EY1 D W UH2 D -BRAIGH B R EY1 -BRAIKENS B R EY1 K IH0 N Z -BRAIL B R EY1 L -BRAILE B R EY1 L -BRAILED B R EY1 L D -BRAILLE B R EY1 L -BRAILS B R EY1 L Z -BRAILSFORD B R EY1 L S F ER0 D 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EY1 K S -BRAKED B R EY1 K T -BRAKELY B R EY1 K L IY0 -BRAKEMAN B R EY1 K M AH0 N -BRAKEMAN'S B R EY1 K M AH0 N Z -BRAKEMEN B R EY1 K M EH0 N -BRAKEN B R EY1 K AH0 N -BRAKES B R EY1 K S -BRAKESMAN B R EY1 K S M AH0 N -BRAKESMEN B R EY1 K S M IH0 N -BRAKESPEARE B R EY1 K S P IY2 R -BRAKFAST B R EY1 K F AH0 S T -BRAKFONTEIN B R EY1 K F AO0 N T AY2 N -BRAKIE B R AE1 K IY0 -BRAKIN B R EY1 K IH0 N -BRAKING B R EY1 K IH0 NG -BRALEY B R AE1 L IY0 -BRALLIGAN B R AE1 L IH0 G AH0 N -BRAM B R AE1 M -BRAM'S B R AE1 M Z -BRAMA B R AE1 M AH0 -BRAMAH B R AA1 M AH0 -BRAMAN B R EY1 M AH0 N -BRAMAN'S B R EY1 M AH0 N Z -BRAMANTE B R AH0 M AA1 N T IY0 -BRAMBER B R AE1 M B ER0 -BRAMBERGER B R AE1 M B ER0 G ER0 -BRAMBLE B R AE1 M B AH0 L -BRAMBLE'S B R AE1 M B AH0 L Z -BRAMBLED B R AE1 M B AH0 L D -BRAMBLEDOWN B R AE1 M B AH0 L D AW2 N -BRAMBLES B R AE1 M B AH0 L Z -BRAMBLETON B R AE1 M B L IH0 T AA0 N -BRAMBLY B R AE1 M B L IY0 -BRAMBOURG B R AE1 M B AO2 R G -BRAMFELL B R AE1 M F EH2 L -BRAMHALL B R AE1 M HH AH0 L -BRAMHAM B R AE1 M HH AH0 M -BRAMHAM'S B R AE1 M HH AH0 M Z -BRAMIMUNDE B R AE1 M IH0 M UW1 N D -BRAMIN B R AE1 M IH0 N -BRAMINICAL B R AE1 M AH0 N IH0 K AH0 L -BRAMINS B R AE1 M IH0 N Z -BRAMINTHO B R AE1 M IH0 N TH OW0 -BRAMLEIGH B R AE1 M L AH0 -BRAMLEIGH'S B R AE1 M L IY0 Z -BRAMLEIGHS B R AE1 M L IY0 Z -BRAMLETT B R AE1 M L IH0 T -BRAMLETT'S B R AE1 M L IH0 T S -BRAMLEY B R AE1 M L IY0 -BRAMMEL B R AE1 M AH0 L -BRAMMLE B R AE1 M L -BRAMMO B R AE1 M OW0 -BRAMPTON B R AE1 M P T AH0 N -BRAMSDEAN B R AE1 M S D IY0 AH0 N -BRAMSEN B R AE1 M S AH0 N -BRAMSTON B R AE1 M S T AH0 N -BRAMWELL B R AE1 M W EH2 L -BRAN B R AE1 N -BRAN' B R AE1 N -BRAN'S B R AE1 N Z -BRANASKO B R AA0 N AA1 S K OW0 -BRANASKO'S B R AA0 N AA1 S K OW0 Z -BRANCA B R AE1 NG K AH0 -BRANCACCIA B R AA0 N K AA1 CH AH0 -BRANCADORI B R AA0 N K AA0 D AO1 R IY0 -BRANCALEONE B R AA0 N K AA1 L IY0 OW2 N -BRANCARDIERS B R AA0 N K AA1 R D IY0 ER0 Z -BRANCAS B R AE1 NG K AH0 Z -BRANCASTER B R AE1 N K AE2 S T ER0 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N D AH0 N B ER0 G ER0 -BRANDENBURGERS B R AE1 N D AH0 N B ER0 G ER0 Z -BRANDENBURGH B R AE1 N D AH0 N B ER0 G -BRANDENBURGHERS B R AE1 N D AH0 N B ER0 G ER0 Z -BRANDER B R AE1 N D ER0 -BRANDER'S B R AE1 N D ER0 Z -BRANDERS B R AE1 N D ER0 Z -BRANDES B R AE1 N D Z -BRANDFORT B R AE1 N D F ER0 T -BRANDIED B R AE1 N D IY0 D -BRANDIES B R AE1 N D IY0 Z -BRANDILES B R AE1 N D AY2 L Z -BRANDIMART B R AE1 N D AY2 M AA2 R T -BRANDIN B R AE1 N D IH0 N -BRANDING B R AE1 N D IH0 NG -BRANDINI B R AE1 N D IY1 N IY0 -BRANDIR B R AE1 N D AY2 ER0 -BRANDIS B R AE1 N D IH0 S -BRANDISH B R AE1 N D IH0 SH -BRANDISH'D B R AE1 N D IH0 SH D -BRANDISHED B R AE1 N D IH0 SH T -BRANDISHES B R AE1 N D IH0 SH IH0 Z -BRANDISHING B R AE1 N D IH0 SH IH0 NG -BRANDLING B R AE1 N D L IH0 NG -BRANDOLINI B R AE1 N D OW0 L IY1 N IY0 -BRANDON B R AE1 N D AH0 N -BRANDON'S B R AE1 N D AH0 N Z -BRANDONS B R AE1 N D AH0 N Z -BRANDOW B R AE1 N D AW2 -BRANDOW'S B R AE1 N D AW2 Z -BRANDRAM B R AE1 N D R AH0 M -BRANDRETH B R AE1 N D R IH0 TH -BRANDRETH'S B R AE1 N D R IH0 TH S -BRANDS B R AE1 N D Z -BRANDT B R AE1 N T -BRANDT'S B R AE2 N T S -BRANDUR B R AE1 N D ER0 -BRANDY B R AE1 N D IY0 -BRANDY'S B R AE1 N D IY0 Z -BRANDYWINE B R AE1 N D IY0 W AY2 N -BRANEHOG B R EY1 N HH AA2 G -BRANEY B R EY1 N IY0 -BRANFORD B R AE1 N F ER0 D -BRANGAENA B R AE1 N G IY1 N AH0 -BRANGAENE B R AE1 N G IY1 N -BRANGEANE B R EY1 N JH AH0 N -BRANGHTON B R AE1 NG T AH0 N -BRANGHTONS B R AE1 NG T AH0 N Z -BRANGIEN B R AE1 N JH IY0 N -BRANGWEN B R AE1 NG W AH0 N -BRANGWEN'S B R AE1 NG W AH0 N Z -BRANGWENS B R AE1 NG W AH0 N Z -BRANGWYN B R AE1 NG W IH0 N -BRANHAM B R AE1 N HH AH0 M -BRANIGAN B R AE1 N IH0 G AH0 N -BRANKOVITCH B R AE1 NG K AH0 V IH0 CH -BRANKS B R AE1 NG K S -BRANKSOME B R AE1 NG K S AH0 M -BRANLING B R AE1 N L IH0 NG -BRANLY B R AE1 N L IY0 -BRANN B R AE1 N -BRANNAD B R AE1 N AH0 D -BRANNAGAN B R AE1 N AH0 G AE0 N -BRANNAN B R AE1 N AH0 N -BRANNAN'S B R AE1 N AH0 N Z -BRANNHARD B R AE1 N HH AA2 R D -BRANNIGAN B R AE1 N IH0 G AH0 N -BRANNING B R AE1 N IH0 NG -BRANNITON B R AE1 N IH0 T AH0 N -BRANNON B R AE1 N AH0 N -BRANNY B R AE1 N IY0 -BRANSBY B R AE1 N S B IY0 -BRANSBY'S B R AE1 N S B IY0 Z -BRANSCOMBE B R AE1 N S K AH0 M -BRANSCOMBE'S B R AE1 N S K AH0 M Z -BRANSCOME B R AE1 N Z K AH2 M -BRANSCOME'S B R AE1 N Z K AH2 M Z -BRANSFORD B R AE1 N S F ER0 D -BRANSFORD'S B R AE1 N S F ER0 D Z -BRANSHAW B R AE1 N SH AO2 -BRANSOME B R AE1 N S AH0 M -BRANSON B R AE1 N S AH0 N -BRANSTOCK B R AE1 N S T AA2 K -BRANSTON B R AE1 N S T AH0 N -BRANSTON'S B R AE1 N S T AH0 N Z -BRANT B R AE1 N T -BRANT'S B R AH0 N T S -BRANTEFIELD B R AE1 N T AH0 F IY0 L D -BRANTFORD B R AE1 N T F ER0 D -BRANTLY B R AE1 N T L IY0 -BRANTOME B R AE1 N T AH0 M IY0 -BRANTS B R AH0 N T S -BRANTWOOD B R AE1 N T W UH2 D -BRANWELL B R AE1 N W EH2 L -BRANWELL'S B R AE1 N W EH2 L Z -BRANWEN B R AE1 N W AH0 N -BRAOSE B R AA0 OW1 Z -BRAQUE B R AE1 K -BRAS B R AA1 S -BRAS B R AE1 S -BRASCHI B R AH0 S K IY0 -BRASCHON B R AE1 SH AH0 N -BRASEN B R AE1 S AH0 N -BRASENOSE B R AH0 S EH1 N OW2 Z -BRASERO B R AH0 S EH1 R OW0 -BRASH B R AE1 SH -BRASHEAR B R AE1 SH IY0 R -BRASHEARS B R AE1 SH IY0 R Z -BRASHER B R AE1 SH ER0 -BRASIDAS B R AH0 S IY1 D AH0 S -BRASIER B R EY1 ZH ER0 -BRASIL B R AE1 S AH0 L -BRASIL B R AH0 S IY1 L -BRASK B R AE1 S K -BRASKA B R AE1 S K AH0 -BRASS B R AE1 S -BRASS'S B R AE1 S IH0 Z -BRASSARD B R AE1 S ER0 D -BRASSARDS B R AE1 S ER0 D Z -BRASSBOUND B R AE1 S B AW2 N D -BRASSBOUND'S B R AE1 S B AW2 N D Z -BRASSE B R AE1 S -BRASSERIE B R AE1 S ER0 IY0 -BRASSERIES B R AE1 S ER0 IY0 Z -BRASSES B R AE1 S IH0 Z -BRASSET B R AE1 S EH0 T -BRASSEUR B R AE1 S ER0 -BRASSEY B R AE1 S IY0 -BRASSEY'S B R AE1 S IY0 Z -BRASSICA B R AE1 S IH0 K AH0 -BRASSIE B R AE1 S IY0 -BRASSIERE B R AE1 S IY0 EH0 R -BRASSING B R AE1 S IH0 NG -BRASSINGTON B R AE1 S IH0 NG T AH0 N -BRASSWARE B R AE1 S W EH2 R -BRASSWORK B R AE1 S W ER2 K -BRASSY B R AE1 S IY0 -BRAST B R AE1 S T -BRASTED B R AE1 S T AH0 D -BRASTER B R AE1 S T ER0 -BRASTIAS B R AE1 S T IY0 AH0 Z -BRASYER B R AE1 S AY0 ER0 -BRAT B R AE1 T -BRAT'S B R AE1 T S -BRATA B R AA1 T AH0 -BRATEN B R AE1 T AH0 N -BRATHAM B R AE1 T AH0 M -BRATHWAITE B R AE1 TH W EY2 T -BRATHWAITE'S B R AE1 TH W EY2 T S -BRATLEY B R AE1 T L IY0 -BRATS B R AE1 T S -BRATTEN B R AE1 T AH0 N -BRATTI B R AE1 T IY0 -BRATTLE B R AE1 T AH0 L -BRATTLE'S B R AE1 T AH0 L Z -BRATTLEBORO B R AE1 T AH0 L B ER0 OW0 -BRATTLES B R AE1 T AH0 L Z -BRATTLESBY B R AE1 T AH0 L S B IY0 -BRATTLING B R AE1 T AH0 L IH0 NG -BRATTON B R AE1 T AH0 N -BRATVOLD B R AA2 T V OW2 L D -BRAUER B R AW1 ER0 -BRAULARD B R AO1 L ER0 D -BRAULT B R AO1 L T -BRAUN B R AO1 N -BRAUN'S B R AO1 N Z -BRAUNAU B R AO1 N AW0 -BRAUNER B R AO1 N ER0 -BRAUNFELS B R AO1 N F AH0 L Z -BRAUNWORTH B R AO1 N W ER2 TH -BRAUT B R AO1 T -BRAUWS B R AW1 Z -BRAV B R AA1 V -BRAVA B R AH0 V AH0 -BRAVADO B R AH0 V AA1 D OW0 -BRAVADOES B R AH0 V AA1 D OW0 Z -BRAVAS B R AA1 V AA0 Z -BRAVE B R EY1 V -BRAVE'S B R EY1 V Z -BRAVED B R EY1 V D -BRAVEES B R EY1 V IY0 Z -BRAVELY B R EY1 V L IY0 -BRAVENESS B R EY1 V N AH0 S -BRAVER B R EY1 V ER0 -BRAVERIES B R EY1 V ER0 IY0 Z -BRAVERY B R EY1 V ER0 IY0 -BRAVES B R EY1 V Z -BRAVEST B R EY1 V AH0 S T -BRAVI B R AA1 V IY0 -BRAVIDA B R AH0 V IY1 D AH0 -BRAVING B R EY1 V IH0 NG -BRAVISSIMA B R EY1 V IY0 S IY1 M AH0 -BRAVISSIMO B R EY1 V IY0 S IY1 M OW0 -BRAVO B R AA1 V OW0 -BRAVO'S B R AA1 V OW0 Z -BRAVOES B R AA1 V OW0 Z -BRAVOS B R AA1 V OW0 S -BRAVURA B R AH0 V Y UH1 R AH0 -BRAVURAS B R AH0 V Y UH1 R AH0 S -BRAW B R AO1 -BRAWER B R AO1 ER0 -BRAWL B R AO1 L -BRAWLED B R AO1 L D -BRAWLER B R AO1 L ER0 -BRAWLERS B R AO1 L ER0 Z -BRAWLING B R AO1 L IH0 NG -BRAWLS B R AO1 L Z -BRAWLY B R AO1 L IY0 -BRAWN B R AO1 N -BRAWN'S B R AO1 N Z -BRAWNE B R AO1 N -BRAWNIEST B R AO1 N IY0 AH0 S T -BRAWNSTON B R AO1 N S T AH0 N -BRAWNY B R AO1 N IY0 -BRAWS B R AO1 Z -BRAXFIELD B R AE1 K S F IY0 L D -BRAXMAR B R AE1 K S M AA2 R -BRAXOME B R AE1 K S OW1 M -BRAXTON B R AE1 K S T AH0 N -BRAXTON'S B R AE1 K S T AH0 N Z 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AH0 S -BRITANNIQUE B R IH0 T AE1 N IY1 K -BRITANNUS B R IH0 T AE1 N AH0 S -BRITANNY B R IH0 T AE1 N IY0 -BRITANY B R IH1 T AH0 N IY0 -BRITCHES B R IH1 CH AH0 Z -BRITCHKA B R IH1 CH K AH0 -BRITHER B R IH1 DH ER0 -BRITHERS B R IH1 DH ER0 Z -BRITHWOOD B R IH1 TH W UH2 D -BRITHWOODS B R IH1 TH W UH2 D Z -BRITIAN B R IH1 SH AH0 N -BRITISH B R IH1 T IH0 SH -BRITISHER B R IH1 T IH0 SH ER0 -BRITISHER'S B R IH1 T IH0 SH ER0 Z -BRITISHERS B R IH1 T IH0 SH ER0 Z -BRITLING B R IH1 T L IH0 NG -BRITO B R IY1 T OW0 -BRITOMART B R IH1 T AH0 M AA2 R T -BRITON B R IH1 T AH0 N -BRITON'S B R IH1 T AH0 N Z -BRITONS B R IH1 T AH0 N Z -BRITT B R IH1 T -BRITT'S B R IH1 T S -BRITTA B R IH1 T AH0 -BRITTA'S B R IH1 T AH0 Z -BRITTAIN B R IH1 T AH0 N -BRITTAN B R IH1 T AH0 N -BRITTANIA B R IH0 T AE1 N IY0 AH0 -BRITTANIA B R IH0 T AE1 N Y AH0 -BRITTANY B R IH1 T AH0 N IY0 -BRITTEN B R IH1 T AH0 N -BRITTISH B R IH1 T IH0 SH -BRITTLE B R IH1 T AH0 L -BRITTLENESS B R IH1 T AH0 L N AH0 S -BRITTLES B R IH1 T AH0 L Z 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L AO2 TH S -BROADEN B R AO1 D AH0 N -BROADENED B R AO1 D AH0 N D -BROADENING B R AO1 D AH0 N IH0 NG -BROADENING B R AO1 D N IH0 NG -BROADENS B R AO1 D AH0 N Z -BROADER B R AO1 D ER0 -BROADEST B R AO1 D IH0 S T -BROADFIRTH B R AO1 D F ER1 TH -BROADFOOT B R AO1 D F UH2 T -BROADHEAD B R AO1 D HH EH2 D -BROADHEM B R AO1 D HH AH0 M -BROADHEM'S B R AO1 D HH AH0 M Z -BROADHURST B R AO1 D HH ER0 S T -BROADHURST'S B R AO1 D HH ER0 S T S -BROADISH B R AO1 D IH0 SH -BROADLANDS B R AO1 D L AE2 N D Z -BROADLY B R AO1 D L IY0 -BROADMEAD B R AO1 D M IY2 D -BROADMINDED B R AO1 D M AY1 N D IH0 D -BROADMINDEDNESS B R AO1 D M AY1 N D IH0 D N AH0 S -BROADMOOR B R AO1 D M UH2 R -BROADMORLANDS B R AO1 D M AO1 R L AH0 N D Z -BROADNESS B R AO1 D N AH0 S -BROADS B R AO1 D Z -BROADSHEET B R OW1 D SH IY2 T -BROADSHEETS B R OW1 D SH IY2 T S -BROADSIDE B R AO1 D S AY2 D -BROADSIDES B R AO1 D S AY2 D Z -BROADSTAIRS B R AO1 D S T EH1 R Z -BROADSTONE B R AO1 D S T OW2 N -BROADSTREET B R AO1 D S T R IY2 T -BROADSWORD B R AO1 D S AO2 R D -BROADSWORDS B R AO1 D S AO2 R D Z -BROADUS B R OW1 D AH0 S -BROADWATER B R AO1 D W AO2 T ER0 -BROADWAY B R AO1 D W EY2 -BROADWAY'S B R AO1 D W EY2 Z -BROADWAYS B R AO1 D W EY2 Z -BROADWELL B R AO1 D W EH2 L -BROADWOOD B R AO1 D W UH2 D -BROADWOOD'S B R AO1 D W UH2 D Z -BROBDIGNAG B R AO2 B D IH0 G N AE1 G -BROBDIGNAGIAN B R AO2 B D IH0 G N AE1 G IY0 AH0 N -BROBDINGNAG B R AO2 B D IH0 G N AE1 G -BROBDINGNAGIAN B R AO2 B D IH0 G N AE1 G IY0 AH0 N -BROBSON B R AA1 B S AH0 N -BROBY B R OW1 B IY0 -BROC B R AA1 K -BROCA B R OW1 K AH0 -BROCADE B R OW0 K EY1 D -BROCADED B R OW0 K EY1 D IH0 D -BROCADES B R OW0 K EY1 D Z -BROCAS B R OW1 K AH0 Z -BROCATELLE B R OW0 K AA0 T EH1 L -BROCCOLI B R AA1 K AH0 L IY0 -BROCCOLI B R AA1 K L IY0 -BROCELIANDE B R OW1 S IY1 L IY0 IH0 N D -BROCHE B R OW1 SH -BROCHET B R OW0 SH EY1 -BROCHETTE B R OW0 SH EH1 T -BROCHT B R AA1 K T -BROCHURE B R OW0 SH UH1 R -BROCHURES B R OW0 SH UH1 R Z -BROCK B R AA1 K -BROCK'S B R AA1 K S -BROCKDEN B R AA1 K 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AA1 D HH EH2 D -BRODIE B R OW1 T IY0 -BRODIE'S B R OW1 T IY0 Z -BRODIUM B R OW1 D IY0 AH0 M -BRODNAX B R AA1 D N AE0 K S -BRODNO B R AA1 D N AH0 -BRODNYX B R AA1 D N IH0 K S -BRODRICK B R AA1 D R IH0 K -BRODRICK'S B R AA1 D R IH0 K S -BRODRICKS B R AA1 D R IH0 K S -BRODY B R OW1 D IY0 -BROECKLYN B R OW1 K L IH0 N -BROEK B R OW1 K -BROEKHUYSEN B R OW1 K HH AY1 S AH0 N -BROENLUND B R OW1 N L AH0 N D -BROER B R OW1 ER0 -BROFFIN B R AA1 F IH0 N -BROFFIN'S B R AA1 F IH0 N Z -BROG B R AA1 G -BROGAN B R OW1 G AH0 N -BROGANS B R OW1 G AH0 N Z -BROGARD B R AA1 G ER0 D -BROGDEN B R AA1 G D AH0 N -BROGHILL B R AA1 G HH IH2 L -BROGLEY B R AA1 G L IY0 -BROGLIE B R AA1 G L IY0 -BROGLIO B R OW1 L Y OW0 -BROGTEN B R OW1 D AH0 N -BROGTEN'S B R OW1 T AH0 N Z -BROGUE B R OW1 G -BROGUES B R OW1 G Z -BROHAN B R OW1 AH0 N -BROHL B R OW1 L -BROIDER B R OY1 D ER0 -BROIDER'D B R OY1 D ER0 D -BROIDERED B R OY1 D ER0 D -BROIDERIES B R OY1 D ER0 IY0 Z -BROIDERING B R OY1 D ER0 IH0 NG -BROIDERY B R OY1 D ER0 IY0 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AH0 N -BRORSON'S B R AO1 R S AH0 N Z -BROS B R OW1 Z -BROSE B R OW1 Z -BROSNAN B R AA1 S N AH0 N -BROSS B R AO1 S -BROSSARD B R AH0 S AA1 R D -BROSSARD'S B R AH0 S AA1 R D Z -BROSSE B R AH0 S -BROSSES B R AO1 S IH0 Z -BROSSETTE B R AH0 S EH1 T -BROSTER B R AA1 S T ER0 -BROT B R AH0 T -BROTAER B R OW1 T EH1 R -BROTH B R AO1 TH -BROTHAH B R AO1 TH AH0 -BROTHEH B R AA1 TH AH0 -BROTHEL B R AA1 TH AH0 L -BROTHELS B R AA1 TH AH0 L Z -BROTHER B R AH1 DH ER0 -BROTHER' B R AH1 DH ER0 -BROTHER'S B R AH1 DH ER0 Z -BROTHERED B R AH1 DH ER0 D -BROTHERHOOD B R AH1 DH ER0 HH UH2 D -BROTHERHOOD'S B R AH1 DH ER0 HH UH2 D Z -BROTHERHOODS B R AH1 DH ER0 HH UH2 D Z -BROTHERLESS B R AH1 DH ER0 L IH0 S -BROTHERLINESS B R AH1 DH ER0 L IY0 N AH0 S -BROTHERLY B R AH1 DH ER0 L IY0 -BROTHERS B R AH1 DH ER0 Z -BROTHERTON B R AH1 DH ER0 T AH0 N -BROTHERTON'S B R AH1 DH ER0 T AH0 N Z -BROTHS B R AO1 TH S -BROTT B R AA1 T -BROTTEAUX B R AH0 T OW1 -BROU B R UW1 -BROUAGE B R W IH0 JH -BROUCHT B R UW1 CH T -BROUGH B R 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L AE1 K -CLACHAN K L AE1 CH AH0 N -CLACK K L AE1 K -CLACKAMAS K L AE1 K AH0 M AH0 S -CLACKED K L AE1 K T -CLACKETY K L AE1 K AH0 T IY0 -CLACKING K L AE1 K IH0 NG -CLACKS K L AE1 K S -CLACTON K L AE1 K T AH0 N -CLAD K L AE1 D -CLADDAGH K L AE1 D AH2 -CLADEL K L AE1 D AH0 L -CLADICH K L AA1 D IH0 CH -CLADS K L AE2 D Z -CLADY K L AH0 D IY0 -CLAELIA K L EY1 L IY0 AH0 -CLAES K L EY1 Z -CLAES'S K L EY1 Z IH0 Z -CLAFLIN K L AE1 F L IH0 N -CLAFLIN'S K L AE1 F L IH0 N Z -CLAGETT K L AE1 JH IH0 T -CLAGGET K L AE1 G IH0 T -CLAGGETT K L AE1 G IH0 T -CLAGHORN K L AE1 G HH ER0 N -CLAGHORN'S K L AE1 G HH AO0 R N Z -CLAGHORNS K L AE1 G HH AO0 R N Z -CLAGNY K L AE1 G N IY0 -CLAH K L AH0 -CLAIB K L EY1 B -CLAIBORNE K L EY1 B ER0 N -CLAIBORNE'S K L EY1 B AO0 R N Z -CLAIBORNE'S K L EY1 B ER0 N Z -CLAIBORNES K L EY1 B AO0 R N Z -CLAIM K L EY1 M -CLAIM'D K L EY1 M D -CLAIM'S K L EY1 M Z -CLAIMANT K L EY1 M AH0 N T -CLAIMANT'S K L EY1 M AH0 N T S -CLAIMANTS K L EY1 M AH0 N T S -CLAIMED K L EY1 M D -CLAIMETH K L EY1 M IH0 TH -CLAIMIN K L EY1 M IH0 N -CLAIMING K L EY1 M IH0 NG -CLAIMS K L EY1 M Z -CLAIN K L EY1 N -CLAIR K L EH1 R -CLAIR'S K L EH1 R Z -CLAIRAUDIENCE K L EH0 R AO1 D IY0 AH0 N S -CLAIRAUDIENT K L EH0 R AO1 D IY0 AH0 N T -CLAIRDELUNE K L EH0 R D EH0 L UW1 N -CLAIRDYCE K L EH0 R D AY2 S -CLAIRE K L EH1 R -CLAIRE'S K L EH1 R Z -CLAIRES K L EH1 R Z -CLAIRETTE K L ER0 EH1 T -CLAIRETTE'S K L ER0 EH1 T S -CLAIRFAIT K L EH0 R F EY1 -CLAIRIERE K L EH0 R IH1 R IY0 -CLAIRIN K L EH0 R IH0 N -CLAIRMONT K L EH1 R M AH0 N T -CLAIROIX K L EH0 R W AH0 -CLAIRON K L EH1 R AA0 N -CLAIRS K L EH1 R Z -CLAIRVAL K L EH0 R V AH0 L -CLAIRVAUX K L EH0 R V OW1 -CLAIRVEND K L EH0 R V AH0 N D -CLAIRVILLE K L EH0 R V IH0 L -CLAIRVILLE'S K L EH0 R V IH0 L Z -CLAIRVOYANCE K L EH0 R V OY1 AH0 N S -CLAIRVOYANT K L EH0 R V OY1 AH0 N T -CLAIRVOYANTE K L EH0 R V OY1 AH0 N T IY0 -CLAIRVOYANTLY K L EH0 R V OY1 AH0 N T L IY0 -CLAIRVOYANTS K L EH0 R V OY1 AH0 N T S -CLAIRY K L EH1 R IY0 -CLAITH K L EY1 TH -CLAK K L AH0 K -CLAKS K L AE1 K S -CLALLAM K L AE1 L AH0 M -CLAM K L AE1 M -CLAMADOS K L AA0 M AA1 D OW0 Z -CLAMANT K L AE1 M AH0 N T -CLAMART K L AE1 M AA2 R T -CLAMAVI K L AH0 M EY1 V IY0 -CLAMBAKE K L AE1 M B EY2 K -CLAMBAKES K L AE1 M B EY2 K S -CLAMBER K L AE1 M B ER0 -CLAMBERED K L AE1 M B ER0 D -CLAMBERING K L AE1 M B ER0 IH0 NG -CLAMBERS K L AE1 M B ER0 Z -CLAMERAN K L AE1 M ER0 AH0 N -CLAMMER K L AE1 M ER0 -CLAMMERS K L AE1 M ER0 Z -CLAMMILY K L AE1 M AH0 L IY0 -CLAMMINESS K L AE1 M IY0 N AH0 S -CLAMMING K L AE1 M IH0 NG -CLAMMY K L AE1 M IY0 -CLAMOR K L AE1 M ER0 -CLAMORED K L AE1 M ER0 D -CLAMORING K L AE1 M ER0 IH0 NG -CLAMORINGS K L AE1 M ER0 IH0 NG Z -CLAMOROUS K L AE1 M ER0 AH0 S -CLAMOROUSLY K L AE1 M ER0 AH0 S L IY0 -CLAMORS K L AE1 M ER0 Z -CLAMOUR K L AE1 M UH0 R -CLAMOURED K L AE1 M UH0 R D -CLAMOURING K L AE1 M UH0 R IH0 NG -CLAMOURINGS K L AE1 M UH0 R IH0 NG Z -CLAMOUROUS K L AE1 M UH0 R AH0 S -CLAMOURS K L AE1 M UH0 R Z -CLAMP K L AE1 M P -CLAMPED K L AE1 M P T -CLAMPING K L AE1 M P IH0 NG -CLAMPS K L AE1 M P S -CLAMS K L AE1 M Z -CLAMSHELL K L AE1 M SH EH2 L -CLAN K L AE1 N -CLAN'S K L AE1 N Z -CLANAHAN K L AE1 N AH0 HH AE0 N -CLANBRASSIL K L AE1 N B R AE0 S IY1 L -CLANCARTY K L AE1 NG K AA2 R T IY0 -CLANCEHURST K L AE1 N S HH ER0 S T -CLANCEY K L AE1 N S IY0 -CLANCY K L AE1 N S IY0 -CLANCY'S K L AE1 N S IY0 Z -CLANCYS K L AE1 N S IY0 Z -CLANDESTINE K L AE0 N D EH1 S T IH0 N -CLANDESTINELY K L AE0 N D EH1 S T AH0 N L IY0 -CLANDEVIL K L AE0 N D EH1 V AH0 L -CLANDON K L AE1 N D AH0 N -CLANDON'S K L AE1 N D AH0 N Z -CLANE K L EY1 N -CLANE'S K L EY1 N Z -CLANED K L EY1 N D -CLANG K L AE1 NG -CLANGED K L AE1 NG D -CLANGER K L AE1 NG ER0 -CLANGING K L AE1 NG IH0 NG -CLANGINGS K L AE1 NG IH0 NG Z -CLANGOR K L AE1 NG G ER0 -CLANGOROUS K L AE1 NG G ER0 AH0 S -CLANGOUR K L AE1 NG ER0 -CLANGS K L AE1 NG Z -CLANHOME K L AE1 N HH OW2 M -CLANK K L AE1 NG K -CLANKED K L AE1 NG K T -CLANKING K L AE1 NG K IH0 NG -CLANKINGS K L AE1 NG K IH0 NG Z -CLANKS K L AE1 NG K S -CLANKWOOD K L AE1 NG K W UH2 D -CLANMORNE K L AE1 N M AO0 R N -CLANN K L AE1 N -CLANNISH K L AE1 N IH0 SH -CLANNISHNESS K L AE1 N IH0 SH N AH0 S -CLANRANALD K L AE1 N R AH0 N AA1 L D -CLANRICARDE K L AE1 N R IH0 K AA1 R D -CLANS K L AE1 N Z -CLANSFORD K L AE1 N S F ER0 D -CLANSHIP K L AE1 N SH IH2 P -CLANSMAN K L AE1 N Z M AH0 N -CLANSMEN K L AE1 N Z M AH0 N -CLANTON K L AE1 N T AH0 N -CLANYA K L AE1 N Y AH0 -CLAP K L AE1 P -CLAP'S K L AE1 P S -CLAPARON K L AE1 P R AH0 N -CLAPART K L AE1 P AA2 R T -CLAPBOARD K L AE1 P B AO2 R D -CLAPBOARDED K L AE1 P B AO2 R D IH0 D -CLAPBOARDS K L AE1 P B AO2 R D Z -CLAPHAM K L AE1 F AH0 M -CLAPHURST K L AE1 F ER0 S T -CLAPP K L AE1 P -CLAPP'D K L AE1 P D -CLAPP'S K L AE1 P S -CLAPPED K L AE1 P T -CLAPPER K L AE1 P ER0 -CLAPPERS K L AE1 P ER0 Z -CLAPPERTON K L AE1 P ER0 T AH0 N -CLAPPERTON'S K L AE1 P ER0 T AH0 N Z -CLAPPIN K L AE1 P IH0 N -CLAPPING K L AE1 P IH0 NG -CLAPPINGS K L AE1 P IH0 NG Z -CLAPS K L AE1 P S -CLAPSADDLE K L AE1 P S AE2 D AH0 L -CLAPT K L AE1 P T -CLAPTON K L AE1 P T AH0 N -CLAPTRAP K L AE1 P T R AE2 P -CLAQUE K L AE1 K -CLAQUEURS K L AE1 K ER1 Z -CLAR K L AA1 R -CLARA K L AE1 R AH0 -CLARA K L EH1 R AH0 -CLARA'S K L AE1 R AH0 Z -CLARA'S K L EH1 R AH0 Z -CLARE K L EH1 R -CLARE'S K L EH1 R Z -CLAREBOROUGH K L AE1 R B ER0 OW0 -CLARED K L EH1 R D -CLAREHAVEN K L AA0 R EH1 HH AH0 V AH0 N -CLAREHAVEN'S K L AA0 R EH1 HH AH0 V AH0 N Z -CLAREMONT K L EH1 R M AA2 N T -CLAREMONT'S K L AE1 R M AA2 N T S -CLAREMORRIS K L AE1 R AH0 M AO1 R IH0 S -CLAREN K L AE1 R AH0 N -CLARENBACH K L EH1 R AH0 N B AA2 K -CLARENCE K L EH1 R AH0 N S -CLARENCE'S K L EH1 R AH0 N S IH0 Z -CLARENCES K L EH1 R AH0 N S IH0 Z -CLARENCEUX K L EH1 R AH0 N S OW1 -CLARENDEL K L AA0 R EH1 N D AH0 L -CLARENDON K L EH1 R AH0 N D AH0 N -CLARENDON'S K L EH1 R AH0 N D AH0 N Z -CLARENHAM K L EH1 R AH0 N HH AE2 M -CLARENS K L EH1 R AH0 N Z -CLARES K L EH1 R Z -CLARET K L EH1 R AH0 T -CLARETIE K L ER0 EH1 T IY0 -CLARETS K L AE1 R AH0 T S -CLARETTE K L ER0 EH1 T -CLARGES K L AA1 R JH IH0 Z -CLARGY K L AA1 R JH IY0 -CLARIAN K L EH1 R IY0 AH0 N -CLARIAN'S K L EH1 R IY0 AH0 N Z -CLARIBEL K L EH1 R AH0 B EH2 L -CLARIBEL'S K L EH1 R AH0 B EH2 L Z -CLARICE K L ER0 IY1 S -CLARICE'S K L AA1 R IH0 S IH0 Z -CLARIDGE K L EH1 R IH0 JH -CLARIDGE'S K L EH1 R IH0 JH AH0 Z -CLARIE K L EH1 R IY0 -CLARIFICATION K L EH2 R AH0 F AH0 K EY1 SH AH0 N -CLARIFIED K L EH1 R AH0 F AY2 D -CLARIFIES K L EH1 R AH0 F AY2 Z -CLARIFY K L EH1 R AH0 F AY2 -CLARIFYING K L EH1 R AH0 F AY2 IH0 NG -CLARIMONDE K L AE1 R IH0 M AH0 N D -CLARIN K L EH2 R AH0 N -CLARINA K L AA0 R IY1 N AH0 -CLARINDA K L ER0 IH1 N D AH0 -CLARINDA'S K L ER0 IH1 N D AH0 Z -CLARINET K L EH2 R AH0 N EH1 T -CLARINETS K L EH2 R AH0 N EH1 T S -CLARION K L EH1 R IY0 AH0 N -CLARION'S K L EH1 R IY0 AH0 N Z -CLARIONET K L EH1 R IY0 AH0 N EH1 T -CLARIONETS K L EH1 R IY0 AH0 N EH1 T S -CLARIONS K L EH1 R IY0 AH0 N Z -CLARIS K L EH1 R IH0 S -CLARISS K L ER0 IH1 S -CLARISSA K L ER0 IH1 S AH0 -CLARISSA'S K L ER0 IH1 S AH0 Z -CLARISSE K L AE1 R IH0 S -CLARISSE K L AH0 R IY1 S -CLARISSE'S K L AA0 R IY1 S IH0 Z -CLARITA K L AA0 R IY1 T AH0 -CLARITY K L EH1 R AH0 T IY0 -CLARITY K L EH1 R IH0 T IY0 -CLARK K L AA1 R K -CLARK'S K L AA1 R K S -CLARKE K L AA1 R K -CLARKE'S K L AA1 R K S -CLARKES K L AA1 R K S -CLARKEVILLE K L AA1 R K V IH0 L -CLARKS K L AA1 R K S -CLARKSBURG K L AA1 R K S B ER0 G -CLARKSON K L AA1 R K S AH0 N -CLARKSON'S K L AA1 R K S AH0 N Z -CLARKSVILLE K L AA1 R K S V IH2 L -CLARO K L AA1 R OW0 -CLAROS K L AA1 R OW0 Z -CLARRIE K L AE1 R IY0 -CLARRY K L AE1 R IY0 -CLARTE K L AA1 R T IY0 -CLARUS K L AA1 R AH0 S -CLARY K L EH1 R IY0 -CLARY'S K L EH1 R IY0 Z -CLAS K L AE1 S -CLASFEMPTA K L AE1 S F EH2 M P T AH0 -CLASH K L AE1 SH -CLASH'D K L AE1 SH IH0 D -CLASHED K L AE1 SH T -CLASHES K L AE1 SH IH0 Z -CLASHING K L AE1 SH IH0 NG -CLASHINGS K L AE1 SH IH0 NG Z -CLASHNICHD K L AE1 SH N IH0 CH D -CLASON K L AE1 S AH0 N -CLASP K L AE1 S P -CLASP'D K L AE1 S P D -CLASPED K L AE1 S P T -CLASPER K L AE1 S P ER0 -CLASPERS K L AE1 S P ER0 Z -CLASPING K L AE1 S P IH0 NG -CLASPINGS K L AE1 S P IH0 NG Z -CLASPS K L AE1 S P S -CLASPT K L AE1 S P T -CLASS K L AE1 S -CLASS'S K L AE1 S IH0 Z -CLASSE K L AE1 S -CLASSED K L AE1 S T -CLASSES K L AE1 S AH0 Z -CLASSES K L AE1 S IH0 Z -CLASSIC K L AE1 S IH0 K -CLASSICAL K L AE1 S IH0 K AH0 L -CLASSICALISM K L AE1 S IH0 K AH0 L IH2 Z AH0 M -CLASSICALITY K L AE1 S IH0 K AE1 L AH0 T IY0 -CLASSICALLY K L AE1 S IH0 K L IY0 -CLASSICISM K L AE1 S IH0 S IH2 Z AH0 M -CLASSICIST K L AE1 S AH0 S AH0 S T -CLASSICISTS K L AE1 S IH0 S IH0 S T S -CLASSICS K L AE1 S IH0 K S -CLASSICUS K L AE1 S IH0 K AH0 S -CLASSIFIABLE K L AE1 S AH0 F AY2 AH0 B AH0 L -CLASSIFICATION K L AE2 S AH0 F AH0 K EY1 SH AH0 N -CLASSIFICATIONS K L AE2 S AH0 F AH0 K EY1 SH AH0 N Z -CLASSIFICATORY K L AE1 S AH0 F AH0 K EY1 T ER0 IY0 -CLASSIFIED K L AE1 S AH0 F AY2 D -CLASSIFIER K L AE1 S AH0 F AY2 ER0 -CLASSIFIERS K L AE1 S AH0 F AY2 ER0 Z -CLASSIFIES K L AE1 S AH0 F AY2 Z -CLASSIFY K L AE1 S AH0 F AY2 -CLASSIFYING K L AE1 S AH0 F AY2 IH0 NG -CLASSING K L AE1 S IH0 NG -CLASSIQUE K L AE1 S IY1 K -CLASSIS K L AE1 S IH0 S -CLASSMAN K L AE1 S M AH0 N -CLASSMATE K L AE1 S M EY2 T -CLASSMATE'S K L AE1 S M EY2 T S -CLASSMATES K L AE1 S M EY2 T S -CLASSMEN K L AE1 S M EH0 N -CLASSON K L AE1 S AH0 N -CLASSROOM K L AE1 S R UW2 M -CLASSROOMS K L AE1 S R UW2 M Z -CLASSY K L AE1 S IY0 -CLAT K L AE1 T -CLATFORD K L AE1 T F ER0 D -CLATSOP K L AE1 T S AA2 P -CLATSOPS K L AE1 T S AA2 P S -CLATTER K L AE1 T ER0 -CLATTERBY K L AE1 T ER0 B IY0 -CLATTERED K L AE1 T ER0 D -CLATTERIN K L AE1 T ER0 IH0 N -CLATTERING K L AE1 T ER0 IH0 NG -CLATTERINGS K L AE1 T ER0 IH0 NG Z -CLATTERMOUTH K L AE1 T ER0 M AH0 TH -CLATTERS K L AE1 T ER0 Z -CLAUD K L AO1 D -CLAUD'S K L AO1 D Z -CLAUDAS K L AO1 D AH0 Z -CLAUDE K L AO1 D -CLAUDE'S K L AO1 D Z -CLAUDES K L AO1 D Z -CLAUDET K L AO0 D EH1 T -CLAUDET'S K L AO0 D EH1 T S -CLAUDIA K L AO1 D IY0 AH0 -CLAUDIA'S K L AO1 D IY0 AH0 Z -CLAUDIAN K L AO1 D IY0 AH0 N -CLAUDIE K L AO1 D IY0 -CLAUDIEUSE K L AO1 D Y UW1 S -CLAUDII K L AO0 D IY1 IY0 -CLAUDINE K L AO0 D IY1 N -CLAUDINE'S K L AO0 D IY1 N Z -CLAUDIO K L AO1 D IY0 OW2 -CLAUDIO'S K L AO1 D IY0 OW0 Z -CLAUDIS K L AO1 D IH0 S -CLAUDIUS K L AO1 D IY0 AH0 S -CLAUDIUS'S K L AO1 D IY0 AH0 S IH0 Z -CLAUDO K L AO1 D OW0 -CLAUGHTON K L AO1 T AH0 N -CLAUS K L AO1 Z -CLAUS'S K L AO1 Z IH0 Z -CLAUSE K L AO1 Z -CLAUSEL K L AO1 Z AH0 L -CLAUSEN K L AW1 S AH0 N -CLAUSEN'S K L AO1 Z AH0 N Z -CLAUSES K L AO1 Z AH0 Z -CLAUSES K L AO1 Z IH0 Z -CLAUSEWITZ K L AO1 Z W IH0 T S -CLAUSIN K L AO1 Z AH0 N -CLAUSIUS K L AO1 Z IY0 IH0 S -CLAUSTHAL K L AO2 S T AH0 L -CLAUSTRAL K L AO2 S T R AH0 L -CLAUSTROPHOBIA K L AO2 S T R AH0 F OW1 B IY0 AH0 -CLAUSUM K L AO1 Z AH0 M -CLAVE K L EY2 V -CLAVECIN K L AH0 V EH1 S IH0 N -CLAVEL K L AE1 V AH0 L -CLAVEL'S K L AH0 V EH1 L Z -CLAVER K L AH0 V ER0 -CLAVER'SE K L AH0 V ER1 Z -CLAVERHOUSE K L AE1 V ER0 HH AW2 S -CLAVERHOUSE'S K L AE1 V ER0 HH AW2 S IH0 Z -CLAVERIAS K L AH0 V EH1 R IY0 AH0 Z -CLAVERING K L AE1 V ER0 IH0 NG -CLAVERING'S K L AE1 V ER0 IH0 NG Z -CLAVERINGS K L AE1 V ER0 IH0 NG Z -CLAVERS K L AE1 V ER0 Z -CLAVERTON K L AE1 V ER0 T AH0 N -CLAVERTON'S K L AE1 V ER0 T AH0 N Z -CLAVES K L EY2 V Z -CLAVICHORD K L AE1 V AH0 K AO2 R D -CLAVICLE K L AE1 V AH0 K AH0 L -CLAVICLE K L AE1 V IH0 K AH0 L -CLAVICLES K L AE1 V AH0 K AH0 L Z -CLAVICULAR K L AE1 V AH0 K Y AH0 L ER0 -CLAVIER K L AE1 V Y ER0 -CLAVIERE K L AE1 V IY0 EH0 R -CLAVIGERA K L AE1 V AH0 JH EH1 R AH0 -CLAVIGERO K L AE1 V AH0 JH EH1 R OW0 -CLAVIS K L AE1 V IH0 S -CLAVIUS K L EY1 V IY0 IH0 S -CLAVO K L AA1 V OW0 -CLAVUS K L AH0 V AH0 S -CLAVVY K L AE1 V IY0 -CLAW K L AO1 -CLAW'S K L AO1 Z -CLAWED K L AO1 D -CLAWHAMMER K L AO1 HH AE2 M ER0 -CLAWIN K L AO1 IY0 N -CLAWING K L AO1 IH0 NG -CLAWLIKE K L AO1 L AY2 K -CLAWS K L AO1 Z -CLAWSON K L AO1 S AH0 N -CLAWY K L AO1 IY0 -CLAXON K L AE1 K S AH0 N -CLAXTON K L AE1 K S T AH0 N -CLAY K L EY1 -CLAY'S K L EY1 Z -CLAYBANK K L EY1 B AE2 NG K 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IY1 V Z -CLEEVER K L IY1 V ER0 -CLEF K L EH1 F -CLEFS K L EH1 F S -CLEFT K L EH1 F T -CLEFTS K L EH1 F T S -CLEGG K L EH1 G -CLEGG'S K L EH1 G Z -CLEGGETT K L EH1 G IH0 T -CLEGGETT'S K L EH1 G EH1 T S -CLEGGS K L EH1 G Z -CLEGHORN K L EH1 G HH ER0 N -CLEGHORNE K L EH1 G HH AO0 R N -CLEIGH K L AH0 -CLEIGH'S K L IY0 Z -CLEIKUM K L IY1 K AH0 M -CLEINIAS K L EY0 IY1 N IY0 AH0 Z -CLEISHBOTHAM K L IY1 SH B AH0 TH AH0 M -CLEISTHENES K L IY1 S TH IY1 N Z -CLEITOPHON K L IY1 T AA1 F AH0 N -CLEL K L EH1 L -CLELAND K L EH1 L AH0 N D -CLELIA K L IY1 L Y AH0 -CLELIA'S K L IY1 L Y AH0 Z -CLELIE K L EH1 L IY0 -CLELLAN K L EH1 L AH0 N -CLELLAN'S K L EH1 L AH0 N Z -CLELLAND K L EH1 L AH0 N D -CLEM K L EH1 M -CLEM'S K L AH0 M Z -CLEMATIS K L AH0 M AE1 T IH0 S -CLEMATIS K L EH1 M AH0 T IH0 S -CLEMENCE K L EH1 M AH0 N S -CLEMENCE'S K L EH1 M AH0 N S IH0 Z -CLEMENCEAU K L EH1 M AH0 N S OW1 -CLEMENCY K L EH1 M AH0 N S IY0 -CLEMENS K L EH1 M AH0 N Z -CLEMENS'S K L EH1 M AH0 N Z IH0 Z -CLEMENSES K L EH1 M AH0 N S IH0 Z -CLEMENT K L EH1 M AH0 N T -CLEMENT'S K L EH1 M AH0 N T S -CLEMENTE K L AH0 M EH1 N T EY0 -CLEMENTE K L AH0 M EH1 N T IY0 -CLEMENTE'S K L AH0 M EH1 N T EY0 Z -CLEMENTE'S K L AH0 M EH1 N T IY0 Z -CLEMENTI K L EY0 M EY1 N T IY0 -CLEMENTIA K L EY0 M EY1 N SH AH0 -CLEMENTINA K L AH0 M EH1 N T AH0 N AH0 -CLEMENTINA'S K L AH0 M EH1 N T AH0 N AH0 Z -CLEMENTINE K L EH1 M AH0 N T AY2 N -CLEMENTINE K L EH1 M AH0 N T IY2 N -CLEMENTINE'S K L AH0 M EH1 N T IY0 N Z -CLEMENTINI K L AH0 M EH1 N T IY1 N IY0 -CLEMENTS K L EH1 M AH0 N T S -CLEMENTS'S K L AH0 M EH1 N T S AH0 Z -CLEMENZA K L AH0 M EH1 N Z AH0 -CLEMM K L EH1 M -CLEMM'S K L EH1 M Z -CLEMMED K L EH1 M D -CLEMMER K L EH1 M ER0 -CLEMMIE K L EH1 M IY0 -CLEMMY K L EH1 M IY0 -CLEN K L EH1 N -CLENA K L EH1 N AH0 -CLENARVON K L EH1 N ER0 V AA0 N -CLENCH K L EH1 N CH -CLENCH'D K L EH1 N CH D -CLENCHED K L EH1 N CH T -CLENCHES K L EH1 N CH AH0 Z -CLENCHING K L EH1 N CH IH0 NG -CLENDENIN K L EH1 N D IH0 N IH0 N -CLENDENNING K L EH2 N D EH1 N IH0 NG -CLENDON K L EH1 N D AH0 N -CLENE K L IY2 N -CLENK K L EH1 NG K -CLENNAM K L EH1 N AH0 M -CLENNAM'S K L EH1 N AH0 M Z -CLENNEL K L EH1 N AH0 L -CLENT K L EH1 N T -CLEO K L IY1 OW0 -CLEO'S K L IY1 OW0 Z -CLEOBULUS K L IY2 AH0 B Y AH0 L AH0 S -CLEOMBROTUS K L IY2 AH0 M B R OW1 T AH0 S -CLEOMENES K L IY2 AH0 M IY2 N Z -CLEON K L IY0 AH0 N -CLEON'S K L IY2 AH0 N Z -CLEONTE K L IY1 AA0 N T -CLEONYMUS K L IY2 AH0 N IH2 M AH0 S -CLEOPATRA K L IY2 AH0 P AE1 T R AH0 -CLEOPATRA'S K L IY2 AH0 P AE1 T R AH0 Z -CLEOPATRAS K L IY2 AH0 P AE1 T R AH0 Z -CLEOPATRE K L IY2 AH0 P AE1 T ER0 -CLEOPHAS K L IY2 AH0 F AH0 Z -CLEOPHON K L IY2 AH0 F AH0 N -CLEOPIS K L IY2 AH0 P IH0 S -CLEOTOS K L IY2 AH0 T OW2 Z -CLEPED K L IY1 P T -CLEPHANE K L EH1 F EY2 N -CLEPHANE'S K L EH1 F EY2 N Z -CLEPSYDRA K L EH2 P S IY0 D R AH0 -CLER K L ER0 -CLERAMBAULT K L EH1 R AH0 M B OW0 -CLERAMBAULT'S K L EH1 R AH0 M B OW0 Z -CLERC K L ER1 K -CLERC'S K L ER1 K S -CLERCQ K L ER1 K -CLERCS K L ER1 K S -CLERE K L ER0 -CLERE'S K L EH1 R Z -CLEREMONT K L EH1 R AH0 M AA1 N T -CLERESTORY K L EH1 R IH0 S T AO1 R IY0 -CLERGEOT K L ER1 JH IY0 AA1 T -CLERGY K L ER1 JH IY0 -CLERGY'S K L ER1 JH IY0 Z -CLERGYMAN K L ER1 JH IY0 M AE2 N -CLERGYMAN K L ER1 JH IY0 M AH0 N -CLERGYMAN'S K L ER1 JH IY0 M AH0 N Z -CLERGYMEN K L ER1 JH IY0 M EH2 N -CLERGYMEN K L ER1 JH IY0 M IH0 N -CLERGYMEN'S K L ER1 JH IY0 M IH0 N Z -CLERGYWOMAN K L ER1 JH IY0 W UH2 M AH0 N -CLERIC K L EH1 R IH0 K -CLERIC'S K L EH1 R IH0 K S -CLERICAL K L EH1 R AH0 K AH0 L -CLERICAL K L EH1 R IH0 K AH0 L -CLERICALISM K L EH1 R IH0 K AH0 L IH2 Z AH0 M -CLERICALLY K L EH1 R IH0 K L IY0 -CLERICALS K L EH1 R IH0 K AH0 L Z -CLERICO K L ER0 IY1 K OW0 -CLERICO'S K L EH1 R IH0 K OW2 Z -CLERICS K L EH1 R IH0 K S -CLERICY K L EH1 R AH0 S IY0 -CLERICY'S K L EH1 R AH0 S IY0 Z -CLERIHEW K L EH1 R AH0 HH Y UW0 -CLERIMONT K L EH1 R IH0 M AA2 N T -CLERIOS K L EH1 R IY0 OW0 Z -CLERK K L ER1 K -CLERK'S K L ER1 K S -CLERKE K L ER1 K -CLERKE'S K L ER1 K S -CLERKED K L ER1 K T -CLERKENWELL K L ER1 K AH0 N W EH2 L -CLERKIN K L ER1 K IH0 N -CLERKING K L ER1 K IH0 NG -CLERKLAND K L ER1 K L AH0 N D -CLERKLY K L ER1 K L IY0 -CLERKS K L ER1 K S -CLERKSHIP K L ER1 K SH IH2 P -CLERKSHIPS K L ER1 K SH IH2 P S -CLERMONT K L EH1 R M AA2 N T -CLERMONT'S K L EH1 R M AA2 N T S -CLERNAND K L ER1 N AH0 N D -CLERNAND'S K L ER1 N IH0 N D Z -CLERRON K L EH1 R AH0 N -CLERTE K L ER1 T -CLERVAL K L EH1 R V AH0 L -CLERY K L EH1 R IY0 -CLERY'S K L EH1 R IY0 Z -CLETA K L EH1 T AH0 -CLETHRA K L IY1 TH R AH0 -CLEUCH K L IY1 AH0 K -CLEUGH K L -CLEVAH K L IY1 V AH0 -CLEVAIRE K L AH0 V EH2 R -CLEVE K L IY1 V -CLEVE'S K L IY1 V Z -CLEVEDEN K L EH1 V D AH0 N -CLEVEDON K L IY1 V AH0 D AO2 N -CLEVELAND K L IY1 V L AH0 N D -CLEVELAND'S K L IY1 V L AH0 N D Z -CLEVELANDS K L IY1 V L AH0 N D Z -CLEVENGER K L EH1 V IH0 N JH ER0 -CLEVER K L EH1 V ER0 -CLEVERER K L EH1 V ER0 ER0 -CLEVEREST K L EH1 V R AH0 S T -CLEVERING K L EH1 V ER0 IH0 NG -CLEVERING'S K L EH1 V ER0 IH0 NG Z -CLEVERISH K L EH1 V R IH0 SH -CLEVERLY K L EH1 V ER0 L IY0 -CLEVERNESS K L EH1 V ER0 N AH0 S -CLEVES K L IY1 V Z -CLEVI K L IY1 V IY0 -CLEVIS K L EH1 V IH0 S -CLEW K L UW1 -CLEWE K L UW1 -CLEWE'S K L EH1 W EY2 Z -CLEWED K L UW1 D -CLEWER K L UW1 ER0 -CLEWES K L UW1 Z -CLEWING K L UW1 IH0 NG -CLEWLINE K L UW1 L AY0 N -CLEWLINES K L UW1 L AY2 N Z -CLEWS K L UW1 Z -CLEYRE K L EH1 R -CLI K L IY0 -CLIA K L IY0 AH0 -CLIANTHA K L IY0 AE1 N TH AH0 -CLIBOROUGH K L AY1 B ER0 OW0 -CLICHE K L IY0 SH EY1 -CLICHES K L IY0 SH EY1 Z -CLICHY K L IY0 SH IY0 -CLICK K L IH1 K -CLICKED K L IH1 K T -CLICKER K L IH1 K ER0 -CLICKETY K L IH1 K AH0 T IY0 -CLICKIN K L IH1 K IH0 N -CLICKING K L IH1 K IH0 NG -CLICKINGS K L IH1 K IH0 NG Z -CLICKITS K L IH1 K IH0 T S -CLICKITY K L IH1 K IH0 T IY0 -CLICKS K L IH1 K S -CLICQUOT K L IH1 K W OW1 -CLIENT K L AY1 AH0 N T -CLIENT'S K L AY1 AH0 N T S -CLIENTAGE K L AY1 AH0 N T IH0 JH -CLIENTELE K L AY2 AH0 N T EH1 L -CLIENTS K L AY1 AH0 N T S -CLIF K L IH1 F -CLIFDEN K L IH1 F D AH0 N 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M AE0 K S IH0 Z -CLIMAXING K L AY1 M AE2 K S IH0 NG -CLIMB K L AY1 M -CLIMB'D K L AY1 M D -CLIMBABLE K L AY1 M B AH0 L -CLIMBED K L AY1 M D -CLIMBER K L AY1 M ER0 -CLIMBER'S K L AY1 M ER0 Z -CLIMBERKIN K L AY1 M ER0 K IH0 N -CLIMBERS K L AY1 M ER0 Z -CLIMBETH K L AY1 M TH -CLIMBIN K L AY1 M IH0 N -CLIMBING K L AY1 M IH0 NG -CLIMBINGS K L AY1 M IH0 NG Z -CLIMBS K L AY1 M Z -CLIME K L AY1 M -CLIMENE K L AY1 M IY2 N -CLIMES K L AY1 M Z -CLIMO K L AY1 M OW0 -CLIMOE K L AY1 M OW0 -CLINABARIANS K L AH0 N EY1 B EH1 R IY0 AH0 N Z -CLINCH K L IH1 N CH -CLINCH'S K L IH1 N CH IH0 Z -CLINCHED K L IH1 N CH T -CLINCHER K L IH1 N CH ER0 -CLINCHES K L IH1 N CH AH0 Z -CLINCHES K L IH1 N CH IH0 Z -CLINCHING K L IH1 N CH IH0 NG -CLINE K L AY1 N -CLING K L IH1 NG -CLINGAN K L IH1 NG G AH0 N -CLINGEST K L IH1 NG G IH0 S T -CLINGETH K L IH1 NG G IH0 TH -CLINGIN K L IH1 NG AH0 N -CLINGING K L IH1 NG IH0 NG -CLINGINGLY K L IH1 NG IH0 NG L IY0 -CLINGMAN K L IH1 NG M AH0 N -CLINGS K L IH1 NG Z -CLINGY K L IH1 NG IY0 -CLINIAS K L IH1 N IY0 AH0 Z -CLINIC K L IH1 N IH0 K -CLINICAL K L IH1 N AH0 K AH0 L -CLINICAL K L IH1 N IH0 K AH0 L -CLINICS K L IH1 N IH0 K S -CLINIQUE K L AH0 N IY1 K -CLINK K L IH1 NG K -CLINKED K L IH1 NG K T -CLINKER K L IH1 NG K ER0 -CLINKERS K L IH1 NG K ER0 Z -CLINKIN K L IH1 NG K IH0 N -CLINKING K L IH1 NG K IH0 NG -CLINKS K L IH1 NG K S -CLINOCK K L IH1 N AH0 K -CLINT K L IH1 N T -CLINT'S K L IH1 N T S -CLINTOCK K L IH1 N T AH0 K -CLINTOCK'S K L IH1 N T AH0 K S -CLINTON K L IH1 N T AH0 N -CLINTON'S K L IH1 N T AH0 N Z -CLINTONIA K L IH2 N T OW1 N IY0 AH0 -CLINTONS K L IH1 N T AH0 N Z -CLIO K L IY1 OW0 -CLIO'S K L IY1 OW0 Z -CLIP K L IH1 P -CLIPBOARD K L IH1 P B AO2 R D -CLIPCLAP K L IH1 P K L AE2 P -CLIPP'D K L IH1 P D -CLIPPED K L IH1 P T -CLIPPER K L IH1 P ER0 -CLIPPER'S K L IH1 P ER0 Z -CLIPPERS K L IH1 P ER0 Z -CLIPPIN K L IH1 P IH0 N -CLIPPING K L IH1 P IH0 NG -CLIPPINGS K L IH1 P IH0 NG Z -CLIPS K L IH1 P S -CLIPSTON K L IH1 P S T AH0 N -CLIPSTONE K L IH1 P S T OW2 N -CLIPT K L IH1 P T -CLIPTON K L IH1 P T AH0 N -CLIQUE K L IY1 K -CLIQUES K L IH1 K S -CLIQUOT K L IY1 K W OW1 -CLISSON K L IH1 S AH0 N -CLISTHENES K L IH2 S TH IY1 N Z -CLITANDRE K L AY0 T AE1 N D ER0 -CLITHERING K L AY1 DH ER0 IH0 NG -CLITHEROE K L IH1 TH ER0 OW0 -CLITHEROW K L IH1 TH ER0 OW0 -CLITORIS K L AY0 T AO1 R IH0 S -CLITTER K L IH1 T ER0 -CLITUMNUS K L AY0 T AH1 M N AH0 S -CLITUS K L AY1 T AH0 S -CLIVE K L AY1 V -CLIVE'S K L AY1 V Z -CLIVEDEN K L AY1 V D AH0 N -CLIVER K L AY1 V ER0 -CLIX K L IH0 K S -CLO K L OW1 -CLO'ES K L OW1 EH0 S -CLO'S K L OW0 Z -CLO'SE K L OW1 S -CLOACA K L OW1 K AH0 -CLOACINA K L OW1 CH IY1 N AH0 -CLOAK K L OW1 K -CLOAK'S K L OW1 K S -CLOAKE K L OW1 K -CLOAKED K L OW1 K T -CLOAKING K L OW1 K IH0 NG -CLOAKROOM K L OW1 K R UW2 M -CLOAKROOMS K L OW1 K R UW2 M Z -CLOAKS K L OW1 K S -CLOAM K L OW1 M -CLOANTHUS K L OW1 N TH AH0 S -CLOAREK K L OW1 ER0 IH0 K -CLOAREK'S K L OW1 ER0 EH2 K S -CLOATH K L OW1 TH -CLOATHED K L OW1 DH D -CLOATHES K L OW1 DH Z -CLOATHING K L OW1 TH IH0 NG -CLOATHS K L OW1 TH S -CLOBBER K L AA1 B ER0 -CLOCA K L OW1 K AH0 -CLOCHE K L OW1 K IY0 -CLOCHEGOURDE K L AA1 K IH0 G AO1 R D -CLOCHES K L AA1 K S -CLOCHETTE K L AA1 K EH1 T -CLOCHONNE K L AA1 K AH0 N -CLOCK K L AA1 K -CLOCK'S K L AA1 K S -CLOCKE K L AA1 K -CLOCKED K L AA1 K T -CLOCKLIKE K L AA1 K L AY2 K -CLOCKMAKER K L AA1 K M EY0 K ER0 -CLOCKMAKER'S K L AA1 K M EY2 K ER0 Z -CLOCKMAKERS K L AA1 K M EY2 K ER0 Z -CLOCKS K L AA1 K S -CLOCKWISE K L AA1 K W AY2 Z -CLOCKWORK K L AA1 K W ER2 K -CLOD K L AA1 D -CLODAGH K L AA1 D AH0 G -CLODAGH'S K L AA1 D AH2 Z -CLODD K L AA1 D -CLODDED K L AA1 D IH0 D -CLODDISH K L AA1 D IH0 SH -CLODDY K L AA1 D IY0 -CLODE K L OW1 D -CLODE'S K L AA1 D Z -CLODFELTER K L AA1 D F EH2 L T ER0 -CLODHOPPER K L AA1 D HH AA2 P ER0 -CLODHOPPERS K L AA1 D HH AA2 P ER0 Z -CLODIA K L OW1 D IY0 AH0 -CLODION K L OW1 D IY0 AH0 N -CLODIUS K L OW1 D IY0 AH0 S -CLODIUS'S K L OW1 D IY0 AH0 S IH0 Z -CLODMAN K L AA1 D M AH0 N -CLODOCHE K L OW0 D OW1 SH -CLODOMIRO K L OW0 D OW1 M IH1 R OW0 -CLODPOLE K L AA1 D P OW2 L -CLODS K L AA1 D Z -CLODWIG K L AA1 D W IH0 G -CLODWIG'S K L AA1 D W IH0 G Z -CLOE K L OW1 -CLOELIA K L OW1 L IY0 AH0 -CLOES K L OW1 Z -CLOETE K L OW1 T IY0 -CLOG K L AA1 G -CLOGG K L AA1 G -CLOGG'D K L AO1 G D -CLOGGED K L AO1 G D -CLOGGING K L AA1 G IH0 NG -CLOGGING K L AO1 G IH0 NG -CLOGHER K L AA1 G ER0 -CLOGS K L AA1 G Z -CLOHERTY K L AA1 HH ER0 T IY0 -CLOISONNE K L OY1 S AH0 N -CLOISTER K L OY1 S T ER0 -CLOISTER'D K L OY1 S T R AH0 D -CLOISTER'S K L OY1 S T ER0 Z -CLOISTERED K L OY1 S T ER0 D -CLOISTERHAM K L OY1 S T ER0 HH AE2 M -CLOISTERS K L OY1 S T ER0 Z -CLOISTRAL K L OY1 S T R AH0 L -CLOITRE K L OY1 T ER0 -CLOKE K L OW1 K -CLOMB K L AA1 M -CLOMBER K L AA1 M B ER0 -CLON K L AA1 N -CLONAKILTY K L OW1 N AH0 K IH1 L T IY0 -CLONARD K L AA1 N ER0 D -CLONARTY K L OW1 N AA0 R T IY0 -CLONBREE K L AA1 N B R IY1 -CLONBRONY K L AA1 N B R OW2 N IY0 -CLONBRONY'S K L AA1 N B R OW1 N IY0 Z -CLONCURRY K L AA1 N K ER1 IY0 -CLONE K L OW1 N -CLONES K L OW1 N Z -CLONMACNOISE K L AA1 N M AE1 K N W AA1 Z -CLONMEL K L AA1 N M AH0 L -CLONMELL K L AA1 N M EY1 L -CLONTARF K L AA1 N T AA1 R F -CLOOM K L UW2 M -CLOOMBER K L UW1 M B ER0 -CLOON K L UW1 N -CLOOSTEDD K L UW1 S T IH0 D -CLOOTZ K L UW1 T S -CLOP K L AA1 P -CLOPIN K L OW1 P IH0 N -CLOPIN'S K L OW1 P IH0 N Z -CLOPPER K L AA1 P ER0 -CLOPPERTY K L AA1 P ER0 T IY0 -CLOPTON K L AA1 P T AH0 N -CLOPTON'S K L AA1 P T AH0 N Z -CLOPTONS K L AA1 P T AH0 N Z -CLOQUET K L OW1 K EY1 -CLORAN K L AO1 R AH0 N -CLORIDAN K L AO1 R IH0 D AH0 N -CLORINDA K L AO0 R IY1 N D AH0 -CLORINDA'S K L AO0 R IY1 N D AH0 Z -CLORINDE K L AO0 R IY1 N D -CLORINDY K L AO0 R IY1 N D IY0 -CLORIS K L AO1 R IH0 S -CLOS K L AA1 S -CLOS'D K L AA1 Z D -CLOS'T K L AA1 Z T -CLOSANA K L AA1 Z AA1 N AH0 -CLOSE K L OW1 S -CLOSE K L OW1 Z -CLOSE'S K L OW1 S IH0 Z -CLOSEBURN K L OW1 S B ER2 N -CLOSED K L OW1 Z D -CLOSELY K L OW1 S L IY0 -CLOSENESS K L OW1 S N IH0 S -CLOSER K L OW1 S ER0 -CLOSER K L OW1 Z ER0 -CLOSER'N K L OW1 S ER2 AH0 N -CLOSERIE K L OW1 S ER0 IY0 -CLOSERS K L OW1 Z ER0 Z -CLOSERSTIL K L OW1 S ER0 S T IH1 L -CLOSES K L OW1 Z IH0 Z -CLOSEST K L OW1 S AH0 S T -CLOSET K L AA1 Z AH0 T -CLOSETED K L AA1 Z AH0 T IH0 D -CLOSETER K L AA1 Z AH0 T ER0 -CLOSETH K L AA1 S IH0 TH -CLOSETS K L AA1 Z AH0 T S -CLOSIN K L AA1 Z AH0 N -CLOSING K L OW1 Z IH0 NG -CLOSKEY K L AO1 S K IY0 -CLOSKEY'S K L AO1 S K IY0 Z -CLOSKY K L AO1 S K IY0 -CLOSS K L AO1 S -CLOST K L AO1 S T -CLOSTE K L OW1 S T -CLOSTER K L AA1 S T ER0 -CLOSURE K L OW1 ZH ER0 -CLOT K L AA1 T -CLOTAIRE K L AA1 T EH1 R -CLOTALDO K L AA1 T AA1 L D OW0 -CLOTE K L OW1 T IY0 -CLOTELLE K L AA1 T EH2 L -CLOTEN K L OW1 T AH0 N -CLOTH K L AO1 TH -CLOTH'D K L OW1 DH D -CLOTH'S K L AO1 TH S -CLOTHE K L OW1 DH -CLOTHED K L OW1 DH D -CLOTHES K L OW1 DH Z -CLOTHES K L OW1 Z -CLOTHESLINE K L OW1 DH Z L AY0 N -CLOTHESLINES K L OW1 DH Z L AY2 N Z -CLOTHESMAN K L OW1 DH Z M AH0 N -CLOTHESMEN K L OW1 DH Z M IH0 N -CLOTHESPIN K L AO1 TH EH1 S P IH0 N -CLOTHESPINS K L AO1 TH EH1 S P IH0 N Z -CLOTHESPRESS K L OW1 DH S P R EH1 S -CLOTHETH K L OW1 DH TH -CLOTHIER K L OW1 DH Y ER0 -CLOTHIER'S K L OW1 DH Y ER0 Z -CLOTHIERS K L OW1 DH Y ER0 Z -CLOTHILDE K L AA1 TH IH0 L D -CLOTHIN K L AA1 TH AH0 N -CLOTHING K L OW1 DH IH0 NG -CLOTHINGS K L OW1 DH IH0 NG Z -CLOTHO K L AA1 TH OW0 -CLOTHS K L AO1 TH S -CLOTHWORKERS K L AO1 TH W ER2 K ER0 Z -CLOTHYARD K L OW1 DH Y AA2 R D -CLOTILDA K L AH0 T IH1 L D AH0 -CLOTILDA'S K L AH0 T IH1 L D AH0 Z -CLOTILDE K L AH0 T IH1 L D -CLOTILDE'S K L AH0 T IH1 L D Z -CLOTS K L AA1 T S -CLOTT K L AA1 T -CLOTTED K L AA1 T AH0 D -CLOTTED K L AA1 T IH0 D -CLOTTING K L AA1 T IH0 NG -CLOTURE K L OW1 CH ER0 -CLOU K L OW1 -CLOUD K L AW1 D -CLOUD'S K L AW1 D Z -CLOUDBANK K L AW1 D B AE2 NG K -CLOUDBERRY K L AW1 D B EH2 R IY0 -CLOUDBURST K L AW1 D B ER2 S T -CLOUDBURSTS K L AW1 D B ER2 S T S -CLOUDED K L AW1 D IH0 D -CLOUDESLEY K L AW1 D AH0 S L IY0 -CLOUDIER K L AW1 D IY0 ER0 -CLOUDIEST K L AW1 D IY0 AH0 S T -CLOUDILY K L AW1 D AH0 L IY0 -CLOUDINESS K L AW1 D IY0 N IH0 S -CLOUDING K L AW1 D IH0 NG -CLOUDINGS K L AW1 D IH0 NG Z -CLOUDLAND K L AW1 D L AH0 N D -CLOUDLESS K L AW1 D L AH0 S -CLOUDLESSLY K L AW1 D L AH0 S L IY0 -CLOUDLET K L AW1 D L EH2 T -CLOUDLETS K L AW1 D L AH0 T S -CLOUDLIKE K L AW1 D L AY2 K -CLOUDS K L AW1 D Z -CLOUDSDALE K L AW1 D Z D EY2 L -CLOUDY K L AW1 D IY0 -CLOUET K L OW1 EY0 T -CLOUGH K L AW1 -CLOUGH'S K L AW1 Z -CLOUR K L AW1 R -CLOUSTON K L AW1 S T AH0 N -CLOUT K L AW1 T -CLOUT'S K L AW1 T S -CLOUTED K L AW1 T IH0 D -CLOUTING K L AW1 T IH0 NG -CLOUTS K L AW1 T S -CLOVE K L OW1 V -CLOVELLY K L OW1 V AH0 L IY0 -CLOVEN K L OW1 V AH0 N -CLOVER K L OW1 V ER0 -CLOVER'S K L OW1 V ER0 Z -CLOVERDALE K L OW1 V ER0 D EY2 L -CLOVERLEA K L OW1 V ER0 L IY1 -CLOVERLEAF K L OW1 V ER0 L IY2 F -CLOVERNOOK K L OW1 V ER0 N UH1 K -CLOVERS K L OW1 V ER0 Z -CLOVERTON K L OW1 V ER0 T AH0 N -CLOVERTOWN K L OW1 V ER0 T AW2 N -CLOVERY K L OW1 V ER0 IY0 -CLOVES K L OW1 V Z -CLOVIO K L OW1 V IY0 OW0 -CLOVIS K L OW1 V IH0 S -CLOVIS'S K L AA1 V IH0 S IH0 Z -CLOVY K L OW1 V IY0 -CLOW K L OW1 -CLOWES K L AW1 Z -CLOWES'S K L AW1 Z IH0 Z -CLOWN K L AW1 N -CLOWN'S K L AW1 N Z -CLOWNING K L AW1 N IH0 NG -CLOWNISH K L AW1 N IH0 SH -CLOWNISHNESS K L AW1 N IH0 SH N AH0 S -CLOWNS K L AW1 N Z -CLOWRIE K L AO1 R IY0 -CLOY K L OY1 -CLOY'D K L OY1 D -CLOYD K L OY1 D -CLOYED K L OY1 D -CLOYING K L OY1 IH0 NG -CLOYINGLY K L OY1 IH0 NG L IY0 -CLOYNE K L OY1 N -CLOYS K L OY1 Z -CLOYSTER K L OY1 S T ER0 -CLOYSTER'S K L OY1 S T ER0 Z -CLOZE K L OW1 Z -CLOZEL K L OW1 Z AH0 L -CLU K L UW1 -CLUB K L AH1 B -CLUB'S K L AH1 B Z -CLUBABLE K L AH1 B AH0 B AH0 L -CLUBBE K L AH1 B -CLUBBED K L AH1 B D -CLUBBER K L AH1 B ER0 -CLUBBING K L AH1 B IH0 NG -CLUBBY K L AH1 B IY0 -CLUBFOOT K L AH1 B F UH2 T -CLUBFOOT'S K L AH1 B F UH2 T S -CLUBFOOTED K L AH1 B F UH1 T AH0 D -CLUBHOUSE K L AH1 B HH AW2 S -CLUBHOUSES K L AH1 B HH AW2 S IH0 Z -CLUBIN K L AH1 B IH0 N -CLUBIN'S K L AH1 B AH0 N Z -CLUBLAND K L AH1 B L AE2 N D -CLUBMAN K L AH1 B M AH0 N -CLUBMAN'S K L AH1 B M AH0 N Z -CLUBMEN K L AH1 B M AH0 N -CLUBROOM K L AH1 B R UW2 M -CLUBROOMS K L AH1 B R UW2 M Z -CLUBS K L AH1 B Z -CLUCK K L AH1 K -CLUCKED K L AH1 K T -CLUCKING K L AH1 K IH0 NG -CLUCKINGS K L AH1 K IH0 NG Z -CLUCKS K L AH1 K S -CLUD K L AH1 D -CLUDED K L UW1 D IH0 D -CLUDEN K L UW1 D AH0 N -CLUE K L UW1 -CLUENTIUS K L UW1 N T IY2 IH0 S -CLUES K L UW1 Z -CLUFF K L AH1 F -CLUFFE K L AH1 F -CLUFFE'S K L AH1 F S -CLUFFINS K L AH1 F IH0 N Z -CLUGNET K L AH1 G N IH0 T -CLUHIR K L AH1 IH2 R -CLUKE K L UW1 K -CLUKE'S K L UW1 K S -CLUM K L AH1 M -CLUMB K L AH1 M -CLUMBER K L AH1 M B ER0 -CLUMP K L AH1 M P -CLUMP'S K L AH1 M P S -CLUMPED K L AH1 M P T -CLUMPING K L AH1 M P IH0 NG -CLUMPS K L AH1 M P S -CLUMPY K L AH1 M P IY0 -CLUMS K L AH1 M Z -CLUMSIER K L AH1 M Z IY0 ER0 -CLUMSIEST K L AH1 M Z IY0 AH0 S T -CLUMSILY K L AH1 M S AH0 L IY0 -CLUMSINESS K L AH1 M Z IY0 N AH0 S -CLUMSY K L AH1 M Z IY0 -CLUN K L UW1 N -CLUNAS K L UW1 N AH0 Z -CLUNE K L UW1 N -CLUNES K L UW1 N Z -CLUNG K L AH1 NG -CLUNIAC K L UW1 N IY0 AE2 K -CLUNIE K L UW1 N IY0 -CLUNIE'S K L UW1 N IY0 Z -CLUNK K L AH1 NG K -CLUNY K L UW1 N IY0 -CLUNY'S K L UW1 N IY0 Z -CLUPP K L AH1 P -CLUPPINS K L AH1 P IH0 N Z -CLUR K L UW1 R -CLURE K L UW1 R -CLURE'S K L UW1 R Z -CLURG K L ER1 G -CLUS K L AH1 S -CLUSERET K L UW1 Z ER0 EY1 -CLUSIUM K L AH1 S IY0 AH0 M -CLUSIUS K L AH1 S IY0 AH0 S -CLUSKY K L AH1 S K IY0 -CLUST'RING K L AH1 S T IH0 R IH0 NG -CLUSTER K L AH1 S T ER0 -CLUSTER'D K L AH1 S T ER0 D -CLUSTERED K L AH1 S T ER0 D -CLUSTERING K L AH1 S T ER0 IH0 NG -CLUSTERS K L AH1 S T ER0 Z -CLUTCH K L AH1 CH -CLUTCH'D K L AH1 CH D -CLUTCHED K L AH1 CH T -CLUTCHES K L AH1 CH AH0 Z -CLUTCHES K L AH1 CH IH0 Z -CLUTCHING K L AH1 CH IH0 NG -CLUTCHINGS K L AH1 CH IH0 NG Z -CLUTSAM K L AH1 T S AH0 M -CLUTTER K L AH1 T ER0 -CLUTTERBUCK K L AH1 T ER0 B AH2 K -CLUTTERBUCK'S K L AH1 T ER0 B AH2 K S -CLUTTERED K L AH1 T ER0 D -CLUTTERIN K L AH1 T ER0 IH0 N -CLUTTERING K L AH1 T ER0 IH0 NG -CLUTTERS K L AH1 T ER0 Z -CLUTTON K L AH1 T AH0 N -CLUYME K L AY1 M -CLXI S IY1 EH1 L EH1 K S AY1 -CLXII K L K S IY1 IY0 -CLXIX K L K S IH0 K S -CLXVI K L K S V IY0 -CLXXI K L K S IH0 -CLXXIII K L K S IH0 IY0 -CLXXIV K L K S IH0 V -CLXXIX K L K S IH0 K S -CLXXV K L K S V -CLXXVI K L K S V IY0 -CLXXVII K L K S V IY0 -CLXXXIX K L Z K S IH0 K S -CLXXXVI K L Z K S V IY0 -CLY K L AY1 -CLYDE K L AY1 D -CLYDE'S K L AY1 D Z -CLYDESDALE K L AY1 D Z D EY2 L -CLYDESDALE'S K L AY1 D Z D EY2 L Z -CLYKES K L AY1 K S -CLYM K L IH1 M -CLYM'S K L AY1 M Z -CLYMENE K L IH0 M IY1 N -CLYMER K L AY1 M ER0 -CLYMER'S K L IH0 M ER0 Z -CLYNE K L AY1 N -CLYNES K L AY1 N Z -CLYNESWORTH K L AY1 N Z W ER0 TH -CLYNTON K L IH1 N T AH0 N -CLYONE K L AY1 AH0 N -CLYST K L IH1 S T -CLYSTER K L IH1 S T ER0 -CLYSTERS K L IH1 S T ER0 Z -CLYTAEMNESTRA K L AY1 T EY1 M N AH0 S T R AH0 -CLYTEMNESTRA K L AY1 T M N AH0 S T R AH0 -CLYTEMNESTRA'S K L AY1 T M N AH0 S T R AH0 Z -CLYTIA K L IH1 T IY0 AH0 -CLYTIE K 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W EH0 S T -CONQUEST'S K AA1 NG K W EH0 S T S -CONQUESTS K AA1 N K W EH2 S T S -CONQUETE K AA1 NG K W AH0 T -CONQUHAR K AA1 NG K W ER0 -CONQUISTA K AA1 N K W IH2 S T AH0 -CONQUISTADOR K AA1 N K W IH2 S T AH0 D AO2 R -CONQUISTADORES K AA1 N K W IH2 S T AH0 D AO1 R Z -CONQUISTADORS K AA1 N K W IH2 S T AH0 D ER0 Z -CONRAD K AA1 N R AE0 D -CONRAD'S K AA1 N R AE0 D Z -CONRADE K AH0 N R EY1 D -CONRADE'S K AH0 N R EY1 D Z -CONRADIN K AH0 N R AA1 D IH0 N -CONRADINO K AA1 N R AH0 D IY1 N OW0 -CONRAN K AA1 N R AH0 N -CONRECTOR K AA1 N R EH1 K T ER0 -CONRIED K AA1 N R IY0 D -CONRIN K AA1 N R IH0 N -CONROTH K AA1 N R AO0 TH -CONROY K AO1 N R OY0 -CONROY'S K AA1 N R OY2 Z -CONROYAL K AA1 N R OY2 AH0 L -CONRY K AA1 N R IY0 -CONS K AA1 N Z -CONSAIT K AH0 N S EY1 T -CONSAITED K AA0 N S AY1 T IH0 D -CONSALVI K AA0 N S AA1 L V IY0 -CONSANGUINEOUS K AH0 N S AE1 NG G W IY1 N IY0 AH0 S -CONSANGUINITY K AH0 N S AA0 NG IY0 N AH0 T IY0 -CONSARN K AA0 N S AA1 R N -CONSARNED K AA0 N S AA1 R N D -CONSARNIN K AH0 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AH0 N S IH1 S T AH0 N S IY0 -CONSISTENCY'S K AH0 N S IH1 S T AH0 N S IY0 Z -CONSISTENT K AH0 N S IH1 S T AH0 N T -CONSISTENTLY K AH0 N S IH1 S T AH0 N T L IY0 -CONSISTETH K AH0 N S IH1 S T IH0 TH -CONSISTING K AH0 N S IH1 S T IH0 NG -CONSISTORIAL K AA1 N S IH0 S T AO1 R IY0 AH0 L -CONSISTORIES K AH0 N S IH1 S T AO2 R IY0 Z -CONSISTORY K AH0 N S IH1 S T AO2 R IY0 -CONSISTS K AH0 N S IH1 S T S -CONSOLACION K AA2 N S AH0 L EY1 SH AH0 N -CONSOLATION K AA2 N S AH0 L EY1 SH AH0 N -CONSOLATIONS K AA2 N S AH0 L EY1 SH AH0 N Z -CONSOLATORY K AA2 N S AH0 L AH0 T AO2 R IY0 -CONSOLE K AA1 N S OW0 L -CONSOLE K AH0 N S OW1 L -CONSOLED K AH0 N S OW1 L D -CONSOLER K AH0 N S OW1 L ER0 -CONSOLERS K AH0 N S OW1 L ER0 Z -CONSOLES K AH0 N S OW1 L Z -CONSOLIDATE K AH0 N S AA1 L IH0 D EY2 T -CONSOLIDATED K AH0 N S AA1 L AH0 D EY2 T AH0 D -CONSOLIDATES K AH0 N S AA1 L IH0 D EY2 T S -CONSOLIDATING K AH0 N S AA1 L AH0 D EY2 T IH0 NG -CONSOLIDATION K AH0 N S AA2 L AH0 D EY1 SH AH0 N -CONSOLIDATIONS K AH0 N S AA2 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AO0 R N IY1 L Y AH0 Z -CORNELIAN K AO0 R N IY1 L Y AH0 N -CORNELIANS K AO0 R N IY1 L Y AH0 N Z -CORNELIE K ER0 N EH1 L IY0 -CORNELIE'S K AO1 R N IH0 L IY0 Z -CORNELII K AO1 R N IH0 L IY0 IY2 -CORNELIO K ER0 N EH1 L IY0 OW0 -CORNELIS K ER0 N EH1 L IH0 S -CORNELISZ K ER0 N EH1 L IH0 SH -CORNELIUS K AO0 R N IY1 L Y AH0 S -CORNELIUS'S K AO0 R N IY1 L Y AH0 S IH0 Z -CORNELL K AO0 R N EH1 L -CORNELL'S K AO0 R N EH1 L Z -CORNELS K ER0 N EH1 L Z -CORNELY K AO1 R N L IY0 -CORNELYS K AO0 R N IY1 L IY0 Z -CORNEMUSE K AO1 R N IH0 M Y UW1 Z -CORNER K AO1 R N ER0 -CORNER'S K AO1 R N ER0 Z -CORNERED K AO1 R N ER0 D -CORNERING K AO1 R N ER0 IH0 NG -CORNERS K AO1 R N ER0 Z -CORNERSTONE K AO1 R N ER0 S T OW2 N -CORNERSTONES K AO1 R N ER0 S T OW2 N Z -CORNERWISE K AO1 R N ER0 W AY2 Z -CORNES K AO1 R N Z -CORNET K AO0 R N EH1 T -CORNET'S K AO1 R N AH0 T S -CORNETCY K ER0 N EY1 S IY0 -CORNETIST K ER0 N EH1 T IH0 S T -CORNETO K ER0 N EH1 T OW0 -CORNETS K AO0 R N EH1 T S -CORNEVILLE K AO0 R N EH1 V IH0 L 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Z -CORSAR K ER0 S AA1 R -CORSCADDEN K ER0 S K AE1 D AH0 N -CORSE K AO1 R S -CORSE'S K AO1 R S AH0 Z -CORSELET K AO1 R S L AH0 T -CORSELETS K AO1 R S L AH0 T S -CORSER K AO1 R S ER0 -CORSES K AO1 R S IH0 Z -CORSET K AO1 R S AH0 T -CORSETED K AO1 R S AH0 T IH0 D -CORSETIERE K ER0 S EH1 T IY0 EH0 R -CORSETS K ER0 S EH1 T S -CORSI K AO1 R S IY0 -CORSICA K AO1 R S IH0 K AH0 -CORSICAN K AO1 R S AH0 K AH0 N -CORSICAN'S K AO1 R S IH0 K AH0 N Z -CORSICANS K AO1 R S IH0 K AH0 N Z -CORSINI K ER0 S IY1 N IY0 -CORSINI'S K ER0 S IY1 N IY0 Z -CORSLET K AO1 R S L IH0 T -CORSLETS K AO1 R S L IH0 T S -CORSO K AO1 R S OW0 -CORSON K AO1 R S AH0 N -CORSON'S K AO1 R S AH0 N Z -CORSTORPHINE K AO1 R S T ER0 F IY2 N -CORT K AO1 R T -CORTA K AO1 R T AH0 -CORTACHY K AO0 R T AA1 CH IY0 -CORTE K AO1 R T -CORTEGE K AO0 R T EH1 ZH -CORTEGES K AO0 R T EH1 ZH -CORTELYON K AO1 R T EH0 L Y AH0 N -CORTELYOU K AO1 R T EH0 L Y UW2 -CORTENAEKEN K AO1 R T N AH0 K AH0 N -CORTES K AO2 R T EH1 Z -CORTESE K ER0 T EY1 Z IY0 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K AO1 R V -CORVEE K AO1 R V IY0 -CORVEES K AO1 R V IY0 Z -CORVER K AO1 R V ER0 -CORVET K AO2 R V EH1 T -CORVET'S K AO2 R V EH1 T S -CORVETTE K AO0 R V EH1 T -CORVETTE'S K AO2 R V EH1 T S -CORVETTES K AO2 R V EH1 T S -CORVETTO K AO2 R V EH1 T OW0 -CORVICK K AO1 R V IH0 K -CORVICK'S K AO1 R V IH0 K S -CORVIGNY K AO2 R V IH1 G N IY0 -CORVINE K AO2 R V IY1 N IY0 -CORVINO K AO2 R V IY1 N OW0 -CORVINUS K AO2 R V IY1 N AH0 S -CORVISART K AO1 R V IH0 S AA0 R T -CORVO K AO1 R V OW0 -CORVSGATE K AO2 R V S G EY2 T -CORVUS K AO1 R V AH0 S -CORVY K AO2 R V IY0 -CORWAY K AO1 R W EY2 -CORWAY'S K AO1 R W EY2 Z -CORWIN K AO1 R W IH0 N -CORWIN'S K AO1 R W IH0 N Z -CORY K AO1 R IY0 -CORY'S K ER0 IY0 Z -CORYBANTES K AO1 R IY0 B AE1 N T S -CORYBANTIC K AO1 R IY0 B AE1 N T IH0 K -CORYDALIS K AO1 R IY0 D AE1 L IH0 S -CORYDALUS K AO1 R IY0 D AE1 L AH0 S -CORYDON K AO1 R IH1 D AH0 N -CORYDON'S K AO1 R IY0 D AH0 N Z -CORYELL K AO1 R AY2 EH1 L -CORYON K AO1 R AY1 AH0 N -CORYON'S K AO1 R IY0 AA0 N Z -CORYPHAEUS K 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-CRISTOFORO K R IY0 S T OW0 F AO1 R OW0 -CRISTOVAL K R IY0 S T OW0 V AA1 L -CRISTY K R IH1 S T IY0 -CRISWELL K R IH1 S W EH0 L -CRIT K R IH1 T -CRITCH K R IH1 CH -CRITCHET K R IH1 CH AH0 T -CRITCHFIELD K R IH1 CH F IY0 L D -CRITCHLOW K R IH1 CH L OW0 -CRITCHLOW'S K R IH1 CH L OW0 Z -CRITERIA K R AY0 T IH1 R IY0 AH0 -CRITERION K R AY0 T IH1 R IY0 AH0 N -CRITERIONS K R AY0 T IH1 R IY0 AH0 N Z -CRITIAS K R IH1 T IY0 AH0 Z -CRITIC K R IH1 T IH0 K -CRITIC'S K R IH1 T IH0 K S -CRITICA K R IH1 T IH0 K AH0 -CRITICAL K R IH1 T IH0 K AH0 L -CRITICALLY K R IH1 T IH0 K AH0 L IY0 -CRITICALLY K R IH1 T IH0 K L IY0 -CRITICISE K R IH1 T IH0 S AY0 Z -CRITICISED K R IH1 T IH0 S AY2 Z D -CRITICISES K R IH1 T IH0 S AY2 Z IH0 Z -CRITICISING K R IH1 T IH0 S AY2 Z IH0 NG -CRITICISM K R IH1 T IH0 S IH2 Z AH0 M -CRITICISMS K R IH1 T IH0 S IH2 Z AH0 M Z -CRITICIZE K R IH1 T IH0 S AY2 Z -CRITICIZED K R IH1 T AH0 S AY2 Z D -CRITICIZES K R IH1 T IH0 S AY2 Z IH0 Z -CRITICIZING K R IH1 T IH0 S AY2 Z IH0 NG -CRITICK K R IH1 T IH0 K -CRITICKS K R IH1 T IH0 K S -CRITICS K R IH1 T IH0 K S -CRITIQUE K R AH0 T IY1 K -CRITIQUE K R IH0 T IY1 K -CRITIQUES K R IH0 T IY1 K S -CRITO K R IY1 T OW0 -CRITTENDEN K R IH0 T EH1 N D AH0 N -CRITTENDEN'S K R IH1 T AH0 N D AH0 N Z -CRITTENDENS K R IH1 T AH0 N D AH0 N Z -CRITTER K R IH1 T ER0 -CRITTER'S K R IH1 T ER0 Z -CRITTERS K R IH1 T ER0 Z -CRITTUR K R IH0 T ER0 -CRITTUR'S K R IH0 T ER0 Z -CRITTURS K R IH0 T ER0 Z -CRITZ K R IH1 T S -CRIVELLI K R IH0 V EH1 L IY0 -CRO K R OW1 -CROAGH K R OW1 AA2 G -CROAK K R OW1 K -CROAKED K R OW1 K T -CROAKER K R OW1 K ER0 -CROAKERS K R OW1 K ER0 Z -CROAKIN K R OW1 K IH0 N -CROAKING K R OW1 K IH0 NG -CROAKINGS K R OW1 K IH0 NG Z -CROAKS K R OW1 K S -CROAKY K R OW1 K IY0 -CROALE K R OW0 EY1 L -CROALE'S K R OW0 EY1 L Z -CROASAN K R OW0 EY1 Z AH0 N -CROAT K R OW1 AA2 T -CROATAN K R OW1 AA2 T AH0 N -CROATIA K R OW0 EY1 SH AH0 -CROATIAN K R OW0 EY1 SH AH0 N -CROATIANS K R OW0 EY1 SH AH0 N Z -CROATS K R OW1 AA2 T S -CROBBLE K R AA1 B AH0 L -CROC K R AA1 K -CROCANS K R AA1 K AH0 N Z -CROCE K R OW1 CH IY0 -CROCE'S K R OW1 CH IY0 Z -CROCHARD K R OW0 SH AA1 R D -CROCHARD'S K R AA1 CH ER0 D Z -CROCHE K R OW1 SH -CROCHET K R OW0 SH EY1 -CROCHETED K R OW0 SH EY1 D -CROCHETING K R OW0 SH EY1 IH0 NG -CROCHETS K R OW0 SH EY1 Z -CROCHETY K R OW2 SH AH0 T IY0 -CROCI K R OW1 S IY0 -CROCK K R AA1 K -CROCKED K R AA1 K T -CROCKER K R AA1 K ER0 -CROCKER'S K R AA1 K ER0 Z -CROCKERS K R AA1 K ER0 Z -CROCKERY K R AA1 K ER0 IY0 -CROCKERYWARE K R AA1 K ER0 IY0 W EH2 R -CROCKET K R AA1 K AH0 T -CROCKETED K R AA1 K AH0 T IH0 D -CROCKETS K R AA1 K AH0 T S -CROCKETT K R AA1 K IH0 T -CROCKETT'S K R AA1 K AH0 T S -CROCKFORD K R AA1 K F ER0 D -CROCKFORD'S K R AA1 K F ER0 D Z -CROCKS K R AA1 K S -CROCKSTON K R AA1 K S T AH0 N -CROCKY K R AA1 K IY0 -CROCODILE K R AA1 K AH0 D AY2 L -CROCODILE'S K R AA1 K AH0 D AY2 L Z -CROCODILES K R AA1 K AH0 D AY2 L Z -CROCODILIAN K R AA2 K AH0 D IH1 L Y AH0 N -CROCS K R AA1 K S -CROCUS K R OW1 K AH0 S -CROCUSES K R OW1 K Y UW0 S IH0 Z -CROESUS K R OW1 S AH0 S -CROESUS'S K R OW1 S AH0 S IH0 Z -CROESUSES K R OW1 S AH0 S IH0 Z -CROFF K R AO1 F -CROFFUT K R AA1 F AH0 -CROFT K R AO1 F T -CROFT'S K R AO1 F T S -CROFTANGRY K R AO1 F T AA0 NG G R IY0 -CROFTER K R AO1 F T ER0 -CROFTER'S K R AO1 F T ER0 Z -CROFTERS K R AO1 F T ER0 Z -CROFTLY K R AO1 F T L IY0 -CROFTON K R AA1 F T AH0 N -CROFTON'S K R AA1 F T AH0 N Z -CROFTONS K R AA1 F T AH0 N Z -CROFTS K R AO1 F T S -CROGHAN K R AA1 G AH0 N -CROGHAN'S K R AA1 G AH0 N Z -CROIA K R OW1 Y AH0 -CROIL K R OY1 L -CROIRE K W AA2 R -CROIS K W AA2 -CROISETTE K R AH0 Z EH1 T -CROISEZ K R OY1 Z EY2 -CROISIC K R OY1 Z IH0 K -CROISIER K R OY1 Z IY0 ER0 -CROISIER'S K R OY1 Z IY0 ER0 Z -CROISILLES K R OY1 S AY1 L Z -CROISNEL K R OY1 S N EH1 L -CROISSANT K R AH0 S AA1 N T -CROISSANT K W AA2 S AA1 N T -CROISSET K W AA2 S EH0 T -CROISSET'S K W AA2 S EH0 T S -CROIT K R OY1 T -CROIX K R OY1 -CROIX'S K R OY1 Z -CROIXILIA K R OY1 IY1 L IY0 AH0 -CROIXILIAN K R OY1 IH1 L Y AH0 N -CROIXILIANS K R OY1 IH1 L Y AH0 N Z -CROIXMARE K R OY1 M EH1 R -CROIZETTE K R AH0 Z EH1 T -CROJACK K R OW1 JH AE2 K -CROKE K R OW1 K -CROKER K R OW1 K ER0 -CROKER'S K R OW1 K ER0 Z -CROLL K R OW1 L -CROLY K R OW1 L IY0 -CROLY'S K R OW1 L IY0 Z -CROM K R AA1 M -CROMA K R OW1 M AH0 -CROMARTIE K R AA1 M AA0 R T IY0 -CROMARTY K R AA1 M AA0 R T IY0 -CROMBIE K R AA1 M B IY0 -CROMBIE'S K R AA1 M B IY0 Z -CROME K R OW1 M -CROMER K R OW1 M ER0 -CROMERING K R OW1 M ER0 IH0 NG -CROMFORD K R AA1 M F ER0 D -CROMLECH K R AA1 M L EH1 K -CROMLECHS K R AA1 M L EH1 K S -CROMMELIN K R AA1 M IH0 L IH0 N -CROMPTON K R AA1 M P T AH0 N -CROMPTON'S K R AA1 M P T AH0 N Z -CROMPTONS K R AA1 M P T AH0 N Z -CROMWEL K R AA1 M W AH0 L -CROMWELL K R AA1 M W AH0 L -CROMWELL'S K R AA1 M W IH0 L Z -CROMWELLIAN K R AA1 M W IH0 L IY0 AH0 N -CROMWELLIANS K R AA1 M W IH0 L Y AH0 N Z -CROMWELLS K R AA1 M W EH2 L Z -CRON K R AA1 N -CRONACA K R AA1 N AH0 K AH0 -CRONAN K R OW1 N AH0 N -CRONDALL K R AA1 N D AH0 L -CRONDALL'S K R AA1 N D AH0 L Z -CRONE K R OW1 N -CRONE'S K R OW1 N Z -CRONENBORG K R OW1 N AH0 N B AO0 R G -CRONES K R OW1 N Z -CRONICA K R AA1 N IH0 K AH0 -CRONIES K R OW1 N IY0 Z -CRONIN K R OW1 N IH0 N -CRONING K R OW1 N IH0 NG -CRONJE K R AA1 N JH -CRONJE'S K R AA1 N JH Z -CRONK K R AA1 NG K -CRONNIN K R AA1 N IH0 N -CRONOS K R OW1 N OW0 S -CRONSHAW K R AA1 N SH AO2 -CRONSHAW'S K R AA1 N SH AO1 Z -CRONSTADT K R AA1 N S T AE2 T -CRONUS K R OW1 N AH0 S -CRONY K R OW1 N IY0 -CRONY'S K R OW1 N IY0 Z -CRONYN K R AH0 N IH0 N -CROOD K R UW1 D -CROOD'S K R UW1 D Z -CROODLIN K R UH1 D L IH0 N -CROOK K R UH1 K -CROOK'D K R UH1 K D -CROOK'S K R UH1 K S -CROOKBACK K R UH1 K B AE2 K -CROOKE K R UH1 K -CROOKED K R UH1 K AH0 D -CROOKEDER K R UH1 K AH0 D ER0 -CROOKEDEST K R UH1 K AH0 D AH0 S T -CROOKEDLY K R UH1 K AH0 D L IY0 -CROOKEDNESS K R UH1 K AH0 D N AH0 S -CROOKENDEN K R UH1 K EH2 N D AH0 N -CROOKER K R UH1 K ER0 -CROOKES K R UH1 K S -CROOKHORN K R UH1 K HH AO2 R N -CROOKING K R UH1 K IH0 NG -CROOKIT K R UH1 K IH0 T -CROOKLEG K R UH1 K L EH2 G -CROOKLYN K R UH1 K L IH2 N -CROOKS K R UH1 K S -CROOKS'S K R UH1 K S IH0 Z -CROOKSHANK K R UH1 K SH AE2 NG K -CROOKSHANKS K R UH1 K SH AE2 NG K S -CROOL K R UW1 L -CROOM K R UW1 M -CROOME K R UW1 M -CROON K R UW1 N -CROONAH K R UW1 N AH0 -CROONED K R UW1 N D -CROONING K R UW1 N IH0 NG -CROONINGS K R UW1 N IH0 NG Z -CROONS K R UW1 N Z -CROOP K R UW1 P -CROOPER K R UW1 P ER0 -CROP K R AA1 P -CROPE K R OW1 P -CROPP'D K R AA1 P D -CROPPED K R AA1 P T -CROPPER K R AA1 P ER0 -CROPPERS K R AA1 P ER0 Z -CROPPIES K R AA1 P IY0 Z -CROPPING K R AA1 P IH0 NG -CROPPINGS K R AA1 P IH0 NG Z -CROPPO K R AA1 P OW0 -CROPPY K R AA1 P IY0 -CROPS K R AA1 P S -CROPSEY K R AA1 P S IY0 -CROPT K R AA1 P T -CROQUART K R OW0 K AO1 R T -CROQUE K R OW0 K -CROQUEMITAINE K R OW0 K M IH0 T EY2 N -CROQUET K R OW0 K EY1 -CROQUETS K R OW0 K EY1 Z -CROQUETTE K R OW0 K EH1 T -CROQUETTES K R OW0 K EH1 T S -CRORE K R AO1 R -CRORES K R AO1 R Z -CRORIDGE K R AA1 R IH0 JH -CROS K R AO1 S -CROSBEIGH K R AA1 S B AY0 -CROSBER K R AA1 S B ER0 -CROSBEY K R AA1 S B IY0 -CROSBIE K R AO1 Z B IY0 -CROSBIE'S K R AO1 Z B IY0 Z -CROSBY K R AO1 Z B IY0 -CROSBY'S K R AO1 Z B IY0 Z -CROSBYS K R AO1 Z B IY0 Z -CROSFIELD K R AO1 Z F IY2 L D -CROSIER K R OW1 Z IY0 ER0 -CROSIERS K R OW1 SH IY0 ER0 Z -CROSLAND K R AA1 S L AH0 N D -CROSNE K R AA1 S N -CROSS K R AO1 S -CROSS'D K R AA1 S D -CROSS'S K R AO1 S IH0 Z -CROSSAN K R AA1 S AH0 N -CROSSBAR K R AO1 S B AA2 R -CROSSBARS K R AO1 S B AA2 R Z -CROSSBEAM K R AO1 S B IY2 M -CROSSBEAMS K R AO1 S B IY1 M Z -CROSSBILL K R AO1 S B IH2 L -CROSSBILLS K R AO1 S B IH2 L Z -CROSSBONES K R AO1 S B OW2 N Z -CROSSBOW K R AO1 S B OW2 -CROSSBOWMEN K R AO1 S B OW2 M AH0 N -CROSSBOWS K R AO1 S B OW2 Z -CROSSCRAB K R AO1 S K R AE1 B -CROSSCUT K R AO1 S K T -CROSSE K R AA1 S -CROSSED K R AO1 S T -CROSSER K R AO1 S ER0 -CROSSES K R AO1 S IH0 Z -CROSSEST K R AO1 S AH0 S T -CROSSETT K R AA1 S IH0 T -CROSSFIELD K R AO1 S F IY2 L D -CROSSFIRE K R AO1 S F AY0 R -CROSSFIRE K R AO1 S F AY2 R -CROSSI K R AO1 S IY0 -CROSSIN K R AA1 S IH0 N -CROSSING K R AO1 S IH0 NG -CROSSINGS K R AO1 S IH0 NG Z -CROSSJAY K R AO1 S JH EY0 -CROSSJAY'S K R AO1 S JH EY0 Z -CROSSKEY K R AO1 S K IY0 -CROSSLAND K R AO1 S L AE2 N D -CROSSLANDS K R AO1 S L AE2 N D Z -CROSSLEGGED K R AA1 S L EH1 G D -CROSSLEY K R AA1 S L IY0 -CROSSLEY'S K R AA1 S L IY0 Z -CROSSLEYS K R AA1 S L IY0 Z -CROSSLY K R AA1 S L IY0 -CROSSMAN K R AO1 S M AH0 N -CROSSNESS K R AA1 S N AH0 S -CROSSON K R AA1 S AH0 N -CROSSPATCH K R AO1 S P AE2 CH -CROSSPIECE K R AO1 S P IY2 S -CROSSPIECES K R AO1 S P IY2 S AH0 Z -CROSSPIECES K R AO1 S P IY2 S IH0 Z -CROSSROAD K R AO1 S R OW2 D -CROSSROADS K R AO1 S R OW2 D Z -CROSSTHWAITE K R AO1 S TH W EY2 T -CROSSTOWN K R AO1 S T AW2 N -CROSSTREES K R AO1 S T R IY1 Z -CROSSWATER K R AA1 S W AA2 T ER0 -CROSSWAY K R AO1 S W EY2 -CROSSWAYS K R AO1 S W EY2 Z -CROSSWISE K R AO1 S W AY2 Z -CROST K R AA1 S T -CROSTHWAITE K R AA1 S TH W AH0 T -CROSWELL K R AA1 S W EH0 L -CROTALUS K R AA1 T AH0 L AH0 S -CROTCH K R AA1 CH -CROTCHED K R AA1 CH T -CROTCHES K R AA1 CH IH0 Z -CROTCHET K R AA1 CH AH0 T -CROTCHETS K R AA1 CH AH0 T S -CROTCHETTY K R AA1 CH IH0 T IY0 -CROTCHETY K R AA1 CH AH0 T IY0 -CROTHERS K R AH1 DH ER0 Z -CROTIN K R AA1 T IH0 N -CROTON K R OW1 T AH0 N -CROTONA K R OW1 T AH0 N AH0 -CROTONS K R OW1 T AH0 N Z -CROTOY K R AA1 T OY0 -CROTTAT K R AA1 T AA1 T -CROTTY K R AA1 T IY0 -CROTUS K R OW1 T AH0 S -CROUBEL K R UW1 B AH0 L -CROUCH K R AW1 CH -CROUCH'D K R AW1 CH D -CROUCH'S K R AW1 CH IH0 Z -CROUCHED K R AW1 CH T -CROUCHER K R AW1 CH ER0 -CROUCHES K R AW1 CH IH0 Z -CROUCHIN K R AW1 CH IH0 N -CROUCHING K R AW1 CH IH0 NG -CROUCHINGLY K R AW1 CH IH0 NG L IY0 -CROUD K R AW1 D -CROUDED K R AW1 D IH0 D -CROUDS K R AW1 D Z -CROUDY K R AW1 D IY0 -CROUP K R UW1 P -CROUPE K R UW1 P -CROUPIER K R UW1 P IY0 ER0 -CROUPIER'S K R UW1 P IY0 ER0 Z -CROUPIERS K R UW1 P IY0 ER0 Z -CROUPS K R UW1 P S -CROUPY K R UW1 P IY0 -CROUSAZ K R AW1 S AE2 Z -CROUSE K R AW1 S -CROUSTADE K R AW1 S T EY1 D -CROUSTILLAC K R AW1 S T IY1 AH0 K -CROUSTILLAC'S K R AW1 S T IY1 AH0 K S -CROUT K R AW1 T -CROUTE K R AW1 T -CROUTEF K R AW1 T F -CROUTHA K R AW1 TH AH0 -CROUTONS K R AW1 T AH0 N Z -CROUY K R AA1 AY0 -CROW K R OW1 -CROW'D K R AW1 D -CROW'S K R OW1 Z -CROWBAIT K R OW1 B EY1 T -CROWBAR K R OW1 B AA2 R -CROWBARS K R OW1 B AA2 R Z -CROWBOROUGH K R OW1 B ER0 OW0 -CROWD K R AW1 D -CROWD'S K R AW1 D Z -CROWDED K R AW1 D AH0 D -CROWDED K R AW1 D IH0 D -CROWDEN K R AW1 D AH0 N -CROWDER K R AW1 D ER0 -CROWDER'S K R AW1 D ER0 Z -CROWDEY K R AW1 D IY0 -CROWDIE K R AW1 D IY0 -CROWDIN K R AW1 D IY0 N -CROWDING K R AW1 D IH0 NG -CROWDS K R AW1 D Z -CROWDY K R AW1 D IY0 -CROWE K R OW1 -CROWE'S K R OW1 Z -CROWED K R OW1 D -CROWELL K R OW1 AH0 L -CROWEN K R OW1 AH0 N -CROWER K R OW1 ER0 -CROWFIELD K R OW1 F IY2 L D -CROWFOOT K R OW1 F UH2 T -CROWFOOT'S K R OW1 F UH2 T S -CROWHEART K R AW1 HH AA2 R T -CROWHEART'S K R AW1 HH AA1 R T S -CROWHURST K R OW1 HH ER0 S T -CROWIN K R OW1 IH0 N -CROWING K R OW1 IH0 NG -CROWINGS K R OW1 IH0 NG Z -CROWL K R AW1 L -CROWL'S K R AW1 L Z -CROWLAND K R OW1 L AH0 N D -CROWLE K R AW1 L -CROWLEIGH K R AW1 L AH0 -CROWLEY K R AW1 L IY0 -CROWLEY'S K R AW1 L IY0 Z -CROWN K R AW1 N -CROWN'D K R AW1 N D -CROWN'S K R AW1 N Z -CROWN'ST K R AW1 N S T -CROWNE K R AW1 N -CROWNED K R AW1 N D -CROWNER K R AW1 N ER0 -CROWNER'S K R AW1 N ER0 Z -CROWNES K R AW1 N Z -CROWNETH K R AW1 N IH0 TH -CROWNHEART K R AW1 N HH AA2 R T -CROWNING K R AW1 N IH0 NG -CROWNINGSHIELD K R AW1 N IH0 NG SH IY2 L D -CROWNINSHIELD K R AW1 N IH0 N SH IY2 L D -CROWNLANDS K R AW1 N L AE2 N D Z -CROWNLESS K R AW1 N L AH0 S -CROWNS K R AW1 N Z -CROWNWALL K R AW1 N W AO0 L -CROWQUILL K R AW1 K IY1 L -CROWS K R OW1 Z -CROWS' K R OW1 Z -CROWSFEATHER K R AW1 S F EH1 DH ER0 -CROWSFEET K R AW1 S F IY1 T -CROWSFOOT K R AW1 S F UH2 T -CROWSWOOD K R AW1 S W UH2 D -CROWTHER K R AW1 DH ER0 -CROXALL K R AA1 K S AO2 L -CROXLEY K R AA1 K S L IY0 -CROXTON K R AA1 K S T AH0 N -CROY K R OY1 -CROYANT K R OY1 AH0 N T -CROYDEN K R OY1 D AH0 N -CROYDEN'S K R OY1 D AH0 N Z -CROYDON K R OY1 D AH0 N -CROYE K R OY1 -CROYEZ K R OY1 EH0 Z -CROYLAND K R OY1 L AH0 N D -CROYLE K R OY1 L -CROYLE'S K R OY1 L Z -CROZ K R AA1 Z -CROZAT K R OW1 Z AE0 T -CROZER K R OW1 Z ER0 -CROZIER K R OW1 ZH ER0 -CROZIER'S K R OW1 ZH ER0 Z -CROZIERS K R OW1 ZH ER0 Z -CROZON K R OW1 Z AH0 N -CRSUS K R S AH0 S -CRU K R UW1 -CRUA K R UW1 AH0 -CRUACH K R UW1 AE0 K -CRUACHAN K R UW0 AE1 CH AH0 N -CRUACHMAA K R UW0 AE1 K M AH0 -CRUAGAN K R UW0 EY1 G AH0 N -CRUCE K R UW1 S -CRUCEM K R UW1 S AH0 M -CRUCES K R UW1 S IY0 Z -CRUCHE K R AH1 CH -CRUCHO K R AH1 CH OW0 -CRUCHOT K R UW1 K AH0 T -CRUCHOTS K R UW1 K AA1 T S -CRUCIAL K R UW1 SH AH0 L -CRUCIAN K R UW1 SH AH0 N -CRUCIANS K R UW1 SH AH0 N Z -CRUCIBLE K R UW1 S AH0 B AH0 L -CRUCIBLES K R UW1 S AH0 B AH0 L Z -CRUCIFIED K R UW1 S AH0 F AY2 D -CRUCIFIERS K R UW1 S AH0 F AY2 ER0 Z -CRUCIFIES K R UW1 S AH0 F AY2 Z -CRUCIFIX K R UW1 S AH0 F 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D EY1 N AH0 -DANA'S D EY1 N AH0 Z -DANAAN D EY1 N AH0 N -DANAANS D EY1 N AH0 N Z -DANAE D AE1 N EY2 -DANAE'S D AE2 N AH0 Z -DANAID D EY1 N EY2 D -DANAIDES D EY1 N AY2 D Z -DANAIDS D EY1 N EY1 D Z -DANAKIL D AE1 N AH0 K IY1 L -DANANN D AE1 N AE0 N -DANAOS D AH0 N EY1 OW0 S -DANAUS D EY1 N AO1 Z -DANAVAS D AA0 N AA1 V AH0 S -DANBRIDGE D AE1 N B R IH2 JH -DANBURY D AE1 N B ER0 IY0 -DANBY D AE1 N B IY0 -DANBY'S D AE1 N B IY0 Z -DANC'D D AE2 NG K D -DANCAIRE D AE1 N K EH1 R -DANCE D AE1 N S -DANCE'S D AE1 N S IH0 Z -DANCED D AE1 N S T -DANCEING D AE1 N S IH0 NG -DANCER D AE1 N S ER0 -DANCER'S D AE1 N S ER0 Z -DANCERS D AE1 N S ER0 Z -DANCES D AE1 N S AH0 Z -DANCES D AE1 N S IH0 Z -DANCETH D AE1 N S IH0 TH -DANCEY D AE1 N S IY0 -DANCIN D AE1 N S IH0 N -DANCIN' D AE1 N S IH0 N -DANCING D AE1 N S IH0 NG -DANCING'S D AE1 N S IH0 NG Z -DANCINGS D AE1 N S IH0 NG Z -DANCLA D AE1 N K L AH0 -DANCOX D AE1 N K AA0 K S -DANCY D AE1 N S IY0 -DANCY'S D AE1 N S IY0 Z -DAND D AE1 N D -DANDALOO D AE1 N D AH0 L UW2 -DANDELINE D AE1 N D AH0 L AY2 N -DANDELION D AE1 N D AH0 L AY2 AH0 N -DANDELIONS D AE1 N D AH0 L AY2 AH0 N Z -DANDENONG D AE1 N D AH0 N AO2 NG -DANDER D AE1 N D ER0 -DANDIACAL D AE1 N D AY1 AH0 K AH0 L -DANDIE D AE1 N D IY0 -DANDIES D AE1 N D IY0 Z -DANDIEST D AE1 N D IY0 AH0 S T -DANDIFIED D AE1 N D IH0 F AY2 D -DANDIN D AE1 N D IH0 N -DANDLE D AE1 N D AH0 L -DANDLED D AE1 N D AH0 L D -DANDLES D AE1 N D AH0 L Z -DANDLING D AE1 N D L IH0 NG -DANDO D AE1 N D OW0 -DANDOINS D AE1 N D OY1 N Z -DANDOLO D AE1 N D OW0 L OW0 -DANDRIDGE D AE1 N D R IH0 JH -DANDRIK D AE1 N D R IH0 K -DANDRUFF D AE1 N D R AH0 F -DANDTAN D AE1 N D T AE2 N -DANDTAN'S D AE1 N D T AE2 N Z -DANDY D AE1 N D IY0 -DANDY'S D AE1 N D IY0 Z -DANDYFIED D AE1 N D IY0 F AY2 D -DANDYISH D AE1 N D IY0 IH2 SH -DANDYISM D AE1 N D IY0 IH2 Z AH0 M -DANE D EY1 N -DANE'S D EY1 N Z -DANEFORD D AE1 N IH0 F ER0 D -DANEGRE D AH0 N EH1 G ER0 -DANEL D AE1 N AH0 L -DANELAGH D AE1 N IH0 L AH0 -DANES D EY1 N Z -DANESBURY D EY1 N Z B EH2 R IY0 -DANESBURY'S D EY1 N Z B EH2 R IY0 Z -DANESFORD D EY1 N Z F ER0 D -DANESH D AE1 N IH0 SH -DANESLY D AH0 N IY1 Z L IY0 -DANEY D EY1 N IY0 -DANEY'S D EY1 N IY0 Z -DANFORD D AE1 N F ER0 D -DANFORTH D AE1 N F ER0 TH -DANFORTH'S D AE1 N F ER0 TH S -DANFORTHS D AE1 N F ER0 TH S -DANG D AE1 NG -DANG'ROUS D AE1 NG R AH0 S -DANGAN D AE1 NG G AH0 N -DANGDEST D AE1 NG D AH0 S T -DANGEAU D EY1 NG G OW1 -DANGEAU'S D EY1 N JH OW1 Z -DANGED D EY1 N JH D -DANGER D EY1 N JH ER0 -DANGER'S D EY1 N JH ER0 Z -DANGEREUSES D EY1 N JH ER0 EY1 AH0 S IH0 Z -DANGEREUX D EY1 N JH ER0 OW0 -DANGERFIELD D EY1 N JH ER0 F IY2 L D -DANGERFIELD'S D EY1 N JH ER0 F IY2 L D Z -DANGEROUS D EY1 N JH ER0 AH0 S -DANGEROUSLY D EY1 N JH ER0 AH0 S L IY0 -DANGEROUSNESS D EY1 N JH ER0 AH0 S N AH0 S -DANGERS D EY1 N JH ER0 Z -DANGERSOME D AE1 NG G ER0 S AH0 M -DANGLAR D AE1 NG G L ER0 -DANGLAR'S D AE1 NG G L ER0 Z -DANGLARS D AE1 NG G L ER0 Z -DANGLE D AE1 NG G AH0 L -DANGLED D AE1 NG G AH0 L D -DANGLER D AE1 NG G AH0 L ER0 -DANGLER D AE1 NG G L ER0 -DANGLERS D AE1 NG G AH0 L ER0 Z -DANGLES D AE1 NG G AH0 L Z -DANGLIN D AE1 NG G L IH0 N -DANGLING D AE1 NG G AH0 L IH0 NG -DANGLING D AE1 NG G L IH0 NG -DANGLOSS D AE1 NG G L AA2 S -DANGO D AE1 NG G OW0 -DANHASCH D AE1 N HH AE1 SH -DANICAN D AE1 N AH0 K AH0 N -DANIDOFF D EY1 N IH0 D AO0 F -DANIEL D AE1 N Y AH0 L -DANIEL'S D AE1 N Y AH0 L Z -DANIELE D AE0 N Y EH1 L -DANIELI D AE0 N Y EH1 L IY0 -DANIELL D AE1 N IY0 L -DANIELLA D AE2 N Y EH1 L AH0 -DANIELLE D AE2 N IY0 EH1 L -DANIELLE D AE2 N Y EH1 L -DANIELS D AE1 N Y AH0 L Z -DANIELSON D AE1 N Y AH0 L S AH0 N -DANIIL D AH0 N IY1 L -DANILO D AE1 N AH0 L OW2 -DANILOVITCH D AE1 N AH0 L AA1 V IH0 CH -DANIRA D AH0 N R AH0 -DANIRA'S D AH0 N ER0 AH0 Z -DANISH D EY1 N IH0 SH -DANITE D AE1 N AH0 T -DANITES D AH0 N AY2 T S -DANIVER D AE1 N AH0 V ER0 -DANJURO D AE1 N JH UW0 R OW0 -DANJURO'S D AE1 N JH UW0 R OW0 Z -DANK D AE1 NG K -DANKALI D AE1 NG K AA1 L IY0 -DANKE D AE1 NG K -DANKLY D AE1 NG K L IY0 -DANKMERE D AE1 NG K M IH2 R -DANKNESS D AE1 NG K N AH0 S -DANKO D AE1 NG K OW0 -DANKS D AE1 NG K S -DANKSHIRE D AE1 NG K SH AY2 R -DANKVILLE D AE1 NG K V IH2 L -DANKWART D AE1 NG K W AO0 R T -DANLEY D AE1 N L IY0 -DANMOUTH D AE1 N M AH0 TH -DANN D AE1 N -DANNA D AE1 N AH0 -DANNEBROG D AE1 N AH0 B R AH0 G -DANNEL D AE1 N AH0 L -DANNER D AE1 N ER0 -DANNER'S D AE1 N ER0 Z -DANNIE D AE1 N IY0 -DANNISBURGH D AE1 N IH0 S B ER0 G -DANNISBURGH'S D AE1 N IH0 S B ER0 G Z -DANNUX D AA0 N UW1 -DANNY D AE1 N IY0 -DANNY'S D AE1 N IY0 Z -DANO D AA1 N OW0 -DANS D AE1 N Z -DANSANT D AE1 N S AH0 N T -DANSANTS D AE1 N S AH0 N T S -DANSBURG D AE1 N S B ER0 G -DANSE D AE1 N S -DANSER D AE1 N S ER0 -DANSEUSE D AE1 N S UW1 S -DANSEUSES D AE1 N S IY0 AH0 S IH0 Z -DANSEY D AE1 N S IY0 -DANSKE D AE1 N S K -DANSON D AE1 N S AH0 N -DANSON'S D AE1 N S AH0 N Z -DANSONS D AE1 N S AH0 N Z -DANSTER D AE1 N S T ER0 -DANSVILLE D AE1 N Z V IH0 L -DANT D AE1 N T -DANTAN D AE1 N T AH0 N -DANTE D AA1 N T EY0 -DANTE'S D AE1 N T IY0 Z -DANTEAN D AE1 N T IY0 AH0 N -DANTERRE D AE1 N T ER0 -DANTERRE'S D AE1 N T ER0 Z -DANTES D AE1 N T IY0 Z -DANTESQUE D AA0 N T EH1 S K -DANTIN D AE1 N T IH0 N -DANTIN'S D AE1 N T AH0 N Z -DANTON D AE1 N T AH0 N -DANTON'S D AE1 N T AH0 N Z -DANTONISTS D AE1 N T AH0 N IH0 S T S -DANTONS D AE1 N T AH0 N Z -DANTOR D AE1 N T ER0 -DANTZIC D AE1 N T S IH0 CH -DANTZIG D AE1 N T Z IH0 G -DANTZIGERS D AE1 N T Z IH0 G ER0 Z -DANU D AA1 N UW0 -DANUBE D AE1 N Y UW0 B -DANUBE'S D AE1 N Y UW0 B Z -DANUBIAN D AE1 N Y UW1 B IY0 AH0 N -DANUSCO D AE1 N Y AH0 S K OW0 -DANUSIA D AE1 N Y UW0 S IY0 AH0 -DANUSIA'S D AE1 N Y UW1 Z IY0 AH0 Z -DANUSKA D EY1 N AH0 S K AH0 -DANUTA D AA0 N UW1 T AH0 -DANVELD D AE1 N V EH2 L D -DANVELD'S D AE1 N V EH2 L D Z -DANVELT D AE1 N V EH2 L T -DANVER D AE1 N V ER0 -DANVERS D AE1 N V ER0 Z -DANVERS'S D AE1 N V ER0 S IH0 Z -DANVILLE D AE1 N V IH2 L -DANVILLE'S D AE1 N V IH0 L Z -DANYERS D AE1 N Y ER0 Z -DANYUL D AE2 N IH0 UH2 L -DANZA D AE1 N Z AH0 -DANZIG D AE1 N Z IH0 G -DANZON D AE1 N Z AO1 N -DAON'T D AA0 OW1 N T -DAOUD D AW1 D -DAOULAS D AW1 L AH0 Z -DAOWN D AA0 OW1 N -DAP D AH0 P -DAPH D AE1 F -DAPHNE D AE1 F N IY0 -DAPHNE'S D AE1 F N IY0 Z -DAPHNES D AE1 F N IY0 Z -DAPHNIS D AE1 F N AH0 S -DAPICHO D AA1 P IH0 CH OW0 -DAPITAN D AA0 P IY0 T AA1 N -DAPPER D AE1 P ER0 -DAPPLE D AE1 P AH0 L -DAPPLED D AE1 P AH0 L D -DAPPLEGRIM D AE1 P AH0 L G R IH0 M -DAPPLEMERE D AE1 P L AH0 M EH2 R -DAPPLES D AE1 P AH0 L Z -DAPPLING D AE1 P L IH0 NG -DAPSUL D AE1 P S AH0 L -DAR D AA1 R -DAR'D D AA2 D -DAR'LL D AA1 R AH0 L -DAR'N'T D AA2 AH0 N T -DAR'S D AA1 R Z -DAR'ST D ER0 S T -DAR'TER D AA2 T ER0 -DARA D AE1 R AH0 -DARA'S D EH1 R AH0 Z -DARAGH D AE1 R AH0 G -DARAGH'S D AE1 R AH0 Z -DARAI D AA1 R EY0 -DARANTE D AA0 R AA1 N T IY0 -DARB D AA1 R B -DARBAR D AA1 R B AA2 R -DARBEY D AA1 R B IY0 -DARBIE D AA1 R B IY0 -DARBIES D AA1 R B IY0 Z -DARBOR D AA1 R B ER0 -DARBY D AA1 R B IY0 -DARBY'S D AA1 R B IY0 Z -DARBYSHIRE D AA1 R B IH0 SH AY2 R -DARBYVILLE D AA1 R B IY0 V IH2 L -DARC D AA1 R K -DARCEY D AA1 R S IY0 -DARCH D AA1 R K -DARCIEUX D AA1 R K IY0 OW0 -DARCY D AA1 R S IY0 -DARCY'S D AA1 R S IY0 Z -DARCYS D AA1 R S IY0 Z -DARD D AA1 R D -DARD'S D AA1 R D Z -DARDALE D AA1 R D EY2 L -DARDAN D AA1 R D AH0 N -DARDANELLES D AA1 R D AH0 N EH1 L Z -DARDANELOV D AA1 R D AE1 N AH0 L AA0 V -DARDANIAN D AA1 R D EY1 N IY0 AH0 N -DARDANIANS D AA1 R D EY1 N IY0 AH0 N Z -DARDANUS D AA1 R D EY1 N AH0 S -DARDAVAN D AA0 R D AA1 V AH0 N -DARDINEL D AA0 R D IY0 N EH1 L -DARDONVILLE D AA0 R D OW1 N V IH0 L -DARDURR D AA1 R D ER1 -DARDUS D AA1 R D AH0 S -DARE D EH1 R -DARE'S D EH1 R Z -DARED D EH1 R D -DAREDEVIL D EH1 R D EH2 V AH0 L -DAREDEVILS D EH1 R D EH2 V AH0 L Z -DARELL D AA0 R EH1 L -DAREM D AE1 R AH0 M -DAREN'T D AE1 R AH0 N T -DARENA D AE1 R IH0 N AH0 -DARENA'S D AE1 R IH0 N AH0 Z -DAREPORT D AE1 R IH0 P AO1 R T -DARES D EH1 R Z -DARES'S D EH1 R S IH0 Z -DARESAY D EH1 R S EY0 -DARESN'T D EH1 R Z AH0 N T -DAREST D AE1 R AH0 S T -DARETH D AE1 R IH0 TH -DAREVILLE D EH1 R V IH2 L -DAREWELL D EH1 R W EH2 L -DARF D AA1 R F -DARFOUR D AA2 F AO0 R -DARFUR D AA2 F UW1 R -DARG D AA1 R G -DARGAI D AA1 R G AY1 -DARGAN D AA1 R G AH0 N -DARGENTAL D AA1 R JH EH1 N T AH0 L -DARGENTAL'S D AA1 R JH EH1 N T AH0 L Z -DARGLE D AA1 R G AH0 L -DARI D AA1 R IY0 -DARIA D AA1 R IY0 AH0 -DARIAN D EH1 R IY0 AH0 N -DARIANS D EH1 R IY0 AH0 N Z -DARIC D AE1 R IH0 K -DARICS D AE1 R IH0 K S -DARIEL D EH1 R IY0 AH0 L -DARIEL'S D EH1 R IY0 AH0 L Z -DARIEN D EH1 R IY0 AH0 N -DARIN D AE1 R IH0 N -DARING D EH1 R IH0 NG -DARINGLY D EH1 R IH0 NG L IY0 -DARINGS D EH1 R IH0 NG Z -DARIO D EH1 R IY0 OW2 -DARIO'S D AA1 R IY0 OW0 Z -DARION D EH1 R IY0 AH0 N -DARIUS D ER0 AY1 AH0 S -DARIUS'S D ER0 AY1 AH0 S IH0 Z -DARJEELING D AA2 R JH IY1 L IH0 NG -DARJEELING D AA2 R ZH IY1 L IH0 NG -DARJILING D AA2 R ZH L IH0 NG -DARK D AA1 R K -DARK'NING D AA1 R K AH0 N IH0 NG -DARK'S D AA1 R K S -DARKE D AA1 R K -DARKEN D AA1 R K AH0 N -DARKEN'D D AA1 R K AH0 N D -DARKENED D AA1 R K AH0 N D -DARKENING D AA1 R K AH0 N IH0 NG -DARKENING D AA1 R K N IH0 NG -DARKENS D AA1 R K AH0 N Z -DARKER D AA1 R K ER0 -DARKEST D AA1 R K AH0 S T -DARKEY D AA1 R K IY0 -DARKEY'S D AA1 R K IY2 Z -DARKEYE D AA1 R K AY2 -DARKEYS D AA1 R K IY0 Z -DARKHAN D AA1 R K HH AE0 N -DARKIE D AA1 R K IY0 -DARKIE'S D AA1 R K IY0 Z -DARKIES D AA1 R K IY0 Z -DARKINGHAM D AA1 R K IH0 NG HH AE2 M -DARKISH D AA1 R K IH0 SH -DARKLE D AA1 R K AH0 L -DARKLING D AA1 R K L IH0 NG -DARKLY D AA1 R K L IY0 -DARKNESS D AA1 R K N AH0 S -DARKNESSE D AA1 R K N IH0 S -DARKNESSES D AA1 R K N AH0 S IH0 Z -DARKNIGHT D AA1 R K N AY2 T -DARKOVAN D AA1 R K AH0 V AH0 N -DARKOVANS D AA1 R K AH0 V AH0 N Z -DARKOVER D AA1 R K AH0 V ER0 -DARKROOM D AA1 R K R UW2 M -DARKS D AA1 R K S -DARKSOME D AA1 R K S AH0 M -DARKUSH D AA1 R K UH1 SH -DARKY D AA1 R K IY0 -DARKY'S D AA1 R K IY0 Z -DARL D AA1 R L -DARL'S D AA1 R L Z -DARLES D AA1 R L Z -DARLETON D AA1 R L T AH0 N -DARLEY D AA1 R L IY0 -DARLEY'S D AA1 R L IY0 Z -DARLFORD D AA1 R L F ER0 D -DARLIN D AA1 R L IH0 N -DARLIN' D AA1 R L IH0 N -DARLIN'S D AA1 R L IH0 N Z -DARLING D AA1 R L IH0 NG -DARLING'S D AA1 R L IH0 NG Z -DARLINGEST D AA1 R L IH0 NG AH0 S T -DARLINGFORD D AA1 R L IH0 NG F ER0 D -DARLINGS D AA1 R L IH0 NG Z -DARLINGTON D AA1 R L IH0 NG T AH0 N -DARLINGTON'S D AA1 R L IH0 NG T AH0 N Z -DARLINK D AA1 R L IH2 NG K -DARLINS D AA1 R L AH0 N Z -DARLINT D AA1 R L IH0 N T -DARLINTS D AA1 R L IH0 N T S -DARLOT D AA1 R L AH0 T -DARMESTETER D AA1 R M IH0 S T T ER0 -DARMSTADT D AA1 R M S T AE2 T -DARN D AA1 R N -DARN'D D AA1 R N D -DARN'T D AA1 R N T -DARNA D AA1 R N AH0 -DARNATION D AA1 R N EY1 SH AH0 N -DARNAY D AA1 R N EY1 -DARNBOROUGH D AA1 R N B ER0 OW0 -DARNCE D AA1 R N S -DARNDEST D AA1 R N D AH0 S T -DARNED D AA1 R N D -DARNEDEST D AA1 R N D EH0 S T -DARNEDEST D AA1 R N EH0 S T -DARNEL D AA1 R N AH0 L -DARNEL'S D AA1 R N AH0 L Z -DARNELL D AA0 R N EH1 L -DARNER D AA1 R N ER0 -DARNFORD D AA1 R N F ER0 D -DARNFORD'S D AA1 R N F ER0 D Z -DARNICK D AA1 R N 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-DISBELIEVED D IH0 S B AH0 L IY1 V D -DISBELIEVER D IH0 S B AH0 L IY1 V ER0 -DISBELIEVERS D IH0 S B AH0 L IY1 V ER0 Z -DISBELIEVES D IH0 S B AH0 L IY1 V Z -DISBELIEVING D IH0 S B AH0 L IY1 V IH0 NG -DISBELIEVINGLY D IH0 S B AH0 L IY1 V IH0 NG L IY0 -DISBROW D IH1 S B R AW0 -DISBURDEN D IH0 S B ER1 D AH0 N -DISBURDENED D IH0 S B ER1 D AH0 N D -DISBURDENING D IH0 S B ER1 D AH0 N IH0 NG -DISBURSE D IH0 S B ER1 S -DISBURSED D IH0 S B ER1 S T -DISBURSEMENT D IH0 S B ER1 S M AH0 N T -DISBURSEMENTS D IH0 S B ER1 S M AH0 N T S -DISBURSING D IH0 S B ER1 S IH0 NG -DISBURTHEN D IH0 S B ER1 TH AH0 N -DISBURTHENED D IH0 S B ER1 TH AH0 N D -DISC D IH1 S K -DISCALCED D IH0 S K AE1 L S T -DISCARD D IH0 S K AA1 R D -DISCARDED D IH0 S K AA1 R D IH0 D -DISCARDING D IH0 S K AA1 R D IH0 NG -DISCARDS D IH0 S K AA1 R D Z -DISCARNATE D IH0 S K AA1 R N EY2 T -DISCARNATED D IH0 S K AA1 R N EY2 T IH0 D -DISCARNATION D IH0 S K AA1 R N EY1 SH AH0 N -DISCE D IH0 S -DISCERN D IH0 S ER1 N -DISCERN'D D IH0 S ER1 N D 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IH0 P L IH0 N EH1 R IY0 AH0 N Z -DISCIPLINARY D IH1 S AH0 P L AH0 N EH2 R IY0 -DISCIPLINE D IH1 S AH0 P L AH0 N -DISCIPLINE'S D IH1 S AH0 P L AH0 N Z -DISCIPLINED D IH1 S AH0 P L AH0 N D -DISCIPLINES D IH1 S AH0 P L AH0 N Z -DISCIPLINING D IH1 S AH0 P L AH0 N IH0 NG -DISCLAIM D IH0 S K L EY1 M -DISCLAIMED D IH0 S K L EY1 M D -DISCLAIMER D IH0 S K L EY1 M ER0 -DISCLAIMERS D IH0 S K L EY1 M ER0 Z -DISCLAIMING D IH0 S K L EY1 M IH0 NG -DISCLAIMS D IH0 S K L EY1 M Z -DISCLOS'D D IH0 S K L OW1 Z D -DISCLOSE D IH0 S K L OW1 Z -DISCLOSED D IH0 S K L OW1 Z D -DISCLOSES D IH0 S K L OW1 Z IH0 Z -DISCLOSING D IH0 S K L OW1 Z IH0 NG -DISCLOSURE D IH0 S K L OW1 ZH ER0 -DISCLOSURES D IH0 S K L OW1 ZH ER0 Z -DISCO D IH1 S K OW0 -DISCO'S D IH1 S K OW0 Z -DISCOBOLUS D IH2 S K AH0 B OW1 L AH0 S -DISCOIDAL D IH0 S K OY1 D AH0 L -DISCOLOR D IH0 S K AH1 L ER0 -DISCOLORATION D IH0 S K AH2 L ER0 EY1 SH AH0 N -DISCOLORATIONS D IH0 S K AH2 L ER0 EY1 SH AH0 N Z -DISCOLORED D IH0 S K AH1 L ER0 D -DISCOLORING D IH0 S K AH1 L ER0 IH0 NG -DISCOLORS D IH0 S K AH1 L ER0 Z -DISCOLOUR D IH0 S K AH1 L ER0 -DISCOLOUR'D D IH0 S K AH1 L ER0 D -DISCOLOURATION D IH0 S K AH1 L ER0 EY1 SH AH0 N -DISCOLOURED D IH0 S K AH1 L ER0 D -DISCOLOURING D IH0 S K AH1 L ER0 IH0 NG -DISCOLOURS D IH0 S K AH1 L ER0 Z -DISCOMETER D IH2 S K AH0 M IY2 T ER0 -DISCOMFIT D IH0 S K AH1 M F IH0 T -DISCOMFITED D IH0 S K AH1 M F IH2 T IH0 D -DISCOMFITING D IH0 S K AH1 M F IH0 T IH0 NG -DISCOMFITTED D IH0 S K AH1 M F IH2 T IH0 D -DISCOMFITURE D IH0 S K AH1 M F IH0 CH ER0 -DISCOMFITURES D IH0 S K AH1 M F IH0 CH ER0 Z -DISCOMFORT D IH0 S K AH1 M F ER0 T -DISCOMFORTABLE D IH0 S K AH1 M F ER0 T AH0 B AH0 L -DISCOMFORTED D IH0 S K AH1 M F ER0 T IH0 D -DISCOMFORTING D IH0 S K AH1 M F ER0 T IH0 NG -DISCOMFORTS D IH0 S K AH1 M F ER0 T S -DISCOMFORTURE D IH0 S K AH1 M F ER0 T UH1 R -DISCOMMODE D IH2 S K AH0 M OW1 D -DISCOMMODED D IH2 S K AH0 M OW1 D IH0 D -DISCOMMODING D IH2 S K AH0 M AA1 D IH0 NG -DISCOMPOSE D IH2 S K AH0 M P OW1 Z -DISCOMPOSED D IH2 S K AH0 M P OW1 Z D -DISCOMPOSES D IH2 S K AH0 M P OW1 Z IH0 Z -DISCOMPOSING D IH2 S K AH0 M P OW1 Z IH0 NG -DISCOMPOSURE D IH2 S K AH0 M P OW1 ZH ER0 -DISCONCERT D IH2 S K AH0 N S ER1 T -DISCONCERTED D IH2 S K AH0 N S ER1 T IH0 D -DISCONCERTEDLY D IH2 S K AH0 N S ER1 T IH0 D L IY0 -DISCONCERTING D IH2 S K AH0 N S ER1 T IH0 NG -DISCONCERTINGLY D IH2 S K AH0 N S ER1 T IH0 NG L IY0 -DISCONCERTION D IH2 S K AH0 N S ER1 SH AH0 N -DISCONCERTMENT D IH2 S K AH0 N S ER1 T M AH0 N T -DISCONCERTS D IH2 S K AH0 N S ER1 T S -DISCONNECT D IH0 S K AH0 N EH1 K T -DISCONNECTED D IH2 S K AH0 N EH1 K T IH0 D -DISCONNECTEDLY D IH2 S K AH0 N EH1 K T IH0 D L IY0 -DISCONNECTEDNESS D IH2 S K AH0 N EH1 K T IH0 D N AH0 S -DISCONNECTING D IH2 S K AH0 N EH1 K T IH0 NG -DISCONNECTION D IH2 S K AH0 N EH1 K SH AH0 N -DISCONNECTS D IH0 S K AH0 N EH1 K T S -DISCONSOLATE D IH0 S K AA2 N S AH0 L EY2 T -DISCONSOLATELY D IH2 S K AH0 N S AA1 L AH0 T L IY0 -DISCONSOLATENESS D IH2 S K AH0 N S AA1 L AH0 T N AH0 S -DISCONSOLATION D IH0 S K AA2 N S AH0 L EY1 SH AH0 N -DISCONTENT D IH0 S K AH0 N T EH1 N T -DISCONTENTED D IH2 S K AH0 N T EH1 N T IH0 D -DISCONTENTEDLY D IH2 S K AH0 N T EH1 N T IH0 D L IY0 -DISCONTENTMENT D IH2 S K AH0 N T EH1 N T M AH0 N T -DISCONTENTS D IH2 S K AH0 N T EH1 N T S -DISCONTINUANCE D IH2 S K AH0 N T IH1 N Y UW0 AH0 N S -DISCONTINUE D IH0 S K AH0 N T IH1 N Y UW0 -DISCONTINUED D IH0 S K AH0 N T IH1 N Y UW0 D -DISCONTINUING D IH0 S K AH0 N T IH1 N Y UW0 IH0 NG -DISCONTINUITY D IH0 S K AA2 N T IH0 N UW1 IH0 T IY0 -DISCONTINUOUS D IH2 S K AH0 N T IH1 N Y UW0 AH0 S -DISCOORSE D IH0 S K UH1 R S -DISCOORSIN D IH0 S K UH1 R S AH0 N -DISCORD D IH1 S K AO0 R D -DISCORDANCE D IH0 S K AO1 R D AH0 N S -DISCORDANCES D IH0 S K AO1 R D AH0 N S IH0 Z -DISCORDANCY D IH0 S K AO1 R D AH0 N S IY0 -DISCORDANT D IH0 S K AO1 R D AH0 N T -DISCORDANTLY D IH0 S K AO1 R D AH0 N T L IY0 -DISCORDIA D IH0 S K AO1 R D IY0 AH0 -DISCORDS D IH0 S K AO1 R D Z -DISCOUNSELLED D IH0 S K AW1 N S AH0 L D -DISCOUNT D IH0 S K AW1 N T -DISCOUNT D IH1 S K AW0 N T -DISCOUNTED D IH1 S K AW2 N IH0 D -DISCOUNTED D IH1 S K AW2 N T IH0 D -DISCOUNTENANCE D IH0 S K AW1 N T AH0 N AH0 N S -DISCOUNTENANCED D IH0 S K AW1 N T AH0 N AH0 N S T -DISCOUNTENANCES D IH0 S K AW1 N T AH0 N AH0 N S IH0 Z -DISCOUNTENANCING D IH0 S K AW1 N T AH0 N AH0 N S IH0 NG -DISCOUNTER D IH1 S K AW2 N T ER0 -DISCOUNTERS D IH0 S K AW1 N T ER0 Z -DISCOUNTERS D IH1 S K AW2 N ER0 R Z -DISCOUNTING D IH1 S K AW2 N IH0 NG -DISCOUNTING D IH1 S K AW2 N T IH0 NG -DISCOUNTS D IH0 S K AW1 N T S -DISCOUNTS D IH1 S K AW2 N T S -DISCOURAGE D IH0 S K ER1 IH0 JH -DISCOURAGED D IH0 S K ER1 AH0 JH D -DISCOURAGED D IH0 S K ER1 IH0 JH D -DISCOURAGEDLY D IH0 S K ER1 AH0 JH D L IY0 -DISCOURAGEMENT D IH0 S K ER1 IH0 JH M AH0 N T -DISCOURAGEMENTS D IH0 S K ER1 IH0 JH M AH0 N T S -DISCOURAGER D IH0 S K ER1 IH0 JH ER0 -DISCOURAGES D IH0 S K ER1 IH0 JH IH0 Z -DISCOURAGIN D IH0 S K ER1 AH0 JH IH0 N -DISCOURAGING D IH0 S K ER1 AH0 JH IH0 NG -DISCOURAGING D IH0 S K ER1 IH0 JH IH0 NG -DISCOURAGINGLY D IH0 S K ER1 AH0 JH IH0 NG L IY0 -DISCOURS D IH0 S K AW1 ER0 Z -DISCOURSE D IH1 S K AO0 R S -DISCOURSED D IH1 S K AO0 R S T -DISCOURSES D IH0 S K AO1 R S IH0 Z -DISCOURSES D IH1 S K AO0 R S IH0 Z -DISCOURSETH D IH1 S K AO0 R S IH0 TH -DISCOURSING D IH0 S K ER1 S IH0 NG -DISCOURTEOUS D IH1 S K AO0 R T IY0 IH0 S -DISCOURTEOUSLY D IH1 S K AO0 R T IY0 AH0 S L IY0 -DISCOURTESIES D IH0 S K ER1 T AH0 S IY0 Z -DISCOURTESY D IH0 S K ER1 T AH0 S IY0 -DISCOVER D IH0 S K AH1 V ER0 -DISCOVER'D D IH0 S K AH1 V R AH0 D -DISCOVERABLE D IH0 S K AH1 V ER0 AH0 B AH0 L -DISCOVERABLE D IH0 S K AH1 V R AH0 B AH0 L -DISCOVERED D IH0 S K AH1 V ER0 D -DISCOVERER D IH0 S K AH1 V ER0 ER0 -DISCOVERER'S D IH0 S K AH1 V ER0 ER0 Z -DISCOVERERS D IH0 S K AH1 V ER0 ER0 Z -DISCOVERETH D IH0 S K AH1 V R IH0 TH -DISCOVERIES D IH0 S K AH1 V ER0 IY0 Z -DISCOVERIN D IH0 S K AH1 V ER0 IH0 N -DISCOVERING D IH0 S K AH1 V ER0 IH0 NG -DISCOVERS D IH0 S K AH1 V ER0 Z -DISCOVERY D IH0 S K AH1 V ER0 IY0 -DISCOVERY D IH0 S K AH1 V R IY0 -DISCOVERY D IH2 S K AH1 V R IY0 -DISCREDIT D IH0 S K R EH1 D AH0 T -DISCREDITABLE D IH0 S K R EH1 D AH0 T AH0 B AH0 L -DISCREDITABLY D IH0 S K R EH1 D AH0 T AH0 B L IY0 -DISCREDITED D IH0 S K R EH1 D IH0 T IH0 D -DISCREDITING D IH0 S K R EH1 D AH0 T IH0 NG -DISCREDITS D IH0 S K R EH1 D AH0 T S -DISCREET D IH0 S K R IY1 T -DISCREETER D IH0 S K R IY1 T ER0 -DISCREETEST D IH0 S K R IY1 T AH0 S T -DISCREETLY D IH0 S K R IY1 T L IY0 -DISCREETNESS D IH0 S K R IY1 T N AH0 S -DISCREPANCIES D IH0 S K R EH1 P AH0 N S IY0 Z -DISCREPANCY D IH0 S K R EH1 P AH0 N S IY0 -DISCREPANT D IH0 S K R EH1 P AH0 N T -DISCRETE D IH0 S K R IY1 T -DISCRETELY D IH0 S K R IY2 T L IY0 -DISCRETION D IH0 S K R EH1 SH AH0 N -DISCRETION'S D IH0 S K R EH1 SH AH0 N Z -DISCRETIONARY D IH0 S K R EH1 SH AH0 N EH2 R IY0 -DISCRETIONS D IH0 S K R EH1 SH AH0 N Z -DISCRIBED D IH0 S K R AY1 B D -DISCRIMINA D IH0 S K R IH1 M AH0 N AH0 -DISCRIMINATE D IH0 S K R IH1 M AH0 N EY2 T -DISCRIMINATED D IH0 S K R IH1 M AH0 N EY2 T AH0 D -DISCRIMINATED D IH0 S K R IH1 M AH0 N EY2 T IH0 D -DISCRIMINATELY D IH0 S K R IH1 M AH0 N AH0 T L IY0 -DISCRIMINATES D IH0 S K R IH1 M AH0 N EY2 T S -DISCRIMINATING D IH0 S K R IH1 M AH0 N EY2 T IH0 NG -DISCRIMINATINGLY D IH0 S K R IH1 M AH0 N EY2 T IH0 NG L IY0 -DISCRIMINATION D IH0 S K R IH2 M AH0 N EY1 SH AH0 N -DISCRIMINATIONS D IH0 S K R IH2 M AH0 N EY1 SH AH0 N Z -DISCRIMINATIVE D IH0 S K R IH1 M AH0 N AH0 T IH0 V -DISCRIMINATORY D IH0 S K R IH1 M AH0 N AH0 T AO2 R IY0 -DISCRIPTION D IH0 S K R IH1 P SH AH0 N -DISCROWN D IH0 S K R OW1 N -DISCROWNED D IH0 S K R AW1 N D -DISCS D IH1 S K S -DISCURSIVE D IH0 S K ER1 S IH0 V -DISCURSIVELY D IH0 S K ER1 S IH0 V L IY0 -DISCURSIVENESS D IH0 S K ER1 S IH0 V N AH0 S -DISCUS D IH1 S K AH0 S -DISCUSS D IH0 S K AH1 S -DISCUSS'D D IH0 S K AH1 SH D -DISCUSSED D IH0 S K AH1 S T -DISCUSSES D IH0 S K AH1 S AH0 Z -DISCUSSES D IH0 S K AH1 S IH0 Z -DISCUSSIN D IH0 S K AH1 S IH0 N -DISCUSSING D IH0 S K AH1 S IH0 NG -DISCUSSION D IH0 S K AH1 SH AH0 N -DISCUSSIONS D IH0 S K AH1 SH AH0 N Z -DISCUST D IH1 S K AH0 S T -DISDAIN D IH0 S D EY1 N -DISDAIN'D D IH0 S D EY1 N D -DISDAINE D IH0 S D EY1 N -DISDAINED D IH0 S D EY1 N D -DISDAINFUL D IH0 S D EY1 N F AH0 L -DISDAINFULLY D IH0 S D EY1 N F AH0 L IY0 -DISDAINING D IH0 S D EY1 N IH0 NG -DISDAINS D IH0 S D EY1 N Z -DISE D AY1 S -DISE D AY1 Z -DISEAS'D D IH0 Z IY1 Z D -DISEASE D IH0 Z IY1 Z -DISEASED D IH0 Z IY1 Z D -DISEASES D IH0 Z IY1 Z AH0 Z -DISEASES D IH0 Z IY1 Z IH0 Z -DISEMBARK D IH0 S EH0 M B AA1 R K -DISEMBARKATION D IH0 S EH2 M B AA0 R K EY1 SH AH0 N -DISEMBARKED D IH0 S EH0 M B AA1 R K T -DISEMBARKING D IH0 S EH0 M B AA1 R K IH0 NG -DISEMBARKS D IH0 S EH0 M B AA1 R K S -DISEMBARRASS D IH0 S IH0 B EH1 R AH0 S -DISEMBARRASSED D IH0 S IH0 B EH1 R AH0 S T -DISEMBARRASSING D IH0 S IH0 B EH1 R AH0 S IH0 NG -DISEMBODIED D IH0 S IH0 B AA1 D IY0 D -DISEMBODIMENT D IH0 S IH0 B AA1 D IY0 M AH0 N T -DISEMBOGUES D IH0 S IH0 B AA1 G Z -DISEMBOWEL D IH0 S IH0 B AW1 AH0 L -DISEMBOWELED D IH0 S IH0 B AW1 AH0 L D -DISEMBOWELING D IH0 S IH0 B AW1 AH0 L IH0 NG -DISEMBOWELLED D IH0 S IH0 B AA1 W EH0 L D -DISEMBOWELLING D IH0 S IH0 B AA1 W EH1 L IH0 NG -DISENCHANT D IH0 S IH0 N CH AE1 N T -DISENCHANTED D IH0 S IH0 N CH AE1 N IH0 D -DISENCHANTED D IH0 S IH0 N CH AE1 N T IH0 D -DISENCHANTING D IH0 S IH0 N CH AE1 N T IH0 NG -DISENCHANTMENT D IH0 S IH0 N CH AE1 N T M AH0 N T -DISENCHANTMENTS D IH0 S IH0 N CH AE1 N T M AH0 N T S -DISENCUMBER D IH0 S IH0 N K AH1 M B ER0 -DISENCUMBERED D IH0 S IH0 N K AH1 M B ER0 D -DISENCUMBERING D IH0 S IH0 N K AH1 M B ER0 IH0 NG -DISENDOWMENT D IH0 S IH0 N D AW1 M AH0 N T -DISENFRANCHISED D IH0 S IH0 N F R AE1 N CH AY2 Z D -DISENGAGE D IH0 S IH0 N G EY1 JH -DISENGAGED D IH0 S IH0 N G EY1 JH D -DISENGAGEMENT D IH0 S IH0 N G EY1 JH M AH0 N T -DISENGAGES D IH0 S IH0 N G EY1 JH IH0 Z -DISENGAGING D IH0 S IH0 N G EY1 JH IH0 NG -DISENT D IH0 S IH0 N T -DISENTANGLE D IH2 S AH0 N T AE1 NG G AH0 L -DISENTANGLED D IH0 S IH0 N T AE1 NG G AH0 L D -DISENTANGLEMENT D IH0 S IH0 N T AE1 NG G AH0 L M AH0 N T -DISENTANGLERS D IH0 S IH0 N T AE1 NG G AH0 L ER0 Z -DISENTANGLES D IH0 S IH0 N T AE1 NG G AH0 L Z -DISENTANGLING D IH0 S IH0 N T AE1 NG G AH0 L IH0 NG -DISENTHRALLED D IH0 S IH0 N TH R AO1 L D -DISENTHRALMENT D IH0 S IH0 N TH R AO1 L M AH0 N T -DISENTOMBED D IH0 S IH0 N T UW1 M D -DISESTABLISH D IH0 S IH0 S T AE1 B L IH0 SH -DISESTABLISHED D IH0 S IH0 S T AE1 B L IH0 SH T -DISESTABLISHING D IH0 S IH0 S T AE1 B L IH0 SH IH0 NG -DISESTABLISHMENT D IH0 S IH0 S T AE1 B L IH0 SH M AH0 N T -DISESTEEM D IH0 S IH0 S T IY1 M -DISESTEEMED D IH0 S IH0 S T IY1 M D -DISFAVOR D IH0 S F EY1 V ER0 -DISFAVOUR D IH0 S F EY1 V ER0 -DISFIGURATION D IH0 S F IH1 G Y ER0 EY1 SH AH0 N -DISFIGURE D IH0 S F IH1 G Y ER0 -DISFIGURED D IH0 S F IH1 G Y ER0 D -DISFIGUREMENT D IH0 S F IH1 G Y ER0 M EH0 N T -DISFIGUREMENTS D IH0 S F IH1 G Y ER0 M AH0 N T S -DISFIGURES D IH0 S F IH1 G Y ER0 Z -DISFIGURING D IH0 S F IH1 G Y ER0 IH0 NG -DISFRANCHISE D IH0 S F R AE2 N CH AY0 Z -DISFRANCHISED D IH0 S F R AE1 N CH AY2 Z D -DISFRANCHISEMENT D IH0 S F R AE1 N CH AY2 Z M AH0 N T -DISFRANCHISING D IH0 S F R AE2 N CH AY0 Z IH0 NG -DISGORGE D IH0 S G AO1 R JH -DISGORGED D IH0 S G AO1 R JH D -DISGORGES D IH0 S G AO1 R JH IH0 Z -DISGORGING D IH0 S G AO1 R JH IH0 NG -DISGRAC'D D IH0 S G R EY1 S D -DISGRACE D IH0 S G R EY1 S -DISGRACED D IH0 S G R EY1 S T -DISGRACEFUL D IH0 S G R EY1 S F AH0 L -DISGRACEFULLY D IH0 S G R EY1 S F AH0 L IY0 -DISGRACES D IH0 S G R EY1 S IH0 Z -DISGRACIN D IH0 S G R EY1 S IH0 N -DISGRACING D IH0 S G R EY1 S IH0 NG -DISGRAZIA D IH0 S G R AA1 Z IY0 AH0 -DISGRUNTLED D IH0 S G R AH1 N AH0 L D -DISGRUNTLED D IH0 S G R AH1 N T AH0 L D -DISGUIS'D D IH0 S G AY1 Z D -DISGUISE D IH0 S G AY1 Z -DISGUISED D IH0 S G AY1 Z D -DISGUISEMENT D IH0 S G AY1 Z M AH0 N T -DISGUISES D IH0 S G AY1 Z IH0 Z -DISGUISING D IH0 S G AY1 Z IH0 NG -DISGUST D IH0 S G AH1 S T -DISGUSTED D IH0 S G AH1 S T AH0 D -DISGUSTED D IH0 S G AH1 S T IH0 D -DISGUSTEDLY D IH0 S G AH1 S T IH0 D L IY0 -DISGUSTFUL D IH0 S G AH1 S T F AH0 L -DISGUSTIN D IH0 S G AH1 S T IH0 N -DISGUSTING D IH0 S G AH1 S T IH0 NG -DISGUSTINGLY D IH0 S G AH1 S T IH0 NG L IY0 -DISGUSTS D IH0 S G AH1 S T S -DISH D IH1 SH -DISHABILLE D IH0 SH AH0 B IH2 L -DISHARMONIES D IH0 S HH AA1 R M AH0 N IY0 Z -DISHARMONY D IH0 S HH AA1 R M AH0 N IY0 -DISHART D IH0 S HH AA2 R T -DISHART'S D IH0 S HH AA1 R T S -DISHCLOTH D IH1 SH K L AO2 TH -DISHCLOTHS D IH1 SH K L AO2 TH S -DISHCLOUT D IH1 SH K L AW1 T -DISHEARTEN D IH0 S HH AA1 R T AH0 N -DISHEARTENED D IH0 S HH AA1 R T AH0 N D -DISHEARTENING D IH0 S HH AA1 R T AH0 N IH0 NG -DISHEARTENING D IH2 S HH AA1 R T N IH0 NG -DISHEARTENINGLY D IH0 S HH AA1 R T AH0 N IH0 NG L IY0 -DISHEARTENMENT D IH0 S HH AA1 R T AH0 N M AH0 N T -DISHEARTENS D IH0 S HH AA1 R T AH0 N Z -DISHED D IH1 SH T -DISHER D IH1 SH ER0 -DISHERITED D IH0 S HH EH1 R AH0 T IH0 D -DISHES D IH1 SH AH0 Z -DISHES D IH1 SH IH0 Z 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AE1 N T L IH0 NG -DISMAS D IH1 Z M AH0 Z -DISMASTED D IH0 S M EY1 S T IH0 D -DISMAY D IH0 S M EY1 -DISMAY'D D IH0 S M EY1 D -DISMAYED D IH0 S M EY1 D -DISMAYING D IH0 S M EY1 IH0 NG -DISMAYINGLY D IH0 S M EY1 IH0 NG L IY0 -DISMAYS D IH0 S M EY1 Z -DISMEMBER D IH0 S M EH1 M B ER0 -DISMEMBERED D IH0 S M EH1 M B ER0 D -DISMEMBERING D IH0 S M EH1 M B ER0 IH0 NG -DISMEMBERMENT D IH0 S M EH1 M B ER0 M AH0 N T -DISMISS D IH0 S M IH1 S -DISMISS'D D IH0 S M IH1 SH D -DISMISSAL D IH0 S M IH1 S AH0 L -DISMISSALS D IH0 S M IH1 S AH0 L Z -DISMISSED D IH0 S M IH1 S T -DISMISSES D IH0 S M IH1 S IH0 Z -DISMISSING D IH0 S M IH1 S IH0 NG -DISMISSION D IH0 S M IH1 SH AH0 N -DISMIST D IH0 S M AH0 S T -DISMOUNT D IH0 S M AW2 N T -DISMOUNTED D IH0 S M AW2 N T IH0 D -DISMOUNTING D IH0 S M AW1 N T IH0 NG -DISMOUNTS D IH0 S M AW2 N T S -DISMUKE D IH1 S M Y UW0 K -DISNA D IH0 S N AH0 -DISNEY D IH1 Z N IY0 -DISNEY'S D IH1 Z N IY0 Z -DISOBEDIENCE D IH2 S AH0 B IY1 D IY0 AH0 N S -DISOBEDIENCE D IH2 S OW0 B IY1 D 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AH0 Z EY1 SH AH0 N -DISORGANIZE D IH0 S AO1 R G AH0 N AY2 Z -DISORGANIZED D IH0 S AO1 R G AH0 N AY2 Z D -DISORGANIZERS D IH0 S AO1 R G AH0 N AY2 Z ER0 Z -DISORGANIZES D IH0 S AO1 R G AH0 N AY2 Z IH0 Z -DISORGANIZING D IH0 S AO1 R G AH0 N AY2 Z IH0 NG -DISORIENTATION D IH0 S AO2 R IY0 AH0 N T EY1 SH AH0 N -DISORIENTED D IH0 S AO1 R IY0 EH2 N T IH0 D -DISOWN D IH0 S OW1 N -DISOWNED D IH0 S OW1 N D -DISOWNING D IH0 S OW1 N IH0 NG -DISOWNS D IH0 S OW1 N Z -DISP D IH1 S P -DISPAIR D IH0 S P EH1 R -DISPARAGE D IH0 S P EH1 R IH0 JH -DISPARAGED D IH0 S P EH1 R IH0 JH D -DISPARAGEMENT D IH0 S P EH1 R IH0 JH M AH0 N T -DISPARAGEMENTS D IH0 S P EH1 R IH0 JH M AH0 N T S -DISPARAGES D IH0 S P EH1 R IH0 JH IH0 Z -DISPARAGING D IH0 S P EH1 R IH0 JH IH0 NG -DISPARAGINGLY D IH0 S P EH1 R IH0 JH IH0 NG L IY0 -DISPARATE D IH0 S P EH1 R IH0 T -DISPARATE D IH1 S P ER0 IH0 T -DISPARITIES D IH0 S P EH1 R AH0 T IY0 Z -DISPARITY D IH0 S P EH1 R AH0 T IY0 -DISPART D IH0 S P AA2 R T -DISPARTED D IH0 S P AA1 R T IH0 D -DISPARTING D IH0 S P AA2 R T IH0 NG -DISPASSION D IH0 S P AE1 SH AH0 N -DISPASSIONATE D IH0 S P AE1 SH AH0 N AH0 T -DISPASSIONATELY D IH0 S P AE1 SH AH0 N AH0 T L IY0 -DISPASSIONATENESS D IH0 S P AE1 SH AH0 N AH0 T N AH0 S -DISPATCH D IH0 S P AE1 CH -DISPATCH'D D IH0 S P AE1 CH IH0 D -DISPATCHED D IH0 S P AE1 CH T -DISPATCHER D IH0 S P AE1 CH ER0 -DISPATCHER D IH1 S P AE2 CH ER0 -DISPATCHER'S D IH0 S P AE1 CH ER0 Z -DISPATCHER'S D IH1 S P AE2 CH ER0 Z -DISPATCHES D IH0 S P AE1 CH IH0 Z -DISPATCHING D IH0 S P AE1 CH IH0 NG -DISPEACE D IH2 S P IY2 S -DISPEL D IH0 S P EH1 L -DISPELL'D D IH0 S P EH1 L D -DISPELLED D IH0 S P EH1 L D -DISPELLER D IH0 S P EH1 L ER0 -DISPELLING D IH0 S P EH1 L IH0 NG -DISPELS D IH0 S P EH1 L Z -DISPENCE D IH0 S P EH1 N S -DISPENSABLE D IH0 S P EH1 N S AH0 B AH0 L -DISPENSARIES D IH1 S P EH0 N S EH2 R IY0 Z -DISPENSARY D IH0 S P EH1 N S ER0 IY0 -DISPENSARY D IH1 S P EH0 N S EH2 R IY0 -DISPENSATION D IH2 S P AH0 N S EY1 SH AH0 N -DISPENSATIONAL D IH2 S P AH0 N S EY1 SH AH0 N AH0 L -DISPENSATIONS D IH2 S P AH0 N S EY1 SH AH0 N Z -DISPENSATOR D IH2 S P AH0 N S EY2 T ER0 -DISPENSATORY D IH0 S P EH1 N S AH0 T AO2 R IY0 -DISPENSE D IH0 S P EH1 N S -DISPENSED D IH0 S P EH1 N S T -DISPENSER D IH0 S P EH1 N S ER0 -DISPENSERS D IH0 S P EH1 N S ER0 Z -DISPENSES D IH0 S P EH1 N S IH0 Z -DISPENSING D IH0 S P EH1 N S IH0 NG -DISPEOPLED D IH0 S P IY2 P AH0 L D -DISPERS'D D IH0 S P ER1 S AH0 D -DISPERSAL D IH0 S P ER1 S AH0 L -DISPERSE D IH0 S P ER1 S -DISPERSED D IH0 S P ER1 S T -DISPERSEDLY D IH0 S P ER1 S AH0 D L IY0 -DISPERSES D IH0 S P ER1 S AH0 Z -DISPERSING D IH0 S P ER1 S IH0 NG -DISPERSION D IH0 S P ER1 ZH AH0 N -DISPERSIONS D IH0 S P ER1 ZH AH0 N Z -DISPERSIVE D IH0 S P ER1 S IH0 V -DISPIRIT D IH0 S P IH1 R IH0 T -DISPIRITED D IH0 S P IH1 R AH0 T IH0 D -DISPIRITEDLY D IH0 S P IH1 R IH0 T AH0 D L IY0 -DISPIRITING D IH0 S P IH1 R IH0 T IH0 NG -DISPLACE D IH0 S P L EY1 S -DISPLACED D IH0 S P L EY1 S T -DISPLACEMENT D IH0 S P L EY1 S M AH0 N T 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-DOUBTFULLY D AW1 T F AH0 L IY0 -DOUBTFULNESS D AW1 T F AH0 L N AH0 S -DOUBTFULS D AW1 T F AH0 L Z -DOUBTIN D AW1 T IH0 N -DOUBTING D AW1 T IH0 NG -DOUBTINGLY D AW1 T IH0 NG L IY0 -DOUBTINGS D AW1 T IH0 NG Z -DOUBTLESS D AW1 T L AH0 S -DOUBTLESSE D AW1 T L AH0 S -DOUBTLESSLY D AW1 T L AH0 S L IY0 -DOUBTS D AW1 T S -DOUCE D UW0 S -DOUCEBELLE D UW0 S B EH2 L -DOUCELY D UW0 S L IY0 -DOUCEMENT D UW0 S M AH0 N T -DOUCET D UW0 S EH1 T -DOUCEUR D UW0 S ER0 -DOUCEURS D UW0 S ER1 Z -DOUCHE D UW1 SH -DOUCHED D AW1 CH T -DOUCHES D AW1 CH IH0 Z -DOUCHING D AW1 CH IH0 NG -DOUDEAUVILLE D AW1 D OW1 V IH2 L -DOUDEVILLE D OW1 D V IH0 L -DOUDEVILLES D OW1 D V IH2 L Z -DOUDNEY D AW1 D N IY0 -DOUDOUX D AW1 D UW1 -DOUE D OW1 EY0 -DOUG D AH1 G -DOUG'S D AH1 G Z -DOUGAL D UW1 G AH0 L -DOUGAL'S D UW1 G AH0 L Z -DOUGALD D UW1 G AH0 L D -DOUGALL D AW1 G AH0 L -DOUGALL D UW1 G AH0 L -DOUGALL'S D UW1 G AH0 L Z -DOUGALLS D UW1 G AH0 L Z -DOUGH D OW1 -DOUGHBOY D OW1 B OY2 -DOUGHBOYS D OW1 B OY2 Z -DOUGHBY D OW1 B IY0 -DOUGHERTY D AA1 G ER0 T IY0 -DOUGHERTY D OW1 ER0 T IY0 -DOUGHFACE D OW1 F EY2 S -DOUGHFACES D OW1 F EY2 S IH0 Z -DOUGHNUT D OW1 N AH2 T -DOUGHNUTS D OW1 N AH2 T S -DOUGHS D OW1 Z -DOUGHT D AO1 T -DOUGHTIEST D AO1 T IY0 AH0 S T -DOUGHTILY D AO1 T AH0 L IY0 -DOUGHTON D AO1 T AH0 N -DOUGHTY D AO1 T IY0 -DOUGHTY'S D AO1 T IY0 Z -DOUGHY D OW1 IY0 -DOUGLAS D AH1 G L AH0 S -DOUGLAS'S D AH1 G L AH0 S IH0 Z -DOUGLASDALE D AH1 G L AH0 S D EY2 L -DOUGLASES D AH1 G L AH0 S IH0 Z -DOUGLASII D AH1 G L AH0 S IY1 IY0 -DOUGLASS D AH1 G L AH0 S -DOUGLASS'S D AH1 G L AH0 S IH0 Z -DOUGLASSES D AH1 G L AH0 S IH0 Z -DOUILLARD D W IY0 L AA1 R D -DOUK D AW1 K -DOUKH D AW1 K -DOUKHOBORS D AW1 K OW0 B AO1 R Z -DOUKHOVA D AW1 K OW1 V AH0 -DOUL D OW1 L -DOULCE D AH1 L S -DOULEBOV D AW1 L B AA0 V -DOULEBOVA D UW0 L EH1 B AH0 V AH0 -DOULENQUES D UW1 L AH0 NG K S -DOULEUR D AW1 L ER1 -DOULLENS D AH1 L AH0 N Z -DOULOUREUX D UW0 L UH0 R OW0 -DOULTON D OW1 L T AH0 N -DOUMER D UW1 M ER0 -DOUMYA D OW1 M IY0 AH0 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AE2 M AH0 T AH0 Z EY1 SH AH0 N -DRAMATISE D R AE1 M AH0 T AY2 Z -DRAMATISED D R AE1 M AH0 T AY2 Z D -DRAMATISING D R AE1 M AH0 T AY2 Z IH0 NG -DRAMATIST D R AA1 M AH0 T IH0 S T -DRAMATIST'S D R AA1 M AH0 T IH0 S T S -DRAMATISTS D R AA1 M AH0 T IH0 S T S -DRAMATIZATION D R AE2 M AH0 T AH0 Z EY1 SH AH0 N -DRAMATIZATIONS D R AE2 M AH0 T AH0 Z EY1 SH AH0 N Z -DRAMATIZE D R AA1 M AH0 T AY2 Z -DRAMATIZE D R AE1 M AH0 T AY2 Z -DRAMATIZED D R AE1 M AH0 T AY2 Z D -DRAMATIZES D R AE1 M AH0 T AY2 Z IH0 Z -DRAMATIZING D R AE1 M AH0 T AY2 Z IH0 NG -DRAMATURGIC D R AA1 M AH0 T ER1 JH IH0 K -DRAMATURGIE D R AA1 M AH0 T ER0 JH IY0 -DRAMATURGY D R AA1 M AH0 T ER0 JH IY0 -DRAME D R EY1 M -DRAMED D R AH0 M EH2 D -DRAMES D R EY1 M Z -DRAMMEN D R AE1 M AH0 N -DRAMS D IY1 R AE2 M Z -DRAMS D R AE1 M Z -DRAMSHOP D R AE1 M SH AA2 P -DRAN D R AH0 N -DRANCES D R AH0 N S IH0 Z -DRANE D R EY1 N -DRANE'S D R EY1 N Z -DRANEY D R EY1 N IY0 -DRANG D R AE1 NG -DRANGEY D R EY1 N JH IY0 -DRANIGO D R EY1 N IY1 G OW0 -DRANK D R AE1 NG K -DRANNAN D R AE1 N AH0 N -DRANOA D R EY1 N OW0 AH0 -DRAP D R AE1 P -DRAPE D R EY1 P -DRAPEAU D R AH0 P OW1 -DRAPED D R EY1 P T -DRAPER D R EY1 P ER0 -DRAPER'S D R EY1 P ER0 Z -DRAPERIED D R EY1 P ER0 IY0 D -DRAPERIES D R EY1 P ER0 IY0 Z -DRAPERS D R EY1 P ER0 Z -DRAPERY D R EY1 P ER0 IY0 -DRAPES D R EY1 P S -DRAPIER D R EY1 P IY0 ER0 -DRAPIER'S D R EY1 P IY0 ER0 Z -DRAPING D R EY1 P IH0 NG -DRAPINGS D R EY1 P IH0 NG Z -DRAPPED D R AE1 P T -DRAPPIE D R AE1 P IY2 -DRAPPIN D R AE1 P IH0 N -DRAPPIT D R AE1 P IH0 T -DRAPS D R AE1 P S -DRAQUE D R AE1 K -DRAS D R AH0 S -DRASSILIS D R AH0 S IH1 L IY0 Z -DRASTIC D R AE1 S T IH0 K -DRASTICALLY D R AE1 S T IH0 K L IY0 -DRAT D R AE1 T -DRATTED D R AE1 T IH0 D -DRAUG D R AO1 G -DRAUGHT D R AE1 F T -DRAUGHTED D R AO1 T IH0 D -DRAUGHTING D R AO1 T IH0 NG -DRAUGHTS D R AE1 F T S -DRAUGHTSMAN D R AE1 F T S M AH0 N -DRAUGHTSMAN'S D R AE1 F T S M AH0 N Z -DRAUGHTSMANSHIP D R AE1 F T S M AH0 N SH IH2 P -DRAUGHTSMEN D R AE1 F T S M IH0 N -DRAUGHTY D R AO1 T IY0 -DRAUPADI D R AO1 P AA1 D IY0 -DRAUPNIR D R AO1 P N ER0 -DRAV D R AE1 V -DRAVE D R EY1 V -DRAVIDIAN D R AH0 V IH1 D IY0 AH0 N -DRAVOT D R AE1 V OW0 -DRAW D R AO1 -DRAW'D D R AO1 D -DRAWBACK D R AO1 B AE2 K -DRAWBACKS D R AO1 B AE2 K S -DRAWBRIDGE D R AO1 B R IH2 JH -DRAWBRIDGES D R AO1 B R IH2 JH IH0 Z -DRAWCANSIR D R AO1 K AH0 N S IH1 R -DRAWD D R AO1 D -DRAWED D R AO1 D -DRAWEN D R AO1 AH0 N -DRAWER D R AO1 R -DRAWERFUL D R AO1 R F AH0 L -DRAWERS D R AO1 R Z -DRAWEST D R AO1 AH0 S T -DRAWETH D R AO1 TH -DRAWFORD D R AO1 F ER0 D -DRAWIN D R AO1 IH0 N -DRAWIN' D R AO1 IH0 N -DRAWING D R AO1 IH0 NG -DRAWING'S D R AO1 IH0 NG Z -DRAWINGROOM D R AO1 IH0 NG R UW2 M -DRAWINGS D R AO1 IH0 NG Z -DRAWL D R AO1 L -DRAWLED D R AO1 L D -DRAWLING D R AO1 L IH0 NG -DRAWLINGLY D R AO1 L IH0 NG L IY0 -DRAWLOCK D R AO1 L AA2 K -DRAWLS D R AO1 L Z -DRAWLY D R AO1 L IY0 -DRAWN D R AO1 N -DRAWNE D R AO1 N -DRAWS D R AO1 Z -DRAWSTRING D R AO1 S T R IH2 NG -DRAWWELL D R AO1 W EH0 L -DRAX D R AE1 K S -DRAXY D R AE1 K S IY0 -DRAXY'S D R AE1 K S IY0 Z -DRAY D R EY1 -DRAYCOTT D R EY1 K AH0 T -DRAYCOTT'S D R EY1 K AH0 T S -DRAYMAN D R EY1 M AH0 N -DRAYMAN'S D R EY1 M AH0 N Z -DRAYMEN D R EY1 M AH0 N -DRAYMOOR D R EY1 M UH2 R -DRAYNE D R EY1 N -DRAYNE'S D R EY1 N Z -DRAYS D R EY1 Z -DRAYSON D R EY1 S AH0 N -DRAYTON D R EY1 T AH0 N -DRAYTON'S D R EY1 T AH0 N Z -DRE D R EY2 -DREA D R IY0 AH0 -DREAD D R EH1 D -DREADED D R EH1 D IH0 D -DREADEST D R EH1 D AH0 S T -DREADFU D R EH1 D F UW0 -DREADFUL D R EH1 D F AH0 L -DREADFULEST D R EH1 D F AH0 L AH0 S T -DREADFULL D R EH1 D F AH0 L -DREADFULLEST D R EH1 D F AH0 L AH0 S T -DREADFULLY D R EH1 D F AH0 L IY0 -DREADFULNESS D R EH1 D F AH0 L N AH0 S -DREADFULS D R EH1 D F AH0 L Z -DREADIN D R EH1 D IY0 N -DREADING D R EH1 D IH0 NG -DREADLESS D R EH1 D L AH0 S -DREADNAUGHT D R EH1 D N AO1 T -DREADNAUGHTS D R EH1 D N AO0 T S -DREADNOUGHT D R EH1 D N AO2 T -DREADNOUGHTS D R EH1 D N AO2 T S -DREADS D R EH1 D Z -DREAM D R IY1 M -DREAM'D D R IY1 M D -DREAM'S D R IY1 M Z -DREAM'ST D R IY1 M S T -DREAME D R IY1 M -DREAMED D R IY1 M D -DREAMER D R IY1 M ER0 -DREAMER'S D R IY1 M ER0 Z -DREAMERIE D R IY1 M ER0 IY0 -DREAMERS D R IY1 M ER0 Z -DREAMEST D R EH1 M AH0 S T -DREAMETH D R IY1 M IH0 TH -DREAMFUL D R IY1 M F UH2 L -DREAMFULLY D R IY1 M F AH0 L IY0 -DREAMIER D R IY1 M IY0 ER0 -DREAMIEST D R IY1 M IY0 IH0 S T -DREAMILY D R IY1 M AH0 L IY0 -DREAMIN D R IY1 M IH0 N -DREAMINESS D R IY1 M IY0 N AH0 S -DREAMING D R IY1 M IH0 NG -DREAMINGLY D R IY1 M IH0 NG L IY0 -DREAMINGS D R IY1 M IH0 NG Z -DREAMLAND D R IY1 M L AE2 N D -DREAMLESS D R IY1 M L AH0 S -DREAMLESSLY D R IY1 M L AH0 S L IY0 -DREAMLIKE D R IY1 M L AY2 K -DREAMPT D R IY1 M T -DREAMS D R IY1 M Z -DREAMT D R EH1 M T -DREAMY D R IY1 M IY0 -DREAR D R IY1 R -DREARIER D R IY1 R IY0 ER0 -DREARIEST D R IY1 R IY0 AH0 S T -DREARILY D R IY1 R AH0 L IY0 -DREARINESS D R IY1 R IY0 N AH0 S -DREARY D R IH1 R IY0 -DREAULOND D R UW1 L AH0 N D -DREAZURE D R IY1 ZH ER0 -DREBBER D R EH1 B ER0 -DRECHSLER D R EH1 K S AH0 L ER0 -DRECHSLER D R EH1 K S L ER0 -DRED D R EH1 D -DREDA D R EY1 D AH0 -DREDA'S D R EH1 D AH0 Z -DREDDLINGTON D R EH1 D L IH0 NG T AH0 N -DREDDLINGTON'S D R EH1 D L IH0 NG T AH0 N Z -DREDE D R IY1 D -DREDFUL D R EH1 D F AH0 L -DREDGE D R EH1 JH -DREDGE'S D R EH1 JH IH0 Z -DREDGED D R EH1 JH D -DREDGER D R EH1 JH ER0 -DREDGERS D R EH1 JH ER0 Z -DREDGES D R EH1 JH AH0 Z -DREDGES D R EH1 JH IH0 Z -DREDGING D R EH1 JH IH0 NG -DREDGINGS D R EH1 JH IH0 NG Z -DREDLINTON D R EH1 D L IH0 N T AH0 N -DREDLINTON'S D R EH1 D L IH0 N T AH0 N Z -DREE D R IY1 -DREED D R IY1 D -DREEME D R IY2 M -DREEN D R IY1 N -DREEPDAILY D R IY1 P D EY1 L IY0 -DREER D R IH1 R -DREEVER D R IY1 V ER0 -DREEVER'S D R IY1 V ER0 Z -DREFFLE D R EH1 F AH0 L -DREFFUL D R EH1 F AH0 L -DREFFULLY D R EH1 F AH0 L IY0 -DREG D R EH1 G -DREGHORN D R EH1 G HH ER0 N -DREGS D R EH1 G Z -DREI D R AY1 -DREIBERG D R AY1 B ER0 G -DREICH D R AY1 K -DREIER D R AY1 ER0 -DREISER D R AY1 S ER0 -DREISSIGER D R AY1 S IH0 G ER0 -DREISSIGER'S D R AY1 S IH0 G ER0 Z -DRELICH D R EH1 L IH0 K -DRELINCOURT D R EH1 L AH0 N K AO2 R T -DRELINCOURT'S D R EH1 L AH0 N K AO2 R T S -DRELMER D R EH1 L M ER0 -DREM D R EH1 M -DREMMEL D R EH1 M AH0 L -DREMMEL'S D R EH1 M AH0 L Z -DREMP D R EH1 M P -DREN D R EH1 N -DRENCH D R EH1 N CH -DRENCH'D D R EH1 N CH D -DRENCHED D R EH1 N CH T -DRENCHES D R EH1 N CH AH0 Z -DRENCHING D R EH1 N CH IH0 NG -DRENCHINGS D R EH1 N CH IH0 NG Z -DRENGO D R EH1 NG G OW0 -DRENNAN D R EH1 N AH0 N -DRENNEN D R EH1 N AH0 N -DRENNEN'S D R EH1 N AH0 N Z -DRENTELL D R EH1 N T AH0 L -DRENTHE D R EH1 N TH IY0 -DRESDEN D R EH1 Z D IH0 N -DRESDENER D R EH1 Z D IH0 N ER0 -DRESHAM D R EH1 SH AH0 M -DRESS D R EH1 S -DRESS'D D R EH1 S D -DRESSCOAT D R EH1 S K OW2 T -DRESSE D R EH1 S -DRESSED D R EH1 S T -DRESSEL D R EH1 S AH0 L -DRESSEL'S D R EH1 S AH0 L Z -DRESSER D R EH1 S ER0 -DRESSER'S D R EH1 S ER0 Z -DRESSERS D R EH1 S ER0 Z -DRESSES D R EH1 S AH0 Z -DRESSES D R EH1 S IH0 Z -DRESSIER D R EH1 S IY0 ER0 -DRESSIN D R EH1 S IH0 N -DRESSIN' D R EH1 S IH0 N -DRESSING D R EH1 S IH0 NG -DRESSINGS D R EH1 S IH0 NG Z -DRESSLER D R EH1 S L ER0 -DRESSLIN D R EH1 S L IH0 N -DRESSMAKER D R EH1 S M EY2 K ER0 -DRESSMAKER'S D R EH1 S M EY2 K ER0 Z -DRESSMAKERS D R EH1 S M EY2 K ER0 Z -DRESSMAKING D R EH1 S M EY2 K IH0 NG -DRESSY D R EH1 S IY0 -DREST D R EH1 S T -DRETFUL D R EH1 T F AH0 L -DRETFULLY D R EH1 T F AH0 L IY0 -DREUX D R OW0 -DREVE D R IY1 V -DREVENOFF D R EH1 V IH0 N AO0 F -DREVER D R EH1 V ER0 -DREW D R UW1 -DREW'S D R UW1 Z -DREWITT D R UW1 IH0 T -DREWS D R UW1 Z -DREWYER D R UW1 IY0 ER0 -DREXEL D R EH1 K S AH0 L -DREXELL D R EH1 K S AH0 L -DREXLEY D R EH1 K S L IY0 -DREXLEY'S D R EH1 K S L IY0 Z -DREYER D R EY1 ER0 -DREYFUS D R AY1 F AH0 S -DREYFUS D R EY1 F AH0 S -DREYMARSH D R EY1 M AA2 R SH -DRI D R IY0 -DRIBBLE D R IH1 B AH0 L -DRIBBLED D R IH1 B AH0 L D -DRIBBLES D R IH1 B AH0 L Z -DRIBBLING D R IH1 B AH0 L IH0 NG -DRIBBLING D R IH1 B L IH0 NG -DRIBE D R AY1 B -DRIBLET D R IH1 B L AH0 T -DRIBLETS D R IH1 B L AH0 T S -DRIBS D R IH1 B Z -DRIE D R AY1 -DRIEBERGEN D R IY1 B ER0 G AH0 N -DRIED D R AY1 D -DRIEL D R IY1 L -DRIER D R AY1 ER0 -DRIERS D R AY1 ER0 Z -DRIES D R AY1 Z -DRIESBACH D R IY1 S B AA0 K -DRIEST D R AY1 AH0 S T -DRIETH D R AY1 IH0 TH -DRIF D R IH1 F -DRIFFIELD D R IH1 F IY0 L D -DRIFT D R IH1 F T -DRIFTAGE D R IH1 F T AH0 JH -DRIFTED D R IH1 F T AH0 D -DRIFTED D R IH1 F T IH0 D -DRIFTER D R IH1 F T ER0 -DRIFTERS D R IH1 F T ER0 Z -DRIFTIN D R IH1 F T IH0 N -DRIFTING D R IH1 F T IH0 NG -DRIFTINGS D R IH1 F T IH0 NG Z -DRIFTS D R IH1 F T S -DRIFTWOOD D R IH1 F T W UH2 D -DRIFTY D R IH1 F T IY0 -DRIGGS D R IH1 G Z -DRILGO D R IH1 L G OW2 -DRILGOES D R IH1 L G OW2 Z -DRILL D R IH1 L -DRILLED D R IH1 L D -DRILLER D R IH1 L ER0 -DRILLERS D R IH1 L ER0 Z -DRILLFORD D R IH1 L F ER0 D -DRILLIN D R IH1 L IH0 N -DRILLING D R IH1 L IH0 NG -DRILLINGS D R IH1 L IH0 NG Z -DRILLOT D R IH1 L AH0 T -DRILLS D R IH1 L Z -DRILY D R AH0 L IY0 -DRIMDARROCH D R IH1 M D AA2 R AH0 K -DRIN D R IH0 N -DRINA D IY1 N AH0 -DRING D R IH1 NG -DRINGENSTADT D R IH1 NG AH0 N S T AE2 T -DRINK D R IH1 NG K -DRINK'S D R IH1 NG K S -DRINKABLE D R IH1 N K AH0 B AH0 L -DRINKABLES D R IH1 N K AH0 B AH0 L Z -DRINKE D R IH1 NG K -DRINKED D R IH1 NG K T -DRINKER D R IH1 NG K ER0 -DRINKER'S D R IH1 NG K ER0 Z -DRINKERS D R IH1 NG K ER0 Z -DRINKEST D R IH1 NG K IH0 S T -DRINKETH D R IH1 NG K IH0 TH -DRINKIN D R IH1 NG K IH0 N -DRINKIN' D R IH1 NG K IH0 N -DRINKING D R IH1 NG K IH0 NG -DRINKINGS D R IH1 NG K IH0 NG Z -DRINKS D R IH1 NG K S -DRINKWATER D R IH1 NG K W AO2 T ER0 -DRINKWATER'S D R IH1 NG K W AO2 T ER0 Z -DRINKWOTTER D R IH1 NG K W AA1 T ER0 -DRIP D R IH1 P -DRIPPED D R IH1 P T -DRIPPIN D R IH1 P IH0 N -DRIPPING D R IH1 P IH0 NG -DRIPPINGLY D R IH1 P IH0 NG L IY0 -DRIPPINGS D R IH1 P IH0 NG Z -DRIPPY D R IH1 P IY0 -DRIPS D R IH1 P S -DRIPSTONE D R IH1 P S T OW2 N -DRISCOE D R IH1 S K OW0 -DRISCOL D R IH1 S K AA0 L -DRISCOLD D R IH1 S K OW1 L D -DRISCOLL D R IH1 S K AH0 L -DRISCOLL'S D R IH1 S K AA0 L Z -DRISCOLLS D R IH1 S K AA0 L Z -DRISHNA D R IH1 SH N AH0 -DRISKOL D R IH1 S K AO0 L -DRITTO D R IH1 T OW0 -DRIV D R IH1 V -DRIV'N D R AY1 V AH0 N -DRIVE D R AY1 V -DRIVEL D R IH1 V AH0 L -DRIVELFORD D R IH1 V AH0 L F ER0 D -DRIVELING D R IH1 V AH0 L IH0 NG -DRIVELLED D R IH1 V AH0 L D -DRIVELLER D R IH1 V AH0 L ER0 -DRIVELLERS D R IH1 V AH0 L ER0 Z -DRIVELLING D R IH1 V AH0 L IH0 NG -DRIVELS D R IH1 V AH0 L Z -DRIVEN D R IH1 V AH0 N -DRIVER D R AY1 V ER0 -DRIVER'S D R AY1 V ER0 Z -DRIVERS D R AY1 V ER0 Z -DRIVES D R AY1 V Z -DRIVEST D R IH1 V AH0 S T -DRIVETH D R IH1 V IH0 TH -DRIVEWAY D R AY1 V W EY2 -DRIVEWAYS D R AY1 V W EY2 Z -DRIVIN D R AY1 V IH0 N -DRIVING D R AY1 V IH0 NG -DRIZZLE D R IH1 Z AH0 L -DRIZZLED D R IH1 Z AH0 L D -DRIZZLES D R IH1 Z AH0 L Z -DRIZZLING D R IH1 Z AH0 L IH0 NG -DRIZZLING D R IH1 Z L IH0 NG -DRIZZLY D R IH1 Z L IY0 -DRO D R OW0 -DROCH D R AA1 K -DROCK D R AA2 K -DROFFS D R AO2 F S -DROGHEDA D R OW0 G EY1 D AH0 -DROGUE D R OW1 G -DROGUET D R OW0 G AH0 T -DROGUET'S D R OW1 G EY1 Z -DROI D R OY0 -DROIT D R OY1 T -DROITE D R OY2 T -DROITS D R OY1 T S -DROITWICH D R OY1 T W IH2 CH -DROITWICHES D R OY1 T W IH0 CH IH0 Z -DROLATIQUES D R OW2 L AH0 T IY1 K S -DROLE D R OW1 L -DROLETTE D R OW0 L EH1 T -DROLL D R OW1 L -DROLLER D R OW1 L ER0 -DROLLERIES D R OW1 L ER0 IY0 Z -DROLLERY D R OW1 L ER0 IY0 -DROLLEST D R OW1 L AH0 S T -DROLLING D R OW1 L IH0 NG -DROLLNESS D R OW1 L N AH0 S -DROLLO D R OW1 L OW2 -DROLLS D R OW1 L Z -DROLLY D R OW1 L IY0 -DROM D R AA1 M -DROMARD D R AA1 M ER0 D -DROMARDS D R AA1 M AA2 R D Z -DROMAS D R OW1 M AH0 Z -DROME D R OW2 M -DROMEDARIES D R AA1 M AH0 D EH2 R IY0 Z -DROMEDARY D R AA1 M AH0 D EH2 R IY0 -DROMEDARY'S D R AA1 M AH0 D EH2 R IY0 Z -DROMIO D R AA1 M IY0 OW0 -DROMIOS D R AA1 M IY0 OW2 Z -DROMO D R OW1 M OW0 -DROMON D R OW1 M AH0 N -DROMORE D R AA1 M AO0 R -DROMOS D R OW2 M OW0 Z -DROMUND D R OW1 M AH0 N D -DROMY D R OW2 M IY0 -DRONA D R OW1 N AH0 -DRONA'S D R OW1 N AH0 Z -DRONE D R OW1 N -DRONE'S D R OW1 N Z -DRONED D R OW1 N D -DRONES D R OW1 N Z -DRONGO D R OW1 NG G OW0 -DRONING D R OW1 N IH0 NG -DRONK D R AA1 NG K -DRONNE D R AA1 N -DRONTHEIM D R AA1 N T HH AY0 M -DROO D R UW1 -DROOCE D R OW1 CH IY0 -DROOD D R UW1 D -DROOD'S D R UW1 D Z -DROOL D R UW1 L -DROOLED D R UW1 L D -DROOLING D R UW1 L IH0 NG -DROOM D R UW2 M -DROON D R UW1 N -DROONED D R UW1 N D -DROOP D R UW1 P -DROOP'D D R UW1 P D -DROOPED D R UW1 P T -DROOPIN D R UW1 P IH0 N -DROOPING D R UW1 P IH0 NG -DROOPINGLY D R UW1 P IH0 NG L IY0 -DROOPS D R UW1 P S -DROOPT D R UW1 P T -DROOPY D R UW1 P IY0 -DROP D R AA1 P -DROP D R AO1 P -DROP'D D R AA1 P D -DROP'S D R AA1 P S -DROPED D R OW1 P T -DROPLET D R AA1 P L AH0 T -DROPLETS D R AA1 P L AH0 T S -DROPLIGHT D R AA1 P L AY2 T -DROPMORE D R AA1 P M AO0 R -DROPP'D D R AA1 P D -DROPPED D R AA1 P T -DROPPER D R AA1 P ER0 -DROPPERS D R AA1 P ER0 Z -DROPPETH D R AA1 P IH0 TH -DROPPIN D R AA1 P IH0 N -DROPPING D R AA1 P IH0 NG -DROPPINGS D R AA1 P IH0 NG Z -DROPS D R AA1 P S -DROPSICAL D R AA1 P S IH0 K AH0 L -DROPSIES D R AA1 P S IY0 Z -DROPSY D R AA1 P S IY0 -DROPT D R AA1 P T -DROQVILLE D R OW1 K V IH2 L -DROQVILLE'S D R OW1 K V IH2 L Z -DROR D R ER0 -DRORED D R ER0 D -DRORIN D R AO1 R IH0 N -DRORIN' D R AO1 R IH0 N -DRORING D R AO1 R IH0 NG -DROSCHKE D R AO1 SH K -DROSCHKY D R AO1 SH K IY0 -DROSERA D R AA1 S EH1 R AH0 -DROSHKY D R OW1 SH K IY0 -DROSKY D R OW1 S K IY0 -DROSS D R AO1 S -DROSSDELL D R AO1 S D AH0 L -DROSSE D R AA1 S -DROSSY D R AO1 S IY0 -DROST D R AA1 S T -DROST'S D R OW1 S T S -DROSTE D R AA1 S T -DROSTE D R AA1 S T AH0 -DROSTE D R OW1 S T -DROT D R OW1 -DROUBLE D R AH1 B AH0 L -DROUET D R UW1 T -DROUGH D R AW1 -DROUGHT D R AW1 T -DROUGHTS D R AW1 T S -DROUGHTY D R AW1 T IY0 -DROUILLARD D R W IY0 L AA1 R D -DROUOT D R W OW1 -DROUTH D R AW1 TH -DROUTHIELAW D R AW1 TH IY0 L AO2 -DROUTHS D R AW1 TH S -DROUTHY D R AW1 TH IY0 -DROUVA D R UW1 V AH0 -DROUYN D R UW1 IH0 N -DROVE D R OW1 V -DROVER D R OW1 V ER0 -DROVER'S D R OW1 V ER0 Z -DROVERS D R OW1 V ER0 Z -DROVES D R OW1 V Z -DROVING D R OW1 V IH0 NG -DROW D R OW2 -DROWN D R AW1 N -DROWN'D D R AW1 N D -DROWND D R AW1 N D -DROWNDED D R AW1 N D IH0 D -DROWNDIN D R AW1 N D IH0 N -DROWNDING D R AW1 N D IH0 NG -DROWNE D R AW1 N -DROWNE'S D R AW1 N Z -DROWNED D R AW1 N D -DROWNIN D R AW1 N IH0 N -DROWNING D R AW1 N IH0 NG -DROWNINGS D R AW1 N IH0 NG Z -DROWNS D R AW1 N Z -DROWS D R AW1 Z -DROWSE D R AW1 Z -DROWSED D R AW1 Z D -DROWSES D R AW1 Z IH0 Z -DROWSIER D R AW1 Z IY0 ER0 -DROWSIEST D R AW1 Z IY0 AH0 S T -DROWSILY D R AW1 Z AH0 L IY0 -DROWSINESS D R AW1 Z IY0 N AH0 S -DROWSING D R AW1 Z IH0 NG -DROWSY D R AW1 Z IY0 -DROZ D R AA1 Z -DROZHKI D R AA1 Z K IY0 -DRS D R Z -DRU D R UW1 -DRU'S D R UW1 Z -DRUB D R AH1 B -DRUBB'D D R AH1 B D -DRUBBED D R AH1 B D -DRUBBING D R AH1 B IH0 NG -DRUBBINGS D R AH1 B IH0 NG Z -DRUCE D R UW1 S -DRUCE'S D R UW1 S IH0 Z -DRUCKEN D R AH1 K AH0 N -DRUCOUR D R AH1 K UH1 R -DRUDGE D R AH1 JH -DRUDGED D R AH1 JH D -DRUDGERIES D R AH1 JH ER0 IY0 Z -DRUDGERY D R AH1 JH ER0 IY0 -DRUDGES D R AH1 JH IH0 Z -DRUDGING D R AH1 JH IH0 NG -DRUE D R UW1 -DRUEL D R UW1 L -DRUFF D R AH1 F -DRUG D R AH1 G -DRUG'S D R AH1 G Z -DRUGA D R UW1 G AH0 -DRUGGED D R AH1 G D -DRUGGER D R AH1 G ER0 -DRUGGET D R AH1 G IH0 T -DRUGGETS D R AH1 G AH0 T S -DRUGGING D R AH1 G IH0 NG -DRUGGIST D R AH1 G IH0 S T -DRUGGIST'S D R AH1 G AH0 S T S -DRUGGIST'S D R AH1 G IH0 S T S -DRUGGISTS D R AH1 G AH0 S T S -DRUGGISTS D R AH1 G IH0 S T S -DRUGLESS D R AH1 G L AH0 S -DRUGS D R AH1 G Z -DRUGSTORE D R AH1 G S T AO2 R -DRUID D R UW1 IH0 D -DRUID'S D R UW1 IH0 D Z -DRUIDIC D R UW1 IH0 D IH0 K -DRUIDICAL D R UW1 IH0 D IH0 K AH0 L -DRUIDISM D R UW1 AH0 D IH2 Z AH0 M -DRUIDS D R UW1 IH0 D Z -DRUILLETES D R UW1 AH0 L EY1 S -DRUIM D R UW1 AH0 M -DRUITT D R UW1 IH0 T -DRUKKER D R UW1 K ER0 -DRUM D R AH1 M -DRUM'S D R AH1 M Z -DRUMAH D R AH1 M AH0 -DRUMBARROW D R AH1 M B EH2 R OW0 -DRUMBEAT D R AH1 M 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K ER0 D Z -DRUNKEN D R AH1 NG K AH0 N -DRUNKENLY D R AH1 NG K AH0 N L IY0 -DRUNKENNESS D R AH1 NG K AH0 N N AH0 S -DRUNKER D R AH1 NG K ER0 -DRUNKEST D R AH1 NG K AH0 S T -DRUNKS D R AH1 NG K S -DRUPADA D R UW1 P AA1 D AH0 -DRUPE D R UW1 P -DRUPES D R UW1 P S -DRURIOLANUS D R UW1 R IY0 OW1 L AH0 N IH0 S -DRURY D R UW1 R IY0 -DRURY'S D R UW1 R IY0 Z -DRUSE D R UW1 Z -DRUSE'S D R UW1 Z IH0 Z -DRUSES D R UW1 Z IH0 Z -DRUSHNEVNA D R AH1 SH N AH0 V N AH0 -DRUSIE D R AH1 S IY0 -DRUSILLA D R UW2 S IH1 L AH0 -DRUSILLA'S D R UW1 S IH1 L AH0 Z -DRUSILLY D R UW1 Z AH0 L IY0 -DRUSUS D R UW1 S AH0 S -DRUTHER D R AH1 DH ER0 -DRUV D R UW1 V -DRUZE D R UW1 Z -DRUZES D R UW1 Z IH0 Z -DRY D R AY1 -DRY'D D R AY1 D -DRYAD D R AY1 AE0 D -DRYAD'S D R AY1 AE0 D Z -DRYADES D R AY1 AH0 D IY2 Z -DRYADS D R AY1 AE2 D Z -DRYAS D R AY1 AH0 S -DRYASDUST D R AY1 AH0 Z D AH2 S T -DRYBONE D R AY1 B OW2 N -DRYBROOK D R AY1 B R UH2 K -DRYBURGH D R AY1 B ER0 G -DRYDEN D R AY1 D AH0 N -DRYDEN'S D R AY1 D AH0 N Z -DRYDOCK 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IH0 NG Z -DUCKINMALLARD D AH1 K AH0 N M AE1 L ER0 D -DUCKLING D AH1 K L IH0 NG -DUCKLING'S D AH1 K L IH0 NG Z -DUCKLINGS D AH1 K L IH0 NG Z -DUCKLOW D AH1 K L OW2 -DUCKLOW'S D AH1 K L OW2 Z -DUCKS D AH1 K S -DUCKWEED D AH1 K W IY2 D -DUCKWORTH D AH1 K W ER2 TH -DUCKWORTH'S D AH1 K W ER2 TH S -DUCKY D AH1 K IY0 -DUCLAUX D AH0 K L AO1 K S -DUCLOS D AH0 K L OW1 Z -DUCO D UW1 K OW0 -DUCO'S D AH0 K OW0 Z -DUCOLOMBIER D AH0 K AH0 L AH1 M B IY0 ER0 -DUCORNEAU D AH0 K ER0 N OW1 -DUCOS D UW0 K AA1 S -DUCOUR D AH0 K UH1 R -DUCREST D UW1 K ER0 IH0 S T -DUCRET D AH0 K R IH0 T -DUCROS D UW1 K R OW0 S -DUCROT D AH0 K R AH0 T -DUCROW D UW1 K R OW0 -DUCROW'S D AH0 K R OW1 Z -DUCROY D AH0 K R OY1 -DUCS D AH1 K S -DUCT D AH1 K T -DUCTILE D AH1 K T AH0 L -DUCTILITY D AH0 K T IH1 L AH0 T IY0 -DUCTLESS D AH1 K T L AH0 S -DUCTOR D AH1 K T ER0 -DUCTS D AH1 K T S -DUCUNT D AH0 K AH1 N T -DUD D AH1 D -DUD'S D AH1 D Z -DUDD D AH1 D -DUDDEN D AH1 D AH0 N -DUDDER D AH1 D ER0 -DUDDINGSTON D AH1 D IH0 NG S T AH0 N -DUDDINGSTONE D AH1 D IH0 NG S T OW2 N -DUDDINGTON D AH1 D IH0 NG T AH0 N -DUDDLESTONE D AH1 D AH0 L S T OW2 N -DUDDON D AH1 D AH0 N -DUDDY D AH1 D IY0 -DUDE D UW1 D -DUDE'S D UW1 D Z -DUDEEN D AH1 D IY0 N -DUDENEY D AH1 D IH0 N IY0 -DUDES D Y UW1 D Z -DUDEVANT D AH1 D IH0 V AH0 N T -DUDGEON D AH1 JH AH0 N -DUDIE D UW1 D IY0 -DUDISH D UW1 D IH0 SH -DUDLEIGH D AH1 D L AH0 -DUDLEIGH'S D AH1 D L IY0 Z -DUDLEY D AH1 D L IY0 -DUDLEY'S D AH1 D L IY0 Z -DUDLEYS D AH1 D L IY0 Z -DUDON D UW1 D AH0 N -DUDON'S D UW1 D AH0 N Z -DUDOUIS D UW1 D UW1 IY0 -DUDS D AH1 D Z -DUDSTONE D AH1 D S T OW2 N -DUDU D AH1 JH UW0 -DUDZEELE D AH1 JH IY1 L -DUE D UW1 -DUE D Y UW1 -DUEHRING D UW1 R IH0 NG -DUEL D UW1 AH0 L -DUELING D UW1 L IH0 NG -DUELIST D UW1 AH0 L IH0 S T -DUELISTS D UW1 AH0 L IH0 S T S -DUELLING D UW1 L IH0 NG -DUELLIST D UW1 L IH0 S T -DUELLISTS D UW1 L IH0 S T S -DUELLO D UW0 EH1 L OW0 -DUELO D UW1 AH0 L OW2 -DUELS D UW1 AH0 L Z -DUENA D UW0 EH1 N AH0 -DUENA D W EY1 N Y AH0 -DUENAS D UW0 EH1 N 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D AH0 D -ENFOLDING IH0 N F OW1 L D IH0 NG -ENFOLDS IH0 N F OW1 L D Z -ENFORCE EH0 N F AO1 R S -ENFORCEABLE EH0 N F AO1 R S AH0 B AH0 L -ENFORCED EH0 N F AO1 R S T -ENFORCEMENT EH0 N F AO1 R S M AH0 N T -ENFORCEMENTS EH0 N F AO1 R S M AH0 N T S -ENFORCER EH0 N F AO1 R S ER0 -ENFORCERS EH0 N F AO1 R S ER0 Z -ENFORCES EH0 N F AO1 R S IH0 Z -ENFORCING EH0 N F AO1 R S IH0 NG -ENFORD EH1 N F ER0 D -ENFRAMED EH0 N F R EY2 M D -ENFRANCHISE IH0 N F R AE1 N CH AY2 Z -ENFRANCHISED EH0 N F R AE1 N CH AY2 Z D -ENFRANCHISEMENT IH0 N F R AE1 N CH AY2 Z M AH0 N T -ENFRANCHISING IH0 N F R AE1 N CH AY2 Z IH0 NG -ENG EH1 NG -ENGADDI EH1 NG G AA1 D IY0 -ENGADINE EH1 NG G AA0 D IY1 N IY0 -ENGAG'D EH0 N G EY1 JH D -ENGAGE EH0 N G EY1 JH -ENGAGED EH0 N G EY1 JH D -ENGAGEMENT EH0 N G EY1 JH M AH0 N T -ENGAGEMENT'S EH0 N G EY1 JH M AH0 N T S -ENGAGEMENTS EH0 N G EY1 JH M AH0 N T S -ENGAGES EH0 N G EY1 JH IH0 Z -ENGAGIN EH0 N G EY1 JH IH0 N -ENGAGING EH0 N G EY1 JH IH0 NG -ENGAGINGLY EH0 N G EY1 JH IH0 NG L IY0 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AH0 N IH1 R IH0 N -ENGINEERING EH1 N JH AH0 N IH1 R IH0 NG -ENGINEERS EH1 N JH AH0 N IH1 R Z -ENGINEMAN EH1 N JH AH0 N M AH0 N -ENGINEMEN EH1 N JH AH0 N M IH0 N -ENGINERY EH1 N JH AH0 N ER0 IY0 -ENGINES EH1 N JH AH0 N Z -ENGIRDLED EH1 NG G ER1 D AH0 L D -ENGIRDLING EH1 NG G ER1 D AH0 L IH0 NG -ENGIRT EH1 NG G ER1 T -ENGIS EH1 N JH AH0 S -ENGLAENDER IH1 NG G L AE1 N D ER0 -ENGLAENDERIN IH1 NG G L AE1 N D ER0 IH0 N -ENGLAN IH1 NG G L AH0 N -ENGLAND IH1 NG G L AH0 N D -ENGLAND'S IH1 NG G L AH0 N D Z -ENGLANDER IH1 NG G L AH0 N D ER0 -ENGLANDER'S IH1 NG G L AH0 N D ER0 Z -ENGLANDERS IH1 NG G L AH0 N D ER0 Z -ENGLANDISM IH1 NG G L AH0 N D IH2 Z AH0 M -ENGLANDS IH1 NG G L AH0 N D Z -ENGLE EH1 NG G AH0 L -ENGLE'S IH1 NG AH0 L Z -ENGLEBOURN EH1 NG G AH0 L B UH1 R N -ENGLEDEW IH1 NG L D UW2 -ENGLEDUE IH1 NG L D UW2 -ENGLEESH IH1 NG L IY1 SH -ENGLEFIELD IH1 NG AH0 L F IY2 L D -ENGLEFIELD'S IH1 NG AH0 L F IY2 L D Z -ENGLEHART IH1 NG AH0 L HH AA0 R T -ENGLER EH1 NG G AH0 L ER0 -ENGLER EH1 NG G L ER0 -ENGLES IH1 NG AH0 L Z -ENGLETON EH1 NG G AH0 L T AH0 N -ENGLETON'S EH1 NG G AH0 L T AH0 N Z -ENGLEWOOD EH1 NG G AH0 L W UH2 D -ENGLEWOOD'S EH1 NG G AH0 L W UH2 D Z -ENGLIS IH1 NG G L IH0 S -ENGLISCHER IH1 NG G L IH0 SH ER0 -ENGLISH IH1 NG G L IH0 SH -ENGLISH IH1 NG L IH0 SH -ENGLISH'S IH1 NG G L IH0 SH IH0 Z -ENGLISHE IH1 NG G L IH0 SH -ENGLISHED IH1 NG G L IH0 SH T -ENGLISHER IH1 NG G L IH0 SH ER0 -ENGLISHERS IH1 NG G L IH0 SH ER0 Z -ENGLISHES IH1 NG G L IH0 SH IH0 Z -ENGLISHMAN IH1 NG G L IH0 SH M AH0 N -ENGLISHMAN'S IH1 NG G L IH0 SH M AH0 N Z -ENGLISHMEN EH2 NG L IH1 SH M AH0 N -ENGLISHMEN'S IH1 NG G L IH0 SH M AH0 N Z -ENGLISHNESS IH1 NG G L IH0 SH N AH0 S -ENGLISHRY IH1 NG G L IH0 SH R IY0 -ENGLISHWOMAN IH1 NG G L IH0 SH W UH2 M AH0 N -ENGLISHWOMAN'S IH1 NG G L IH0 SH W UH2 M AH0 N Z -ENGLISHWOMEN IH1 NG G L IH0 SH W IH2 M AH0 N -ENGLISHY IH1 NG G L IH0 SH IY0 -ENGO EH1 NG G OW0 -ENGORGED IH0 N G AO1 R JH D -ENGORGEMENT IH0 N G AO1 R JH M AH0 N T -ENGRACIA IH0 N G R EY1 SH AH0 -ENGRAFT IH0 N G R AH0 F T -ENGRAFTED IH0 N G R AE1 F T IH0 D -ENGRAFTING IH0 N G R AE1 F T IH0 NG -ENGRAILED IH0 N G R EY1 L D -ENGRAINED IH0 N G R EY1 N D -ENGRAVE IH0 N G R EY1 V -ENGRAVED IH0 N G R EY1 V D -ENGRAVEN IH0 N G R EY1 V AH0 N -ENGRAVER IH0 N G R EY1 V ER0 -ENGRAVER'S IH0 N G R EY1 V ER0 Z -ENGRAVERS IH0 N G R EY1 V ER0 Z -ENGRAVES IH0 N G R EY1 V Z -ENGRAVING IH0 N G R EY1 V IH0 NG -ENGRAVINGS IH0 N G R EY1 V IH0 NG Z -ENGRO IH0 N G R OW0 -ENGROSS IH0 N G R OW1 S -ENGROSS'D IH0 N G R OW1 S IH0 D -ENGROSSED IH0 N G R OW1 S T -ENGROSSER IH0 N G R OW1 S ER0 -ENGROSSES IH0 N G R OW1 S IH0 Z -ENGROSSING IH0 N G R OW1 S IH0 NG -ENGROSSINGLY IH0 N G R OW1 S IH0 NG L IY0 -ENGROSSMENT IH0 N G R OW1 S M AH0 N T -ENGSTRAND EH1 NG S T R AH0 N D -ENGUERRAND EH1 NG G EH1 R AH0 N D -ENGULF IH0 N G AH1 L F -ENGULFED IH0 N G AH1 L F T -ENGULFING IH0 N G AH1 L F IH0 NG -ENGULFMENT IH0 N G AH1 L F M AH0 N T -ENGULFS IH0 N G AH1 L F S -ENGULPH IH0 N G AH1 L F -ENGULPHED IH0 N G AH1 L F T -ENGULPHING IH0 N G AH1 L F IH0 NG -ENHALOED EH0 N HH AE1 L OW2 D -ENHANCE EH0 N HH AE1 N S -ENHANCED EH0 N HH AE1 N S T -ENHANCED IH0 N HH AE1 N S T -ENHANCEMENT EH0 N HH AE1 N S M AH0 N T -ENHANCEMENTS EH0 N HH AE1 N S M AH0 N T S -ENHANCER EH0 N HH AE1 N S ER0 -ENHANCES EH0 N HH AE1 N S IH0 Z -ENHANCING EH0 N HH AE1 N S IH0 NG -ENHARMONIC EH0 N HH AA0 R M AA1 N IH0 K -ENHEARTENED EH0 N HH AA1 R T AH0 N D -ENHEARTENING EH0 N HH AA1 R T AH0 N IH0 NG -ENIAC IY1 N IY0 AE2 K -ENID IY1 N IH0 D -ENID'S IY1 N IH0 D Z -ENIGMA IH0 N IH1 G M AH0 -ENIGMAS IH0 N IH1 G M AH0 Z -ENIGMATIC EH2 N IH0 G M AE1 T IH0 K -ENIGMATICAL EH2 N IH0 G M AE1 T IH0 K AH0 L -ENIGMATICALLY EH2 N IH0 G M AE1 T IH0 K L IY0 -ENIM IY1 N IH0 M -ENIMIE IY1 N IH0 M IY0 -ENIMIES IY1 N IH0 M IY0 Z -ENISLED IH0 N IH1 S AH0 L D -ENIUS IY1 N IY0 AH0 S -ENJ'Y EH0 N JH EY1 IY0 -ENJOIN EH0 N JH OY1 N -ENJOIN IH0 N JH OY1 N -ENJOIN'D EH0 N JH OY1 N D -ENJOINED EH0 N JH OY1 N D -ENJOINETH EH0 N JH OY1 N TH -ENJOINING EH0 N JH 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AA1 R JH -ENLARGED EH0 N L AA1 R JH D -ENLARGED IH0 N L AA1 R JH D -ENLARGEMENT IH0 N L AA1 R JH M AH0 N T -ENLARGEMENTS IH0 N L AA1 R JH M AH0 N T S -ENLARGES IH0 N L AA1 R JH IH0 Z -ENLARGING IH0 N L AA1 R JH IH0 NG -ENLIGHTEN EH0 N L AY1 T AH0 N -ENLIGHTEN'D EH0 N L AY1 T AH0 N D -ENLIGHTENED EH0 N L AY1 T AH0 N D -ENLIGHTENER EH0 N L AY1 T AH0 N ER0 -ENLIGHTENING EH0 N L AY1 T AH0 N IH0 NG -ENLIGHTENING EH0 N L AY1 T N IH0 NG -ENLIGHTENMENT EH0 N L AY1 T AH0 N M AH0 N T -ENLIGHTENS EH0 N L AY1 T AH0 N Z -ENLIL EH0 N L IH1 L -ENLIST EH0 N L IH1 S T -ENLISTED EH0 N L IH1 S T IH0 D -ENLISTED IH0 N L IH1 S T AH0 D -ENLISTIN EH0 N L IH1 S T IH0 N -ENLISTING EH0 N L IH1 S T IH0 NG -ENLISTING IH0 N L IH1 S T IH0 NG -ENLISTMENT EH0 N L IH1 S T M AH0 N T -ENLISTMENTS EH0 N L IH1 S T M AH0 N T S -ENLISTS EH0 N L IH1 S T S -ENLIVEN EH0 N L AY1 V AH0 N -ENLIVEN'D EH0 N L AY1 V AH0 N D -ENLIVENED EH0 N L AY1 V AH0 N D -ENLIVENING EH0 N L AY1 V AH0 N IH0 NG -ENLIVENMENT EH0 N L AY1 V AH0 N M AH0 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K T AH0 N T S -EXPECTATION EH2 K S P EH0 K T EY1 SH AH0 N -EXPECTATIONS EH2 K S P EH0 K T EY1 SH AH0 N Z -EXPECTED IH0 K S P EH1 K T AH0 D -EXPECTED IH0 K S P EH1 K T IH0 D -EXPECTETH IH0 K S P EH1 K T IH0 TH -EXPECTIN IH0 K S P EH1 K T IH0 N -EXPECTING IH0 K S P EH1 K T IH0 NG -EXPECTORATE IH0 K S P EH1 K T ER0 AH0 T -EXPECTORATED IH0 K S P EH1 K T ER0 EY2 T IH0 D -EXPECTORATING IH0 K S P EH1 K T ER0 EY2 T IH0 NG -EXPECTORATION IH0 K S P EH1 K T ER0 EY1 SH AH0 N -EXPECTORATIONS IH0 K S P EH1 K T ER0 EY1 SH AH0 N Z -EXPECTS IH0 K S P EH1 K S -EXPECTS IH0 K S P EH1 K T S -EXPEDIENCE IH0 K S P IY1 D IY0 AH0 N S -EXPEDIENCIES IH0 K S P IY1 D IY0 AH0 N S IY0 Z -EXPEDIENCY IH0 K S P IY1 D IY0 AH0 N S IY0 -EXPEDIENT IH0 K S P IY1 D IY0 AH0 N T -EXPEDIENTES IH0 K S P IY1 D IY0 AH0 N T EH0 S -EXPEDIENTS IH0 K S P IY1 D IY0 AH0 N T S -EXPEDITE EH1 K S P IH0 D AY2 T -EXPEDITED EH1 K S P IH0 D AY2 T IH0 D -EXPEDITER EH1 K S P IH0 D AY2 T ER0 -EXPEDITES EH1 K S P IH0 D AY2 T S -EXPEDITING EH1 K S 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AH0 K EY2 T IH0 D -EXPLICATION EH2 K S P L AH0 K EY1 SH AH0 N -EXPLICATIONS IH0 K S P L AH0 K EY1 SH AH0 N Z -EXPLICIT IH0 K S P L IH1 S AH0 T -EXPLICITLY IH0 K S P L IH1 S AH0 T L IY0 -EXPLICITNESS IH0 K S P L IH1 S AH0 T N AH0 S -EXPLODE IH0 K S P L OW1 D -EXPLODED IH0 K S P L OW1 D AH0 D -EXPLODED IH0 K S P L OW1 D IH0 D -EXPLODER IH0 K S P L OW1 D ER0 -EXPLODES IH0 K S P L OW1 D Z -EXPLODING IH0 K S P L OW1 D IH0 NG -EXPLOIT EH1 K S P L OY2 T -EXPLOIT EH2 K S P L OY1 T -EXPLOITABLE IH0 K S P L OY2 T AH0 B AH0 L -EXPLOITATION EH2 K S P L OY2 T EY1 SH AH0 N -EXPLOITATIONS IH0 K S P L OY2 T EY1 SH AH0 N Z -EXPLOITED EH1 K S P L OY2 T AH0 D -EXPLOITED IH0 K S P L OY1 T AH0 D -EXPLOITER IH0 K S P L OY1 T ER0 -EXPLOITERS IH0 K S P L OY1 T ER0 Z -EXPLOITING EH1 K S P L OY2 T IH0 NG -EXPLOITING IH0 K S P L OY1 T IH0 NG -EXPLOITS EH1 K S P L OY2 T S -EXPLOR'D IH0 K S P L ER0 D -EXPLORATION EH2 K S P L AO0 R EY1 SH AH0 N -EXPLORATION EH2 K S P L ER0 EY1 SH AH0 N -EXPLORATIONS EH1 K S P L AO0 R EY1 SH AH0 N Z -EXPLORATIONS EH2 K S P L ER0 EY1 SH AH0 N Z -EXPLORATORY IH0 K S P L AO1 R AH0 T AO2 R IY0 -EXPLORE IH0 K S P L AO1 R -EXPLORED IH0 K S P L AO1 R D -EXPLORER IH0 K S P L AO1 R ER0 -EXPLORER'S IH0 K S P L AO1 R ER0 Z -EXPLORERS IH0 K S P L AO1 R ER0 Z -EXPLORES IH0 K S P L AO1 R Z -EXPLORIN IH0 K S P L AO1 R IH0 N -EXPLORING IH0 K S P L AO1 R IH0 NG -EXPLORINGLY IH0 K S P L AO1 R IH0 NG L IY0 -EXPLOSION IH0 K S P L OW1 ZH AH0 N -EXPLOSIONS IH0 K S P L OW1 ZH AH0 N Z -EXPLOSIVE IH0 K S P L OW1 S IH0 V -EXPLOSIVELY EH2 K S P L OW1 S IH0 V L IY0 -EXPLOSIVENESS IH0 K S P L OW1 S IH0 V N AH0 S -EXPLOSIVES IH0 K S P L OW1 S IH0 V Z -EXPONENT EH1 K S P OW2 N AH0 N T -EXPONENTS IH0 K S P OW1 N AH0 N T S -EXPORT EH1 K S P AO0 R T -EXPORTABLE EH0 K S P AO1 R T AH0 B AH0 L -EXPORTATION EH2 K S P AO0 R T EY1 SH AH0 N -EXPORTATIONS EH2 K S P AO0 R T EY1 SH AH0 N Z -EXPORTED IH0 K S P AO1 R T AH0 D -EXPORTER IH0 K S P AO1 R T ER0 -EXPORTERS IH0 K S P AO1 R T ER0 Z -EXPORTING IH0 K S P AO1 R T IH0 NG -EXPORTS EH1 K S P AO0 R T S -EXPOS'D IH0 K S P OW1 ZH D -EXPOSE IH0 K S P OW1 Z -EXPOSED IH0 K S P OW1 Z D -EXPOSER IH0 K S P OW1 Z ER0 -EXPOSES IH0 K S P OW1 Z IH0 Z -EXPOSIN EH0 K S P AH0 Z IY1 N -EXPOSING IH0 K S P OW1 Z IH0 NG -EXPOSITION EH2 K S P AH0 Z IH1 SH AH0 N -EXPOSITIONS EH2 K S P AH0 Z IH1 SH AH0 N Z -EXPOSITOR IH0 K S P OW1 Z IH0 T ER0 -EXPOSITORS IH0 K S P OW1 Z IH0 T ER0 Z -EXPOSITORY EH0 K S P AH0 Z AH0 T AO2 R IY0 -EXPOSTULATE IH0 K S P AA1 S CH AH0 L EY2 T -EXPOSTULATED IH0 K S P AA1 S CH AH0 L EY2 T IH0 D -EXPOSTULATES IH0 K S P AA1 S CH AH0 L EY2 T S -EXPOSTULATING IH0 K S P AA1 S CH AH0 L EY2 T IH0 NG -EXPOSTULATION IH0 K S P AA1 S CH AH0 L EY1 SH AH0 N -EXPOSTULATIONS IH0 K S P AA1 S CH AH0 L EY1 SH AH0 N Z -EXPOSTULATORY IH0 K S P AA1 S CH AH0 L AH0 T AO2 R IY0 -EXPOSURE IH0 K S P OW1 ZH ER0 -EXPOSURES IH0 K S P OW1 ZH ER0 Z -EXPOUND IH0 K S P AW1 N D -EXPOUNDED IH0 K S P AW1 N D AH0 D -EXPOUNDER IH0 K S P AW1 N D ER0 -EXPOUNDERS IH0 K S P AW1 N D ER0 Z -EXPOUNDING IH0 K S P AW1 N D IH0 NG -EXPOUNDS IH0 K S P AW1 N D Z -EXPRES IH0 K S P R AH0 S -EXPRESLY IH0 K S P R IH0 S L IY0 -EXPRESS IH0 K S P R EH1 S -EXPRESS'D IH0 K S P R EH2 SH D -EXPRESSAGE IH0 K S P R EH1 S AH0 JH -EXPRESSE IH0 K S P R EH1 S -EXPRESSED IH0 K S P R EH1 S T -EXPRESSES IH0 K S P R EH1 S AH0 Z -EXPRESSES IH0 K S P R EH1 S IH0 Z -EXPRESSETH IH0 K S P R EH1 S IH0 TH -EXPRESSIBLE IH0 K S P R EH1 S AH0 B AH0 L -EXPRESSIN IH0 K S P R EH1 S IH0 N -EXPRESSING IH0 K S P R EH1 S IH0 NG -EXPRESSION IH0 K S P R EH1 SH AH0 N -EXPRESSIONAL IH0 K S P R EH1 SH AH0 N AH0 L -EXPRESSIONLESS IH0 K S P R EH2 SH AH0 N L IH0 S -EXPRESSIONLESSLY IH0 K S P R EH1 SH AH0 N L AH0 S L IY0 -EXPRESSIONS IH0 K S P R EH1 SH AH0 N Z -EXPRESSIVE IH0 K S P R EH1 S IH0 V -EXPRESSIVELY IH0 K S P R EH1 S IH0 V L IY0 -EXPRESSIVENESS IH0 K S P R EH1 S IH0 V N AH0 S -EXPRESSLY EH0 K S P R EH1 S L IY0 -EXPRESSMAN IH0 K S P R EH1 S M AH0 N -EXPRESSMAN'S IH0 K S P R EH1 S M AH0 N Z -EXPRESSMEN IH0 K S P R EH1 S M AH0 N -EXPREST IH0 K S P R AH0 S T -EXPROPRIATE EH0 K S P R OW1 P R IY0 EY2 T -EXPROPRIATED EH0 K S P R OW1 P R IY0 EY2 T IH0 D -EXPROPRIATION EH2 K S P R OW2 P R IY0 EY1 SH AH0 N -EXPULSED IH0 K S P AH1 L S T -EXPULSION IH0 K S P AH1 L SH AH0 N -EXPULSIONS IH0 K S P AH1 L SH AH0 N Z -EXPULSIVE IH0 K S P AH1 L S IH0 V -EXPUNGE IH0 K S P AH1 N JH -EXPUNGED IH0 K S P AH1 N JH D -EXPUNGING IH0 K S P AH1 N JH IH0 NG -EXPURGATE IH0 K S P ER1 G AH0 T -EXPURGATED IH0 K S P ER1 G EY2 T IH0 D -EXPURGATION IH0 K S P ER0 G EY1 SH AH0 N -EXPURGATORIUS IH0 K S P ER1 G AH0 T AO2 R IY0 IH0 S -EXQUIS EH1 K S K W IH1 Z -EXQUISITE EH1 K S K W AH0 Z AH0 T -EXQUISITELY EH2 K S K W IH1 Z IH0 T L IY0 -EXQUISITENESS EH1 K S K W AH0 Z AH0 T N AH0 S -EXQUISITES EH1 K S K W AH0 Z AH0 T S -EXTACIES EH1 K S T AH0 S IY0 Z -EXTACY EH1 K S T EY1 S IY0 -EXTANT EH1 K S T AH0 N T -EXTASIES EH1 K S T AH0 S IY0 Z -EXTASY EH1 K S T AH0 S IY0 -EXTATIC EH1 K S T AE1 T IH0 K -EXTEE IH0 K S T IY0 -EXTEMPORAL EH1 K S T EH2 M P ER0 AH0 L -EXTEMPORANEOUS EH1 K S T EH2 M P ER0 EY1 N IY0 AH0 S -EXTEMPORANEOUSLY EH1 K S T EH2 M P ER0 EY1 N IY0 AH0 S L IY0 -EXTEMPORARY EH1 K S T EH2 M P ER0 EH2 R IY0 -EXTEMPORE EH1 K S T EH2 M P AO1 R IY0 -EXTEMPORISE EH1 K S T EH2 M P ER0 AY1 Z -EXTEMPORISED EH1 K S T EH2 M P ER0 AY1 Z D -EXTEMPORISING EH1 K S T EH2 M P ER0 AY1 Z IH0 NG -EXTEMPORIZE EH1 K S T EH2 M P ER0 AY2 Z -EXTEMPORIZED EH1 K S T EH2 M P ER0 AY2 Z D -EXTEMPORIZING EH1 K S T EH2 M P ER0 AY2 Z IH0 NG -EXTENCIVE IH0 K S T EH1 N S IH0 V -EXTEND IH0 K S T EH1 N D -EXTENDED IH0 K S T EH1 N D AH0 D -EXTENDED IH0 K S T EH1 N D IH0 D -EXTENDETH IH0 K S T EH1 N D IH0 TH -EXTENDIN IH0 K S T EH1 N D IH0 N -EXTENDING IH0 K S T EH1 N D IH0 NG -EXTENDS IH0 K S T EH1 N D Z -EXTENSIBLE IH0 K S T EH1 N S AH0 B AH0 L -EXTENSION IH0 K S T EH1 N SH AH0 N -EXTENSIONS IH0 K S T EH1 N SH AH0 N Z -EXTENSIVE IH0 K S T EH1 N S IH0 V -EXTENSIVELY IH0 K S T EH1 N S IH0 V L IY0 -EXTENSIVENESS IH0 K S T EH1 N S IH0 V N AH0 S -EXTENSO IH0 K S T EH1 N S OW0 -EXTENSOR IH0 K S T EH1 N S ER0 -EXTENT IH0 K S T EH1 N T -EXTENTS IH0 K S T EH1 N T S -EXTENUATE IH0 K S T EH1 N Y UW0 EY2 T -EXTENUATED IH0 K S T EH1 N Y UW0 EY2 T IH0 D -EXTENUATES IH0 K S T EH1 N Y UW0 EY2 T S -EXTENUATING IH0 K S T EH1 N Y UW0 EY2 T IH0 NG -EXTENUATION IH0 K S T EH1 N Y UW0 EY1 SH AH0 N -EXTENUATIONS IH0 K S T EH1 N Y UW0 EY1 SH AH0 N Z -EXTER IH0 K S T ER0 -EXTERIOR IH0 K S T IH1 R IY0 ER0 -EXTERIORLY IH0 K S T IH1 R IY0 ER0 L IY0 -EXTERIORS EH0 K S T IH1 R IY0 ER0 Z -EXTERMINATE IH0 K S T ER1 M AH0 N EY2 T -EXTERMINATED IH0 K S T ER1 M AH0 N EY2 T IH0 D -EXTERMINATES IH0 K S T ER1 M AH0 N EY2 T S -EXTERMINATING IH0 K S T ER1 M AH0 N EY2 T IH0 NG -EXTERMINATION IH0 K S T ER2 M AH0 N EY1 SH AH0 N -EXTERMINATOR IH0 K S T ER1 M AH0 N EY2 T ER0 -EXTERMINATORS IH0 K S T ER1 M AH0 N EY2 T ER0 Z -EXTERN IH0 K S T ER1 N -EXTERNAL IH0 K S T ER1 N AH0 L -EXTERNALISM IH0 K S T ER1 N AH0 L IH2 Z AH0 M -EXTERNALITIES IH0 K S T ER1 N AH0 L 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AE1 G Z -FLAGITIOUS F L AH0 JH IH1 SH AH0 S -FLAGLER F L AE1 G L ER0 -FLAGMAN F L AE1 G M AE2 N -FLAGMEN F L AE1 G M EH2 N -FLAGON F L AE1 G AO0 N -FLAGONS F L AE1 G AH0 N Z -FLAGPOLE F L AE1 G P OW2 L -FLAGRANCY F L EY1 G R AH0 N S IY0 -FLAGRANT F L EY1 G R AH0 N T -FLAGRANTE F L AH0 G R AA1 N T IY0 -FLAGRANTLY F L EY1 G R AH0 N T L IY0 -FLAGS F L AE1 G Z -FLAGSHIP F L AE1 G SH IH2 P -FLAGSHIP'S F L AE1 G SH IH2 P S -FLAGSHIPS F L AE1 G SH IH2 P S -FLAGSTAFF F L AE1 G S T AE2 F -FLAGSTAFFS F L AE1 G S T AE2 F S -FLAGSTONE F L AE1 G S T OW2 N -FLAGSTONES F L AE1 G S T OW2 N Z -FLAGWAY F L AE1 G W EY2 -FLAHAULT F L OW0 -FLAHAUT F L AE1 HH AW2 T -FLAHERTY F L EH1 R T IY0 -FLAHERTY'S F L EY1 HH ER0 T IY0 Z -FLAID F L EY1 D -FLAIL F L EY1 L -FLAILED F L EY1 L D -FLAILING F L EY1 L IH0 NG -FLAILS F L EY1 L Z -FLAIR F L EH1 R -FLAK F L AE1 K -FLAKE F L EY1 K -FLAKED F L EY1 K T -FLAKER F L EY1 K ER0 -FLAKES F L EY1 K S -FLAKING F L EY1 K IH0 NG -FLAKY F L EY1 K IY0 -FLAM F L AE1 M -FLAM'S F L AH0 M Z -FLAMAND F L AE1 M AH0 N D -FLAMANDE F L AH0 M AE1 N D -FLAMARENS F L AH0 M EH1 R AH0 N Z -FLAMBARD F L AE0 M B ER0 D -FLAMBEAU F L AE0 M B OW1 -FLAMBEAU'S F L AE0 M B OW1 Z -FLAMBEAUS F L AE0 M B OW1 Z -FLAMBEAUX F L AE0 M B OW1 -FLAMBERGE F L AE1 M B ER0 JH -FLAMBOROUGH F L AE0 M B ER2 OW0 -FLAMBOROUGH'S F L AE0 M B ER2 OW0 Z -FLAMBOYANCE F L AE0 M B OY1 AH0 N S -FLAMBOYANT F L AE0 M B OY1 AH0 N T -FLAMBOYANTLY F L AE0 M B OY1 AH0 N T L IY0 -FLAMBOYANTS F L AE0 M B OY1 AH0 N T S -FLAME F L EY1 M -FLAME'S F L EY1 M Z -FLAMECHE F L AH0 M IY1 CH IY0 -FLAMED F L EY1 M D -FLAMEL F L AE1 M AH0 L -FLAMEL'S F L EY1 M L Z -FLAMELESS F L EY1 M L AH0 S -FLAMELETS F L EY1 M L AH0 T S -FLAMELIKE F L EY1 M L AY2 K -FLAMEN F L EY1 M AH0 N -FLAMENCA F L AH0 M EH1 NG K AH0 -FLAMENCO F L AH0 M EH1 NG K OW2 -FLAMENG F L AH0 M EH1 NG -FLAMENS F L EY1 M AH0 N Z -FLAMENT F L AH0 M AH0 N T -FLAMES F L EY1 M Z -FLAMIN F L AH0 M IH1 N -FLAMINEO F L AH0 M IH1 N IY0 OW0 -FLAMING F L EY1 M IH0 NG 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G AA1 R T ER0 Z -GARTH G AA1 R TH -GARTH'S G AA1 R TH S -GARTHDALE G AA1 R TH D EY2 L -GARTHIE G AA1 R TH IY0 -GARTHMYLE G AA1 R TH M AY2 L -GARTHORNE G AA2 R TH AO0 R N -GARTHORNE'S G AA2 R TH AO0 R N Z -GARTHOWEN G AA1 R TH AH0 W AH0 N -GARTHS G AA1 R TH S -GARTHWAITE G AA1 R TH W EY2 T -GARTLAND G AA1 R T L AH0 N D -GARTLAUBEN G AA1 R T L AW1 B AH0 N -GARTLEY G AA1 R T L IY0 -GARTNER G AA1 R T N ER0 -GARTNEY G AA0 R T N IY0 -GARTNEY'S G AA0 R T N IY0 Z -GARTOK G AA1 R T AH0 K -GARTON G AA1 R T AH0 N -GARTRAM G AA1 R T R AE2 M -GARTRAM'S G AA1 R T R AE2 M Z -GARTSIDE G AA1 R T S AY2 D -GARTSIDE'S G AA1 R T S AY2 D Z -GARUDA G AH0 R UW1 D AH0 -GARULY G AH0 R UW1 L IY0 -GARUM G AE1 R AH0 M -GARV G AA1 R V -GARVACE G AA1 R V AA1 CH IY0 -GARVAGE G AA1 R V IH0 JH -GARVALD G AA1 R V AA1 L D -GARVAN G AA1 R V AH0 N -GARVELL G AA0 R V EY1 L -GARVER G AA1 R V ER0 -GARVESTAD G AA1 R V AH0 S T AE2 D -GARVET G AA1 R V T -GARVEY G AA1 R V IY0 -GARVEY'S G AA1 R V IY0 Z -GARVEZ G AA1 R V EH0 Z -GARVIAN G AA1 R V IY0 AH0 N -GARVICE G AA1 R V AH0 S -GARVIE G AA1 R V IY0 -GARVIN G AA1 R V IH0 N -GARVIN'S G AA1 R V IH0 N Z -GARVINGTON G AA1 R V IH0 NG T AH0 N -GARVINGTON'S G AA1 R V IH0 NG T AH0 N Z -GARWOOD G AA1 R W UH2 D -GARWOOD'S G AA1 R W UH2 D Z -GARY G AE1 R IY0 -GARY G EH1 R IY0 -GARY'S G AE1 R IY0 Z -GARY'S G EH1 R IY0 Z -GARYVILLE G EH1 R IY0 V IH2 L -GARZA G AA1 R Z AH0 -GARZIA G AA1 R Z IY0 AH0 -GARZIN G AA1 R Z IH0 N -GAS G AE1 S -GASCA G AA1 S K AH0 -GASCOGNE G AE1 S K OW1 N -GASCOIGNE G AE1 S K OY1 N Y -GASCOIGNE'S G AE1 S K OY1 N Z -GASCON G AE1 S K AH0 N -GASCON'S G AE1 S K AH0 N Z -GASCONADE G AE1 S K AH0 N EY1 D -GASCONADES G AE1 S K AH0 N EY1 D Z -GASCONADING G AE1 S K AH0 N EY1 D IH0 NG -GASCONS G AE1 S K AH0 N Z -GASCONY G AE1 S K AH0 N IY0 -GASCOYNE G AE1 S K OY0 N -GASCOYNE'S G AE1 S K OY1 N Z -GASCOYNES G AE1 S K OY1 N Z -GASELEE G AE1 S IH0 L IY0 -GASELIER G EY1 Z L IY0 ER0 -GASEOUS G AE1 S IY0 AH0 S -GASES G AE1 S AH0 Z -GASES G AE1 S IH0 Z -GASFITTER G AE1 S F IH1 T ER0 -GASGOYNE G AE1 S G OY1 N -GASH G AE1 SH -GASH'D G AE1 SH D -GASHA G AE1 SH AH0 -GASHED G AE1 SH T -GASHES G AE1 SH AH0 Z -GASHFORD G AE1 SH F ER0 D -GASHING G AE1 SH IH0 NG -GASHIRA G AA0 SH IH1 R AH0 -GASHLEIGH G AE1 SH L AH0 -GASHLY G AE1 SH L IY0 -GASHMU G AE1 SH M UW1 -GASHWILER G AE1 SH W AY2 L ER0 -GASKELL G AE1 S K AH0 L -GASKELL'S G AE1 S K AH0 L Z -GASKET G AE1 S K AH0 T -GASKETS G AE1 S K AH0 T S -GASKETTE G AE1 S K EH1 T -GASKHO G AE1 S K OW0 -GASKILL G AE1 S K IH2 L -GASKINS G AE1 S K IH0 N Z -GASKO G AE1 S K OW0 -GASLIGHT G AE1 S L AY0 T -GASLIGHTS G AE1 S L AY0 T S -GASLIT G AE1 S L AY0 T -GASOLENE G AE1 S AH0 L IY2 N -GASOLINE G AE1 S AH0 L IY2 N -GASOMETER G AE1 S AH0 M IY2 T ER0 -GASOMETERS G AE1 S AH0 M IY2 T ER0 Z -GASOWASHINE G AE1 S AH0 W AA2 SH AY2 N -GASP G AE1 S P -GASP'D G AA0 S P D -GASPACHO G AA0 S P AA1 CH OW0 -GASPAR G AE1 S P ER0 -GASPAR'S G AH0 S P AA1 R Z -GASPARA G AH0 S P AA1 R AH0 -GASPARD G AH0 S P AA1 R D -GASPARD'S G AE1 S P ER0 D Z -GASPARE G AH0 S P EH1 R -GASPARE'S G AH0 S P EH1 R Z -GASPARIN G AE1 S P ER0 IH0 N -GASPARO G AH0 S P AA1 R OW0 -GASPE G AE1 S P IY0 -GASPED G AE1 S P T -GASPEE G AA0 S P IY0 -GASPER G AE1 S P ER0 -GASPEREAU G AE1 S P ER0 OW2 -GASPIN G AE1 S P IH0 N -GASPING G AE1 S P IH0 NG -GASPINGLY G AE1 S P IH0 NG L IY0 -GASPINGS G AE1 S P IH0 NG Z -GASPIPE G AA0 S P AY2 P -GASPON G AA0 S P AH0 N -GASPS G AE1 S P S -GASPY G AE1 S P IY0 -GASQUET G AE1 S K EY1 -GASS G AE1 S -GASSE G AE1 S -GASSED G AE1 S T -GASSEN G AE1 S AH0 N -GASSENDI G AE1 S IH0 N D IY0 -GASSER G AE1 S ER0 -GASSES G AE1 S IH0 Z -GASSIN G AE1 S IH0 N -GASSING G AE1 S IH0 NG -GASSION G AE1 SH AH0 N -GASSNER G AE1 S N ER0 -GASSY G AE1 S IY0 -GAST G AE1 S T -GASTANG G AH0 S T AE2 NG -GASTEIN G AH0 S T IY0 N -GASTELL G AH0 S T EH1 L -GASTER G AE1 S T ER0 -GASTERINI G AE1 S T ER0 IY1 N IY0 -GASTEROPODA G AE1 S T ER0 AA2 P AH0 D AH0 -GASTHAUS G AE1 S T HH AW2 S -GASTHOF G AE1 S T HH AO0 F -GASTON G AE1 S T AH0 N -GASTON'S G AE1 S T AH0 N Z -GASTONG G AE1 S T AO0 NG -GASTRALGIA G AE1 S T R AH0 L JH AH0 -GASTRELL G AE1 S T R AH0 L -GASTRELL'S G AE1 S T R AH0 L Z -GASTRIC G AE1 S T R IH0 K -GASTRITIS G AE0 S T R AY1 T AH0 S -GASTRO G AE1 S T R OW0 -GASTRON'S G AE1 S T R AH0 N Z -GASTRONOME G AE0 S T R OW0 N OW1 M -GASTRONOMIC G AH0 S T R AA2 N AA1 M IH0 K -GASTRONOMICAL G AE1 S T R AH0 N AA1 M IH0 K AH0 L -GASTRONOMY G AE0 S T R AA1 N AH0 M IY0 -GASWORKS G AE1 S W ER2 K S -GAT G AE1 T -GATA G AA1 T AH0 -GATACRE G AE1 T AH0 K ER0 -GATACRE'S G AE1 T AH0 K ER0 Z -GATE G EY1 T -GATE'S G EY1 T S -GATEAU G AA0 T OW1 -GATEAUX G AA0 T OW1 -GATEBY G EY1 T B IY0 -GATED G EY1 T IH0 D -GATEHOUSE G EY1 T HH AW2 S -GATEKEEPER G EY1 T K IY2 P ER0 -GATEKEEPER'S G EY1 T K IY2 P ER0 Z -GATEKEEPERS G EY1 T K IY2 P ER0 Z -GATELESS G EY1 T L IH0 S -GATELY G EY1 T L IY0 -GATEMAN G EY1 T M AH0 N -GATEMEN G EY1 T M AH0 N -GATEPOST G EY1 T P OW2 S T -GATEPOSTS G EY1 T P OW2 S T S -GATER G EY1 T ER0 -GATES G EY1 T S -GATES'S G EY1 T S -GATES'S G EY1 T S IH0 Z -GATESBORO G EY1 T S B ER0 OW0 -GATESHEAD G EY1 T Z HH EH2 D -GATESTONE G EY1 T S T OW2 N -GATEWARD G EY1 T W ER0 D -GATEWAY G EY1 T W EY2 -GATEWAYS G EY1 T W EY2 Z -GATEWOOD G EY1 T W UH2 D -GATEWOOD'S G EY1 T W UH2 D Z -GATH G AE1 TH -GATH'RING G AE1 TH R IH0 NG -GATHAS G AE1 TH AH0 Z -GATHBROKE G AE1 TH B R OW2 K -GATHER G AE1 DH ER0 -GATHER'D G AE1 DH ER0 D -GATHERCOLE G AE1 DH ER0 K OW1 L IY0 -GATHERED G AE1 DH ER0 D -GATHERER G AE1 DH ER0 ER0 -GATHERER'S G AE1 DH ER0 ER0 Z -GATHERERS G AE1 DH ER0 ER0 Z -GATHEREST G AE1 DH ER0 IH0 S T -GATHERETH G AE1 DH ER0 IY1 TH -GATHERGOLD G AE1 DH ER0 G OW2 L D -GATHERIN G AE1 DH ER0 IH0 N -GATHERING G AE1 DH ER0 IH0 NG -GATHERINGS G AE1 DH ER0 IH0 NG Z -GATHERS G AE1 DH ER0 Z -GATHERUM G AE1 DH ER0 AH0 M -GATHO G AE1 TH OW0 -GATHOL G AE1 TH AH0 L -GATHOLIAN G AE1 TH L IY0 AH0 N -GATHON G AE1 TH AH0 N -GATHORNE G AE1 TH AO0 R N -GATIEN G EY1 T Y EH0 N -GATIEN'S G AE1 SH AH0 N Z -GATIENNE G AE1 T IY0 EH0 N -GATINAIS 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G L EH1 R IY0 -GLAS G L AE1 S -GLASBY G L AE1 S B IY0 -GLASCOCK G L AE1 S K AH0 K -GLASCOW G L AE1 S K OW2 -GLASDALE G L AO1 S D EY2 L -GLASENAPP G L AE1 S IH0 N AE1 P -GLASER G L EY1 Z ER0 -GLASGIE G L AE1 S G IY0 -GLASGOW G L AE1 S G OW2 -GLASGOW G L AE1 S K OW2 -GLASGOW'S G L AE1 S K OW2 Z -GLASHA G L AE1 SH AH0 -GLASHAN G L AE1 SH AH0 N -GLASHBURN G L AE1 SH B ER2 N -GLASHER G L AE1 SH ER0 -GLASHGAR G L AE1 SH G AA2 R -GLASHRUACH G L AH0 SH R UW1 AH0 K -GLASLYN G L AE1 S L IH0 N -GLASNABINNIE G L AE1 S N AH0 B IH1 N IY0 -GLASNEVIN G L AE1 S N EH1 V AH0 N -GLASPELL G L AE1 S P EH1 L -GLASPYTH G L AE1 S P IH0 TH -GLASS G L AE1 S -GLASS'D G L AE1 S IH0 D -GLASS'S G L AE1 S IH0 Z -GLASSDALE G L AE1 S D EY2 L -GLASSE G L AE1 S -GLASSED G L AE1 S T -GLASSER G L AE1 S ER0 -GLASSES G L AE1 S AH0 Z -GLASSES G L AE1 S IH0 Z -GLASSFELL G L AE1 S F AH0 L -GLASSFORD G L AE1 S F AO0 R D -GLASSFUL G L AE1 S F AH0 L -GLASSFULS G L AE1 S F AH0 L Z -GLASSHOUSE G L AE1 S HH AW2 S -GLASSILY G L AE1 S AH0 L IY0 -GLASSINESS G L AE1 S IY0 N AH0 S -GLASSING G L AE1 S IH0 NG -GLASSITE G L AE1 S AY0 T -GLASSLESS G L AE1 S L AH0 S -GLASSLIKE G L AE1 S L AY2 K -GLASSMANIKIN G L AE1 S M AE2 N IH0 K IH0 N -GLASSON G L AE1 S AH0 N -GLASSON'S G L AE1 S AH0 N Z -GLASSWARE G L AE1 S W EH2 R -GLASSY G L AE1 S IY0 -GLASTER G L AE1 S T ER0 -GLASTON G L AE1 S T AH0 N -GLASTONBURY G L AE1 S T AH0 N B EH2 R IY0 -GLAT G L AE1 T -GLATHION G L AE1 TH IY0 AO0 N -GLATIGNY G L AE1 T AH0 N IY0 -GLATZ G L AE1 T S -GLAUBE G L AO1 B -GLAUBEN G L AW1 B AH0 N -GLAUBER G L AW1 B ER0 -GLAUBER'S G L AW1 B ER0 Z -GLAUBUS G L AO1 B AH0 S -GLAUCA G L AO0 K AH0 -GLAUCIA G L AO1 SH AH0 -GLAUCIAS G L AO1 S IY0 AH0 Z -GLAUCOMA G L AO0 K OW1 M AH0 -GLAUCOMATOUS G L AO0 K OW1 M AH0 T AH0 S -GLAUCON G L AO0 K AA2 N -GLAUCON'S G L AO0 K AH0 N Z -GLAUCOUS G L AO1 K AH0 S -GLAUCUS G L AO1 K AH0 S -GLAUKE G L AO1 K -GLAUKIAS G L AO1 K IY1 AH0 Z -GLAUM G L AO1 M -GLAUR G L AO1 R -GLAVE G L EY1 V -GLAVIS G L AE1 V IH0 S -GLAZ'D G L EY1 Z AH0 D -GLAZE G L EY1 Z -GLAZEBROOK G L EY1 Z B R UH2 K -GLAZED G L EY1 Z D -GLAZES G L EY1 Z AH0 Z -GLAZES G L EY1 Z IH0 Z -GLAZIER G L EY1 Z IY0 ER0 -GLAZIER'S G L EY1 Z IY0 ER0 Z -GLAZIERS G L EY1 Z IY0 ER0 Z -GLAZING G L EY1 Z IH0 NG -GLAZOUNOV G L EY1 Z UW0 N AA0 V -GLAZY G L EY1 Z IY0 -GLE G AH0 L -GLEAM G L IY1 M -GLEAM'D G L IY1 M D -GLEAMED G L IY1 M D -GLEAMEIL G L IY1 M IY1 L -GLEAMIN G L IY1 M IH0 N -GLEAMING G L IY1 M IH0 NG -GLEAMINGLY G L IY1 M IH0 NG L IY0 -GLEAMINGS G L IY1 M IH0 NG Z -GLEAMS G L IY1 M Z -GLEAMY G L IY1 M IY0 -GLEAN G L IY1 N -GLEAN'D G L IY1 N D -GLEANED G L IY1 N D -GLEANER G L IY1 N ER0 -GLEANER'S G L IY1 N ER0 Z -GLEANERS G L IY1 N ER0 Z -GLEANING G L IY1 N IH0 NG -GLEANINGS G L IY1 N IH0 NG Z -GLEANS G L IY1 N Z -GLEAR G L IY1 R -GLEASON G L IY1 S AH0 N -GLEASON'S G L IY1 S AH0 N Z -GLEAT G L IY1 T -GLEAZEN G L IY1 Z AH0 N -GLEAZEN'S G L IY1 Z AH0 N Z -GLEBAE G L IY1 B AY1 -GLEBE G L IY1 B -GLEBES G L IY1 B Z -GLEBESHIRE G L EH1 B IH0 SH AY2 R -GLECK G L EH1 K -GLED G AH0 L D -GLEDE G L IY1 D -GLEDWARE G L EH1 D W EH2 R -GLEDWARE'S G L EH1 D W EH2 R Z -GLEE G L IY1 -GLEED G L IY1 D -GLEEFUL G L IY1 F AH0 L -GLEEFULLY G L IY1 F AH0 L IY0 -GLEEK G L IY1 K -GLEEMAIDEN G L IY1 M EY1 D AH0 N -GLEEMAN G L IY1 M AH0 N -GLEEMEN G L IY1 M EH0 N -GLEER G L IH1 R -GLEES G L IY1 Z -GLEESOME G L IY1 S AH0 M -GLEESON G L IY1 Z AH0 N -GLEESON'S G L IY1 Z AH0 N Z -GLEG G L EH1 G -GLEGG G L EH1 G -GLEGG'S G L EH1 G Z -GLEICESTER G L EH1 S T ER0 -GLEICH G L AY1 K -GLEICHEN G L AY1 K AH0 N -GLEIG G L IY1 G -GLEIM G L IY1 M -GLEISSERT G L AY1 S ER0 T -GLEIWITZ G L AY1 W IH0 T S -GLEK G L EH1 K -GLEN G L EH1 N -GLEN'S G L EH1 N Z -GLENAIRE G L EH2 N EH1 R -GLENALADALE G L EH1 N AH0 L AA1 D EY2 L -GLENALBERT G L EH2 N AE1 L B ER0 T -GLENALDER G L EH1 N AO1 L D ER0 -GLENALLA G L EH2 N AE1 L AH0 -GLENALLAN G L EH2 N AE1 L AH0 N -GLENALLAN'S G L EH2 N AE1 L AH0 N Z -GLENALTA G L EH1 N AH0 L T AH0 -GLENALVON G L EH1 N AE0 L V AO1 N -GLENARK G L EH1 N ER0 K -GLENARM G L EH2 N EH1 R M -GLENARM'S G L EH2 N EH1 R M Z -GLENARTNEY G L EH1 N ER0 T N IY0 -GLENARVAN G L EH2 N EH1 R V AH0 N -GLENBARTH G L EH1 N B AA0 R TH -GLENBOGIE G L EH1 N B UH1 G IY0 -GLENBURY G L EH1 N B EH2 R IY0 -GLENBY G L EH1 N B IY0 -GLENCAID G L EH1 N K EY1 D -GLENCAIRN G L EH1 N K EH1 R N -GLENCAIRN'S G L EH1 N K EH1 R N Z -GLENCARYLL G L EH1 N K EH1 R AH0 L -GLENCOE G L EH1 N K OW1 -GLENCORA G L EH1 N K AO2 R AH0 -GLENCORA'S G L EH1 N K AO2 R AH0 Z -GLENCORE G L EH1 N K AO2 R -GLENCORE'S G L EH1 N K AO2 R Z -GLENDALE G L EH1 N D EY2 L -GLENDALOUGH G L EH1 N D AH0 L AW0 -GLENDAY G L EH1 N D EY2 -GLENDENNING G L EH1 N D IH0 N IH0 NG -GLENDIN G L EH1 N D IH0 N -GLENDINNING G L EH1 N D IH0 N IH0 NG -GLENDINNING'S G L EH1 N D IH0 N IH0 NG Z -GLENDIVE G L EH1 N D AH0 V -GLENDON G L EH1 N D OW0 N -GLENDON'S G L EH1 N D AH0 N Z -GLENDORA G L EH1 N D AO1 R AH0 -GLENDORE G L EH1 N D ER0 -GLENDOW G L EH1 N D OW0 -GLENDOWER G L EH1 N D OW0 ER0 -GLENDOWER'S G L EH1 N D OW0 ER0 Z -GLENEESH G L EH1 N IY1 SH -GLENELG G L EH1 N IH0 L G -GLENFALLEN G L EH1 N F AO2 L AH0 N -GLENFALLEN'S G L EH1 N F AO2 L AH0 N Z -GLENFERN G L EH1 N F ER1 N -GLENFERNIE G L EH1 N F ER1 N IY0 -GLENFERNIE'S G L EH1 N F ER1 N IY0 Z -GLENFIELD G L EH1 N F IY0 L D -GLENFINLAS G L EH1 N F IH2 N L AH0 Z -GLENFINLASS G L EH1 N F IH2 N L AE0 S -GLENFINNAN G L EH1 N F IH1 N AH0 N -GLENGALL G L EH2 N G AH0 L -GLENGARIFF G L EH2 N G EH1 R AH0 F -GLENGARRY G L EH2 N G EH1 R IY0 -GLENGARY G L EH2 N G EH1 R IY0 -GLENGOWRA G L EH2 N G AO1 R AH0 -GLENGYLE G L EH2 N G AY2 L -GLENHAM G L EH1 N AH0 M -GLENHAM G L EH1 N HH AH0 M -GLENHAVEN G L EH1 N HH EY2 V AH0 N -GLENINCH G L EH1 N IH0 N CH -GLENISTER G L EH1 N IH0 S T ER0 -GLENISTER'S G L EH1 N IH0 S T ER0 Z -GLENKENS G L EH1 NG K AH0 N Z -GLENLIVAT G L EH1 N L IH1 V AH0 T -GLENLIVET G L EH1 N L IH1 V AH0 T -GLENLOCH G L EH1 N L AH0 K -GLENLYON G L EH1 N L IY0 AA0 N -GLENMORE G L EH1 N M AO2 R -GLENMORISTON G L EH1 N M AO2 R IH0 S T AA0 N 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V W AA1 -GLENWILLIAM G L EH1 N W IH1 L Y AH0 M -GLENWILLIAM'S G L EH1 N W IH1 L Y AH0 M Z -GLENWITH G L EH1 N W IH2 TH -GLENWOOD G L EH1 N W UH2 D -GLESCA G L EH1 S K AH0 -GLEW G L UW1 -GLEY G L IY0 -GLEYRE G L EH1 R -GLI G L IY0 -GLIB G L IH1 B -GLIBLY G L IH1 B L IY0 -GLIBNESS G L IH1 B N AH0 S -GLICKHICAN G L IH1 K HH IY1 K AH0 N -GLIDDEN G L IH1 D AH0 N -GLIDDERY G L IH1 D ER0 IY0 -GLIDDON G L IH1 D AH0 N -GLIDE G L AY1 D -GLIDED G L AY1 D IH0 D -GLIDER G L AY1 D ER0 -GLIDERS G L AY1 D ER0 Z -GLIDES G L AY1 D Z -GLIDEST G L AY1 D AH0 S T -GLIDETH G L AY1 D IH0 TH -GLIDIN G L AY1 D IH0 N -GLIDING G L AY1 D IH0 NG -GLIDINGLY G L AY1 D IH0 NG L IY0 -GLIEVE G L IY1 V -GLIFF G L IH0 F -GLIM G L IH1 M -GLIMM'RING G L IH1 M R IH0 NG -GLIMMER G L IH1 M ER0 -GLIMMER'D G L IH1 M ER0 AH0 D -GLIMMERED G L IH1 M ER0 D -GLIMMERIN G L IH1 M ER0 IH0 N -GLIMMERING G L IH1 M ER0 IH0 NG -GLIMMERINGLY G L IH1 M ER0 IH0 NG L IY0 -GLIMMERINGS G L IH1 M ER0 IH0 NG Z -GLIMMERS G L IH1 M ER0 Z -GLIMMINGE G 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G L IH1 T ER0 D -GLITTERED G L IH1 T ER0 D -GLITTERIN G L IH1 T ER0 IH0 N -GLITTERING G L IH1 T ER0 IH0 NG -GLITTERINGLY G L IH1 T ER0 IH0 NG L IY0 -GLITTERINGS G L IH1 T ER0 IH0 NG Z -GLITTERS G L IH1 T ER0 Z -GLITTERY G L IH1 T ER0 IY0 -GLO G L OW1 -GLO'STER G L OW0 S T ER0 -GLOAK G L OW1 K -GLOAM G L OW1 M -GLOAMIN G L OW1 M IH0 N -GLOAMING G L OW1 M IH0 NG -GLOAMINGS G L OW1 M IH0 NG Z -GLOAT G L OW1 T -GLOATED G L OW1 T IH0 D -GLOATING G L OW1 T IH0 NG -GLOATINGLY G L OW1 T IH0 NG L IY0 -GLOATS G L OW1 T S -GLOB G L AA1 B -GLOBAL G L OW1 B AH0 L -GLOBE G L OW1 B -GLOBE'S G L OW1 B Z -GLOBED G L OW1 B D -GLOBER G L OW1 B ER0 -GLOBES G L OW1 B Z -GLOBESKA G L OW1 B EH1 S K AH0 -GLOBESKI G L OW1 B EH1 S K IY0 -GLOBIGERINA G L OW1 B AH0 JH EH0 R IY1 N AH0 -GLOBIGERINAE G L OW1 B AH0 JH EH1 R IH0 N EY2 -GLOBO G L OW1 B OW0 -GLOBOSE G L OW1 B OW0 S -GLOBULAR G L AA1 B Y AH0 L ER0 -GLOBULE G L AA1 B Y AH0 L -GLOBULES G L AA1 B Y AH0 L Z -GLOCESTER G L AW1 S T ER0 -GLOCKEN G L AA1 K AH0 N 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AE2 D Z -GRANDADA G R AE1 N D AE2 D AH0 -GRANDADDY G R AE1 N D AE1 D IY0 -GRANDADDY'S G R AE1 N D AE1 D IY0 Z -GRANDAIRS G R AE0 N D EH1 R Z -GRANDALE G R AE1 N D EY2 L -GRANDALL G R AE1 N D AH0 L -GRANDALL'S G R AE1 N D AH0 L Z -GRANDAM G R AE1 N D AE2 M -GRANDAM'S G R AE1 N D AE2 M Z -GRANDAME G R AE0 N D EY1 M -GRANDAMES G R AE0 N D EY1 M Z -GRANDAMS G R AE1 N D AE2 M Z -GRANDAUNT G R AE1 N D AO1 N T -GRANDAUNTS G R AE1 N D AO1 N T S -GRANDCHILD G R AE1 N CH AY2 L D -GRANDCHILD G R AE1 N D CH AY2 L D -GRANDCHILD'S G R AE1 N D CH AY2 L D Z -GRANDCHILDREN G R AE1 N CH IH2 L D R AH0 N -GRANDCHILDREN G R AE1 N D CH IH2 L D R AH0 N -GRANDCHILDREN'S G R AE1 N CH IH2 L D R AH0 N Z -GRANDCHILDREN'S G R AE1 N D CH IH2 L D R AH0 N Z -GRANDCOURT G R AE1 N D K AO2 R T -GRANDCOURT'S G R AE1 N D K AO1 R T S -GRANDDAD G R AE1 N D AH0 D -GRANDDAD'S G R AE1 N D AO2 D Z -GRANDDADDY G R AE1 N D AE2 D IY0 -GRANDDAM G R AE1 N D AA1 M -GRANDDAUGHTER G R AE1 N D AO2 T ER0 -GRANDDAUGHTER'S G R AE1 N D AO2 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AH0 N -GRANDMAMA G R AE1 M AA2 M AH0 -GRANDMAMA'S G R AE1 N M AA1 M AH0 Z -GRANDMAMMA G R AE1 N M AE1 M AH0 -GRANDMAMMA'S G R AE1 N M AE1 M AH0 Z -GRANDMAMMAS G R AE1 N M AE1 M AH0 Z -GRANDMARM G R AE1 M AA2 R M -GRANDMAS G R AE1 M AA2 Z -GRANDMAS G R AE1 N D M AA2 Z -GRANDMAS G R AE1 N M AA2 Z -GRANDMERE G R AE1 N D M IH2 R -GRANDMOTHER G R AE1 M AH2 DH ER0 -GRANDMOTHER G R AE1 N D M AH2 DH ER0 -GRANDMOTHER G R AE1 N M AH2 DH ER0 -GRANDMOTHER'S G R AE1 M AH2 DH ER0 Z -GRANDMOTHER'S G R AE1 N D M AH2 DH ER0 Z -GRANDMOTHER'S G R AE1 N M AH2 DH ER0 Z -GRANDMOTHERLY G R AE1 M AH2 DH ER0 L IY0 -GRANDMOTHERLY G R AE1 N D M AH2 DH ER0 L IY0 -GRANDMOTHERLY G R AE1 N M AH2 DH ER0 L IY0 -GRANDMOTHERS G R AE1 M AH2 DH ER0 Z -GRANDMOTHERS G R AE1 N D M AH2 DH ER0 Z -GRANDMOTHERS G R AE1 N M AH2 DH ER0 Z -GRANDNEPHEW G R AE1 N D N EH1 F Y UW0 -GRANDNESS G R AE1 N D N AH0 S -GRANDNIECE G R AE1 N D N IY1 S -GRANDNIECES G R AE1 N D N IY1 S IH0 Z -GRANDOIS G R AE1 N W AA1 -GRANDOLPH G R AE1 N D AA0 L F -GRANDON G R AE1 N D AA0 N -GRANDON'S G R AE1 N D AH0 N Z -GRANDONI G R AE1 N D OW1 N IY0 -GRANDPA G R AE1 M P AA2 -GRANDPA G R AE1 N D P AA2 -GRANDPA G R AE1 N P AA2 -GRANDPA'S G R AE1 N P AH0 Z -GRANDPAP G R AE1 N D P AE2 P -GRANDPAP'S G R AE1 N P AA2 P S -GRANDPAPA G R AE1 N P AA2 P AH0 -GRANDPAPA'S G R AE1 N P AA2 P AH0 Z -GRANDPAPAS G R AE1 N P AA2 P AH0 Z -GRANDPARENT G R AE1 N D P EH2 R AH0 N T -GRANDPARENT G R AE1 N P EH2 R AH0 N T -GRANDPARENTS G R AE1 N D P EH2 R AH0 N T S -GRANDPARENTS G R AE1 N P EH2 R AH0 N T S -GRANDPAS G R AE1 N P AA2 S -GRANDPAW G R AE1 N P AA2 -GRANDPERE G R AE1 N P IH1 R -GRANDPOP G R AE1 N P AA2 P -GRANDPRE G R AE1 N D P R EY2 -GRANDS G R AE1 N D Z -GRANDSIR G R AE1 N S IH1 R -GRANDSIRE G R AE1 N S AY0 R -GRANDSIRE'S G R AE1 N S AY0 R Z -GRANDSIRES G R AE1 N S AY1 R Z -GRANDSON G R AE1 N D S AH2 N -GRANDSON G R AE1 N S AH2 N -GRANDSON'S G R AE1 N D S AH2 N Z -GRANDSON'S G R AE1 N S AH2 N Z -GRANDSONS G R AE1 N D S AH2 N Z -GRANDSONS G R AE1 N S AH2 N Z -GRANDSTAND G R AE1 N D S T AE2 N D -GRANDSTAND G R AE1 N S T AE2 N D -GRANDSTANDER G R AE1 N S T AE2 N D ER0 -GRANDSTANDS G R AE1 N S T AE2 N D Z -GRANDUNCLE G R AE1 N D AH1 NG K AH0 L -GRANDVAL G R AE1 N D V AH0 L -GRANDVILAIN G R AE1 N D V IH2 L EY2 N -GRANDVILAIN'S G R AE1 N D V IH2 L AH0 N Z -GRANDVILLE G R AE1 N D V IH2 L -GRANDY G R AE1 N D IY0 -GRANDY'S G R AE1 N D IY0 Z -GRANE G R EY1 N -GRANET G R AE1 N IH0 T -GRANET'S G R AE1 N AH0 T S -GRANEY G R EY1 N IY0 -GRANEY'S G R AE1 N IY0 Z -GRANFA G R AE1 N F AH0 -GRANFER G R AE1 N F ER0 -GRANGE G R EY1 N JH -GRANGE'S G R EY1 N JH IH0 Z -GRANGER G R EY1 N JH ER0 -GRANGER'S G R EY1 N JH ER0 Z -GRANGERS G R EY1 N JH ER0 Z -GRANGES G R EY1 N JH IH0 Z -GRANGEVILLE G R EY1 N JH V IH0 L -GRANGEWOOD G R EY1 N JH W UH2 D -GRANGIOIA G R AA0 N JH OW1 Y AH0 -GRANI G R AE1 N IY0 -GRANIA G R EY1 N IY0 AH0 -GRANIANI G R AA0 N IY0 AA1 N IY0 -GRANICE G R AA0 N IY1 S -GRANICE'S G R AE2 N IH0 S IH0 Z -GRANICUS G R AE1 N AH0 K AH0 S -GRANIER G R 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OY1 Z AH0 N -GROIX G R OY1 -GROLES G R OW1 L Z -GROLIER G R OW1 L Y ER0 -GROLLE G R OW1 L -GROLLMANN G R OW1 L M AH0 N -GROM G R AA1 M -GROM'S G R AA1 M Z -GROMMER G R AA1 M ER0 -GRONAU G R OW1 N AW0 -GRONAU'S G R OW1 N AW0 Z -GRONER G R OW1 N ER0 -GRONG G R AO1 NG -GRONINGEN G R OW1 N IH0 NG AH0 N -GRONOW G R AA1 N OW0 -GRONVOLD G R AA1 N V OW2 L D -GROOM G R UW1 M -GROOM'S G R UW1 M Z -GROOMBRIDE G R UW1 M B R AY1 D -GROOMBRIDGE G R UW1 M B R IH2 JH -GROOMBRIDGE'S G R UW1 M B R IH2 JH IH0 Z -GROOME G R UW1 M -GROOME'S G R UW1 M Z -GROOMED G R UW1 M D -GROOMING G R UW1 M IH0 NG -GROOMS G R UW1 M Z -GROOMSMAN G R UW1 M Z M AH0 N -GROOMSMEN G R UW1 M Z M IH0 N -GROOS G R UW1 Z -GROOT G R UW1 T -GROOTE G R UW1 T -GROOTENHEIMER G R UW1 T AH0 N HH AY2 M ER0 -GROOTON G R UW1 T AH0 N -GROOTZ G R UW1 T S -GROOTZ'S G R UW1 T S IH0 Z -GROOVE G R UW1 V -GROOVED G R UW1 V D -GROOVES G R UW1 V Z -GROOVING G R UW1 V IH0 NG -GROPE G R OW1 P -GROPED G R OW1 P T -GROPER G R OW1 P ER0 -GROPERS G R OW1 P ER0 Z -GROPES G R OW1 P S -GROPIN G R OW1 P IH0 N -GROPING G R OW1 P IH0 NG -GROPINGLY G R OW1 P IH0 NG L IY0 -GROPINGS G R OW1 P IH0 NG Z -GROPPHUSEN G R AA1 P HH UW0 S AH0 N -GROS G R OW1 S -GROSART G R OW1 Z ER0 T -GROSBEAK G R OW1 S B IY2 K -GROSBEAKS G R OW1 S B IY2 K S -GROSBOIS G R OW1 S B W AA1 -GROSCHEN G R AO1 SH AH0 N -GROSCHENS G R AO1 SH AH0 N Z -GROSCHUT G R AO1 SH AH0 T -GROSE G R OW1 Z -GROSE'S G R OW1 Z IH0 Z -GROSEMAN G R OW1 Z M AH0 N -GROSKET G R OW1 S K AH0 T -GROSLOW G R AA1 S L OW0 -GROSMONT G R AO1 Z M AH0 N T -GROSNOFF G R AO1 S N AO0 F -GROSPIERRE G R OW1 S P IY0 EH1 R -GROSS G R OW1 S -GROSS'S G R OW1 S IH0 Z -GROSSBEAK G R OW1 S B IY2 K -GROSSBECK G R OW1 S B EH2 K -GROSSBY G R OW1 S B IY0 -GROSSE G R AA1 S -GROSSE'S G R OW1 S IH0 Z -GROSSEN G R OW1 S AH0 N -GROSSENSTECK G R OW1 S EH0 N S T IH0 K -GROSSER G R OW1 S ER0 -GROSSES G R OW1 S IH0 Z -GROSSEST G R OW1 S AH0 S T -GROSSET G R OW1 S IH0 T -GROSSI G R OW1 S IY0 -GROSSIER G R OW1 S IY0 ER0 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AA1 -HAAB HH AA1 B -HAABUNAI HH AA1 B AH0 N AY2 -HAAF HH AA1 F -HAAK HH AA1 K -HAAKON HH AA1 K AA0 N -HAAMDAANEE HH AA1 M D AA1 N IY1 -HAAMOURA HH AA1 M UH1 R AH0 -HAAN HH AA1 N -HAAR HH AA1 R -HAARFAGER HH AA1 R F EY1 G ER0 -HAARFAGR HH AA1 R F AA1 G R -HAARLEM HH AA1 R L AH0 M -HAARLEM'S HH AA1 R L AH0 M Z -HAARLEMER HH AA1 R AH0 L M ER0 -HAARLEMMER HH AA2 R L EH1 M ER0 -HAAS HH AA1 S -HAASE HH AA1 S -HAASE'S HH AA1 S IH0 Z -HAAVE HH AA2 V -HAB HH AE2 B -HABAKKUK HH AH0 B AE1 K AH0 K -HABAKUK HH AE1 B AH0 K AH0 K -HABAL HH AE1 B AH0 L -HABAL'S HH AE1 B AH0 L Z -HABANA HH AH0 B AE1 N AH0 -HABANERA HH EY1 B AH0 N ER1 AH0 -HABBERTON HH AE1 B ER0 T AH0 N -HABBY HH AE1 B IY0 -HABE HH EY1 B -HABEAS HH AE1 B IY0 AH0 S -HABEEBAH HH AE1 B IY0 B AH1 -HABELSCHWERT HH AA0 B EH1 L SH W ER1 T -HABEMUS HH AE1 B IH0 M IH0 S -HABEN HH AE1 B AH0 N -HABENECK HH AE1 B IH0 N EH0 K -HABENT HH AE1 B IH0 N T -HABEO HH AE1 B IY0 OW0 -HABER HH EY1 B ER0 -HABERDASHER HH AE1 B ER0 D AE2 SH ER0 -HABERDASHER'S HH AE1 B ER0 D AE2 SH ER0 Z -HABERDASHERS HH AE1 B ER0 D AE2 SH ER0 Z -HABERDASHERY HH AE1 B ER0 D AE2 SH ER0 IY0 -HABERDEN HH AE1 B ER0 D AH0 N -HABERE HH AE1 B ER0 -HABERECHT HH AE1 B ER0 EH0 K T -HABERGEON HH AE1 B ER0 JH IH0 N -HABERGEONS HH AE1 B ER0 JH AH0 N Z -HABERI HH AA0 B EH1 R IY0 -HABERLAND HH AE1 B ER0 L AH0 N D -HABERMANN HH EY1 B ER0 M AH0 N -HABERMANN'S HH AE1 B ER0 M AH0 N Z -HABERSHAM HH AE1 B ER0 SH AE2 M -HABERSHAW HH AE1 B ER0 SH AO2 -HABERSTRO HH AA0 B EH1 R S T R OW0 -HABERT HH AE1 B ER0 T -HABES HH EY1 B Z -HABESH HH AE1 B IH0 SH -HABET HH AE1 B IH0 T -HABIB HH AH0 B IY1 B -HABILE HH AE1 B AH0 L -HABILIMENT HH AH0 B IH1 L AH0 M AH0 N T -HABILIMENTS HH AH0 B IH1 L AH0 M AH0 N T S -HABIN HH AE1 B IH0 N -HABINAS HH AH0 B IY1 N AH0 Z -HABINGTON HH AE1 B IH0 NG T AH0 N -HABINNAS HH AE1 B IH0 N AH0 Z -HABIT HH AE1 B AH0 T -HABIT'S HH AE1 B AH0 T S -HABITA HH AE1 B AH0 T AH0 -HABITABILITY HH AE1 B IH0 T AH0 B IH1 L IH0 T IY0 -HABITABLE HH AE1 B AH0 T AH0 B AH0 L -HABITACION HH AE2 B AH0 T EY1 SH AH0 N -HABITAN HH AE1 B IH0 T AH0 N -HABITANS HH AE1 B AH0 T AH0 N Z -HABITANT HH AE1 B IH0 T AH0 N T -HABITANTS HH AE1 B IH0 T AH0 N T S -HABITAT HH AE1 B AH0 T AE2 T -HABITATION HH AE2 B AH0 T EY1 SH AH0 N -HABITATIONS HH AE2 B AH0 T EY1 SH AH0 N Z -HABITATS HH AE1 B AH0 T AE2 T S -HABITE HH AE1 B IH0 T -HABITED HH AE1 B AH0 T AH0 D -HABITING HH AE1 B IH0 T IH0 NG -HABITS HH AE1 B AH0 T S -HABITUAL HH AH0 B IH1 CH UW0 AH0 L -HABITUALLY HH AH0 B IH1 CH UW0 AH0 L IY0 -HABITUALLY HH AH0 B IH1 CH UW0 L IY0 -HABITUATE HH AH0 B IH1 CH UW0 EY2 T -HABITUATED HH AH0 B IH1 CH UW0 EY2 T IH0 D -HABITUATES HH AH0 B IH1 CH UW0 EY2 T S -HABITUATING HH AH0 B IH1 CH UW0 EY2 T IH0 NG -HABITUATION HH AH0 B IH1 CH UW0 EY1 SH AH0 N -HABITUDE HH AE1 B AH0 T UW2 D -HABITUDES HH AE1 B AH0 T UW2 D Z -HABITUE HH AE1 B IH0 CH UW0 -HABITUEE HH AE1 B IH0 CH UW0 IY0 -HABITUES HH AE1 B IH0 CH UW0 Z -HABITUS HH AE1 B AH0 T AH0 S -HABLA HH AE1 B L AH0 -HABLE HH EY1 B AH0 L -HABLETON HH EY1 B AH0 L T AH0 N -HABLOT HH AE1 B L AH0 T -HABN'T HH EY1 B AH0 N T -HABOGI HH AE1 B OW1 G IY0 -HABR HH AE1 B R -HABRUNT HH AE1 B R AH1 N T -HABRUNT'S HH AE1 B R AH0 N T S -HABSBURG HH AE1 P S B ER0 G -HABSBURGS HH AE1 P S B ER0 G Z -HABTI HH AE1 B T IY0 -HABU HH AE1 B UW0 -HABUIT HH AE1 B UW0 IH0 T -HAC HH AH0 K -HACCIDENT HH AE1 K S AH0 D AH0 N T -HACE HH AA1 CH IY0 -HACENDADO HH AA0 CH EH0 N D AA1 D OW0 -HACENDERO HH AE2 S AH0 N D EH1 R OW0 -HACENDERO'S HH AE2 S AH0 N D EH1 R OW0 Z -HACENDEROS HH AE2 S AH0 N D EH1 R OW0 Z -HACHA HH AE1 CH AH0 -HACHE HH AE1 CH -HACHESTO HH AH0 SH EH1 S T OW0 -HACHETTE HH AH0 SH EH1 T -HACHIMAN HH AE1 K IH0 M AH0 N -HACHIRO HH AE1 CH IH1 R OW0 -HACHMONI HH AH0 SH M OW1 N IY0 -HACHOVNIK HH AE1 CH OW1 V N IH0 K -HACIENDA HH AE2 S IY0 EH1 N D AH0 -HACIENDADO HH AE2 S IY0 EH1 N D AA1 D OW0 -HACIENDAS HH AE2 S IY0 EH1 N D AH0 Z -HACK HH AE1 K -HACK'D HH AE1 K D -HACK'S HH AE1 K S -HACKAMORE HH AE1 K AH0 M AO2 R -HACKBERRY HH AE1 K B EH2 R IY0 -HACKBUTT HH AE1 K B AH0 T -HACKE HH AE1 K -HACKEBORN HH AE1 K IH0 B ER0 N -HACKED HH AE1 K T -HACKELBERG HH AE1 K AH0 L B ER0 G -HACKENSACK HH AE1 K AH0 N S AE2 K -HACKER HH AE1 K ER0 -HACKER'S HH AE1 K ER0 Z -HACKERIS HH AE1 K ER0 IH0 S -HACKERS HH AE1 K ER0 Z -HACKET HH AE1 K IH0 T -HACKET'S HH AE1 K AH0 T S -HACKETT HH AE1 K IH0 T -HACKETT'S HH AE1 K IH0 T S -HACKING HH AE1 K IH0 NG -HACKIT HH AE1 K IH0 T -HACKLE HH AE1 K AH0 L -HACKLED HH AE1 K AH0 L D -HACKLEFORD HH AE1 K AH0 L F ER0 D -HACKLER HH AE1 K AH0 L ER0 -HACKLER HH AE1 K L ER0 -HACKLES HH AE1 K AH0 L Z -HACKLEY HH AE1 K L IY0 -HACKLING HH AE1 K L IH0 NG -HACKMAN HH AE1 K M AE2 N -HACKMAN HH AE1 K M AH0 N -HACKMATACK HH AE1 K M AH0 T AE2 K -HACKMEN HH AE1 K M EH2 N -HACKNEY HH AE1 K N IY0 -HACKNEYED HH AE1 K N IY0 D -HACKNEYS HH AE1 K N IY0 Z -HACKNIED HH AE1 K N IY0 D -HACKS HH AE1 K S -HACKSTON HH AE1 K S T AH0 N -HACKSTOUN HH AE1 K S T AW1 N -HACKTON HH AE1 K T AH0 N -HACO HH AA1 K OW0 -HACON HH EY1 K AH0 N -HACT HH AE1 K T -HACTENUS HH AE1 K T IY1 N AH0 S -HACTION HH AE1 K SH AH0 N -HAD HH AE1 D -HAD'NT HH AE1 D AH0 N T -HAD'ST HH AE1 D S T -HADAD HH AE1 D AH0 D -HADASSAH HH AE1 D AE2 S AH0 -HADASSAH'S HH AE1 D AH0 S AA1 Z -HADD HH AE1 D -HADDA HH AE1 D AH0 -HADDAD HH AE1 D AH0 D -HADDAM HH AE1 D AH0 M -HADDAN HH AE1 D AH0 N -HADDE HH AE1 D -HADDEN HH AE1 D AH0 N -HADDEN'S HH AE1 D AH0 N Z -HADDENS HH AE1 D AH0 N Z -HADDICK HH AE1 D IH0 K -HADDIE HH AE1 D IY0 -HADDIES HH AE1 D IY0 Z -HADDINGTON HH AE1 D IH0 NG T AH0 N -HADDO HH AE1 D OW0 -HADDO'S HH AE1 D OW0 Z -HADDOCK HH AE1 D AH0 K -HADDOCK'S HH AE1 D AH0 K S -HADDOCKS HH AE1 D AH0 K S -HADDON HH AE1 D AH0 N -HADDON'S HH AE1 D AH0 N Z -HADDONFIELD HH AE1 D AH0 N F IY0 L D -HADDS HH AE1 D Z -HADE HH EY1 D -HADEN HH EY1 D AH0 N -HADEN'S HH EY1 D AH0 N Z -HADENDOA HH AE1 D EH0 N D OW1 AH0 -HADENDOWAS HH AE1 D EH0 N D AW1 AH0 Z -HADENT HH EY1 D AH0 N T -HADES HH EY1 D IY0 Z -HADES HH EY1 D Z -HADFIELD HH AE1 D F IY2 L D -HADGER HH AE1 JH ER0 -HADGI HH AE1 JH IY0 -HADI HH AA1 D IY0 -HADIDJA HH AE1 D AH0 D Y AH0 -HADING HH EY1 D IH0 NG -HADITH HH EY1 D IH0 TH -HADJ HH AE1 JH -HADJ'S HH AE1 JH EY1 Z -HADJI HH AE1 JH IY0 -HADJI'S HH AE1 JH IY0 Z -HADJIS HH AE1 JH IY0 Z -HADLAI HH AE1 D L AA1 IY0 -HADLEIGH HH AE1 D L AH0 -HADLEY HH AE1 D L IY0 -HADLEY'S HH AE1 D L IY0 Z -HADLEYBURG HH AE1 D L IY0 B ER0 G -HADLEYS HH AE1 D L IY0 Z -HADLOW HH AE1 D L OW2 -HADLOW'S HH AE1 D L OW2 Z -HADLOWS HH AE1 D L OW2 Z -HADN HH AE1 D AH0 N -HADN'T HH AE1 D AH0 N -HADN'T HH AE1 D AH0 N T -HADNA HH AE1 D N AH0 -HADNOCK HH AE1 D N AH0 K -HADNT HH AE1 D N T -HADOW HH AE1 D OW0 -HADOWAY HH AE1 D OW0 W EY2 -HADRATHUS HH AE1 D R AH0 TH AH0 S -HADRIA HH AE1 D R IY0 AH0 -HADRIA'S HH EY1 D R IY0 AH0 Z -HADRIAN HH AE1 D R IY0 AH0 N -HADRIAN HH EY1 D R IY0 AH0 N -HADRIAN'S HH EY1 D R IY0 AH0 N Z -HADRIATIC HH EY1 D R IY0 AE1 T IH0 K -HADRON HH AE1 D R AO2 N -HADST HH AE1 D S T -HADSTONE HH AE1 D S T OW2 N -HADUMOTH HH AE1 D AH0 M AH0 TH -HADWELL HH AE1 D W EH2 L -HADWELL'S HH AE1 D W EH2 L Z -HADWIG HH AE1 D W IH0 G -HADWIG'S HH AE1 D W IH0 G Z -HADWIN HH AE1 D W IH0 N -HADWIN'S HH AE1 D W AH0 N Z -HADWINS HH AE1 D W IH0 N Z -HADYN HH AE1 D IH0 N -HADYN'S HH AE1 D IH0 N Z -HAE HH EY1 -HAE'T HH IY1 T -HAEC HH EH1 K -HAECKEL HH EH1 K AH0 L -HAECKEL'S HH EH1 K AH0 L Z -HAECKELHEIMER HH EH1 K AH0 L HH AY2 M ER0 -HAEIN HH IY1 N -HAEL HH EH1 L -HAELEN HH EH1 L AH0 N -HAEMATITE HH EH1 M AH0 T AY2 T -HAEMON HH IY1 M AH0 N -HAEMORRHAGE HH EH1 M ER0 IH0 JH -HAEMORRHOIDS HH EH1 M ER0 OY2 D Z -HAEMUS HH IY1 M AH0 S -HAENA HH IY1 N AH0 -HAENDEL HH EH1 N D AH0 L -HAENDEL'S HH EH1 N D AH0 L Z -HAENNCHEN HH EH1 N K AH0 N -HAENSEL HH EH1 N S AH0 L -HAER HH EH1 R -HAER'S HH AA1 ER0 Z -HAERE HH EH1 R -HAERET HH EH1 R AH0 T -HAERLEM HH EH1 R L AH0 M -HAES HH AE1 -HAESIT HH EH1 Z IH0 T -HAESPELE HH EH1 S P EH1 L EY0 -HAEUNING HH AW1 N IH0 NG -HAF HH AE1 F -HAFBUR HH AE1 F B ER0 -HAFE HH EY1 F -HAFED HH EY1 F T -HAFELA HH AE1 F AH0 L AH0 -HAFEN HH AE1 F AH0 N -HAFER HH EY1 F ER0 -HAFF HH AE1 F -HAFFEN HH AE1 F AH0 N -HAFFETS HH AE1 F AH0 T S -HAFFGO HH AE1 F G OW2 -HAFI HH AA1 F IY0 -HAFID HH AA0 F IY1 D -HAFIR HH AA0 F IY1 R -HAFITZ HH AE1 F IH0 T S -HAFIZ HH AA0 F IY1 Z -HAFLIDI HH AE1 F L IY1 D IY0 -HAFNER HH AE1 F N ER0 -HAFNER'S HH AE1 F N ER0 Z -HAFOD HH AE1 F AH0 D -HAFRAN HH AE1 F R AH0 N -HAFSA HH AE1 F S AH0 -HAFT HH AE1 F T -HAFTA HH AE1 F T AH0 -HAFTED HH AE1 F T IH0 D -HAFTER HH AE1 F T ER0 -HAFTS HH AE1 F S -HAFTS HH AE1 F T S -HAG HH AE1 G -HAG'S HH AE1 G Z -HAGA HH AA1 G AH0 -HAGADORN HH AE1 G AH0 D AO0 R N -HAGAN HH EY1 G AH0 N -HAGAN'S HH EY1 G AH0 N Z -HAGAR HH EY1 G AA0 R -HAGAR HH EY1 G ER0 -HAGAR'S HH EY1 G ER0 Z -HAGARS HH EY1 G ER0 Z -HAGBERD HH AE1 G B ER0 D -HAGBURN HH AE1 G B ER0 N -HAGE HH EY1 JH -HAGEB HH AE1 G IH0 B -HAGEDORN HH AE1 G IH0 D ER0 N -HAGEL HH AE1 G AH0 L -HAGEN HH EY1 G AH0 N -HAGEN'S HH EY1 G AH0 N Z -HAGENAU HH EY1 G AH0 N OW1 -HAGENBACH HH AE1 G IH0 N B AA0 K -HAGENBECK HH AE1 G IH0 N B EH0 K -HAGENHOF HH AE1 G IH0 N HH AH0 F -HAGER HH EY1 G ER0 -HAGERSTOWN HH EY1 G ER0 Z T AW2 N -HAGERUP HH EY1 G ER0 AH2 P -HAGG HH AE1 G -HAGGAGE HH AE1 G AH0 JH -HAGGAI HH AE1 G AY2 -HAGGARD HH AE1 G ER0 D -HAGGARD'S HH AE1 G ER0 D Z -HAGGARDLY HH AE1 G ER0 D L IY0 -HAGGARDNESS HH AE1 G ER0 D N AH0 S -HAGGART HH AE1 G ER0 T -HAGGART'S HH AE1 G ER0 T S -HAGGARTY HH AE1 G AA2 R T IY0 -HAGGER HH AE1 G ER0 -HAGGERDY HH AE1 G ER0 D IY0 -HAGGERSTONE HH AE1 G ER0 S T OW2 N -HAGGERT HH AE1 G ER0 T -HAGGERTY HH AE1 G ER0 T IY0 -HAGGERTY'S HH AE1 G ER0 T IY0 Z -HAGGIS HH AE1 G AH0 S -HAGGITH HH AE1 G AH0 TH -HAGGLE HH AE1 G AH0 L -HAGGLED HH AE1 G AH0 L D -HAGGLER HH AE1 G AH0 L ER0 -HAGGLES HH AE1 G AH0 L Z -HAGGLING HH AE1 G AH0 L IH0 NG -HAGGLING HH AE1 G L IH0 NG -HAGHAR HH AE1 G AA2 R -HAGHARS HH AE1 G AA2 R Z -HAGI HH AE1 G IY0 -HAGIA HH AE1 G IY0 AH0 -HAGIOLOGY HH AE1 G IY0 AA1 L AH0 JH IY0 -HAGIOS HH AE1 G IY0 OW2 Z -HAGIWARA HH AA2 G IH0 W AA1 R AH0 -HAGLEY HH AE1 G L IY0 -HAGNON HH AE1 G N AH0 N -HAGON HH AE1 G AH0 N -HAGS HH AE1 G Z -HAGSTROM HH AE1 G S T R AH0 M -HAGTHORPE HH AE1 G TH AO0 R P -HAGUE HH EY1 G -HAGUENIN HH EY1 G EY0 N IY1 N -HAGUNA HH AH0 G UW1 N AH0 -HAGWORTH HH AE1 G W ER0 TH -HAH HH AA1 -HAHA HH AA0 HH AA0 -HAHD HH AA1 D -HAHN HH AA1 N -HAHN'S HH AA1 N Z -HAHNEMANN HH AA1 N M AH0 N -HAHNEMANN'S HH AA1 N M AH0 N Z -HAHNKE HH AE1 NG K -HAHNSOME HH AA1 N S AH0 M -HAI HH AY1 -HAIATALNEFOUS HH AY1 AH0 T AA1 L N EH0 F AH0 S -HAICK HH AY1 K -HAICKS HH AY1 K S -HAID HH EY1 D -HAIDA HH AY1 D AH0 -HAIDAR HH AY1 D AA2 R -HAIDAS HH AY1 D AH0 S -HAIDE HH EY1 D -HAIDED HH EY1 D IH0 D -HAIDEE HH EY1 D IY0 -HAIDEE'S HH EY1 D IY0 Z -HAIDEMAKEN HH EY1 D M EY1 K AH0 N -HAIDEN HH EY1 D AH0 N -HAIDIA HH EY1 D Y AH0 -HAIDIA'S HH EY1 D IY0 AH0 Z -HAIDPLATZ HH EY1 D P L AE1 T S -HAIDS HH EY1 D Z -HAIDUK HH EY1 D AH0 K -HAIDUKS HH EY1 D AH1 K S -HAIE HH AY1 -HAIEN HH AY1 AH0 N -HAIFA HH AY1 F AH0 -HAIG HH EY1 G -HAIG'S HH EY1 G Z -HAIGH HH EY1 -HAIGH'S HH EY1 Z -HAIGHT HH EY1 T -HAIGHT'S HH EY1 T S -HAIK HH EY1 K -HAIKS HH EY1 K S -HAIL HH EY1 L -HAIL'D HH EY1 L D -HAILE HH AY1 L IY0 -HAILE HH EY1 L -HAILED HH EY1 L D -HAILES HH AY1 L IY0 Z -HAILES HH EY1 L Z -HAILEY HH EY1 L IY0 -HAILEY'S HH EY1 L IY0 Z -HAILEYBURY HH EY1 L IY0 B EH2 R IY0 -HAILIN HH EY1 L IH0 N -HAILING HH EY1 L IH0 NG -HAILL HH EY1 L -HAILLON HH EY1 L AH0 N -HAILO HH AY1 L OW0 -HAILS HH EY1 L Z -HAILSHAM HH EY1 L S HH AH0 M -HAILSTONE HH EY1 L S T OW2 N -HAILSTONES HH EY1 L S T OW2 N Z -HAILSTORM HH EY1 L S T AO2 R M -HAILSTORMS HH EY1 L S T AO2 R M Z -HAIM HH AY1 M -HAIM HH EY1 M -HAIM'S HH EY1 M Z -HAIMANOT HH EY1 M AH0 N AA0 T -HAIMANOUT HH EY1 M AH0 N AW2 T -HAIN HH EY1 N -HAIN'T HH EY1 N T -HAINAN HH EY1 N AH0 N -HAINAULT HH EY1 N OW1 -HAINBURG HH EY1 N B ER0 G -HAINE HH EY1 N -HAINES HH EY1 N Z -HAINES'S HH EY1 N Z IH0 Z -HAINS HH EY1 N Z -HAINT HH EY1 N T -HAINTEROH HH EY1 N T EH1 R OW0 -HAIR HH EH1 R -HAIR'D HH EH1 D -HAIR'S HH EH1 R Z -HAIRBRAINED HH EH1 R B R EY2 N D -HAIRBREADTH HH EH1 R B R EH1 D TH -HAIRBRUSH HH EH1 R B R AH2 SH -HAIRBRUSHES HH EH1 R B R AH2 SH IH0 Z -HAIRCLOTH HH EH1 R K L AO2 TH -HAIRCUT HH EH1 R K AH2 T -HAIRCUTTING HH EH1 R K AH2 T IH0 NG -HAIRD HH EH1 R D -HAIRDRESSER HH EH1 R D R EH2 S ER0 -HAIRDRESSER'S HH EH1 R D R EH2 S ER0 Z -HAIRDRESSERS HH EH1 R D R EH2 S ER0 Z -HAIRDRESSING HH EH1 R D R EH2 S IH0 NG -HAIRE HH EH1 R -HAIRED HH EH1 R D -HAIRES HH EH1 R Z -HAIRIER HH EH1 R IY0 ER0 -HAIRINESS HH EH1 R IY0 N AH0 S -HAIRLESS HH EH1 R L AH0 S -HAIRLESSNESS HH EH1 R L AH0 S N AH0 S -HAIRLIKE HH EH1 R L AY2 K -HAIRLINE HH EH1 R L AY2 N -HAIRPIN HH EH1 R P IH0 N -HAIRPINS HH EH1 R P IH0 N Z -HAIRS HH EH1 R Z -HAIRSBREADTH HH AY1 R S B R EH1 D TH -HAIRST HH EH1 R S T -HAIRSTON HH AY1 R S T AH0 N -HAIRT HH EH1 R T -HAIRY HH EH1 R IY0 -HAIS HH EY1 -HAIT HH EY1 T -HAITH HH EY1 TH -HAITI HH EY1 T IY0 -HAITIAN HH EY1 SH AH0 N -HAITIANS HH EY1 SH AH0 N Z -HAITIEN HH EY1 SH AH0 N -HAITIENS HH EY1 SH AH0 N Z -HAIUTO HH EY1 UW1 T OW0 -HAJ HH AY1 -HAJA HH AY1 AH0 -HAJI HH AE1 JH IY0 -HAJJ HH AE1 JH -HAJJAJ HH AA0 Y AA1 JH -HAJJEE HH AA0 Y EY1 -HAJJI HH AA0 Y IY0 -HAJO HH AA1 Y OW0 -HAJUS HH AY1 IH0 S -HAK HH AH0 K -HAKA HH AA1 K AH0 -HAKADAH HH AH0 K AA1 D AH0 -HAKAMA HH AA0 K AA1 M AH0 -HAKAU HH AH0 K AA1 UW0 -HAKE HH EY1 K -HAKE'S HH EY1 K S -HAKEEM HH AA0 K IY1 M -HAKEM HH EY1 K AH0 M -HAKEMOUTH HH AA0 K IY1 M AH0 TH -HAKIM AA0 K IY1 M -HAKIM HH AA0 K IY1 M -HAKIM'S AA0 K IY1 M Z -HAKIM'S HH AA0 K IY1 M Z -HAKIMA HH AA0 K IY1 M AH0 -HAKKUT HH AE1 K AH0 T -HAKLUYT HH AE1 K L AY1 T -HAKLUYT'S HH AE1 K L AY1 T S -HAKM HH AA0 K M -HAKO HH AA1 K OW0 -HAKODATE HH AE1 K AH0 D EY2 T -HAKON HH AE1 K AH0 N -HAKON'S HH AE1 K AH0 N Z -HAKONE HH AE1 K AH0 N -HAKONSON HH AE1 K AH0 N S AH0 N -HAL HH AE1 L -HAL'S HH AE1 L Z -HALA HH AA1 L AH0 -HALAKAZI HH AA1 L AH0 K AA1 Z IY0 -HALB HH AE1 L B -HALBACH HH AE1 L B AA0 K -HALBAN HH AE1 L B AH0 N -HALBE HH AE1 L B -HALBECK HH AE1 L B EH2 K -HALBER HH AE1 L B ER0 -HALBERD HH AE1 L B ER0 D -HALBERDIER HH AE1 L B ER0 D IH1 R -HALBERDIERS HH AE1 L B ER0 D IH1 R Z -HALBERDS HH AE1 L B ER0 D Z -HALBERG HH AE1 L B ER0 G -HALBERSTADT HH AE1 L B ER0 SH T AE0 T -HALBERT HH AE1 L B ER0 T -HALBERT'S HH AE1 L B ER0 T S -HALBERTS HH AE1 L B ER0 T S -HALBOROUGH HH AE1 L B ER0 OW0 -HALBPFENNIG HH AE1 L B F EH1 N IH0 G -HALBRANE HH AE1 L B R EY2 N -HALBY HH AE1 L B IY0 -HALCHEE HH AE1 L CH IY0 -HALCOMBE HH AE1 L K AH0 M -HALCOMBE'S HH AE1 L K AH0 M Z -HALCON HH AE1 L K AH0 N -HALCRO HH AE1 L K R OW0 -HALCROW HH AE1 L K R OW0 -HALCYON HH AE1 L S IY0 AH0 N -HALCYON'S HH AE1 L S IY0 AH0 N Z -HALCYONDALE HH AE1 L S IY0 AH0 N D EY2 L -HALCYONE HH AE1 L S IY0 OW0 N -HALCYONS HH AE1 L S IY0 AH0 N Z -HALD HH AO1 L D -HALDANE HH AH0 L D AE1 N -HALDANE'S HH AE1 L D AH0 N Z -HALDEMAN HH AA1 L D AH0 M AH0 N -HALDEMAN HH AA1 L D M AH0 N -HALDEN HH AO1 L D AH0 N -HALDENBRUNN HH AO1 L D AH0 N B R AH1 N -HALDER HH AO1 L D ER0 -HALDERSCHRODT HH AO1 L D ER0 SH R AA1 T -HALDGREN HH AO1 L D G R EH0 N -HALDIMAND HH AO1 L D AH0 M AH0 N D -HALDIMAR HH AO1 L D IY2 M AA0 R -HALDIMAR'S HH AO1 L D IH0 M AA2 R Z -HALDIN HH AO1 L D IH0 N -HALDIN'S HH AO1 L D AH0 N Z -HALDING HH AO1 L D IH0 NG -HALDON HH AE1 L D AH0 N -HALDOR HH AO1 L D ER0 -HALDOR'S HH AO1 L D ER0 Z -HALDORSTEDE HH AO1 L D ER0 S T IY1 D -HALDWELL HH AO1 L D W EH0 L -HALE HH EY1 L -HALE'S HH EY1 L Z -HALEAKALA HH EY1 L IY0 AH0 K AA1 L AH0 -HALED HH EY1 L D -HALEEL HH AE1 L IY0 L -HALEEMA HH AE1 L IY0 M AH0 -HALEINE HH AH0 L IY1 N -HALER HH EY1 L ER0 -HALES HH EY1 L Z -HALES'S HH EY1 L Z IH0 Z -HALESOME HH EY1 L S AH0 M -HALESUS HH AH0 L IY1 S AH0 S -HALESWORTH HH EY1 L Z W ER0 TH -HALET HH AH0 L EY1 -HALET'S HH AH0 L EY1 Z -HALEVI HH AH0 L EY1 V IY0 -HALEVI HH AH0 L IY1 V IY0 -HALEVY HH AH0 L IY1 V IY0 -HALEVY'S HH AH0 L IY1 V IY0 Z -HALEWITZ HH AE1 L AH0 W IH0 T S -HALEWYN HH AE1 L AH0 W IH0 N -HALEY HH EY1 L IY0 -HALEY'S HH EY1 L IY0 Z -HALEYVILLE HH EY1 L IY0 V IH2 L -HALF HH AE1 F -HALF'S HH AE1 F S -HALFA HH AE1 L F AH0 -HALFBACK HH AE1 F B AE2 K -HALFBREED HH AE1 F B R IY1 D -HALFBREED'S HH AE1 F B R IY1 D Z -HALFBREEDS HH AE1 F B R IY1 D Z -HALFDAN HH AE1 L F D AH0 N -HALFDAN'S HH AE1 L F D AH0 N Z -HALFE HH AE1 F -HALFHEARTED HH AE1 F HH AA2 R T IH0 D -HALFHEARTEDLY HH AE1 F HH AA2 R T IH0 D L IY0 -HALFLIN HH AE1 F L IH0 N -HALFMAN HH AE1 F M AH0 N -HALFMAN'S HH AE1 F M AH0 N Z -HALFMOON HH AE1 F M UW2 N -HALFNESS HH AE1 F N AH0 S -HALFOFF HH AE1 F AO0 F -HALFONT HH AE1 F AA1 N T -HALFORD HH AE1 F ER0 D -HALFORD'S HH AE1 F ER0 D Z -HALFPAST HH AE1 F P S T -HALFPENCE HH AE1 F P EH1 N S -HALFPENNIES HH AE1 F P EH1 N IY0 Z -HALFPENNY HH AE1 F P EH2 N IY0 -HALFPENNY'S HH AE1 F P EH1 N IY0 Z -HALFPENNYWORTH HH AE1 F P EH1 N IY0 W ER0 TH -HALFRED HH AE1 L F R IH0 D -HALFRED'S HH AE1 L F R IH0 D Z -HALFS HH AE1 F S -HALFTONE HH AE1 F T OW2 N -HALFVORSON HH AE1 F V AO1 R S AH0 N -HALFWAY HH AE1 F W EY1 -HALHED HH AE1 L T -HALI HH AA1 L IY0 -HALIBURTON HH AE1 L IH0 B ER2 T AH0 N -HALIBURTON'S HH AE1 L IH0 B ER2 T AH0 N Z -HALIBUT HH AE1 L AH0 B AH0 T -HALICARNASSUS HH AE1 L IH0 K AA2 R N AE1 S AH0 S -HALICTI HH AE1 L IH0 K T IY0 -HALICTUS HH AE1 L IH0 K T AH0 S -HALID HH AA1 L IH0 D -HALIDAME HH AE2 L AH0 D EY1 M -HALIDOM HH AH0 L IH1 D AH0 M -HALIDOME HH AE1 L AY2 D OW2 M -HALIDON HH AE1 L AY2 D AH0 N -HALIEUS HH EY1 L Y UW1 S -HALIFAX HH AE1 L IH0 F AE2 K S -HALIFAX'S HH AH0 L IY1 F AE2 K S IH0 Z -HALIL HH AE1 L AH0 L -HALIL'S HH AE1 L AH0 L Z -HALIM HH AH0 L IY1 M -HALIM'S HH AH0 L IY1 M Z -HALIMA HH AH0 L IY1 M AH0 -HALIMAH HH AH0 L IY1 M AH0 -HALING HH EY1 L IH0 NG -HALINGRE HH AE1 L IH0 N G R IY1 -HALIOTIS HH AA0 L IY0 OW1 T IH0 S -HALITHERSES HH AH0 L AY1 DH ER0 S AH0 Z -HALJAN HH AE1 L JH AH0 N -HALKET HH AE1 L K IH0 T -HALKETT HH AE1 L K IH0 T -HALKETT'S HH AE1 L K AH0 T S -HALKIN HH AE1 L K IH0 N -HALKON HH AE1 L K AO0 N -HALL HH AO1 L -HALL'S HH AO1 L Z -HALLA HH AE1 L AH0 -HALLAJ HH AE1 L AY2 -HALLAM HH AE1 L AH0 M -HALLAM'S HH AE1 L AH0 M Z -HALLAMS HH AE1 L AH0 M Z -HALLAN HH AE1 L AH0 N -HALLAND HH AE1 L AH0 N D -HALLARD HH AE1 L ER0 D -HALLASON HH AE1 L AH0 S AH0 N -HALLAYS HH AH0 L EY1 Z -HALLBERG HH AO1 L B ER0 G -HALLBIORN HH AO1 L B IY0 AO0 R N -HALLBLITHE HH AO1 L B L AY1 DH -HALLBLITHE'S HH AO1 L B L AY1 TH IH0 Z -HALLBOY HH AO1 L B OY2 -HALLDOOR HH AE1 L D AO2 R -HALLDOR HH AE1 L D AO0 R -HALLE HH AE1 L -HALLE HH AE1 L IY0 -HALLECK HH AE1 L IH0 K -HALLECK'S HH AE1 L IH0 K S -HALLELUIAH HH AE2 L AH0 L UW1 AH0 -HALLELUJAH HH AE2 L AH0 L UW1 Y AH0 -HALLELUJAHS HH AE2 L AH0 L UW1 Y AH0 Z -HALLEN HH AO1 L AH0 N -HALLEN'S HH AO1 L AH0 N Z -HALLER HH AO1 L ER0 -HALLERPFORT HH AE1 L ER0 P F ER0 T -HALLES HH EY1 L Z -HALLET HH AE1 L IH0 T -HALLET'S HH AE1 L IH0 T S -HALLETT HH AE1 L IH0 T -HALLETT'S HH AE1 L AH0 T S -HALLETTS HH AE1 L IH0 T S -HALLEY HH AE1 L IY0 -HALLEY HH EY1 L IY0 -HALLEY'S HH AE1 L IY0 Z -HALLFRED HH AE1 L F ER0 D -HALLGERD HH AE1 L G ER0 D -HALLGERD'S HH AE1 L G ER0 D Z -HALLGERDA HH AE1 L G ER1 D AH0 -HALLGROVE HH AE1 L G R OW2 V -HALLIARD HH AE1 L Y ER0 D -HALLIARDS HH AE1 L Y ER0 D Z -HALLIBURN HH AE1 L IH0 B ER2 N -HALLIBURN'S HH AE1 L IH0 B ER0 N Z -HALLIBURTON HH AE1 L IH0 B ER2 T AH0 N -HALLIBURTON'S HH AE1 L IH0 B ER2 T AH0 N Z -HALLIBURTONS HH AE1 L IH0 B ER2 T AH0 N Z -HALLIBURTT HH AE1 L IH0 B ER1 T -HALLIDAY HH AE1 L IH0 D EY2 -HALLIDAY'S HH AE1 L IH0 D EY2 Z -HALLIDAYS HH AE1 L IH0 D EY2 Z -HALLIE HH AO1 L IY0 -HALLIE'S HH AE1 L IY0 Z -HALLIFAX HH AE1 L IH0 F AE2 K S -HALLIFORD HH AE1 L IH0 F ER0 D -HALLIGAN HH AE1 L IH0 G AH0 N -HALLIJOHN HH AE1 L IH0 JH AA0 N -HALLIJOHN'S HH AE1 L AY1 JH AA2 N Z -HALLIN HH AE1 L IH0 N -HALLIN'S HH AE1 L IH0 N Z -HALLING HH AO1 L IH0 NG -HALLINGDAL HH AO1 L IH0 NG G D AH0 L -HALLISS HH AE1 L IH0 S -HALLIWAY HH AE1 L AH0 W EY2 -HALLIWAY'S HH AE1 L AH0 W EY2 Z -HALLIWELL HH AE1 L AH0 W EH0 L -HALLMAN HH AO1 L M AH0 N -HALLMARK HH AA1 L M AA2 R K -HALLMARKS HH AO1 L M AA2 R K S -HALLMUND HH AO1 L M AH0 N D -HALLO HH AE1 L OW0 -HALLOA HH AE1 L OW1 AH0 -HALLOAED HH AE1 L OW0 AH0 D -HALLOAING HH AE1 L OW0 EY1 NG -HALLOCK HH AE1 L AH0 K -HALLOCK'S HH AE2 L AA1 K S -HALLOED HH AE1 L OW0 D -HALLOH HH AE2 L AH0 -HALLOING HH AE1 L OW0 IH0 NG -HALLOO HH AE1 L UW2 -HALLOO'D HH AE1 L UW0 D -HALLOOED HH AH0 L UW1 D -HALLOOING HH AH0 L UW1 IH0 NG -HALLOOS HH AH0 L UW1 Z -HALLORAN HH AE1 L ER0 AH0 N -HALLORAN'S HH AE1 L ER0 AH0 N Z -HALLOW HH AE1 L OW0 -HALLOW'D HH AE1 L OW0 D -HALLOWAY HH AE1 L OW0 W EY2 -HALLOWAY'S HH AE1 L OW0 W EY2 Z -HALLOWE'EN HH AE2 L AH0 W IY1 N -HALLOWED HH AE1 L OW0 D -HALLOWEEN HH AE2 L AH0 W IY1 N -HALLOWELL HH AE1 L AH0 W EH0 L -HALLOWELL'S HH AE1 L OW0 IH0 L Z -HALLOWELLS HH AE2 L AH0 W EH0 L Z -HALLOWING HH AE1 L OW0 IH0 NG -HALLOWS HH AE1 L OW0 Z -HALLS HH AO1 L Z -HALLSTOCK HH AE1 L S T AA2 K -HALLTON HH AO1 L T AH0 N -HALLTOWN HH AO1 L T AW2 N -HALLUCINATED HH AH0 L UW1 S AH0 N EY0 T IH0 D -HALLUCINATING HH AH0 L UW1 S AH0 N EY0 T IH0 NG -HALLUCINATING HH AH0 L UW1 S IH0 N EY0 T IH0 NG -HALLUCINATION HH AH0 L UW2 S AH0 N EY1 SH AH0 N -HALLUCINATIONS HH AH0 L UW2 S AH0 N EY1 SH AH0 N Z -HALLUCINATORY HH AH0 L UW1 S AH0 N AH0 T AO2 R IY0 -HALLUS HH AE1 L AH0 S -HALLVARD HH AE1 L V ER0 D -HALLWARD HH AO1 L W ER0 D -HALLWARD'S HH AO1 L W ER0 D Z -HALLWAY HH AO1 L W EY2 -HALLWAYS HH AO1 L W EY2 Z -HALLY HH AE1 L IY0 -HALM HH AA1 M -HALMA HH AA1 L M AH0 -HALMAR HH AO1 L M ER0 -HALNAKEHAM HH AE1 L N AH0 K AH0 M -HALO HH EY1 L OW0 -HALOED HH EY1 L OW0 D -HALOES HH EY1 L OW0 Z -HALOO HH AH0 L UW2 -HALOS HH EY1 L OW0 Z -HALP HH AE1 L P -HALPIN HH AE1 L P IH0 N -HALS HH AH0 L Z -HALSA HH AO1 L S AH0 -HALSBURY HH AO1 L Z B EH0 R IY0 -HALSE HH AE1 L S -HALSE'S HH AE1 L S IH0 Z -HALSELL HH AE1 L S AH0 L -HALSEY HH AE1 L S IY0 -HALSEY'S HH AE1 L S IY0 Z -HALSING HH AE1 L S IH0 NG -HALSON HH AE1 L S AH0 N -HALSTEAD HH AE1 L S T EH0 D -HALSTEAD'S HH AE1 L S T EH2 D Z -HALSTED HH AE1 L S T IH0 D -HALSWOOD HH AE1 L S W UH2 D -HALT HH AO1 L T -HALTE HH EY1 L T -HALTED HH AO1 L T AH0 D -HALTED HH AO1 L T IH0 D -HALTER HH AO1 L T ER0 -HALTERED HH AO1 L T ER0 D -HALTERING HH AO1 L T ER0 IH0 NG -HALTERS HH AO1 L T ER0 Z -HALTING HH AO1 L T IH0 NG -HALTINGLY HH AO1 L T IH0 NG L IY0 -HALTINGS HH AO1 L T IH0 NG Z -HALTON HH AE1 L T AH0 N -HALTREN HH AO1 L T R AH0 N -HALTS HH AO1 L T S -HALTWHISTLE HH AO1 L T W IH1 S AH0 L -HALVARD HH AE1 L V ER0 D -HALVDAN HH AE1 V D AH0 N -HALVE HH AE1 V -HALVED HH AE1 V D -HALVERTON HH AE1 L V ER0 T AH0 N -HALVES HH AE1 V Z -HALVEY HH AE1 L V IY0 -HALVING HH AE1 V IH0 NG -HALVOR HH AE1 L V ER0 -HALVORSEN HH AE0 L V AO1 R S AH0 N -HALWAYS HH AO1 L W EY2 Z -HALY HH AH0 L IY0 -HALYARD HH AO1 L Y ER0 D -HALYARDS HH AE1 L Y AA0 R D Z -HALYS HH AE1 L IY0 Z -HAM HH AE1 M -HAM'S HH AE1 M Z -HAMA HH AA1 M AH0 -HAMA'S HH AA1 M AH0 Z -HAMADAH HH AE1 M AH0 D AH0 -HAMADAN HH AE1 M AH0 D AH0 N -HAMADCHAS HH AE1 M AH0 D CH AH0 Z -HAMADI HH AH0 M AA1 D IY0 -HAMADIAN HH AH0 M AA1 D IY0 AH0 N -HAMADRYAD HH AE1 M AH0 D R AY1 AE0 D -HAMADRYADS HH AE1 M AH0 D R AY1 AE2 D Z -HAMAGE HH AE1 M IH0 JH -HAMAKO HH AE1 M AH0 K OW0 -HAMAKUA HH AE1 M AH0 K Y UW0 AH0 -HAMAL HH EY1 M AH0 L -HAMALS HH EY1 M AH0 L Z -HAMAN HH EY1 M AH0 N -HAMAN'S HH EY1 M AH0 N Z -HAMANDO HH AH0 M AA1 N D OW0 -HAMANN HH AA1 M AH0 N -HAMAR HH AH0 M AA1 R -HAMARI HH AA0 M AA1 R IY0 -HAMATH HH AE1 M AH0 TH -HAMBA HH AE1 M B AH0 -HAMBARD HH AE1 M B ER0 D -HAMBLE HH AE1 M B AH0 L -HAMBLEBY HH AE1 M B AH0 L B IY0 -HAMBLEDON HH AE1 M B AH0 L D AH0 N -HAMBLETON HH AE1 M B AH0 L T AA0 N -HAMBLETONIAN HH AE1 M B L IH0 T OW1 N IY0 AH0 N -HAMBLIN HH AE1 M B L IH0 N -HAMBLIN'S HH AE1 M B L IH0 N Z -HAMBLY HH AE1 M B L IY0 -HAMBLYN HH AE1 M B L IH0 N -HAMBO HH AE1 M B OW0 -HAMBOURG HH AE1 M B AO2 R G -HAMBURG HH AE1 M B ER0 G -HAMBURG'S HH AE1 M B ER0 G Z -HAMBURGER HH AE1 M B ER0 G ER0 -HAMBURGERS HH AE1 M B ER0 G ER0 Z -HAMBURGH HH AE1 M B ER0 G -HAMBURGS HH AE1 M B ER0 G Z -HAMBY HH AE1 M B IY0 -HAMDEN HH AE1 M D AH0 N -HAMDI HH AE1 M D IY0 -HAMDIR HH AE1 M D ER0 -HAME HH EY1 M -HAMEAU HH AE1 M OW1 -HAMED HH AE1 M D -HAMED'S HH AA1 M EH0 D Z -HAMEL HH AE1 M AH0 L -HAMEL'S HH AE1 M AH0 L Z -HAMELIN HH AE1 M AH0 L IH0 N -HAMELIN HH AE1 M L IH2 N -HAMELINE HH AE1 M L AY2 N -HAMELN HH AE1 M AH0 L N -HAMELY HH AE1 M L IY0 -HAMELYN HH AE1 M IH0 L IH0 N -HAMER HH AE1 M ER0 -HAMER'S HH AE1 M ER0 Z -HAMERS HH AE1 M ER0 Z -HAMERSLEY HH AE1 M ER0 S L IY0 -HAMERSLEY'S HH AE1 M ER0 S L IY0 Z -HAMERTON HH AE1 M ER0 T AH0 N -HAMERTON'S HH AE1 M ER0 T AH0 N Z -HAMES HH EY1 M Z -HAMET HH AE1 M IH0 T -HAMET'S HH AE1 M AH0 T S -HAMEWARTS HH AE1 M W AO1 R T S -HAMID HH AH0 M IY1 D -HAMID'S HH AE1 M IH0 D Z -HAMIDA HH AH0 M IY1 D AH0 -HAMIL HH AE1 M AH0 L -HAMILCAR HH AE1 M AH0 L K AA2 R -HAMILCAR'S HH AE1 M AH0 L K AA1 R Z -HAMILL HH AE1 M AH0 L -HAMILT HH AE1 M AH0 L T -HAMILTON HH AE1 M AH0 L T AH0 N -HAMILTON'S HH AE1 M AH0 L T AH0 N Z -HAMILTONIAN HH AE1 M AH0 L T OW1 N IY0 AH0 N -HAMILTONS HH AE1 M AH0 L T AH0 N Z -HAMIR HH AA0 M IH1 R -HAMISH HH AE1 M IH0 SH -HAMISH'S HH AE1 M IH0 SH IH0 Z -HAMITIC HH AE0 M IH1 T IH0 K -HAMLEN HH AE1 M L AH0 N -HAMLEN'S HH AE1 M L AH0 N Z -HAMLET HH AE1 M L AH0 T -HAMLET HH AE1 M L IH0 T -HAMLET'S HH AE1 M L AH0 T S -HAMLETS HH AE1 M L AH0 T S -HAMLEY HH AE1 M L IY0 -HAMLIKE HH AE1 M L AY2 K -HAMLIN HH AE1 M L IH0 N -HAMLIN'S HH AE1 M L IH0 N Z -HAMLYN HH AE1 M L IH0 N -HAMLYN'S HH AE1 M L IH0 N Z -HAMLYNS HH AE1 M L IH0 N Z -HAMM HH AE1 M -HAMMA HH AE1 M AH0 -HAMMAL HH AE1 M AH0 L -HAMMAM HH AE1 M AH0 M -HAMMAMAT HH AE1 M AH0 M AE0 T -HAMMED HH AE1 M D -HAMMER HH AE1 M ER0 -HAMMER'D HH AE1 M ER0 AH0 D -HAMMER'S HH AE1 M ER0 Z -HAMMERCLOTH HH AE1 M ER0 K L AO2 TH -HAMMERED HH AE1 M ER0 D -HAMMERER HH AE1 M ER0 ER0 -HAMMERER'S HH AE1 M ER0 ER0 Z -HAMMERFEST HH AE1 M ER0 F EH2 S T -HAMMERFIELD HH AE1 M ER0 F IY2 L D -HAMMERIN HH AE1 M ER0 IH0 N -HAMMERING HH AE1 M ER0 IH0 NG -HAMMERINGS HH AE1 M ER0 IH0 NG Z -HAMMERLESS HH AE1 M ER0 L AH0 S -HAMMERLOCK HH AE1 M ER0 L AA2 K -HAMMERMAN HH AE1 M ER0 M AH0 N -HAMMERMEN HH AE1 M ER0 M IH0 N -HAMMERPOND HH AE1 M ER0 P AH0 N D -HAMMERS HH AE1 M ER0 Z -HAMMERSLEY HH AE1 M ER0 S L IY0 -HAMMERSLEY'S HH AE1 M ER0 S L IY0 Z -HAMMERSLY HH AE1 M ER0 S L IY0 -HAMMERSMITH HH AE1 M ER0 S M IH2 TH -HAMMERSMITH'S HH AE1 M ER0 S M IH2 TH S -HAMMERSTEIN HH AE1 M ER0 S T AY2 N -HAMMERSTEIN HH AE1 M ER0 S T IY2 N -HAMMERSTEIN'S HH AE1 M ER0 S T AY2 N Z -HAMMERSTEIN'S HH AE1 M ER0 S T IY2 N Z -HAMMERTON HH AE1 M ER0 T AH0 N -HAMMET HH AE1 M IH0 T -HAMMETH HH AE1 M IH0 TH -HAMMETT HH AE1 M IH0 T -HAMMICK HH AE1 M IH0 K -HAMMOCK HH AE1 M AH0 K -HAMMOCKS HH AE1 M AH0 K S -HAMMON HH AE1 M AH0 N -HAMMON'S HH AE1 M AH0 N Z -HAMMOND HH AE1 M AH0 N D -HAMMOND'S HH AE1 M AH0 N D Z -HAMMONDS HH AE1 M AH0 N D Z -HAMMONTON HH AE1 M AH0 N T AH0 N -HAMMURABI HH AE1 M UH0 R AA1 B IY0 -HAMMURABI'S HH AE1 M UH0 R AA0 B IY1 Z -HAMMY HH AE1 M IY0 -HAMN HH AE1 M N -HAMNER HH AE1 M N ER0 -HAMNET HH AE1 M N EH2 T -HAMO HH AA1 M OW0 -HAMOAZE HH AE1 M EY1 Z -HAMON HH AE1 M AH0 N -HAMON'S HH EY1 M AH0 N Z -HAMOND HH AE1 M AH0 N D -HAMOOD HH AE1 M UW2 D -HAMOR HH AE1 M ER0 -HAMP HH AE1 M P -HAMPDEN HH AE1 M P D AH0 N -HAMPDEN'S HH AE1 M P D AH0 N Z -HAMPDENS HH AE1 M P D AH0 N Z -HAMPDENSHIRE HH AE1 M P D IH0 N SH AY2 R -HAMPER HH AE1 M P ER0 -HAMPERED HH AE1 M P ER0 D -HAMPERING HH AE1 M P ER0 IH0 NG -HAMPERS HH AE1 M P ER0 Z -HAMPOLE HH AE1 M P OW2 L -HAMPS HH AE1 M P S -HAMPS'S HH AE1 M P S IH0 Z -HAMPSHIRE HH AE1 M P SH AY0 ER0 -HAMPSHIRE HH AE1 M P SH ER0 -HAMPSHIRE HH AE1 M SH AY0 ER0 -HAMPSHIRE HH AE1 M SH ER0 -HAMPSHIRE'S HH AE1 M P SH AY0 ER0 Z -HAMPSHIRE'S HH AE1 M P SH ER0 Z -HAMPSHIRE'S HH AE1 M SH AY0 ER0 Z -HAMPSHIRE'S HH AE1 M SH ER0 Z -HAMPSHIRES HH AE1 M P SH AY0 ER0 Z -HAMPSON HH AE1 M P S AH0 N -HAMPSON'S HH AE1 M P S AH0 N Z -HAMPSTEAD HH AE1 M P S T EH2 D -HAMPSTEAD'S HH AE1 M P S T EH2 D Z -HAMPTON HH AE1 M P T AH0 N -HAMPTON'S HH AE1 M P T AH0 N Z -HAMPTONS HH AE1 M P T AH0 N Z -HAMPTONSHIRE HH AE1 M P T AH0 N SH ER0 -HAMRAN HH AE1 M R AH0 N -HAMS HH AE1 M Z -HAMSHAW HH AE1 M SH AO2 -HAMSON HH AE1 M S AH0 N -HAMSTEAD HH AE1 M S T EH2 D -HAMSTER HH AE1 M S T ER0 -HAMSTERS HH AE1 M S T ER0 Z -HAMSTRING HH AE1 M S T R IH2 NG -HAMSTRINGING HH AE1 M S T R IH2 NG IH0 NG -HAMSTRINGS HH AE1 M S T R IH2 NG Z -HAMSTRUNG HH AE1 M S T R AH0 NG -HAMSUN HH AE1 M S AH2 N -HAMSUN'S HH AE1 M S AH0 N Z -HAMTHWAITE HH AE1 M TH W EY2 T -HAMTON HH AE0 M T AH0 N -HAMVERT HH AE1 M V ER1 T -HAMYLTON HH AE1 M IH0 L T AH0 N -HAMZA HH AE1 M Z AH0 -HAMZAH HH AE1 M Z AH0 -HAN HH AA1 N -HAN HH AE1 N -HAN' HH AE1 N -HAN'FUL HH AE1 N F AH0 L -HAN'KERCHIEF HH AE1 N K ER1 CH AH0 F -HAN'LE HH AE1 N AH0 L -HAN'S HH AA1 N Z -HAN'S HH AE1 N Z -HAN'SOME HH AE1 N S AH0 M -HAN'SOMER HH AE1 N S AH0 M ER0 -HAN'T HH AE1 N T -HANA HH AE1 N AH0 -HANADRA HH AA0 N AA1 D R AH0 -HANAFORD HH AE1 N AH0 F AO0 R D -HANAK HH AA1 N AH0 K -HANALEI HH AE1 N AH0 L IY0 -HANAN HH EY1 N AH0 N -HANANI HH AA0 N AA1 N IY0 -HANANIAH HH AE1 N AH0 N AY1 AH0 -HANAP HH AE1 N AH0 P -HANARURO HH AE1 N ER0 UW1 R OW0 -HANAU HH AE1 N AW0 -HANAUD HH AE1 N OW1 -HANAUD'S HH AE1 N AW0 D Z -HANAVAVE HH AE1 N AH0 V AE2 V -HANBOROUGH HH AE1 N B ER0 OW0 -HANBRIDGE HH AE1 N B R IH2 JH -HANBURY HH AE1 N B EH2 R IY0 -HANBURY'S HH AE1 N B EH2 R IY0 Z -HANBURYS HH AE1 N B EH2 R IY0 Z -HANBY HH AE1 N B IY0 -HANC HH AE1 NG K -HANCE HH AE1 N S -HANCHEN HH AE1 N CH AH0 N -HANCHETT HH AE1 N CH IH0 T -HANCKEL HH AE1 NG K AH0 L -HANCOCK HH AE1 N K AA2 K -HANCOCK'S HH AE1 N K AA2 K S -HANCOCKS HH AE1 N K AA2 K S -HAND HH AE1 N D -HAND' HH AE1 N -HAND'S HH AE1 N D Z -HANDASSAH HH AE1 N D AE2 S AH0 -HANDBAG HH AE1 N D B AE2 G -HANDBAGS HH AE1 N D B AE2 G Z -HANDBALL HH AE1 N D B AO2 L -HANDBARROW HH AE1 N D B EH2 R OW0 -HANDBELL HH AE1 N D B EH0 L -HANDBILL HH AE1 N D B IH2 L -HANDBILLS HH AE1 N D B IH2 L Z -HANDBOOK HH AE1 N D B UH2 K -HANDBOOKS HH AE1 N D B UH2 K S -HANDBREADTH HH AE1 N D B R EH2 D TH -HANDBY HH AE1 N D B IY0 -HANDCAR HH AE1 N D K AA2 R -HANDCART HH AE1 N D K AA2 R T -HANDCARTS HH AE1 N D K AA2 R T S -HANDCLAPPING HH AE1 N D K L AE2 P IH0 NG -HANDCLASP HH AE1 N D K L AE2 S P -HANDCLASPS HH AE1 N D K L AE2 S P S -HANDCOCK HH AE1 N D K AA2 K -HANDCRAFT HH AE1 N D K R AE2 F T -HANDCUFF HH AE1 N D K AH2 F -HANDCUFFED HH AE1 N D K AH2 F T -HANDCUFFING HH AE1 N D K AH2 F IH0 NG -HANDCUFFS HH AE1 N D K AH2 F S -HANDE HH AE1 N D -HANDED HH AE1 N D AH0 D -HANDED HH AE1 N D IH0 D -HANDEDLY HH AE1 N D IH0 D L IY0 -HANDEDNESS HH AE1 N D AH0 D N AH0 S -HANDEL HH AE1 N D AH0 L -HANDEL'S HH AE1 N D AH0 L Z -HANDELS HH AE1 N D AH0 L Z -HANDER HH AE1 N D ER0 -HANDERS HH AE1 N D ER0 Z -HANDES HH AE1 N D IY0 Z -HANDFAST HH AE1 N D F AE2 S T -HANDFORD HH AE1 N D F ER0 D -HANDFU HH AE1 N D F UW0 -HANDFUL HH AE1 N D F UH2 L -HANDFULL HH AE1 N D F AH0 L -HANDFULLS HH AE1 N D F AH0 L Z -HANDFULS HH AE1 N D F UH2 L Z -HANDGLASS HH AE1 N D G L AE2 S -HANDGRIP HH AE1 N D G R IH0 P -HANDGRIPS HH AE1 N D G R IH1 P S -HANDGUN HH AE1 N D G AH2 N -HANDHOLD HH AE1 N D HH OW2 L D -HANDHOLDS HH AE1 N D HH OW2 L D Z -HANDI HH AE1 N D IY0 -HANDICAP HH AE1 N D IY0 K AE2 P -HANDICAPPED HH AE1 N D IY0 K AE2 P T -HANDICAPPER HH AE1 N D IY0 K AE2 P ER0 -HANDICAPPING HH AE1 N D IY0 K AE2 P IH0 NG -HANDICAPS HH AE1 N D IY0 K AE2 P S -HANDICRAFT HH AE1 N D IY0 K R AE2 F T -HANDICRAFTS HH AE1 N D IY0 K R AE2 F T S -HANDICRAFTSMAN HH AE1 N D IY0 K R AE2 F T S M AH0 N -HANDICRAFTSMEN HH AE1 N D IY0 K R AE2 F T S M EH0 N -HANDIER HH AE1 N D IY0 ER0 -HANDIES HH AE1 N D IY0 Z -HANDIEST HH AE1 N D IY0 AH0 S T -HANDILY HH AE1 N D AH0 L IY0 -HANDIN HH AE1 N D IH0 N -HANDINESS HH AE1 N D IY0 N AH0 S -HANDING HH AE1 N D IH0 NG -HANDIWORK HH AE1 N D IY0 W ER2 K -HANDIWORKS HH AE1 N D IY0 W ER2 K S -HANDJAR HH AE1 N JH ER0 -HANDKERCHEIF HH AE1 NG K ER0 CH IY1 F -HANDKERCHER HH AE1 NG K ER0 CH ER0 -HANDKERCHERS HH AE1 NG K ER0 CH ER0 Z -HANDKERCHIEF HH AE1 NG K ER0 CH IH0 F -HANDKERCHIEF HH AE1 NG K ER0 CH IY0 F -HANDKERCHIEF'S HH AE1 NG K ER0 CH IY1 F S -HANDKERCHIEFS HH AE1 NG K ER0 CH AH0 F S -HANDKERCHIEFS HH AE1 NG K ER0 CH IY0 F S -HANDKERCHY HH AE1 NG K ER0 CH IY0 -HANDLE HH AE1 N D AH0 L -HANDLE'S HH AE1 N D AH0 L Z -HANDLEBARS HH AE1 N D AH0 L B AA2 R Z -HANDLED HH AE1 N D AH0 L D -HANDLELESS HH AE1 N D AH0 L L AH0 S -HANDLER HH AE1 N D AH0 L ER0 -HANDLER HH AE1 N D L ER0 -HANDLERS HH AE1 N D AH0 L ER0 Z -HANDLERS HH AE1 N D L ER0 Z -HANDLES HH AE1 N D AH0 L Z -HANDLESS HH AE1 N D L AH0 S -HANDLETH HH AE1 N D AH0 L TH -HANDLEY HH AE1 N D L IY0 -HANDLIKE HH AE1 N D L AY2 K -HANDLIN HH AE1 N D L IH0 N -HANDLING HH AE1 N D AH0 L IH0 NG -HANDLING HH AE1 N D L IH0 NG -HANDLINGS HH AE1 N D L IH0 NG Z -HANDLON HH AE1 N D L AH0 N -HANDLOOM HH AE1 N D L UW2 M -HANDMADE HH AE1 N D M EY1 D -HANDMADE HH AE1 N M EY1 D -HANDMAID HH AE1 N D M EY2 D -HANDMAIDEN HH AE1 N D M EY1 D AH0 N -HANDMAIDEN'S HH AE1 N D M EY1 D AH0 N Z -HANDMAIDENS HH AE1 N D M EY1 D AH0 N Z -HANDMAIDS HH AE1 N D M EY2 D Z -HANDMILL HH AE1 N D M IH2 L -HANDOUT HH AE1 N D AW2 T -HANDOUTS HH AE1 N D AW2 T S -HANDPHONE HH AE1 N D F OW2 N -HANDRAIL HH AE1 N D R EY2 L -HANDRAILS HH AE1 N D R EY2 L Z -HANDROSAN HH AE1 N D R OW0 S AA1 N -HANDS HH AE1 N D Z -HANDS HH AE1 N Z -HANDS' HH AE1 N D Z -HANDS'LL HH AE1 N S AH0 L -HANDSAW HH AE1 N D S AO2 -HANDSBREADTH HH AE1 N D Z B R EH1 D TH -HANDSCOMBE HH AE1 N D S K OW1 M B IY0 -HANDSEL HH AE1 N D S AH0 L -HANDSELL HH AE1 N D S AH0 L -HANDSELLED HH AE1 N D Z EH1 L D -HANDSET HH AE1 N D S EH2 T -HANDSFUL HH AE1 N S F AH0 L -HANDSHAKE HH AE1 N D SH EY2 K -HANDSHAKES HH AE1 N D SH EY2 K S -HANDSHAKING HH AE1 N D SH EY2 K IH0 NG -HANDSHAKINGS HH AE1 N D SH EY2 K IH0 NG Z -HANDSLEY HH AE1 N D S L IY0 -HANDSOM HH AE1 N S AH0 M -HANDSOME HH AE1 N S AH0 M -HANDSOMEBODY HH AE1 N D S AH2 M B AA2 D IY0 -HANDSOMEBODY'S HH AE1 N D S AH2 M B AA2 D IY0 Z -HANDSOMELY HH AE1 N S AH0 M L IY0 -HANDSOMENESS HH AE1 N S AH0 M N AH0 S -HANDSOMER HH AE1 N S AH0 M ER0 -HANDSOMEST HH AE1 N D S AH2 M AH0 S T -HANDSOMLY HH AE1 N S AH0 M L IY0 -HANDSPIKE HH AE1 N D S P IH0 K -HANDSPIKES HH AE1 N D S P AY1 K S -HANDSPRING HH AE1 N D S P R IH0 NG -HANDSPRINGS HH AE1 N D S P R IH1 NG Z -HANDSTONE HH AE1 N D S T OW2 N -HANDSUM HH AE1 N Z AH0 M -HANDSWORTH HH AE1 N D Z W ER2 TH -HANDWORK HH AE1 N D W ER2 K -HANDWRITE HH AE1 N D R AY2 T -HANDWRITIN HH AE1 N D R AY2 T IH0 N -HANDWRITING HH AE1 N D R AY2 T IH0 NG -HANDWRITINGS HH AE1 N D R AY2 T IH0 NG Z -HANDWRITTEN HH AE1 N D R IH2 T AH0 N -HANDY HH AE1 N D IY0 -HANDY'S HH AE1 N D IY0 Z -HANDYCOCK HH AE1 N D IY0 K AO2 K -HANDYMAN HH AE1 N D IY0 M AE2 N -HANDYSIDE HH AE1 N D IY0 S AY2 D -HANDYSIDE'S HH AE1 N D IY0 S AY2 D Z -HANDYWORK HH AE1 N D IY0 W ER2 K -HANE HH EY1 N -HANEEMAR HH AH0 N EY1 EY0 M AA1 R -HANEM HH AE1 N IH0 M -HANENWINKEL HH AE1 N IH0 N W IH1 NG K AH0 L -HANER HH EY1 N ER0 -HANES HH EY1 N Z -HANEY HH EY1 N IY0 -HANEY'S HH EY1 N IY0 Z -HANEYS HH EY1 N IY0 Z -HANFIELD HH AE1 N F IY2 L D -HANFORD HH AE1 N F ER0 D -HANFORD'S HH AE1 N F ER0 D Z -HANFSTAENGL HH AE1 N F S T AE0 IH1 NG AH0 L -HANG HH AE1 NG -HANG'D HH AE1 NG D -HANGAR HH AE1 NG ER0 -HANGARS HH AE1 NG ER0 Z -HANGCHOW HH AE1 NG CH OW0 -HANGDOG HH AE1 NG D AO2 G -HANGED HH AE1 NG D -HANGEL HH AE1 NG G AH0 L -HANGELINA HH AE1 N JH AH0 L IY1 N AH0 -HANGER HH AE1 NG ER0 -HANGER'S HH AE1 NG ER0 Z -HANGERS HH AE1 NG ER0 Z -HANGEST HH AE1 NG G AH0 S T -HANGETH HH AE1 NG AH0 TH -HANGIN HH AE1 NG G IH0 N -HANGIN'S HH AE1 NG G IH0 N Z -HANGING HH AE1 NG G IH0 NG -HANGING HH AE1 NG IH0 NG -HANGING'S HH AE1 NG G IH0 NG Z -HANGINGS HH AE1 NG G IH0 NG Z -HANGIT HH AE1 NG G IH0 T -HANGLIP HH AE1 NG L IH0 P -HANGMAN HH AE1 NG M AH0 N -HANGMAN'S HH AE1 NG M AH0 N Z -HANGMEN HH AE1 NG M AH0 N -HANGOUT HH AE1 NG AW2 T -HANGOVER HH AE1 NG OW2 V ER0 -HANGS HH AE1 NG Z -HANGT HH AE1 NG T -HANGTOWN HH AE1 NG T AW2 N -HANGWAN HH AE1 NG G W AA0 N -HANIERRI HH AE1 N Y EH1 R IY0 -HANIMAL HH AE1 N AH0 M AH0 L -HANIMALS HH AE1 N AH0 M AH0 L Z -HANK HH AE1 NG K -HANK'S HH AE1 N K S -HANKA HH AE1 NG K AH0 -HANKER HH AE1 NG K ER0 -HANKERCHER HH AE1 NG K ER1 K ER0 -HANKERCHIEF HH AE1 NG K ER1 CH AH0 F -HANKERED HH AE1 NG K ER0 D -HANKERIN HH AE1 NG K ER0 IH0 N -HANKERING HH AE1 NG K ER0 IH0 NG -HANKERINGS HH AE1 NG K ER0 IH0 NG Z -HANKERS HH AE1 NG K ER0 Z -HANKES HH AE1 NG K S -HANKES'S HH AE1 NG K S IH0 Z -HANKEY HH AE1 NG K IY0 -HANKEY'S HH AE1 NG K IY0 Z -HANKIES HH AE1 NG K IY0 Z -HANKIN HH AE1 NG K IH0 N -HANKINS HH AE1 NG K IH0 N Z -HANKINSON HH AE1 NG K IH0 N S AH0 N -HANKLY HH AE1 NG K L IY0 -HANKOS HH AE1 NG K OW0 Z -HANKOW HH AE1 NG K OW0 -HANKS HH AE1 NG K S -HANKY HH AE1 NG K IY0 -HANKY'S HH AE1 NG K IY0 Z -HANKYCHY HH AE1 NG K Y CH IY0 -HANLEY HH AE1 N L IY0 -HANLEY'S HH AE1 N L IY0 Z -HANLIN HH AE1 N L IH0 N -HANLON HH AE1 N L AH0 N -HANLON'S HH AE1 N L AH0 N Z -HANLY HH AE1 N L IY0 -HANMER HH AE1 N M ER0 -HANMER'S HH AE1 N M ER0 Z -HANN HH AE1 N -HANNA HH AE1 N AH0 -HANNA'S HH AE1 N AH0 Z -HANNAFORD HH AE1 N AH0 F ER0 D -HANNAFORD'S HH AE1 N AH0 F ER0 D Z -HANNAH HH AE1 N AH0 -HANNAH'S HH AE1 N AH0 Z -HANNAN HH AE1 N AH0 N -HANNAN'S HH AE1 N AH0 N Z -HANNAS HH AE1 N AH0 Z -HANNASYDE HH AE1 N AH0 S AY1 D -HANNAWAY HH AE1 N AH0 W EY0 -HANNAY HH AE1 N EY0 -HANNE HH AE1 N -HANNE'S HH AE1 N Z -HANNEH HH AE1 N IH0 HH -HANNELE HH AE1 N AH0 L -HANNEN HH AE1 N AH0 N -HANNEQUIN HH AE1 N AH0 K IH0 N -HANNER HH AE1 N ER0 -HANNES HH AE1 N Z -HANNIBAL HH AE1 N IH0 B AH0 L -HANNIBAL'S HH AE1 N AH0 B AH0 L Z -HANNIGAN HH AE1 N IH0 G AH0 N -HANNING HH AE1 N IH0 NG -HANNINGTON HH AE1 N IH0 NG T AH0 N -HANNINGTON'S HH AE1 N IH0 NG T AH0 N Z -HANNO HH AE1 N OW0 -HANNO'S HH AE1 N OW0 Z -HANNOCK HH AE1 N AH0 K -HANNOCK'S HH AE1 N AH0 K S -HANNON HH AE1 N AH0 N -HANNOVER HH AE1 N OW2 V ER0 -HANNS HH AE1 N Z -HANNY HH AE1 N IY0 -HANNY'S HH AE1 N IY0 Z -HANOTAUX HH AE1 N OW0 T OW1 -HANOTEAU HH AE1 N AH0 T OW1 -HANOUM HH AH0 N UW1 M -HANOVER HH AE1 N OW0 V ER0 -HANOVERIAN HH AE2 N OW0 V IH1 R IY0 AH0 N -HANOVERIANS HH AE2 N OW0 V IH1 R IY0 AH0 N Z -HANOVERTOWN HH AE1 N OW0 V ER0 T AW2 N -HANOVRE HH AE1 N OW0 V ER0 -HANRAHAN HH AE1 N R AH0 HH AE0 N -HANRAHAN'S HH AE1 N R AH0 HH AE0 N Z -HANS HH AA1 N S -HANS HH AE1 N Z -HANS'S HH AE1 N Z IH0 Z -HANSA HH AE1 N S AH0 -HANSAG HH AE1 N S AE0 G -HANSARD HH AE1 N S ER0 D -HANSARD'S HH AE1 N S ER0 D Z -HANSCHA HH AE1 N SH AH0 -HANSCOM HH AE1 N S K AH0 M -HANSE HH AE1 N S -HANSEATIC HH AE2 N S IY0 AE1 T IH0 K -HANSEI HH AA1 N S EY2 -HANSEL HH AE1 N S AH0 L -HANSEL'S HH AE1 N S AH0 L Z -HANSELL HH AE1 N S AH0 L -HANSEN HH AE1 N S AH0 N -HANSEN'S HH AE1 N S AH0 N Z -HANSENS HH AE1 N S AH0 N Z -HANSERL HH AE1 N S ER0 L -HANSERL'S HH AE1 N S ER0 L Z -HANSFORD HH AE1 N S F ER0 D -HANSFORD'S HH AE1 N S F ER0 D Z -HANSGEORGE HH AE1 N Z JH AO1 R JH -HANSHEW HH AE1 N SH UW0 -HANSI HH AE1 N S IY0 -HANSKA HH AE1 N S K AH0 -HANSKA'S HH AE1 N S K AH0 Z -HANSKE HH AE1 N S K -HANSKI HH AE1 N S K IY0 -HANSLEIN HH AE1 N S L AY2 N -HANSLICK HH AE1 N S L IH0 K -HANSOM HH AE1 N S AH0 M -HANSOM'S HH AE1 N S AH0 M Z -HANSOMBODY HH AE1 N S AH0 M B AA1 D IY0 -HANSOME HH AE1 N S AH0 M -HANSOMS HH AE1 N S AH0 M Z -HANSON HH AE1 N S AH0 N -HANSON'S HH AE1 N S AH0 N Z -HANSONS HH AE1 N S AH0 N Z -HANSSON HH AE1 N S AH0 N -HANSTEEN HH AE1 N S T IY2 N -HANSUM HH AE1 N S AH0 M -HANSWER HH AE1 N S ER0 -HANT HH AE1 N T -HANTE HH AA1 N T IY0 -HANTED HH AE1 N IH0 D -HANTEE HH AE1 N T IY0 -HANTLE HH AE1 N T AH0 L -HANTS HH AE1 N T S -HANU HH AA1 N UW0 -HANUM HH AA1 N AH0 M -HANUMAN HH AA1 N AH0 M AH0 N -HANUN HH AA1 N AH0 N -HANWAY HH AE1 N W EY2 -HANWAY'S HH AE1 N W EY2 Z -HANWAYS HH AE1 N W EY2 Z -HANWELL HH AE1 N W EH2 L -HANWOOD HH AE1 N W UH2 D -HANY HH EY1 N IY0 -HANYTHING HH EY1 N IY0 TH IH2 NG -HANYTHINK HH EY1 N IY0 TH IH2 NG K -HANZLI HH AE1 N Z L IY0 -HAO HH AW1 -HAOKAH HH AA0 OW1 K AH0 -HAOLE HH EY1 OW2 L -HAOMA HH AA0 OW1 M AH0 -HAORI HH AW1 R IY0 -HAOUSE HH AW1 Z -HAOW HH EY1 OW0 -HAP HH AE1 P -HAP'ORTH HH AE1 P AO2 R TH -HAP'S HH AE1 P S -HAPAA HH AE1 P AH0 -HAPAAS HH AE1 P AA1 Z -HAPARANDA HH AE1 P ER0 AE1 N D AH0 -HAPE HH EY1 P -HAPENDEKI HH AE1 P AH0 N D EH1 K IY0 -HAPGOOD HH AE1 P G UH2 D -HAPGOOD'S HH AE1 P G UH2 D Z -HAPHAZARD HH AE0 P HH AE1 Z ER0 D -HAPHAZARDLY HH AE0 P HH AE1 Z ER0 D L IY0 -HAPHAZARDLY HH AE1 F AH0 Z ER0 D L IY0 -HAPI HH AE1 P IY0 -HAPLESS HH AE1 P L AH0 S -HAPLEY HH AE1 P L IY0 -HAPLY HH AE1 P L IY0 -HAPNED HH AE1 P N D -HAPORTH HH EY1 P ER0 TH -HAPP HH AE1 P -HAPP'D HH AE1 P D -HAPPAR HH AE1 P ER0 -HAPPARS HH AE1 P ER0 Z -HAPPED HH AE1 P T -HAPPEN HH AE1 P AH0 N -HAPPEN'D HH AE1 P AH0 N D -HAPPENCHANCE HH AE1 P IH0 N CH AE1 N S -HAPPENED HH AE1 P AH0 N D -HAPPENETH HH AE1 P AH0 N EH1 TH -HAPPENIN HH AE1 P AH0 N IH1 N -HAPPENIN'S HH AE1 P AH0 N IH0 N Z -HAPPENING HH AE1 P AH0 N IH0 NG -HAPPENING HH AE1 P N IH0 NG -HAPPENINGS HH AE1 P AH0 N IH0 NG Z -HAPPENINGS HH AE1 P N IH0 NG Z -HAPPENS HH AE1 P AH0 N Z -HAPPENSTANCE HH AE1 P AH0 N S T AE2 N S -HAPPENT HH AE1 P AH0 N T -HAPPER HH AE1 P ER0 -HAPPERSETT HH AE1 P ER0 S AH0 T -HAPPERTON HH AE1 P ER0 T AH0 N -HAPPETITE HH AE1 P AH0 T AY2 T -HAPPIE HH AE1 P IY0 -HAPPIER HH AE1 P IY0 ER0 -HAPPIEST HH AE1 P IY0 AH0 S T -HAPPILY HH AE1 P AH0 L IY0 -HAPPINESS HH AE1 P IY0 N AH0 S -HAPPINESSE HH AE1 P IY0 N IH0 S -HAPPINESSES HH AE1 P IY0 N AH0 S IH0 Z -HAPPUCH HH AE1 P AH0 K -HAPPY HH AE1 P IY0 -HAPPY'S HH AE1 P IY0 Z -HAPPYHEART HH AE1 P IY0 HH AA2 R T -HAPPYLAND HH AE1 P IY0 L AE2 N D -HAPS HH AE1 P S -HAPSBURG HH AE1 P S B ER0 G -HAPSBURGERS HH AE1 P S B ER0 G ER0 Z -HAPSBURGS HH AE1 P S B ER0 G Z -HAPU HH AE1 P Y UW0 -HAQ HH AE1 K -HAR HH AA1 R -HARA HH EH1 R AH0 -HARAB HH AE1 R AH0 B -HARADEN HH AH0 R AE1 D AH0 N -HARAK HH AE1 R AH0 K -HARAKIRI HH AE1 R AH0 K IH1 R IY0 -HARALD HH AA1 R AH0 L D -HARALD'S HH AA1 R AH0 L D Z -HARALSON HH AE1 R AH0 L S AH0 N -HARAM HH AE1 R AH0 M -HARAMOK HH AA0 R AA1 M AA0 K -HARAMONT HH AE1 R AH0 M AA1 N T -HARAN HH AE1 R AH0 N -HARANGUE HH ER0 AE1 NG -HARANGUED HH ER0 AE1 NG D -HARANGUES HH ER0 AE1 NG Z -HARANGUING HH ER0 AE1 NG IH0 NG -HARAR HH ER0 AA1 R -HARARI HH ER0 AA1 R IY0 -HARAS HH AA1 R AH0 Z -HARASS HH ER0 AE1 S -HARASS'D HH ER0 AE1 SH D -HARASSED HH ER0 AE1 S T -HARASSES HH ER0 AE1 S IH0 Z -HARASSING HH ER0 AE1 S IH0 NG -HARASSMENT HH ER0 AE1 S M AH0 N T -HARASSMENTS HH ER0 AE1 S M AH0 N T S -HARB HH AA1 R B -HARBELL HH AA1 R B EH2 L -HARBEN HH AA1 R B AH0 N -HARBER HH AA1 R B ER0 -HARBERG HH AA1 R B ER0 G -HARBERTON HH AA1 R B ER0 T AH0 N -HARBIN HH AA1 R B IH0 N -HARBINGER HH AA1 R B IH0 N JH ER0 -HARBINGERS HH AA1 R B IH0 NG ER0 Z -HARBISON HH AA1 R B IH0 S AH0 N -HARBISON'S HH AA1 R B IH0 S AH0 N Z -HARBOR HH AA1 R B ER0 -HARBOR'S HH AA1 R B ER0 Z -HARBORAGE HH AA1 R B ER0 AH0 JH -HARBORD HH AA1 R B ER0 D -HARBORED HH AA1 R B ER0 D -HARBORING HH AA1 R B ER0 IH0 NG -HARBORNE HH AA1 R B ER0 N -HARBORNE'S HH AA1 R B ER0 N Z -HARBOROUGH HH AA1 R B ER2 OW0 -HARBOROUGH'S HH AA1 R B ER2 OW0 Z -HARBORS HH AA1 R B ER0 Z -HARBOTTLE HH AA1 R B AO2 T AH0 L -HARBOTTLE'S HH AA1 R B AO2 T AH0 L Z -HARBOTTLES HH AA1 R B AO2 T AH0 L Z -HARBOUR HH AA1 R B ER0 -HARBOUR'D HH AA1 R B ER0 D -HARBOUR'S HH AA1 R B ER0 Z -HARBOURAGE HH AA1 R B ER0 AA1 ZH -HARBOURED HH AA1 R B ER0 D -HARBOURER HH AA1 R B ER0 ER0 -HARBOURING HH AA1 R B ER0 IH0 NG -HARBOURLESS HH AA1 R B ER0 L AH0 S -HARBOURS HH AA1 R B ER0 Z -HARBURG HH AA1 R B ER0 G -HARBURG'S HH AA1 R B ER0 G Z -HARBURGER HH AA1 R B ER0 G ER0 -HARBURY HH AA1 R B ER0 IY0 -HARBUTT HH AA1 R B AH0 T -HARBY HH AA1 R B IY0 -HARCH HH AA1 R K -HARCLAY HH AA1 R K L EY0 -HARCOMB HH AA1 R K AH0 M -HARCOMBE HH AA1 R K AH0 M -HARCOURT HH AA1 R K AO2 R T -HARCOURT'S HH AA1 R K ER0 T S -HARCOURTS HH AA1 R K ER1 T S -HARCOUT HH AA1 R K AW0 T -HARCRAFT HH AA1 R K R AE2 F T -HARD HH AA1 R D -HARD'S HH AA1 R D Z -HARDA HH AA1 R D AH0 -HARDACRE HH AA1 R D EY2 K ER0 -HARDAGE HH AA1 R D IH0 JH -HARDANGER HH AA1 R D EY1 N JH ER0 -HARDAWAY HH AA1 R D AH0 W EY2 -HARDBAKE HH AA1 R D B EY2 K -HARDBOTTLE HH AA1 R D B AO2 T AH0 L -HARDBROOKE HH AA1 R D B R UH0 K -HARDCAP HH AA1 R D K AE2 P -HARDCASE HH AA1 R D K EY2 S -HARDCASTLE HH AA1 R D K AE2 S AH0 L -HARDCASTLE'S HH AA1 R D K AE2 S AH0 L Z -HARDCOME HH AA1 R D K AH2 M -HARDCOME'S HH AA1 R D K AH2 M Z -HARDE HH AA1 R D -HARDEE HH AA1 R D IY1 -HARDEE'S HH AA1 R D IY1 Z -HARDEN HH AA1 R D AH0 N -HARDEN'D HH AA1 R D AH0 N D -HARDEN'S HH AA1 R D AH0 N Z -HARDENBERG HH AA1 R D AH0 N B ER0 G -HARDENBERG'S HH AA1 R D AH0 N B ER0 G Z -HARDENBURGH HH AA1 R D AH0 N B ER0 G -HARDENED HH AA1 R D AH0 N D -HARDENETH HH AA1 R D N IH0 TH -HARDENING HH AA1 R D AH0 N IH0 NG -HARDENING HH AA1 R D N IH0 NG -HARDENOW HH AA1 R D N OW0 -HARDENS HH AA1 R D AH0 N Z -HARDER HH AA1 R D ER0 -HARDER'N HH AA1 R D ER0 AH0 N -HARDEST HH AA1 R D AH0 S T -HARDESTY HH AA1 R D AH0 S T IY0 -HARDFELLOW HH AA1 R D F EH2 L OW0 -HARDGRAVE HH AA1 R D G R EY2 V -HARDHACK HH AA1 R D HH AE0 K -HARDHACK'S HH AA1 R D HH AE2 K S -HARDHAND HH AA1 R D HH AE2 N D -HARDHEADED HH AA1 R D HH EH2 D IH0 D -HARDHEARTED HH AA1 R D HH AA2 R T IH0 D -HARDHEARTEDNESS HH AA1 R D HH AA2 R T IH0 D N AH0 S -HARDHOLD HH AA1 R D HH OW2 L D -HARDI HH AA1 R D IY0 -HARDICANUTE HH AA1 R D IH0 K AH0 N UW1 T -HARDIE HH AA1 R D IY0 -HARDIE'S HH AA1 R D IY2 Z -HARDIER HH AA1 R D IY0 ER0 -HARDIES HH AA1 R D IY0 Z -HARDIEST HH AA1 R D IY0 AH0 S T -HARDIHOOD HH AA1 R D IY0 HH UH2 D -HARDILY HH AA1 R D IH0 L IY0 -HARDIMAN HH AA1 R D IH0 M AH0 N -HARDIMAN'S HH AA1 R D IH0 M AH0 N Z -HARDIMENT HH AA1 R D IH0 M AH0 N T -HARDIN HH AA1 R D IH0 N -HARDIN'S HH AA1 R D IH0 N Z -HARDINESS HH AA1 R D IY0 N AH0 S -HARDING HH AA1 R D IH0 NG -HARDING'S HH AA1 R D IH0 NG S -HARDINGE HH AA1 R D IH0 N G -HARDINGE'S HH AA1 R D IH0 N G Z -HARDINGHAM HH AA1 R D IH0 NG HH AE2 M -HARDINGS HH AA1 R D IH0 NG Z -HARDISH HH AA1 R D IH0 SH -HARDLANDS HH AA1 R D L AE2 N D Z -HARDLEY HH AA1 R D L IY0 -HARDLINES HH AA1 R D L AY2 N Z -HARDLY HH AA1 R D L IY0 -HARDMAN HH AA1 R D M AH0 N -HARDMAN'S HH AA1 R D M AH0 N Z -HARDNED HH AA1 R D N D -HARDNESS HH AA1 R D N AH0 S -HARDNESSES HH AA1 R D N AH0 S IH0 Z -HARDOCK HH AA1 R D AA0 K -HARDON HH AA1 R D AH0 N -HARDON'S HH AA1 R D AH0 N Z -HARDOUIN HH AA1 R D OW0 AH0 N -HARDPAN HH AA1 R D P AE2 N -HARDRAADE HH AA1 R D R AA1 D -HARDRADA HH AA1 R D R AA1 D AH0 -HARDRAT HH AA1 R D R AH0 T -HARDRAT'S HH AA1 R D R AE2 T S -HARDRAW HH AA1 R D R AO1 -HARDRESS HH AA1 R D R EH2 S -HARDRESS'S HH AA1 R D R AH0 S IH0 Z -HARDROSS HH AA1 R D R AO1 S -HARDS HH AA1 R D Z -HARDSCRABBLE HH AA1 R D S K R AE2 B AH0 L -HARDSETT HH AA1 R D S EH2 T -HARDSHAW HH AA1 R D SH AO2 -HARDSHELL HH AA1 R D SH AH0 L -HARDSHIP HH AA1 R D SH IH0 P -HARDSHIPS HH AA1 R D SH IH0 P S -HARDT HH AA1 R T -HARDTACK HH AA1 R D T AE2 K -HARDUP HH AA1 R D AH2 P -HARDWAR HH AA1 R D W AO0 R -HARDWARE HH AA1 R D W EH2 R -HARDWAY HH AA1 R D W EY2 -HARDWICK HH AA1 R D W IH2 K -HARDWICK'S HH AA1 R D W IH0 K S -HARDWICKE HH AA1 R D W IH0 K -HARDWICKE'S HH AA1 R D W IH0 K S -HARDWOOD HH AA1 R D W UH2 D -HARDWOODS HH AA1 R D W UH2 D Z -HARDWORKED HH AA1 R D W ER2 K T -HARDWORKING HH AA1 R D W ER2 K IH0 NG -HARDY HH AA1 R D IY0 -HARDY'S HH AA1 R D IY0 Z -HARDYMAN HH AA1 R D IY0 M AE2 N -HARDYMAN'S HH AA1 R D IY0 M AH0 N Z -HARDYNGE HH AA1 R D AY2 N JH -HARDYS HH AA1 R D IY0 Z -HARE HH EH1 R -HARE'S HH EH1 R Z -HAREAU HH EH1 R OW1 -HAREBELL HH EH1 R B EH2 L -HAREBELLS HH EH1 R B EH2 L Z -HAREBRAINED HH EH1 R B R EY2 N D -HAREDALE HH EH1 R D EY2 L -HAREDALE'S HH EH1 R D EY2 L Z -HAREEM HH ER0 IY1 M -HAREEMS HH ER0 IY1 M Z -HAREFIELD HH AE1 R IH0 F IY0 L D -HAREFIELD'S HH AE1 R IH0 F IY0 L D Z -HAREFOOT HH EH1 R F UH2 T -HAREK HH AA1 R EH0 K -HAREL HH EH1 R AH0 L -HARELIP HH AE1 R IH0 L IH0 P -HARELIPPED HH AE1 R IH0 L IH1 P T -HAREM HH EH1 R AH0 M -HAREM'S HH EH1 R AH0 M Z -HAREMLIK HH EH1 R AH0 M L IH0 K -HAREMS HH EH1 R AH0 M Z -HARERO HH EH0 R EH1 R OW0 -HARES HH EH1 R Z -HARET HH EH1 R AH0 T -HARET'S HH EH1 R AH0 T S -HARETON HH AE1 R IH0 T AA0 N -HAREVILLE HH EH1 R AH0 V IH2 L -HAREWOOD HH EH1 R W UH2 D -HAREWOOD'S HH EH1 R W UH2 D Z -HARF HH AA1 R F -HARFINK HH AA1 R F IH2 NG K -HARFLEUR HH AA1 R F L ER1 -HARFORD HH AA1 R F ER0 D -HARFORD'S HH AA1 R F ER0 D Z -HARG HH AA1 R G -HARGATE HH AA1 R G EY2 T -HARGER HH AA1 R G ER0 -HARGIS HH AA1 R G IH0 S -HARGRAVE HH AA1 R G R EY2 V -HARGRAVE'S HH AA1 R G R EY2 V Z -HARGRAVES HH AA1 R G R EY2 V Z -HARGREAVE HH AA1 R G R IY1 V -HARGREAVE'S HH AA1 R G R IY1 V Z -HARGREAVES HH AA1 R G R IY2 V Z -HARGREW HH AA1 R G R UW1 -HARGROVE HH AA1 R G R OW2 V -HARGROVE'S HH AA1 R G R AH0 V Z -HARGUS HH AA1 R G AH0 S -HARI HH AA1 R IY0 -HARI'S HH AA1 R IY0 Z -HARICOT HH EH1 R IH0 K AA2 T -HARICOTS HH EH1 R IH0 K AA2 T S -HARIDAS HH EH1 R IH0 D AH0 Z -HARIETTA HH AA1 R IY0 EH1 T AH0 -HARIETTA'S HH AA1 R IY0 EH1 T AH0 Z -HARIKAR HH AE1 R IH0 K AA2 R -HARIM HH EH0 R IY1 M -HARINGTON HH EH1 R IH0 NG T AH0 N -HARIOT HH EH1 R IY0 AH0 T -HARIRI HH ER0 IH1 R IY0 -HARIS HH AA1 R IY0 S -HARIS HH EH1 R IH0 S -HARISARMAN HH EH1 R IH0 S AA1 R M AH0 N -HARISCHANDRA HH EH1 R IH0 S CH AA1 N D R AH0 -HARISH HH EH1 R IH0 SH -HARISON HH EH1 R IH0 S AH0 N -HARISWAMI HH AA1 R IY0 S W AA1 M IY0 -HARITH HH ER0 IH1 TH -HARJES HH AA1 R Y AH0 S -HARK HH AA1 R K -HARK'E HH AA1 R K EY2 -HARK'EE HH AA1 R K IY0 -HARKAMAN HH AA1 R K AH0 M AH0 N -HARKAMAN'S HH AA1 R K AH0 M AH0 N Z -HARKAVY HH AA1 R K AH0 V IY0 -HARKAWAY HH AA1 R K AH0 W EY2 -HARKAWAY'S HH AA1 R K AH0 W EY2 Z -HARKED HH AA1 R K T -HARKEE HH AA1 R K IY1 -HARKEN HH AA1 R K AH0 N -HARKENED HH AA1 R K AH0 N D -HARKENING HH AA1 R K N IH0 NG -HARKER HH AA1 R K ER0 -HARKER'S HH AA1 R K ER0 Z -HARKEY HH AA1 R K IY0 -HARKIN HH AA1 R K IH0 N -HARKING HH AA1 R K IH0 NG -HARKINGS HH AA1 R K IH0 NG Z -HARKINS HH AA1 R K IH0 N Z -HARKNESS HH AA1 R K N AH0 S -HARKNESS'S HH AA1 R K N AH0 S IH0 Z -HARKNESSES HH AA1 R K N AH0 S IH0 Z -HARKOV HH AA1 R K AO2 V -HARKS HH AA1 R K S -HARKUTT HH AA1 R K AH0 T -HARKUTT'S HH AA1 R K UH1 T S -HARKVILLE HH AA1 R K V IH2 L -HARKY HH AA1 R K IY0 -HARKY'S HH AA1 R K IY0 Z -HARKYE HH AA1 R K IY0 -HARL HH AA1 R L -HARLAEM HH AA1 R L EY1 M -HARLAN HH AA1 R L AH0 N -HARLAN'S HH AA1 R L AH0 N Z -HARLAND HH AA1 R L AH0 N D -HARLAND'S HH AA1 R L AH0 N D Z -HARLAW HH AA1 R L AO2 -HARLAY HH AA1 R L EY0 -HARLE HH AA1 R AH0 L -HARLECH HH AA1 R AH0 L CH -HARLED HH AA1 R L D -HARLEIAN HH AA1 R L IY2 AH0 N -HARLEIGH HH AA1 R L AH0 -HARLEM HH AA1 R L AH0 M -HARLEQUIN HH AA1 R L AH0 K W AH0 N -HARLEQUIN'S HH AA1 R L AH0 K IH0 N Z -HARLEQUINADE HH AA1 R L AH0 K IH0 N EY1 D -HARLEQUINS HH AA1 R L AH0 K IH0 N Z -HARLESDEN HH AA1 R L Z D AH0 N -HARLESTON HH AA1 R L S T AH0 N -HARLESTON'S HH AA1 R L S T AH0 N Z -HARLETH HH AA1 R L AH0 TH -HARLEY HH AA1 R L IY0 -HARLEY'S HH AA1 R L IY0 Z -HARLEYS HH AA1 R L IY0 Z -HARLINGEN HH AA1 R L IH0 NG AH0 N -HARLINGTON HH AA1 R L IH0 NG T AH0 N -HARLOT HH AA1 R L AH0 T -HARLOT'S HH AA1 R L AH0 T S -HARLOTRY HH AA1 R L AH0 T R IY0 -HARLOTS HH AA1 R L AH0 T S -HARLOW HH AA1 R L OW0 -HARLOW'S HH AA1 R L OW2 Z -HARLOWE HH AA1 R L OW2 -HARLOWE'S HH AA1 R L OW2 Z -HARLOWES HH AA1 R L OW2 Z -HARM HH AA1 R M -HARM'D HH AA1 R M D -HARM'S HH AA1 R M Z -HARMAC HH AA2 R M AH0 K -HARMACHIS HH AA2 R M AH0 K IH1 Z -HARMAKHIS HH AA2 R M AH0 K IH0 S -HARMAN HH AA1 R M AH0 N -HARMAN'S HH AA1 R M AH0 N Z -HARMANS HH AA1 R M AH0 N Z -HARMAR HH AA0 R M AA1 R -HARMAS HH AA1 R M AH0 Z -HARMATTAN HH AA2 R M AH0 T AE1 N -HARME HH AA1 R M -HARMED HH AA1 R M D -HARMER HH AA1 R M ER0 -HARMER'S HH AA1 R M ER0 Z -HARMERS HH AA1 R M ER0 Z -HARMES HH AA1 R M Z -HARMFUL HH AA1 R M F AH0 L -HARMFULLY HH AA1 R M F AH0 L IY0 -HARMFULNESS HH AA1 R M F AH0 L N AH0 S -HARMHABI HH AA1 R M HH AE2 B IY0 -HARMING HH AA1 R M IH0 NG -HARMLESS HH AA1 R M L AH0 S -HARMLESSLY HH AA1 R M L AH0 S L IY0 -HARMLESSNESS HH AA1 R M L AH0 S N AH0 S -HARMODIUS HH AA0 R M OW1 D IY0 AH0 S -HARMON HH AA1 R M AH0 N -HARMON'S HH AA1 R M AH0 N Z -HARMONIA HH AA0 R M OW1 N IY0 AH0 -HARMONIC HH AA0 R M AA1 N IH0 K -HARMONICA HH AA0 R M AA1 N IH0 K AH0 -HARMONICAL HH AA0 R M AA1 N IH0 K AH0 L -HARMONICALLY HH AA0 R M AA1 N IH0 K L IY0 -HARMONICAS HH AA0 R M AA1 N IH0 K AH0 Z -HARMONICON HH AA0 R M AA1 N IH0 K AA2 N -HARMONICS HH AA0 R M AA1 N IH0 K S -HARMONIE HH AA1 R M AH0 N IY0 -HARMONIES HH AA1 R M AH0 N IY0 Z -HARMONIOUS HH AA0 R M OW1 N IY0 AH0 S -HARMONIOUSLY HH AA0 R M OW1 N IY0 AH0 S L IY0 -HARMONISE HH AA1 R M AH0 N IH0 Z -HARMONISED HH AA1 R M AH0 N IH0 Z D -HARMONISES HH AA1 R M AH0 N IH0 Z IH0 Z -HARMONISING HH AA1 R M AH0 N IH0 Z IH0 NG -HARMONIST HH AA1 R M AH0 N IH0 S T -HARMONISTS HH AA1 R M AH0 N IH0 S T S -HARMONIUM HH AA0 R M OW1 N IY0 AH0 M -HARMONIUMS HH AA0 R M OW1 N IY0 AH0 M Z -HARMONIZATION HH AA2 R M AH0 N IH0 Z EY1 SH AH0 N -HARMONIZE HH AA1 R M AH0 N AY2 Z -HARMONIZED HH AA1 R M AH0 N AY2 Z D -HARMONIZES HH AA1 R M AH0 N AY2 Z IH0 Z -HARMONIZING HH AA1 R M AH0 N AY2 Z IH0 NG -HARMONS HH AA1 R M AH0 N Z -HARMONY HH AA1 R M AH0 N IY0 -HARMONY'S HH AA1 R M AH0 N IY0 Z -HARMOST HH AA1 R M OW2 S T -HARMOUTH HH AA1 R M AH0 TH -HARMS HH AA1 R M Z -HARMSWORTH HH AA1 R M S W ER0 TH -HARMY HH AA1 R M IY0 -HARN HH AA1 R N -HARNACH HH AA1 R N AH0 K -HARNACK HH AA1 R N AH0 K -HARNED HH AA1 R N D -HARNESS HH AA1 R N AH0 S -HARNESS HH AA1 R N IH0 S -HARNESS'D HH AA1 R N AH0 S D -HARNESSED HH AA1 R N AH0 S T -HARNESSES HH AA1 R N AH0 S AH0 Z -HARNESSES HH AA1 R N AH0 S IH0 Z -HARNESSING HH AA1 R N AH0 S IH0 NG -HARNEY HH AA1 R N IY0 -HARNEY'S HH AA1 R N IY0 Z -HARNHAM HH AA1 R N AH0 M -HARNISH HH AA1 R N IH0 SH -HARNISS HH AA1 R N IH0 S -HARNOSH HH AA1 R N AH0 SH -HARNS HH AA1 R N Z -HARNSOME HH AA1 R N S AH0 M -HARNT HH AA1 R N T -HARO HH AA1 R OW0 -HAROL HH EH1 R AH0 L -HAROLD HH EH1 R AH0 L D -HAROLD'S HH EH1 R AH0 L D Z -HAROLDS HH EH1 R AH0 L D Z -HAROOD HH ER0 UW1 D -HAROON HH ER0 UW1 N -HAROUN HH AA1 R UW1 N -HAROUN'S HH ER0 AW1 N Z -HARP HH AA1 R P -HARP'S HH AA1 R P S -HARPAGAS HH AA1 R P AA1 G AH0 Z -HARPAGON HH AA1 R P AH0 G AA2 N -HARPAGUS HH AA1 R P AH0 G AH0 S -HARPALUS HH AA1 R P AH0 L AH0 S -HARPAR HH AA1 R P AA2 R -HARPE HH AA1 R P -HARPE'S HH AA1 R P S -HARPED HH AA1 R P T -HARPEN HH AA1 R P AH0 N -HARPENDEN HH AA0 R P EH1 N D AH0 N -HARPENDING HH AA0 R P EH1 N D IH0 NG -HARPER HH AA1 R P ER0 -HARPER'S HH AA1 R P ER0 Z -HARPERS HH AA1 R P ER0 Z -HARPETH HH AA1 R P IH0 TH -HARPIES HH AA1 R P IY0 Z -HARPIGNIES HH AA1 R P IH0 G N IY0 Z -HARPIN HH AA0 R P AE1 N -HARPING HH AA1 R P IH0 NG -HARPINGS HH AA1 R P IH0 NG Z -HARPIST HH AA1 R P IH0 S T -HARPISTS HH AA1 R P IH0 S T S -HARPIT HH AA1 R P IH0 T -HARPOCRATES HH AA1 R P AH0 K R EY2 T S -HARPOON HH AA0 R P UW1 N -HARPOONED HH AA0 R P UW1 N D -HARPOONEER HH AA0 R P UW1 N IH1 R -HARPOONEERS HH AA0 R P UW1 N IH1 R Z -HARPOONER HH AA0 R P UW1 N ER0 -HARPOONERS HH AA0 R P UW1 N ER0 Z -HARPOONING HH AA0 R P UW1 N IH0 NG -HARPOONS HH AA0 R P UW1 N Z -HARPS HH AA1 R P S -HARPSICHORD HH AA1 R P S AH0 K AO2 R D -HARPSICHORDS HH AA1 R P S AH0 K AO2 R D Z -HARPSTENAH HH AA1 R P S T EY1 N AH0 -HARPSTRING HH AA1 R P S T R IH2 NG -HARPSTRINGS HH AA1 R P S T R IH1 NG Z -HARPSWELL HH AA1 R P S W EH2 L -HARPUR HH AA1 R P ER0 -HARPY HH AA1 R P IY0 -HARQUEBUS HH AA1 R K W AH0 B AH0 S -HARQUEBUSS HH AA1 R K W AH0 B AH0 S -HARRACLES HH AE1 R AH0 K AH0 L Z -HARRADEN HH AE1 R AH0 D EH2 N -HARRAN HH AE1 R AH0 N -HARRAN'S HH AE1 R AH0 N Z -HARRAP HH AE1 R AH0 P -HARRAS HH AE1 R AH0 Z -HARRASS HH EH1 R AH0 S -HARRASSED HH EH1 R AH0 S T -HARRASSING HH EH1 R AH0 S IH0 NG -HARRD HH AE1 R D -HARREL HH AE1 R AH0 L -HARREL'S HH AE1 R AH0 L Z -HARRELS HH AE1 R AH0 L Z -HARRER HH AA1 R ER0 -HARRICANE HH AE1 R AH0 K EY2 N -HARRICUTT HH AE1 R AH0 K AH0 T -HARRIDAN HH EH1 R IH0 D AH0 N -HARRIDANS HH EH1 R IH0 D AH0 N Z -HARRIE HH EH1 R IY0 -HARRIE'S HH EH1 R IY0 Z -HARRIED HH EH1 R IY0 D -HARRIER HH EH1 R IY0 ER0 -HARRIERS HH EH1 R IY0 ER0 Z -HARRIES HH EH1 R IY0 Z -HARRIET HH EH1 R IY0 AH0 T -HARRIET'S HH EH1 R IY0 AH0 T S -HARRIETT HH AE1 R IY0 EH0 T -HARRIETT'S HH AE1 R IY0 EH0 T S -HARRIETTE HH AE1 R IY0 EH0 T -HARRIGAN HH EH1 R IH0 G AH0 N -HARRIGAN'S HH AE1 R IH0 G AH0 N Z -HARRIGANS HH AE1 R IH0 G AH0 N Z -HARRIGATE HH AE1 R AH0 G EY2 T -HARRIHAN HH EH1 R IH0 HH AE0 N -HARRIK HH AE1 R IH0 K -HARRIMAN HH EH1 R IH0 M AH0 N -HARRIMAN'S HH EH1 R IH0 M AH0 N Z -HARRINGAY HH EH1 R IH0 NG EY2 -HARRINGFORD HH EH1 R IH0 NG F ER0 D -HARRINGTON HH EH1 R IH0 NG T AH0 N -HARRINGTON'S HH EH1 R IH0 NG T AH0 N Z -HARRINGTONS HH EH1 R IH0 NG T AH0 N Z -HARRIOT HH AE1 R IY0 AH0 T -HARRIOT'S HH AE1 R IY0 AH0 T S -HARRIOTT HH AE1 R IY0 AH0 T -HARRIS HH EH1 R IH0 S -HARRIS'S HH EH1 R IH0 S IH0 Z -HARRISBURG HH AE1 R IH0 S B ER0 G -HARRISBURG HH EH1 R IH0 S B ER0 G -HARRISES HH AE1 R IH0 S IH0 Z -HARRISMITH HH EH1 R IH0 S M IH2 TH -HARRISON HH EH1 R IH0 S AH0 N -HARRISON'S HH EH1 R IH0 S AH0 N Z -HARRISONBURG HH EH1 R IH0 S AH0 N B ER0 G -HARRISONS HH EH1 R IH0 S AH0 N Z -HARRISSE HH AE1 R IH0 S -HARRISSON HH AE1 R IH0 S AH0 N -HARRITY HH AE1 R IH0 T IY0 -HARRIWELL HH EH1 R IH0 W EH0 L -HARROD HH EH1 R AH0 D -HARROD'S HH EH1 R AH0 D Z -HARRODS HH EH1 R AH0 D Z -HARRODSBURG HH EH1 R AH0 D Z B ER0 G -HARRODSBURGH HH EH1 R AH0 D Z B ER0 G -HARROGATE HH EH1 R AH0 G EY2 T -HARROP HH EH1 R AH0 P -HARROR HH AE1 R ER0 -HARROVIAN HH AE1 R OW0 V IY1 AH0 N -HARROVIANS HH AE1 R OW1 V IY0 AH0 N Z -HARROW HH AE1 R OW0 -HARROW'S HH AE1 R OW0 Z -HARROWBY HH AE1 R OW0 B IY0 -HARROWBY'S HH AE1 R OW0 B IY0 Z -HARROWDEN HH AE1 R OW0 D AH0 N -HARROWED HH AE1 R OW0 D -HARROWELL'S HH AE1 R OW0 W EH2 L Z -HARROWFIELD HH AE1 R OW0 F IY2 L D -HARROWFORK HH AE1 R OW0 F AO2 R K -HARROWGATE HH AE1 R OW0 G EY2 T -HARROWIN HH AE1 R OW0 IH0 N -HARROWING HH EH1 R OW0 IH0 NG -HARROWINGLY HH AE1 R OW0 IH0 NG L IY0 -HARROWS HH AE1 R OW0 Z -HARRUM HH AE1 R AH0 M -HARRY HH EH1 R IY0 -HARRY'S HH EH1 R IY0 Z -HARRYING HH EH1 R IY0 IH0 NG -HARRYMAN HH AE1 R IY0 M AH0 N -HARRYS HH AE1 R IY0 Z -HARSANYI HH AA1 R S AA1 N Y IY0 -HARSE HH AA1 R S -HARSES HH AA1 R S IH0 Z -HARSH HH AA1 R SH -HARSHA HH AA1 R SH AH0 -HARSHAW HH AA1 R SH AO2 -HARSHENED HH AA1 R SH AH0 N D -HARSHER HH AA1 R SH ER0 -HARSHEST HH AA1 R SH AH0 S T -HARSHLY HH AA1 R SH L IY0 -HARSHNESS HH AA1 R SH N AH0 S -HARSHNESSES HH AA1 R SH N AH0 S IH0 Z -HARSON HH AA1 R S AH0 N -HARSTON HH AA1 R S T AH0 N -HART HH AA1 R T -HART'S HH AA1 R T S -HARTE HH AA1 R T -HARTE'S HH AA1 R T EY2 Z -HARTEBEEST HH AA1 R T AH0 B IY1 IH0 S T -HARTEBEESTE HH AA1 R T AH0 B IY1 S T -HARTEBEESTS HH AA1 R T AH0 B AH0 S T S -HARTENSTEIN HH AA1 R T AH0 N S T AY2 N -HARTENSTEIN HH AA1 R T AH0 N S T IY2 N -HARTER HH AA1 R T ER0 -HARTES HH AA1 R T IY0 Z -HARTFIELD HH AA1 R T F IY2 L D -HARTFORD HH AA1 R T F ER0 D -HARTFORD'S HH AA1 R T F ER0 D Z -HARTHBOROUGH HH AA1 R TH B ER2 OW0 -HARTHE HH AA1 R T -HARTHILD HH AA1 R T AY2 L D -HARTHOVER HH AA1 R T OW0 V ER0 -HARTIGAN HH AA1 R T IH0 G AH0 N -HARTIGAN'S HH AA1 R T IH0 G AH0 N Z -HARTILL HH AA1 R T IH0 L -HARTILL'S HH AA1 R T IH0 L Z -HARTILY HH AA1 R T AH0 L IY0 -HARTING HH AA1 R T IH0 NG -HARTINGTON HH AA1 R T IH0 NG T AH0 N -HARTINGTON'S HH AA1 R T IH0 NG T AH0 N Z -HARTIST HH AA1 R T IH0 S T -HARTLAND HH AA1 R T L AH0 N D -HARTLEDON HH AA1 R T AH0 L D AH0 N -HARTLEDON'S HH AA1 R T AH0 L D AH0 N Z -HARTLEPOD HH AA1 R T L AH0 P AA2 D -HARTLEPOOL HH AA1 R T L AH0 P UW2 L -HARTLETOP HH AA1 R T AH0 L T AA2 P -HARTLEY HH AA1 R T L IY0 -HARTLEY'S HH AA1 R T L IY0 Z -HARTLEYS HH AA1 R T L IY0 Z -HARTLY HH AA1 R T L IY0 -HARTMAN HH AA1 R T M AH0 N -HARTMAN'S HH AA1 R T M AH0 N Z -HARTMANN HH AA1 R T M AH0 N -HARTMANN'S HH AA1 R T M AH0 N Z -HARTNESS HH AA1 R T N AH0 S -HARTNETT HH AA1 R T N IH0 T -HARTNOLL HH AA1 R T N OW2 L -HARTOG HH AA1 R T AH0 G -HARTON HH AA1 R T AH0 N -HARTOP HH AA1 R T AO0 P -HARTOPP HH AA1 R T AH0 P -HARTOPP'S HH AA1 R T AH0 P S -HARTRATH HH AA1 R T R AH0 TH -HARTREY HH AA1 R T R IY0 -HARTRICH HH AA1 R T R IH0 K -HARTRIDGE HH AA1 R T R IH0 JH -HARTRIGHT HH AA1 R T R AY2 T -HARTRIGHT'S HH AA1 R T R AY2 T S -HARTROTT HH AA1 R T R AA1 T -HARTS HH AA1 R T S -HARTSCHWERT HH AA1 R CH W ER0 T -HARTSED HH AA1 R T S T -HARTSEL HH AA1 R T S AH0 L -HARTSEL'S HH AA1 R T S AH0 L Z -HARTSHORN HH AA1 R T S HH AO2 R N -HARTSHORNE HH AA1 R CH ER0 N -HARTSON HH AA1 R T S AH0 N -HARTSOOK HH AA1 R T S UH0 K -HARTSTEIN HH AA1 R T S T AY2 N -HARTSTEIN HH AA1 R T S T IY2 N -HARTSTONGUE HH AA1 R T S T AA1 NG G -HARTSUFF HH AA1 R CH AH0 F -HARTSVILLE HH AA1 R T S V IH0 L -HARTVIG HH AA1 R T V IH0 G -HARTVIK HH AA1 R T V IH0 K -HARTWELL HH AA1 R T W EH2 L -HARTWELL'S HH AA1 R T W EH2 L Z -HARTWICH HH AA1 R T W IH0 CH -HARTWICH'S HH AA1 R T W IH0 CH IH0 Z -HARTWIG HH AA1 R T W IH0 K -HARTWIG'S HH AA1 R T W IH0 G Z -HARTY HH AA1 R T IY0 -HARTZ HH AA1 R T S -HARTZENBOSCH HH AA1 R T S AH0 N B AO2 SH -HARTZENBUSCH HH AA1 R T S AH0 N B UH0 SH -HARTZMANN HH AA1 R T S M AH0 N -HARU HH AA1 R UW0 -HARU'S HH AA1 R UW0 Z -HARUHIKU HH AA0 R UW0 HH IY1 K UW2 -HARUM HH EH1 R AH0 M -HARUN HH ER0 UW1 N -HARUSPEX HH AA0 R UW1 S P EH2 K S -HARUSPICES HH AA0 R UW1 S P AH0 S IH0 Z -HARUT HH AA0 R UW1 T -HARV HH AA1 R V -HARVARD HH AA1 R V ER0 D -HARVARD'S HH AA1 R V ER0 D Z -HARVE HH AA1 R V -HARVEST HH AA1 R V AH0 S T -HARVEST'S HH AA1 R V AH0 S T S -HARVESTED HH AA1 R V AH0 S T AH0 D -HARVESTED HH AA1 R V AH0 S T IH0 D -HARVESTER HH AA1 R V AH0 S T ER0 -HARVESTER'S HH AA1 R V AH0 S T ER0 Z -HARVESTERS HH AA1 R V AH0 S T ER0 Z -HARVESTIN HH AA1 R V AH0 S T IH0 N -HARVESTING HH AA1 R V AH0 S T IH0 NG -HARVESTMAN HH AA1 R V AH0 S T M AH0 N -HARVESTMEN HH AA1 R V EH1 S M AH0 N -HARVESTON HH AA1 R V AH0 S T AH0 N -HARVESTS HH AA1 R V AH0 S T S -HARVEY HH AA1 R V IY0 -HARVEY'S HH AA1 R V IY0 Z -HARVEYS HH AA1 R V EY1 Z -HARVIE HH AA1 R V IY0 -HARVILLE HH AA1 R V IH2 L -HARVILLE'S HH AA1 R V IH2 L Z -HARVILLES HH AA1 R V IH2 L Z -HARVINGTON HH AA1 R V IH0 NG T AH0 N -HARVISS HH AA1 R V IH0 S -HARVY HH AA1 R V IY0 -HARWELL HH AA1 R W EH2 L -HARWICH HH AA1 R W IH0 CH -HARWOOD HH AA1 R W UH2 D -HARWOOD'S HH AA1 R W UH2 D Z -HARZ HH ER0 Z -HARZA HH AA1 R Z AH0 -HAS HH AE1 Z -HAS HH AH0 Z -HAS'T HH AE1 S AH0 T -HASAN HH EY1 Z AH0 N -HASARD HH AE1 S ER0 D -HASAYAMPA HH AE1 S AA0 Y AA1 M P AH0 -HASBROOK HH AE1 S B R UH0 K -HASBROOK'S HH AE1 Z B R UH0 K S -HASBROUCK HH AE1 Z B R UH2 K -HASBROUCK'S HH AE1 Z B R UH2 K S -HASCHICH HH AE1 SH IH0 K -HASCHISCH HH AE1 SH IH0 SH -HASCHISH HH AE1 SH IH0 SH -HASDRUBAL HH AE1 S D R UW0 B AE1 L -HASDRUBAL'S HH AE1 S D R AH2 B AH0 L Z -HASE HH EY1 Z -HASEBROEK HH AE1 S IH0 B R OW0 K -HASELFORD HH AE1 S IH0 L F ER0 D -HASELTINE HH AE1 S IH0 L T IY0 N -HASELTON HH AH0 S EH1 L T AH0 N -HASENCLEVER HH AE1 S IH0 N K L IY1 V ER0 -HASH HH AE1 SH -HASHA HH AE1 SH AH0 -HASHED HH AE1 SH T -HASHEESH HH AH0 SH IY1 SH -HASHEM HH AE1 SH IH0 M -HASHES HH AE1 SH AH0 Z -HASHI HH AE1 SH IY2 -HASHING HH AE1 SH IH0 NG -HASHISH HH AE1 SH IH0 SH -HASHISH HH AH0 SH IY1 SH -HASHISHIN HH AH0 SH IY1 SH IH0 N -HASHMI HH AA1 SH M IY0 -HASHMI HH AE1 SH M IY0 -HASHNU HH AE1 SH N UW0 -HASHOLOM HH AE1 SH AH0 L AH0 M -HASIDIM HH AH0 S IH1 D IH0 M -HASIDIM HH AH0 S IY1 D IH0 M -HASIDISM HH AH0 S IY1 D IH0 Z AH0 M -HASISADRA HH EY1 S IH0 S AA1 D R AH0 -HASJELTI HH AE1 S JH EH1 L T IY0 -HASK HH AE1 S K -HASKALAH HH AE1 S K AA1 L AH0 -HASKELL HH AE1 S K AH0 L -HASKELL'S HH AE1 S K AH0 L Z -HASKERS HH AE1 S K ER0 Z -HASKET HH AE1 S K AH0 T -HASKETT HH AE1 S K IH0 T -HASKETT'S HH AE1 S K IH0 T S -HASKINS HH AE1 S K IH0 N Z -HASLAM HH AE1 S L AH0 M -HASLAM'S HH AE1 S L AH0 M Z -HASLAR HH AE1 S L ER0 -HASLEM HH AE1 S L IH0 M -HASLEMERE HH AE1 S L IH0 M IH2 R -HASLETT HH AE1 S L IH0 T -HASLUCK HH AE1 S L AH0 K -HASN HH AE1 Z AH0 N -HASN'T HH AE1 Z AH0 N T -HASNA HH AE1 S N AH0 -HASNIP HH AE1 S N IH1 P -HASNT HH AE1 Z N T -HASP HH AE1 S P -HASPARREN HH AE1 S P AA1 R AH0 N -HASPED HH AE1 S P T -HASPS HH AE1 S P S -HASS HH AE1 S -HASSAL HH AE1 S AH0 L -HASSALL HH AE1 S AH0 L -HASSAM HH AE1 S AH0 M -HASSAN HH AH0 S AA1 N -HASSAN'S HH AE1 S AA0 N Z -HASSARD HH AE1 S ER0 D -HASSAYAMP HH AE1 S AY1 AE0 M P -HASSAYAMPA HH AE1 S IY0 AA1 M P AH0 -HASSE HH AA1 S -HASSEBU HH AE1 S AH0 B UW0 -HASSEEBOO HH AE1 S IY0 B UW2 -HASSEL HH AE1 S AH0 L -HASSELL HH AE1 S AH0 L -HASSELT HH AE1 S IH0 L T -HASSEN HH AE1 S AH0 N -HASSENREUTER HH AE1 S AH0 N R OY1 T ER0 -HASSETT HH AE1 S IH0 T -HASSIEBROCK HH AE1 S IY1 B R AH0 K -HASSIM HH AE1 S IH0 M -HASSIM'S HH AE1 S IH0 M Z -HASSLE HH AE1 S AH0 L -HASSLER HH AE1 S L ER0 -HASSLING HH AE1 S AH0 L IH0 NG -HASSLING HH AE1 S L IH0 NG -HASSOCK HH AE1 S AH0 K -HASSOCKS HH AE1 S AH0 K S -HAST HH AE1 S T -HASTA AA1 S T AH0 -HASTA HH AE1 S T AH0 -HASTAIN HH EY1 S T EY1 N -HASTATI HH EY1 S T AA1 T IY0 -HASTE HH EY1 S T -HASTED HH EY1 S T IH0 D -HASTEN HH EY1 S AH0 N -HASTEN'D HH EY1 S AH0 N D -HASTENBROOK HH EY1 S AH0 N B R UH2 K -HASTENED HH EY1 S AH0 N D -HASTENETH HH EY1 S AH0 N EH1 TH -HASTENING HH EY1 S AH0 N IH0 NG -HASTENING HH EY1 S N IH0 NG -HASTENS HH EY1 S AH0 N Z -HASTES HH EY1 S T S -HASTETH HH EY1 S T IH0 TH -HASTIE HH EY1 S T IY0 -HASTIE'S HH EY1 S T IY0 Z -HASTIER HH EY1 S T IY0 ER0 -HASTIEST HH EY1 S T IY0 AH0 S T -HASTILY HH EY1 S T AH0 L IY0 -HASTINESS HH EY1 S T IY0 N AH0 S -HASTING HH EY1 S T IH0 NG -HASTING'S HH EY1 S T IH0 NG Z -HASTINGS HH EY1 S T IH0 NG Z -HASTINGS'S HH EY1 S T IH0 NG Z IH0 Z -HASTOR HH AE1 S T ER0 -HASTUR HH AE1 S T ER0 -HASTURS HH AE1 S T ER0 Z -HASTY HH EY1 S T IY0 -HASWELL HH AE1 S W EH0 L -HASWELL'S HH AE1 S W EH2 L Z -HAT HH AE1 T -HAT'S HH AE1 T S -HATA HH AA1 T AH0 -HATAMOTO HH AE1 T AH0 M OW1 T OW0 -HATASOO HH AE1 T AH0 S UW1 -HATASU HH AE1 T AH0 S UW1 -HATBAND HH AE1 T B AE2 N D -HATBANDS HH AE1 T B AE2 N D Z -HATBORO HH AE1 T B ER0 OW0 -HATBOX HH AE1 T B AA2 K S -HATBRIM HH AE1 T B R IH1 M -HATBURN HH AE1 T B ER2 N -HATBURNS HH AE1 T B ER2 N Z -HATCH HH AE1 CH -HATCH'D HH AE1 CH IH0 D -HATCH'S HH AE1 CH IH0 Z -HATCHAM HH AE1 CH AH0 M -HATCHARD HH AE1 CH ER0 D -HATCHARD'S HH AE1 CH ER0 D Z -HATCHED HH AE1 CH T -HATCHELL HH AE1 CH AH0 L -HATCHER HH AE1 CH ER0 -HATCHER'S HH AE1 CH ER0 Z -HATCHERIES HH AE1 CH ER0 IY0 Z -HATCHERY HH AE1 CH ER0 IY0 -HATCHES HH AE1 CH IH0 Z -HATCHET HH AE1 CH AH0 T -HATCHETS HH AE1 CH AH0 T S -HATCHETT HH AE1 CH IH0 T -HATCHIE HH AE1 CH IY0 -HATCHIN HH AE1 CH IH0 N -HATCHING HH AE1 CH IH0 NG -HATCHINGS HH AE1 CH IH0 NG Z -HATCHMENT HH AE1 CH M AH0 N T -HATCHMENTS HH AE1 CH M AH0 N T S -HATCHWAY HH AE1 CH W EY2 -HATCHWAY'S HH AE1 CH W EY2 Z -HATCHWAYS HH AE1 CH W EY2 Z -HATE HH EY1 T -HATE'S HH EY1 T S -HATED HH EY1 T AH0 D -HATED HH EY1 T IH0 D -HATEETA HH EY1 T IY1 T AH0 -HATEETAH HH EY1 T IY1 T AH0 -HATEFUL HH EY1 T F AH0 L -HATEFULEST HH EY1 T F AH0 L AH0 S T -HATEFULLEST HH EY1 T F AH0 L AH0 S T -HATEFULLY HH EY1 T F AH0 L IY0 -HATEFULNESS HH EY1 T F AH0 L N AH0 S -HATER HH EY1 T ER0 -HATERIUS HH AE1 T IY1 R IY0 AH0 S -HATERS HH EY1 T ER0 Z -HATES HH EY1 T S -HATEST HH EY1 T AH0 S T -HATETH HH AE1 T IH0 TH -HATFIELD HH AE1 T F IY2 L D -HATFIELD'S HH AE1 T F IY2 L D Z -HATFIELDS HH AE1 T F IY2 L D Z -HATFUL HH AE1 T F AH0 L -HATFULS HH AE1 T F AH0 L Z -HATH HH AE1 TH -HATHA HH AE1 TH AH0 -HATHAWAY HH AE1 TH AH0 W EY2 -HATHAWAY'S HH AE1 TH AH0 W EY2 Z -HATHAWAYS HH AE1 TH AH0 W EY2 Z -HATHE HH EY1 DH -HATHELSBOROUGH HH AE1 TH AH0 L Z B ER0 OW0 -HATHEN HH AE1 TH AH0 N -HATHERCLEUGH HH AE1 TH ER0 K L -HATHERELL HH AE1 TH ER0 AH0 L -HATHERLEY HH EY1 DH ER0 L IY0 -HATHERLY HH EY1 DH ER0 L IY0 -HATHERSAGE HH EY1 DH ER0 S IH0 JH -HATHERTON HH AE1 TH ER0 T AH0 N -HATHI HH AE1 TH IY0 -HATHI'S HH AE1 TH IY0 Z -HATHIPOTA HH AE1 TH IH0 P OW1 T AH0 -HATHN'T HH AE1 TH N T -HATHNIT HH AE1 TH N IH1 T -HATHOR HH AE1 TH ER0 -HATHORN HH AA1 TH AO0 R N -HATHORN'S HH AA1 TH AO0 R N Z -HATHORNE HH AE1 TH ER0 N -HATHORNE'S HH AA1 TH AO0 R N Z -HATHORNES HH AA1 TH AO0 R N Z -HATHORS HH AE1 TH ER0 Z -HATI HH AA1 T IY0 -HATIF HH AA0 T IY1 F -HATIHEU AE2 T IH0 HH Y UW0 -HATIM HH AE1 T IH2 M -HATIN HH AE1 T AH0 N -HATING HH EY1 T IH0 NG -HATLESS HH EY1 T L AH0 S -HATO HH AE1 T OW0 -HATOR HH EY1 T ER0 -HATORASK HH AE1 T AO2 R AE1 S K -HATOS HH AE1 T OW0 Z -HATOUL HH AE1 T UW1 L -HATPIN HH EY1 T P IH0 N -HATPINS HH EY1 T P IH2 N Z -HATRACK HH AE1 T R AE2 K -HATREAUMONT HH AE1 T R OW1 M AO0 N T -HATRED HH EY1 T R AH0 D -HATRED'S HH EY1 T R AH0 D Z -HATREDS HH EY1 T R AH0 D Z -HATRY HH EY1 T R IY0 -HATS HH AE1 T S -HATSHEPSU HH EY1 T SH EH2 P S UW0 -HATSHEPSU'S HH EY1 T SH EH2 P S UW2 Z -HATSHEPSUT HH EY1 T SH EH2 P S AH0 T -HATSHOPSITU HH T SH AA2 P S IH0 T UW0 -HATSTAND HH AE1 T S T AE2 N D -HATT HH AE1 T -HATTA HH AE1 T AH0 -HATTE HH AE1 T -HATTED HH AE1 T IH0 D -HATTER HH AE1 T ER0 -HATTER'S HH AE1 T ER0 Z -HATTERAICK HH AE1 T ER0 IH0 K -HATTERAICK'S HH AE1 T ER0 IH0 K S -HATTERAS HH AE1 T ER0 AH0 S -HATTERS HH AE1 T ER0 Z -HATTERSLEY HH AE1 T ER0 Z L IY0 -HATTHA HH AE1 T AH0 -HATTI HH AE1 T IY0 -HATTIE HH AE1 T IY0 -HATTIE'S HH AE1 T IY0 Z -HATTIN HH AE1 T IH0 N -HATTINGH HH AE1 T IH0 NG -HATTO HH AE1 T OW0 -HATTO'S HH AE1 T OW0 Z -HATTOCK HH AE1 T AH0 K -HATTON HH AE1 T AH0 N -HATTON'S HH AE1 T AH0 N Z -HATTOY HH AE1 T OY1 -HATTY HH AE1 T IY0 -HATTY'S HH AE1 T IY0 Z -HATYK HH AH0 T IH0 K -HATZE HH AE1 T Z -HATZFELD HH AE1 T S F EH0 L D -HATZFELDT HH AE1 T S F EH0 L T -HAU HH AW1 -HAUB HH AO1 B -HAUBERK HH AW1 B ER0 K -HAUBERK'S HH AW1 B ER0 K S -HAUBERKS HH AW1 B ER2 K S -HAUCK HH AO1 K -HAUCK'S HH AO1 K S -HAUD HH AO1 D 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HH AO1 L Y AA2 R D Z -HAUN HH AO1 N -HAUNCH HH AO1 N CH -HAUNCHED HH AO1 N CH T -HAUNCHES HH AO1 N CH IH0 Z -HAUND HH AO1 N D -HAUNS HH AO1 N Z -HAUNT HH AO1 N T -HAUNTE HH AO1 N T -HAUNTED HH AO1 N T AH0 D -HAUNTED HH AO1 N T IH0 D -HAUNTER HH AO1 N T ER0 -HAUNTERS HH AO1 N T ER0 Z -HAUNTETH HH AO1 N T IH0 TH -HAUNTIN HH AO1 N T IH0 N -HAUNTING HH AO1 N T IH0 NG -HAUNTINGLY HH AO1 N T IH0 NG L IY0 -HAUNTINGS HH AO1 N T IH0 NG Z -HAUNTS HH AO1 N T S -HAUPT HH AO1 P T -HAUPTMANN HH AW1 P T M AH0 N -HAUPTMANN'S HH AW1 P T M AH0 N Z -HAURAKI HH AO1 R AA1 K IY0 -HAURAN HH AO1 R AH0 N -HAUS HH AW1 S -HAUSA HH AW1 S AH0 -HAUSAS HH AW1 S AH0 S -HAUSBERG HH AW1 S B ER0 G -HAUSE HH AW1 S -HAUSEN HH AW1 Z AH0 N -HAUSER HH AW1 Z ER0 -HAUSER'S HH AW1 Z ER0 Z -HAUSERMAN HH AW1 Z ER0 M AH0 N -HAUSFRAU HH AW1 S F R AW1 -HAUSKULD HH AO1 S K AH0 L D -HAUSMANN HH AW1 S M AH0 N -HAUSSA HH AW1 S AH0 -HAUSSAS HH AW1 S AH0 S -HAUSSET HH AW1 S EH0 T -HAUSSMANN HH AW1 S M AH0 N -HAUSTUS HH AO1 S T AH0 S -HAUT 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AE1 Z AH0 L -HAZELNUT HH EY1 Z AH0 L N AH2 T -HAZELNUTS HH EY1 Z AH0 L N AH2 T S -HAZELRIG HH EY1 Z EH0 L R IH0 G -HAZELS HH EY1 Z AH0 L Z -HAZELTINE HH EY1 Z AH0 L T AY2 N -HAZELTINE'S HH EY1 Z AH0 L T AY2 N Z -HAZELTINES HH EY1 Z AH0 L T AY2 N Z -HAZELTON HH EY1 Z AH0 L T AH0 N -HAZELTON'S HH EY1 Z AH0 L T AH0 N Z -HAZELWOOD HH EY1 Z AH0 L W UH2 D -HAZEN HH EY1 Z AH0 N -HAZEN'S HH EY1 Z AH0 N Z -HAZER HH EY1 Z ER0 -HAZERS HH AE1 Z ER0 Z -HAZES HH EY1 Z AH0 Z -HAZIER HH EY1 Z IY0 ER0 -HAZIEST HH EY1 Z IY0 AH0 S T -HAZILY HH AE1 Z AH0 L IY0 -HAZINESS HH EY1 Z IY0 N AH0 S -HAZING HH EY1 Z IH0 NG -HAZIRA HH EY1 Z IH1 R AH0 -HAZIRATU'L HH AE1 Z AH0 R AE2 CH UW0 AH0 L -HAZLE HH EY1 Z AH0 L -HAZLEHURST HH EY1 Z AH0 L HH ER0 S T -HAZLEHURST'S HH EY1 Z AH0 L HH ER0 S T S -HAZLET HH AE1 Z L IH0 T -HAZLET'S HH AE1 Z L IH0 T S -HAZLETINE HH EY1 Z AH0 L T IY0 N -HAZLETON HH EY1 Z AH0 L T AH0 N -HAZLETON'S HH EY1 Z AH0 L T AH0 N Z -HAZLETT HH AE1 Z L IH0 T -HAZLEWOOD HH EY1 Z AH0 L W UH2 D 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EH1 D S EH2 T S -HEADSHAKE HH EH1 D SH EY2 K -HEADSHAKES HH EH1 D SH EY2 K S -HEADSHIP HH EH1 D SH IH2 P -HEADSIZE HH EH1 D S AY2 Z -HEADSMAN HH EH1 D Z M AH0 N -HEADSMAN'S HH EH1 D Z M AH0 N Z -HEADSMEN HH EH1 D Z M IH0 N -HEADSTALL HH EH1 D S T AO1 L -HEADSTALLS HH EH1 D S T AO1 L Z -HEADSTONE HH EH1 D S T OW2 N -HEADSTONE'S HH EH1 D S T OW2 N Z -HEADSTONES HH EH1 D S T OW2 N Z -HEADSTRONG HH EH1 D S T R AO2 NG -HEADWAITER HH EH1 D W EY0 T ER0 -HEADWATER HH EH1 D W AO2 T ER0 -HEADWATERS HH EH1 D W AO2 T ER0 Z -HEADWAY HH EH1 D W EY2 -HEADWIND HH EH1 D W IH2 N D -HEADWINDS HH EH1 D W IH2 N D Z -HEADWORK HH EH1 D W ER2 K -HEADY HH EH1 D IY0 -HEAH HH IY1 AH0 -HEAH'S HH IY1 Z -HEAHS HH IY1 Z -HEAL HH IY1 L -HEAL'D HH IY1 L D -HEALD HH IY1 L D -HEALE HH IY1 L -HEALE'S HH IY1 L Z -HEALED HH IY1 L D -HEALER HH IY1 L ER0 -HEALER'S HH IY1 L ER0 Z -HEALERS HH IY1 L ER0 Z -HEALEST HH IY1 L IH0 S T -HEALETH HH IY1 L EH2 TH -HEALEY HH IY1 L IY0 -HEALIN HH IY1 L IH0 N -HEALING HH IY1 L IH0 NG 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T B R OW2 K AH0 N L IY0 -HEARTBURN HH AA1 R T B ER2 N -HEARTBURNING HH AA1 R T B ER2 N IH0 NG -HEARTBURNINGS HH AA1 R T B ER2 N IH0 NG Z -HEARTED HH AA1 R T AH0 D -HEARTED HH AA1 R T IH0 D -HEARTEDLY HH AA1 R T IH0 D L IY0 -HEARTEDNESS HH AA1 R T IH0 D N AH0 S -HEARTEN HH AA1 R T AH0 N -HEARTENED HH AA1 R T AH0 N D -HEARTENING HH AA1 R T AH0 N IH0 NG -HEARTENING HH AA1 R T N IH0 NG -HEARTENINGLY HH AA1 R T AH0 N IH0 NG L IY0 -HEARTENS HH AA1 R T AH0 N Z -HEARTFELT HH AA1 R T F EH2 L T -HEARTFREE HH AA1 R T F R IY2 -HEARTFREE'S HH AA1 R T F R IY2 Z -HEARTFUL HH AA1 R T F AH0 L -HEARTFULLY HH AA1 R T F AH0 L IY0 -HEARTH HH AA1 R TH -HEARTH'S HH AA1 R TH S -HEARTHRUG HH AA1 R T R AH1 G -HEARTHRUGS HH AA1 R T R AH1 G Z -HEARTHS HH AA1 R TH S -HEARTHSIDE HH AA1 R TH S AY2 D -HEARTHSTONE HH AA1 R TH S T OW2 N -HEARTHSTONES HH AA1 R TH S T OW2 N Z -HEARTIER HH AA1 R T IY0 ER0 -HEARTIES HH AA1 R T IY0 Z -HEARTIEST HH AA1 R T IY0 AH0 S T -HEARTILY HH AA1 R T AH0 L IY0 -HEARTINESS HH AA1 R T IH0 N IH0 S -HEARTING HH AA1 R T IH0 NG -HEARTLESS HH AA1 R T L AH0 S -HEARTLESSLY HH AA1 R T L AH0 S L IY0 -HEARTLESSNESS HH AA1 R T L AH0 S N AH0 S -HEARTLY HH AA1 R T L IY0 -HEARTRENDING HH AA1 R T R EH1 N D IH0 NG -HEARTS HH AA1 R T S -HEARTSEASE HH AA1 R T S IY1 Z -HEARTSELL HH AA1 R T S AH0 L -HEARTSICK HH AA1 R T S IH0 K -HEARTSICKNESS HH AA1 R T S IH2 K N AH0 S -HEARTSOME HH AA1 R T S AH0 M -HEARTSORE HH AA1 R T S ER0 -HEARTSTRINGS HH AA1 R T S T R IH1 NG Z -HEARTWELL HH AA1 R T W EH2 L -HEARTY HH AA1 R T IY0 -HEARTY'S HH AA1 R T IY0 Z -HEARTYS HH AA1 R T IY0 Z -HEASLIP HH IY1 S L IH0 P -HEASY HH IY1 S IY0 -HEAT HH IY1 T -HEAT'S HH IY1 T S -HEATE HH IY1 T -HEATED HH IY1 T AH0 D -HEATED HH IY1 T IH0 D -HEATEDLY HH IY1 T IH0 D L IY0 -HEATER HH IY1 T ER0 -HEATERS HH IY1 T ER0 Z -HEATGUN HH IY1 T G AH2 N -HEATGUNS HH IY1 T G AH2 N Z -HEATH HH IY1 TH -HEATH'S HH IY1 TH S -HEATHBRIDGE HH IY1 TH B R IH2 JH -HEATHCLIFF HH EH1 TH K L IH0 F -HEATHCLIFF'S HH EH1 TH K L IH0 F S -HEATHCOCK HH EH1 TH K AH0 K -HEATHCOTE HH EH1 TH K AH0 T -HEATHCOTE'S HH EH1 TH K AH0 T S -HEATHCOTES HH EH1 TH K AH0 T S -HEATHCOTT HH EH1 TH K AH0 T -HEATHCROFT HH EH1 TH K R AO2 F T -HEATHEN HH IY1 DH AH0 N -HEATHEN'S HH IY1 DH AH0 N Z -HEATHENDOM HH IY1 DH AH0 N D AH0 M -HEATHENESSE HH IY1 DH AH0 N EH1 S -HEATHENISH HH IY1 TH EH1 N IH0 SH -HEATHENISM HH IY1 DH AH0 N IH2 Z AH0 M -HEATHENRY HH IY1 TH EH1 N R IY0 -HEATHENS HH IY1 TH AH0 N Z -HEATHER HH EH1 DH ER0 -HEATHER'S HH EH1 DH ER0 Z -HEATHERBY HH EH1 DH ER0 B IY0 -HEATHERCLIFFE HH EH1 DH ER0 K L IH0 F -HEATHERDALE HH EH1 DH ER0 D EY2 L -HEATHERED HH EH1 DH ER0 D -HEATHERFORD HH EH1 DH ER0 F ER0 D -HEATHERLAND HH EH1 DH ER0 L AH0 N D -HEATHERLANDS HH EH1 DH ER0 L AE2 N D Z -HEATHERLEGH HH EH1 DH ER0 L IH0 G -HEATHERLEIGH HH EH1 DH ER0 L AH0 -HEATHERLY HH EH1 DH ER0 L IY0 -HEATHERPOINT HH EH1 DH ER0 P OY2 N T -HEATHERSTONE HH EH1 DH ER0 S T OW2 N -HEATHERTON HH EH1 DH ER0 T AH0 N -HEATHERY HH EH1 DH ER0 IY0 -HEATHERY'S HH EH1 DH ER0 IY0 Z 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ER0 L AW0 F -HEBERT HH EH1 B ER0 T -HEBERT'S HH EH1 B ER0 T S -HEBERTISTS HH EH1 B ER0 T IH0 S T S -HEBERTS HH EH1 B ER0 T S -HEBES HH IY1 B Z -HEBETUDE HH EH1 B AH0 T UW2 D -HEBLER HH EH1 B L ER0 -HEBR HH EH1 B R -HEBRAIC HH AH0 B R EY1 IH0 K -HEBRAISM HH EH1 B R EY0 IH2 Z AH0 M -HEBRAIST HH EH1 B R AA1 IH0 S T -HEBRAISTS HH EH1 B R AH0 S -HEBREW HH IY1 B R UW0 -HEBREW'S HH IY1 B R UW2 Z -HEBREWS HH IY1 B R UW2 Z -HEBRID HH EH1 B R IH0 D -HEBRIDEAN HH EH1 B R IH0 D IY0 AH0 N -HEBRIDES HH EH1 B R IH0 D IY0 Z -HEBRON HH EH1 B R AH0 N -HEBRON HH EH1 B R AO2 N -HEBRON'S HH EH1 B R AO2 N Z -HEBRUS HH EH1 B R AH0 S -HEC HH EH1 K -HECATE HH EH1 K EY0 T -HECATE'S HH EH1 K AH0 T S -HECATEUS HH EH1 K AH0 T IY0 AH0 S -HECATOMB HH EH1 K AH0 T UW1 M -HECATOMBS HH EH1 K AH0 T UW1 M Z -HECH HH EH1 K -HECHAM HH EH1 CH AH0 M -HECHNAHOUL HH EH1 K N AH0 UW1 L -HECHO HH EH1 K OW0 -HECHT HH EH1 K T -HECHT'S HH EH1 K T S -HECK HH EH1 K -HECKEPFENNIG HH EH1 K EH0 F EH1 N IH0 G -HECKER HH EH1 K ER0 -HECKEWELDER HH EH0 K Y UW1 W EH2 L D ER0 -HECKEWELDER'S HH EH0 K Y UW1 W EH2 L D ER0 Z -HECKLE HH EH1 K AH0 L -HECKLED HH EH1 K AH0 L D -HECKLEFIELD HH EH1 K AH0 L F IY2 L D -HECKLER HH EH1 K L ER0 -HECKLESTON HH EH1 K AH0 L S T AH0 N -HECKLING HH EH1 K L IH0 NG -HECKSCHER HH EH1 K SH ER0 -HECKSHER HH EH1 K SH ER0 -HECLA HH EH1 K L AH0 -HECTARE HH EH1 K T AA2 R -HECTARES HH EH1 K T AA2 R Z -HECTIC HH EH1 K T IH0 K -HECTICALLY HH EH1 K T IH0 K L IY0 -HECTOGRAPH HH EH1 K T AH0 G R AE2 F -HECTOR HH EH1 K T ER0 -HECTOR'S HH EH1 K T ER0 Z -HECTORED HH EH1 K T ER0 D -HECTORIN HH EH1 K T AH0 R IH0 N -HECTORING HH EH1 K T ER0 IH0 NG -HECTORS HH EH1 K T ER0 Z -HECUBA HH EH1 K Y AH0 B AH0 -HECUBA HH EH1 K Y UW0 B AH0 -HED HH EH1 D -HEDDA HH EH1 D AH0 -HEDDEGAN HH EH1 D AH0 G AH0 N -HEDDEN HH EH1 D AH0 N -HEDDINGTON HH EH1 D IH0 NG T AH0 N -HEDDON HH EH1 D AH0 N -HEDE HH IY1 D -HEDER HH EH1 D ER0 -HEDGE HH EH1 JH -HEDGE'S HH EH1 JH IH0 Z -HEDGED HH EH1 JH D -HEDGEFLOWER HH EH1 JH F L AW2 ER0 -HEDGEHOG HH EH1 JH HH AA2 G -HEDGEHOG'S HH EH1 JH HH AA2 G Z -HEDGEHOGS HH EH1 JH HH AA2 G Z -HEDGELESS HH EH1 JH L AH0 S -HEDGER HH EH1 JH ER0 -HEDGER'S HH EH1 JH ER0 Z -HEDGEROW HH EH1 JH ER0 OW0 -HEDGEROWS HH EH1 JH ER0 OW0 Z -HEDGERS HH EH1 JH ER0 Z -HEDGES HH EH1 JH IH0 Z -HEDGESIDE HH EH1 JH S AY2 D -HEDGEVILLE HH EH1 JH V IH0 L -HEDGING HH EH1 JH IH0 NG -HEDIGER HH EH1 D IH0 G ER0 -HEDIN HH EH1 D IH0 N -HEDINGHAM HH EH1 D IH0 NG HH AE2 M -HEDJAZ HH EH1 JH AA1 Z -HEDLEY HH EH1 D L IY0 -HEDN'T HH EH1 D AH0 N T -HEDON HH IY1 D AH0 N -HEDONISM HH IY1 D AH0 N IH2 Z AH0 M -HEDONIST HH IY1 D AH0 N IH0 S T -HEDONISTIC HH IY2 D AH0 N IH1 S T IH0 K -HEDONISTS HH IY1 D AH0 N IH0 S T S -HEDRICK HH EH1 D R IH0 K -HEDRICK'S HH EH1 D R IH0 K S -HEDRIN HH EH1 D R IH0 N -HEDULIO HH EH1 D UW1 L IY0 OW0 -HEDULIO'S HH EH1 D UW1 L IY0 OW0 Z -HEDVIG HH EH1 D V IH2 G -HEDWIG HH EH1 D W IH0 G -HEDWIG'S HH EH1 D W IH0 G Z -HEDWIGE HH EH1 D W IH0 G -HEDWORTH HH EH1 D W ER2 TH -HEDZOFF HH EH1 D Z AO0 F -HEE HH IY1 -HEE'S HH IY1 Z -HEEAD HH IY1 EH0 D -HEEARD HH IY1 ER0 D -HEED HH IY1 D -HEEDED HH IY1 D AH0 D -HEEDED HH IY1 D IH0 D -HEEDEST HH IY1 D AH0 S T -HEEDETH HH IY1 D IH0 TH -HEEDFUL HH IY1 D F AH0 L -HEEDFULLY HH IY1 D F AH0 L IY0 -HEEDFULNESS HH IY1 D F AH0 L N AH0 S -HEEDIN HH IY1 D IH0 N -HEEDING HH IY1 D IH0 NG -HEEDLESS HH IY1 D L AH0 S -HEEDLESSLY HH IY1 D L AH0 S L IY0 -HEEDLESSNESS HH IY1 D L AH0 S N AH0 S -HEEDS HH IY1 D Z -HEEF HH IY1 F -HEEL HH IY1 L -HEEL'D HH IY1 L D -HEELBARE HH IY1 L B EH2 R -HEELED HH IY1 L D -HEELER HH IY1 L ER0 -HEELER'S HH IY1 L ER0 Z -HEELERS HH IY1 L ER0 Z -HEELES HH IY1 L Z -HEELEY HH IY1 L IY0 -HEELING HH IY1 L IH0 NG -HEELLESS HH IY1 L L AH0 S -HEELS HH IY1 L Z -HEELS'S HH IY1 L Z IH0 Z -HEELTAP HH IY1 L T AE2 P -HEELTAPS HH IY1 L T AE2 P S -HEEM HH IY1 M -HEEMSELF HH IY1 M S EH1 L F -HEEMSKERK HH IY1 M S K ER1 K -HEENAN HH IY1 N AH0 N -HEENY HH IY1 N IY0 -HEENY'S HH IY1 N IY0 Z -HEEP HH IY1 P -HEEP'S HH IY1 P S -HEEPS HH IY1 P S -HEER HH IY1 ER0 -HEER'D HH IH1 R D -HEERA HH IH1 R AH0 -HEERBRAND HH IH1 R B R AE2 N D -HEERD HH IH1 R D -HEERDEN HH IH1 R D AH0 N -HEERDEN'S HH IH1 R D AH0 N Z -HEERE HH IH1 R -HEERED HH IH1 R D -HEEREN HH IH1 R AH0 N -HEERN HH IH1 R N -HEES HH IY1 Z -HEET HH IY1 T -HEF HH EH1 F -HEFFER HH EH1 F ER0 -HEFFERNAN HH EH1 F ER0 N AH0 N -HEFFERNAN'S HH EH1 F ER0 N AH0 N Z -HEFFEROM HH EH1 F ER0 AA1 M -HEFFLEFINGER HH EH1 F AH0 L F IH2 NG G ER0 -HEFFNER HH EH1 F N ER0 -HEFFTERDINGT HH EH1 F T ER0 D IH0 NG T -HEFFY HH EH1 F IY0 -HEFT HH EH1 F T -HEFTED HH EH1 F T IH0 D -HEFTING HH EH1 F T IH0 NG -HEFTS HH EH1 F T S -HEFTY HH EH1 F T IY0 -HEG HH EH1 G -HEGAN HH EH1 G AH0 N -HEGAN'S HH EY1 G AH0 N Z -HEGARTY HH EH1 G AA0 R T IY0 -HEGARTY'S HH EH1 G AA0 R T IY0 Z -HEGAU HH EH1 G OW1 -HEGEL HH EH1 G AH0 L -HEGEL'S HH EH1 G AH0 L Z -HEGELIAN HH IY0 JH IY1 L IY0 AH0 N -HEGELIANISM HH IY0 JH IY1 L IY0 AH0 N IH0 Z AH0 M -HEGELIANS HH IY0 JH IY1 L IY0 AH0 N Z -HEGELMANN HH EH1 G AH0 L M AH0 N -HEGEMONY HH IY0 JH EH1 M AH0 N IY0 -HEGER HH IY1 G ER0 -HEGERMANN HH EH1 G ER0 M AH0 N -HEGESIPPUS HH EH1 G IH0 S IH1 P AH0 S -HEGG HH EH1 G -HEGGIE HH EH1 G IY0 -HEGGS HH EH1 G Z -HEGH HH EH1 G -HEGIRA HH IY0 JH IH1 R AH0 -HEGNER HH EH1 G N ER0 -HEGUMEN HH EH1 G Y AH0 M AH0 N -HEGUMEN'S HH EH1 G Y AH0 M AH0 N Z -HEH HH EH1 -HEHRMANN HH EH1 R M AH0 N -HEHRMANN'S HH EH1 R M AH0 N Z -HEI HH AY1 -HEIAU HH AY1 AW0 -HEIBER HH AY1 B ER0 -HEIBERG HH AY1 B ER0 G -HEID HH AY1 D -HEIDE HH AY1 D -HEIDECK HH AY1 D IH0 K -HEIDEGARDS HH AY1 D IH0 G AA1 R D Z -HEIDEGGER HH AY1 D IH0 G ER0 -HEIDEGGER'S HH AY1 D IH0 G ER0 Z -HEIDEL HH AY1 D AH0 L -HEIDELBERG HH AY1 D AH0 L B ER0 G -HEIDELMANN HH AY1 D AH0 L M AH0 N -HEIDENDORF HH AY1 D IH0 N D AO0 R F -HEIDI HH AY1 D IY0 -HEIDLEMANN HH AY1 D AH0 L M AH0 N -HEIDLEMANNS HH AY1 D AH0 L M AH0 N Z -HEIDREK HH AY1 D R EH0 K -HEIDREK'S HH AY1 D R EH2 K S -HEIDS HH AY1 D Z -HEIDSIECK HH AY1 D S IY1 K -HEIDT HH AY1 D T -HEIF HH IY1 F -HEIFER HH AY1 F ER0 -HEIFER HH EH1 F ER0 -HEIFER'S HH EH1 F ER0 Z -HEIFERS HH AY1 F ER0 Z -HEIFERS HH EH1 F ER0 Z -HEIFETZ HH AY1 F IH0 T S -HEIGH HH AY1 -HEIGHAM HH AY1 AH0 M -HEIGHO HH AY1 OW0 -HEIGHT HH AY1 T -HEIGHTEN HH AY1 T AH0 N -HEIGHTENED HH AY1 T AH0 N D -HEIGHTENING HH AY1 T AH0 N IH0 NG -HEIGHTENING HH AY1 T N IH0 NG -HEIGHTENS HH AY1 T AH0 N Z -HEIGHTH HH AY1 TH -HEIGHTS HH AY1 T S -HEIGHTY HH AY1 T IY0 -HEIKE HH AY1 K -HEIL HH AY1 L -HEILBORN HH AY1 L B ER0 N -HEILBRON HH AY1 L B R AH0 N -HEILBRONN HH AY1 L B R AA1 N -HEILIG HH AY1 L IH0 G -HEILIGE HH AY1 L IH0 JH -HEILIGEN HH AY1 L IH0 JH EH0 N -HEILIGENBERG HH AY1 L IH0 G AH0 N B ER0 G -HEILIGENGELT HH AY1 L IH0 G EH1 NG G IH0 L T -HEILIGENSTADT HH AY1 L IH0 G AH0 N S T AE2 T -HEILIGENSTERN HH AY1 L IH0 G AH0 N S T ER0 N -HEILMANN HH AY1 L M AH0 N -HEILSBERG HH AY1 L Z B ER0 G -HEIM HH AY1 M -HEIM'S HH AY2 M Z -HEIMA HH AY1 M AH0 -HEIMAN HH AY1 M AH0 N -HEIMAT HH AY1 M AE0 T -HEIMBERT HH AY1 M B ER0 T -HEIMBURG HH AY1 M B ER0 G -HEIMCHEN HH AY1 M CH AH0 N -HEIMDAL HH AY1 M D AH0 L -HEIMDALL HH AY1 M D AH0 L -HEIMERT HH AY1 M ER0 T -HEIMRA HH AY1 M R AH0 -HEIMSKRINGLA HH AY1 M S K R IH1 NG G L AH0 -HEIMWEH HH AY1 M W EH2 -HEIN HH AY1 N -HEINDE HH AY1 N D -HEINE HH AY1 N -HEINE'S HH AY1 N Z -HEINECKE HH AY1 N IH0 K -HEINECKEN HH AY1 N IH0 K AH0 N -HEINEMAN HH AY1 N M AH0 N -HEINEMANN HH AY1 N M AH0 N -HEINEMANN'S HH AY1 N M AH0 N Z -HEINFETTER HH AY1 N F EH1 T ER0 -HEINHOLD HH AY1 N HH OW0 L D -HEINIE HH AY1 N IY0 -HEINIES HH AY1 N IY0 Z -HEINK HH AY1 NG K -HEINOUS HH EY1 N AH0 S -HEINOUSLY HH EY1 N AH0 S L IY0 -HEINOUSNESS HH EY1 N AH0 S N AH0 S -HEINRICH HH AY1 N R IH0 K -HEINRICH'S HH AY1 N R IH0 K S -HEINRICK HH AY1 N R IH0 K -HEINSIUS HH AY1 N S IY0 AH0 S -HEINTZELMAN HH AY1 N T Z AH0 L M AH0 N -HEINTZELMAN'S HH AY1 N T Z AH0 L M AH0 N Z -HEINY HH AY1 N IY0 -HEINZ HH AY1 N Z -HEINZ'S HH AY1 N Z IH0 Z -HEINZE HH AY1 N Z -HEINZEL HH AY1 N Z AH0 L -HEINZLEMAN HH AY1 N Z AH0 L M AH0 N -HEINZMAN HH AY1 N Z M AH0 N -HEINZMAN'S HH AY1 N Z M AH0 N Z -HEIR EH1 R -HEIR'S EH1 R Z -HEIRE HH AY0 R -HEIRED EH1 R D -HEIRESS EH1 R AH0 S -HEIRESS'S EH1 R AH0 S IH0 Z -HEIRESSES EH1 R AH0 S IH0 Z -HEIRI EH1 R IY0 -HEIRI'S EH1 R IY0 Z -HEIRLESS EH1 R L AH0 S -HEIRLOOM EH1 R L UW2 M -HEIRLOOMS EH1 R L UW2 M Z -HEIRS EH1 R Z -HEIRSHIP EH1 R SH IH2 P -HEISE HH AY1 S -HEISH HH IY1 IH0 SH -HEISSE HH AY1 S -HEISSLER HH AY1 S L ER0 -HEITMAN HH AY1 T M AH0 N -HEITZ HH AY1 T S -HEIZER HH AY1 Z ER0 -HEJ HH EH1 JH -HEJAZ HH EH1 JH AA1 Z -HEK HH EH0 K -HEKALU HH EH2 K AA1 L UW0 -HEKALU'S HH EH2 K AH0 L UW2 Z -HEKE HH IH0 K -HEKE'S HH EH2 K S -HEKEKIAN HH EH2 K EY1 K IY0 AH0 N -HEKEKYAN HH EH1 K AH0 K Y AH0 N -HEKKA HH EH1 K AH0 -HEKKADOR HH EH1 K AH0 D AO2 R -HEKKI HH EH1 K IY0 -HEKT HH EH2 K T -HEL HH EH1 L -HEL'S HH EH1 L Z -HELA HH EH1 L AH0 -HELA'S HH EH1 L AH0 Z -HELAS HH EH1 L AH0 S -HELBECK HH EH1 L B EH2 K -HELBECK'S HH EH1 L B EH2 K S -HELBERSON HH EH1 L B ER0 S AH0 N -HELBON HH EH1 L B AH0 N -HELCA HH EH1 L K AH0 -HELD HH EH1 L D -HELDA HH EH1 L D AH0 -HELDAR HH EH1 L D AA2 R -HELDBURG HH EH1 L D B ER0 G -HELDEN HH EH1 L D AH0 N -HELDENFELD HH EH1 L D IH0 N F EH0 L D -HELDER HH EH1 L D ER0 -HELDEREN HH EH1 L D IH1 R AH0 N -HELDERS HH EH1 L D ER0 Z -HELDEST HH EH1 L D AH0 S T -HELDING HH EH1 L D IH0 NG -HELDON HH EH1 L D AH0 N -HELDRE HH EH1 L D R -HELDRE'S HH EH1 L D R Z -HELE HH EH1 L -HELEIGH HH EH1 L AH0 -HELEN HH EH1 L AH0 N -HELEN'S HH EH1 L IH0 N Z -HELENA HH EH1 L AH0 N AH0 -HELENA'S HH EH1 L IH0 N AH0 Z -HELENE HH AH0 L IY1 N -HELENE'S HH AH0 L IY1 N Z -HELENS HH EH1 L AH0 N Z -HELENSVILLE HH EH1 L AH0 N Z V IH2 L -HELENUS HH AH0 L IY1 N AH0 S -HELEPHANT HH EH1 L AH0 F AH0 N T -HELEPHANTS HH EH1 L AH0 F AH0 N T S -HELFEN HH EH1 L F AH0 N -HELFFERICH HH EH1 L F ER0 IH0 K -HELFORD HH EH1 L F ER0 D -HELGA HH EH1 L G AH0 -HELGA'S HH EH1 L G AH0 Z -HELGE HH EH1 L JH -HELGE'S HH EH1 L G Z -HELGELAND HH EH1 L G IH0 L AH0 N D -HELGERS HH EH1 L G ER0 Z -HELGI HH EH1 L G IY0 -HELGI'S HH EH1 L G IY0 Z -HELGOLAND HH EH1 L G OW1 L AH0 N D -HELHEIM HH EH1 L HH AY2 M -HELI HH EH1 L IY0 -HELIACAL HH IY1 L IY0 AE2 K AH0 L -HELIANTHUS HH IY1 L IY0 AH0 N TH AH0 S -HELICAL HH EH1 L IH0 K AH0 L -HELICANUS HH EH1 L IH0 K AH0 N AH0 S -HELICES HH EH1 L IH0 S IH0 Z -HELICON HH EH1 L IH0 K AA2 N -HELICON'S HH EH1 L IH0 K AA2 N Z -HELICONIAN HH EH1 L IH0 K OW1 N IY0 AH0 N -HELICONIAS HH EH1 L IH0 K AA2 N IY0 AH0 Z -HELICOPTER HH EH1 L IH0 K AA2 P T ER0 -HELICOPTERS HH EH1 L IH0 K AA2 P T ER0 Z -HELIDOR HH IY1 L IY0 D AO1 R -HELIE HH EH1 L IY0 -HELIER HH IY1 L IY0 ER0 -HELIER'S HH IY1 L IY0 ER0 Z -HELIERS HH EH1 L Y ER0 Z -HELIET HH IY1 L IY0 EH2 T -HELIET'S HH IY1 L IY0 EH2 T S -HELIGOLAND HH EH1 L IH0 G AA1 L AH0 N D -HELIM HH EH1 L AH0 M -HELIO HH IY1 L IY0 OW0 -HELIOBAS HH IY1 L IY0 AA2 B AH0 Z -HELIOCENTRIC HH IY2 L IY0 OW0 S EH1 N T R IH0 K -HELIODORA HH IY1 L IY0 AH0 D AO1 R AH0 -HELIODORE HH IY1 L IY0 AH0 D AO2 R -HELIODORUS HH IY1 L IY0 AH0 D AO1 R AH0 S -HELIOGABALUS HH IY2 L IY0 AA1 G AH0 B AH0 L AH0 S -HELIOGRAPH HH IY1 L IY0 AH0 G R AE2 F -HELIOGRAPHED HH IY1 L IY0 AH0 G R AE2 F T -HELIOGRAPHIC HH IY1 L IY0 AH0 G R AE1 F IH0 K -HELIOGRAPHING HH IY2 L IY0 AA1 G R AH0 F IH0 NG -HELIOGRAPHS HH IY1 L IY0 AH0 G R AE2 F S -HELIOPOLIS HH IY2 L IY0 AA1 P AH0 L AH0 S -HELIOPOLITAN HH IY2 L IY0 AA1 P AH0 L IH1 T AH0 N -HELIOS HH IY1 L IY0 AA2 S -HELIOT HH IY1 L IY0 AH0 T -HELIOTROPE HH IY1 L IY0 AH0 T R OW2 P -HELIOTROPES HH IY1 L IY0 AH0 T R OW2 P S -HELIUM HH IY1 L IY0 AH0 M -HELIUM'S HH IY1 L IY0 AH0 M Z -HELIUMITE HH IY1 L IY0 AH0 M AY2 T -HELIUS HH IY1 L IY0 IH0 S -HELIX HH IY1 L IH0 K S -HELKA HH EH1 L K AH0 -HELL HH EH1 L -HELL' HH EH1 L -HELL'S HH EH1 L Z -HELLA HH EH1 L AH0 -HELLAJ HH EH1 L AY2 -HELLANICUS HH EH1 L AE1 N AH0 K AH0 S -HELLAR HH EH1 L ER0 -HELLAS HH EH1 L AH0 Z -HELLBEAM HH EH1 L B IY2 M -HELLBEAM'S HH EH1 L B IY2 M Z -HELLDORF HH EH1 L D AO0 R F -HELLE HH EH1 L -HELLEBERGENE HH EH1 L AH0 B ER0 JH IY2 N -HELLEBORE HH EH1 L AH0 B AO0 R -HELLELIL HH EH1 L AH0 L IH1 L -HELLEN HH EH1 L AH0 N -HELLENA HH EH1 L IH0 N AH0 -HELLENE HH EH1 L IY2 N -HELLENES HH EH1 L IY2 N Z -HELLENIC HH AH0 L EH1 N IH0 K -HELLENISM HH EH1 L AH0 N IH2 Z AH0 M -HELLENIST HH EH1 L AH0 N IH0 S T -HELLENISTIC HH EH2 L AH0 N IH1 S T IH0 K -HELLENISTS HH EH1 L AH0 N IH0 S T S -HELLENIZED HH EH1 L AH0 N AY2 Z D -HELLENS HH EH1 L AH0 N Z -HELLENY HH EH1 L AH0 N IY0 -HELLER HH EH1 L ER0 -HELLERAU HH EH1 L ER0 AW0 -HELLERS HH EH1 L ER0 Z -HELLES HH EH1 L Z -HELLESPONT HH EH1 L AH0 S P AA2 N T -HELLESTON HH EH1 L AH0 S T AH0 N -HELLFDE HH EH1 L F D -HELLFIRE HH EH1 L F AY2 R -HELLHOUND HH EH1 L HH AW2 N D -HELLHOUNDS HH EH1 L HH AW2 N D Z -HELLICANUS HH EH1 L IH0 K AH0 N AH0 S -HELLIFIELD HH EH1 L IH0 F IY2 L D -HELLING HH EH1 L IH0 NG -HELLINGFORD HH EH1 L IH0 NG F ER0 D -HELLINGSLEY HH EH1 L IH0 NG Z L IY0 -HELLION HH EH1 L Y AH0 N -HELLIONS HH EH1 L Y AH0 N Z -HELLISH HH EH1 L IH0 SH -HELLISHLY HH EH1 L IH0 SH L IY0 -HELLISHNESS HH EH1 L IH0 SH N AH0 S -HELLNER HH EH1 L N ER0 -HELLO HH AH0 L OW1 -HELLO HH EH0 L OW1 -HELLOA HH AH0 L OW1 AH0 -HELLOS HH EH1 L OW0 Z -HELLS HH EH1 L Z -HELLUM HH EH1 L AH0 M -HELLUP HH EH1 L AH0 P -HELLUVA HH EH2 L UW1 V AH0 -HELLWARD HH EH1 L W ER0 D -HELLWARDS HH EH1 L W ER0 D Z -HELLWIG HH EH1 L W IH0 G -HELLY HH EH1 L IY0 -HELLYER HH EH1 L IY0 ER0 -HELM HH EH1 L M -HELM'S HH EH1 L M Z -HELMA HH EH1 L M AH0 -HELMA'S HH EH1 L M AH0 Z -HELMAN HH EH1 L M AH0 N -HELMANOFF HH EH1 L M AH0 N AO0 F -HELMAR HH EH1 L M ER0 -HELMAS HH EH1 L M AH0 Z -HELMBOE HH EH1 L M B OW0 -HELMBRECHT HH EH1 L M B R IH0 K T -HELMED HH EH1 L M D -HELMER HH EH1 L M ER0 -HELMER'S HH EH1 L M ER0 Z -HELMET HH EH1 L M AH0 T -HELMET'S HH EH1 L M AH0 T S -HELMETED HH EH1 L M AH0 T IH0 D -HELMETH HH EH1 L M AH0 TH -HELMETS HH EH1 L M AH0 T S -HELMHOLTZ HH EH1 L M HH OW2 L T S -HELMINE HH EH1 L M IH0 N -HELMLESS HH EH1 L M L AH0 S -HELMONT HH EH1 L M AA2 N T -HELMS HH EH1 L M Z -HELMSDALE HH EH1 L M Z D EY2 L -HELMSLEY HH EH1 L M Z L IY0 -HELMSLEY'S HH EH1 L M Z L IY0 Z -HELMSMAN HH EH1 L M Z M AE2 N -HELMSMAN'S HH EH1 L M Z M AE2 N Z -HELMSMEN HH EH1 L M Z M IH0 N -HELMSTONE HH EH1 L M S T OW2 N -HELMSTONE'S HH EH1 L M S T OW2 N Z -HELMSTONES HH EH1 L M S T OW2 N Z -HELMSWOMAN HH EH1 L M Z W UH2 M AH0 N -HELMSWORTH HH EH1 L M Z W ER2 TH -HELMUND HH EH1 L M AH0 N D -HELMUT HH EH1 L M AH0 T -HELMUTH HH EH1 L M UW2 TH -HELMY HH IH0 L M IY0 -HELOISE HH EH1 L OW0 IY0 Z -HELOISE'S HH EH1 L OW0 IY0 Z IH0 Z -HELON HH EH1 L AH0 N -HELOT HH EH1 L AH0 T -HELOTS HH EH1 L AH0 T S -HELOUAN HH EH1 L AH0 W AA1 N -HELOUIN HH EH1 L W IY1 N -HELP HH EH1 L P -HELP'D HH EH1 L P D -HELP'S HH EH1 L P S -HELPE HH EH1 L P -HELPED HH EH1 L P T -HELPER HH EH1 L P ER0 -HELPER'S HH EH1 L P ER0 Z -HELPERS HH EH1 L P ER0 Z -HELPEST HH EH1 L P AH0 S T -HELPETH HH EH1 L P IH0 TH -HELPFUL HH EH1 L P F AH0 L -HELPFULLY HH EH1 L P F AH0 L IY0 -HELPFULNESS HH EH1 L P F AH0 L N AH0 S -HELPIN HH EH1 L P IH0 N -HELPING HH EH1 L P IH0 NG -HELPINGS HH EH1 L P IH0 NG Z -HELPIT HH EH1 L P IH0 T -HELPLESS HH EH1 L P L AH0 S -HELPLESSLY HH EH1 L P L AH0 S L IY0 -HELPLESSNESS HH EH1 L P L AH0 S N AH0 S -HELPMAKAAR HH EH1 L P M AH0 K AA1 R -HELPMAN HH EH1 L P M AH0 N -HELPMATE HH EH1 L P M EY2 T -HELPMATES HH EH1 L P M EY2 T S -HELPMEET HH EH1 L P M IY1 T -HELPMEETS HH EH1 L P M IY1 T S -HELPS HH EH1 L P S -HELPSTON HH EH1 L P S T AH0 N -HELPSTONE HH EH1 L P S T OW2 N -HELPT HH EH1 L P T -HELSA HH EH1 L S AH0 -HELSETH HH EH1 L S IH0 TH -HELSING HH EH1 L S IH0 NG -HELSING'S HH EH1 L S IH0 NG Z -HELSINGBORG HH EH1 L S IH0 NG B AO0 R G -HELSINGFORS HH EH1 L S IH0 NG F ER0 Z -HELSINGHAM HH EH1 L S IH0 NG HH AE2 M -HELST HH EH1 L S T -HELSTON HH EH1 L S T AH0 N -HELSTONE HH EH1 L S T OW2 N -HELSTONLEIGH HH EH1 L S OW2 N L AH0 -HELT HH EH1 L T -HELTER HH EH1 L T ER0 -HELTH HH EH1 L TH -HELU HH EH1 L UW0 -HELUVA HH EH1 L UW1 V AH0 -HELVA HH EH1 L V AH0 -HELVE HH EH1 L V -HELVELLYN HH EH1 L V EH1 L IH0 N -HELVES HH EH1 L V Z -HELVETIA HH EH1 L V AH0 T Y AH0 -HELVETIAN HH EH1 L V IY1 SH AH0 N -HELVETIANS HH EH1 L V IY1 SH Y IH0 N Z -HELVETIC HH EH1 L V AH0 T IH2 K -HELVETII HH EH1 L V AH0 T IY0 -HELVETIUS HH EH1 L V AH0 T IY0 IH0 S -HELVIDIUS HH EH1 L V IH0 D IY0 AH0 S -HELVOETSLUYS HH EH1 L V OW1 S L AY1 Z -HELWYSE HH EH1 L W AY0 S -HELWYSE'S HH EH1 L W AY1 Z IH0 Z -HELY HH EH1 L IY0 -HELZEPHRON HH EH1 L Z EH1 F R AA0 N -HELZEPHRON'S HH EH1 L Z EH1 F R AA0 N Z -HEM HH EH1 M -HEM'D HH EH1 M D -HEMAN HH IY1 M AH0 N -HEMAN'S HH EH1 M AH0 N Z -HEMANGINI HH EH1 M AA0 NG G IY1 N IY0 -HEMANS HH EH1 M AH0 N Z -HEMANS'S HH EH1 M AH0 N Z IH0 Z -HEMANTA HH AH0 M AA1 N T AH0 -HEMATITE HH EH1 M AH0 T AY2 T -HEMBURG HH EH1 M B ER0 G -HEMENWAY HH EH1 M AH0 N W EY2 -HEMERLINGUE HH EH1 M ER0 L IH0 NG -HEMERLINGUE'S HH EH1 M ER0 L IH0 NG Z -HEMI HH EH1 M IY0 -HEMICYCLE HH EH2 M AH0 S AY2 K AH0 L -HEMING HH EH1 M IH0 NG -HEMINGE HH EH1 M IH0 NG -HEMINGWAY HH EH1 M IH0 NG W EY2 -HEMINGWAY'S HH EH1 M IH0 NG W EY2 Z -HEMINGWAYS HH EH1 M IH0 NG W EY2 Z -HEMINWAY HH EH1 M IH0 N W EY2 -HEMIPLEGIA HH EH2 M AH0 P L IY1 JH IY0 AH0 -HEMIPLEGIA HH EH2 M AH0 P L IY1 JH Y AH0 -HEMIPO HH EH2 M AH0 P OW0 -HEMIPTERA HH EH2 M AH0 P T EH1 R AH0 -HEMISPHERE HH EH1 M IH0 S F IH2 R -HEMISPHERES HH EH2 M AH0 S F IH1 R Z -HEMISPHERIC HH EH2 M AH0 S F IH1 R IH0 K -HEMISPHERICAL HH EH2 M AH0 S F EH1 R IH0 K AH0 L -HEMLOCK HH EH1 M L AA2 K -HEMLOCKS HH EH1 M L AA2 K S -HEMM'D HH EH1 M D -HEMMED HH EH1 M D -HEMMELINE HH EH1 M IH0 L AY2 N -HEMMENSHAW HH EH1 M IH0 N SH AO2 -HEMMING HH EH1 M IH0 NG -HEMMING'S HH EH1 M IH0 NG Z -HEMMINGS HH EH1 M IH0 NG Z -HEMMINGWAY HH EH1 M IH0 NG W EY2 -HEMMINGWELL HH EH1 M IH0 NG W EH2 L -HEMMY HH EH1 M IY0 -HEMOGLOBIN HH IY2 M AH0 G L OW1 B AH0 N -HEMORRHAGE HH EH1 M ER0 IH0 JH -HEMORRHAGE HH EH1 M R AH0 JH -HEMORRHAGES HH EH1 M ER0 IH0 JH IH0 Z -HEMORRHOIDS HH EH1 M ER0 OY2 D Z -HEMP HH EH1 M P -HEMPEL HH EH1 M P AH0 L -HEMPEL'S HH EH1 M P AH0 L Z -HEMPEN HH EH1 M P AH0 N -HEMPFIELD HH EH1 M P F IY2 L D -HEMPHILL HH EH1 M P HH IH2 L -HEMPIE HH EH1 M P IY0 -HEMPSEED HH EH1 M P S IY0 D -HEMPSTEAD HH EH1 M P S T EH0 D -HEMS HH EH1 M Z -HEMSON HH EH1 M S AH0 N -HEMSTITCH HH EH1 M S T IH0 CH -HEMSTITCHED HH EH1 M S T IH1 CH T -HEMSTITCHING HH EH1 M S T IH1 CH IH0 NG -HEMTI HH EH1 M T IY0 -HEMU HH EH1 M UW0 -HEMYOCK HH EH1 M IY0 AH0 K -HEN HH EH1 N -HEN'S HH EH1 N Z -HENA HH EH1 N AH0 -HENARES HH EH1 N ER0 Z -HENAREZ HH EH1 N ER0 EH2 Z -HENAULT HH EH1 N AW0 L T -HENBANE HH EH1 N B EY2 N -HENCASTLE HH EH1 N K AE2 S AH0 L -HENCE HH EH1 N S -HENCEFORTH HH EH1 N S F AO1 R TH -HENCEFORWARD HH EH1 N S F AO1 R W ER0 D -HENCEFORWARDS HH EH1 N S F AO1 R W ER0 D Z -HENCHARD HH EH1 N CH ER0 D -HENCHARD'S HH EH1 N CH ER0 D Z -HENCHMAN HH EH1 N CH M AH0 N -HENCHMAN'S HH EH1 N CH M AH0 N Z -HENCHMEN HH EH1 N CH M AH0 N -HENCHY HH EH1 N CH IY0 -HENCKEL HH EH1 N K AH0 L -HENCKLEY HH EH1 NG K L IY0 -HENCOOP HH EH1 N K UW1 P -HENCOOPS HH EH1 N K UW1 P S -HEND HH EH1 N D -HENDAYE HH EH1 N D EY1 -HENDER HH EH1 N D ER0 -HENDER'S HH EH1 N D ER0 Z -HENDERLAND HH EH1 N D ER0 L AH0 N D -HENDERLEY HH EH1 N D ER0 L IY0 -HENDERLIN HH EH1 N D ER0 L IH0 N -HENDERS HH EH1 N D ER0 Z -HENDERSON HH EH1 N D ER0 S AH0 N -HENDERSON'S HH EH1 N D ER0 S AH0 N Z -HENDERSONS HH EH1 N D ER0 S AH0 N Z -HENDERSONVILLE HH EH1 N D ER0 S AH0 N V IH2 L -HENDIE HH EH1 N D IY0 -HENDON HH EH1 N D OW0 N -HENDREN HH EH1 N D ER0 AH0 N -HENDRIC HH EH1 N D R IH0 K -HENDRICK HH EH1 N D R IH0 K -HENDRICK'S HH EH1 N D R IH0 K S -HENDRICKS HH EH1 N D R IH0 K S -HENDRIE HH EH1 N D ER0 IY0 -HENDRIE'S HH EH1 N D ER0 IY0 Z -HENDRIK HH EH1 N D R IH0 K -HENDRIK'S HH EH1 N D R IH0 K S -HENDRIKA HH EH1 N D R IY1 K AH0 -HENDRIKSEN HH EH1 N D R IH0 K S AH0 N -HENDRIX HH EH1 N D R IH0 K S -HENDRY HH EH1 N D R IY0 -HENDRY'S HH EH1 N D R IY0 Z -HENDRYX HH EH1 N D R IH0 K S -HENDY HH EH1 N D IY0 -HENEAGE HH EH1 N IY0 AH0 JH -HENERY HH EH1 N ER0 IY0 -HENERY'S HH EH1 N ER0 IY0 Z -HENEY HH EH1 N IY0 -HENEY'S HH EH1 N IY0 Z -HENFREY HH EH1 N F R IY0 -HENG HH EH1 NG -HENGEST HH EH1 NG G AH0 S T -HENGIST HH EH1 N JH IH0 S T -HENGLAND HH EH1 NG G L AH0 N D -HENGLER'S HH EH1 NG L ER0 Z -HENGLISH HH EH1 NG G AH0 L IH0 SH -HENGLY HH EH1 NG L IY0 -HENGO HH EH1 NG G OW0 -HENGSTENBERG HH EH1 NG G S T AH0 N B ER0 G -HENHOUSE HH EH1 N HH AW2 S -HENKE HH EH1 NG K -HENKEL HH EH1 NG K AH0 L -HENKEL'S HH EH1 NG K AH0 L Z -HENKER HH EH1 NG K ER0 -HENLE HH EH1 N AH0 L -HENLEIGH HH EH1 N L AH0 -HENLEY HH EH1 N L IY0 -HENLEY'S HH EH1 N L IY0 Z -HENLOPEN HH EH1 N L OW1 P AH0 N -HENLY HH EH1 N L IY0 -HENN HH EH1 N -HENNA HH EH1 N AH0 -HENNE HH EH1 N -HENNE HH EH1 N IY0 -HENNEBON HH EH1 N IH0 B AO0 N -HENNELEINLEIN HH EH1 N AH0 L AY1 N L AY2 N -HENNELL HH EH1 N AH0 L -HENNEPIN HH EH1 N IH0 P IH0 N -HENNEPIN'S HH EH1 N IH0 P IH0 N Z -HENNEQUIN HH EH1 N AH0 K IH0 N -HENNER HH EH1 N ER0 -HENNERY HH EH1 N ER0 IY0 -HENNESEY HH EH1 N IH0 S IY0 -HENNESSEY HH EH1 N AH0 S IY0 -HENNESSEY'S HH EH1 N AH0 S IY0 Z -HENNESSY HH EH1 N AH0 S IY0 -HENNESSY'S HH EH1 N IH0 S IY0 Z -HENNESY HH EH1 N AH0 S IY0 -HENNEY HH EH1 N IY0 -HENNIBUL HH EH1 N IY0 B UW1 L -HENNIE HH EH1 N IY0 -HENNIFER HH EH1 N IH0 F ER0 -HENNIKER HH EH1 N IH0 K ER0 -HENNIKER'S HH EH1 N IH0 K ER0 Z -HENNIKERS HH EH1 N IH0 K ER0 Z -HENNING HH EH1 N IH0 NG -HENNINGER HH EH1 N IH0 NG ER0 -HENNINGS HH EH1 N IH0 NG Z -HENNINGSEN HH EH1 N IH0 NG S AH0 N -HENNION HH EH1 N Y AH0 N -HENNO HH EH1 N OW0 -HENNY HH EH1 N IY0 -HENNY'S HH EH1 N IY0 Z -HENO HH EH1 N OW0 -HENORY HH EH1 N ER0 IY0 -HENPECK HH EH1 N P EH2 K -HENPECKED HH EH1 N P EH2 K T -HENRI AA2 N R IY1 -HENRI AO2 R IY1 -HENRI HH EH1 N R IY0 -HENRI'S HH EH2 N R IY0 Z -HENRIADE HH EH2 N R IY0 EY2 D -HENRICA HH EH1 N R IH0 K AH0 -HENRICA'S HH EH1 N R IH0 K AH0 Z -HENRICI AA0 N R IY1 CH IY0 -HENRICO AA0 N R IY1 K OW0 -HENRICUS HH EH1 N R IH0 K AH0 S -HENRIE HH EH1 N ER0 IY0 -HENRIES HH EH1 N ER0 IY0 Z -HENRIET HH EH2 N R IY0 EH1 T -HENRIETTA HH EH2 N R IY0 EH1 T AH0 -HENRIETTA'S HH EH2 N R IY0 EH1 T AH0 Z -HENRIETTE HH EH2 N R IY0 EH1 T -HENRIETTE'S HH EH2 N R IY0 EH1 T S -HENRIK HH EH1 N R IH0 K -HENRIK'S HH EH1 N R IH0 K S -HENRIKSEN HH EH1 N R IH0 K S AH0 N -HENRIKSEN'S HH EH1 N R IH0 K S AH0 N Z -HENRIOT HH EH2 N R IY0 AH0 T -HENRIOT'S HH EH2 N R IY0 AH0 T S -HENRIQUE AA0 N R IY1 K -HENRIQUES AA0 N R IY1 K -HENRIQUES HH EH0 N R IY1 K EH0 Z -HENRIQUEZ AA0 N R IY1 K -HENRIQUEZ HH EH0 N R IY1 K EH0 Z -HENROOST HH EH1 N R UW1 S T -HENROOSTS HH EH1 N R UW1 S T S -HENRY HH EH1 N R IY0 -HENRY'LL HH EH1 N ER0 IY0 AH0 L -HENRY'S HH EH1 N R IY0 Z -HENRYK HH EH1 N ER0 IH0 K -HENRYS HH EH1 N ER0 IY0 Z -HENS HH EH1 N Z -HENSCHEL HH EH1 N SH AH0 L -HENSE HH EH1 N S -HENSEL HH EH1 N S AH0 L -HENSELT HH EH1 N S AH0 L T -HENSEN HH EH1 N S AH0 N -HENSHAW HH EH1 N SH AO2 -HENSHAW'S HH EH1 N SH AO2 Z -HENSHAWS HH EH1 N SH AO2 Z -HENSLAER HH EH1 N Z L IH1 R -HENSLER HH EH1 N S AH0 L ER0 -HENSLER HH EH1 N S L ER0 -HENSLEY HH EH1 N Z L IY0 -HENSLOW HH EH1 N S L OW2 -HENSLOW'S HH EH1 N S L OW2 Z -HENSLOWE HH EH1 N S L OW2 -HENSLOWE'S HH EH1 N S L OW1 Z -HENSON HH EH1 N S AH0 N -HENSON'S HH EH1 N S AH0 N Z -HENSOR HH EH1 N S ER0 -HENSTEAD HH EH1 N S T EH2 D -HENT HH EH1 N T -HENTIG HH EH1 N T IH0 G -HENTLEY HH EH1 N T L IY0 -HENTY HH EH1 N T IY0 -HENTY'S HH EH1 N IY0 Z -HENTYS HH EH1 N IY0 Z -HENTZ HH EH1 N T S -HENTZAU HH EH1 N T S OW1 -HENTZI HH EH1 N T Z IY0 -HENTZI'S HH EH1 N T Z IY0 Z -HENWIFE HH EH1 N W AY2 F -HENYARD HH EH1 N Y AA2 R D -HENZEL HH EH1 N Z AH0 L -HENZY HH EH1 N Z IY0 -HENZY'S HH EH1 N Z IY0 Z -HEOROT HH IY2 AH0 R OW1 -HEOW HH IY1 OW0 -HEP HH EH1 P -HEPATIC HH AH0 P AE1 T IH0 K -HEPATICA HH AH0 P AE1 T IH0 K AH0 -HEPATICAS HH AH0 P AE1 T IH0 K AH0 S -HEPBURN HH EH1 P B ER0 N -HEPBURN'S HH EH1 P B ER0 N Z -HEPE HH EH1 P IY0 -HEPE'S HH EH1 P S -HEPHAESTION HH EH1 P EH1 S CH AH0 N -HEPHAESTUS HH EH1 P EH1 S T AH0 S -HEPHAISTOS HH EH1 F AY0 IY1 S T OW0 Z -HEPHZIBAH HH EH1 F Z IY1 B AH0 -HEPHZIBAH'S HH EH1 F Z IY1 B AH0 Z -HEPHZY HH EH1 F Z IY0 -HEPHZY'S HH EH1 F Z IY0 Z -HEPNER HH EH1 P N ER0 -HEPPEL HH EH1 P AH0 L -HEPPELWHITE HH EH1 P AH0 L W AY2 T -HEPPIE HH EH1 P IY0 -HEPPLEWHITE HH EH1 P AH0 L W AY2 T -HEPPNER HH EH1 P N ER0 -HEPPNER'S HH EH1 P N ER0 Z -HEPPY HH EH1 P IY0 -HEPSA HH EH1 P S AH0 -HEPSEBA HH EH1 P S IY1 B AH0 -HEPSEY HH EH1 P S IY0 -HEPSEY'S HH EH1 P S IY0 Z -HEPSIE HH EH1 P S IY0 -HEPSY HH EH1 P S IY0 -HEPSY'S HH EH1 P S IY0 Z -HEPTAGON HH EH0 P T AE1 G AH0 N -HEPTAMERON HH EH0 P T AE1 M ER0 AH0 N -HEPTARCHY HH EH1 P T AA2 R K IY0 -HEPTONSTALL HH EH0 P T AA1 N S T AH0 L -HEPWORTH HH EH1 P W ER0 TH -HEPWORTH'S HH EH1 P W ER0 TH S -HEPWORTHS HH EH1 P W ER0 TH S -HEPZEBIAH HH EH1 P Z AH0 B AY1 AH0 -HEPZIBAH HH EH1 P Z IY1 B AH0 -HEPZIBAH'S HH EH1 P Z IY1 B AH0 Z -HEQUET EH2 K EY1 -HER HH ER0 -HER HH ER1 -HER' HH ER1 -HER'D HH ER1 D -HER'LL HH ER1 L -HER'N HH ER1 AH0 N -HER'S HH ER1 Z -HERA HH IH1 R AH0 -HERA'S HH EH1 R AH0 Z -HERACLEA HH ER1 AH0 K L IY0 AH0 -HERACLEIDAE HH ER1 AH0 K L IY1 D EY2 -HERACLEIDES HH EH1 R AH0 K AH0 L AY2 D Z -HERACLEITEAN HH ER1 AH0 K L IY1 T IY1 N -HERACLEITUS HH ER1 AH0 K L IY1 T AH0 S -HERACLES HH EH1 R AH0 K AH0 L Z -HERACLIDAE HH ER1 AH0 K L IH0 D EY0 -HERACLIDES HH ER1 AH0 K L AY2 D Z -HERACLITUS HH ER1 AH0 K L AY1 T AH0 S -HERACLIUS HH ER1 AH0 K L IY0 IH0 S -HERAIA HH EH0 R AA1 Y AH0 -HERAKLEOPHORBIA HH EH0 R AE1 K L IY0 OW0 F ER0 B IY0 AH0 -HERAKLEOPOLIS HH EH0 R AE1 K L IY0 AO1 P AH0 L IH0 S -HERAKLES HH ER1 AH0 K AH0 L Z -HERALD HH EH1 R AH0 L D -HERALD'S HH EH1 R AH0 L D Z -HERALDED HH EH1 R AH0 L D IH0 D -HERALDIC HH EH0 R AE1 L D IH0 K -HERALDICALLY HH EH0 R AE1 L D IH0 K L IY0 -HERALDING HH EH1 R AH0 L D IH0 NG -HERALDRY HH EH1 R AH0 L D R IY0 -HERALDS HH EH1 R AH0 L D Z -HERAPATH HH ER1 AH0 P AE2 TH -HERAPATH'S HH ER1 AH0 P AE1 TH S -HERAT HH ER0 AA1 T -HERATY HH ER1 AH0 T IY0 -HERAUD HH ER0 AO1 D -HERAUGIERE HH ER0 AO0 G IH1 R -HERAULT HH ER0 OW0 -HERB ER1 B -HERB HH ER1 B -HERB'S ER1 B Z -HERB'S HH ER1 B Z -HERBA HH ER1 B AH0 -HERBACEOUS ER0 B EY1 SH AH0 S -HERBAGE HH ER1 B IH0 JH -HERBAL ER1 B AH0 L -HERBAL HH ER1 B AH0 L -HERBALIST ER1 B AH0 L AH0 S T -HERBALIST HH ER1 B AH0 L AH0 S T -HERBALIST'S ER1 B AH0 L AH0 S T S -HERBALIST'S HH ER1 B AH0 L AH0 S T S -HERBALISTS ER1 B AH0 L AH0 S -HERBALISTS ER1 B AH0 L AH0 S T S -HERBALISTS HH ER1 B AH0 L AH0 S -HERBALISTS HH ER1 B AH0 L AH0 S T S -HERBALS HH ER1 B AH0 L Z -HERBARIUM ER0 B EH1 R IY0 AH0 M -HERBARIUM HH ER0 B EH1 R IY0 AH0 M -HERBART HH ER1 B AA2 R T -HERBART'S ER1 B AA0 R T S -HERBARTIAN HH ER0 B EH1 R SH AH0 N -HERBARTIANS HH ER0 B EH1 R SH Y IH0 N Z -HERBAUT HH ER1 B AO1 T -HERBAUT'S HH ER1 B AO0 T S -HERBE HH ER1 B -HERBECK HH ER1 B EH0 K -HERBEGG HH ER1 B IH0 G -HERBENFELDER HH ER1 B IH0 N F EH0 L D ER0 -HERBERGE HH ER1 B ER0 JH -HERBERT HH ER1 B ER0 T -HERBERT'S HH ER1 B ER0 T S -HERBERTS HH ER1 B ER0 T S -HERBERTSON HH ER1 B ER0 T S AH0 N -HERBES HH ER1 B Z -HERBEY HH ER1 B IY0 -HERBIE HH ER1 B IY0 -HERBIN HH ER1 B IH0 N -HERBIVORA HH ER1 B IH0 V AO2 R AH0 -HERBIVORES HH ER1 B IH0 V AO2 R Z -HERBIVOROUS ER0 B IH1 V ER0 AH0 S -HERBIVOROUS HH ER0 B IH1 V ER0 AH0 S -HERBLESS HH ER1 B L AH0 S -HERBOIS HH ER1 B W AA1 -HERBOLD HH ER1 B OW0 L D -HERBS ER1 B Z -HERBUX ER1 B -HERBY HH ER1 B IY0 -HERC HH ER1 K -HERCULANEUM HH ER1 K Y AH0 L AH0 N IY1 AH0 M -HERCULE HH ER0 K Y UW2 L -HERCULEAN HH ER0 K Y UW1 L IY0 AH0 N -HERCULEM HH ER1 K Y AH0 L AH0 M -HERCULES HH ER1 K Y AH0 L IY2 Z -HERCULES'S HH ER0 K Y UW2 L Z IH0 Z -HERCULIS HH ER1 K Y AH0 L IH0 S -HERCUS HH ER1 K AH0 S -HERCYNIAN HH EH2 R T S IH1 N IY0 AH0 N -HERD HH ER1 D -HERD'S HH ER1 D Z -HERDAL HH EH1 R D AH0 L -HERDBOY HH ER1 D B OY2 -HERDED HH ER1 D IH0 D -HERDEGEN HH ER1 D IH0 G AH0 N -HERDEGEN'S HH ER1 D IH0 JH EH2 N Z -HERDER HH EH1 R D ER0 -HERDER HH ER1 D ER0 -HERDER'S HH ER1 D ER0 Z -HERDERS HH ER1 D ER0 Z -HERDHOLT HH ER1 D HH OW2 L T -HERDIC HH ER1 D IH0 K -HERDICKER HH ER1 D IH0 K ER0 -HERDIN HH ER1 D IH0 N -HERDING HH ER1 D IH0 NG -HERDMAN HH ER1 D M AH0 N -HERDMANSTONE HH ER1 D M AH0 N S T OW2 N -HERDMEN HH ER1 D M AH0 N -HERDS HH ER1 D Z -HERDSMAN HH ER1 D Z M AH0 N -HERDSMAN'S HH ER1 D Z M AH0 N Z -HERDSMEN HH ER1 D Z M IH0 N -HERDSMEN'S HH ER1 D Z M IH0 N Z -HERE HH IY1 R -HERE' HH IH1 R -HERE'D HH EH1 R D -HERE'LL HH IH1 R AH0 L -HERE'S HH IH1 R Z -HEREABOUT HH IH1 R AH0 B AW2 T -HEREABOUTS HH IH1 R AH0 B AW2 T S -HEREAFTER HH IH0 R AE1 F T ER0 -HEREAT HH IH0 R AE1 T -HEREAWAY HH IH1 R AH0 W EY2 -HEREAWAYS HH IH1 R AH0 W EY2 Z -HEREBY HH IH0 R B AY1 -HEREDIA HH ER0 EH1 D IY0 AH0 -HEREDITAMENTS HH ER0 EH1 D AH0 T AH0 M AH0 N T S -HEREDITARILY HH ER0 EH1 D AH0 T EH1 R AH0 L IY0 -HEREDITARY HH ER0 EH1 D AH0 T EH2 R IY0 -HEREDITIES HH ER0 EH1 D AH0 T IY0 Z -HEREDITY HH ER0 EH1 D AH0 T IY0 -HEREFORD HH EH1 R AH0 F ER0 D -HEREFORD'S HH IH0 R F ER0 D Z -HEREFORDS HH IH0 R F ER0 D Z -HEREFORDSHIRE HH IH0 R F ER0 D SH AY2 ER0 -HEREFROM HH IH0 R F R AA2 M -HEREGAR HH IH0 R G ER0 -HEREGAR'S HH IH0 R G ER0 Z -HEREIN HH IH0 R IH1 N -HEREINAFTER HH IH0 R IH1 N AE2 F T ER0 -HEREINBEFORE HH IH0 R IH1 N B IY2 F AO1 R -HEREM HH EH1 R AH0 M -HERENDEN HH ER0 EH1 N D AH0 N -HERENNIUS HH ER0 EH1 N IY0 AH0 S -HEREOF HH EH2 R AH1 V -HEREON HH EH0 R AO1 N -HERERO HH ER0 EH1 R OW0 -HERES HH IH1 R Z -HERESIARCH HH ER0 EH1 S IY0 AA2 R K -HERESIARCHS HH ER0 EH1 S IY0 AA2 R K S -HERESIE HH ER0 EH1 S IY0 -HERESIES HH EH1 R AH0 S IY0 Z -HERESY HH EH1 R AH0 S IY0 -HERETIC HH EH1 R AH0 T IH0 K -HERETIC'S HH ER0 EH1 T IH0 K S -HERETICAL HH ER0 EH1 T IH0 K AH0 L -HERETICK HH EH1 R AH0 T IH2 K -HERETICKS HH EH1 R AH0 T IH2 K S -HERETICO HH ER0 EH1 T IH0 K OW2 -HERETICS HH ER0 EH1 T IH0 K S -HERETO HH ER0 EH1 T OW0 -HERETOFORE HH IH2 R T AH0 F AO1 R -HEREUNDER HH IH0 R OY1 N D ER0 -HEREUNTO HH IH0 R UW1 N T OW2 -HEREUPON HH IH0 R AH0 P AA1 N -HEREWARD HH IH1 R W ER0 D -HEREWARD'S HH IH1 R W ER0 D Z -HEREWITH HH IH1 R W IH1 TH -HEREWITHIN HH IH1 R W IH0 DH IH1 N -HEREZUELO HH EH1 R IH0 Z W EY1 L OW0 -HERFORD HH ER1 F ER0 D -HERFRIDA HH ER1 F R IY1 D AH0 -HERGESHEIMER HH ER1 G IH0 S HH AY0 M ER0 -HERGESHEIMER'S HH ER1 G Z HH AY2 M ER0 Z -HERGEST HH ER1 G IH0 S T -HERHOR HH ER1 HH AO0 R -HERI HH EH1 R IY0 -HERIBALD HH EH1 R AH0 B AO2 L D -HERIBERT HH EH1 R IH0 B ER0 T -HERICOURT HH ER1 IH0 K AO2 R T -HERING HH ER1 IH0 NG -HERIOT HH IH1 R IY0 AA0 T -HERIOT'S HH IH1 R IY0 AH0 T S -HERIOTS HH IH1 R IY0 AH0 T S -HERISAU HH EH1 R IH0 S AO0 -HERITABLE HH EH1 R AH0 T AH0 B AH0 L -HERITAGE HH EH1 R AH0 T AH0 JH -HERITAGE HH EH1 R IH0 T IH0 JH -HERITAGE'S HH EH1 R AH0 T IH0 JH IH0 Z -HERITAGES HH EH1 R IH0 T IH0 JH AH0 Z -HERITOR HH EH1 R AH0 T ER0 -HERITORS HH EH1 R AH0 T ER0 Z -HERITZBURG HH EH1 R IH0 T S B ER0 G -HERIULF HH ER1 IY0 AH0 L F -HERKIMER HH ER1 K IH0 M ER0 -HERKIMER'S HH ER1 K AY2 M ER0 Z -HERKOMER HH ER1 K OW1 M ER0 -HERKU HH ER1 K UW0 -HERKUS HH ER1 K IH0 S -HERKY HH ER1 K IY0 -HERLAND HH ER1 L AH0 N D -HERLIHY HH ER1 L IH0 HH IY0 -HERLUIN HH ER1 L UW1 IH0 N -HERM HH ER1 M -HERMAE HH ER1 M IY2 -HERMAN HH ER1 M AH0 N -HERMAN'S HH ER1 M AH0 N Z -HERMANA HH ER0 M AA1 N AH0 -HERMANCE HH ER1 M AH0 N S -HERMANDAD HH ER1 M AH0 N D AA1 D 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Z -HERMIL HH ER1 M IH0 L -HERMINE HH ER1 M IH0 N -HERMINE'S HH ER1 M AH0 N Z -HERMING HH ER1 M IH0 NG -HERMINIA HH EH0 R M IY1 N IY0 AH0 -HERMINIA'S HH ER1 M IH0 N IY0 AH0 Z -HERMINIE HH ER1 M IH0 N IY0 -HERMINIE'S HH ER1 M IH0 N IY0 Z -HERMINIUS HH ER1 M IH0 N IY0 IH0 S -HERMIONE HH ER1 M IY0 OW1 N IY0 -HERMIONE'S HH ER1 M IY0 OW1 N Z -HERMIPPUS HH ER1 M IY0 P AH0 S -HERMISTON HH ER1 M IH0 S T AA0 N -HERMIT HH ER1 M AH0 T -HERMIT'S HH ER1 M IH0 T S -HERMITAGE HH ER1 M AH0 T AH0 JH -HERMITAGES HH ER1 M IH0 T IH0 JH IH0 Z -HERMITE HH ER1 M AY0 T -HERMITS HH ER1 M AH0 T S -HERMNAEN HH ER1 M N EY2 N -HERMOCRATES HH ER0 M AH0 K R EY2 T S -HERMOD HH ER1 M AH0 D -HERMOGEN HH ER0 M AH0 JH AH0 N -HERMOGENES HH ER0 M AH0 JH IY1 N Z -HERMOISES HH ER1 M OY1 Z IH0 Z -HERMON HH ER1 M AH0 N -HERMON'S HH ER0 M AH0 N Z -HERMONTHIS HH ER0 M AH0 N TH IH0 S -HERMOPOLIS HH ER1 M AO1 P AH0 L IH0 S -HERMOSA HH EH0 R M OW1 S AH0 -HERMOSILLO HH ER0 M AH0 S IH1 L OW0 -HERMOTIMUS HH EH0 R M OW1 T IH0 M IH0 S -HERMSDORF HH ER1 M S D AO0 R F -HERMUND HH EH0 R M AH0 N D -HERMUS HH EH0 R M AH0 S -HERMY HH EH0 R M IY0 -HERN HH ER1 N -HERN'S HH ER1 N Z -HERNAN HH ER1 N AH0 N -HERNAN'S HH ER1 N IH0 N Z -HERNANDEZ HH ER0 N AE1 N D EH0 Z -HERNANDO HH ER0 N AA1 N D OW0 -HERNANI HH ER0 N AA1 N IY0 -HERNCASTLE HH ER1 N K AE2 S AH0 L -HERNDA HH ER1 N D AH0 -HERNDON HH ER1 N D AH0 N -HERNDON'S HH ER1 N D AH0 N Z -HERNE HH ER1 N -HERNE'S HH ER1 N Z -HERNHUT HH ER1 N HH AH0 T -HERNIA HH ER1 N IY0 AH0 -HERNICIANS HH ER0 N IH1 SH AH0 N Z -HERNSHAW HH ER1 N SH AO2 -HERO HH IH1 R OW0 -HERO HH IY1 R OW0 -HERO'S HH IH1 R OW0 Z -HERO'S HH IY1 R OW0 Z -HEROD HH EH1 R AH0 D -HEROD'S HH EH1 R AH0 D Z -HERODE HH IY1 R OW0 D -HERODE'S HH EH1 R OW2 D Z -HERODED HH IH0 R OW1 D AH0 D -HERODES HH IY1 R OW0 D Z -HERODIADE HH EH1 R AH0 D AY1 AH0 D -HERODIAN HH IH0 R OW1 D IY0 AH0 N -HERODIANS HH IH0 R OW1 D IY0 AH0 N Z -HERODIAS HH IY1 R OW0 D IY1 AH0 Z -HERODICUS HH EH1 R AH0 D IH0 K AH0 S -HERODING HH IH0 R OW1 D IH0 NG 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HH EH1 R Z -HERRAN HH EH1 R AH0 N -HERRAN'S HH EH1 R AH0 N Z -HERRARA HH ER0 AA1 R AH0 -HERRARA'S HH ER0 AA1 R AH0 Z -HERRE HH AH1 R IY0 -HERRE HH EH1 R -HERREGUD HH EH1 R AH0 G AH0 D -HERREN HH EH1 R AH0 N -HERRENDEEN HH ER0 EH1 N D IY0 N -HERRENHAUSEN HH EH1 R AH0 N HH AW2 Z AH0 N -HERRERA HH ER0 EH1 R AH0 -HERRERA'S HH ER0 EH1 R AH0 Z -HERREROS HH EH0 R EH1 R OW0 Z -HERRESFORD HH EH1 R Z F ER0 D -HERRESFORD'S HH EH1 R Z F ER0 D Z -HERRETS HH EH1 R AH0 T S -HERRICK HH EH1 R IH0 K -HERRICK'S HH EH1 R IH0 K S -HERRICKS HH EH1 R IH0 K S -HERRIES HH EH1 R IY0 Z -HERRIN HH EH1 R IH0 N -HERRING HH EH1 R IH0 NG -HERRING'S HH EH1 R IH0 NG Z -HERRINGBONE HH EH1 R IH0 NG B OW2 N -HERRINGHAME HH EH1 R IH0 NG AH0 M -HERRINGS HH EH1 R IH0 NG Z -HERRINGTON HH EH1 R IH0 NG T AH0 N -HERRION HH EH1 R IY0 AH0 N -HERRIOT HH EH1 R IY0 AA0 T -HERRIOT'S HH EH1 R IY0 AH0 T S -HERRIOTT HH EH1 R IY0 AA0 T -HERRIOTT HH EH1 R IY0 AH0 T -HERRIOTT'S HH EH1 R IY0 AH0 T S -HERRIT HH EH1 R IH0 T -HERRITON HH EH1 R IH0 T AO1 N -HERRMANN HH EH1 R M AH0 N -HERRN HH EH1 R AH0 N -HERRNHUT HH ER0 N HH AH0 T -HERRON HH EH1 R AH0 N -HERRON'S HH EH1 R AH0 N Z -HERRSCHAFT HH EH1 R AH0 SH AE0 F T -HERRSCHAFTEN HH EH1 R AH0 SH AE0 F T AH0 N -HERS HH ER0 Z -HERS HH ER1 Z -HERSANT HH ER1 S AH0 N T -HERSCHEL HH ER1 SH AH0 L -HERSCHEL'S HH ER1 SH AH0 L Z -HERSCHELL HH ER1 SH AH0 L -HERSCHELS HH ER1 SH AH0 L Z -HERSE HH ER1 S -HERSE'F HH ER1 S IH0 F -HERSE'S HH ER1 S IH0 Z -HERSEBOM HH ER1 S B AA2 M -HERSEL HH ER1 S AH0 L -HERSELF HH ER0 S EH1 L F -HERSELF'S HH ER0 S EH1 L F S -HERSELFE HH ER0 S EH1 L F -HERSELL HH ER0 S EH2 L -HERSEN HH ER0 S AH0 N -HERSEY HH ER1 S IY0 -HERSEY'S HH ER1 Z IY0 Z -HERSHEIMMER HH ER1 SH IH1 M ER0 -HERSHEL HH ER1 SH AH0 L -HERSHEY HH ER1 SH IY0 -HERSHEY'S HH ER1 SH IY0 Z -HERSHIE HH ER1 SH IY0 -HERSHIE'S HH ER1 SH IY0 Z -HERSILF HH ER1 S IH1 L F -HERSILIE HH ER0 S IY1 L IY0 -HERSIR HH ER1 S IH1 R -HERT HH ER1 T -HERTE HH ER1 T -HERTEL HH ER1 T AH0 L -HERTER HH ER1 T ER0 -HERTER'S HH ER1 T ER0 Z -HERTFORD HH ER1 T F ER0 D -HERTFORD'S HH ER1 T F ER0 D Z -HERTFORDSHIRE HH ER1 T F ER0 D SH AY2 ER0 -HERTHA HH ER1 TH AH0 -HERTHA'S HH ER1 TH AH0 Z -HERTIG HH ER1 T IH0 G -HERTLING HH ER1 T L IH0 NG -HERTS HH ER1 T S -HERTUG HH ER1 T AH0 G -HERTZ HH EH1 R T S -HERTZ HH ER1 T S -HERTZIAN HH ER1 T Z IY0 AH0 N -HERTZOG HH ER1 T Z AA2 G -HERTZOG HH ER1 T Z AH0 G -HERU HH EH1 R UW0 -HERULES HH ER0 UW1 L Z -HERULI HH ER0 UW1 L IY0 -HERULIAN HH ER0 UW1 L IY0 AH0 N -HERULIANS HH ER0 UW1 L Y AH0 N Z -HERVAGAULT HH ER1 V AH0 G OW1 -HERVAL HH ER0 V AH0 L -HERVART HH ER1 V ER0 T -HERVART'S HH ER1 V ER0 T S -HERVE HH ER1 V -HERVE HH ER1 V EY0 -HERVE'S HH ER1 V Z -HERVEY HH ER0 V EY1 -HERVEY'S HH ER0 V EY1 Z -HERVEYS HH ER0 V EY1 Z -HERVIEU HH ER1 V Y UW1 -HERWARTH HH ER1 W ER0 TH -HERWARTH'S HH ER1 W ER0 TH S -HERWEGH HH ER1 W IY0 G -HERWIG HH ER1 W IH0 G -HERZ HH ER1 Z -HERZBERG HH ER1 Z B ER0 G -HERZCHEN HH ER1 Z K AH0 N -HERZEGOVINA HH EH2 R T S AH0 G OW0 V IY1 N AH0 -HERZEGOVINA HH ER2 T S AH0 G OW0 V IY1 N AH0 -HERZEGOVINIAN HH ER2 T S AH0 G OW0 V IY1 N IY0 AH0 N -HERZEN HH ER1 Z AH0 N -HERZENSTUBE HH ER2 T S AH0 N S T UW1 B -HERZL HH ER1 Z L -HERZL'S HH ER1 Z L Z -HERZLICH HH ER1 Z L IH0 K -HERZMANN HH ER1 Z M AH0 N -HERZMANN'S HH ER1 Z M AH0 N Z -HERZOG HH ER1 Z AA0 G -HERZOG'S HH ER1 Z AA0 G Z -HES HH EH1 Z -HESA HH EH1 S AH0 -HESBA HH EH1 S B AH0 -HESCOCK'S HH EH1 S K AA2 K S -HESDEN HH EH1 Z D AH0 N -HESDEN'S HH EH1 Z D AH0 N Z -HESDIN HH EH1 S D IH0 N -HESEA HH IY1 S IY0 AH0 -HESED HH EH1 S T -HESELF HH IH0 S EH1 L F -HESELRIGGE HH EH1 S AH0 L R IH0 G -HESELTINE HH EH1 S AH0 L T AY2 N -HESELTINE HH EH1 S AH0 L T IY2 N -HESELTON HH IH0 S EH1 L T AH0 N -HESH HH EH1 SH -HESHBON HH EH1 SH B AH0 N -HESIOD HH IY1 S IY0 AH0 D -HESIOD'S HH IY1 S IY0 AH0 D Z -HESITANCE HH EH1 Z IH0 T AH0 N S -HESITANCIES HH EH1 Z IH0 T AH0 N S IY0 Z -HESITANCY HH EH1 Z IH0 T AH0 N S IY0 -HESITANT HH EH1 Z IH0 T AH0 N T -HESITANTLY HH EH1 Z IH0 T AH0 N T L IY0 -HESITATE HH EH1 Z AH0 T EY2 T -HESITATED HH EH1 Z IH0 T EY2 T IH0 D -HESITATES HH EH1 Z AH0 T EY2 T S -HESITATIN HH EH2 Z AH0 T EY1 T IH0 N -HESITATING HH EH1 Z AH0 T EY2 T IH0 NG -HESITATINGLY HH EH1 Z AH0 T EY2 T IH0 NG L IY0 -HESITATION HH EH2 Z AH0 T EY1 SH AH0 N -HESITATIONS HH EH2 Z AH0 T EY1 SH AH0 N Z -HESKETH HH EH1 S K IH0 TH -HESKETH'S HH EH1 S K IH0 TH S -HESKETT HH EH1 S K IH0 T -HESPER HH EH1 S P ER0 -HESPERIA HH EY0 S P EH1 R IY0 AH0 -HESPERIAN HH EY0 S P EH1 R IY0 AH0 N -HESPERIDES HH EH1 S P ER0 AY2 D Z -HESPERIDUM HH EY0 S P EH1 R IH0 D AH0 M -HESPERUS HH EY0 S P EH1 R AH0 S -HESS HH EH1 S -HESSE HH EH1 S -HESSE'S HH EH1 S IH0 Z -HESSEL HH EH1 S AH0 L -HESSELIUS HH EH1 S AH0 L IY0 IH0 S -HESSELS HH EH1 S AH0 L Z -HESSEN HH EH1 S AH0 N -HESSET HH EH1 S AH0 T -HESSEY HH EH1 S IY0 -HESSIA HH EH1 SH AH0 -HESSIAN HH EH1 SH AH0 N -HESSIANS HH EH1 SH AH0 N Z -HESSIE HH EH1 S IY0 -HESSLEIN HH EH1 S L AY2 N -HEST HH EH1 S T -HESTER HH EH1 S T ER0 -HESTER'S HH EH1 S T ER0 Z -HESTIA HH EH1 S T IY0 AH0 -HESTON HH EH1 S T AH0 N -HESTRI HH EH1 S T R IY0 -HESTS HH EH1 S T S -HESUS HH EH1 S AH0 S -HET HH EH1 T -HET'S HH EH1 T S -HETAERA HH EH1 T EH1 R AH0 -HETAERAE HH EH1 T EH2 R EY2 -HETAIRA HH EH1 T AA0 IH1 R AH0 -HETAIRAE HH AH0 T EH1 R EY2 -HETAIRAI HH EH2 T EH2 R AA1 IY0 -HETAIRISM HH EH1 T ER0 IH2 Z AH0 M -HETCH HH EH1 CH -HETCHY HH EH1 CH IY0 -HETEP HH EH2 T P -HETERO HH EH2 T ER0 OW0 -HETERODOX HH EH2 T ER0 AH0 D AA2 K S -HETERODOXIES HH EH2 T ER0 AH0 D AA2 K S IY0 Z -HETERODOXY HH EH1 T ER0 AH0 D AA2 K S IY0 -HETERODYNED HH EH2 T ER0 AH0 D AY2 N D -HETEROGENEITY HH EH2 T ER0 AH0 JH IH0 N EY1 AH0 T IY0 -HETEROGENEITY HH EH2 T ER0 AH0 JH IH0 N IY1 AH0 T IY0 -HETEROGENEOUS HH EH2 T ER0 AH0 JH IY1 N Y AH0 S -HETEROGENEOUSLY HH EH2 T ER0 AH0 JH IY1 N IY0 AH0 S L IY0 -HETEROGENOUS HH EH2 T ER0 AH0 JH AH0 N AH0 S -HETERONOMY HH EH2 T ER0 AA1 N AH0 M IY0 -HETFALU HH EH1 T F AA1 L UW0 -HETFALUSY HH EH1 T F AE1 L IH0 S IY0 -HETH HH EH1 TH -HETH'S HH EH1 TH S -HETHCOTE HH EH2 TH K AH0 T -HETHENCOURT HH EH1 TH AH0 N K AO2 R T -HETHER HH EH1 DH ER0 -HETHERFORD HH EH1 DH ER0 F ER0 D -HETHERIDGE HH EH1 TH ER0 IH2 JH -HETHERINGTON HH EH1 DH ER0 IH0 NG T AH0 N -HETHERINGTON'S HH EH1 DH ER0 IH0 NG T AH0 N Z -HETHERTON HH EH1 DH ER0 T AH0 N -HETLEY HH EH1 T L IY0 -HETMAN HH EH1 T M AH0 N -HETMAN'S HH EH1 T M AH0 N Z -HETMANS HH EH1 T M AH0 N Z -HETON HH EH1 T AH0 N -HETT HH EH1 T -HETTA HH EH1 T AH0 -HETTERMAN HH EH1 T ER0 M AH0 N -HETTIE HH EH1 T IY0 -HETTIE'S HH EH1 T IY0 Z -HETTY HH EH1 T IY0 -HETTY'S HH EH1 T IY0 Z -HEU HH OY1 -HEUGEL HH Y UW1 G AH0 L -HEUGH HH Y UW1 -HEUKBANE HH Y UW1 K B EY2 N -HEUMANN HH Y UW1 M AH0 N -HEURE HH Y UW1 R -HEURES HH Y UH0 R Z -HEUREUSE HH Y UH0 R UW1 Z -HEUREUSEMENT HH Y UH0 R UW1 Z M AH0 N T -HEUREUX HH Y UW2 R UH1 -HEURISTIC HH Y UH0 R IH1 S T IH0 K -HEURNE HH Y UH0 R N -HEURTEBISE HH Y UH0 R T IH0 B AY0 Z -HEUS HH Y UW1 Z -HEUSCHRECKE HH OY1 SH R EH1 K -HEUTE HH Y UW1 T -HEUZEY HH Y UW1 Z IY0 -HEV HH EH1 V -HEVA HH EY1 V AH0 -HEVE HH EH1 V IY0 -HEVEA HH EH1 V IY0 AH0 -HEVELIUS HH IH0 V EY1 L IY0 IH0 S -HEVEN HH EH1 V AH0 N -HEVEN'T HH EH1 V AH0 N T -HEVENIN HH EH1 V IY0 N IH0 N -HEVENING HH IY1 V N IH0 NG -HEVER HH EH1 V ER0 -HEVERY HH EH1 V R IY0 -HEVERYTHING HH EH1 V R IY0 TH IH2 NG -HEVERYTHINK HH EH1 V R IY0 TH IH2 NG K -HEVEYTH HH IH0 V EY1 TH -HEVIN HH EH1 V IH0 N -HEVN'T HH EH1 V N T -HEVY HH EH1 V IY0 -HEW HH Y UW1 -HEW'D HH Y UW1 D -HEWBY HH Y UW1 B IY0 -HEWED HH Y UW1 D -HEWEN HH Y UW1 AH0 N -HEWER HH Y UW1 ER0 -HEWERS HH Y UW1 ER0 Z -HEWES HH Y UW1 Z -HEWET HH Y UW1 IH0 T -HEWET'S HH Y UW1 IH0 T S -HEWETT HH Y UW1 IH0 T -HEWETT'S HH Y UW1 IH0 T S -HEWETTS HH Y UW1 IH0 T S -HEWING HH Y UW1 IH0 NG -HEWIT HH Y UW1 IH0 T -HEWITSON HH Y UW1 IH0 T S AH0 N -HEWITT HH Y UW1 IH0 T -HEWITT'S HH Y UW1 IH0 T S -HEWLETT HH Y UW1 L IH0 T -HEWLETT'S HH Y UW1 L IH0 T S -HEWLEY HH Y UW1 L IY0 -HEWLITT HH Y UW1 L IH0 T -HEWN HH Y UW1 N -HEWS HH Y UW1 Z -HEWSON HH Y UW1 S AH0 N -HEWSON'S HH Y UW1 S AH0 N Z -HEWSTON HH Y UW1 S T AH0 N -HEX HH EH1 K S -HEXAGON HH EH1 K S AH0 G AA2 N -HEXAGONAL HH EH0 K S AE1 G AH0 N AH0 L -HEXAGONS HH EH0 K S AE1 G AH0 N Z -HEXAM HH EH1 K S AH0 M -HEXAM'S HH EH1 K S AH0 M Z -HEXAMETER HH EH0 K S AE1 M AH0 T ER0 -HEXAMETERS HH EH0 K S AE1 M AH0 T ER0 Z -HEXAN HH EH1 K S AH0 N -HEXANS HH EH1 K S AH0 N Z -HEXAPODE HH EH1 K S AH0 P OW1 D -HEXCUSE HH EH1 K S K Y UW1 Z -HEXHAM HH EH1 K S AH0 M -HEXIE HH EH1 K S IY0 -HEXOXEN HH EH1 K S AA1 K S AH0 N -HEXTON HH EH1 K S T AH0 N -HEXTRA HH EH1 K S T R AH0 -HEXTRY HH EH1 K S T R IY0 -HEY HH EY1 -HEYDAY HH EY1 D EY2 -HEYDE HH EY1 D -HEYDECK HH EY1 D EH0 K -HEYDECK'S HH EY1 D EH0 K S -HEYDEN HH EY1 D AH0 N -HEYDEN'S HH EY1 D AH0 N Z -HEYDEY HH EY1 D IY0 -HEYDINGER HH EY1 D IH0 NG ER0 -HEYDOCKE HH EY1 D AA1 K -HEYDON HH EY1 D AH0 N -HEYDUKE HH EY1 D UW2 K -HEYDUKES HH EY1 D UW1 K S -HEYE HH AY1 -HEYERDAHL HH AY1 ER1 D AA0 L -HEYES HH AY1 Z -HEYL HH EY1 L -HEYLER HH EY1 L ER0 -HEYLIGER HH EY1 L IH0 G ER0 -HEYLING HH EY1 L IH0 NG -HEYM HH EY1 M -HEYMANN HH EY1 M AH0 N -HEYMES HH EY1 M Z -HEYNE HH EY1 N -HEYNES HH EY1 N Z -HEYSE HH EY1 S -HEYST HH EY1 S T -HEYST'S HH EY1 S T S -HEYTESBURY HH EY1 T S B EH0 R IY0 -HEYTHORP HH EY1 TH AO0 R P -HEYTHORP'S HH EY1 TH AO0 R P S -HEYTON HH EY1 T AH0 N -HEYTON'S HH EY1 T AH0 N Z -HEYWARD HH EY1 W ER0 D -HEYWOOD HH EY1 W UH2 D -HEYWOOD'S HH EY1 W UH2 D Z -HEYWOODS HH EY1 W UH2 D Z -HEYWORTH HH EY1 W ER2 TH -HEZ HH EH0 Z -HEZ'S HH EH0 Z IH0 Z -HEZEKIAH HH EH1 Z AH0 K AY1 AH0 -HEZEKIAH'S HH EH1 Z AH0 K AY1 AH0 Z -HEZILO HH EH0 Z IY1 L OW0 -HEZZY HH EH1 Z IY0 -HH HH -HHETT HH IH0 T -HI HH AY1 -HI'LL HH AY1 L -HI'M HH AY1 M -HI'S HH AY1 Z -HI'VE HH AY1 V -HIA HH IY0 AH0 -HIALMAR HH AY2 AH0 L M ER0 -HIALTI HH AY2 AH0 L T IY0 -HIAN HH AY1 AH0 N -HIAQUA HH AY2 AH0 K W AH0 -HIARANDI HH AY1 ER0 AE1 N D IY0 -HIATUS HH AY0 EY1 T AH0 S -HIATUSES HH AY1 AH0 T AH0 S IH0 Z -HIAWASSEE HH AY2 AH0 W AA1 S IY0 -HIAWATHA HH AY2 AH0 W AA1 TH AH0 -HIAWATHA'S HH AY2 AH0 W AA1 TH AH0 Z -HIB HH IH1 B -HIBACHI HH AH0 B AA1 CH IY0 -HIBACHI HH IY0 B AA1 CH IY0 -HIBBARD HH IH1 B ER0 D -HIBBEN HH IH1 B AH0 N -HIBBERT HH IH1 B ER0 T -HIBBERT'S HH IH1 B ER0 T S -HIBBERTY HH IH1 B ER0 T IY0 -HIBBINS HH IH1 B IH0 N Z -HIBBS HH IH1 B Z -HIBERNATE HH AY1 B ER0 N EY2 T -HIBERNATED HH AY2 B ER0 N EY2 T AH0 D -HIBERNATES HH AY2 B ER0 N EY2 T S -HIBERNATING HH AY2 B ER0 N EY2 T IH0 NG -HIBERNATION HH AY2 B ER0 N EY1 SH AH0 N -HIBERNIA HH AY2 B ER1 N IY0 AH0 -HIBERNIAN HH AY2 B ER1 N IY0 AH0 N -HIBERNIANS HH AY2 B ER1 N IY0 AH0 N Z -HIBERNICISM HH AY2 B ER1 N IH0 S IH2 Z AH0 M -HIBISCUS HH AY2 B IH1 S K AH0 S -HIC HH IH1 K -HICCOUGH HH IH1 K AH0 P -HICCOUGHED HH IH1 K AH1 F T -HICCOUGHING HH IH1 K AO1 F IH0 NG -HICCOUGHS HH IH1 K AH0 P S -HICCUP HH IH1 K AH0 P -HICCUPED HH IH1 K AH0 P T -HICCUPING HH IH1 K AH0 P IH0 NG -HICCUPPED HH IH1 K AH0 P T -HICCUPPING HH IH1 K AH0 P IH0 NG -HICCUPS HH IH1 K AH0 P S -HICH HH IH0 CH -HICHENS HH IH1 K AH0 N Z -HICK HH IH1 K -HICK'RY HH IH1 R IY0 -HICKATHRIFT HH IH1 K AH0 TH R IH1 F T -HICKATHRIFT'S HH IH1 K AH0 TH R IH2 F T S -HICKERY HH IH1 K ER0 IY0 -HICKES HH IH1 K S -HICKETY HH IH1 K AH0 T IY0 -HICKEY HH IH1 K IY0 -HICKEY'S HH IH1 K IY0 Z -HICKIE HH IH1 K IY0 -HICKLEY HH IH1 K L IY0 -HICKLEYBROW HH IH1 K L IY0 B R AW0 -HICKLING HH IH1 K L IH0 NG -HICKMAN HH IH1 K M AH0 N -HICKMAN'S HH IH1 K M AH0 N Z -HICKMANS HH IH1 K M AH0 N Z -HICKNEY HH IH1 K N IY0 -HICKOCK HH IH1 K AA0 K -HICKOCK'S HH IH1 K AA0 K S -HICKOK HH IH1 K AH0 K -HICKORIES HH IH1 K ER0 IY0 Z -HICKORY HH IH1 K ER0 IY0 -HICKORY HH IH1 K R IY0 -HICKORY'S HH IH1 K ER0 IY0 Z -HICKOX HH IH1 K AA0 K S -HICKOX'S HH IH1 K AA0 K S IH0 Z -HICKS HH IH1 K S -HICKS'S HH IH1 K S IH0 Z -HICKSES HH IH1 K S IH0 Z -HICKSEY HH IH1 K S IY0 -HICKSITE HH IH1 K S AY2 T -HICKSITES HH IH1 K S AY2 T S -HICKSON HH IH1 K S AH0 N -HICKSON'S HH IH1 K S AH0 N Z -HICKSWORTHY HH IH1 K S W ER0 DH IY0 -HICKTON HH IH1 K T AH0 N -HICKWOOD HH IH1 K W UH2 D -HICKY HH IH1 K IY0 -HID HH IH1 D -HIDALGO HH AH0 D AE1 L G OW2 -HIDALGO'S HH AH0 D AE1 L G OW2 Z -HIDALGOS HH AH0 D AE1 L G OW2 Z -HIDDEKEL HH IH1 D AH0 K AH0 L -HIDDEN HH IH1 D AH0 N -HIDDIGEIGEI HH IH1 D IH0 G EY1 G EY1 -HIDE HH AY1 D -HIDE'S HH AY1 D Z -HIDEAWAY HH AY1 D AH0 W EY2 -HIDEBOUND HH AY1 D B AW2 N D -HIDED HH AY1 D IH0 D -HIDEOUS HH IH1 D IY0 AH0 S -HIDEOUSLY HH IH1 D IY0 AH0 S L IY0 -HIDEOUSNESS HH IH1 D IY0 AH0 S N AH0 S -HIDEOUT HH AY1 D AW2 T -HIDER HH AY1 D ER0 -HIDERS HH AY1 D ER0 Z -HIDES HH AY1 D Z -HIDESATO HH IY2 D EY0 S AA1 T OW0 -HIDEST HH AY1 D AH0 S T -HIDETADA HH IH1 D IY0 T AA1 D AH0 -HIDETH HH AY1 D IH0 TH -HIDEYOSHI HH IH0 D EY1 Y OW1 SH IY0 -HIDIN HH AY1 D IH0 N -HIDIN' HH AY1 D IH0 N -HIDING HH AY1 D IH0 NG -HIDINGS HH AY1 D IH0 NG Z -HIDY HH AY1 D IY0 -HIE HH AY1 -HIED HH AY1 D -HIEING HH AY1 IH0 NG -HIEL HH IY1 L -HIELAN HH IY1 L AH0 N -HIELAN'MAN HH IY1 L AE0 N M AH0 N -HIELAN'S HH IY1 L AH0 N Z -HIELAND HH IY1 L AH0 N D -HIELANDERS HH IY1 L AH0 N D ER0 Z -HIELANDMAN HH AY1 L AE1 N D M AH0 N -HIELANDMEN HH AY1 L AE1 N D M AH0 N -HIELANDS HH IY1 L AH0 N D Z -HIELEN HH IY1 L AH0 N -HIEMPSAL HH IY1 M P S AH0 L -HIEN HH IY1 N -HIENS HH IY1 N Z -HIER HH AY1 ER0 -HIERA HH IY1 R AH0 -HIERAKONPOLIS HH AY1 ER0 AA2 K AA2 N P AH0 L IH0 S -HIERAPOLIS HH AY1 R AE1 P AH0 L IH0 S -HIERARCH HH AY1 ER0 AA2 R K -HIERARCHIC HH AY1 ER0 AA2 R K IH0 K -HIERARCHICAL HH AY2 R AA1 R K AH0 K AH0 L -HIERARCHIES HH AY1 R AA2 R K IY0 Z -HIERARCHS HH AY1 ER0 AA2 R K S -HIERARCHY HH AY1 ER0 AA2 R K IY0 -HIERARCHY HH AY1 R AA2 R K IY0 -HIERATIC HH AY1 ER0 AE1 T IH0 K -HIERAX HH AY1 R AE0 K S -HIERIE HH AY1 ER0 IY0 -HIERO HH AY2 R OW0 -HIERO'S HH AY2 R OW0 Z -HIEROCLES HH AY2 R OW0 K AH0 L Z -HIERODULES HH AY1 ER0 AA1 JH UW0 L Z -HIEROGLYPH HH AY2 R OW0 G L IH1 F -HIEROGLYPHIC HH AY2 R OW0 G L IH1 F IH0 K -HIEROGLYPHICAL HH AY2 R OW0 G L IH1 F IH0 K AH0 L -HIEROGLYPHICS HH AY2 R OW0 G L IH1 F IH0 K S -HIEROGLYPHS HH AY2 R OW0 G L IH1 F S -HIEROME HH AY2 R OW0 M -HIERON HH AY1 ER0 AH0 N -HIERONIMO HH AY1 ER0 OW0 N IY1 M OW0 -HIERONYMO HH AY2 R OW0 N AH0 M OW0 -HIERONYMUS HH AY2 R OW0 N AH0 M AH0 S -HIEROPHANT HH AY2 R OW0 F AH0 N T -HIEROPHANTIC HH AY2 R OW0 F AE1 N T IH0 K -HIEROPHANTS HH AY2 R OW0 F AH0 N T S -HIERUSALEM HH AY1 ER0 UW1 S AH0 L AH0 M -HIES HH AY1 Z -HIF HH AY1 F -HIFALUTIN HH AY1 F AH0 L UW1 T IH0 N -HIGBEE HH IH1 G B IY2 -HIGBEE'S HH IH1 G B IY2 Z -HIGBIE HH IH1 G B IY0 -HIGBY HH IH1 G B IY0 -HIGDEN HH IH1 G D AH0 N -HIGG HH IH1 G -HIGG'S HH IH1 G Z -HIGGINBOTHAM HH IH0 G IH0 N B AA1 TH AH0 M -HIGGINBOTHAM'S HH IH1 G IH0 N B AA1 TH AH0 M Z -HIGGINBOTTOM HH IH0 G IH0 N B AA1 T AH0 M -HIGGINS HH IH1 G IH0 N Z -HIGGINS'S HH IH1 G IH0 N Z IH0 Z -HIGGINSON HH IH1 G IH0 N S AH0 N -HIGGINSON'S HH IH1 G IH0 N S AH0 N Z -HIGGLE HH IH1 G AH0 L -HIGGLEBY'S HH IH1 G AH0 L B IY0 Z -HIGGLED HH IH1 G AH0 L D -HIGGLEDEE HH IH1 G AH0 L D IY0 -HIGGLEDY HH IH1 G AH0 L D IY0 -HIGGLER HH IH1 G AH0 L ER0 -HIGGLESTON HH IH1 G AH0 L S T AH0 N -HIGGLING HH IH1 G L IH0 NG -HIGGS HH IH1 G Z -HIGGS'S HH IH1 G Z IH0 Z -HIGH HH AY1 -HIGH'S HH AY1 Z -HIGHAM HH AY1 AH0 M -HIGHBALL HH AY1 B AO2 L -HIGHBALLS HH AY1 B AO2 L Z -HIGHBINDERS HH AY1 B AY2 N D ER0 Z -HIGHBORN HH AY1 B ER0 N -HIGHBOURNE HH AY1 B ER0 N -HIGHBOY HH AY1 B OY2 -HIGHBOYS HH AY1 B OY2 Z -HIGHBRED HH AY1 B R EH1 D -HIGHBROW HH AY1 B R AW2 -HIGHBROWS HH AY1 B R AW2 Z -HIGHBURY HH AY1 B EH0 R IY0 -HIGHCAMP HH AY1 K AE1 M P -HIGHCLIFF HH AY1 K L IH0 F -HIGHCLIFFE HH AY1 K L IH0 F -HIGHCLOSE HH AY1 K L OW1 Z -HIGHCOMBE HH AY1 K AH0 M -HIGHCOURT HH AY1 K AO2 R T -HIGHER HH AY1 ER0 -HIGHER'N HH AY1 AH0 AH0 N -HIGHEST HH AY1 AH0 S T -HIGHFALUTIN HH AY2 F AH0 L UW1 T IH0 N -HIGHFALUTING HH AY1 F AH0 L UW1 T IH0 NG -HIGHFIELD HH AY1 F IY2 L D -HIGHFLIER HH AY1 F L AY2 ER0 -HIGHFLOWN HH AY1 F L OW1 N -HIGHFLYER HH AY1 F L AY2 ER0 -HIGHFLYERS HH AY1 F L AY2 ER0 Z -HIGHGATE HH AY1 G EY2 T -HIGHGRADER HH AY1 G R EY1 D ER0 -HIGHGRADING HH AY1 G R EY2 D IH0 NG -HIGHHANDED HH AY1 HH AE2 N D IH0 D -HIGHLAND HH AY1 L AH0 N D -HIGHLANDER HH AY1 L AE2 N D ER0 -HIGHLANDER'S HH AY1 L AE2 N D ER0 Z -HIGHLANDERS HH AY1 L AE2 N D ER0 Z -HIGHLANDMAN HH AY1 L AE2 N D M AH0 N -HIGHLANDMEN HH AY1 L AE2 N D M AH0 N -HIGHLANDS HH AY1 L AH0 N D Z -HIGHLIGHT HH AY1 L AY2 T -HIGHLIGHTS HH AY1 L AY2 T S -HIGHLOWS HH AY1 L OW0 Z -HIGHLY HH AY1 L IY0 -HIGHMARKET HH AY1 M AA2 R K IH0 T -HIGHMINDED HH AY1 M AY1 N D IH0 D -HIGHMINDEDNESS HH AY1 M AY1 N D IH0 D N AH0 S -HIGHMORE HH AY1 M AO0 R -HIGHNESS HH AY1 N AH0 S -HIGHNESS'S HH AY1 N AH0 S IH0 Z -HIGHNESSES HH AY1 N AH0 S IH0 Z -HIGHPOCKETS HH AY1 P AA2 K AH0 T S -HIGHPOINT HH AY1 P OY2 N T -HIGHROAD HH AY1 R OW1 D -HIGHROADS HH AY1 R OW2 D Z -HIGHROB HH AY1 R AA2 B -HIGHS HH AY1 Z -HIGHSTRUNG HH AY1 S T R AH0 NG -HIGHT HH AY1 T -HIGHTOFT HH AY1 T AA1 F T -HIGHTON HH AY1 T AH0 N -HIGHTOWER HH AY1 T AW2 ER0 -HIGHTOWN HH AY1 T AW2 N -HIGHTS HH AY1 T S -HIGHTUM HH AY1 T AH0 M -HIGHTY HH AY1 T IY0 -HIGHWATER HH AY1 W AO2 T ER0 -HIGHWAY HH AY1 W EY2 -HIGHWAYMAN HH AY1 W EY1 M AH0 N -HIGHWAYMAN'S HH AY1 W EY2 M AH0 N Z -HIGHWAYMEN HH AY1 W EY2 M AH0 N -HIGHWAYS HH AY1 W EY2 Z -HIGLEY HH IH1 G L IY0 -HIGLI HH IY0 G L IY0 -HIGLINSON HH IH1 G L IH0 N S AH0 N -HIGNETT HH IH1 G N IH0 T -HIGNETT'S HH IH1 G N AH0 T S -HIGNORANT HH IH1 G N ER0 AH0 N T -HIGUEROTA HH IY0 G EH0 R OW1 T AH0 -HIH HH IH1 -HIJACKERS HH AY1 JH AE2 K ER0 Z -HIJAZ HH AY1 JH AE2 Z -HIJO HH AY1 JH OW0 -HIJOS HH AY1 JH OW0 Z -HIJRAH HH AY1 JH R AH0 -HIKE HH AY1 K -HIKED HH AY1 K T -HIKER HH AY1 K ER0 -HIKERS HH AY1 K ER0 Z -HIKES HH AY1 K S -HIKIN HH AY1 K IH0 N -HIKING HH AY1 K IH0 NG -HIKOROKU HH AY1 K AH0 R OW1 K UW2 -HIKOZAEMON HH AY1 K AA2 Z EY1 M AH0 N -HIKUERU HH AY1 K UW0 EY1 R UW0 -HIL HH IH1 L -HIL'RY HH IH1 L R IY0 -HILAIRE HH IH0 L EH1 R -HILAIRE'S HH IH0 L EY1 R Z -HILAL HH IH1 L AH0 L -HILAND HH IH1 L AH0 N D -HILARIA HH IY0 L AA1 R IY0 AH0 -HILARIO HH IY0 L AA1 R IY0 OW0 -HILARIO'S HH IH0 L EH1 R IY0 OW0 Z -HILARION HH IH0 L EH1 R IY0 AH0 N -HILARION'S HH IH0 L EH1 R IY0 AH0 N Z -HILARIOUS HH IH0 L EH1 R IY0 AH0 S -HILARIOUSLY HH IH0 L EH1 R IY0 AH0 S L IY0 -HILARIOUSNESS HH IH0 L EH1 R IY0 AH0 S N AH0 S -HILARITIES HH IH0 L EH1 R AH0 T IY0 Z -HILARITY HH IH0 L EH1 R AH0 T IY0 -HILARIUS HH IH0 L EH1 R IY0 AH0 S -HILARO HH IY0 L AA1 R OW0 -HILARUS HH IY0 L AA1 R AH0 S -HILARY HH IH1 L ER0 IY0 -HILARY'S HH IH0 L EH1 R IY0 Z -HILARYS HH IH0 L EH1 R IY0 Z -HILAS HH IH1 L AH0 Z -HILBERRY HH IH1 L B EH2 R IY0 -HILBERT HH IH1 L B ER0 T -HILBERY HH IH1 L B ER0 IY0 -HILBERY'S HH IH1 L B ER0 IY0 Z -HILBERYS HH IH1 L B ER0 IY0 Z -HILBOROUGH HH IH1 L B ER0 OW0 -HILBROUGH HH IH1 L B R AW0 -HILBROUGH'S HH IH1 L B R AW0 Z -HILBROUGHS HH IH1 L B R AW0 Z -HILBRUN HH IH1 L B R AH0 N -HILCHESTER HH IH1 L CH EH2 S T ER0 -HILD HH IH1 L D -HILDA HH IH1 L D AH0 -HILDA'S HH IH1 L D AH0 Z -HILDEBRAND HH IH1 L D IH0 B R AE2 N D -HILDEBRAND'S HH IH1 L D IH0 B R AE2 N D Z -HILDEBRANDS HH IH1 L D IH0 B R AE2 N D Z -HILDEBRANDT HH IH1 L D IH0 B R AH0 N T -HILDEBRANT HH IH1 L D IH0 B R AH0 N T -HILDEBROD HH IH1 L D IH0 B R AH0 D -HILDEGARD HH IH1 L D AH0 G AA2 R D -HILDEGARDE HH IH1 L D IH0 G AA2 R D -HILDEGARDE'S HH IH1 L D AH0 G AA2 R D Z -HILDEGARDIS HH IH1 L D AH0 G AA2 R D IH0 S -HILDEGUND HH IH1 L D AH0 G AH0 N D -HILDEGUNDE HH IH1 L D IH0 G AH1 N D -HILDEMALLER HH IH1 L D IH0 M AO1 L ER0 -HILDEMALLER'S HH IH1 L D IH0 M AO1 L ER0 Z -HILDEN HH AY1 L D AH0 N -HILDENHEIM HH IH1 L D IH0 N HH AY0 M -HILDERIC HH IH1 L D ER0 IH0 K -HILDERMAN HH IH1 L D ER0 M AH0 N -HILDERMAN'S HH IH1 L D ER0 M AH0 N Z -HILDERTON HH IH1 L D ER0 T AH0 N -HILDESHEIM HH IH1 L D Z HH AY2 M -HILDGUND HH IH1 L D G AH0 N D -HILDING HH IH1 L D IH0 NG -HILDITCH HH IH1 L D IH0 CH -HILDITCH'S HH IH1 L D IH1 CH IH0 Z -HILDRED HH IH1 L D R IH0 D -HILDRETH HH IH1 L D R IH0 TH -HILDRETH'S HH IH1 L D R IH0 TH S -HILDUR HH IH1 L D UH1 R -HILDY HH IH1 L D IY0 -HILDYARD HH IH1 L D Y ER0 D -HILDYARD'S HH IH1 L D Y ER0 D Z -HILES HH AY1 L Z -HILGARD HH IH1 L G AA2 R D -HILGARDE HH IH1 L G AA2 R D -HILGENDORF HH IH1 L G IH0 N D AO0 R F -HILI HH IH1 L IY0 -HILKIAH HH IH1 L K AY1 AH0 -HILL HH IH1 L -HILL'S HH IH1 L Z -HILLA HH IH1 L AH0 -HILLAH HH IH1 L AH0 -HILLARD HH IH1 L ER0 D -HILLARD'S HH IH1 L ER0 D Z -HILLARY HH IH1 L ER0 IY0 -HILLAS HH IH1 L AH0 S -HILLATON HH IH1 L AH0 T AA1 N -HILLBILLY HH IH1 L B IH0 L IY0 -HILLBRIDGE HH IH1 L B R IH0 JH -HILLCREST HH IH1 L K R EH0 S T -HILLCREST'S HH IH1 L K R EH0 S T S -HILLCRIST HH IH1 L K R IH2 S T -HILLEBOE HH IH1 L IH0 B OW1 -HILLEBRANT HH IH1 L IH0 B R AH0 N T -HILLED HH IH1 L D -HILLEL HH IH2 L EH1 L -HILLER HH IH1 L ER0 -HILLER'S HH IH1 L ER0 Z -HILLERN HH IH1 L ER0 N -HILLERS HH IH1 L ER0 Z -HILLERTON HH IH1 L ER0 T AH0 N -HILLES HH IH1 L IY0 Z -HILLEY HH IH1 L IY0 -HILLFORD HH IH1 L F ER0 D -HILLGROVE HH IH1 L G R OW2 V -HILLHOUSE HH IH1 L HH AW2 S -HILLIARD HH IH1 L Y AA0 R D -HILLIARD'S HH IH1 L Y ER0 D Z -HILLIER HH IH1 L IY0 ER0 -HILLIGOSS HH IH1 L IH0 G AA0 S -HILLING HH IH1 L IH0 NG -HILLINGSWORTH HH IH1 L IH0 NG Z W ER2 TH -HILLIS HH IH1 L IH0 S -HILLISTON HH IH1 L IH0 S T AA0 N -HILLISTON'S HH IH1 L IH0 S T AA0 N Z -HILLITES HH IH1 L AY0 T S -HILLMAN HH IH1 L M AE2 N -HILLMAN'S HH IH1 L M AH0 N Z -HILLMEN HH IH1 L M EH0 N -HILLMER HH IH1 L M ER0 -HILLMER'S HH IH1 L M ER0 Z -HILLNER HH IH1 L N ER0 -HILLO HH IH1 L OW0 -HILLOA HH IH1 L OW1 AH0 -HILLOAH HH IH1 L OW1 AH0 -HILLOCK HH IH1 L AH0 K -HILLOCKS HH IH1 L AH0 K S -HILLOCKY HH IH1 L AH0 K IY0 -HILLOH HH IH1 L AH0 -HILLPORT HH IH1 L P AO2 R T -HILLS HH IH1 L Z -HILLSBORO HH IH1 L Z B ER0 OW0 -HILLSBOROUGH HH IH1 L Z B ER0 OW0 -HILLSBRO HH IH1 L Z B R OW0 -HILLSCOMBE HH IH1 L S K OW1 M B IY0 -HILLSDALE HH IH1 L Z D EY2 L -HILLSIDE HH IH1 L S AY2 D -HILLSIDE'S HH IH1 L S AY2 D Z -HILLSIDES HH IH1 L S AY2 D Z -HILLSLOPE HH IH1 L S L OW1 P -HILLSMAN HH IH1 L S M AH0 N -HILLSMEN HH IH1 L S M IH0 N -HILLSOVER HH IH1 L S AH0 V ER0 -HILLSTOKE HH IH1 L S OW2 K -HILLSVILLE HH IH1 L S V IH2 L -HILLTON HH IH1 L T AH0 N -HILLTONIANS HH IH1 L T OW1 N IY0 AH0 N Z -HILLTOP HH IH1 L T AA2 P -HILLTOPS HH IH1 L T AA2 P S -HILLTOWN HH IH1 L T AW2 N -HILLVIEW HH IH1 L V Y UW2 -HILLWARD HH IH1 L W ER0 D -HILLY HH IH1 L IY0 -HILLYAR HH IH1 L Y AA2 R -HILLYARD HH IH1 L Y AA2 R D -HILLYARD'S HH IH1 L Y AA2 R D Z -HILLYER HH IH1 L IY0 ER0 -HILMA HH IH1 L M AH0 -HILMA'S HH IH1 L M AH0 Z -HILMAN HH IH1 L M AH0 N -HILMAR HH IH1 L M ER0 -HILNARIC HH IH1 L N ER0 IH0 K -HILO HH IY1 L OW0 -HILP HH IH1 L P -HILS HH IH1 L Z -HILSBORN HH IH1 L Z B ER0 N -HILSE HH IH1 L S -HILSENHOFF HH IH1 L S IH0 N HH AO0 F -HILSEY HH IH1 L S IY0 -HILSON HH IH1 L S AH0 N -HILST HH IH1 L S T -HILT HH IH1 L T -HILTED HH IH1 L T IH0 D -HILTNER HH IH1 L T N ER0 -HILTNERS HH IH1 L T N ER0 Z -HILTON HH IH1 L T AH0 N -HILTON'S HH IH1 L T AH0 N Z -HILTONS HH IH1 L T AH0 N Z -HILTS HH IH1 L T S -HILTZE HH IH1 L T S -HILVERSUM HH IH1 L V ER0 S AH0 M -HILYARD HH AH0 L Y AA1 R D -HILYARD'S HH AH0 L Y AA1 R D Z -HIM HH IH1 M -HIM IH0 M -HIM' HH IH1 M -HIM'LL HH IH1 M AH0 L -HIM'S HH IH1 M Z -HIMAGE HH IH2 M IH0 JH -HIMALAYA HH IH2 M AH0 L AY1 AH0 -HIMALAYA HH IH2 M AH0 L EY1 AH0 -HIMALAYAN HH IH2 M AH0 L AY1 AH0 N -HIMALAYAN HH IH2 M AH0 L EY1 AH0 N -HIMALAYAS HH IH2 M AH0 L AY1 AH0 S -HIMALAYAS HH IH2 M AH0 L EY1 AH0 S -HIMATION HH AH0 M EY1 SH AH0 N -HIMAVAT HH IH2 M AH0 V AA1 T -HIME HH AY1 M -HIMEGIMI HH IH1 M IH0 JH IY1 M IY0 -HIMEJI HH AY1 M JH IY0 -HIMENE HH IH0 M IY2 N -HIMENES HH AY1 M N Z -HIMERA IH0 M EH1 R AH0 -HIMILCO HH IH0 M IH1 L K OW0 -HIMILTRUDE HH IH0 M IH1 L T R UW0 D -HIMINE HH IH2 M AH0 N -HIMKOF HH IH1 M K AO0 F -HIMMEL HH IH1 M AH0 L -HIMMELS HH IH1 M AH0 L Z -HIMS HH IH1 M Z -HIMSE'F HH IH0 M S EH1 F -HIMSEEMED HH IH0 M S IY1 M D -HIMSEL HH IH0 M S EH1 L -HIMSELF HH IH0 M S EH1 L F -HIMSELF'S HH IH0 M S EH1 L F S -HIMSELFE HH IH0 M S EH1 L F -HIMSELL HH IH0 M S EH1 L -HIMSILF HH IH0 M S AH0 L F -HIN HH IH1 N -HINA HH AY1 N AH0 -HINAUS HH IH1 N AO1 Z -HINAYANA HH IH1 N AH0 Y AA1 N AH0 -HINC HH IH1 NG K -HINCH HH IH1 N CH -HINCHCLIFFE HH IH1 N CH K L IH0 F -HINCHEL HH IH1 N K AH0 L -HINCHEY HH IH1 N CH IY0 -HINCHFORD HH IH1 N CH F ER0 D -HINCHFORD'S HH IH1 N CH F ER0 D Z -HINCHFORDS HH IH1 N CH F ER0 D Z -HINCHINBROOK HH IH1 N CH IH0 N B R UH2 K -HINCHINGBROKE HH IH1 N CH IH0 NG B R OW2 K -HINCHMAN HH IH1 NG K M AH0 N -HINCKELDEYM HH IH1 NG K AH0 L D IH2 IH1 M -HINCKLEY HH IH1 NG K L IY0 -HINCKLEY'S HH IH1 NG K L IY0 Z -HINCKMAN HH IH1 NG K M AH0 N -HINCKS HH IH1 NG K S -HINCMAR HH IH1 NG K M AA0 R -HIND HH AY1 N D -HIND'S HH AY1 N D Z -HINDAL HH IH1 N D AH0 L -HINDBAD HH AY1 N D B AE0 D -HINDE HH IH1 N D -HINDELOOPEN HH IH1 N D AH0 L UW2 P AH0 N -HINDENBERG HH IH1 N D AH0 N B ER0 G -HINDENBURG HH IH1 N D AH0 N B ER0 G -HINDENBURG'S HH IH1 N D AH0 N B ER0 G Z -HINDER HH IH1 N D ER0 -HINDERANCE HH IH1 N D ER0 AH0 N S -HINDERANCES HH IH1 N D ER0 AH0 N S AH0 Z -HINDERED HH IH1 N D ER0 D -HINDERER HH IH1 N D ER0 ER0 -HINDERERS HH IH1 N D ER0 ER0 Z -HINDERETH HH IH1 N D ER0 IH0 TH -HINDERIN HH IH1 N D ER0 IH0 N -HINDERING HH IH1 N D ER0 IH0 NG -HINDERMOST HH IH1 N D ER0 M OW2 S T -HINDERS HH IH1 N D ER0 Z -HINDES HH IH1 N D Z -HINDFORD HH IH1 N D F ER0 D -HINDHEAD HH IH1 N D HH EH2 D -HINDI HH IH1 N D IY0 -HINDLE HH IH1 N D AH0 L -HINDLEGS HH IH1 N D L EH2 G Z -HINDLEY HH IH1 N D L IY0 -HINDLIP HH IH1 N D L IH2 P -HINDMAN HH AY1 N D M AH0 N -HINDMAN'S HH IH1 N D M AH0 N Z -HINDMOST HH IH1 N D M OW2 S T -HINDOO HH AY1 N D UW2 -HINDOO'S HH IH1 N JH UW1 Z -HINDOOISM HH IH1 N D AO2 IH2 Z AH0 M -HINDOOS HH IH1 N JH UW1 Z -HINDOOSTAN HH IH1 N D AO2 S T AE2 N -HINDOOSTANEE HH IH1 N D AO2 S T AH0 N IY1 -HINDOSTAN HH IH1 N D OW0 S T AA1 N -HINDOSTANEE HH IH1 N D OW0 S T AA1 N IY1 -HINDOSTANI HH IH1 N D OW0 S T AA1 N IY0 -HINDQUARTER HH AY1 N D K AO2 R T ER0 -HINDQUARTER HH AY1 N D K W AO2 R T ER0 -HINDQUARTERS HH AY1 N D K AO2 R T ER0 Z -HINDQUARTERS HH AY1 N D K W AO2 R T ER0 Z -HINDRANCE HH IH1 N D R AH0 N S -HINDRANCES HH IH1 N D R AH0 N S IH0 Z -HINDRED HH IH1 N D R IH0 D -HINDRICKSON HH IH1 N D R IH0 K S AH0 N -HINDS HH AY1 N D Z -HINDSIDE HH AY1 N D S AY2 D -HINDSIGHT HH AY1 N D S AY2 T -HINDSIGHT HH AY1 N S AY2 T -HINDU HH IH1 N D UW2 -HINDU'S HH IH1 N D UW2 Z -HINDUISM HH IH1 N JH UW0 IH2 Z AH0 M -HINDUS HH IH1 N D UW2 Z -HINDUSTAN HH IH1 N D UW0 S T AE2 N -HINDUSTANEE HH IH1 N D UW0 S T AE2 N IY1 -HINDUSTANI HH IH1 N D UW0 S T AA1 N IY0 -HINE HH AY1 N -HINE'S HH AY1 N Z -HINEIN HH AY1 N AY2 N -HINES HH AY1 N Z -HINFANT HH IH1 N F AH0 N T -HING HH IH1 NG -HINGE HH IH1 N JH -HINGED HH IH1 N JH D -HINGES HH IH1 N JH IH0 Z -HINGHAM HH IH1 NG AH0 M -HINGIN HH IH1 NG AH0 N -HINGING HH IH1 NG IH0 NG -HINGLAND HH IH1 NG G L AH0 N D -HINGLISH HH IH1 NG G L IH0 SH -HINGS HH IH1 NG Z -HINGSTON HH IH1 NG S T AH0 N -HINGSTON'S HH IH1 NG S T AH0 N Z -HINKLE HH IH1 NG K AH0 L -HINKLE'S HH IH1 NG K AH0 L Z -HINKLING HH IH1 NG K L IH0 NG -HINKMAN HH IH1 NG K M AH0 N -HINKS HH IH1 NG K S -HINKSEY HH IH1 NG K S IY0 -HINKSON HH IH1 NG K S AH0 N -HINKSVILLE HH IH1 NG K S V IH2 L -HINMAN HH IH1 N M AH0 N -HINNA HH IH1 N AH0 -HINNERCENT HH IH1 N ER0 S EH1 N T -HINNOCENT HH IH1 N AH0 S AH0 N T -HINNOM HH IH1 N AH0 M -HINNY HH IH1 N IY0 -HINOE HH IY0 N OW0 -HINOMISAKI HH IH0 N OW1 M IY0 S AA1 K IY0 -HINPOHA HH IH1 N P OW1 HH AH0 -HINPOHA'S HH IH1 N P OW2 AH0 Z -HINRICH HH IH1 N R IH0 K -HINS HH IH1 N Z -HINSDALE HH IH1 N S D EY2 L -HINSETH HH IH1 N S IH0 TH -HINSHAW HH IH1 N SH AO2 -HINSON HH IH1 N S AH0 N -HINT HH IH1 N T -HINTED HH IH1 N IH0 D -HINTED HH IH1 N T AH0 D -HINTED HH IH1 N T IH0 D -HINTER HH IH1 N T ER0 -HINTEREST HH IH1 N T R AH0 S T -HINTERLAND HH IH1 N T ER0 L AE2 N D -HINTERLANDS HH IH1 N T ER0 L AE2 N D Z -HINTIN HH IH1 N T IH0 N -HINTING HH IH1 N T IH0 NG -HINTINGS HH IH1 N T IH0 NG Z -HINTOCK HH IH1 N T AH0 K -HINTON HH IH1 N T AH0 N -HINTON'S HH IH1 N T AH0 N Z -HINTS HH IH1 N T S -HINTZA HH IH1 N T Z AH0 -HINZ HH IH1 N Z -HINZA HH IH1 N Z AH0 -HINZE HH IH1 N Z -HINZELMANN HH IH1 N Z AH0 L M AH0 N -HIO HH AY1 OW0 -HIORBA HH AO1 R B AH0 -HIORDIS HH AO1 R D IH0 S -HIP HH IH1 P -HIPCROFT HH IH1 P K R AO2 F T -HIPGRAVE HH IH1 P G R EY2 V -HIPOLITO IY1 P OW0 L IY1 T OW0 -HIPP HH IH1 P -HIPPALCA HH IH1 P AE1 L K AH0 -HIPPANTHIGH HH IH1 P AH0 N TH AY1 -HIPPARCHIA HH IH1 P AA1 R K IY0 AH0 -HIPPARCHUS HH IH1 P ER0 CH AH0 S -HIPPARETE HH IH1 P ER0 IY2 T -HIPPARION HH IH1 P EH2 R IY0 AH0 N -HIPPASUS HH IH1 P AH0 S AH0 S -HIPPE HH IH1 P -HIPPED HH IH1 P T -HIPPEL HH IH1 P AH0 L -HIPPERTY HH IH1 P ER0 T IY0 -HIPPESLEY HH IH1 P IH0 S L IY0 -HIPPETTS HH IH1 P IH0 T S -HIPPETY HH IH1 P AH0 T IY0 -HIPPI HH IH1 P IY0 -HIPPIAS HH IH1 P IY0 AH0 S -HIPPICUS HH IH1 P IH0 K AH0 S -HIPPISLEY HH IH1 P IY0 Z L IY0 -HIPPITY HH IH1 P AH0 T IY0 -HIPPLE HH IH1 P AH0 L -HIPPO HH IH1 P OW0 -HIPPO'S HH IH2 P OW0 Z -HIPPOCAMPUS HH IH2 P OW0 K AE1 M P AH0 S -HIPPOCRAS HH IH0 P AO1 K R AH0 Z -HIPPOCRATES HH IH0 P AO1 K R AH0 T IY0 Z -HIPPOCRATES HH IH1 P AH0 K R EY2 T S -HIPPOCRATIC HH IH0 P AH0 K R AE1 T IH0 K -HIPPOCRENE HH IH1 P AH0 K R IY0 N -HIPPODAMIA HH IH1 P AH0 D EY1 M IY0 AH0 -HIPPODROME HH IH1 P AH0 D R OW2 M -HIPPODROME'S HH IH1 P AH0 D R OW2 M Z -HIPPODROMES HH IH1 P AH0 D R OW2 M Z -HIPPOGRIF HH IH1 P AH0 G R IH0 F -HIPPOGRIFF HH IH1 P AH0 G R IH0 F -HIPPOGRIFFS HH IH1 P AH0 G R IH0 F S -HIPPOGRYPH HH IH1 P AH0 G R IH1 F -HIPPOI HH IH1 P OY0 -HIPPOLITA HH IH2 P OW0 L IY1 T AH0 -HIPPOLITA'S HH IH2 P OW0 L IY1 T AH0 Z -HIPPOLITO HH IH2 P OW0 L IY1 T OW0 -HIPPOLITO'S HH IH2 P OW0 L IY1 T OW0 Z -HIPPOLITUS HH IH2 P OW0 L IY1 T AH0 S -HIPPOLOCHUS HH IH1 P AH0 L AA1 K AH0 S -HIPPOLYTA HH IH2 P AA2 L IH0 T AH0 -HIPPOLYTE HH IH1 P AH0 L AY2 T -HIPPOLYTE'S HH IH1 P AH0 L AY2 T S -HIPPOLYTUS HH AH0 P AA1 L AH0 T AH0 S -HIPPOMENES HH IH1 P AH0 M IY2 N Z -HIPPONAX HH IH1 P AH0 N AE0 K S -HIPPONICUS HH IH2 P AA1 N AH0 K AH0 S -HIPPOPOTAMI HH IH2 P AH0 P AA1 T AH0 M IY0 -HIPPOPOTAMUS HH IH2 P AH0 P AA1 T AH0 M AH0 S -HIPPOPOTAMUSES HH IH2 P AH0 P AA1 T AH0 M AH0 S IH0 Z -HIPPOS HH IH1 P OW0 Z -HIPPS HH IH1 P S -HIPPUS HH IH1 P AH0 S -HIPPY HH IH1 P IY0 -HIPPY'S HH IH1 P IY0 Z -HIPS HH IH1 P S -HIPSLEY HH IH1 P S L IY0 -HIR HH IH2 R -HIR'D HH ER1 D -HIRA HH IH1 R AH0 -HIRABUL HH IH0 R AH0 B UW1 L -HIRAM HH AY1 R AH0 M -HIRAM'S HH IH0 R AA1 M Z -HIRAMAN HH IH0 R AA1 M AH0 N -HIRAMANI HH IH0 R AH0 M AA1 N IY0 -HIRANYA HH IH0 R AA1 N Y AH0 -HIRATA HH IH0 R AA1 T AH0 -HIRD HH ER1 D -HIRE HH AY1 ER0 -HIRE HH AY1 R -HIRED HH AY1 ER0 D -HIRELING HH AY1 ER0 L IH0 NG -HIRELING'S HH AY1 ER0 L IH0 NG Z -HIRELINGS HH AY1 ER0 L IH0 NG Z -HIRER HH AY1 ER0 R -HIRERS HH AY1 ER0 ER0 Z -HIRES HH AY1 ER0 Z -HIRES HH AY1 R Z -HIRIN HH IH1 R IH0 N -HIRING HH AY1 R IH0 NG -HIRISH HH AY1 R IH0 SH -HIRLAJ HH ER1 L AY2 -HIRLAJI HH ER1 L AA1 JH IY0 -HIRNIO HH ER1 N IY0 OW0 -HIRO HH IH1 R OW0 -HIROM HH IH2 R OW0 M -HIRONDELLE HH IH2 R OW0 N D EH1 L -HIROSHIMA HH IH2 R OW0 SH IY1 M AH0 -HIROSHIMA HH IH2 R OW1 SH IH0 M AH0 -HIROSHIMI HH IH2 R OW0 SH IY1 M IY0 -HIRPINGTON HH ER1 P IH0 NG T AH0 N -HIRPINGTON'S HH ER1 P IH0 NG T AH0 N Z -HIRPLE HH ER1 P AH0 L -HIRPLING HH ER1 P AH0 L IH0 NG -HIRSCH HH ER1 SH -HIRSCH'S HH ER1 SH IH0 Z -HIRSCHBERG HH ER1 SH B ER0 G -HIRSCHHORN HH ER1 SH HH ER0 N -HIRSCHVOGEL HH ER1 SH V OW2 G AH0 L -HIRSFELD HH IH1 R S F EH2 L D -HIRST HH ER1 S T -HIRST'S HH ER1 S T S -HIRSUTE HH ER0 S UW1 T -HIRTIUS HH ER1 T IY0 IH0 S -HIRUNDO HH IH1 R UW2 N D OW0 -HIS HH IH0 Z -HIS HH IH1 Z -HIS'N HH IH1 AH0 N -HISAR HH IH0 S AA1 R -HISCOCK HH IH1 S K AH0 K -HISEN HH IH1 Z AH0 N -HISGINS HH IH0 S G IH0 N Z -HISH HH IH1 SH -HISHAM HH IH1 SH AH0 M -HISI HH IH1 S IY0 -HISLOP HH IH1 S L AH0 P -HISN HH IH1 S N -HISPAL HH IH1 S P AH0 L -HISPANIA HH IH0 S P AE1 N Y AH0 -HISPANIC HH IH0 S P AE1 N IH0 K -HISPANIOLA HH IH0 S P AA2 NG OW1 L AH0 -HISPANO HH IH0 S P AA1 N OW0 -HISS HH IH1 S -HISS'D HH IH1 SH D -HISSAR HH IH1 S ER0 -HISSE'F HH IH1 S IH0 F -HISSED HH IH1 S T -HISSEF HH IH1 S EH0 F -HISSEFF HH IH2 S EH1 F -HISSEL HH IH1 S AH0 L -HISSELF HH IH2 S EH1 L F -HISSEN HH IH1 S AH0 N -HISSES HH IH1 S IH0 Z -HISSILF HH IH1 S AH0 L F -HISSIN HH IH1 S IH0 N -HISSING HH IH1 S IH0 NG -HISSINGLY HH IH1 S IH0 NG L IY0 -HISSINGS HH IH1 S IH0 NG Z -HISSODECHA HH IH1 S AO2 D EH1 CH AH0 -HIST HH IH0 S T -HIST'RY HH IH1 S T R IY0 -HISTAH HH IH1 S T AH0 -HISTED HH IH1 S T IH0 D -HISTER HH IH0 S T ER0 -HISTIAEUS HH IH1 S T IY0 EY1 AH0 S -HISTIN HH IH1 S T IH0 N -HISTOIRE HH IH1 S T OY2 R -HISTOIRES HH IH0 S T W AA2 Z -HISTOLOGICAL HH IH1 S T AH0 L AA1 JH IH0 K AH0 L -HISTOLOGY HH IH0 S T AA1 L AH0 JH IY0 -HISTORIA HH IH0 S T AO1 R IY0 AH0 -HISTORIAE HH IH0 S T AO1 R IY0 AH0 -HISTORIAN HH IH0 S T AO1 R IY0 AH0 N -HISTORIAN'S HH IH0 S T AO1 R IY0 AH0 N Z -HISTORIANS HH IH0 S T AO1 R IY0 AH0 N Z -HISTORIC HH IH0 S T AO1 R IH0 K -HISTORICAL HH IH0 S T AO1 R IH0 K AH0 L -HISTORICALLY HH IH0 S T AO1 R IH0 K AH0 L IY0 -HISTORICALLY HH IH0 S T AO1 R IH0 K L IY0 -HISTORICITY HH IH0 S T AO1 R IH0 S IH2 T IY0 -HISTORICO HH IH0 S T AO1 R IH0 K OW2 -HISTORIE HH IH0 S T AO1 R IY0 -HISTORIES HH IH1 S T ER0 IY0 Z -HISTORIES HH IH1 S T R IY0 Z -HISTORIETTES HH IH0 S T AO1 R IY0 EH1 T S -HISTORIOGRAPHER HH IH0 S T AO2 R IY0 AA1 G R AH0 F ER0 -HISTORIOGRAPHERS HH IH0 S T AO2 R IY0 AA1 G R AH0 F ER0 Z -HISTORIQUE HH IH1 S T ER0 IY1 K -HISTORIQUES HH IH1 S T ER0 IY1 K S -HISTORY HH IH1 S T ER0 IY0 -HISTORY HH IH1 S T R IY0 -HISTORY'S HH IH1 S T ER0 IY0 Z -HISTORY'S HH IH1 S T R IY0 Z -HISTRIO HH IH1 S T R IY0 OW0 -HISTRION HH IH1 S T R IY0 AH0 N -HISTRIONIC HH IH2 S T R IY0 AA1 N IH0 K -HISTRIONICALLY HH IH1 S T R IY0 AA1 N IH0 K L IY0 -HISTRIONICS HH IH2 S T R IY0 AA1 N IH0 K S -HISTRIONISM HH IH1 S T R IY0 AH0 N IH2 Z AH0 M -HIT HH IH1 T -HIT'LL HH AY1 T AH0 L -HIT'S HH IH0 T S -HITACHI HH IH0 T AA1 CH IY0 -HITANI HH IH0 T AA1 N IY0 -HITCH HH IH1 CH -HITCHCOCK HH IH1 CH K AA2 K -HITCHCOCK'S HH IH1 CH K AA2 K S -HITCHED HH IH1 CH T -HITCHENER HH IH1 CH N ER0 -HITCHENER'S HH IH1 CH N ER0 Z -HITCHER HH IH1 CH ER0 -HITCHES HH IH1 CH IH0 Z -HITCHIN HH IH1 CH IH0 N -HITCHIN'S HH IH1 CH IH0 N Z -HITCHING HH IH1 CH IH0 NG -HITCHINS HH IH1 CH IH0 N Z 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IH0 T IY0 -HIXLEY HH IH1 K S L IY0 -HIXON HH IH1 K S AH0 N -HIYA HH IY1 AH0 -HIYU HH Y UW1 -HIZ HH IH0 Z -HIZZIE HH IH1 Z IY0 -HIZZIES HH IH1 Z IY0 Z -HIZZY HH IH1 Z IY0 -HJALMAR HH Y AA1 L M ER0 -HJALTI HH Y AA1 L T IY0 -HJORTH HH AH0 Y AO1 R TH -HLABULANA HH L AE1 B Y UW0 L AE1 N AH0 -HLALA HH L AA1 L AH0 -HLANGANI HH L AA1 NG G AH0 N IY0 -HLANGWANE HH L AE1 NG G W EY0 N -HLAWA HH L AA1 W AH0 -HLO HH L OW0 -HLOMA HH L OW1 M AH0 -HLORG HH L AO0 R G -HLUBI HH L AH0 B IY0 -HLUBIS HH L UW1 B IH0 S -HM HH AH0 M -HMELNITSKI HH AH0 M EH1 L N IH0 T S K IY0 -HMM HH M -HMMM HH M M -HN N -HNOSSA HH N OW1 S AH0 -HO HH OW1 -HO'D HH OW1 D -HO'S HH OW1 Z -HOA HH OW1 AH0 -HOAD HH OW1 D -HOADLEY HH OW1 D L IY0 -HOADLY HH OW1 D L IY0 -HOAEM HH OW1 AH0 M -HOAG HH OW1 G -HOAK HH OW1 K -HOAM HH OW1 M -HOAN HH OW1 N -HOANG HH OW1 NG -HOANGTI HH OW1 NG K T IY2 -HOAPE HH OW1 P -HOAR HH AO1 R -HOAR'S HH AO1 R Z -HOARD HH AO1 R D -HOARDED HH AO1 R D IH0 D -HOARDER HH AO1 R D ER0 -HOARDERS HH AO1 R D ER0 Z 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AA1 B AH0 L D EH1 HH OY0 -HOBBLEDEHOYS HH AA1 B AH0 L D EH1 HH OY0 Z -HOBBLES HH AA1 B AH0 L Z -HOBBLIN HH AA1 B L IH0 N -HOBBLING HH AA1 B AH0 L IH0 NG -HOBBLING HH AA1 B L IH0 NG -HOBBS HH AA1 B Z -HOBBS'S HH AA1 B Z IH0 Z -HOBBY HH AA1 B IY0 -HOBBY'S HH AA1 B IY0 Z -HOBBYHORSE HH AA1 B IY0 HH AO2 R S -HOBDAY HH AA1 B D EY2 -HOBDEN HH AA1 B D AH0 N -HOBDEN'S HH AA1 B D AH0 N Z -HOBGOBLIN HH AA1 B G AA1 B L IH0 N -HOBGOBLINS HH AA1 B G AA1 B L IH0 N Z -HOBHOUSE HH AA1 B HH AW2 S -HOBHOUSE'S HH AA1 B HH AW2 S IH0 Z -HOBIN HH OW1 B IH0 N -HOBKIRK HH AA1 B K ER0 K -HOBLER HH OW1 B L ER0 -HOBNAIL HH AA1 B N EY2 L -HOBNAILED HH AA1 B N EY2 L D -HOBNAILS HH AA1 B N EY2 L Z -HOBNELL HH AA1 B N AH0 L -HOBNOB HH AA1 B N AA2 B -HOBNOBBED HH AA1 B N AA2 B D -HOBNOBBING HH AA1 B N AA2 B IH0 NG -HOBO HH OW1 B OW0 -HOBO'S HH OW1 B OW0 Z -HOBOES HH OW1 B OW0 Z -HOBOING HH OW1 B OW0 IH0 NG -HOBOKEN HH OW1 B OW0 K AH0 N -HOBOLAND HH OW1 B OW0 L AH0 N D -HOBOMOK HH OW1 B OW0 M AA1 K -HOBOS HH OW1 B OW0 Z -HOBOTOV HH OW1 B OW0 T AA2 F -HOBS HH AA1 B Z -HOBSON HH AA1 B S AH0 N -HOBSON'S HH AA1 B S AH0 N Z -HOBY HH OW1 B IY0 -HOC HH AA1 K -HOCH HH AA1 K -HOCHBERG HH AA1 K B ER0 G -HOCHBURG HH AA1 K B ER0 G -HOCHE HH AA1 CH -HOCHELAGA HH AA1 K AH0 L AA1 G AH0 -HOCHENHEIMER HH AO1 K AH0 N HH AY2 M ER0 -HOCHFELS HH AA1 K F AH0 L Z -HOCHHEIM HH AA1 K HH AY0 M -HOCHHEIMER HH AA1 K HH AY0 M ER0 -HOCHKIRCH HH AA1 K K ER0 CH -HOCHMULLER HH AA1 K M AH1 L ER0 -HOCHON HH AA1 CH AH0 N -HOCHON'S HH AA1 K AH0 N Z -HOCHS HH AA1 K S -HOCHST HH AA1 K S T -HOCHSTADT HH AA1 K S T AE0 T -HOCHSTEDT HH AA1 K S T IH0 T -HOCHSTEIN HH AA1 K S T AY0 N -HOCHSTEIN HH AA1 K S T IY0 N -HOCHSTETTEN HH AA1 K S T EH0 T AH0 N -HOCHSTETTER HH AA1 K S T IH0 T ER0 -HOCHWALD HH AA1 K W AO0 L D -HOCHWALD'S HH AA1 K W AO2 L D Z -HOCHWOHLGEBOREN HH AA1 K W OW2 L G IH0 B AO1 R AH0 N -HOCK HH AA1 K -HOCKED HH AA1 K T -HOCKER HH AA1 K ER0 -HOCKEY HH AA1 K IY0 -HOCKHEIMER HH AA1 K HH AY2 M ER0 -HOCKHOCKING HH AA1 K HH AA1 K IH0 NG -HOCKIN HH AA1 K IH0 N -HOCKING HH AA1 K IH0 NG -HOCKING'S HH AA1 K IH0 NG Z -HOCKLEY HH AA1 K L IY0 -HOCKLING HH AA1 K L IH0 NG -HOCKS HH AA1 K S -HOCOMO HH AA1 K OW1 M OW0 -HOCUS HH OW1 K AH0 S -HOCUSSED HH OW1 K IH0 S T -HOD HH AA1 D -HODCARRIERS HH AA1 D K AE2 R Y ER0 Z -HODD HH AA1 D -HODDAM HH AA1 D AA1 M -HODDAN HH AA1 D AH0 N -HODDAN'S HH AA1 D AH0 N Z -HODDEN HH AA1 D AH0 N -HODDEN'S HH AA1 D AH0 N Z -HODDER HH AA1 D ER0 -HODDER'S HH AA1 D ER0 Z -HODDIDODDI HH AA1 D IH0 D AA1 D IY0 -HODDON HH AA1 D AH0 N -HODDY HH AA1 D IY0 -HODE HH OW1 D -HODEN HH OW1 D AH0 N -HODEN'S HH OW1 D AH0 N Z -HODENOSAUNEE HH AA1 D EH1 N AH0 S AO1 N IY0 -HODER HH OW1 D ER0 -HODGE HH AA1 JH -HODGE'S HH AA1 JH IH0 Z -HODGEN HH AA1 JH AH0 N -HODGEPODGE HH AA1 JH P AA2 JH -HODGES HH AA1 JH IH0 Z -HODGES'S HH AA1 JH IH0 Z IH0 Z -HODGESON HH AA1 JH S AH0 N -HODGETT HH AA1 JH IH0 T -HODGKIN HH AA1 JH K IH0 N -HODGKINS HH AA1 JH K IH0 N Z -HODGKINSON HH AA1 JH K IH0 N S AH0 N -HODGMAN HH AA1 JH M AH0 N 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HH AA1 F ER0 -HOFFERMAN HH AO1 F ER0 M AH0 N -HOFFICER HH AO1 F IH0 S ER0 -HOFFLAND HH AO1 F L AH0 N D -HOFFMAN HH AO1 F M AH0 N -HOFFMAN'S HH AO1 F M AH0 N Z -HOFFMANN HH AO1 F M AH0 N -HOFFMANN'S HH AO1 F M AH0 N Z -HOFFMEIER HH AO1 F M AY0 ER0 -HOFFMEISTER HH AO1 F M AY2 S T ER0 -HOFFMEYER HH AO1 F M AY0 ER0 -HOFFNER HH AO1 F N ER0 -HOFFNUNG HH AO1 F N AH0 NG -HOFFORD HH AA1 F ER0 D -HOFGARTEN HH AA1 F G AA0 R T AH0 N -HOFLAND HH AA1 F L AH0 N D -HOFMAN HH AA1 F M AH0 N -HOFMANN HH AA1 F M AH0 N -HOFMANN'S HH AA1 F M AH0 N Z -HOFMANNSTHAL HH AA1 F M AH0 N Z TH AH0 L -HOFMANNSTHAL'S HH AA1 F M AH0 N Z TH AH0 L Z -HOFMEISTER HH AA1 F M AY0 S T ER0 -HOFMYER HH AA1 F M IY0 ER0 -HOFRA HH AA1 F R AH0 -HOFRAT HH AA1 F R AE2 T -HOFRATH HH AA1 F R AH0 TH -HOFUS HH AA1 F AH0 S -HOFWYL HH AA1 F W IH0 L -HOG HH AA1 G -HOG'EM HH AA1 G EH1 M -HOG'S HH AA1 G Z -HOGAN HH OW1 G AA2 N -HOGAN HH OW1 G AH0 N -HOGAN'S HH OW1 G AA2 N Z -HOGANS HH OW1 G AA2 N Z -HOGANS HH OW1 G AH0 N Z -HOGARTH HH OW1 G AA2 R TH -HOGARTH'S HH OW1 G AA2 R TH S -HOGARTHIAN HH OW1 G AA2 R TH IY0 AH0 N -HOGARTHS HH OW1 G AA2 R TH S -HOGBACK HH AA1 G B AE2 K -HOGBACKS HH AA1 G B AE2 K S -HOGBEN HH AA1 G B AH0 N -HOGBIN HH AA1 G B IH0 N -HOGE HH OW1 JH -HOGEN HH AA1 G AH0 N -HOGENDORP HH AA1 G IH0 N D AO0 R P -HOGES HH AA1 JH IH0 Z -HOGG HH AA1 G -HOGG'S HH AA1 G Z -HOGGAN HH AA1 G AH0 N -HOGGARTY HH AA1 G ER0 T IY0 -HOGGE HH AA1 G -HOGGED HH AA1 G D -HOGGENBACK HH AA1 G IH0 N B AE2 K -HOGGETT HH AA1 G IH0 T -HOGGETT'S HH AA1 G IH0 T S -HOGGIE HH AA1 G IY0 -HOGGIN HH AA1 G IH0 N -HOGGINARMO HH AA1 G IH0 N AA1 R M OW0 -HOGGING HH AO1 G IH0 NG -HOGGINS HH AA1 G IH0 N Z -HOGGISH HH AA1 G IH0 SH -HOGGISHNESS HH AA1 G IH0 SH N AH0 S -HOGGS HH AA1 G Z -HOGHTON HH AA1 T AH0 N -HOGJAW HH AA1 G JH AO1 -HOGMANAY HH AA1 G M AE2 N EY0 -HOGNI HH AA1 G N IY0 -HOGS HH AA1 G Z -HOGSDEN HH AA1 G Z D AH0 N -HOGSFLESH HH AA1 G S F L EH2 SH -HOGSHEAD HH AA1 G S HH EH2 D -HOGSHEADS HH AA1 G S HH EH2 D Z -HOGSON HH AA1 G S AH0 N -HOGSTEAD HH AA1 G S T EH2 D -HOGSTRATEN HH AA1 G S T R EY1 T AH0 N -HOGTIE HH AA1 G T IY0 -HOGUE HH OW1 G -HOGVARDT HH OW1 G AH0 V EH0 R T -HOGWASH HH AA1 G W AA2 SH -HOH HH OW1 -HOHASTILLPILP HH OW1 HH AA0 S T IH1 L P IH0 L P -HOHE HH OW1 -HOHEN HH OW1 AH0 N -HOHENASPERG HH OW1 AH0 N AE1 S P ER0 G -HOHENAU HH OW1 AH0 N OW1 -HOHENBERG HH OW1 AH0 N B ER0 G -HOHENBURG HH OW1 AH0 N B ER0 G -HOHENBURGERHOF HH OW1 AH0 N B ER0 G ER0 HH AO0 F -HOHENDAHL HH OW1 AH0 N D AA2 L -HOHENECK HH OW1 AH0 N EH2 K -HOHENFELS HH AA0 HH IH0 N F AH0 L Z -HOHENHEIM HH OW1 AH0 N HH AY2 M -HOHENHOERN HH OW1 AH0 N HH AO1 R N -HOHENKRAEHEN HH OW1 AH0 N K R EY1 AH0 N -HOHENLINDEN HH OW1 AH0 N L IH2 N D AH0 N -HOHENLOHE HH OW1 AH0 N L OW1 -HOHENLOHE'S HH OW1 AH0 N L OW1 AH0 Z -HOHENSTAUFEN HH OW1 AH0 N S T AO2 F AH0 N -HOHENSTAUFENS HH OW1 AH0 N S T AO2 F AH0 N Z -HOHENSTAUFFEN HH OW1 AH0 N S T AO1 F AH0 N -HOHENSTEIN HH OW1 AH0 N S T AY0 N -HOHENSTEIN HH OW1 AH0 N S T IY0 N -HOHENSTIEL HH OW1 AH0 N S T IY2 L 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AH0 M -HOKUS HH OW1 K AH0 S -HOKUSAI HH AA1 K Y UW0 S AY2 -HOKY HH OW1 K IY0 -HOL HH OW1 L -HOLA HH OW1 L AH0 -HOLABIRD HH OW1 L AH0 B ER2 D -HOLAR HH OW1 L ER0 -HOLATA HH OW0 L AA1 T AH0 -HOLATI HH OW0 L AA1 T IY0 -HOLBACH HH OW1 L B AA2 K -HOLBEACH HH OW1 L B IY2 CH -HOLBEAUT HH OW1 L B OW1 T -HOLBEIN HH OW1 L B AY0 N -HOLBEIN'S HH OW1 L B AY0 N Z -HOLBEINS HH OW1 L B AY0 N Z -HOLBERG HH OW1 L B ER0 G -HOLBERG'S HH OW1 L B ER0 G Z -HOLBERTON HH OW1 L B ER0 T AH0 N -HOLBORN HH OW1 L B ER0 N -HOLBORNE HH OW1 L B AO0 R N -HOLBOROUGH HH OW1 L B ER0 OW0 -HOLBROOK HH OW1 L B R UH2 K -HOLBROOK'S HH OW1 L B R UH0 K S -HOLBROOKS HH OW1 L B R UH0 K S -HOLBURY HH OW1 L B EH2 R IY0 -HOLBY HH OW1 L B IY0 -HOLCHESTER HH OW1 L CH EH2 S T ER0 -HOLCHESTER'S HH OW1 L CH EH2 S T ER0 Z -HOLCOMB HH OW1 L K AH0 M -HOLCOMB'S HH OW1 L K AH0 M Z -HOLCOMBE HH OW1 L K AH0 M -HOLCOMBE'S HH OW1 L K AH0 M Z -HOLCROFT HH OW1 L K R AO2 F T -HOLCROFT'S HH OW1 L K R AO2 F T S -HOLCUS HH OW1 L K AH0 S -HOLD HH OW1 L D 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HH OW1 L M Z IH0 Z -HOLMES'S HH OW1 M Z IH0 Z -HOLMESCROFT HH OW1 M Z K R AO2 F T -HOLMESES HH OW1 M Z IH0 Z -HOLMESKIRK HH OW1 M S K ER0 K -HOLMESY HH OW1 M Z IY0 -HOLMGANG HH OW1 L M G AE2 NG -HOLMHURST HH OW1 L M HH ER0 S T -HOLMLEIGH HH OW1 L M L AH0 -HOLMLOCK HH OW1 L M L AA2 K -HOLMNESS HH OW1 L M N AH0 S -HOLMS HH OW1 L M Z -HOLMSTOKE HH OW1 L M S T OW1 K -HOLMWOOD HH OW1 L M W UH2 D -HOLO HH OW1 L OW0 -HOLOCAUST HH AA1 L AH0 K AO2 S T -HOLOCAUSTS HH AA1 L AH0 K AO2 S T S -HOLOFERNES HH AA1 L AH0 F ER1 N Z -HOLOGRAPH HH AA1 L AH0 G R AE2 F -HOLOHAN HH AA1 L AH0 HH AE0 N -HOLOKU HH AA1 L AH0 K UW0 -HOLOTHURIA HH AA1 L AH0 TH UH1 R IY0 AH0 -HOLOTHURIAN HH AA1 L AH0 TH UH1 R IY0 AH0 N -HOLP HH OW1 L P -HOLPED HH OW1 L P T -HOLPEN HH OW1 L P EH2 N -HOLROYD HH OW1 L R OY2 D -HOLROYD'S HH OW1 L R OY2 D Z -HOLROYDE HH OW1 L R OY2 D -HOLS HH OW1 L Z -HOLSCHE HH OW1 L SH -HOLST HH OW1 L S T -HOLSTEIN HH OW1 L S T IY2 N -HOLSTEINER HH OW1 L S T AY0 N ER0 -HOLSTEINS HH OW1 L S T IY0 N Z 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AH0 S -HUNYAD HH AH1 N Y AE0 D -HUNYADES HH AH1 N Y AA1 D Z -HUNYADI HH AH1 N Y AA1 D IY0 -HUNYADY HH AH1 N Y AA1 D IY0 -HUNYADY'S HH AH1 N Y AA1 D IY0 Z -HUNZA HH AH1 N Z AH0 -HUON HH Y UW1 AH0 N -HUON'S HH Y UW1 AH0 N Z -HUP HH AH1 P -HUPEH HH AH1 P EH2 -HUPFELD HH AH1 P F EH2 L D -HUPFELDT HH AH1 P F IH0 L T -HUPFELDT'S HH AH1 P F IH0 L T S -HUPP HH AH1 P -HUPP'S HH AH1 P S -HUPPER HH AH1 P ER0 -HUQ HH UW1 K -HUR HH ER1 -HUR'S HH ER1 Z -HURD HH ER1 D -HURD'S HH ER1 D Z -HURDA HH ER1 D AH0 -HURDIES HH ER1 D IY0 Z -HURDLE HH ER1 D AH0 L -HURDLED HH ER1 D AH0 L D -HURDLES HH ER1 D AH0 L Z -HURDLESTONE HH ER1 D AH0 L S T OW2 N -HURDLESTONE'S HH ER1 D AH0 L S T OW2 N Z -HURDLING HH ER1 D AH0 L IH0 NG -HURDLING HH ER1 D L IH0 NG -HURDLY HH ER1 D L IY0 -HURDLY'S HH ER1 D L IY0 Z -HURDON HH ER1 D AH0 N -HURDS HH ER1 D Z -HURDWAR HH ER1 D W AO0 R -HURDY HH ER1 D IY0 -HURE HH Y UH1 R -HURFORD HH ER1 F ER0 D -HURIERE HH ER1 IY0 EH0 R -HURKARU HH ER1 K ER0 UW1 -HURL HH ER1 L -HURL'D HH ER1 L D 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UH1 R UW0 -HUS HH AH1 S -HUS'LING HH UW0 AH0 L IH0 NG -HUSAIN HH AH1 S AY0 N -HUSAK HH UW1 S AH0 K -HUSAYN HH AH1 S EY1 N -HUSB HH AH1 S B -HUSBAN HH AH1 Z B AH0 N -HUSBAN'S HH AH1 Z B AH0 N Z -HUSBAND HH AH1 Z B AH0 N D -HUSBAND'S HH AH1 Z B AH0 N D Z -HUSBANDED HH AH1 Z B AH0 N D IH0 D -HUSBANDING HH AH1 Z B AH0 N D IH0 NG -HUSBANDLESS HH AH1 Z B AH0 N D L AH0 S -HUSBANDLY HH AH1 Z B AH0 N D L IY0 -HUSBANDMAN HH AH1 Z B AH0 N D M AH0 N -HUSBANDMAN'S HH AH1 Z B AH0 N D M AH0 N Z -HUSBANDMEN HH AH1 Z B AH0 N D M AH0 N -HUSBANDRY HH AH1 Z B AH0 N D R IY0 -HUSBANDS HH AH1 Z B AH0 N D Z -HUSEBY HH AH1 S IH0 B IY0 -HUSEIN HH UW1 S EY2 N -HUSEYN HH UW1 S EY1 N -HUSH HH AH1 SH -HUSH'D HH AH1 SH D -HUSHABY HH AH1 SH AH0 B IY0 -HUSHAI HH AH1 SH AY1 -HUSHBY HH AH1 SH B IY0 -HUSHED HH AH1 SH T -HUSHES HH AH1 SH IH0 Z -HUSHIEL HH AH1 SH IY2 L -HUSHING HH AH1 SH IH0 NG -HUSHMUT HH AH1 SH M AH0 T -HUSHNOLY HH AH1 SH N AA2 L IY0 -HUSHT HH AH1 SH T -HUSK HH AH1 S K -HUSKED HH AH1 S K T -HUSKER HH AH1 S K ER0 -HUSKERS HH AH1 S K ER0 Z -HUSKEY HH AH1 S K IY0 -HUSKIE HH AH1 S K IY0 -HUSKIER HH AH1 S K IY0 ER0 -HUSKIES HH AH1 S K IY0 Z -HUSKIEST HH AH1 S K IY0 AH0 S T -HUSKILY HH AH1 S K AH0 L IY0 -HUSKIN HH AH1 S K IH0 N -HUSKINESS HH AH1 S K IY0 N AH0 S -HUSKING HH AH1 S K IH0 NG -HUSKINGS HH AH1 S K IH0 NG Z -HUSKINS HH AH1 S K IH0 N Z -HUSKISSON HH AH1 S K IH0 S AH0 N -HUSKS HH AH1 S K S -HUSKY HH AH1 S K IY0 -HUSKY'S HH AH1 S K IY0 Z -HUSS HH AH1 S -HUSSAIN HH UW0 S EY1 N -HUSSAN HH UW0 S AH0 N -HUSSAR HH AH1 S ER0 -HUSSAR'S HH AH1 S ER0 Z -HUSSARS HH AH1 S ER0 Z -HUSSBANDT HH UW0 S B AE1 N T -HUSSEIN HH UW0 S EY1 N -HUSSEIN'S HH UW0 S EY1 N Z -HUSSEY HH AH1 S IY0 -HUSSIES HH AH1 S IY0 Z -HUSSIN HH AH1 S IH0 N -HUSSITE HH AH1 S AY2 T -HUSSITES HH AH1 S AY0 T S -HUSSMAN HH AH1 S M AH0 N -HUSSON HH AH1 S AH0 N -HUSSON'S HH AH1 S AH0 N Z -HUSSONNET HH AH1 S AH0 N EH2 T -HUSSY HH AH1 S IY0 -HUSSY'S HH AH1 S IY0 Z -HUST HH AH1 S T -HUSTED HH AH1 S T IH0 D -HUSTINGS HH AH1 S T IH0 NG Z 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AH1 T ER0 -HUTTER'S HH AH1 T ER0 Z -HUTTING HH AH1 T IH0 NG -HUTTOFT HH AH1 T AH0 F T -HUTTON HH AH1 T AH0 N -HUTTON'S HH AH1 T AH0 N Z -HUTTONIAN HH AH1 T OW1 N IY0 AH0 N -HUTUKTU HH UW1 T UW2 K T UW1 -HUVANE HH UW0 V EY1 N -HUXFORD HH AH1 K S F ER0 D -HUXHAM HH AH1 K S AH0 M -HUXHAM'S HH AH1 K S AH0 M Z -HUXLEY HH AH1 K S L IY0 -HUXLEY'S HH AH1 K S L IY0 Z -HUXLEYS HH AH1 K S L IY0 Z -HUXTABLE HH AH1 K S T AH0 B AH0 L -HUXTABLE'S HH AH1 K S T AH0 B AH0 L Z -HUXTER HH AH1 K S T ER0 -HUXTER'S HH AH1 K S T ER0 Z -HUXWELL HH AH1 K S W EH2 L -HUY HH AY1 -HUYA HH AY1 AH0 -HUYGENS HH AY1 G AH0 N Z -HUYGHENS HH AY1 G AH0 N Z -HUYLER HH AY1 L ER0 -HUYLER'S HH AY1 L ER0 Z -HUYLIGER HH AY1 L IH0 G ER0 -HUYN HH AY1 N -HUYS HH AY1 Z -HUYSEN HH AY1 S AH0 N -HUYSMAN HH AY1 S M AH0 N -HUYSMAN'S HH AY1 S M AH0 N Z -HUYSMANS HH AY1 Z M AH0 N Z -HUYSMANS'S HH AY1 Z M AH0 N Z IH0 Z -HUYSUM HH AY1 Z AH0 M -HUYTENS HH AY1 T AH0 N Z -HUZ HH AH1 Z -HUZIM HH AH1 Z IY1 M -HUZOOR HH Y UW1 Z UH1 R -HUZOOR'S HH 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AH0 -HYGELAC HH AY2 JH EY1 L AH0 K -HYGIENE HH AY1 JH IY2 N -HYGIENIC HH AY2 G IY1 N IH0 K -HYGIENICALLY HH AY2 G IY1 N IH0 K L IY0 -HYGIENIST HH AY2 G EH1 N IH0 S T -HYGIENIST HH AY2 G IY1 N IH0 S T -HYGIENISTS HH AY2 G EH1 N IH0 S -HYGIENISTS HH AY2 G EH1 N IH0 S T S -HYGIENISTS HH AY2 G IY1 N IH0 S -HYGIENISTS HH AY2 G IY1 N IH0 S T S -HYGROMETER HH AY0 G R AA1 M AH0 T ER0 -HYGROMETERS HH AY2 G R AA1 M AH0 T ER0 Z -HYGROMETRIC HH AY2 G R OW0 M EH1 T R IH0 K -HYKE HH AY1 K -HYKSOS HH AY1 K S OW2 Z -HYLA HH AY1 L AH0 -HYLACTOR HH IH0 L AE1 K T ER0 -HYLAND HH AY1 L AH0 N D -HYLAND'S HH AY1 L AH0 N D Z -HYLAS HH IH1 L AH0 Z -HYLAX HH AY1 L AE2 K S -HYLDA HH AY1 L D AH0 -HYLDEBRAND HH AY1 L D IH0 B R AE2 N D -HYLE HH AY1 L -HYLEG HH AY1 L AH0 G -HYLLUS HH IH1 L AH0 S -HYLTON HH IH1 L T AH0 N -HYM HH IH1 M -HYMAN HH AY1 M AH0 N -HYMBERCOURT HH IH1 M B ER0 K AO2 R T -HYMEN HH AY1 M AH0 N -HYMEN'S HH AY1 M AH0 N Z -HYMENAEUS HH AY1 M AH0 N IY1 AH0 S -HYMENEAL HH AY1 M AH0 N IY1 L -HYMENOP HH AY1 M AH0 N AA2 P -HYMENOPS HH AY1 M AH0 N AA2 P S -HYMENOPTERA HH AY1 M AH0 N AA2 P T EH1 R AH0 -HYMENOPTERON HH AY1 M AH0 N AA2 P T ER0 AH0 N -HYMETTUS HH AY1 M EH1 T AH0 S -HYMIE HH AY1 M IY0 -HYMIR HH IH1 M IH0 R -HYMIR'S HH AY1 M IH1 R Z -HYMN HH IH1 M -HYMNAL HH IH1 M N AH0 L -HYMNALS HH IH1 M N AH0 L Z -HYMNBOOK HH IH1 M N B UH2 K -HYMNE HH IH1 M -HYMNED HH IH1 M D -HYMNING HH IH1 M IH0 NG -HYMNODY HH IH1 M N AH0 D IY0 -HYMNOLOGY HH IH0 M N AA1 L AH0 JH IY0 -HYMNS HH IH1 M Z -HYMNWRITER HH IH1 M N R AY2 T ER0 -HYMNWRITERS HH IH1 M N R AY2 T ER0 Z -HYND HH IH1 N D -HYNDE HH IH1 N D -HYNDMAN HH IH1 N D M AH0 N -HYNE HH AY1 N -HYNES HH AY1 N Z -HYOSCYAMUS HH Y AO1 S AY1 AH0 M IH0 S -HYP HH AY1 P -HYPAETHRAL HH AY2 P IY1 TH R AH0 L -HYPATIA HH AY2 P AA1 SH AH0 -HYPATIA'S HH AY1 P EY2 SH AH0 Z -HYPE HH AY1 P -HYPER HH AY1 P ER0 -HYPERAEMIA HH AY2 P ER0 AE0 IY1 M IY0 AH0 -HYPERAESTHESIA HH AY2 P ER0 AE0 S TH IY1 ZH AH0 -HYPERAESTHETIC HH AY2 P ER0 AE0 S TH EH1 T IH0 K -HYPERBOLA HH AY0 P ER1 B AH0 L AH0 -HYPERBOLE HH AY0 P ER1 B AH0 L IY2 -HYPERBOLES HH AY0 P ER1 B AH0 L IY2 Z -HYPERBOLIC HH AY2 P ER0 B AA1 L IH0 K -HYPERBOLICAL HH AY2 P ER0 B AA1 L IH0 K AH0 L -HYPERBOLICALLY HH AY2 P ER0 B AA1 L IH0 K L IY0 -HYPERBOREAN HH AY2 P ER0 B AO1 R IY0 AH0 N -HYPERBOREANS HH AY2 P ER0 B AO1 R IY0 AH0 N Z -HYPERCRITICAL HH AY2 P ER0 K R IH1 T IH0 K AH0 L -HYPERCRITICISM HH AY2 P ER0 K R IH1 T IH0 S IH2 Z AH0 M -HYPERDRIVE HH AY2 P ER0 D R AY2 V -HYPERFILM HH AY2 P ER0 F IH2 L M -HYPERICUM HH AY0 P ER1 IH0 K AH0 M -HYPERIDES HH AY2 P ER0 AY2 D Z -HYPERION HH AY0 P IH1 R IY0 AH0 N -HYPERION'S HH AY0 P IH1 R IY0 AH0 N Z -HYPERKRITE HH AY2 P ER0 K R AY2 T -HYPERMNESTRA HH AY1 P ER0 M N AH0 S T R AH0 -HYPERPHYSICAL HH AY2 P ER0 F IH1 Z IH0 K AH0 L -HYPERSENSITIVE HH AY2 P ER0 S EH1 N S IH0 T IH0 V -HYPERSENSITIVENESS HH AY2 P ER0 S EH1 N S AH0 T IH0 V N AH0 S -HYPERSHIP HH AY2 P ER0 SH IH2 P -HYPERSPACE HH AY2 P ER0 S P EY2 S -HYPERTHYROID HH AY2 P ER0 TH AY1 R OY0 D 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K AA1 N D R IY0 AH0 -HYPOCHONDRIAC HH AY2 P AH0 K AA1 N D R IY0 AE0 K -HYPOCHONDRIACAL HH AY2 P AH0 K AA1 N D R IY0 AE0 K AH0 L -HYPOCHONDRIACISM HH AY2 P AH0 K AA1 N D R IY0 AH0 S IH2 Z AH0 M -HYPOCHONDRIACS HH AY2 P AH0 K AA1 N D R IY0 AE0 K S -HYPOCHONDRIASIS HH AY2 P AH0 K AA1 N D R IY0 AH0 S IH0 S -HYPOCRAS HH AY2 P AH0 K R AE1 S -HYPOCRISIE HH IH0 P AA1 K R AH0 S IY0 -HYPOCRISIES HH IH0 P AA1 K R AH0 S IY0 Z -HYPOCRISY HH IH0 P AA1 K R AH0 S IY0 -HYPOCRITE HH IH1 P AH0 K R IH2 T -HYPOCRITE'S HH AY2 P AH0 K R IH2 T S -HYPOCRITES HH IH1 P AH0 K R IH2 T S -HYPOCRITIC HH AY2 P AH0 K R IH1 T IH0 K -HYPOCRITICAL HH IH2 P AH0 K R IH1 T IH0 K AH0 L -HYPOCRITICALLY HH AY2 P AH0 K R IH1 T IH0 K L IY0 -HYPODERMIC HH AY2 P AH0 D ER1 M IH0 K -HYPODERMICALLY HH AY2 P AH0 D ER1 M IH0 K L IY0 -HYPODERMICS HH AY2 P AH0 D ER1 M IH0 K S -HYPOGASTRIC HH AY2 P OW0 G AE2 S T R IH0 K -HYPOGEUM HH AY2 P OW0 G IY1 AH0 M -HYPOLITE HH AY1 P AH0 L AY0 T -HYPOLITO HH AY2 P OW0 L IY1 T OW0 -HYPOSTASES HH AY2 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-KALLIOPE'S K AE1 L AY1 AH0 P IY2 Z -KALLOLO K AE1 L OW1 L OW0 -KALLOLO'S K AE1 L OW0 L OW0 Z -KALLUNDBORG K AE1 L AH1 N D B AO0 R G -KALLY K AE1 L IY0 -KALLYOPE K AE1 L AY0 OW1 P -KALM K AE1 L M -KALM'S K AE1 L M Z -KALMA K AE1 L M AH0 -KALMAK K AE1 L M AE0 K -KALMAKOFF K AE1 L M AH0 K AO0 F -KALMAN K AE1 L M AH0 N -KALMAN'S K AE1 L M AH0 N Z -KALMAR K AE1 L M ER0 -KALMIA K AE1 L M IY0 AH0 -KALMIAS K AE1 L M IY0 AH0 Z -KALMON K AE1 L M AH0 N -KALMUCK K AE1 L M AH1 K -KALMUCKS K AE1 L M AH2 K S -KALMUK K AE1 L M IH0 K -KALMUKS K AE1 L M IH0 K S -KALO K EY1 L OW0 -KALOMO K AH0 L OW1 M OW0 -KALON K AE1 L AA2 N -KALONAY K AE1 L AA2 N EY0 -KALONDAI K AE1 L AA2 N D AY1 -KALONYMOS K AE1 L AH0 N IH2 M OW0 Z -KALOOLAH K AH0 L UW1 L AH0 -KALOOMAH K AH0 L UW1 M AH0 -KALOON K AH0 L UW2 N -KALORA K AH0 L AO1 R AH0 -KALORET K AH0 L AO1 R EH0 T -KALPA K AE1 L P AH0 -KALPAG K AE1 L P AH0 G -KALPAS K AE1 L P AH0 Z -KALPI K AE1 L P IY0 -KALSKI K AA1 L S K IY0 -KALSOMINE K AE1 L S AH0 M IY1 N IY0 -KALSOMINED K AE1 L S AH0 M IY1 N D -KALSOW K AE1 L S OW0 -KALTOFF K AA1 L T AO2 F -KALUBI K AH0 L UW1 B IY0 -KALUBIS K AH0 L UW1 B IH0 S -KALUDARI K AE2 L Y UW0 D AA1 R IY0 -KALUGA K AH0 L UW1 G AH0 -KALULU K AH0 L UW1 L UW0 -KALULU'S K AH0 L UW1 L UW0 Z -KALUM K AE1 L AH0 M -KALUNA K AH0 L UW1 N AH0 -KALUSHES K AH0 L AH1 SH IH0 Z -KALUTUNAH K AH0 L UW1 T UW2 N AH0 -KALVERSTRAAT K AE1 L V ER0 S T R AA1 T -KALVIK K AE1 L V IH0 K -KALYB K AH0 L IH2 B -KALYBA K AH0 L IH1 B AH0 -KALYDOR K AE1 L IH0 D AO2 R -KALZOW K AE1 L Z OW0 -KAM K AE1 M -KAMA K AA1 M AH0 -KAMA'S K AA1 M AH0 Z -KAMAIAKAN K AE1 M EY0 EY1 K AH0 N -KAMAK K AA2 M AH0 K -KAMAKURA K AA2 M AH0 K UH1 R AH0 -KAMAL K EY1 M AH0 L -KAMALA K AH0 M AA1 L AH0 -KAMALIA K AH0 M AA1 L Y AH0 -KAMANAKO K AA2 M AA0 N AA1 K OW0 -KAMAR K AH0 M AA1 R -KAMARAD K AE1 M ER0 AE0 D -KAMAS K AA1 M AH0 Z -KAMASURA K AA2 M AA0 S UH1 R AH0 -KAMASWAMI K AA2 M AH0 S W AA1 M IY0 -KAMATLAH K AH0 M AE1 T L AH0 -KAMATTAWA K AA2 M AA0 T AA1 W AH0 -KAMBA K AE1 M B AH0 -KAMBIRA K AE1 M AH0 B IH1 R AH0 -KAMBOH K AA1 M B OW0 -KAMBULA K AE1 M B UW1 L AH0 -KAMCHATKA K AA2 M CH AA1 T K AH0 -KAMCHATKA K AE2 M CH AE1 T K AH0 -KAMCO K AE1 M K OW0 -KAME K EY1 M -KAMEEL K AE1 M IY0 L -KAMEHAMEHA K AH0 M EY1 AH0 M EY1 AH0 -KAMEL K AA1 M AH0 L -KAMENOPHIS K EY1 M EH0 N AH0 F IH2 S -KAMENYETS K EY1 M EH0 N Y AH0 T S -KAMENZ K EY1 M EH0 N Z -KAMERAD K AE1 M ER0 AE0 D -KAMERUN K AE1 M R AH1 N -KAMES K EY1 M Z -KAMI K AA1 M IY0 -KAMI'S K AE1 M IY0 Z -KAMIDANA K AE1 M IH0 D AE1 N AH0 -KAMIENSZKA K EY1 M IY0 AH0 N SH K AH0 -KAMIL K AH0 M IH1 L -KAMILAROI K AA2 M AH0 L AE1 R OY1 -KAMIMURA K AA1 M IY0 M UH1 R AH0 -KAMINER K AE1 M IH0 N ER0 -KAMINISTIKWIA K AE1 M IH0 N IH1 S T IH0 K V IY0 AH0 -KAMINSKI K AH0 M IH1 N S K IY0 -KAMIS K AE1 M IH0 S -KAMKE K AE1 M K IY0 -KAMLEITER'S K AE1 M L AY2 T ER0 Z -KAMLOOPS K AE1 M L UW1 P S -KAMM K AE1 M -KAMMER K AE1 M ER0 -KAMMERBUND K AE1 M ER0 B AH0 N D -KAMMERJUNKER K AE1 M ER0 JH AH1 NG K ER0 -KAMMERRATH K AE1 M ER0 R AH0 TH -KAMO K AA1 M OW0 -KAMON K EY1 M AH0 N -KAMP K AE1 M P -KAMP'S K AE1 M P S -KAMPALA K AH0 M P AA1 L AH0 -KAMPEN K AE1 M P AH0 N -KAMPF K AE1 M P F -KAMPONG K AE1 M P AO1 NG -KAMPONGS K AE1 M P AO1 NG Z -KAMRAN K AE1 M R AH0 N -KAMRASI K AE1 M R AH0 S IY0 -KAMRASI'S K AE1 M R AH0 S IY0 Z -KAMSCHATKA K AE1 M S K AA1 T K AH0 -KAMSKATKA K AE1 M S K AA1 T K AH0 -KAMTCHADALES K AA2 M CH AH0 D AA1 L EH0 S -KAMTCHATKA K AA2 M CH AA1 T K AH0 -KAMTSCHATKA K AA2 M CH AE1 T K AH0 -KAMUSO K AA2 M UW1 S OW0 -KAMWORTH K AE1 M W ER0 TH -KAMYAKA K AA2 M Y AA1 K AH0 -KAN K AE1 N -KAN'T K AE1 N T -KANA K AE1 N AH0 -KANAB K AH0 N AA1 B -KANADAY K AE1 N AH0 D EY2 -KANAGAWA K AA2 N AA0 G AA1 W AH0 -KANAKA K AA0 N AA1 K AH0 -KANAKA'S K AA0 N AA1 K AH0 Z -KANAKAS K AA0 N AA1 K AH0 Z -KANANA K AH0 N AE1 N AH0 -KANANA'S K AA2 N AA1 N AH0 Z -KANARI K AH0 N AA1 R IY0 -KANARIS K AE1 N ER0 IH0 S -KANARY K AE1 N ER0 IY0 -KANATS K AE1 N AH0 T S -KANAUJ K AA2 N AA0 UW1 JH -KANAWHA K AH0 N AO1 HH AH0 -KANAYA K AH0 N AY1 AH0 -KANCABA K AA0 N K AA1 B AH0 -KANDA K AE1 N D AH0 -KANDAHAR K AE1 N D AH0 HH AA0 R -KANDAR K AE1 N D ER0 -KANDAR'S K AE1 N D ER0 Z -KANDARIAN K AH0 N D EH1 R IY0 AH0 N -KANDAULES K AE1 N D AW0 L Z -KANDAVU K AA0 N D AA1 V UW2 -KANDERSTEG K AE1 N D ER0 S T EH2 G -KANDIAN K AE1 N D IY0 AH0 N -KANDIDAT K AE1 N D AH0 D AH0 T -KANDIN K AE2 N D IH0 N -KANDY K AE1 N D IY0 -KANE K EY1 N -KANE'S K EY1 N Z -KANELE K AE1 N AH0 L -KANEM K AE2 N EH0 M -KANEMY K AE1 N AH0 M IY0 -KANENGE K AE1 N AH0 N JH -KANESVILLE K EY1 N Z V IH2 L -KANG K AA1 NG -KANG K AE1 NG -KANGA K AE1 NG G AH0 -KANGAROO K AE2 NG G ER0 UW1 -KANGAROO'S K AE2 NG G ER0 UW1 Z -KANGAROOS K AE2 NG G ER0 UW1 Z -KANGERTLUKSOAK K AE2 NG G ER0 T L AH0 K S OW1 K -KANGHI K AE1 N G IY0 -KANGMA K AE1 NG M AH0 -KANGOUROU K AE2 NG G UW1 R UW2 -KANGWE K AE1 NG W -KANIKI K AA0 N IY1 K IY0 -KANINE K AA0 N IY1 N IY0 -KANISHKA K AA0 N IY1 SH K AH0 -KANKA K AE1 N K AH0 -KANKA K AE1 NG K AH0 -KANKAD K AA2 N K AA1 D -KANKAD'S K AA2 N K AA1 D Z -KANKAKEE K AE1 NG K IH0 K IY0 -KANKER K AE1 NG K ER0 -KANN K AE1 N -KANNAKA K AE1 N AA1 K AH0 -KANNEBERG K AE1 N B ER0 G -KANNOT K AE1 N AA0 T -KANNUSHI K AE1 N UW1 SH IY0 -KANO K AA1 N OW0 -KANO'S K AA1 N OW0 Z -KANONSIONNI K EY1 N AH0 N CH AH0 N IY0 -KANOPIC K EY1 N AH0 P IH0 K -KANOPUS K AH0 N OW1 P AH0 S -KANOU K AA0 N OW0 UW1 -KANOURI K AE1 N UH1 R IY0 -KANOWIT K AE1 N AH0 W IH0 T -KANOWITS K AE1 N AH0 W IH0 T S -KANS K AH0 N Z -KANSA K AE1 N Z AH0 -KANSAN K AE1 N Z AH0 N -KANSANS K AE1 N Z AH0 N Z -KANSAS K AE1 N Z AH0 S -KANSU K AE1 N Z UW0 -KANT K AE1 N T -KANT'S K AE1 N T S -KANTARA K AE1 N T AA1 R AH0 -KANTELE K AA0 N T EH1 L -KANTIAN K AE1 N T IY0 AH0 N -KANTIANS K AE1 N T IY0 AH0 N Z -KANTO K AE1 N T OW0 -KANTOR K AE1 N T ER0 -KANTOS K AE1 N T OW0 Z -KANTURK K AE1 N T ER2 K -KANU K AA1 N UW0 -KANUS K EY1 N IH0 S -KANYA K AA1 N Y AH0 -KANZAS K AE1 N Z AH0 Z -KANZLER K AE1 N Z L ER0 -KAO K AW1 -KAO K EY1 OW2 -KAOHA K EY2 OW1 HH AH0 -KAOL K AW1 L -KAOLIAN K EY2 OW1 L IY0 AH0 N -KAOLIANS K AW1 L Y AH0 N Z -KAOLIN K AW1 L IH0 N -KAOLIN K EY1 OW0 L IH0 N -KAOR K AO1 R -KAOTSOU K AW1 T S UW1 -KAOU K EY1 OW2 -KAOUES K AW1 EH0 S -KAP K AE1 P -KAPA K AA1 P AH0 -KAPAA'A K AE1 P AA1 -KAPALI K AH0 P AA1 L IY0 -KAPE K EY1 P -KAPELLE K AH0 P EH1 L -KAPELLMEISTER K AH0 P EH1 L M AY0 S T ER0 -KAPERTON K AE1 P ER0 T AH0 N -KAPES K EY1 P S -KAPHENGST K AE1 P HH EH1 NG G S T -KAPI K AE1 P IY0 -KAPIK K AE1 P IH0 K -KAPILA K AE1 P IH0 L AH0 -KAPILAVASTU K AE1 P IH0 L AA1 V AH0 S T UW0 -KAPILAWOT K AE1 P AH0 L AO2 AO1 T -KAPIN K AE1 P IH0 N -KAPING K AE1 P IH0 NG -KAPIOLANI K AE1 P IY0 OW0 L AA1 N IY0 -KAPIT K AE1 P IH0 T -KAPITAL K AE1 P IH0 T AH0 L -KAPITAN K AE1 P IH0 T AH0 N -KAPITEIN K AE1 P IH0 T AY2 N -KAPITI K AH0 P IY1 T IY0 -KAPITOLINA K AE1 P IH0 T AH0 L IY1 N AH0 -KAPITON K AE1 P IH0 T AH0 N -KAPJE K AE1 P Y EH2 -KAPLAN K AE1 P L AH0 N -KAPOIOLANI K AH0 P OW1 IY0 OW0 L AA1 N IY0 -KAPOK K AH0 P AA1 K -KAPOLNA K AH0 P OW1 L N AH0 -KAPOSIA K AH0 P OW1 S IY0 AH0 -KAPOTAI K AE1 P OW0 T AA1 IY0 -KAPOU K AH0 P UW1 -KAPP K AE1 P -KAPPA K AE1 P AH0 -KAPPAN K AE1 P AH0 N -KAPPANS K AE1 P AH0 N Z -KAPSTAAD K AE1 P S T AA1 D -KAPTCHITCH K AE1 P T SH IH0 CH -KAPU K AE1 P -KAPUDAN K AH0 P AH1 D AH0 N -KAPUT K AE1 P AH0 T -KAR K AA2 R -KARA K EH1 R AH0 -KARA'S K EH1 R AH0 Z -KARABOGHAZJI K ER0 AE1 B AH0 G AH0 Z JH IY0 -KARACHI K AA0 R AA1 CH IY0 -KARACSAY K ER0 AE0 K S EY0 -KARADEUCQ K ER0 AA1 D UW1 K -KARAGEORGE K AE1 R AH0 JH AO1 R JH -KARAGEORGES K AE1 R AH0 JH AO1 R JH IH0 Z -KARAGEORGEVITCH K AE1 R AH0 JH AO1 R JH AH0 V IH0 CH -KARAGWAH K AA0 R AA1 G W AH0 -KARAGWE K AA0 R AA1 G W -KARAIN K ER0 EY1 N -KARAIT K ER0 EY1 T -KARAITES K EH2 R IY0 AY2 T S -KARAITI K ER0 EY1 T IY0 -KARAIYO K EH2 R IY0 IH0 Y OW0 -KARAKA K AA1 R AH0 K AH0 -KARAKALPAKS K AA1 R AH0 K AA2 L P AE0 K S -KARAKOEE K AA1 R AH0 K AO2 IY0 -KARAKOROM K AA1 R AH0 K AO2 R AH0 M -KARAKORUM K AA1 R AH0 K AO2 R AH0 M -KARAM K AE1 R AH0 M -KARAMANEH K AE1 R AH0 M AA1 N EH2 -KARAMANEH'S K AE1 R AH0 M AA1 N EH2 Z -KARAMAZOV K AE1 R AH0 M AE1 Z AA0 V -KARAMAZOVS K ER0 AE1 M AH0 Z AA2 F S -KARAMZIN K ER0 AE1 M Z IH0 N -KARAN K EH1 R AH0 N -KARARA K AA0 R AA1 R AH0 -KARARA'S K ER0 AA1 R AH0 Z -KARASJOK K ER0 AA1 S JH AH0 K -KARASOWSKI K AA2 R AH0 S AO1 F S K IY0 -KARAT K EH1 R AH0 T -KARATAYEFF K ER0 AA1 T AH0 Y EH1 F -KARAZ K AA1 R AA0 Z -KARB K AA1 R B -KARDAR K AA1 R D AA2 R -KARDELJ K AA1 D EH1 L JH -KARDON K AA1 R D AH0 N -KARE K EH1 R -KAREE K ER0 IY1 -KAREEMA K ER0 IY1 M AA0 -KAREL K EH1 R AH0 L -KAREL'S K EH1 R AH0 L Z -KARELA K ER0 IY1 L AH0 -KARELY K EH1 R L IY0 -KAREMA K AH0 R EH1 M AH0 -KAREMAKU K AH0 R EH1 M AH0 K UW0 -KAREN K EH1 R AH0 N -KAREN'S K EH1 R AH0 N Z -KARENIN K EH2 R EH0 N IY1 N -KARENINA K AH0 R EH1 N IH0 N AH0 -KARENINA K EH2 R EH0 N IY1 N AH0 -KARENNA K AH0 R EH1 N AH0 -KARENS K EH1 R AH0 N Z -KARETSKY K AH0 R EH1 T S K IY0 -KAREWENDA K EH2 R W EH1 N D AH0 -KARF K AA1 R F -KARF'S K AA1 R F S -KARFA K AA1 R F AH0 -KARFA'S K AA1 R F AH0 Z 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R L S AH0 N Z -KARLSRUHE K AA1 R L Z R UW2 -KARLSTADT K AA1 R L S T AE2 T -KARLSTRASSE K AA1 R L S T R AE1 S -KARLUK K AA1 R L AH0 K -KARMA K AA1 R M AH0 -KARMIC K AA1 R M IH0 K -KARMOS K AA1 R M OW0 Z -KARN K AA1 R N -KARNA K AA1 R N AH0 -KARNA'S K AA1 R N AH0 Z -KARNAC K AA1 R N AH0 K -KARNAK K AA1 R N AH0 K -KARNAL K AA1 R N AH0 L -KARNEGIE K AA1 R N AH0 G IY0 -KARNER K AA1 R N ER0 -KARNER'S K AA1 R N ER0 Z -KARNES K AA1 R N Z -KARNIS K AA1 R N IY0 Z -KARNOS K AA1 R N OW0 Z -KARNS K AA1 R N Z -KARNSTEIN K AA1 R N S T AY2 N -KARO K AA1 R OW0 -KAROL K EH1 R AO0 L -KAROLIDES K EH2 R AH0 L AY1 D Z -KAROLINE K EH1 R AH0 L AY2 N -KARONYA K ER0 OW1 N Y AH0 -KAROO K ER0 UW1 -KAROON K ER0 UW1 N -KAROSS K EH1 R AO0 S -KAROSSES K EH2 R AH0 Z IH0 Z -KARP K AA1 R P -KARPATFALVA K AA2 R P AE1 T F AA1 L V AH0 -KARPATHY K AA1 R P AE2 TH IY0 -KARPATHY'S K AA2 R P AE1 TH IY0 Z -KARPIK K AA1 R P IH0 K -KARPIN K AA1 R P IH0 N -KARPIN'S K AA1 R P IH0 N Z -KARPOVNA K AA1 R P AA2 V N AH0 -KARR K AA1 R -KARR'S K AA1 R Z -KARRACK K AE1 R AH0 K -KARRAJE K AE1 R AH0 JH -KARRAJE'S K AE1 R AH0 JH Z -KARRI K AA1 R IY0 -KARRO K AA1 R OW0 -KARROO K AA1 R UW0 -KARS K AA1 R Z -KARSAVINA K AA1 R S AH0 V IY1 N AH0 -KARSCHOFF K AA1 R S K HH AO0 F -KARSHISH K AA1 R SH IY1 SH -KARSKI K AA1 R S K IY0 -KARSLAKE K AA1 R S L EY2 K -KARST K AA1 R S T -KARSTEN K AA1 R S T AH0 N -KARSTEN'S K AA1 R S T EH0 N Z -KARSWELL K AA1 R S W EH2 L -KARTA K AA1 R T AH0 -KARTAUS K AA0 R T AE2 IH0 S -KARTEL K AA1 R T AH0 L -KARTHLOS K AA1 R TH L OW0 Z -KARTIK K AA1 R T IH0 K -KARTINKIN K AA2 R T IH1 NG K IH0 N -KARTOUM K AA1 R T UW1 M -KARUMA K ER0 UW1 M AH0 -KARUN K ER0 UW1 N -KARUSKA K ER0 AH1 S K AH0 -KARVA K AA1 R V AH0 -KARVALL K AA1 R V AA1 L -KARVARINA K AA1 R V ER0 IY1 N AH0 -KARVAY K AA1 R V EY0 -KAS K AH0 S -KASA K AA1 S AH0 -KASAI K AH0 S AA1 IY0 -KASAN K AH0 S AA1 N -KASANA K AH0 S AA1 N AH0 -KASANA'S K AH0 S AA1 N AH0 Z -KASAR K AH0 S AA1 R -KASATSKY K AH0 S AA1 T S K IY0 -KASBA K AE1 S B AH0 -KASBAH K AE1 S B AH0 -KASCHTA K AE1 SH T AH0 -KASE K EY1 Z -KASEGARA K AE1 S EH0 G AA1 R AH0 -KASER K EY1 Z ER0 -KASGI K AE1 S G IY0 -KASH K AE1 SH -KASHA K AA1 SH AH0 -KASHALLA K AH0 SH AA1 L AH0 -KASHAN K AE1 SH AH0 N -KASHAQUA K AH0 SH AA1 K AH0 -KASHGAR K AE1 SH G AA2 R -KASHI K AA1 SH IY0 -KASHIKU K AA2 SH IY0 K UW1 -KASHIRIN K AA0 SH IH1 R AH0 N -KASHMIR K AE1 SH M IH0 R -KASHMIR K AE1 SH M IY0 R -KASHMIRI K AE0 SH M IY1 R IY0 -KASHMIRIS K AE0 SH M IY1 R IY0 Z -KASHNA K AE1 SH N AH0 -KASHTANKA K AA1 SH T AE1 NG K AH0 -KASHTANOV K AA1 SH T AA0 N AA0 V -KASHTSHEI K AA1 SH T SH AY0 -KASI K AA1 Z IY0 -KASIA K EY1 ZH AH0 -KASIDAH K AA1 S IH0 D AH0 -KASIH K AH0 S IH1 -KASIM K AE1 S IH0 M -KASIM'S K AE1 S IH0 M Z -KASIN K AE1 S IH0 N -KASKASKIA K AA1 S K AH0 S K IY0 AH0 -KASKASKIAS K AA1 S K AH0 S K IY1 AH0 Z -KASKER K AE1 S K ER0 -KASKER'S K AE1 S K ER0 Z -KASKISOON K AE1 S K IH0 S UW1 N -KASLO K AA1 S L OW0 -KASPAR K AE1 S P ER0 -KASPAR'S K AE1 S P ER0 Z -KASPER K AE1 S P ER0 -KASR K AE1 S R -KASS K AE1 S -KASSA K AE1 S AH0 -KASSALA K AE1 S AH0 L AH0 -KASSANDANE K AE1 S AA0 N D EY1 N -KASSANDRA K AE1 S AA1 N D R AH0 -KASSIM K AE1 S IH0 M -KASSIMERE K AE1 S IH0 M IH2 R -KASSITE K AE1 S AY0 T -KASSON K AE1 S AH0 N -KAST K AE1 S T -KASTEEL K AH0 S T IY1 L -KASTELIZ K AH0 S T EH1 L IH0 Z -KASTEN K AE1 S T AH0 N -KASTNER K AE1 S T N ER0 -KASUKAH K AA1 S UW0 K AH0 -KASVIN K AE1 S V IH0 N -KASYA K AE1 S IY0 AH0 -KASYAPA K AE1 S AY0 AA1 P AH0 -KAT K AE1 T -KAT'S K AE2 T S -KATA K AA1 T AH0 -KATADA K AH0 T AA1 D AH0 -KATAHDIN K AA0 T AA0 D IH0 N -KATAHDIN'S K AA0 T AA0 D AH0 N Z -KATAK K AE1 T AH0 K -KATAKI K AH0 T AA1 K IY0 -KATALAMBULA K AA0 T AA0 L AA0 M B UW1 L AH0 -KATALAMBULA'S K AA0 T AA0 L AA0 M B UW1 L AH0 Z -KATALLA K AA0 T AA0 L AH0 -KATANA K AA0 T AA1 N AH0 -KATANGA K AA0 T AA0 NG G AH0 -KATARINA K AA2 T ER0 IY1 N AH0 -KATCH K AE1 CH -KATCHA K AE1 CH AH0 -KATE K EY1 T -KATE'S K EY1 T S -KATEL K AH0 T EH1 L -KATENKA K AA0 T EH1 NG K AH0 -KATER K EY1 T ER0 -KATER'S K EY1 T ER0 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AH0 L AA1 M AH0 T ER0 -KILOMETER K IH1 L AH0 M IY2 T ER0 -KILOMETERS K AH0 L AA1 M AH2 T ER0 Z -KILOMETERS K IH1 L AH0 M IY2 T ER0 Z -KILOMETRE K IH2 L AH0 M IY1 T ER0 -KILOMETRE K IH2 L AO1 M AH2 T ER0 -KILOMETRES K IH2 L AH0 M IY1 T ER0 Z -KILOMETRES K IH2 L AO1 M AH2 T ER0 Z -KILOS K IY1 L OW2 Z -KILOWATT K IH1 L AH0 W AA2 T -KILOWATTS K IH1 L AH0 W AA2 T S -KILPATRICK K IH2 L P AE1 T R IH0 K -KILPATRICK'S K IH2 L P AE1 T R IH0 K S -KILPONT K IH2 L P AA2 N T -KILQUHANITY K IH1 L K W HH AE1 N AH0 T IY0 -KILQUHANITY'S K IH1 L K W HH AE1 N IH0 T IY0 Z -KILQUYT K IH1 L K W IY1 T -KILRAIN K IH1 L R EY2 N -KILREA K IH1 L R IY0 AH0 -KILRONAN K IH1 L R OW1 N AH0 N -KILROY K IH1 L R OY2 -KILROY'S K IH1 L R OY2 Z -KILROYS K IH1 L R OY2 Z -KILRUSH K IH1 L R AH0 SH -KILSIP K IH1 L S IH0 P -KILSPINDIE K IH1 L S P IH1 N D IY0 -KILSYTH K IH1 L S AY2 TH -KILT K IH1 L T -KILTA K IH1 L T AH0 -KILTARTAN K IH1 L T AA0 R T AE0 N -KILTED K IH1 L T IH0 D -KILTER K IH1 L T ER0 -KILTIES K IH1 L T IY0 Z -KILTS K IH1 L T S -KILWA K IH1 L W AH0 -KILWARDEN K IH1 L W ER0 D AH0 N -KILWEH K IH1 L W EH2 -KIM K IH1 M -KIM'S K IH1 M Z -KIMASH K IH1 M AE2 SH -KIMBAL K IH1 M B AH0 L -KIMBALL K IH1 M B AH0 L -KIMBALL'S K IH1 M B AH0 L Z -KIMBALLTON K IH1 M B AH0 L T AH0 N -KIMBER K IH1 M B ER0 -KIMBERLEY K IH1 M B ER0 L IY0 -KIMBERLEY'S K IH1 M B ER0 L IY0 Z -KIMBERLY K IH1 M B ER0 L IY0 -KIMBLE K IH1 M B AH0 L -KIMBO K IH1 M B OW0 -KIMBUCK K IH1 M B AH0 K -KIMCHI K IH1 M CH IY0 -KIMI K IH1 M IY0 -KIMIKA K IH1 M IH0 K AH0 -KIMIKO K IY0 M IY1 K OW0 -KIMMEL K IH1 M AH0 L -KIMMER K IH1 M ER0 -KIMO K AH0 M OW2 -KIMONA K AH0 M OW1 N AH0 -KIMONAS K AH0 M OW1 N AH0 Z -KIMONO K AH0 M OW1 N AH0 -KIMONOS K AH0 M OW1 N AH0 Z -KIMRY K IH1 M R IY0 -KIMYERA K IH1 M IY0 ER0 AH0 -KIN K IH1 N -KIN'LY K IH1 N AH0 L IY0 -KIN'S K IH1 N Z -KINA K IY1 N AH0 -KINALDEN K IY0 N AA1 L D AH0 N -KINBRAS K IH1 N B R AH0 Z -KINCAID K IH2 N K EY1 D -KINCAID'S K IH2 N K EY1 D Z -KINCAIDE K IH2 N K EY1 D -KINCARDINE K IH2 N K 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N D AH0 L TH -KINDLIER K IH1 N D L IY0 ER0 -KINDLIEST K IH1 N D L IY0 AH0 S T -KINDLIN K IH1 N D L IH0 N -KINDLIN'S K IH1 N D L IH0 N Z -KINDLINESS K IH1 N D L IY0 N AH0 S -KINDLING K IH1 N D L IH0 NG -KINDLINGS K IH1 N D AH0 L IH0 NG Z -KINDLY K AY1 N D L IY0 -KINDNESS K AY1 N D N AH0 S -KINDNESSE K AY1 N D N AH0 S -KINDNESSES K AY1 N D N AH0 S IH0 Z -KINDO K IH1 N D OW0 -KINDRED K IH1 N D R IH0 D -KINDRED'S K IH1 N D R IH0 D Z -KINDREDS K IH1 N D R AH0 D Z -KINDS K AY1 N D Z -KINDS K AY1 N Z -KINDT K IH1 N T -KINE K IY2 N -KINEMA K IH2 N IH0 M AH0 -KINEMATOGRAPH K IH2 N IH0 M AH0 T AA1 G R AH0 F -KINEMON K IH0 N EH1 M AH0 N -KINEO K IH1 N IY0 OW0 -KINESASIS K IH2 N AH0 S EY1 S IH0 S -KINESASIS'S K IH2 N AH0 S EY1 S IH0 S IH0 Z -KINESMA K IH2 N AH0 S M AH0 -KINETIC K AH0 N EH1 T IH0 K -KINETIC K IH0 N EH1 T IH0 K -KINETOSCOPE K AH0 N EH1 T AH0 S K OW2 P -KINEY K IH1 N IY0 -KINFAUNS K IH1 N F AA1 N Z -KINFOLK K IH1 N F OW2 K -KINFOLKS K IH1 N F OW1 K S -KING K IH1 NG -KING' K IH1 NG 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K IH1 NG Z M AH0 N -KINGSMEAD K IH1 NG S M IY1 D -KINGSMILL K IH1 NG S M IH2 L -KINGSNORTH K IH1 NG S N AO0 R TH -KINGSNORTH'S K IH1 NG S N AO0 R TH S -KINGSPORT K IH1 NG S P AO2 R T -KINGSTAIRS K IH1 NG S T EH1 R Z -KINGSTON K IH1 NG S T AH0 N -KINGSTON'S K IH1 NG S T AH0 N Z -KINGSTONE K IH1 NG S T OW2 N -KINGSTONIANS K IH1 NG S T OW0 N IY0 AH0 N Z -KINGSTOWN K IH1 NG Z T AW2 N -KINGSWAY K IH1 NG G Z W EY0 -KINGSWEAR K IH1 NG G Z W EH2 R -KINGSWELL K IH1 NG G Z W EH0 L -KINGSWOOD K IH1 NG G Z W UH2 D -KINGSWORTH K IH1 NG Z W ER2 TH -KINGSWORTH'S K IH1 NG Z W ER2 TH S -KINGTHORPE K IH1 NG TH AO0 R P -KINGU K IH1 NG UW1 -KINGUSSIE K IH1 NG G AH1 S IY0 -KINI K IY1 N IY0 -KINJURO K IH1 N JH UH1 R OW0 -KINK K IH1 NG K -KINK'S K IH1 NG K S -KINKA K IH1 NG K AH0 -KINKAID K IH1 NG K EY2 D -KINKED K IH1 NG K T -KINKEL K IH1 NG K AH0 L -KINKING K IH1 NG K IH0 NG -KINKS K IH1 NG K S -KINKY K IH1 NG K IY0 -KINLAY K IH1 N L IY0 -KINLAY'S K IH1 N L IY0 Z -KINLESS K IH1 N L AH0 S -KINLEY K IH1 N L IY0 -KINLEY'S K IH1 N L IY0 Z -KINLOCH K IH1 N L AH0 K -KINLOCH'S K IH1 N L AA1 K S -KINMONT K IH1 N M AA2 N T -KINNAIRD K IH1 N ER0 D -KINNAKULLA K IH1 N AH0 K UW1 L AH0 -KINNEAR K IH1 N IH2 R -KINNEIL K IH1 N IY1 L -KINNENEH K IH1 N IH0 N EH1 -KINNEPATOOS K IH1 N IH0 P AH0 T UW2 Z -KINNESASIS K IH1 N AH0 S EY1 S IH0 S -KINNEY K IH1 N IY0 -KINNEY'S K IH1 N IY0 Z -KINNEYS K IH1 N IY0 Z -KINNICUTT K IH1 N IH0 K AH0 T -KINNIKINICK K IH1 N AH0 K IY1 N IH0 K -KINNIKINNICK K IH1 N IH0 K IH1 N IH0 K -KINNISON K IH1 N IH0 S AH0 N -KINNISON'S K IH1 N IH0 S AH0 N Z -KINNON K IH1 N AH0 N -KINNOUL K IH1 N UW1 L -KINNOULL K IH1 N UW1 L -KINO K IY0 N OW0 -KINOSLING K IH1 N AH0 S L IH0 NG -KINRAID K IH1 N R EY2 D -KINRAID'S K IH1 N R EY2 D Z -KINROSS K IH1 N R AA2 S -KINS K IH1 N Z -KINSALE K IH1 N S EY2 L -KINSELL K IH1 N S AH0 L -KINSELLA K IY0 N S EH1 L AH0 -KINSELLA'S K IY0 N S EH1 L AH0 Z -KINSEY K IH1 N Z IY0 -KINSFOLK K IH1 N Z F OW2 K -KINSFOLKS K IH1 N Z F OW2 K S -KINSHIP K IH1 N SH IH2 P -KINSHIPS K IH0 N SH IH2 P S -KINSINGTON K IH1 N S IH0 NG T AH0 N -KINSKY K IH1 N S K IY0 -KINSLEY K IH1 N Z L IY0 -KINSMAN K IH1 N Z M AE2 N -KINSMAN'S K IH1 N S M AH0 N Z -KINSMEN K IH1 N Z M AH0 N -KINSMEN'S K IH1 N Z M AH0 N Z -KINSOLVING K IH1 N S AA1 L V IH0 NG -KINSPEOPLE K IH1 N Z P IY2 P AH0 L -KINSTON K IH1 N S T AH0 N -KINSTRY K IH1 N S T R IY0 -KINSTRY'S K IH1 N S T R IY0 Z -KINSWOMAN K IH1 N S W UH2 M AH0 N -KINSWOMAN'S K IH1 N S W UH2 M AH0 N Z -KINSWOMEN K IH1 N Z W IH2 M AH0 N -KINTAIL K IH1 N T EY2 L -KINTARO K IH1 N T AA1 R OW0 -KINTBURY K IH1 N T B EH2 R IY0 -KINTCHIN K IH1 N CH IH0 N -KINTLA K IH1 N T L AH0 -KINTRY K IH1 N T R IY0 -KINTU K IH1 N T UW0 -KINTUCK K IH1 N T AH1 K -KINTUCKY K IH1 N T AH1 K IY0 -KINTYRE K IH1 N T AY2 R -KINVARA K IH1 N V AA1 R AH0 -KINVIG K IH1 N V IH2 G -KINZER K IH1 N Z ER0 -KINZER'S K IH1 N Z ER0 Z -KINZERS K IH1 N Z ER0 Z -KINZEY K IH1 N Z IY0 -KINZIE K IH1 N Z IY0 -KINZIE'S K IH1 N Z IY0 Z -KINZIG K IH1 N Z IH0 G -KIO K IY1 OW0 -KIOMI K IY0 OW1 M IY0 -KIOMI'S K IY1 AO2 M IY0 Z -KIOMIDZU K IY1 AO2 M AH0 D Z UW0 -KIOPO K IY1 AO2 P OW0 -KIOPO'S K IY1 AO2 P OW0 Z -KIORAM K AY1 ER0 AH0 M -KIOSK K IY1 AO2 S K -KIOSKS K IY1 AO2 S K S -KIOSQUE K IY1 AO2 S K -KIOSQUES K IY1 AO2 S K S -KIOTA K IY2 OW1 T AH0 -KIOTE K AY1 OW2 T -KIOTO K IY0 OW1 T OW0 -KIOUNI K AY1 UW1 N IY0 -KIOW K AY1 AW0 -KIOWA K AY1 AH0 W AH0 -KIOWAS K AY1 AH0 W AH0 Z -KIP K IH1 P -KIP'S K IH1 P S -KIPLING K IH1 P L IH0 NG -KIPLING'S K IH1 P L IH0 NG Z -KIPP K IH1 P -KIPPEEN K IH1 P IY0 N -KIPPEN K IH1 P AH0 N -KIPPENBERG K IH1 P AH0 N B ER0 G -KIPPENBERG'S K IH1 P AH0 N B ER0 G Z -KIPPENBROCK K IH1 P AH0 N B R AH0 K -KIPPENREITER K IH1 P IH0 N R AY0 T ER0 -KIPPER K IH1 P ER0 -KIPPERED K IH1 P ER0 D -KIPPERS K IH1 P ER0 Z -KIPPERSON K IH1 P ER0 S AH0 N -KIPPEWA K IH1 P AH0 W AA2 -KIPPING K IH1 P IH0 NG -KIPPING'S K IH1 P IH0 NG Z -KIPPIS K IH1 P IY0 Z -KIPPLETRINGAN K IH1 P AH0 L T R IY1 NG G AH0 N -KIPPOD K IH1 P AH0 D -KIPPS K IH1 P S -KIPPSES K IH1 P S IH0 Z -KIPPUR K IH1 P ER0 -KIPPY K IH1 P IY0 -KIPS K IH1 P S -KIPT K IH1 P T -KIR K IH1 R -KIRANGOZI K IH1 R AE0 NG G OW1 Z IY0 -KIRBEY K ER1 B IY0 -KIRBY K ER1 B IY0 -KIRBY'S K ER1 B IY0 Z -KIRBYS K ER1 B IY0 Z -KIRCHE K ER1 K -KIRCHEISEN K ER1 K AY0 S AH0 N -KIRCHER K ER1 K ER0 -KIRCHHEIM K ER1 K HH AY0 M -KIRCHHOFF K ER1 K HH AO0 F -KIRCHNER K ER1 K N ER0 -KIRCHOFF K ER1 K HH AO0 F -KIRCONNELL K ER1 K AA1 N AH0 L -KIRDALL K ER1 D AA0 L -KIRDY K ER1 D IY0 -KIRDY'S K ER1 D IY0 Z -KIRGAN K ER1 G AH0 N -KIRGHIS K ER1 G IH0 S -KIRGHIZ K ER1 G IY1 Z -KIRI K IH1 R IY0 -KIRIL K IH1 R IH0 L -KIRILIN K IH1 R IH0 L IH0 N -KIRILL K IH1 R AH0 L -KIRILLA K IH2 R IH1 L AH0 -KIRILLOVITCH K IH2 R IH1 L AH0 V IH0 CH -KIRILOV K IH1 R IH0 L AA0 V -KIRIS K IH1 R IH0 S -KIRITIN K IH1 R IH0 T IH0 N -KIRJATH K ER1 JH AE2 TH -KIRJIPA K ER1 JH IY1 P AH0 -KIRK K ER1 K -KIRK'S K ER1 K S -KIRKALDY K ER1 K AA1 L D IY0 -KIRKANDREW K ER1 K AH0 N D R UW2 -KIRKBRIDE K ER1 K B R AY0 D -KIRKBURN K ER1 K B ER2 N -KIRKBY K ER1 K B IY0 -KIRKCALDY K ER0 K AO1 L D IY0 -KIRKCAPLE K ER0 K EY1 P AH0 L -KIRKCONNELL K ER0 K AA1 N AH0 L -KIRKCUDBRIGHT K ER0 K AH1 D B R AY2 T -KIRKDALE K ER1 K D EY2 L -KIRKE K ER1 K -KIRKE'S K ER1 K S -KIRKER K ER1 K ER0 -KIRKES K ER1 K S -KIRKHAM K ER1 K HH AH0 M -KIRKHAM'S K ER1 K HH AH0 M Z -KIRKLAND K ER1 K L AH0 N D -KIRKLAND'S K ER1 K L AH0 N D Z -KIRKLANDS K ER1 K L AH0 N D Z -KIRKLEATHAM K ER1 K L IY1 TH AH0 M -KIRKLEY K ER1 K L IY0 -KIRKMAN K ER1 K M AH0 N -KIRKMAN'S K ER1 K M AH0 N Z -KIRKMICHAEL K ER1 K M AY1 K AH0 L -KIRKOSWALD K ER0 K AO1 Z W AO0 L D -KIRKPATRICK K ER0 K P AE1 T R IH0 K -KIRKPATRICK K ER0 P AE1 T R IH0 K -KIRKPATRICK'S K ER0 P AE1 T R IH0 K S -KIRKPATRICKS K ER0 P AE1 T R IH0 K S -KIRKS K ER1 K S -KIRKSTALL K ER1 K S T AH0 L -KIRKSTONE K ER1 K S T OW2 N -KIRKSVILLE K ER1 K S V IH2 L -KIRKTON K ER1 K T AH0 N -KIRKUP K IH1 R K AH0 P -KIRKWALL K ER1 K W AO2 L -KIRKWELL K ER1 K W EH2 L -KIRKWOOD K ER1 K W UH0 D -KIRKWOOD'S K ER1 K W UH2 D Z -KIRKWOODS K ER1 K W UH2 D Z -KIRKYAIRD K ER1 K Y ER0 D -KIRKYARD K ER1 K Y AA2 R D -KIRKYARDS K ER1 K Y AA2 R D Z -KIRMANSHAH K ER1 M AH0 N SH AA1 -KIRMENBACH K ER1 M IH0 N B AA0 K -KIRN K ER1 N -KIROS K IH1 R OW0 Z -KIRPO K IH1 R P OW0 -KIRREE K ER1 IY0 -KIRRY K ER1 IY0 -KIRSCH K ER1 SH -KIRSCHEN K ER1 SH AH0 N -KIRSCHNER K ER1 SH N ER0 -KIRSCHWASSER K ER1 SH W AA1 S ER0 -KIRSHA K ER1 SH AH0 -KIRSTEN K ER1 S T AH0 N -KIRSTIN K ER1 S T IH0 N -KIRSTY K ER1 S T IY0 -KIRSTY'S K ER1 S T IY0 Z -KIRTAN K ER1 T AH0 N -KIRTELL K ER1 T AH0 L -KIRTLAND K ER1 T L AH0 N D -KIRTLE K ER1 T AH0 L -KIRTLED K ER1 T AH0 L D -KIRTLES K ER1 T AH0 L Z -KIRTLEY K ER1 T L IY0 -KIRTLEY'S K ER1 T L IY0 Z -KIRTON K ER1 T AH0 N -KIRTON'S K ER1 T AH0 N Z -KIRU K IH1 R UW0 -KIRV K ER1 V -KIRWAN K ER1 W AO0 N -KIRWAN'S K ER1 W AO0 N Z -KIRWIN K ER1 W IH0 N -KIRWIN'S K ER1 W AH0 N Z -KIRYAK K IH1 R Y AE0 K -KIRYAKOV K IH2 R Y AA1 K AO2 V -KIRYLO K ER0 IH1 L OW0 -KIS K IH1 S -KISA K IH1 S AH0 -KISARI K IY0 S AA1 R IY0 -KISCADDEN K IH1 S K AE1 D AH0 N -KISCO K IH1 S K OW0 -KISER K AY1 Z ER0 -KISESA K IH1 S EH1 S AH0 -KISH K IH1 SH -KISHIMOTO K IY1 SH IY0 M OW1 T OW0 -KISHINEV K IY1 SH IH0 N EH0 F -KISHLAK K IH1 SH L AE0 K -KISHLAKI K IH1 SH L AA1 K IY0 -KISHNU K IH1 SH N UW0 -KISHON K IH1 SH AH0 N -KISHRA K IH1 SH R AH0 -KISHU K IY1 SH UW2 -KISINGTON K AY1 Z IH0 NG T AH0 N -KISKA K IH1 S K AH0 -KISKADDEN K IH1 S K AE1 D AH0 N -KISLAR K IH1 S L ER0 -KISLIAKOV K IH1 S L AY2 AH0 K AA0 V -KISLOVODSK K IH1 S L OW0 V AA1 D S K -KISMET K IH1 Z M IH0 T -KISMINE K IH1 Z M AH0 N -KISS K IH1 S -KISS'D K IH1 SH D -KISSABLE K IH1 S AH0 B AH0 L -KISSAM K IH1 S AH0 M -KISSE K IH1 S -KISSED K IH1 S T -KISSEL K IH1 S AH0 L -KISSER K IH1 S ER0 -KISSES K IH1 S AH0 Z -KISSES K IH1 S IH0 Z -KISSETH K IH1 S IH0 TH -KISSIMMEE K IH0 S IH1 M IY0 -KISSIN K IH1 S IH0 N -KISSING K IH1 S IH0 NG -KISSINGEN K IH1 S IH0 NG G EH0 N -KISSINGS K IH1 S IH0 NG Z -KISSOCK K IH1 S AH0 K -KISSOFF K IH1 S AO0 F -KIST K IH1 S T -KISTNAGHERRY K IH1 S T N AH0 G EH1 R IY0 -KISTS K IH1 S T S -KISTUNOV K IH1 S T UW0 N AA0 V -KISUMU K IH1 S UW1 M UW0 -KISWAH K IH1 S W AH0 -KISWAHILI K IH1 S W AA0 HH IY1 L IY0 -KIT K IH1 T -KIT'S K IH1 T S -KITAB K IH1 T AH0 B -KITBAG K IH1 T B AE2 G -KITCAT K IH1 T K AE2 T -KITCH K IH1 CH -KITCHED K IH1 CH T -KITCHELL K IH1 CH AH0 L -KITCHEN K IH1 CH AH0 N -KITCHEN'S K IH1 CH AH0 N Z -KITCHENER K IH1 CH AH0 N ER0 -KITCHENER K IH1 CH N ER0 -KITCHENER'S K IH1 CH N ER0 Z -KITCHENETTE K IH2 CH AH0 N EH1 T -KITCHENMAID K IH1 CH AH0 N M EY2 D -KITCHENMAIDS K IH1 CH AH0 N M EY2 D Z -KITCHENS K IH1 CH AH0 N Z -KITCHENWARD K IH1 CH AH0 N W ER0 D -KITCHENWARE K IH1 CH AH0 N W EH2 R -KITCHI K IH1 CH IY0 -KITCHIN K IH1 CH IH0 N -KITCHINER K IH1 CH IH0 N ER0 -KITCHING K IH1 CH IH0 NG -KITE K AY1 T -KITE'S K AY1 T S -KITED K AY1 T IH0 D -KITELY K AY1 T L IY0 -KITELY'S K AY1 T L IY0 Z -KITES K AY1 T S -KITH K IH1 TH -KITHAERON K AY1 TH EH1 R AH0 N -KITHNEB K IH1 TH N AH0 B -KITI K IH1 T IY0 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IH0 Z -KITTRICK K IH1 T R IH0 K -KITTRIDGE K IH1 T R IH0 JH -KITTRIDGE'S K IH1 T R IH2 JH IH0 Z -KITTS K IH1 T S -KITTY K IH1 T IY0 -KITTY'S K IH1 T IY0 Z -KITTYWAKE K IH1 T IY0 W EY2 K -KITUTA K IY0 T UW1 T AH0 -KITUWIA K IH1 CH UW0 V IY0 AH0 -KITZUKI K IH1 T Z UW1 K IY0 -KIU K IY1 UW0 -KIUKIANG K AY1 AH0 K Y AA1 NG -KIUPRILE K AY1 AH0 P R AH0 L -KIUSHIU K AY1 AH0 SH UW1 -KIVA K IY1 V AH0 -KIVAS K IY1 V AH0 Z -KIVELSON K IH1 V IH0 L S AH0 N -KIVELSON'S K IH1 V IH0 L S AH0 N Z -KIVER K AY1 V ER0 -KIVERED K IH1 V ER0 D -KIVERS K AY1 V ER0 Z -KIVI K IH1 V IY0 -KIVIL K IH1 V AH0 L -KIWI K IY1 W IY0 -KIWYEH K IY1 W Y AH0 -KIYI K IY2 IY0 -KIYO K IY2 OW0 -KIYOMORI K IY2 OW0 M AO1 R IY0 -KIYOSAI K IY2 OW0 S AA1 IY0 -KIZIL K AY1 Z AH0 L -KIZLAR K AY1 Z L ER0 -KIZZIE K IH1 Z IY0 -KIZZY K IH1 Z IY0 -KJARTAN K Y AA1 R T AH0 N -KJARTAN'S K Y AA1 R T AH0 N Z -KJELGAARD K Y EH1 L G AA2 R D -KJELLIN K Y EH1 L IH0 N -KJERSTI K Y ER1 S T IY0 -KJOEGE K Y OW1 JH -KLA K L AH0 -KLA'UNS K L AH0 N Z -KLAAS K L AA1 S -KLAAS'S K L AA1 S IH0 Z -KLAE K L EY1 -KLAEDEN K L IY1 D AH0 N -KLAERE K L IH1 R -KLAHOWYA K L AE1 AW0 Y AH0 -KLAKEE K L EY1 K IY0 -KLAMATH K L AE1 M AH0 TH -KLAN K L AE1 N -KLANG K L AE1 NG -KLANNER K L AE1 N ER0 -KLANNER'S K L AE1 N ER0 Z -KLANS K L AE1 N Z -KLANSMAN K L AE1 N Z M AH0 N -KLANSMEN K L AE1 N Z M AH0 N -KLAPKA K L EY1 P K AH0 -KLAPKA'S K L EY1 P K AH0 Z -KLAPPAN K L AE1 P AH0 N -KLAPROTH K L AE1 P R AO2 TH -KLARA K L AA1 R AH0 -KLARNOOD K L AA1 R N UH0 D -KLAS K L AE1 S -KLASSAN K L AE1 S AH0 N -KLATAWA K L AE1 T AH0 W AA2 -KLATSCH K L AE1 CH -KLAUBER K L AW1 B ER0 -KLAUS K L AW1 S -KLAUS'S K L AH0 S IH0 Z -KLAUSEN K L AW1 S AH0 N -KLAUSEN'S K L AW1 S AH0 N Z -KLAUSENBERG K L AW1 S AH0 N B ER0 G -KLAUSENBURG K L AW1 S AH0 N B ER0 G -KLAUSNER K L AW1 S N ER0 -KLAUSOFF K L AW1 S AO0 F -KLAUSS K L AW1 S -KLAV K L EY1 V -KLAVAN K L EY1 V AH0 N -KLAVS K L AA1 V Z -KLAW K L AO2 -KLAWS K L AO2 Z -KLAXON K L AE1 K S AH0 N -KLAYU K L EY1 UW0 -KLE K AH0 L -KLEA K L IY1 AH0 -KLEA'S K L IY1 AH0 Z -KLEAGLE K L IY1 G AH0 L -KLEAGLES K L IY1 G AH0 L Z -KLEBER K L IY1 B ER0 -KLEBER'S K L IY1 B ER0 Z -KLEBS K L EH1 B Z -KLECZYNSKI K L EH1 CH IH1 N S K IY0 -KLEEF K L IY1 F -KLEEK K L IY1 K -KLEESMAN K L IY1 Z M AH0 N -KLEESMAN'S K L IY1 S M AH0 N Z -KLEGG K L EH1 G -KLEIG K L IY1 G -KLEIMAN K L AY1 M AH0 N -KLEIN K L AY1 N -KLEIN'S K L AY1 N Z -KLEINE K L AY1 N -KLEINER K L AY1 N ER0 -KLEINERT K L AY1 N ER0 T -KLEINMICHEL K L AY1 N M IH2 K AH0 L -KLEINSEITE K L AY1 N S AY2 T -KLEINTEICH K L AY1 N T AY0 K -KLEINWALDE K L AY1 N W AO2 L D -KLEINWORT K L AY1 N W ER0 T -KLEINWORT'S K L AY1 N W ER0 T S -KLEISER K L AY1 S ER0 -KLEIST K L AY1 S T -KLEIST'S K L AY1 S T S -KLEM K L EH1 M -KLEMENTI K L EH1 M AH0 N T IY0 -KLEMM K L EH1 M -KLEMM'S K L EH1 M Z -KLEON K L IY1 AH0 N -KLEOPATRA K L IY2 AH0 P AE1 T R AH0 -KLEPPER K L EH1 P ER0 -KLEPTOMANIA K L EH2 P T AH0 M EY1 N IY0 AH0 -KLEPTOMANIAC K L EH2 P T AH0 M EY1 N IY0 AE0 K -KLEPTOMANIACS K L EH2 P T AH0 M EY1 N IY0 AE2 K S -KLERK K L ER1 K -KLERKSDORP K L ER1 K S D AO0 R P -KLESMER K L EH1 S M ER0 -KLESMER'S K L EH1 S M ER0 Z -KLETTE K L EH1 T -KLETZINGK K L EH1 T S IH2 NG K -KLEW K L UW0 -KLICK K L IH1 K -KLIEWER K L IY1 W ER0 -KLIK K L IH0 K -KLIM K L IH1 M -KLIMOV K L IH0 M AA0 V -KLINDWORTH K L IH1 N D W ER0 TH -KLINE K L AY1 N -KLING K L IH1 NG -KLING'S K L IH1 NG Z -KLINGENBERG K L IH1 NG AH0 N B ER0 G -KLINGENSMITH K L IH1 NG G IH0 N S M IH0 TH -KLINGENSPIEL K L IH1 NG AH0 N S P IY2 L -KLINGER K L IH1 NG ER0 -KLINGSOHR K L IH1 NG Z AO1 R -KLINGSOR K L IH1 NG Z ER0 -KLINGSOR'S K L IH1 NG Z ER0 Z -KLINK K L IH1 NG K -KLINKAERT K L IH1 NG K EH1 R T -KLINKER K L IH1 NG K ER0 -KLINKER'S K L IH1 NG K ER0 Z -KLIP K L IH1 P -KLIPSPRINGER K L IH1 P S P R IH1 NG ER0 -KLIPSPRINGERS K L IH1 P S P R IH1 NG ER0 Z -KLITZ K L IH1 T S -KLITZING K L IH1 T Z IH0 NG -KLOCK K L AA1 K -KLOED K L OW1 D -KLOENG K L OW1 NG -KLOFE K L AA1 F -KLOH K L OW1 -KLONDIKE K L AA1 N D AY2 K -KLONDIKER K L AA1 N D AY2 K ER0 -KLONDIKERS K L AA1 N D AY2 K ER0 Z -KLONDYKE K L AA1 N D AY2 K -KLONDYKE'S K L AA1 N D AY2 K S -KLOOCH K L UW1 CH -KLOOCHMAN K L UW1 CH M AH0 N -KLOOF K L UW1 F -KLOOFS K L UW1 F S -KLOOMIRIA K L UW1 M IH0 R IY1 AH0 -KLOON K L UW1 N -KLOON'S K L UW1 N Z -KLOOTCHMAN K L UW1 CH M AH0 N -KLOOTS K L UW1 T S -KLOP K L AA1 P -KLOPEN K L OW1 P AH0 N -KLOPP K L AA1 P -KLOPSTOCK K L AA1 P S T AA2 K -KLOPSTOCK'S K L AA1 P S T AA2 K S -KLOPTON K L AA1 P T AH0 N -KLORAN K L ER0 AH0 N -KLOSHE K L AA1 SH -KLOSKING K L AA1 S K IH0 NG -KLOSKING'S K L AA1 S K IH0 NG Z -KLOSTER K L AO1 S T ER0 -KLOTA K L OW1 T AH0 -KLOTCHKOV K L AA1 T CH K AO2 V -KLOTZ K L AA1 T S -KLOW K L AW0 -KLU K L UW1 -KLUCK K L AH1 K -KLUCK'S K L AH1 K S -KLUCKERS K L AH1 K ER0 Z -KLUCKHUHN K L AH1 K HH AH1 N -KLUEBER K L UH1 B ER0 -KLUECKING K L UH1 K IH0 NG -KLUG K L AH1 G -KLUGE K L UW1 JH -KLUMPKE K L AH1 M P K -KLUTZ K L AH1 T S -KLUX K L AH1 K S -KLUXES K L AH1 K S AH0 Z -KLUXISM K L AH1 K S IH0 Z AH0 M -KLYDA K L IH1 D AH0 -KMITA K AH0 M IY1 T AH0 -KMITA'S K AH0 M IY1 T AH0 Z -KMOCH K AH0 M AA1 K -KN K EY1 EH1 N -KNABE N EY1 B -KNACK N AE1 K -KNACKER N AE1 K ER0 -KNACKER'S N AE1 K ER0 Z -KNACKERS N AE1 K ER0 Z -KNACKS N AE1 K S -KNAG N AE1 G -KNAGGS N AE1 G Z -KNAKIAS N EY1 K IY0 AH0 Z -KNAOW N EY1 OW0 -KNAOWED N EY1 OW0 D -KNAP N AE1 P -KNAPDALE N AE1 P D EY2 L -KNAPF N AE1 P F -KNAPP N AE1 P -KNAPP'S N AE1 P S -KNAPPE N AE1 P -KNAPPING N AE1 P IH0 NG -KNAPPS N AE1 P S -KNAPSACK N AE1 P S AE2 K -KNAPSACKS N AE1 P S AE2 K S -KNAPWEED N EY1 P W IY2 D -KNAPWURST N EY1 P W ER0 S T -KNARESBOROUGH N AE1 R Z B ER0 OW0 -KNAT N AE1 T -KNAT'S N AE1 T S -KNATCHBULL N AE1 CH B AH0 L -KNATCHETT N AE1 CH IH0 T -KNATS N AE1 T S -KNAUS N AO1 Z -KNAVE N EY1 V -KNAVE'S N EY1 V Z -KNAVERIES N EY1 V ER0 IY0 Z -KNAVERY N EY1 V ER0 IY0 -KNAVES N EY1 V Z -KNAVISH N EY1 V IH0 SH -KNAVISHLY N EY1 V IH0 SH L IY0 -KNAW N AO2 -KNAW'D N AW1 D -KNAWED N AO1 D -KNAWIN N AE1 W IH0 N -KNAWN N AO1 N -KNAWS N AO1 Z -KNEAD N IY1 D 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AO1 R AH0 -KORA'S K AO1 R AH0 Z -KORABLEVA K AO0 R AE1 B L EY1 V AH0 -KORAH K AO1 R AH0 -KORAK K AO1 R AH0 K -KORAK'S K AO1 R AE2 K S -KORAKS K AO0 R AE1 K S -KORAN K AO0 R AA1 N -KORANIC K AO0 R AE1 N IH0 K -KORANNA K AO0 R AA1 N AH0 -KORANNAS K AO0 R AA1 N AH0 Z -KORANS K AO0 R AE1 N Z -KORASOV K R IH0 S AO0 V -KORB K AO1 R B -KORBES K AO1 R B Z -KORCHAGIN K AO1 R CH EY1 G IH0 N -KORCHAGINS K AO1 R CH AE1 G IH0 N Z -KORDE K AO1 R D -KORDETSKI K AO0 R D EH1 T S K IY0 -KORDOFAN K AO0 R D OW0 F AE2 N -KORE K AO1 R IY0 -KOREA K AO0 R IY1 AH0 -KOREA K ER0 R IY1 AH0 -KOREA K R IY1 AH0 -KOREAN K AO0 R IY1 AH0 N -KOREAN K ER0 R IY1 AH0 N -KOREAN K R IY1 AH0 N -KOREANS K AO0 R IY1 AH0 N Z -KOREANS K ER0 R IY1 AH0 N Z -KOREANS K R IY1 AH0 N Z -KOREB K AO2 R EH1 B -KOREFF K AO2 R EH1 F -KOREISH K R IY1 IH0 SH -KOREN K AO1 R AH0 N -KORENTAL K AO1 R IH0 N T AH0 L -KORENTONA K AO1 R IH0 N T AH0 N AH0 -KORFF K AO1 R F -KORGUM K AO1 R G AH0 M -KORI K AO1 R IY0 -KORIHOR K AO0 R IY0 HH ER0 -KORINDA K AO0 R IY1 N D AH0 -KORINNA K AO0 R IH1 N AH0 -KORINNA'S K AO1 R IH0 N AH0 Z -KORITTO K AO0 R IY1 T OW0 -KORITZA K AO0 R IH1 T Z AH0 -KORKYRA K AO1 R K AY1 R AH0 -KORLIN K AO1 R L IH0 N -KORMORAN K AO1 R M ER0 AH0 N -KORMT K AO1 R M T -KORN K AO1 R N -KORNER K AO1 R N ER0 -KORNER'S K AO1 R N ER0 Z -KORNICKER K AO1 R N IH0 K ER0 -KORNILOFF K AO1 R N AH0 L AO0 F -KORNIS K AO1 R N IY0 Z -KORO K AO1 R OW0 -KOROBOTCHKA K AO1 R OW0 B AA1 CH K AH0 -KOROLENKO K AO0 R AA2 L EH1 NG K OW0 -KOROLEVICH K AO0 R AA2 L IH0 V IH0 CH -KOROLEVITCH K AO0 R AA2 L AH0 V IH0 CH -KOROLEVNA K AO0 R AA2 L AH0 V N AH0 -KOROLYOV K AO0 R AA2 L Y AH0 V -KORONG K AO1 R AO0 NG -KORORAREKA K AO1 R AO0 R AA0 R EH1 K AH0 -KORORARIKA K AO1 R AO0 R AA0 R IY1 K AH0 -KOROS K AO1 R OW0 Z -KOROSKO K AO0 R OW1 S K OW0 -KORPANOFF K ER0 P AH0 N AO0 F -KORPER K AO1 R P ER0 -KORPS K AO1 R P S -KORRIGAN K AO1 R IH0 G AH0 N -KORRIGANS K AO1 R IH0 G AH0 N Z -KORSAKOFF K AO0 R S EY1 K AO0 F -KORSAKOV K AO0 R S EY1 K AA0 V -KORSAKOVSK K AO0 R S EY1 K AA1 F S K -KORSE K AO1 R S -KORSOER K AO1 R S OW0 ER0 -KORTE K AO1 R T -KORTI K AO1 R T IY0 -KORTLANDT K AO1 R T L AH0 N T -KORUS K AO1 R AH0 S -KORUST K AO1 R AH0 S T -KORVAN K AO1 R V AH0 N -KORVIN K AO1 R V IH0 N -KORWSKY K AO1 R W S K IY0 -KORY K AO1 R IY0 -KORYNTHIA K AO1 R IH1 N TH IY0 AH0 -KOS K AA1 S -KOSAKA K AH0 S AH0 K AH0 -KOSALA K AH0 S AA1 L AH0 -KOSATO K AH0 S AA1 T OW0 -KOSCAM K OW1 S K AE0 M -KOSCIEY K AH0 S CH IY0 -KOSCIUSKO K AH0 S CH UW1 S K OW0 -KOSCIUSKO'S K AH0 S CH UW1 S K OW0 Z -KOSCIUSZKO K AH0 S CH UW1 SH K OW0 -KOSCZIELNA K AH0 S CH IH0 L N AA1 -KOSEK K OW1 S EH0 K -KOSEKIN K AA1 S IH0 K IH0 N -KOSERITZ K OW1 S EH0 R IH0 T S -KOSERITZES K OW1 Z ER0 IH1 T S IH0 Z -KOSH K AA1 SH -KOSHAR K OW1 SH AA2 R -KOSHARE K OW1 SH EH2 R -KOSHCHEI K OW1 SH AY0 -KOSHEH K OW1 SH -KOSHER K OW1 SH ER0 -KOSHIN K OW1 SH IH0 N -KOSHINGA K OW1 SH IY1 NG G AH0 -KOSHIU K OW1 SH UW1 -KOSHNILI K OW1 SH N IY1 L IY0 -KOSIS K OW1 S IH0 S -KOSLOV K AH0 S L AA2 V -KOSMA K AH0 S M AH0 -KOSMAROFF K AH0 S M AA1 R AO0 F -KOSMAROFF'S K AA2 Z M ER0 AO0 F S -KOSMOS K AA2 Z M OW0 Z -KOSO K OW1 S OW0 -KOSOGOL K AH0 S AO1 G AH0 L -KOSOVO K OW1 S OW0 V OW2 -KOSRU K AA1 Z R UW2 -KOSSOVO K AA1 S OW0 V OW2 -KOSSUTH K AA1 S AH0 TH -KOSSUTH'S K AA1 S AH0 TH S -KOST K AA1 S T -KOSTANZHOGLO K AH0 S T AA1 N Z AH0 G L OW1 -KOSTER K AA1 S T ER0 -KOSTI K AA1 S T IY0 -KOSTIA K AA1 S T IY0 AH0 -KOSTIEI K AA1 S T IY0 EY2 -KOSTKA K AA1 S T K AH0 -KOSTROMA K AH0 S T R OW1 M AH0 -KOSTUILIN K AA1 S T UW0 L IH0 N -KOSTYA K OW1 S T Y AH0 -KOSUKE K AH0 S UW0 K -KOSUTH K AH0 S UW1 TH -KOSWELL K AA1 S W EH0 L -KOSZTA K AA1 S T AH0 -KOT K AA1 T -KOTA K OW1 T AH0 -KOTAH K AH0 T AA1 -KOTAL K OW1 T AH0 L -KOTCH K AA1 CH -KOTCHED K AA1 CH T -KOTCHEVOY K AA1 CH IH0 V OY2 -KOTE K OW1 T -KOTEI K OW1 T EY2 -KOTH K AA1 TH -KOTI K OW1 T IY0 -KOTICK K AH0 T IH2 K -KOTIK K AA1 T IH0 K -KOTKELL K AA1 T K AH0 L -KOTLICKI K AA1 T L IH1 T S K IY0 -KOTO K OW1 T OW0 -KOTOK K OW1 T AA2 K -KOTOWING K AH0 T OW1 IH0 NG -KOTRINA K AH0 T R IY1 N AH0 -KOTTAH K AA1 T AH0 -KOTTENNER K AA1 T EH1 N ER0 -KOTTWITZ K AA1 T W IH0 T S -KOTUKO K AH0 T UW1 K OW0 -KOTUL K AH0 T UW1 L -KOTWAL K AA1 T W AH0 L -KOTZE K OW1 T Z -KOTZEBUE K AA1 T S AH0 B Y UW2 -KOTZEBUE'S K AA1 T S IH0 B Y UW2 Z -KOU K UW1 -KOUA K UW1 AH0 -KOUAGA K W AA1 G AH0 -KOUANG K W AA1 NG -KOUBAN K UW1 B AH0 N -KOUBBA K AW1 B AH0 -KOUIDAR K UW1 AH0 D AA2 R -KOUKA K UW1 K AH0 -KOULIKOFF K AW1 L IH0 K AO2 F -KOUM K OW1 M -KOUME K UW1 M -KOUMISS K UW1 M IH0 S -KOUMONGOE K UW1 M AA2 NG G OW0 -KOUN K AW1 N -KOUPRIANE K UW1 P R IY0 AE1 N -KOUPRIANE'S K UW1 P R IY0 AE1 N Z -KOURAGINE K UW1 R AA0 JH IY1 N IY0 -KOURAKIN K UW1 R AH0 K IH0 N -KOURATA K UW1 R AA1 T AH0 -KOURBASH K AW1 R B AE2 SH -KOUROPATKINE K UW1 R AH0 P AE1 T K AH0 N -KOURSK K UW1 R S K -KOUS K AH0 S -KOUSKI K UW1 S K IY0 -KOUSKOUS K UW1 S K AH0 S -KOUSMA K AW1 S M AH0 -KOUSMINSKI K AW1 S M IH1 N S K IY0 -KOUSSEVITZKY K UW1 S EH0 V IH1 T S K IY0 -KOUTONEPI K AW1 T AH0 N IY1 P IY0 -KOVAK K OW1 V AE0 K -KOVALCHUK K AH0 V AA1 L HH AH0 K -KOVALENKO K OW1 V AA0 L EH1 NG K OW0 -KOVALEVSKY K OW1 V AA0 L EH1 V S K IY0 -KOVALOFF K AH0 V AA1 L AO0 F -KOVALSKI K AH0 V AA1 L S K IY0 -KOVANDASWAMY K AH0 V AA1 D AH0 S W AA1 M IY0 -KOVEN K OW1 V AH0 N -KOVNO K AA1 V N OW0 -KOVRIN K OW1 V R IH0 N -KOVUDOO K OW1 V AH0 D UW2 -KOW K AW0 -KOWA K AW1 AH0 -KOWALSKI K AH0 W AA1 L S K IY0 -KOWALT K AW1 AH0 L T -KOWEN K AW1 AH0 N -KOWFAT K OW1 F AE2 T -KOWLIANG K OW1 L Y AE1 NG -KOWNO K AW0 N OW0 -KOWRIES K AW1 R IY0 Z -KOWSHING K OW1 SH IH0 NG -KOWTERSKI K AW1 T IH1 R S K IY0 -KOWTOW K AW1 T AW1 -KOWTOW K OW1 T OW1 -KOWTOWED K OW1 T OW1 D -KOWTOWING K OW1 T OW1 IH0 NG -KOY K OY1 -KOYALA K OW0 Y AA1 L AH0 -KOYALA'S K OW0 Y AA1 L AH0 Z -KOYOKUK K OY1 OW0 K AH0 K -KOYUK K OW0 Y UW1 K -KOYUKUK K OW0 Y UW1 K AH0 K -KOZAK K OW1 Z AH0 K -KOZO K OW1 Z OW0 -KR K R -KRAAL K R AA1 L -KRAALBOOM K R AA1 L B UW2 M -KRAALED K R AH1 L AH0 D -KRAALS K R AA1 L Z -KRADEN K R EY1 D AH0 N -KRAEVESK K R EY1 V EH0 S K -KRAFFT K R AE1 F T -KRAFFT'S K R AE1 F T S -KRAFT K R AE1 F T -KRAG K R AE1 G -KRAGAN K R EY1 G AH0 N -KRAGANS K R EY1 G AH0 N Z -KRAGLIEVIC K R AE1 G L Y AH0 V IH0 K -KRAILL K R AH0 L -KRAILL'S K R EY2 L Z -KRAKA K R AA1 K AH0 -KRAKATAU K R AA1 K AH0 T AW0 -KRAKATOA K R AA1 K AH0 T OW0 AH0 -KRAKAUER K R AE1 K AW0 R -KRAKEN K R EY1 K AH0 N -KRAKER K R EY1 K ER0 -KRAKOW K R AA1 K AA0 V -KRAKOW K R AA1 K AW0 -KRAKOW K R AE1 K AW0 -KRAKOWITZ K R AE1 K AH0 W IH0 T S -KRALAHOME K R AE1 L AH0 HH OW1 M -KRALINSKY K R AH0 L IH1 N S K IY0 -KRALITZ K R AA1 L IH0 T S -KRAMENIN K R AE1 M IH0 N IH0 N -KRAMER K R EY1 M ER0 -KRAMER'S K R EY1 M ER0 Z -KRAMM K R AE1 M -KRAN K R AH0 N -KRANATH K R AE1 N AH0 TH -KRANATH'S K R AE1 N AH0 TH S -KRANITSKI K R AH0 N IH1 T S K IY0 -KRANK K R AE1 NG K -KRANNON K R AE1 N AH0 N -KRANS K R AE1 N Z -KRANSBERG K R AE1 N Z B ER0 G -KRANT K R AE1 N T -KRANTZ K R AE1 N T S -KRANTZER K R AE1 N T Z ER0 -KRANTZES K R AE1 N T S IH0 Z -KRANZ K R AE1 N Z -KRANZLER K R AE1 N Z L ER0 -KRAP K R AE1 P -KRAPACH K R AA1 P AH0 CH -KRAPAK K R AE1 P AH0 K -KRAPF K R AE1 P F -KRAPP K R AE1 P -KRAPP'S K R AE1 P S -KRASINSKA K R AH0 S IH1 N S K AH0 -KRASINSKI K R AH0 S IH1 N S K IY0 -KRASIPPE K R AH0 S IH1 P -KRASNOI K R AE0 S N OY0 -KRASNOIARSK K R AA1 S N OW0 IY0 AA1 R S K -KRASNOYARSK K R AE0 S N OY1 AA0 R S K -KRASSIN K R AE1 S IH0 N -KRASSNOV K R AE1 S N AA0 V -KRASSOTKIN K R AE1 S OW0 T K IH1 N -KRATES K R EY1 T S -KRATON K R AE1 T AH0 N -KRAUS K R AW1 S -KRAUSE K R AO1 S -KRAUSE'S K R AW1 S IH0 Z -KRAUSS K R AW1 S -KRAUT K R AW1 T -KRAYBO K R EY1 B OW2 -KRAYENHOFF K R EY1 AH0 N HH AO2 F -KRAYTON K R EY1 T AH0 N -KRAZINSKI K R AE1 Z IH1 N S K IY0 -KRAZY K R EY1 Z IY0 -KREBS K R EH1 B Z -KREGER K R IY1 G ER0 -KREHBIEL K R EH1 B IY0 L -KREIGER K R AY1 G ER0 -KREIS K R IY1 Z -KREISGERICHTSRATH K R AY1 S G ER0 IH0 K T S R AH0 TH -KREISLER K R AY1 S L ER0 -KREISLER'S K R AY1 S L ER0 Z -KREISLERIANA K R AY1 S L ER0 IY0 AA1 N AH0 -KREISPHYSIKUS K R AY1 S F AH0 Z IH1 K AH0 S -KREISS K R AY1 S -KRELI K R EH1 L IY0 -KRELI'S K R EH1 L IY0 Z -KREMLIN K R EH1 M L AH0 N -KREMLIN K R EH1 M L IH0 N -KREMNITZ K R EH1 M N IH0 T S -KREMPE K R EH1 M P -KRENNER K R EH1 N ER0 -KRENOJ K R EH1 N OW0 JH -KRENSKA K R EH1 N S K AH0 -KRENSKI K R EH1 N S K IY0 -KREPETSKI K R EH0 P EH1 T S K IY0 -KREPETSKIS K R EH0 P EH1 T S K IY0 Z -KRESPEL K R EH1 S P AH0 L -KRESPEL'S K R EH1 S P AH0 L Z -KRESS K R EH1 S -KRESTOWSKY K R EH2 S T AW1 S K IY0 -KRETA K R EH1 T AH0 -KRETSCHMER K R EH1 CH M ER0 -KREUTZ K R UW1 T S -KREUTZER K R OY1 T Z ER0 -KREUTZERS K R OY1 T Z ER0 Z -KREUTZKAMMER K R OY1 T S K AE1 M ER0 -KREUZ K R UW1 Z -KREUZER K R UW1 Z ER0 -KREUZERS K R UW1 Z ER0 Z -KREVIN K R EH1 V AH0 N -KREWE K R UW1 -KREYMBORG K R IH2 M B AO0 R G -KREYNBORG K R EY1 N B AO0 R G -KRIEFF K R IY2 F -KRIEG K R IY1 G -KRIEGER K R IY1 G ER0 -KRIEGSPIEL K R IY1 G S P IY0 L -KRIEMHILD K R IY1 AY2 L D -KRIEMHILD'S K R IY1 M HH IH0 L D Z -KRIEMHILDA K R IY1 M HH IY1 L D AH0 -KRIGE K R IH0 JH -KRIGE'S K R AY1 JH IH0 Z -KRIGES K R AY1 JH IH0 Z -KRIGGER K R IH1 G ER0 -KRIIJORL K R IY1 Y AO1 R L -KRIJGSRAAD K R IH0 JH Z R AA1 D -KRILOFF K R IH1 L AO0 F -KRIM K R IH1 M -KRIMGIRAI K R IH0 M JH IH0 R AA1 IY0 -KRINGLE K R IH1 NG G AH0 L -KRINK K R IH1 NG K -KRINKEN K R IH1 NG K AH0 N -KRIPA K R IY1 P AH0 -KRIS K R IH1 S -KRISBAY K R IH1 Z B EY2 -KRISCHAN K R IH1 SH AH0 N -KRISES K R AY1 Z IH0 Z -KRISHNA K R IH1 SH N AH0 -KRISHNA K R IY1 SH N AH0 -KRISHNA'S K R IH1 SH N AH0 Z -KRISHNOS K R IH1 SH N OW0 S -KRISHT K R IH1 SH T -KRISHTS K R IH1 SH T S -KRISS K R IH1 S -KRISSED K R IH1 S T -KRISSES K R IH1 S IH0 Z -KRIST K R IH1 S T -KRISTEN K R IH1 S T AH0 N -KRISTIAN K R IH1 S T IY0 AH0 N -KRISTOPO K R IH0 S T AA1 P OW2 -KRISTY K R IH1 S T IY0 -KRISTY'S K R IH1 S T IY0 Z -KRITA K R IY1 T AH0 -KRITHIA K R IH1 TH IY0 AH0 -KRITIK K R IH1 T IH0 K -KRITZINGER K R IH1 T Z IH0 NG ER0 -KRIVER K R IH1 V ER0 -KRIVOSCIA K R IH1 V OW1 S CH AH0 -KRIZSANOWSKI K R IH1 Z S AH0 N AO1 F S K IY0 -KRO K R OW1 -KROCHMAL K R AA1 K M AE1 L -KROEKER K R OW1 K ER0 -KROG K R AA1 G -KROGSKOGEN K R AA1 G S K AA1 G AH0 N -KROGSTAD K R AA1 G S T AH0 D -KROLL K R AO1 L -KROLL'S K R AO1 L Z -KROLLER K R OW1 L ER0 -KROM K R AA1 M -KROMITZKA K R OW1 M IH1 T S K AH0 -KROMITZKI K R OW1 M IH1 T S K IY0 -KROMITZKI'S K R OW1 M IH1 T S K IY0 Z -KROMODEOR K R OW1 M AH0 D IY0 ER0 -KRON K R AA1 N -KRONAS K R OW1 N AH0 Z -KRONBERG K R AA1 N B ER0 G -KRONBORG K R AA1 N B AO0 R G -KRONBORG'S K R AA1 N B AO0 R G Z -KRONBORGS K R AA1 N B AO0 R G Z -KRONE K R OW1 N AH0 -KRONEN K R OW1 N AH0 N -KRONER K R OW1 N ER0 -KRONERS K R OW1 N ER0 Z -KRONES K R OW1 N Z -KRONION K R OW1 N Y AH0 N -KRONISCHE K R AA1 N IH0 SH IY0 -KRONK K R AA1 NG K -KRONOS K R OW1 N OW0 S -KRONPRINZ K R AA1 N P R IH1 N Z -KRONSTADT K R AA1 N S T AE2 T -KROO K R UW0 -KROOBOYS K R UW1 B OY2 Z -KROOK K R UH2 K -KROOMAN K R UW1 M AH0 N -KROOMEN K R UW2 M AH0 N -KROONSTAD K R UW1 N S T AE2 D -KROPFSBERG K R AA1 P F S B ER0 G 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AO0 F -KRYN K R IH1 N -KRYSIA K R IH1 S IY0 AH0 -KRYSIA'S K R IH1 S IY0 AH0 Z -KRYUKOV K R Y UW0 K AA2 V -KRZESNIA K R EH1 Z N IY0 AH0 -KRZYZAK K R IH0 Z AH0 K -KRZYZAKS K R IH0 Z AE0 K S -KS K S -KSEW K S UW0 -KSHATRIYA K SH AH0 T R IY1 AH0 -KSHATRIYAS K SH AE1 T R IH0 Y AA1 S -KSHATTRIYA K SH AA2 T R IY1 AH0 -KSHIR K SH ER0 -KSIONDZ K SH AH0 D Z -KT K T -KU K UW1 -KU'S K UW2 Z -KUA K Y UW1 AH0 -KUAN K W AA1 N -KUBA K Y UW1 B AH0 -KUBAN K Y UW1 B AH0 N -KUBAR K AH0 B AA1 R -KUBAYS K UW0 B EY2 Z -KUBBADAR K AH1 B AH0 D AA1 R -KUBBEE K AH1 B IY2 -KUBBELING K AH1 B AH0 L IH0 NG -KUBEL K UW1 B AH0 L -KUBELIK K UW1 B EH2 L IH0 K -KUBI K UW1 B IY0 -KUBIK K Y UW1 B IH0 K -KUBIN K Y UW1 B IH0 N -KUBLA K AH1 B L AH0 -KUBLAI K AH1 B L AA1 IY0 -KUBO K Y UW1 B OW0 -KUBOKO K UW0 B OW1 K OW0 -KUBOKO'S K UW0 B OW1 K OW0 Z -KUBWA K AH1 B W AH0 -KUC K AH1 K -KUCHEN K AH1 K AH0 N -KUCHENG K UW2 CH EH1 NG -KUCHIN K AH0 CH IH1 N -KUCHING K AH1 CH IH0 NG -KUCSUK K AH1 K S AH1 K -KUD K AH1 D -KUDAN K UW1 D 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AH0 T -LAMPMAN L AE1 M P M AH0 N -LAMPON L AE1 M P AO0 N -LAMPOON L AE0 M P UW1 N -LAMPOONED L AE0 M P UW1 N D -LAMPOONING L AE0 M P UW1 N IH0 NG -LAMPOONS L AE0 M P UW1 N Z -LAMPORT L AE1 M P AO2 R T -LAMPOURDE L AE0 M P AO1 R D -LAMPPOST L AE1 M P OW2 S T -LAMPPOST L AE1 M P P OW2 S T -LAMPPOSTS L AE1 M P P OW2 S T S -LAMPREY L AE1 M P R IY0 -LAMPREYS L AE1 M P R IY0 Z -LAMPRIAS L AE1 M P R IY1 AH0 Z -LAMPS L AE1 M P S -LAMPSACUS L AE1 M P S AE1 K IH0 S -LAMPSHADE L AE1 M SH EY2 D -LAMPSHADES L AE1 M SH EY2 D Z -LAMPSON L AE1 M P S AH0 N -LAMPTON L AE1 M P T AH0 N -LAMPTON'S L AE1 M P T AH0 N Z -LAMPTONS L AE1 M P T AH0 N Z -LAMPUGNANI L AE1 M P Y UW1 N AA1 N IY0 -LAMSON L AE1 M S AH0 N -LAMSON'S L AE1 M S AH0 N Z -LAMU L AA0 M UW1 -LAMUSE L AH0 M Y UW1 Z -LAMY L EY1 M IY0 -LAMY'S L EY1 M IY0 Z -LAN L AE1 N -LAN'LORD L AE1 N L AO2 R D -LAN'S L AE1 N Z -LANA L AE1 N AH0 -LANA'S L AE1 N AH0 Z -LANAHAN L AE1 N AH0 HH AE0 N -LANAHERNE L AE1 N AH0 HH ER1 N -LANAI L AH0 N AY1 -LANAO L AH0 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F ER0 Z -LANFRANC L AE1 N F R AE1 NG K -LANFRANC'S L AE1 N F R AE1 NG K S -LANFRANCHI L AH0 N F R AA1 N K IY0 -LANFREY L AE1 N F R IY0 -LANG L AE1 NG -LANG'S L AE1 NG Z -LANGAGE L AE1 NG IH0 JH -LANGAN L AE1 NG G AH0 N -LANGAR L AE1 NG ER0 -LANGBAINE L AE1 NG B EY1 N -LANGBOROUGH L AE1 NG B ER0 OW0 -LANGBOURNE L AE1 NG B ER0 N -LANGDALE L AE1 NG D EY2 L -LANGDALE'S L AE1 NG D EY2 L Z -LANGDEN L AE1 NG D EH2 N -LANGDON L AE1 NG D AH0 N -LANGDON'S L AE1 NG D AH0 N Z -LANGDONS L AE1 NG D AH0 N Z -LANGE L AE1 NG -LANGE'S L AE1 NG Z -LANGEAC L EY1 NG G IY1 K -LANGEAIS L AE1 NG IY0 AH0 S -LANGELAND L EY1 NG G IH0 L AH0 N D -LANGELAND L EY1 NG G L AH0 N D -LANGELLEY L AE1 NG G AH0 L IY0 -LANGEN L AE1 NG AH0 N -LANGENBURG L AE1 NG AH0 N B ER0 G -LANGENGRAD L AE1 NG AH0 N G R AE2 D -LANGENI L EY1 NG G IH0 N IY0 -LANGENS L AE1 NG G AH0 N Z -LANGENSALZA L EY1 NG G IH0 N S AE1 L Z AH0 -LANGEOOG L EY1 NG G IH0 UW1 G -LANGER L AE1 NG ER0 -LANGERNAULT L AE1 NG ER0 N AO2 L T -LANGERON L EY1 NG G ER0 AH0 N -LANGEST L AE1 NG G AH0 S T -LANGETHAL L EY1 NG G IH0 TH AH0 L -LANGEVIN L EY1 NG G IH0 V IH0 N -LANGFELDT L AE1 NG F EH0 L T -LANGFORD L AE1 NG F ER0 D -LANGFORD'S L AE1 NG F ER0 D Z -LANGHAM L AE1 NG AH0 M -LANGHAM L AE1 NG HH AE0 M -LANGHAM'S L AE1 NG AH0 M Z -LANGHEINRICH L AE1 NG HH AY1 N R IH0 K -LANGHETTI L AE1 NG EH1 T IY0 -LANGHETTI'S L AE1 NG EH1 T IY0 Z -LANGHOLM L AE1 NG HH OW0 L M -LANGHOLM'S L AE1 NG HH OW2 L M Z -LANGHOPE L AE1 NG HH OW2 P -LANGHOPE'S L AE1 NG HH OW2 P S -LANGHORN L AE1 NG HH AO0 R N -LANGHORNE L AE1 NG HH AO0 R N -LANGHORNE'S L AE1 NG HH AO0 R N Z -LANGIS L AE1 NG AH0 S -LANGLADE L AE1 NG L AH0 D -LANGLAND L AE1 NG L AH0 N D -LANGLE L AE1 NG G AH0 L -LANGLER L AE1 NG G L ER0 -LANGLER'S L AE1 NG L ER0 Z -LANGLESS L AE1 NG L AH0 S -LANGLEY L AE1 NG G L IY0 -LANGLEY L AE1 NG L IY0 -LANGLEY'S L AE1 NG G L IY0 Z -LANGLEYS L AE1 NG G L IY0 Z -LANGLOIS L AH0 NG L W AA1 -LANGLUME L AE1 NG L UW1 M -LANGLY L AE1 NG L IY0 -LANGMAID L AE1 NG M EY2 D -LANGMAID'S L AE1 NG M EY2 D Z -LANGMORE L AE1 NG M AO0 R -LANGMORE'S L AE1 NG M AO2 R Z -LANGON L AH0 NG G AO1 N -LANGONA L AA0 NG G OW1 N AH0 -LANGOUR L AE1 NG G ER0 -LANGRAGE L AE1 NG G R AH0 JH -LANGRES L AE1 NG G R Z -LANGRIDGE L AE1 NG G R IH0 JH -LANGRIDGE'S L AE1 NG G R IH2 JH IH0 Z -LANGRIGG L AE1 NG G R IH1 G -LANGRISH L AE1 NG G R IH0 SH -LANGRISHE L AE1 NG G R IY1 SH -LANGRUNE L AE1 NG G R UW1 N -LANGRUNE'S L AE1 NG G R UW0 N Z -LANGRYE L AE1 NG G R IY0 -LANGS L AE1 NG Z -LANGSDORFF L AE1 NG Z D AO0 R F -LANGSIDE L AE1 NG S AY1 D -LANGSTON L AE1 NG S T AH0 N -LANGSTON'S L AE1 NG S T AH0 N Z -LANGSTROTH L AE1 NG S T R AO0 TH -LANGSYNE L AE1 NG S AY0 N -LANGTON L AE1 NG T AH0 N -LANGTON'S L AE1 NG T AH0 N Z -LANGTRY L AE1 NG T R IY0 -LANGUAGE L AE1 NG G W AH0 JH -LANGUAGE L AE1 NG G W IH0 JH -LANGUAGED L AE1 NG G W IH0 JH D -LANGUAGES L AE1 NG G W AH0 JH AH0 Z -LANGUAGES L AE1 NG G W IH0 JH IH0 Z -LANGUE L AE1 NG -LANGUEDOC L AE1 NG G W EH1 D AH0 K -LANGUEDOCIAN L AE1 NG G W EY0 D OW1 CH AH0 N -LANGUEO L AE1 NG G W EY1 OW0 -LANGUES L AE1 NG Z -LANGUID L AE1 NG G W AH0 D -LANGUIDLY L AE1 NG G W AH0 D L IY0 -LANGUIDNESS L AE1 NG G W AH0 D N AH0 S -LANGUISH L AE1 NG G W IH0 SH -LANGUISH'D L AE1 NG G W IH0 SH D -LANGUISHED L AE1 NG G W IH0 SH T -LANGUISHES L AE1 NG G W IH0 SH IH0 Z -LANGUISHETH L AE1 NG G W IH0 SH IH0 TH -LANGUISHING L AE1 NG G W IH0 SH IH0 NG -LANGUISHINGLY L AE1 NG G W IH0 SH IH0 NG L IY0 -LANGUISHMENT L AE1 NG G W IH0 SH M AH0 N T -LANGUISHMENTS L AE1 NG G W IH0 SH M AH0 N T S -LANGUOR L AE1 NG G W AO1 R -LANGUOROUS L AE1 NG G W ER0 AH0 S -LANGUOROUSLY L AE1 NG G W ER0 AH0 S L IY0 -LANGUORS L AE1 NG G W AO1 R Z -LANGUR L AA0 NG G ER0 -LANGURANT L AE1 NG G Y ER0 AH0 N T -LANGURS L AA0 NG G ER0 Z -LANGWERTH L AE1 NG W ER0 TH -LANGWIDERE L AE1 NG G W IH0 D IH2 R -LANGWIDGE L AE1 NG G W IH0 JH -LANGWORTH L AE1 NG G W ER0 TH -LANGWORTHY L AE1 NG W ER0 DH IY0 -LANGY L AE0 N JH IY0 -LANHAM L AE1 N AH0 M -LANI L AA1 N IY0 -LANIER L AH0 N IH1 R 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K IY0 -MIELLYN M IY1 L IH0 N -MIEMON M IY1 M AH0 N -MIEN M IY1 N -MIENNE M IY0 EH2 N -MIENS M IY1 N Z -MIER M AY1 ER0 -MIERIS M AY1 ER0 IH0 S -MIERRE M IY0 EH1 R -MIERS M AY1 ER0 Z -MIETTE M IY1 T -MIETTE'S M IY1 T S -MIEUX M Y UW1 -MIEVILLE M IY2 V IH0 L -MIEW M IY0 UW0 -MIEZE M IY1 Z -MIFF M IH1 F -MIFFED M IH1 F T -MIFFLIN M IH1 F L IH0 N -MIFFLIN'S M IH1 F L IH0 N Z -MIFROID M IH1 F R OY0 D -MIG M IH1 G -MIGADAWON M IY0 G AA1 D AH0 W AA0 N -MIGDOL M IH1 G D AH0 L -MIGGLES M IH1 G AH0 L Z -MIGGS M IH1 G Z -MIGGS'S M IH1 G Z IH0 Z -MIGHELS M AY1 AH0 L Z -MIGHT M AY1 T -MIGHT'S M AY1 T S -MIGHT'ST M AY1 T S T -MIGHT'VE M AY1 T AH0 V -MIGHTA M AY1 T AH0 -MIGHTE M AY1 T -MIGHTEST M AY1 T AH0 S T -MIGHTIE M AY1 T IY0 -MIGHTIER M AY1 T IY0 ER0 -MIGHTIES M AY1 T IY0 Z -MIGHTIEST M AY1 T IY0 AH0 S T -MIGHTILY M AY1 T AH0 L IY0 -MIGHTINESS M AY1 T IY0 N AH0 S -MIGHTINESSES M AY1 T IY0 N AH0 S IH0 Z -MIGHTLY M AY1 T L IY0 -MIGHTN'T M AY1 T AH0 N T -MIGHTS M AY1 T S -MIGHTST M AY1 T S T 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IH1 Z ER0 AH0 B L IY0 -MISERABLY M IH1 Z R AH0 B L IY0 -MISERE M IH1 Z ER0 -MISERERE M IH1 Z ER0 IH2 R -MISERERES M IH1 Z R IY1 R Z -MISERES M IH1 Z R Z -MISERIA M IH1 Z ER0 IY1 AH0 -MISERICORDE M IH1 Z R IH0 K AO2 R D -MISERICORDIA M IH1 Z R IH0 K AO1 R D IY0 AH0 -MISERICORDIAE M IH1 Z R IH0 K AO1 R D IY0 AH0 -MISERICORDIAM M IH1 Z R IH0 K AO1 R D IY0 AH0 M -MISERIE M IH1 Z ER0 IY0 -MISERIES M IH1 Z ER0 IY0 Z -MISERIS M IH1 Z R IH0 S -MISERLINESS M AY1 Z ER0 L IY0 N AH0 S -MISERLY M AY1 Z ER0 L IY0 -MISERO M IH1 Z R OW0 -MISERRIMUS M IH1 Z ER0 R AY1 M AH0 S -MISERS M AY1 Z ER0 Z -MISERUM M IH1 Z R AH0 M -MISERY M IH1 Z ER0 IY0 -MISERY'S M IH1 Z R IY0 Z -MISES M AY1 Z IH0 Z -MISFIRE M IH0 S F AY1 ER0 -MISFIRED M IH0 S F AY1 ER0 D -MISFIRING M IH0 S F AY1 ER0 IH0 NG -MISFIT M IH1 S F IH2 T -MISFITS M IH1 S F IH2 T S -MISFITTING M IH1 S F IH2 T IH0 NG -MISFORMED M IH0 S F AO1 R M D -MISFORT'NIT M IH0 S F AO1 R CH AH0 N IH0 T -MISFORTIN M IH0 S F AO1 R T IH0 N -MISFORTUN M IH0 S F AO1 R CH AH0 N -MISFORTUNATE M IH0 S F AO1 R CH UW0 N AH0 T -MISFORTUNE M IH0 S F AO1 R CH AH0 N -MISFORTUNE'S M IH0 S F AO1 R CH UW0 N Z -MISFORTUNES M IH0 S F AO1 R CH AH0 N Z -MISGAVE M IH0 S G EY1 V -MISGIVE M IH0 S G IH1 V -MISGIVES M IH0 S G IH1 V Z -MISGIVING M IH0 S G IH1 V IH0 NG -MISGIVINGS M IH0 S G IH1 V IH0 NG Z -MISGOVERN M IH0 S G AH1 V ER0 N -MISGOVERNED M IH0 S G AH1 V ER0 N D -MISGOVERNMENT M IH0 S G AH1 V ER0 N M AH0 N T -MISGUIDANCE M IH0 S G AY1 D AH0 N S -MISGUIDE M IH0 S G AY1 D -MISGUIDED M IH0 S G AY1 D IH0 D -MISGUIDING M IH0 S G AY1 D IH0 NG -MISH M IH1 SH -MISHA M IH1 SH AH0 -MISHA'S M IH1 SH AH0 Z -MISHAEL M IH1 SH AH0 L -MISHANDLE M IH0 S HH AE1 N D AH0 L -MISHANDLED M IH0 S HH AE1 N D AH0 L D -MISHANDLING M IH0 S HH AE1 N D L IH0 NG -MISHAP M IH1 S HH AE2 P -MISHAPS M IH1 S HH AE2 P S -MISHAUMOK M IH1 SH OW0 M AA1 K -MISHCHA M IH1 SH HH AH0 -MISHE M IY1 SH -MISHEARD M IH2 S HH ER1 D -MISHELL M IH1 SH AH0 L -MISHKA M IY1 SH K AH0 -MISHKA'S M IY1 SH K AH0 Z 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L EY1 IH0 NG -MISLEAD M IH0 S L IY1 D -MISLEADER M IH0 S L IY1 D ER0 -MISLEADING M IH0 S L IY1 D IH0 NG -MISLEADINGLY M IH0 S L IY1 D IH0 NG L IY0 -MISLEADS M IH0 S L IY1 D Z -MISLED M IH0 S L EH1 D -MISLETOE M IH0 S L T OW2 -MISLIKE M IH0 S L AY1 K -MISLIKED M IH0 S L AY1 K T -MISLIKES M IH0 S L AY1 K S -MISLIKETH M IH0 S L AY1 K TH -MISLIKING M IH0 S L AY1 K IH0 NG -MISMANAGE M IH0 S M AE1 N IH0 JH -MISMANAGED M IH0 S M AE1 N IH0 JH D -MISMANAGEMENT M IH0 S M AE1 N IH0 JH M AH0 N T -MISMANAGEMENTS M IH0 S M AE1 N IH0 JH M AH0 N T S -MISMANAGING M IH0 S M AE1 N IH0 JH IH0 NG -MISMATCHED M IH0 S M AE1 CH T -MISMATED M IH0 S M AE1 D -MISNAME M IH0 S N EY2 M -MISNAMED M IH0 S N EY2 M D -MISNOMER M IH0 S N OW1 M ER0 -MISNOMERS M IH0 S N OW1 M ER0 Z -MISOGYNIST M IH1 Z AH0 JH IH0 N IH0 S T -MISOGYNISTIC M IH1 Z AH0 JH IH0 N IH0 S T IH0 K -MISOGYNISTS M IH1 Z AH0 JH IH0 N IH0 S -MISOGYNISTS M IH1 Z AH0 JH IH0 N IH0 S T S -MISOGYNY M IH1 Z AH0 JH IH0 N IY0 -MISONNE M IH2 S AH0 N -MISPLACE M IH0 S P L EY1 S -MISPLACED M IH0 S P L EY1 S T -MISPLACEMENT M IH0 S P L EY1 S M AH0 N T -MISPLACES M IH0 S P L EY1 S IH0 Z -MISPLACING M IH0 S P L EY1 S IH0 NG -MISPLAY M IH0 S P L EY1 -MISPRINT M IH1 S P R IH1 N T -MISPRINTED M IH1 S P R IH1 N T AH0 D -MISPRINTS M IH1 S P R IH1 N T S -MISPRISION M IH0 S P R IH1 ZH AH0 N -MISPRIZED M IH0 S P R AY1 Z D -MISPRONOUNCE M IH0 S P R AH0 N AW1 N S -MISPRONOUNCED M IH0 S P R AH0 N AW1 N S T -MISPRONOUNCING M IH0 S P R AH0 N AW1 N S IH0 NG -MISPRONUNCIATION M IH0 S P R AH0 N AH2 N S IY0 EY1 SH AH0 N -MISPRONUNCIATIONS M IH0 S P R AH0 N AH2 N S IY0 EY1 SH AH0 N Z -MISQUITH M IH0 S K W IH0 TH -MISQUOTATION M IH0 S K W OW0 T EY1 SH AH0 N -MISQUOTATIONS M IH0 S K W OW0 T EY1 SH AH0 N Z -MISQUOTE M IH0 S K W OW1 T -MISQUOTED M IH0 S K W OW1 T IH0 D -MISQUOTES M IH0 S K W OW1 T S -MISQUOTING M IH0 S K W OW1 T IH0 NG -MISRAIM M IH0 S R AH0 M -MISREAD M IH0 S R EH1 D -MISREAD M IH0 S R IY1 D -MISREADING M IH0 S R IY1 D IH0 NG -MISREMEMBER M IH0 S R IY0 M EH1 M B ER0 -MISREPORTED M IH0 S R IH0 P AO1 R T IH0 D -MISREPRESENT M IH0 S R EH2 P R AH0 Z EH1 N T -MISREPRESENTATION M IH0 S R EH2 P R IH0 Z EH0 N T EY1 SH AH0 N -MISREPRESENTATIONS M IH0 S R EH2 P R IH0 Z EH0 N T EY1 SH AH0 N Z -MISREPRESENTATIVE M IH0 S R EH2 P R AH0 Z EH1 N AH0 T IH0 V -MISREPRESENTED M IH2 S R EH0 P R IH0 Z EH1 N IH0 D -MISREPRESENTED M IH2 S R EH0 P R IH0 Z EH1 N T IH0 D -MISREPRESENTING M IH0 S R EH2 P R AH0 Z EH1 N IH0 NG -MISREPRESENTING M IH0 S R EH2 P R AH0 Z EH1 N T IH0 NG -MISREPRESENTS M IH0 S R EH2 P R AH0 Z EH1 N S -MISREPRESENTS M IH0 S R EH2 P R AH0 Z EH1 N T S -MISRULE M IH0 S R UW1 L -MISRULED M IH0 S R UW1 L D -MISS M IH1 S -MISS'D M IH0 S SH D -MISS'S M IH0 S IH0 Z -MISSA M IH1 S AH0 -MISSAEL M IH0 S EY1 L -MISSAGUASH M IH1 S AH0 G W AO1 SH -MISSAL M IH1 S AH0 L -MISSALS M IH1 S AH0 L Z -MISSANABIE M IH1 S AH0 N AE1 B IY0 -MISSE M IH0 S -MISSED M IH1 S T -MISSEE M IH0 S IY1 -MISSEL M IH1 S AH0 L -MISSELTHWAITE M IH1 S AH0 L TH W EY2 T 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M IH1 S T OW0 -MISTOO M IH1 S T UW2 -MISTOOK M IH0 S T UH1 K -MISTOVER M IH1 S T OW2 V ER0 -MISTRAL M IH1 S T R AH0 L -MISTRAL'S M IH1 S T R AH0 L Z -MISTRANSLATED M IH0 S T R AE1 N S L EY2 T AH0 D -MISTRANSLATION M IH0 S T R AE0 N Z L EY1 SH AH0 N -MISTRANSLATIONS M IH0 S T R AE0 N Z L EY1 SH AH0 N Z -MISTREAT M IH0 S T R IY1 T -MISTREATED M IH0 S T R IY1 T IH0 D -MISTREATING M IH0 S T R IY1 T IH0 NG -MISTREATMENT M IH0 S T R IY1 T M AH0 N T -MISTRESS M IH1 S T R AH0 S -MISTRESS'S M IH0 S T R EH1 S IH0 Z -MISTRESSE M IH0 S T R EH1 S -MISTRESSES M IH1 S T R AH0 S AH0 Z -MISTRESSES M IH1 S T R AH0 S IH0 Z -MISTRIAL M IH0 S T R AY1 AH0 L -MISTRIAL M IH1 S T R AY2 AH0 L -MISTRIS M IH0 S T R AH0 S -MISTRISS M IH0 S T R IH1 S -MISTRUST M IH0 S T R AH1 S T -MISTRUSTED M IH0 S T R AH1 S T IH0 D -MISTRUSTFUL M IH0 S T R AH1 S F AH0 L -MISTRUSTFUL M IH0 S T R AH1 S T F AH0 L -MISTRUSTFULLY M IH0 S T R AH1 S T F AH0 L IY0 -MISTRUSTIN M IH0 S T R AH1 S T IH0 N -MISTRUSTING M IH0 S T R AH1 S T IH0 NG -MISTRUSTS M IH0 S T R AH1 S -MISTRUSTS M IH0 S T R AH1 S T S -MISTS M IH1 S T S -MISTUH M IH1 S T UW0 -MISTUS M IH1 S T AH0 S -MISTY M IH1 S T IY0 -MISUNDERSTAND M IH2 S AH0 N D ER0 S T AE1 N D -MISUNDERSTANDIN M IH2 S AH0 N D ER0 S T AE1 N D IH0 N -MISUNDERSTANDING M IH2 S AH0 N D ER0 S T AE1 N D IH0 NG -MISUNDERSTANDINGS M IH2 S AH0 N D ER0 S T AE1 N D IH0 NG Z -MISUNDERSTANDS M IH2 S AH0 N D ER0 S T AE1 N D Z -MISUNDERSTOOD M IH2 S AH0 N D ER0 S T UH1 D -MISUS M IH2 S AH0 S -MISUSE M IH0 S Y UW1 S -MISUSE M IH0 S Y UW1 Z -MISUSED M IH0 S Y UW1 Z D -MISUSES M IH0 S Y UW1 S IH0 Z -MISUSES M IH0 S Y UW1 Z IH0 Z -MISUSING M IH0 S Y UW1 Z IH0 NG -MIT EH1 M AY1 T IY1 -MIT M IH1 T -MITA M IY1 T AH0 -MITAI M IH0 T AY2 -MITAINE M IH0 T EY2 N -MITAKOS M IY0 T AA1 K OW0 Z -MITANNI M IY0 T AA1 N IY0 -MITAU M IY2 T AW0 -MITCH M IH1 CH -MITCHAM M IH1 CH AH0 M -MITCHEL M IH1 CH AH0 L -MITCHEL'S M IH1 CH AH0 L Z -MITCHELBOURNE M IH1 CH IH0 L B ER0 N -MITCHELDEAN M IH1 CH IH0 L D AH0 N 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N -PELLEGRINA P EH2 L EH0 G R IY1 N AH0 -PELLEGRINI P EH2 L EH0 G R IY1 N IY0 -PELLEGRINO P EH2 L EH0 G R IY1 N OW0 -PELLENORE P EH1 L IH0 N ER0 -PELLERIN P EH1 L ER0 IH0 N -PELLERIN'S P EH1 L ER0 IH0 N Z -PELLERINISM P EH1 L ER0 IH0 N IH2 Z AH0 M -PELLERY P EH1 L ER0 IY0 -PELLES P EH1 L Z -PELLET P EH1 L AH0 T -PELLETAN P EH0 L AH0 T AA0 N -PELLETIER P EH0 L AH0 T IH1 R -PELLETS P EH1 L AH0 T S -PELLEW P EH0 L UW1 -PELLEY P EH1 L IY0 -PELLICAN P EH1 L IH0 K AH0 N -PELLICANUS P EH1 L IH0 K AH0 N AH0 S -PELLICLE P EH1 L IH0 K AH0 L -PELLICLES P EH1 L IH0 K AH0 L Z -PELLICO P EH1 L IH0 K OW0 -PELLING P EH1 L IH0 NG -PELLINGER P EH1 L IH0 NG ER0 -PELLINORE P EH1 L IH0 N ER0 -PELLISSIER P EH0 L IY0 S IY0 ER0 -PELLISSON P EH0 L IY0 S AH0 N -PELLITORY P EH1 L IH0 T AO2 R IY0 -PELLMELL P EH1 L M AH0 L -PELLMELLI P EH1 L M EH1 L IY0 -PELLS P EH1 L Z -PELLUCID P EH0 L UW1 S AH0 D -PELLUCIDAR P EH0 L UW1 S AH0 D AA2 R -PELLUCIDARIAN P EH0 L UW1 S AH0 D EH1 R IY0 AH0 N -PELLY P EH0 L IY0 -PELLY'S P EH0 L IY0 Z -PELOFF P EH1 L AO0 F -PELONE P EH0 L OW1 N IY0 -PELOPAEUS P EH2 L AH0 P EY1 AH0 S -PELOPIDAS P EH0 L OW1 P IH0 D AH0 Z -PELOPONNESE P EH2 L AH0 P AH0 N IY1 Z -PELOPONNESIAN P EH2 L AH0 P AH0 N IY1 ZH AH0 N -PELOPONNESIANS P EH2 L AH0 P AH0 N IY1 ZH AH0 N Z -PELOPONNESUS P EH2 L AH0 P AH0 N IY1 S AH0 S -PELOPS P EH2 L AH0 P S -PELORUS P EH1 L OW0 R AH1 S -PELOTA P EH0 L OW1 T AH0 -PELOTI P EH0 L OW1 T IY0 -PELOTON P EH0 L OW1 T AH0 N -PELOUSE P EH1 L AW1 S -PELOUX P EH1 L UW1 -PELS P EH1 L Z -PELSALL P EH1 L S AH0 L -PELT P EH1 L T -PELT'S P EH1 L T S -PELTASTS P EH1 L T AE2 S -PELTED P EH1 L T IH0 D -PELTER P EH1 L T ER0 -PELTER'S P EH1 L T ER0 Z -PELTERS P EH1 L T ER0 Z -PELTIER P EH1 L T IY0 ER0 -PELTIN P EH1 L T IH0 N -PELTING P EH1 L T IH0 NG -PELTINGS P EH1 L T IH0 NG Z -PELTON P EH1 L T AH0 N -PELTON'S P EH1 L T AH0 N Z -PELTRIE P EH1 L T ER0 IY0 -PELTRIES P EH1 L T R IY0 Z -PELTRY P EH1 L T R IY0 -PELTS P EH1 L T S -PELTY P EH1 L T IY0 -PELTZER P EH1 L T Z ER0 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EH1 N F AA0 N T AA1 N Y UW1 -PENFIELD P EH1 N F IY2 L D -PENFIELD'S P EH1 N F IY0 L D Z -PENFOLD P EH1 N F OW2 L D -PENFOLD'S P EH1 N F OW2 L D Z -PENFOLDS P EH1 N F OW2 L D Z -PENFORD P EH1 N F ER0 D -PENG P EH1 NG -PENGARTH P EH1 NG G AA2 R TH -PENGARVAN P EH1 NG G AA0 R V AH0 N -PENGE P EH1 N JH -PENGELLY P EH1 NG G AH0 L IY0 -PENGELLY'S P EH1 NG G AH0 L IY0 Z -PENGER P EH1 NG G ER0 -PENGILLY P EH1 NG G AH0 L IY0 -PENGOWAN P EH1 NG G AW1 AH0 N -PENGUIN P EH1 NG G W AH0 N -PENGUIN'S P EH1 NG G W AH0 N Z -PENGUINIA P EH1 NG G W AH0 N IY0 AH0 -PENGUINS P EH1 NG G W AH0 N Z -PENH P EH1 N -PENHALIGON P EH1 N HH AH0 L IH1 G AH0 N -PENHALLOW P EH1 N HH AH0 L AW1 -PENHALLOW'S P EH1 N HH AH0 L AW1 Z -PENHALLOWS P EH1 N HH AH0 L AW1 Z -PENHEIM P AH0 N HH AY2 M -PENHOLDER P EH1 N HH OW2 L D ER0 -PENHOLDERS P EH1 N HH OW2 L D ER0 Z -PENIBLE P EH2 N AH0 B AH0 L -PENICILLIUM P EH2 N AH0 S IH1 L IY0 AH0 M -PENICUIK P EH2 N AH0 S IH0 K -PENIEL P EH2 N IY1 L -PENIMAN P EH1 N IH0 M AH0 N -PENIMAN'S P EH1 N IH0 M AH0 N Z -PENINAH P AH0 N IH1 N AH0 -PENINE P EH2 N AH0 N -PENINGTON P EH1 N IH0 NG T AH0 N -PENINNAH P AH0 N IH1 N AH0 -PENINSULA P AH0 N IH1 N S AH0 L AH0 -PENINSULAR P AH0 N IH1 N S AH0 L ER0 -PENINSULAS P AH0 N IH1 N S AH0 L AH0 Z -PENIS P IY1 N IH0 S -PENISTON P EH1 N IH0 S T AA0 N -PENISTON'S P IY1 N IH0 S T AA0 N Z -PENISTONE P IY1 N IH0 S T OW2 N -PENITENCE P EH1 N IH0 T IH0 N S -PENITENCES P EH2 N IH0 T EH1 N S AH0 Z -PENITENT P EH1 N IH0 T IH0 N T -PENITENT'S P EH1 N IH0 T IH0 N T S -PENITENTE P EH1 N IH0 T IH0 N T -PENITENTES P EH1 N IH0 T IH0 N T EH0 S -PENITENTIAL P EH2 N IH0 T EH1 N CH AH0 L -PENITENTIALLY P EH2 N IH0 T EH1 N CH AH0 L IY0 -PENITENTIARIES P EH2 N IH0 T EH1 N CH ER0 IY0 Z -PENITENTIARY P EH2 N IH0 T EH1 N CH ER0 IY0 -PENITENTLY P EH1 N IH0 T IH0 N T L IY0 -PENITENTS P EH1 N IH0 T IH0 N T S -PENITUS P EH1 N IH0 T AH0 S -PENKAWR P EH0 NG K AA1 R -PENKETHMAN P EH0 NG K EH1 TH M AH0 N -PENKNIFE P AH0 N AY2 F -PENKNIVES P AH0 N AY1 V Z -PENKRIDGE P EH0 NG K R IH2 JH -PENLEY P EH1 N L IY0 -PENLOGAN P EH1 N L OW1 G AH0 N -PENLOGANS P EH1 N L OW1 G AH0 N Z -PENLOP P EH1 N L AH0 P -PENMAENMAWR P EH1 N M EH1 N M AA1 R -PENMAN P EH1 N M AH0 N -PENMANSHIP P EH1 N M AH0 N SH IH2 P -PENMEN P EH1 N M EH0 N -PENMORGAN P EH1 N M AO2 R G AH0 N -PENN P EH1 N -PENN'D P EH1 N D -PENN'ORTH P EH2 N S AO2 R TH -PENN'ORTHS P EH2 N S AO0 R TH S -PENN'S P EH1 N Z -PENNA P EH1 N AH0 -PENNA P EH2 N S IH0 L V EY1 N Y AH0 -PENNACOOK P EH1 N AH0 K UH1 K -PENNANT P EH1 N AH0 N T -PENNANT'S P EH1 N AH0 N T S -PENNANTS P EH1 N AH0 N T S -PENNE P EH1 N -PENNED P EH1 N D -PENNEFATHER P EH1 N IH0 F AA2 DH ER0 -PENNEL P EH1 N AH0 L -PENNEL'S P EH1 N AH0 L Z -PENNELL P EH1 N AH0 L -PENNELL'S P EH2 N EH1 L Z -PENNELLINI P EH1 N AH0 L IY1 N IY0 -PENNEY P EH1 N IY0 -PENNI P EH1 N IY0 -PENNICOTE P EH1 N IY0 K OW1 T -PENNICUT P EH1 N IH0 K AH0 T -PENNIE P EH1 N IY0 -PENNIE'S P EH1 N IY0 Z -PENNIES P EH1 N IY0 Z -PENNIFEATHER P EH1 N IH0 F EH1 DH ER0 -PENNIKET P EH1 N 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NG K S -RANKE R AE1 NG K -RANKE'S R AE1 NG K S -RANKED R AE1 NG K T -RANKEILLOR R AE1 NG K IY1 L ER0 -RANKEN R AE1 NG K AH0 N -RANKER R AE1 NG K ER0 -RANKERS R AE1 NG K ER0 Z -RANKEST R AE1 NG K AH0 S T -RANKIN R AE1 NG K IH0 N -RANKIN'S R AE1 NG K IH0 N Z -RANKINE R AE1 NG K AY2 N -RANKING R AE1 NG K IH0 NG -RANKINS R AE1 NG K IH0 N Z -RANKLE R AE1 NG K AH0 L -RANKLED R AE1 NG K AH0 L D -RANKLES R AE1 NG K AH0 L Z -RANKLING R AE1 NG K L IH0 NG -RANKLY R AE1 NG K L IY0 -RANKNESS R AE1 NG K N AH0 S -RANKS R AE1 NG K S -RANLEIGH R AE1 N L AH0 -RANN R AE1 N -RANN'S R AE1 N Z -RANNA R AE1 N AH0 -RANNAG R AE1 N AH0 G -RANNE R AE1 N -RANNEY R AE1 N IY0 -RANNIE R AE1 N IY0 -RANNION R AE1 N Y AH0 N -RANNOCH R AE1 N AH0 K -RANNS R AE1 N Z -RANNU R AE1 N UW0 -RANNVEIG R AE1 N V IY1 G -RANNY R AE1 N IY0 -RANNY'S R AE1 N IY0 Z -RANOVICA R AE1 N AH0 V IH0 K AH0 -RANQUIST R AE1 N K W IH2 S T -RANS R AE1 N Z -RANSACK R AE1 N S AE2 K -RANSACKED R AE1 N S AE2 K T -RANSACKING R AE1 N S AE2 K IH0 NG 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AH0 L F -RANULPH R AE1 N AH0 L F -RANULPH'S R AE1 N AH0 L F S -RANUNCULUS R AE1 N AH0 NG K Y AH0 L AH0 S -RANUNCULUSES R AE1 N AH0 NG K Y AH0 L AH0 S IH0 Z -RANUZI R AA0 N UW1 Z IY0 -RANWORTH R AE1 N W ER2 TH -RANZ R AE1 N Z -RANZA R AE1 N Z AH0 -RANZO R AE1 N Z OW0 -RAO R AW1 -RAO'S R AW1 Z -RAOUL R AA0 UW1 L -RAOUL'S R AA0 UW1 L Z -RAOULA R AA0 UW1 L AH0 -RAOUN R AA0 UW1 N -RAOUND R AA0 AW2 N D -RAP R AE1 P -RAPACIOUS R AH0 P AE1 SH IH0 S -RAPACIOUS R AH0 P EY1 SH IH0 S -RAPACIOUSLY R AH0 P EY1 SH AH0 S L IY0 -RAPACIOUSNESS R AH0 P EY1 SH AH0 S N AH0 S -RAPACITY R AH0 P AE1 S AH0 T IY0 -RAPAHOE R AE1 P AH0 HH OW2 -RAPAJU R AH0 P EY1 JH UW0 -RAPALLO R AH0 P AE1 L OW0 -RAPALLY R AE1 P AH0 L IY0 -RAPAUD R AE1 P AO0 D -RAPE R EY1 P -RAPED R EY1 P T -RAPER R EY1 P ER0 -RAPES R EY1 P S -RAPET R AH0 P EH2 T -RAPHAEL R AA2 F AY0 EH1 L -RAPHAEL R AA2 F Y EH1 L -RAPHAEL R EY1 F IY0 AH0 L -RAPHAEL'S R EY1 F IY0 AH0 L Z -RAPHAELE R AA2 F AY0 EH1 L EY0 -RAPHAELESQUE R EY1 F IY0 AH0 L EH1 S K 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AH0 T S -RARED R EH1 R D -RAREE R EH1 R IY1 -RAREFACTION R EH1 R AH0 F AE1 K SH AH0 N -RAREFIED R EH1 R AH0 F AY0 D -RAREFIES R EH1 R AH0 F AY0 Z -RAREFY R EH1 R AH0 F AY0 -RARELY R EH1 R L IY0 -RAREMENT R EH1 R AH0 M AH0 N T -RARENESS R EH1 R N IH0 S -RARER R EH1 R ER0 -RARES R EH1 R Z -RAREST R EH1 R AH0 S T -RAREY R EH1 R IY0 -RAREY'S R EH1 R IY0 Z -RARI R AE1 R EY2 -RARIFIED R EH1 R AH0 F AY2 D -RARIFY'D R EH1 R IH0 F AY1 D -RARIK R AE1 R IH0 K -RARIN R EH1 R IH0 N -RARING R EH1 R IH0 NG -RARITAN R EH1 R IH0 T AH0 N -RARITAN'S R EH1 R IH0 T AH0 N Z -RARITIES R EH1 R IH0 T IY0 Z -RARITY R EH1 R AH0 T IY0 -RARITY R EH1 R IH0 T IY0 -RARO R AA1 R OW0 -RAROTONGA R AE1 R AH0 T AA1 N G AH0 -RARX R AA1 R K S -RAS R AE1 S -RAS'S R AH0 S IH0 Z -RASA R AA1 S AH0 -RASAY R AE1 S EY0 -RASBERRIES R AE1 S B EH2 R IY0 Z -RASCAL R AE1 S K AH0 L -RASCAL'S R AE1 S K AH0 L Z -RASCALDOM R AE1 S K AH0 L D AH0 M -RASCALITIES R AE0 S K AE1 L AH0 T IY0 Z -RASCALITY R AE0 S K AE1 L AH0 T IY0 -RASCALLY R AE1 S K AH0 L IY0 -RASCALS R AE1 S K AH0 L Z -RASCHI R AE1 SH IY0 -RASCHID R AE1 SH IH0 D -RASCHKE R AE1 SH K -RASCIAN R AE1 S K AH0 N -RASCZINSKY R AE1 S K Z IH1 N S K IY0 -RASE R EY1 Z -RASED R EY1 S T -RASELLI R AH0 S EH1 L IY0 -RASH R AE1 SH -RASHBOROUGH R AE1 SH B ER2 OW0 -RASHBURN R AE1 SH B ER2 N -RASHCLIFFE R AE1 SH K L IH0 F -RASHER R AE1 SH ER0 -RASHERS R AE1 SH ER0 Z -RASHES R AE1 SH IH0 Z -RASHEST R AH0 SH AH0 S T -RASHEVITCH R AE1 SH V IH2 CH -RASHID R AA0 SH IY1 D -RASHID R AH0 SH IY1 D -RASHID'S R AA0 SH IY1 D Z -RASHIE R AE1 SH IY0 -RASHKOFF R AH0 SH K AO2 F -RASHLEIGH R AE1 SH L AH0 -RASHLEIGH'S R AE1 SH L IY0 Z -RASHLY R AH0 SH L IY0 -RASHNESS R AE1 SH N AH0 S -RASKA R AA1 S K AH0 -RASKIL R AE1 S K AH0 L -RASKILS R AE1 S K AH0 L Z -RASKOL R AE1 S K AO0 L -RASKOLNIKOV R AE1 S K OW1 L N IH0 K AO2 V -RASKOLNIKOV'S R AE1 S K OW1 L N IH0 K AA2 V Z -RASKOLNIKS R AE1 S K OW1 L N IH0 K S -RASLANEI R AE1 S L AA0 N EY0 -RASMER R AE1 S AH0 M ER0 -RASMITH R AE1 S M IH2 TH -RASMUS R 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IH0 K EY1 -RASTIGNAC R AE1 S T IH0 N Y AE2 K -RASTIGNAC'S R AE1 S T IH0 N Y AE2 K S -RASTLE R AE1 S AH0 L -RASTLIN R AE1 S T L IH0 N -RASTRO R AE1 S T R OW0 -RASTUS R AE1 S T AH0 S -RASUL R AE1 S AH0 L -RAT R AE1 T -RAT'S R AE1 T S -RATA R AE1 T AH0 -RATAFIA R AE1 T AH0 F IY0 AH0 -RATAN R AE1 T AH0 N -RATANS R AE1 T AH0 N Z -RATAPLAN R AA1 T AH0 P L AH0 N -RATAZIAEV R AE1 T AH0 Z IY1 EY1 V -RATCATCHER R AE1 T K AE2 CH ER0 -RATCATCHER'S R AE1 T K AE2 CH ER0 Z -RATCHAM R AE1 CH AH0 M -RATCHCALI R AE0 T CH K AA1 L IY0 -RATCHET R AE0 T CH AH0 T -RATCHET R AE1 CH AH0 T -RATCHETS R AE1 CH AH0 T S -RATCHETT R AE1 CH IH0 T -RATCLIFF R AE1 T K L IH0 F -RATCLIFFE R AE1 T K L IH0 F -RATCLIFFE'S R AE1 T K L IH0 F S -RATE R EY1 T -RATEABLE R AE1 T AH0 B AH0 L -RATEAU R AE0 T OW1 -RATED R EY1 T AH0 D -RATED R EY1 T IH0 D -RATEL R AE1 T AH0 L -RATEPAYER R EY1 T P EY2 ER0 -RATEPAYERS R EY1 T P EY2 ER0 Z -RATER R EY1 T ER0 -RATERS R EY1 T ER0 Z -RATES R EY1 T S -RATH R AE1 TH -RATH'S R AE1 TH S -RATHA R AE1 TH AH0 -RATHAH R AA1 T AH2 -RATHAUS R AE1 T HH AW2 S -RATHBONE R AE1 TH B OW2 N -RATHBONE'S R AE1 TH B OW2 N Z -RATHBORNE R AE1 TH B AO2 R N -RATHBUN R AE1 TH B AH0 N -RATHBURN R AE1 TH B ER0 N -RATHBURN'S R AE1 TH B ER0 N Z -RATHBURY R AE1 TH B ER0 IY0 -RATHBY R AE1 TH B IY0 -RATHCLARE R AE1 TH K L EH1 R -RATHDALE R AE1 TH D EY2 L -RATHE R EY1 DH -RATHEE R AE1 TH IY1 -RATHEES R AE1 TH IY0 Z -RATHENAU R AE1 TH IH0 N OW1 -RATHENOW R AE1 TH AH0 N OW0 -RATHER R AE1 DH ER0 -RATHER R AH1 DH ER0 -RATHER'N R AE1 DH ER0 AH0 N -RATHERBY R AE1 DH ER0 B IY0 -RATHHAUS R AE1 TH HH AW2 S -RATHIE R AE1 TH IY0 -RATHIE'S R AE1 TH IY0 Z -RATHILLET R AE1 TH IY0 L EH1 T -RATHLIN R AE1 TH L IH0 N -RATHMINES R AE1 TH M AH0 N Z -RATHMORE R AE1 TH M AO2 R -RATHMULLAN R AE1 TH M AH1 L AH0 N -RATHOLE R AE1 T HH OW2 L -RATHS R AE1 TH S -RATHSHERR R AE1 TH SH EH1 R -RATHSKELLER R AE1 TH S K EH2 L ER0 -RATI R AA1 T IY0 -RATIBOR R AE1 T IH0 B ER0 -RATICHON R AE1 T IH0 CH AH0 N -RATIE R EY1 T IY0 -RATIFIA R 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K R UW1 T M AH0 N T -RECRUITS R AH0 K R UW1 T S -RECRUITS R IH0 K R UW1 T S -RECRUITS R IY0 K R UW1 T S -RECRUITY R IH0 K R UW1 T IY0 -RECT R EH1 K T -RECTA R EH1 K T AH0 -RECTAL R EH1 K T AH0 L -RECTANGLE R EH1 K T AE0 NG G AH0 L -RECTANGLES R EH1 K T AE2 NG G AH0 L Z -RECTANGULAR R EH0 K T AE1 NG G Y AH0 L ER0 -RECTE R EH1 K T -RECTI R EH1 K T IY0 -RECTIFICATION R EH2 K T AH0 F AH0 K EY1 SH AH0 N -RECTIFICATIONS R EH1 K T AH0 F AH0 K EY1 SH AH0 N Z -RECTIFIE R EH1 K T AH0 F AY2 -RECTIFIED R EH1 K T AH0 F AY2 D -RECTIFIER R EH1 K T AH0 F AY2 ER0 -RECTIFIERS R EH1 K T AH0 F AY2 ER0 Z -RECTIFIES R EH1 K T AH0 F AY2 Z -RECTIFY R EH1 K T AH0 F AY2 -RECTIFYING R EH1 K T AH0 F AY2 IH0 NG -RECTILINEAL R EH1 K T IH0 L IH1 N IY0 AH0 L -RECTILINEAR R EH1 K T IH0 L IH1 N IY0 ER0 -RECTITUDE R EH1 K T IH0 T UW2 D -RECTIUS R IY0 K SH AH0 S -RECTO R EH1 K T OW0 -RECTOR R EH1 K T ER0 -RECTOR'S R EH1 K T ER0 Z -RECTORIAL R EH0 K T AO1 R IY0 AH0 L -RECTORIES R EH1 K T ER0 IY0 Z -RECTORS R EH1 K T ER0 Z 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IH0 F L EH1 K SH AH0 N -REFLECTION'S R IH0 F L EH1 K SH AH0 N Z -REFLECTIONS R IH0 F L EH1 K SH AH0 N Z -REFLECTIVE R IH0 F L EH1 K T IH0 V -REFLECTIVELY R IH0 F L EH1 K T IH0 V L IY0 -REFLECTIVENESS R IH0 F L EH1 K T IH0 V N AH0 S -REFLECTOR R IH0 F L EH1 K T ER0 -REFLECTORS R IH0 F L EH1 K T ER0 Z -REFLECTS R IH0 F L EH1 K S -REFLECTS R IH0 F L EH1 K T S -REFLEX R IY1 F L EH0 K S -REFLEXED R AH0 F L EH1 K S T -REFLEXES R IY1 F L EH0 K S AH0 Z -REFLEXION R AH0 F L EH1 K S IY0 AA0 N -REFLEXIONS R IY1 F L EH0 K SH AH0 N Z -REFLEXIVE R AH0 F L EH1 K S IH0 V -REFLEXIVELY R IY0 F L EH1 K S IH0 V L IY0 -REFLUENT R IY0 F L UW1 AH0 N T -REFLUX R IY0 F L AH0 K S -REFOLD R IY1 F OW2 L D -REFOLDED R IY0 F OW1 L D AH0 D -REFOLDING R IY0 F OW1 L D IH0 NG -REFORESTATION R IY2 F AO0 R AH0 S T EY1 SH AH0 N -REFORM R AH0 F AO1 R M -REFORM R IH0 F AO1 R M -REFORM'D R IH0 F AO1 R M D -REFORMATION R EH2 F ER0 M EY1 SH AH0 N -REFORMATIONS R IH0 F AO1 R M EY1 SH AH0 N Z -REFORMATIVE R IH0 F AO1 R M AH0 T 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T EY1 T -REINSTATED R IY2 IH0 N S T EY1 T IH0 D -REINSTATEMENT R IY2 IH0 N S T EY1 T M AH0 N T -REINSTATES R IY2 IH0 N S T EY1 T S -REINSTATING R IY2 IH0 N S T EY1 T IH0 NG -REINSTROM R IY2 IH0 N S T R AH0 M -REINSURANCE R IY2 IH0 N SH UH1 R AH0 N S -REINTEGRATION R IY0 IH1 N T AH0 G R EY2 SH AH0 N -REINTERPRETED R IY0 IH0 N T ER1 P R AH0 T IH0 D -REINTERRED R IY0 IH0 N T ER1 D -REINTRODUCE R IY0 IH0 N T R AH0 D UW1 S -REINTRODUCED R IY0 IH0 N T R AH0 D UW1 S T -REINTRODUCING R IY0 IH0 N T R AH0 D UW1 S IH0 NG -REINTRODUCTION R IY0 IH0 N T R AH0 D AH1 K SH AH0 N -REINVEST R IY2 IH0 N V EH1 S T -REINVESTED R IY2 IH0 N V EH1 S T IH0 D -REINVESTMENT R IY2 IH0 N V EH1 S M AH0 N T -REINVESTMENT R IY2 IH0 N V EH1 S T M AH0 N T -REINVIGORATE R IY2 IH0 N V IH1 G ER0 EY2 T -REINVIGORATED R IY2 IH0 N V IH1 G ER0 EY2 T IH0 D -REINVIGORATING R IY2 IH0 N V IH1 G ER0 EY2 T IH0 NG -REINVIGORATION R IY0 IH0 N V IH2 G ER0 EY1 SH AH0 N -REIPUBLICAE R IY1 P AH1 B L IH0 K EY1 -REIS R IY1 Z -REISACHER R 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IY0 -RELUME R IH0 L UW1 M -RELUX R IY0 L AH1 K S -RELVIL R EH1 L V AH0 L -RELY R IH0 L AY1 -RELY R IY0 L AY1 -RELYEA R EH1 L Y EY2 -RELYIN R IY0 L AY1 IH0 N -RELYING R IY0 L AY1 IH0 NG -REM R EH1 M -REMA R EH1 M AH0 -REMADE R IY0 M EY1 D -REMAILED R IY0 M EY1 L D -REMAIN R IH0 M EY1 N -REMAIN R IY0 M EY1 N -REMAIN'D R IH0 M EY1 N D -REMAINDER R IH0 M EY1 N D ER0 -REMAINDER R IY0 M EY1 N D ER0 -REMAINDERS R IH0 M EY1 N D ER0 Z -REMAINE R IH0 M EY1 N -REMAINED R IH0 M EY1 N D -REMAINED R IY0 M EY1 N D -REMAINEST R IH0 M EY1 N AH0 S T -REMAINETH R IH0 M EY1 N IH0 TH -REMAININ R IH0 M EY1 N IH0 N -REMAINING R IH0 M EY1 N IH0 NG -REMAINING R IY0 M EY1 N IH0 NG -REMAINS R IH0 M EY1 N Z -REMAINS R IY0 M EY1 N Z -REMAKE R IY1 M EY1 K -REMAKES R IY1 M EY1 K S -REMAKING R IY1 M EY1 K IH0 NG -REMAND R IH0 M AE1 N D -REMANDED R IH0 M AE1 N D IH0 D -REMANDING R IH0 M AE1 N D IH0 NG -REMANJOU R IH0 M AE1 N JH UW0 -REMARK R IH0 M AA1 R K -REMARK R IY0 M AA1 R K -REMARK'D R IY0 M AA1 R K D -REMARKABLE 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Z -RESPONDS R IY0 S P AA1 N D Z -RESPONSE R IH0 S P AA1 N S -RESPONSE R IY0 S P AA1 N S -RESPONSES R IH0 S P AA1 N S IH0 Z -RESPONSES R IY0 S P AA1 N S AH0 Z -RESPONSES R IY0 S P AA1 N S IH0 Z -RESPONSIBILITIES R IY0 S P AA2 N S AH0 B IH1 L AH0 T IY0 Z -RESPONSIBILITY R IY0 S P AA2 N S AH0 B IH1 L AH0 T IY0 -RESPONSIBLE R IY0 S P AA1 N S AH0 B AH0 L -RESPONSIBLENESS R IY0 S P AA2 N S AH0 B AH0 L N AH0 S -RESPONSIBLY R IH0 S P AA1 N S AH0 B L IY0 -RESPONSIONS R IH0 S P AA1 N SH AH0 N Z -RESPONSIVE R IH0 S P AA1 N S IH0 V -RESPONSIVELY R IH0 S P AA1 N S IH0 V L IY0 -RESPONSIVENESS R IH0 S P AA1 N S IH0 V N AH0 S -RESPUBLICA R IY0 S P AH1 B L IH0 K AH0 -RESS R EH1 S -RESSAIDAR R EH1 S AY1 D AA2 R -RESSALDAR R EH1 S AH0 L D AA2 R -RESSEMBLANCE R EH2 S EH1 M B L AH0 N S -RESSEMBLE R EH2 S EH1 M B AH0 L -RESSEMBLENT R EH2 S EH1 M B AH0 L AH0 N T -RESSORT R EH1 S ER0 T -RESSOURCE R EH1 S AO2 R S -RESSOURCES R EH1 S AO1 R S AH0 Z -REST R EH1 S T -REST' R IY0 S T IH0 Z -REST'LL R IY0 S T AH0 L -REST'S R EH1 S T S -RESTA R EH1 S T AH0 -RESTABIT R IY0 S T EY2 B AH0 T -RESTALRIG R IY0 S T AA1 L R IH0 G -RESTANTE R EH0 S T AA0 N T -RESTART R IY0 S T AA1 R T -RESTARTED R IY0 S T AA1 R T IH0 D -RESTARTING R IY0 S T AA1 R T IH0 NG -RESTATE R IY0 S T EY1 T -RESTATED R IY0 S T EY1 T IH0 D -RESTATEMENT R IY0 S T EY1 T M AH0 N T -RESTATING R IY0 S T EY1 T IH0 NG -RESTAUD R EH1 S T D -RESTAUD'S R EH1 S T D Z -RESTAURANT R EH1 S T ER0 AA2 N T -RESTAURANT R EH1 S T R AA2 N T -RESTAURANT'S R EH1 S T ER0 AA2 N T S -RESTAURANT'S R EH1 S T R AA2 N T S -RESTAURANTS R EH1 S T ER0 AA2 N T S -RESTAURANTS R EH1 S T R AA2 N T S -RESTAURATEUR R EH2 S T ER0 AH0 T ER1 -RESTAURATEUR R EH2 S T R AH0 T ER1 -RESTAURATEUR'S R EH1 S T R AH0 T ER1 Z -RESTAURATEURS R EH2 S T ER0 AH0 T ER1 Z -RESTAURATEURS R EH2 S T R AH0 T ER1 Z -RESTAURATION R EH1 S T ER0 EY1 SH AH0 N -RESTE R EH1 S T -RESTED R EH1 S T AH0 D -RESTED R EH1 S T IH0 D -RESTER R EH1 S T ER0 -RESTES R EH1 S T IY0 Z -RESTETH R IH0 S T IH0 TH 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-RESTORING R IH0 S T AO1 R IH0 NG -RESTRAIN R IY0 S T R EY1 N -RESTRAIN'D R IY0 S T R EY1 N D -RESTRAINED R IY0 S T R EY1 N D -RESTRAINEDLY R IY0 S T R EY1 N D L IY0 -RESTRAINER R IY0 S T R EY1 N ER0 -RESTRAINING R IY0 S T R EY1 N IH0 NG -RESTRAININGLY R IY0 S T R EY1 N IH0 NG L IY0 -RESTRAINS R IY0 S T R EY1 N Z -RESTRAINT R IH0 S T R EY1 N T -RESTRAINT R IY0 S T R EY1 N T -RESTRAINTS R IH0 S T R EY1 N T S -RESTRAINTS R IY0 S T R EY1 N T S -RESTRICT R IY0 S T R IH1 K T -RESTRICTED R IY0 S T R IH1 K T AH0 D -RESTRICTED R IY0 S T R IH1 K T IH0 D -RESTRICTING R IY0 S T R IH1 K T IH0 NG -RESTRICTION R IY0 S T R IH1 K SH AH0 N -RESTRICTIONS R IY0 S T R IH1 K SH AH0 N Z -RESTRICTIVE R IY0 S T R IH1 K T IH0 V -RESTRICTS R IY0 S T R IH1 K T S -RESTRONGUET R IY0 S T R AO1 NG W EH2 T -RESTRONGUET'S R IY0 S T R AO1 NG W EH2 T S -RESTRUNG R IY0 S T R AH1 NG -RESTS R EH1 S T S -RESTU R IY0 S T UW0 -RESTY R EH1 S T IY0 -RESULT R IH0 Z AH1 L T -RESULT R IY0 Z AH1 L T -RESULT'S R IY0 Z AH1 L T S -RESULTANT R IY0 Z AH1 L T AH0 N T -RESULTANTS R IY0 Z AH1 L T AH0 N T S -RESULTED R IH0 Z AH1 L T IH0 D -RESULTED R IY0 Z AH1 L T AH0 D -RESULTED R IY0 Z AH1 L T IH0 D -RESULTING R IH0 Z AH1 L T IH0 NG -RESULTING R IY0 Z AH1 L T IH0 NG -RESULTLESS R IY0 Z AH1 L T L AH0 S -RESULTS R IH0 Z AH1 L T S -RESULTS R IY0 Z AH1 L T S -RESUM'D R IY0 Z AH1 M D -RESUME R EH1 Z AH0 M EY2 -RESUME R IH0 Z UW1 M -RESUME R IY0 Z UW1 M -RESUMED R IH0 Z UW1 M D -RESUMED R IY0 Z UW1 M D -RESUMES R EH1 Z AH0 M EY2 Z -RESUMES R IH0 Z UW1 M Z -RESUMES R IY0 Z UW1 M Z -RESUMING R IH0 Z UW1 M IH0 NG -RESUMING R IY0 Z UW1 M IH0 NG -RESUMPTION R IH0 Z AH1 M P SH AH0 N -RESUMPTION R IH0 Z AH1 M SH AH0 N -RESUMPTION R IY0 Z AH1 M P SH AH0 N -RESUMPTION R IY0 Z AH1 M SH AH0 N -RESURGAM R IY0 S ER1 G AH0 M -RESURGED R IY0 S ER1 JH D -RESURGENCE R IY0 S ER1 JH AH0 N S -RESURGENT R IH0 S ER1 JH AH0 N T -RESURGENT R IY0 S ER1 JH AH0 N T -RESURGING R IY0 S ER1 JH IH0 NG -RESURRECT R EH2 Z ER0 EH1 K T -RESURRECTED R EH2 Z ER0 EH1 K T IH0 D -RESURRECTING R EH2 Z ER0 EH1 K T IH0 NG -RESURRECTION R EH2 Z ER0 EH1 K SH AH0 N -RESURRECTIONIST R EH2 Z ER0 EH1 K SH AH0 N IH0 S T -RESURRECTIONISTS R EH2 Z ER0 EH1 K SH AH0 N IH0 S T S -RESURRECTIONS R EH2 Z ER0 EH1 K SH AH0 N Z -RESUSCITATE R IH0 S AH1 S IH0 T EY2 T -RESUSCITATE R IY0 S AH1 S IH0 T EY2 T -RESUSCITATED R IH0 S AH1 S IH0 T EY2 T IH0 D -RESUSCITATES R IH0 S AH1 S IH0 T EY2 T S -RESUSCITATING R IH0 S AH1 S IH0 T EY2 T IH0 NG -RESUSCITATION R IH0 S AH2 S IH0 T EY1 SH AH0 N -RESZKE R IY0 SH K IY0 -RET R EH1 T -RETAIL R IY1 T EY2 L -RETAILED R IY1 T EY2 L D -RETAILER R IY1 T EY2 L ER0 -RETAILERS R IY1 T EY2 L ER0 Z -RETAILING R IY1 T EY2 L IH0 NG -RETAILS R IY1 T EY2 L Z -RETAIN R IH0 T EY1 N -RETAIN R IY0 T EY1 N -RETAIN'D R IY1 T EY2 N D -RETAINED R IH0 T EY1 N D -RETAINED R IY0 T EY1 N D -RETAINER R IH0 T EY1 N ER0 -RETAINER R IY0 T EY1 N ER0 -RETAINER'S R IH0 T EY1 N ER0 Z -RETAINERS R IH0 T EY1 N ER0 Z -RETAINETH R IH0 T EY1 N TH -RETAINING R IH0 T EY1 N IH0 NG -RETAINING R IY0 T EY1 N IH0 NG -RETAINS R IH0 T EY1 N Z -RETAINS R IY0 T EY1 N Z -RETAKE R IY0 T EY1 K -RETAKE R IY1 T EY1 K -RETAKEN R IY0 T EY1 K AH0 N -RETAKES R IY0 T EY1 K S -RETAKING R IY0 T EY1 K IH0 NG -RETALIATE R IH0 T AE1 L IY0 EY2 T -RETALIATE R IY0 T AE1 L IY0 EY2 T -RETALIATED R IH0 T AE1 L IY0 EY2 T IH0 D -RETALIATED R IY0 T AE1 L IY0 EY2 T IH0 D -RETALIATES R IH0 T AE1 L IY0 EY2 T S -RETALIATING R IH0 T AE1 L IY0 EY2 T IH0 NG -RETALIATION R IY0 T AE2 L IY0 EY1 SH AH0 N -RETALIATIONS R IY0 T AE2 L IY0 EY1 SH AH0 N Z -RETALIATORY R IY0 T AE1 L Y AH0 T AO2 R IY0 -RETAMOSO R EH1 T AH0 M OW1 S OW0 -RETARD R IH0 T AA1 R D -RETARD R IY0 T AA1 R D -RETARDATION R IY0 T AA0 R D EY1 SH AH0 N -RETARDED R IH0 T AA1 R D IH0 D -RETARDED R IY0 T AA1 R D AH0 D -RETARDED R IY0 T AA1 R D IH0 D -RETARDER R IY0 T AA1 R D ER0 -RETARDING R IH0 T AA1 R D IH0 NG -RETARDING R IY0 T AA1 R D IH0 NG -RETARDS R IH0 T AA1 R D Z -RETARDS R IY0 T AA1 R D Z -RETAUX R IY1 T AO1 K S -RETCH R EH1 CH -RETCH'S R EH1 CH IH0 Z -RETCHED R EH1 CH T -RETCHING R EH1 CH IH0 NG -RETCHINGS R EH1 CH IH0 NG Z -RETCLIFFE R EH1 T K L IH0 F -RETE R IY0 T -RETEAU R IY2 T OW1 -RETELL R IY0 T EH1 L -RETELLING R IY0 T EH1 L IH0 NG -RETENTION R IY0 T EH1 N SH AH0 N -RETENTIVE R IY0 T EH1 N T IH0 V -RETENTIVENESS R IY0 T EH1 N T IH0 V N AH0 S -RETENUE R IY0 T EH1 N Y UW0 -RETFORD R EH1 T F ER0 D -RETHEL R EH1 TH AH0 L -RETHER R EH1 DH ER0 -RETHERTON R IH0 TH ER1 T AH0 N -RETIARIUS R IY0 T IY0 EH1 R IY0 AH0 S -RETICENCE R EH1 T IH0 S AH0 N S -RETICENCES R EH1 T IH0 S AH0 N S IH0 Z -RETICENT R EH1 T IH0 S AH0 N T -RETICENTLY R EH1 T IH0 S AH0 N T L IY0 -RETICULATED R IH0 T IH1 K Y AH0 L EY2 T IH0 D -RETICULATION R IH0 T IH1 K Y AH0 L EY1 SH AH0 N -RETICULATIONS R IH0 T IH1 K Y AH0 L EY1 SH AH0 N Z -RETICULE R EH1 T AH0 K Y UW2 L -RETICULES R EH1 T AH0 K Y UW2 L Z -RETIE R EH1 T IY0 -RETIED R IH0 T AY1 D -RETIEF R IH0 T AY1 F -RETIEF'S R IH0 T AY1 F S -RETINA R EH1 T AH0 N AH0 -RETINAL R EH1 T AH0 N AH0 L -RETINAS R IH0 T AY1 N AH0 Z -RETINUE R EH1 T AH0 N UW2 -RETINUES R EH1 T AH0 N UW2 Z -RETIR'D R IY0 T AY1 ER0 D -RETIRACY R IH0 T AY1 R AH0 S IY0 -RETIRE R IH0 T AY1 R -RETIRE R IY0 T AY1 R -RETIRE R IY2 T AY1 ER0 -RETIRED R IH0 T AY1 R D -RETIRED R IY0 T AY1 ER0 D -RETIRED R IY0 T AY1 R D -RETIREMENT R IH0 T AY1 ER0 M AH0 N T -RETIREMENT R IY0 T AY1 ER0 M AH0 N T -RETIREMENTS R IH0 T AY1 R M AH0 N T S -RETIREMENTS R IY0 T AY1 R M AH0 N T S -RETIRER R IY0 T AY1 ER0 R -RETIRES R IH0 T AY1 R Z -RETIRES R IY0 T AY1 ER0 Z -RETIRES R IY0 T AY1 R Z -RETIRIN R IH0 T AY1 R IH0 N -RETIRING R IH0 T AY1 R IH0 NG -RETIRING R IY0 T AY1 ER0 IH0 NG -RETIRING R IY0 T AY1 R IH0 NG -RETIRO R IH0 T AY1 R OW0 -RETOC R IY0 T AO1 K -RETOC'S R IY0 T AO1 K S -RETOLD R EH1 T OW0 L D -RETOOK R IY0 T UH1 K -RETORT R IY1 T AO2 R T -RETORTED R IY0 T AO1 R T IH0 D -RETORTII R IY1 T AO2 R T IY1 IY0 -RETORTING R IY0 T AO1 R T IH0 NG -RETORTS R IH0 T AO1 R T S -RETORTS R IY0 T AO1 R T S -RETOUCH R IY0 T AH1 CH -RETOUCHED R IY0 T AH1 CH T -RETOUCHER R IY0 T AH1 CH ER0 -RETOUCHING R IY0 T AH1 CH IH0 NG -RETOUR R IY0 T UH1 R -RETOURNE R IY0 T UH1 R N -RETOURNER R IY0 T ER1 N ER0 -RETRACE R IY0 T R EY1 S -RETRACED R IY0 T R EY1 S T -RETRACES R IY0 T R EY1 S IH0 Z -RETRACING R IY0 T R EY1 S IH0 NG -RETRACT R IY0 T R AE1 K T -RETRACTATION R IY0 T R AE1 K T EY1 SH AH0 N -RETRACTATIONS R IY0 T R AE1 K T EY1 SH AH0 N Z -RETRACTED R IY0 T R AE1 K T AH0 D -RETRACTILE R IY0 T R AE1 K T AH0 L -RETRACTING R IY0 T R AE1 K T IH0 NG -RETRACTION R IY0 T R AE1 K SH AH0 N -RETRACTIONS R IY0 T R AE1 K SH AH0 N Z -RETRACTS R IY0 T R AE1 K T S -RETRAITE R IY0 T R EY1 T -RETRANSLATED R IY2 T R AE0 N Z L EY1 T AH0 D -RETRANSLATION R IY2 T R AE0 N Z L EY1 SH AH0 N -RETREAD R IY0 T R EH1 D -RETREAT R IY0 T R IY1 T -RETREATED R IY0 T R IY1 T AH0 D -RETREATED R IY0 T R IY1 T IH0 D -RETREATIN R IY0 T R IY1 T AH0 N -RETREATING R IY0 T R IY1 T IH0 NG -RETREATS R IY0 T R IY1 T S -RETRENCH R IY0 T R EH1 N CH 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N IH0 S T -RETURNETH R IY0 T ER1 N AH0 TH -RETURNIN R IY0 T ER1 N IH0 N -RETURNING R IH0 T ER1 N IH0 NG -RETURNING R IY0 T ER1 N IH0 NG -RETURNINGS R IY0 T ER1 N IH0 NG Z -RETURNLESS R IY0 T ER1 N L AH0 S -RETURNS R IH0 T ER1 N Z -RETURNS R IY0 T ER1 N Z -RETY R IH0 T IY0 -RETY'S R IY0 T IY0 Z -RETYING R IY0 T IY0 IH0 NG -RETYS R IY0 T IY0 Z -RETZ R EH1 T S -RETZ'S R EH1 T S IH0 Z -RETZOW R EH1 T Z OW0 -REUBEN R UW1 B AH0 N -REUBEN'S R UW1 B AH0 N Z -REUBENS R UW1 B AH0 N Z -REUBIN R UW1 B IH0 N -REUBY R UW1 B IY0 -REUCH'S R OY1 K S -REUCHLIN R OY1 K L IH0 N -REUEL R UW1 AH0 L -REUILLY R IY0 UW1 L IY0 -REULAH R UW1 L AH0 -REUMONT R UW1 M AA2 N T -REUNION R IY0 UW1 N Y AH0 N -REUNIONS R IY0 UW1 N Y AH0 N Z -REUNIS R IY0 UW1 N AH0 S -REUNITE R IY2 UW0 N AY1 T -REUNITED R IY2 UW0 N AY1 T IH0 D -REUNITES R IY2 UW0 N AY1 T S -REUNITING R IY2 UW0 N AY1 T IH0 NG -REUS R IY0 UW1 Z -REUSS R UW1 S -REUTER R OY1 T ER0 -REUTER'S R OY1 T ER0 Z -REUTHER R OY1 DH ER0 -REUTHER'S R OY1 DH ER0 Z 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AY1 T L IY0 -RIGHTNESS R AY1 T N AH0 S -RIGHTO R AY1 T OW0 -RIGHTS R AY1 T S -RIGHTWISE R AY1 T W AY2 Z -RIGHTY R AY1 T IY0 -RIGI R IH1 JH IY0 -RIGID R IH1 JH AH0 D -RIGID R IH1 JH IH0 D -RIGIDITIES R IH0 JH IH1 D IH0 T IY0 Z -RIGIDITY R IH0 JH IH1 D AH0 T IY0 -RIGIDLY R IH1 JH IH0 D L IY0 -RIGIDNESS R IH1 JH AH0 D N AH0 S -RIGIMENT R IH1 JH AH0 M AH0 N T -RIGIMINT R IH1 JH AH0 M IH0 N T -RIGINAL R IH1 JH AH0 N AH0 L -RIGLAR R IH1 G L ER0 -RIGMAROLE R IH1 G M ER0 OW2 L -RIGMAROLES R IH1 G M ER0 OW2 L Z -RIGNAUT R IH1 G N AO2 T -RIGNOLD R IH1 G N OW0 L D -RIGOLET R IH2 G AH0 L EH1 T -RIGOLETS R IH2 G AH0 L EH1 T S -RIGOLETTE R IH2 G AH0 L EH1 T -RIGOLETTO R IH2 G AH0 L EH1 T OW0 -RIGOLOT R IH2 G AH0 L AA1 T -RIGOR R IH1 G ER0 -RIGORISTS R IH1 G ER0 IH0 S T S -RIGOROUS R IH1 G ER0 AH0 S -RIGOROUSLY R IH1 G ER0 AH0 S L IY0 -RIGORS R IH1 G ER0 Z -RIGOU R IH1 G UW0 -RIGOU'S R IH1 G UW1 Z -RIGOUR R IH1 G ER0 -RIGOURS R IH1 G AO1 R Z -RIGS R IH1 G Z -RIGSDAK R IH1 G Z D AE0 K -RIGSWORTH R IH1 G Z W ER2 TH -RIGUEUR R IH0 G Y UW1 R -RIGUEUR R IH1 G ER0 -RII R IY0 -RIIS R IY1 Z -RIJEKA R IH0 JH EH1 K AH0 -RIJN R IY1 AH0 N -RIK R IH1 K -RIKA R IY1 K AH0 -RIKCHEN R IH1 K AH0 N -RIKE R AY1 K -RIKER R AY1 K ER0 -RIKI R IH1 K IY0 -RIKIU R IH1 K Y UW0 -RIKKI R IH1 K IY0 -RIKLI R IH1 K L IY0 -RIKOR R IH1 K ER0 -RIKSDAG R IH1 K S D AE0 G -RIL R IH0 L -RILAS R AY1 L AH0 Z -RILE R AY1 L -RILED R AY1 L D -RILES R AY1 L Z -RILEY R AY1 L IY0 -RILEY'S R AY1 L IY0 Z -RILIEVO R IH1 L Y AH0 V OW0 -RILIN R IH0 L IH0 N -RILING R AY1 L IH0 NG -RILKE R IH1 L K -RILKE'S R IH1 L K S -RILL R IH1 L -RILLA R IH1 L AH0 -RILLA'S R IH1 L AH0 Z -RILLED R IH1 L D -RILLER R IH1 L ER0 -RILLET R IH1 L IH0 T -RILLETTE R IH0 L EH1 T -RILLING R IH1 L IH0 NG -RILLINGDON R IH1 L IH0 NG D AH0 N -RILLS R IH1 L Z -RILLY R IH1 L IY0 -RILSTON R AY1 L S T AH0 N -RILTH R IH1 L TH -RILTON R IH1 L T AH0 N -RILY R AH0 L IY0 -RIM R IH1 M -RIMA R IY1 M AH0 -RIMA'S R IY1 M AH0 Z -RIMAC R IH1 M AE0 K -RIMBAUD R IH1 M B OW1 -RIME R AY1 M -RIMED R AY1 M D -RIMES R AY1 M Z -RIMINGTON R AY1 M IH0 NG T AH0 N -RIMINGTON'S R AY1 M IH0 NG T AH0 N Z -RIMINI R IH1 M AH0 N IY0 -RIMLESS R IH1 M L AH0 S -RIMMED R IH1 M D -RIMMEL R IH1 M AH0 L -RIMMER R IH1 M ER0 -RIMMING R IH1 M IH0 NG -RIMMON R IH1 M AH0 N -RIMMON'S R IH1 M AH0 N Z -RIMN R IH1 M N -RIMOUSKI R IH1 M AH1 S K IY0 -RIMPI R IH2 M P IY0 -RIMPIS R IH1 M P IH0 S -RIMROCK R IH1 M R AA2 K -RIMROCK'S R IH1 M R AA2 K S -RIMS R IH1 M Z -RIMSKY R IH1 M S K IY0 -RIMU R IH1 M UW0 -RIMY R AY1 M IY0 -RIN R IH0 N -RINA R IY1 N AH0 -RINALDI R IY0 N AA1 L D IY0 -RINALDINI R IH0 N AA1 L D IY1 N IY0 -RINALDO R IH0 N AA1 L D OW0 -RINALDO'S R IH0 N AA1 L D OW0 Z -RINCER R IH1 N S ER0 -RINCON R IH1 NG K AH0 N -RINCONA R IH1 NG K OW1 N AH0 -RINCONADA R IH1 NG K AH0 N AA1 D AH0 -RIND R AY1 N D -RINDA R IH1 N D AH0 -RINDED R AY1 N D IH0 D -RINDERPEST R IH1 N D ER0 P AH0 S T -RINDGE R IH1 N JH -RINDING R AY1 N D IH0 NG -RINDS R AY1 N D Z -RINDY R IH1 N D IY0 -RINE R AY1 N -RINEHART R AY1 N HH AA2 R T -RINEHART'S R AY1 N HH AA2 R T S -RINELDO R AY1 N L D OW0 -RINEY R AY1 N IY0 -RING R IH1 NG -RING'D R IH1 NG D -RING'S R IH1 NG Z -RINGAMY R IH1 NG G AH0 M IY0 -RINGAN R IH1 NG G AH0 N -RINGAN'S R IH1 NG G AH0 N Z -RINGBOLT R IH1 NG B OW2 L T -RINGBOLTS R IH1 NG B OW2 L T S -RINGBONE R IH1 NG B OW2 N -RINGDOVE R IH1 NG D AH0 V -RINGDOVE'S R IH1 NG D AH0 V Z -RINGDOVES R IH1 NG D UW1 V Z -RINGE R IH1 NG -RINGED R IH1 NG D -RINGER R IH1 NG ER0 -RINGER'S R IH1 NG ER0 Z -RINGERS R IH1 NG ER0 Z -RINGETH R IH1 NG G IH0 TH -RINGFIELD R IH1 NG F IY2 L D -RINGFIELD'S R IH1 NG F IY2 L D Z -RINGG R IH1 NG G -RINGG'S R IH1 NG G Z -RINGGAN R IH1 NG G AH0 N -RINGGAN'S R IH1 NG G AH0 N Z -RINGGOLD R IH1 NG G OW2 L D -RINGGOLDS R IH1 NG G OW2 L D Z -RINGHEIM R IH1 NG HH AY2 M -RINGIN R IH1 NG AH0 N -RINGING R IH1 NG IH0 NG -RINGINGLY R IH1 NG IH0 NG L IY0 -RINGINGS R IH1 NG IH0 NG Z -RINGLEADER R IH1 NG L IY2 D ER0 -RINGLEADERS R IH1 NG L IY2 D ER0 Z -RINGLESS R IH1 NG L AH0 S -RINGLET R IH1 NG G AH0 L T -RINGLETED R IH1 NG G AH0 L T IH0 D -RINGLETS R IH1 NG G AH0 L AH0 T S -RINGLETTED R IH1 NG G L EH1 T IH0 D -RINGLEY R IH1 NG G L IY0 -RINGMASTER R IH1 NG M AE2 S T ER0 -RINGO R IY1 NG G OW0 -RINGO'S R IH1 NG G OW0 Z -RINGOLD R IH1 NG G OW0 L D -RINGROPE R IH1 NG G R OW1 P -RINGROSE R IH1 NG G R OW2 Z -RINGS R IH1 NG Z -RINGSEND R IH1 NG Z AH0 N D -RINGSIDE R IH1 NG S AY2 D -RINGSTETTEN R IH1 NG S T IH0 T AH0 N -RINGSTRASSE R IH1 NG S T R AE1 S -RINGTAIL R IH1 NG T EY2 L -RINGTOP R IH1 NG T AA2 P -RINGTOWN R IH1 NG T AW2 N -RINGWAAK R IH1 NG W AA1 K -RINGWOOD R IH1 NG W UH2 D -RINGWORM R IH1 NG G W ER0 M -RINK R IH1 NG K -RINKA R IH1 NG K AH0 -RINKELMANN R IH1 NG K AH0 L M AH0 N -RINKING R IH1 NG K IH0 NG -RINKITINK R IH1 NG K IH0 T IH0 NG K -RINKITINK'S R IH1 NG K IH0 T IH1 NG K S -RINKS R IH1 NG K S -RINKTUM R IH1 NG K T AH0 M -RINNIN R IH1 N IH0 N -RINNING R IH1 N IH0 NG -RINOLFO R IY0 N OW1 L F OW0 -RINS R IH1 N Z -RINSE R IH1 N S -RINSED R IH1 N S T -RINSES R IH1 N S AH0 Z -RINSING R IH1 N S IH0 NG -RINSINGS R IH1 N S IH0 NG Z -RINT R IH0 N T -RINTOUL R IH1 N T UW1 L -RINTOUL'S R IH1 N T UW1 L Z -RINTS R IH0 N T S -RINTZEVA R IH1 N T Z EY1 V AH0 -RIO R IY1 OW0 -RIO'S R IY1 OW0 Z -RIOBAMBA R IY1 OW0 B AE1 M B AH0 -RIOJA R IY1 OW0 Y AH0 -RIOL R IY0 AH0 L -RIOLAMA R IY0 AH0 L AA1 M AH0 -RIOM R IY1 AH0 M -RION R AY1 AH0 N -RIONGA R IY1 OW0 NG G AH0 -RIORDAN R IH1 R D AH0 N -RIORDON R IH1 R D AH0 N -RIOS R IY1 OW0 S -RIOS'S R IY1 OW0 S IH0 Z -RIOSA R IY0 OW1 S AH0 -RIOT R AY1 AH0 T -RIOT'S R AY1 AH0 T S -RIOTED R AY1 AH0 T IH0 D -RIOTER R AY1 AH0 T ER0 -RIOTERS R AY1 AH0 T ER0 Z -RIOTING R AY1 AH0 T IH0 NG -RIOTINGS R AY1 AH0 T IH0 NG Z -RIOTOUS R AY1 AH0 T AH0 S -RIOTOUSLY R AY1 AH0 T AH0 S L IY0 -RIOTOUSNESS R AY1 AH0 T AH0 S N AH0 S -RIOTS R AY1 AH0 T S -RIOU R IY1 UW0 -RIOWAG R IY1 OW0 W AE2 G -RIP R IH1 P -RIP'S R IH1 P S -RIPA R IY1 P AH0 -RIPARIAN R IH1 P EH1 R IY0 AH0 N -RIPE R AY1 P -RIPELY R AY1 P AH0 L IY0 -RIPEN R AY1 P AH0 N -RIPEN'D R AY1 P AH0 N D -RIPENED R AY1 P AH0 N D -RIPENESS R AY1 P N AH0 S -RIPENIN R AY1 P AH0 N IH1 N -RIPENING R AY1 P AH0 N IH0 NG -RIPENING R AY1 P N IH0 NG -RIPENS R AY1 P AH0 N Z -RIPER R AY1 P ER0 -RIPER'S R AY1 P ER0 Z -RIPES R AY1 P S -RIPEST R AY1 P AH0 S T -RIPETTA R IH1 P EH1 T AH0 -RIPLEY R IH1 P L IY0 -RIPLEY'S R IH1 P L IY0 Z -RIPLEYS R IH1 P L IY0 Z -RIPOGENUS R IH1 P AH0 JH IY1 N AH0 S -RIPOLI R IH1 P AH0 L IY0 -RIPON R IH1 P AA2 N -RIPOSO R IH0 P OW1 S OW0 -RIPOSTE R IH0 P OW1 S T -RIPOSTED R IH0 P OW1 S T IH0 D -RIPOSTES R IH0 P AO1 S T S -RIPPED R IH1 P T -RIPPENGER R IH1 P IH0 N JH ER0 -RIPPENGER'S R IH1 P IH0 N JH ER0 Z -RIPPER R IH1 P ER0 -RIPPERS R IH1 P ER0 Z -RIPPIN R IH1 P IH0 N -RIPPING R IH1 P IH0 NG -RIPPINGILLE R IH1 P IH0 NG G IY1 L IY0 -RIPPINGLY R IH1 P IH0 NG L IY0 -RIPPLE R IH1 P AH0 L -RIPPLE'S R IH1 P AH0 L Z -RIPPLED R IH1 P AH0 L D -RIPPLELESS R IH1 P AH0 L L AH0 S -RIPPLES R IH1 P AH0 L Z -RIPPLETON R IH1 P AH0 L T AH0 N -RIPPLIN R IH1 P L IH0 N -RIPPLING R IH1 P AH0 L IH0 NG -RIPPLING R IH1 P L IH0 NG -RIPPLINGLY R IH1 P AH0 L IH0 NG L IY0 -RIPPLINGS R IH1 P AH0 L IH0 NG Z -RIPPLY R IH1 P L AY1 -RIPPON R IH1 P AH0 N -RIPS R IH1 P S -RIPSY R IH1 P S IY0 -RIPT R IH1 P T -RIPTON R IH1 P T AH0 N -RIPTON'S R IH1 P T AH0 N Z -RIQU'L R IY1 G AH0 L -RIQUET R IY1 K EY0 T -RIQUET'S R IY1 K EH0 T S -RIQUETTE R IY1 K EH1 T -RIQUETTI R IY1 K EH1 T IY0 -RIQUIER R IH1 K Y ER0 -RIRE R AY1 ER0 -RIS R IH1 S -RIS'N R IH1 S AH0 N -RISALDAR R AY1 Z AH0 L D AA2 R -RISALDAR'S R AY1 Z AH0 L D AA2 R Z -RISCHENHEIM R IH1 SH AH0 N HH AY2 M -RISCHENHEIM'S R IH1 SH AH0 N HH AY2 M Z -RISE R AY1 Z -RISEHOLME R IH1 Z AH0 HH OW0 L M -RISEING R AY1 Z IH0 NG -RISEN R IH1 Z AH0 N -RISER R AY1 Z ER0 -RISERS R AY1 Z ER0 Z -RISES R AY1 Z AH0 Z -RISES R AY1 Z IH0 Z -RISEST R AY1 Z AH0 S T -RISETH R IH1 S IH0 TH -RISH R IH1 SH -RISHI R IY1 SH IY0 -RISHIS R IH0 SH IH0 S -RISHYAS R IH1 SH Y AH0 S -RISIBILITIES R IH1 S IH0 B IH1 L IH0 T IY0 Z -RISIBILITY R IH1 S IH0 B IH1 L IH0 T IY0 -RISIBLE R IH1 Z AH0 B AH0 L -RISIBLES R IH1 Z AH0 B AH0 L Z -RISIN R AY1 Z IH0 N -RISING R AY1 Z IH0 NG -RISINGHAM R AY1 Z IH0 NG HH AE2 M -RISINGS R AY1 Z IH0 NG Z -RISK R IH1 S K -RISK'D R IH1 S K D -RISK'S R IH1 S K S -RISKED R IH1 S K T -RISKIEST R IH1 S K IY0 AH0 S T -RISKIN R IH1 S K IH0 N -RISKINESS R IH1 S K IY0 N AH0 S -RISKING R IH1 S K IH0 NG -RISKS R IH1 S K S -RISKY R IH1 S K IY0 -RISLER R IH1 S L ER0 -RISLEY R IH1 Z L IY0 -RISLEY'S R IH1 Z L IY0 Z -RISO R IY1 S OW0 -RISORGIMENTO R IY2 S AO1 R JH AH0 M EH1 N T OW0 -RISOTTO R IY2 S AO1 T OW2 -RISPECT R IY0 S P EH2 K T -RISPECTS R IY0 S P EH2 K T S -RISPETTO R IY0 S P EH1 T OW0 -RISPONDI R IY0 S P OW1 N D IY0 -RISPOSE R IY0 S P OW1 Z -RISQUE R IH2 S K EY1 -RISQUED R IH2 S K D -RISQUES R IH2 S K S -RISSALA R IH1 S AH0 L AH0 -RISSALDAR R IH1 S AH0 L D AA2 R -RISSOLE R IH0 S OW1 L -RISSOLES R IH0 S OW1 L Z -RIST R IH1 S T -RISTAL R IH1 S T AH0 L -RISTIGOUCHE R IH1 S T IH0 G UW1 SH -RISTOFALO R IY0 S T OW0 F AA1 L OW0 -RISTON R IH1 S T AH0 N -RISTORANTE R IH0 S T ER0 AA1 N T EY0 -RISTORI R IH1 S T AO0 R IY0 -RISU R IH1 S UW2 -RISUM R IH1 Z AH0 M -RISUS R IH1 S AH0 S -RIT R IH1 T -RITA R IY1 T AH0 -RITA'S R IY1 T AH0 Z -RITCHEY R IH1 CH IY0 -RITCHIE R IH1 CH IY0 -RITCHIE'S R IH1 CH IY0 Z -RITCHING R IH1 CH IH0 NG -RITE R AY1 T -RITES R AY1 T S -RITHMETIC R IH0 TH M EH1 T IH0 K -RITIN R IH1 T IH0 N -RITING R AY1 T IH0 NG -RITO R IY1 T OW0 -RITORNELLO R IH0 T AO2 R N EH1 L OW0 -RITSON R IH1 T S AH0 N -RITSON'S R IH1 T S AH0 N Z -RITT R IH1 T -RITTA R IH1 T AH0 -RITTEN R IH1 T AH0 N -RITTENHEIM R IH1 T AH0 N HH AY2 M -RITTENHEIM'S R IH1 T AH0 N HH AY2 M Z -RITTENHOUSE R IH1 T AH0 N HH AW2 S -RITTER R IH1 T ER0 -RITTER'S R IH1 T ER0 Z -RITTERS R IH1 T ER0 Z -RITTERSAAL R IH1 T ER0 S AA1 L -RITTERSHEIM R IH1 T ER0 S HH AY0 M -RITTI R IY1 T IY0 -RITTMEISTER R IH1 T M AY0 S T ER0 -RITUAL R IH1 CH UW0 AH0 L -RITUALISM R IH2 CH UW0 AH0 L IH2 Z AH0 M -RITUALIST R IH0 CH AH0 W AH0 L IH0 S T -RITUALISTIC R IH2 CH UW0 AH0 L IH1 S T IH0 K -RITUALISTS R IH0 CH AH0 W AH0 L IH0 S T S -RITUALLY R IH1 CH UW0 AH0 L IY0 -RITUALS R IH1 CH UW0 AH0 L Z -RITUEL R IH2 CH UW0 AH0 L -RITZ R IH1 T S -RITZ'S R IH1 T S IH0 Z -RITZER R IH1 T Z ER0 -RITZMORE R IH1 T S M AO0 R -RITZTON R IH1 T S T AH0 N -RIV R IH1 V -RIV'RENCE R IH1 V R AH0 N S -RIVA R IY1 V AH0 -RIVAGE R AY1 V AH0 JH -RIVAL R AY1 V AH0 L -RIVAL'S R AY1 V AH0 L Z -RIVALED R AY1 V AH0 L D -RIVALING R AY1 V AH0 L IH0 NG -RIVALING R AY1 V L IH0 NG -RIVALLED R AY1 V AH0 L D -RIVALLING R AY1 V AH0 L IH0 NG -RIVALRIES R AY1 V AH0 L R IY0 Z -RIVALRY R AY1 V AH0 L R IY0 -RIVALS R AY1 V AH0 L Z -RIVALSHIP R AY1 V AH0 L SH IH2 P -RIVALSHIPS R AY1 V AH0 L SH IH2 P S -RIVANNA R IH0 V AE1 N AH0 -RIVAR R IH1 V ER0 -RIVARDI R IH1 V ER0 D IY0 -RIVAREZ R IH1 V ER0 EH2 Z -RIVAROL R IH0 V AE1 R AH0 L -RIVAROL'S R IH0 V AE1 R AH0 L Z -RIVAS R IY1 V AA0 Z -RIVAZ R IH0 V AA2 Z -RIVE R AY1 V -RIVED R AY1 V D -RIVEDERCI R IH1 V IH0 D IH1 R CH IY0 -RIVEH R IH1 V AH0 -RIVEN R IH1 V AH0 N -RIVENOAK R IH1 V IH0 N OW1 K -RIVER R IH1 V ER0 -RIVER'S R IH1 V ER0 Z -RIVERA R IH0 V EH1 R AH0 -RIVERA'S R IH1 V ER0 AH0 Z -RIVERAIN R IH1 V ER0 EY1 N -RIVERBANK R IH1 V ER0 B AE2 NG K -RIVERBANKS R IH1 V ER0 B AE2 NG K S -RIVERBED R IH1 V ER0 B EH2 D -RIVERBEDS R IH1 V ER0 B EH2 D Z -RIVERBEND R IH1 V ER0 B EH2 N D -RIVERBORO R IH1 V ER0 B ER0 OW0 -RIVERBY R IH1 V ER0 B IY0 -RIVERCOURT R IH1 V ER0 K AO2 R T -RIVERDALE R IH1 V ER0 D EY2 L -RIVERDENE R IH0 V EH1 R D IY0 N -RIVEREDGE R IH1 V ER0 EH0 JH -RIVERENCE R IH1 V ER0 AH0 N S -RIVERENZA R IH1 V ER0 EH1 N Z AH0 -RIVERFIELD R IH1 V ER0 F IY2 L D -RIVERFRONT R IH1 V ER0 F R AH2 N T -RIVERHEAD R IH1 V ER0 HH EH2 D -RIVERINA R IH1 V ER0 IY1 N AH0 -RIVERINCE R IH1 V ER0 IH0 N S -RIVERINE R IH1 V ER0 IY1 N IY0 -RIVERLAWN R IH1 V ER0 L AO2 N -RIVERLAWNS R IH1 V ER0 L AO1 N Z -RIVERMAN R IH1 V ER0 M AH0 N -RIVERMAN'S R IH1 V ER0 M AH0 N Z -RIVERMEAD R IH1 V ER0 M EH2 D -RIVERMEN R IH1 V ER0 M IH0 N -RIVERMOUTH R IH1 V ER0 M AH0 TH -RIVERO R IY0 V EH1 R OW0 -RIVEROLA R IH1 V ER0 OW1 L AH0 -RIVEROS R IY0 V EH1 R OW0 Z -RIVERPORT R IH1 V ER0 P AO2 R T -RIVERS R IH1 V ER0 Z -RIVERS'S R IH1 V ER0 Z IH0 Z -RIVERSBOROUGH R IH1 V ER0 Z B ER0 OW0 -RIVERSBROOK R IH1 V ER0 S B R UH2 K -RIVERSDALE R IH1 V ER0 S D EY2 L -RIVERSEDGE R IH1 V ER0 S EH2 JH -RIVERSIDE R IH1 V ER0 S AY2 D -RIVERSLEY R IH1 V ER0 S L IY0 -RIVERSMOUTH R IH1 V ER0 S M AH0 TH -RIVERSTOWN R IH1 V ER0 S T AW2 N -RIVERTON R IH1 V ER0 T AH0 N -RIVERTOWN R IH1 V ER0 T AW2 N -RIVERVALE R IH1 V ER0 V EY2 L -RIVERVIEW R IH1 V ER0 V Y UW2 -RIVERWARD R IH1 V ER0 W ER0 D -RIVERWARDS R IH1 V ER0 W ER0 D Z -RIVERWOOD R IH1 V ER0 W UH2 D -RIVES R AY1 V Z -RIVET R IH1 V AH0 T -RIVETED R IH1 V AH0 T IH0 D -RIVETER R IH1 V AH0 T ER0 -RIVETERS R IH1 V AH0 T ER0 Z -RIVETING R IH1 V AH0 T IH0 NG -RIVETS R IH1 V AH0 T S -RIVETTED R IH0 V EH1 T IH0 D -RIVETTING R IH0 V EH1 T IH0 NG -RIVI R IH2 V IY0 -RIVIERA R IH2 V IY0 EH1 R AH0 -RIVIERE R IH1 V IY0 EH0 R -RIVIERE'S R IH0 V Y EH1 R Z -RIVIERES R IH0 V Y IH1 R EH0 Z -RIVING R AY1 V IH0 NG -RIVINGTON R AY1 V IH0 NG T AH0 N -RIVINGTON'S R AY1 V IH0 NG T AH0 N Z -RIVO R IY1 V OW0 -RIVOLI R IH0 V OW1 L IY0 -RIVOLI'S R IH1 V AH0 L IY0 Z -RIVULET R IH1 V AH1 L IH0 T -RIVULET'S R IH1 V AH1 L EY1 Z -RIVULETS R IH1 V AH1 L AH0 T S -RIX R IH1 K S -RIXA R IH1 K S AH0 -RIXDOLLARS R IH1 K S D AO2 L ER0 Z -RIYA R IY0 AH0 -RIZ R IH1 Z -RIZA R IY1 Z AH0 -RIZAL R IY0 Z AA1 L -RIZAL'S R IY0 Z AA1 L Z -RIZOFF R IH1 Z AO0 F -RIZPAH R IH1 T S P AA2 -RIZZI R IH1 Z IY0 -RIZZIO R IH1 Z IY0 OW0 -RIZZIO'S R IY0 T S IY0 OW0 Z -RIZZO R IH1 Z OW0 -RIZZON R IH0 Z OW1 N -RJUKAN R JH AH0 K AH0 N -RJUKANFOS R JH AH0 K AE1 N F OW0 Z -RK R K -RL R L -RM R M -RN R N -RNER R N ER0 -RNSON R N S AH0 N -RNSTJERNE R N S T JH ER1 N -RO R OW1 -RO'D R OW1 D -ROA R OW1 AH0 -ROAB R OW1 AE0 B -ROABE R OW0 EY1 B -ROACH R OW1 CH -ROACH'S R OW1 CH IH0 Z -ROACHBACK R OW1 CH B AE2 K -ROACHED R OW1 CH T -ROACHES R OW1 CH 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AO2 R M Z -SANDTOFT S AE1 N D T AA1 F T -SANDTOWN S AE1 N D T AW2 N -SANDTOWN S AE1 N T AW2 N -SANDU S AE0 N D UW1 -SANDUS S AE1 N D IH0 S -SANDUSKY S AE0 N D AH1 S K IY0 -SANDUSKY'S S AE0 N D AH1 S K IY0 Z -SANDWICH S AE1 M W IH0 CH -SANDWICH S AE1 N D W IH0 CH -SANDWICH S AE1 N W IH0 CH -SANDWICH'S S AE1 N W IH0 CH IH0 Z -SANDWICHED S AE1 M W IH2 CH T -SANDWICHED S AE1 N D W IH2 CH T -SANDWICHED S AE1 N W IH2 CH T -SANDWICHES S AE1 M W IH0 CH IH0 Z -SANDWICHES S AE1 N D W IH0 CH IH0 Z -SANDWICHES S AE1 N W IH0 CH IH0 Z -SANDWICHING S AE1 N W IH0 CH IH0 NG -SANDWICK S AE1 N D W IH2 K -SANDWOOD S AE1 N D W UH2 D -SANDWORTH S AE1 N D W ER0 TH -SANDWORTH'S S AE1 N D W ER2 TH S -SANDY S AE1 N D IY0 -SANDY'S S AE1 N D IY0 Z -SANDYCLIFFE S AE1 N D IY0 K L IH0 F -SANDYFACE S AE1 N D IY0 F EY2 S -SANDYMERE S AE1 N D IY0 M IH2 R -SANDYMOUNT S AE1 N D IY0 M AW2 N T -SANDYS S AE1 N D IY0 Z -SANE S EY1 N -SANEHAT S AA0 N EY1 AH0 T -SANELY S EY1 N L IY0 -SANENESS S AE1 N AH0 S -SANER S EY1 N ER0 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IY0 -SANGUINARIA S AE1 NG G W AH0 N EH1 R IY0 AH0 -SANGUINARY S AE1 NG G W AH0 N EH2 R IY0 -SANGUINE S AE1 NG G W IH0 N -SANGUINEA S AE1 NG G W IH0 N IY1 AH0 -SANGUINELY S AE1 NG G W AH0 N L IY0 -SANGUINEM S AA0 NG IY0 N EH1 M -SANGUINENESS S AE1 NG G W AH0 N IH0 S -SANGUINEOUS S AA0 NG IY0 N IY0 AH0 S -SANGUINETTI S AA0 NG IY0 N EH1 T IY0 -SANGUINIS S AA0 NG IY0 N IY0 Z -SANGUIS S AA0 NG IY0 Z -SANHEDRIM S AE2 N HH IY1 D R IH0 M -SANHEDRIN S AE2 N HH IY1 D R IH0 N -SANI S AE1 N IH0 -SANI S AE1 N IY0 -SANIEL S AE1 N Y AH0 L -SANIEL'S S AE1 N Y AH0 L Z -SANIES S AH0 N IY0 Z -SANIETTE S AE2 N IY0 EH1 T -SANIEZ S AE2 N IY0 EH1 Z -SANIN S AE1 N IH0 N -SANIN'S S AE1 N IH0 N Z -SANINE S AH0 N IY1 N -SANINE'S S AH0 N IY1 N Z -SANITAETSRAT S AE1 N IH0 T AE2 T S R AE2 T -SANITAIRE S AE2 N AH0 T EH1 R -SANITARIUM S AE2 N AH0 T EH1 R IY0 AH0 M -SANITARIUMS S AE2 N AH0 T EH1 R IY0 AH0 M Z -SANITARS S AE1 N IH0 T AA1 R Z -SANITARY S AE1 N IH0 T EH2 R IY0 -SANITAS S AA0 N IY1 T AH0 S -SANITATION S AE2 N AH0 T EY1 SH AH0 N -SANITATION S AE2 N IH0 T EY1 SH AH0 N -SANITIES S AE1 N IH0 T IY0 Z -SANITORIUM S AE2 N IH0 T AO1 R IY0 AH0 M -SANITY S AE1 N AH0 T IY0 -SANJAK S AA0 N Y AH0 K -SANJO S AA0 N Y OW0 -SANK S AE1 NG K -SANKA S AE1 NG K AH0 -SANKARA S AA0 N K AA1 R AH0 -SANKATY S AA0 N K AA1 T IY0 -SANKER S AE1 NG K ER0 -SANKER'S S AE1 NG K ER0 Z -SANKERS S AE1 NG K ER0 Z -SANKEY S AE1 NG K IY0 -SANKEY'S S AE1 NG K IY0 Z -SANKHYA S AA0 N K Y AH0 -SANKIRTAN S AE1 NG K ER0 T AE1 N -SANKJEWITCH S AA0 N K Y IH0 W IH1 CH -SANKO S AE1 NG K OW0 -SANKS S AE1 NG K S -SANKUM S AE1 NG K AH0 M -SANNA S AE1 N AH0 -SANNA'S S AE1 N AH0 Z -SANNAES S AE1 N EY1 Z -SANNASPOST S AE1 N AH0 S P OW2 S T -SANNAZARO S AE1 N AH0 Z AA1 R OW0 -SANNIE S AE1 N IY0 -SANNIE'S S AE1 N IY0 Z -SANNINI S AA0 N IY1 N IY0 -SANNOX S AE1 N AH0 K S -SANNY S AE1 N IY0 -SANNYASI S AE1 N Y AA1 S IY0 -SANNYASIS S AE1 N Y AH0 S IH0 S -SANO S AA1 N OW0 -SANOM S AE1 N AH0 M -SANOMS S AE1 N AH0 M Z -SANPEUR S AE1 N P ER0 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-SCABS S K AE1 B Z -SCACCIATI S K AA1 CH AA1 T IY0 -SCADDER S K AE1 D ER0 -SCADDHAN S K AE1 D AH0 N -SCADS S K AE1 D Z -SCAEAN S K IY1 AH0 N -SCAEVOLA S K AA0 EH0 V OW1 L AH0 -SCAFELL S K AA0 F AH0 L -SCAFFOLD S K AE1 F AH0 L D -SCAFFOLDED S K AE1 F AH0 L D IH0 D -SCAFFOLDING S K AE1 F AH0 L D IH0 NG -SCAFFOLDINGS S K AE1 F AH0 L D IH0 NG Z -SCAFFOLDS S K AE1 F AH0 L D Z -SCAGLIOLA S K AE2 G L IY0 OW1 L AH0 -SCAIFE S K EY1 F -SCAIFE'S S K EY1 F S -SCAIRT S K ER0 T -SCAIS S K EY1 Z -SCAIT S K EY1 T -SCAITH S K EY1 TH -SCAL'D S K EY1 L D -SCALA S K AA1 L AH0 -SCALARIA S K AA0 L AA1 R IY0 AH0 -SCALASTRO S K AA0 L AA1 S T R OW0 -SCALAWAG S K AA1 L AH0 W AE2 G -SCALAWAGS S K AA1 L AH0 W AE1 G Z -SCALD S K AO1 L D -SCALDED S K AO1 L D IH0 D -SCALDIN S K AO1 L D IH0 N -SCALDING S K AO1 L D IH0 NG -SCALDINO S K AO1 L D IY1 N OW0 -SCALDS S K AO1 L D Z -SCALE S K EY1 L -SCALE'S S K EY1 L Z -SCALED S K EY1 L D -SCALELESS S K EY1 L L AH0 S -SCALENE S K EY1 L IY0 N -SCALER S K EY1 L ER0 -SCALERY S K AE1 L ER0 IY0 -SCALES S K EY1 L Z -SCALES'S S K EY1 L Z IH0 Z -SCALEY S K EY1 L IY0 -SCALI S K AA1 L IY0 -SCALI S K EY1 L IY0 -SCALIGER S K EY1 L IH0 G ER0 -SCALIGERS S K AA1 L AY0 G ER0 Z -SCALING S K EY1 L IH0 NG -SCALLAMERA S K AE1 L AH0 M EH1 R AH0 -SCALLAWAG S K AE1 L AH0 W AE2 G -SCALLAWAGS S K AE1 L AH0 W AE1 G Z -SCALLOP S K AE1 L AH0 P -SCALLOPED S K AA1 L AH0 P T -SCALLOPING S K AE1 L AH0 P IH0 NG -SCALLOPS S K AE1 L AH0 P S -SCALLOWA S K AE1 L AW1 AH0 -SCALLY S K AE1 L IY0 -SCALLYWAG S K AE1 L IY0 W AE2 G -SCALLYWAGS S K AE1 L IY0 W AE1 G Z -SCALP S K AE1 L P -SCALPA S K AE1 L P AH0 -SCALPED S K AE1 L P T -SCALPEL S K AE1 L P AH0 L -SCALPELS S K AE1 L P AH0 L Z -SCALPER S K AE1 L P ER0 -SCALPER'S S K AE1 L P ER0 Z -SCALPERS S K AE1 L P ER0 Z -SCALPIN S K AE1 L P IH0 N -SCALPING S K AE1 L P IH0 NG -SCALPINGS S K AE1 L P IH0 NG Z -SCALPLESS S K AE1 L P L AH0 S -SCALPLOCK S K AE1 L P L AH0 K -SCALPS S K AE1 L P S -SCALY S K EY1 L IY0 -SCAMANDER S K AE1 M AH0 N D ER0 -SCAMANDER'S S K AE1 M AH0 N D ER0 Z -SCAMMEL S K AE1 M AH0 L -SCAMMON S K AE1 M AH0 N -SCAMMONY S K AE1 M AH0 N IY0 -SCAMP S K AE1 M P -SCAMP'S S K AE1 M P S -SCAMPED S K AE1 M P T -SCAMPER S K AE1 M P ER0 -SCAMPER'D S K AE1 M P ER0 D -SCAMPERDALE S K AE1 M P ER0 D EY2 L -SCAMPERDALE'S S K AE1 M P ER0 D EY2 L Z -SCAMPERED S K AE1 M P ER0 D -SCAMPERING S K AE1 M P ER0 IH0 NG -SCAMPERINGS S K AE1 M P ER0 IH0 NG Z -SCAMPERS S K AE1 M P ER0 Z -SCAMPFORD S K AE1 M P F ER0 D -SCAMPING S K AE1 M P IH0 NG -SCAMPISH S K AE1 M P IH0 SH -SCAMPS S K AE1 M P S -SCAN S K AE1 N -SCAN'LOUS S K AE1 N L AH0 S -SCAND'LOUS S K AE1 N L AH0 S -SCANDAL S K AE1 N D AH0 L -SCANDAL'S S K AE1 N D AH0 L Z -SCANDALE S K AE1 N D EY2 L -SCANDALEUSE S K AE1 N D AH0 L UW1 S -SCANDALISE S K AE1 N D AH0 L IY1 S -SCANDALISED S K AE1 N D AH0 L AY2 Z D -SCANDALISING S K AE1 N D AH0 L AY2 Z IH0 NG -SCANDALIZE S K AE1 N D AH0 L AY2 Z -SCANDALIZED S K AE1 N D AH0 L AY2 Z D -SCANDALIZES S K AE1 N D AH0 L AY2 Z IH0 Z -SCANDALIZING S K AE1 N D AH0 L AY2 Z IH0 NG -SCANDALMONGER S K AE1 N D AH0 L M AA2 NG G ER0 -SCANDALMONGERING S K AE1 N D AH0 L M AA2 NG G ER0 IH0 NG -SCANDALMONGERS S K AE1 N D AH0 L M AA2 NG G ER0 Z -SCANDALOUS S K AE1 N D AH0 L AH0 S -SCANDALOUSLY S K AE1 N D AH0 L AH0 S L IY0 -SCANDALS S K AE1 N D AH0 L Z -SCANDALUM S K AE1 N D AH0 L AH0 M -SCANDENS S K AE1 N D AH0 N Z -SCANDERBEG S K AE1 N D ER0 B EH2 G -SCANDERBEG'S S K AE1 N D ER0 B EH2 G Z -SCANDEROON S K AE1 N D ER0 UW2 N -SCANDIANO S K AE1 N D IY0 AA1 N OW0 -SCANDINAVIA S K AE2 N D IH0 N EY1 V IY0 AH0 -SCANDINAVIAN S K AE2 N D IH0 N EY1 V IY0 AH0 N -SCANDINAVIANS S K AE2 N D IH0 N EY1 V IY0 AH0 N Z -SCANIA S K AA1 N IY0 AH0 -SCANLAN S K AE1 N L AH0 N -SCANLAN'S S K AE1 N L AH0 N Z -SCANLON S K AE1 N L AH0 N -SCANLON'S S K AE1 N L AH0 N Z -SCANN'D S K AE1 N D -SCANNED S K AE1 N D -SCANNELL S K AE1 N AH0 L -SCANNER S K AE1 N ER0 -SCANNERS S K AE1 N ER0 Z -SCANNING S K AE1 N IH0 NG -SCANS S K AE1 N Z -SCANSION S K AE1 N SH AH0 N -SCANT S K AE1 N T -SCANTED S K AE1 N T IH0 D -SCANTER S K AE1 N T ER0 -SCANTEST S K AE1 N T AH0 S T -SCANTIER S K AE1 N T IY0 ER0 -SCANTIEST S K AE1 N T IY0 AH0 S T -SCANTILY S K AE1 N T AH0 L IY0 -SCANTINESS S K AE1 N T IY0 N AH0 S -SCANTLEBRAY S K AE1 N T L AH0 B R EY2 -SCANTLEBRAY'S S K AE1 N T L AH0 B R EY2 Z -SCANTLEBURY S K AE1 N T AH0 L B EH2 R IY0 -SCANTLING S K AE1 N T L IH0 NG -SCANTLINGS S K AE1 N T L IH0 NG Z -SCANTLY S K AE1 N T L IY0 -SCANTNESS S K AE1 N T N AH0 S -SCANTY S K AE1 N T IY0 -SCAP'D S K EY1 P IY1 D -SCAPA S K AA1 P AH0 -SCAPE S K EY1 P -SCAPED S K EY1 P T -SCAPEGOAT S K EY1 P G OW2 T -SCAPEGOATS S K EY1 P G OW2 T S -SCAPEGRACE S K EY1 P G R EY1 S -SCAPEGRACES S K EY1 P G R EY1 S IH0 Z -SCAPES S K EY1 P S -SCAPHIO S K AE1 F IY0 OW0 -SCAPIN S K EY1 P IH2 N -SCAPING S K EY1 P IH0 NG -SCAPULA S K AE1 P Y AH0 L AH0 -SCAPULAR S K AE1 P Y AH0 L ER0 -SCAPULARIES S K AE1 P Y AH0 L ER0 IY0 Z -SCAPULARS S K AE1 P Y AH0 L ER0 Z -SCAPULARY S K AE1 P Y AH0 L EH2 R IY0 -SCAR S K AA1 R -SCAR'D S K AA0 R D -SCAR'S S K AA1 R Z -SCARAB S K AE1 R AH0 B -SCARABAEI S K EH1 R AH0 B AE0 EY2 -SCARABAEUS S K AE1 R AH0 B EY1 AH0 S -SCARABEE S K AE1 R AH0 B IY0 -SCARABEUS S K AA0 R AA1 B EY0 IH0 S -SCARABS S K AE1 R AH0 B Z -SCARAMMUCCIA S K EH1 R AH0 M Y UW0 CH AH0 -SCARAMOUCH S K AE1 R AH0 M AW2 CH -SCARAMOUCHE S K EH1 R AH0 M AW2 CH -SCARAMOUCHES S K EH1 R AH0 M AW2 CH IH0 Z -SCARBERRY S K AA1 R B EH2 R IY0 -SCARBERRY'S S K AA1 R B EH2 R IY0 Z -SCARBORO S K AA1 R B ER0 OW0 -SCARBOROUGH S K AA1 R B ER2 OW0 -SCARBOROUGH'S S K AA1 R B ER2 OW0 Z -SCARBY S K AA1 R B IY0 -SCARCE S K EH1 R S -SCARCELY S K EH1 R S L IY0 -SCARCENESS S K EH1 R S N AH0 S -SCARCER S K EH1 R S ER0 -SCARCEST S K EH1 R S AH0 S T -SCARCITY S K EH1 R S IH0 T IY0 -SCARCLIFF S K AA0 R K L IH2 F -SCARCLIFFS S K AA0 R K L IH2 F S -SCARDALE S K AA1 R D EY2 L -SCARE S K EH1 R -SCARECROW S K AE1 R K R OW0 -SCARECROW'S S K AE1 R K R OW0 Z -SCARECROWS S K AE1 R K R OW0 Z -SCARED S K EH1 R D -SCAREHEAD S K EH1 R HH EH0 D -SCAREHEADS S K EH1 R HH EH2 D Z -SCARER S K EH1 R ER0 -SCARES S K EH1 R Z -SCARESBY S K AA0 R EH1 S B IY0 -SCARET S K AA0 R EH1 T -SCAREY S K EH1 R IY0 -SCARF S K AA1 R F -SCARFACE S K AA1 R F EY2 S -SCARFE S K AA1 R F -SCARFED S K AA1 R F T -SCARFIELD S K AA1 R F IY2 L D -SCARFORD S K AA1 R F ER0 D -SCARFPIN S K AA1 R F P AE1 N -SCARFS S K AA1 R F S -SCARGATE S K AA1 R G EY2 T -SCARGILL S K AA1 R G IH2 L -SCARHAVEN S K AA1 R HH EY2 V AH0 N -SCARIEST S K EH1 R IY0 AH0 S T -SCARIFF S K EH1 R IH0 F -SCARIFICATION S K EH1 R IH0 F IH0 K EY1 SH AH0 N -SCARIFIED S K EH1 R AH0 F AY2 D -SCARIFY S K EH1 R AH0 F AY2 -SCARIFYING S K EH1 R AH0 F AY2 IH0 NG -SCARIN S K AE1 R IH0 N -SCARING S K EH1 R IH0 NG -SCARISBRICK S K EH1 R IH0 S B R IH2 K -SCARISBRICKS S K EH1 R IH0 S B R IH2 K S -SCARLATINA S K AA1 R L AH0 T IY1 N AH0 -SCARLATTI S K AA0 R L AA1 T IY0 -SCARLATTI'S S K AA1 R L AH0 T IY0 Z -SCARLET S K AA1 R L AH0 T -SCARLET'S S K AA1 R L AH0 T S -SCARLETS S K AA1 R L AH0 T S -SCARLETT S K AA1 R L IH0 T -SCARLETT'S S K AA1 R L AH0 T S -SCARMANN S K AA1 R M AH0 N -SCARMELLI S K AA0 R M EH1 L IY0 -SCARNHAM S K AA1 R N AH0 M -SCAROOYADI S K EH1 R UW0 AA1 D IY0 -SCARP S K AA1 R P -SCARPA S K AA1 R P AH0 -SCARPE S K AA1 R P -SCARPED S K AA1 R P T -SCARPIA S K AA0 R P IY0 AH0 -SCARPIA'S S K AA0 R P IY0 AH0 Z -SCARPS S K AA1 R P S -SCARR'D S K AA1 R D -SCARRED S K AA1 R D -SCARREN S K AA1 R AH0 N -SCARRING S K AA1 R IH0 NG -SCARRON S K AE1 R AH0 N -SCARRON'S S K AE1 R AH0 N Z -SCARROW S K AE1 R OW0 -SCARROWBY S K AE1 R OW0 B IY0 -SCARROWMANIA S K AE1 R OW0 M EY1 N IY0 AH0 -SCARS S K AA1 R Z -SCARSDALE S K AA1 R Z D EY2 L -SCARSELLA S K AA2 R S EH1 L AH0 -SCARSMERE S K AA1 R Z M IH2 R -SCART S K AA2 R T -SCARTH S K AA0 R TH -SCARTHEY S K AA1 R DH IY0 -SCARTHOE S K AA1 R TH OW2 -SCARUM S K EH1 R AH0 M -SCARVES S K AA1 R V Z -SCARY S K EH1 R IY0 -SCAT S K AE1 T -SCATCHARD S K AE1 CH ER0 D -SCATCHERD S K AE1 CH ER0 D -SCATCHERD'S S K AE1 CH ER0 D Z -SCATCHERDS S K AE1 CH ER0 D Z -SCATCHY S K AE1 CH IY0 -SCATE S K EY2 T -SCATES S K EY1 T S -SCATH S K AE1 TH -SCATHE S K EY1 DH -SCATHED S K EY1 DH D -SCATHELESS S K EY1 DH L AH0 S -SCATHES S K EY1 DH Z -SCATHING S K EY1 DH IH0 NG -SCATHINGLY S K EY1 DH IH0 NG L IY0 -SCATHLESS S K AE1 TH L AH0 S -SCATT S K AE1 T -SCATTER S K AE1 T ER0 -SCATTER'D S K AE1 T ER0 D -SCATTERALL S K AE1 T ER0 AO2 L -SCATTERATION S K AE1 T ER0 EY1 SH AH0 N -SCATTERBRAIN S K AE1 T ER0 B R EY2 N -SCATTERBRAIN'S S K AE1 T ER0 B R EY1 N Z -SCATTERBRAINED S K AE1 T ER0 B R EY2 N D -SCATTERBRAINS S K AE1 T ER0 B R EY1 N Z -SCATTERCASH S K AE1 T ER0 K AE2 SH -SCATTERCASH'S S K AE1 T ER0 K AE2 SH IH0 Z -SCATTERED S K AE1 T ER0 D -SCATTERER S K AE1 T ER0 ER0 -SCATTEREST S K AE1 T ER0 AH0 S T -SCATTERETH S K AE1 T ER0 EH1 TH -SCATTERGOOD S K AE1 T ER0 G UH2 D -SCATTERIN S K AE1 T ER0 IH0 N -SCATTERING S K AE1 T ER0 IH0 NG -SCATTERINGLY S K AE1 T ER0 IH0 NG L IY0 -SCATTERINGS S K AE1 T ER0 IH0 NG Z -SCATTERS S K AE1 T ER0 Z -SCAUR S K AA1 R -SCAURS S K AO2 R Z -SCAURUS S K AO1 R AH0 S -SCAVAIG S K AH0 V EY1 G -SCAVENGED S K AE1 V AH0 N JH D -SCAVENGER S K AE1 V AH0 N JH ER0 -SCAVENGER'S S K AE1 V AH0 N JH ER0 Z -SCAVENGERS S K AE1 V AH0 N JH ER0 Z -SCAVENGING S K AE1 V AH0 N JH IH0 NG -SCAVOIR S K AH0 V W AA1 R -SCAW S K AO1 -SCAWFELL S K AO1 F AH0 L -SCAWTHORNE S K AO1 TH AO0 R N -SCEAUX S OW1 -SCELECTED S IH0 L EH1 K T IH0 D -SCELERAT S EH2 L ER0 EY1 T -SCELUS S K EH1 L AH0 S -SCENA S IY1 N AH0 -SCENARIO S IH0 N EH1 R IY0 OW0 -SCENARIOS S IH0 N EH1 R IY0 OW0 Z -SCENE S IY1 N -SCENE'S S IY1 N Z -SCENERIES S IY1 N ER0 IY0 Z -SCENERY S IY1 N ER0 IY0 -SCENES S IY1 N Z -SCENIC S IY1 N IH0 K -SCENICAL S IY1 N IH0 K AH0 L -SCENICALLY S IY1 N IH0 K L IY0 -SCENT S EH1 N T -SCENTED S EH1 N T IH0 D -SCENTIN S EH1 N T IH0 N -SCENTING S EH1 N T IH0 NG -SCENTLESS S EH1 N T L AH0 S -SCENTS S EH1 N T S -SCENTW S IH0 N T W -SCENTWELL S EH1 N T W EH2 L -SCEPTER S K EH1 P T ER0 -SCEPTERED S K EH1 P T ER0 D -SCEPTERS S K EH1 P T ER0 Z -SCEPTIC S EH1 P T IH0 K -SCEPTIC'S S EH1 P T IH0 K S -SCEPTICAL S EH1 P T IH0 K AH0 L -SCEPTICALLY S EH1 P T IH0 K L IY0 -SCEPTICISM S EH1 P T IH0 S IH2 Z AH0 M -SCEPTICISMS S EH1 P T IH0 S IH2 Z AH0 M Z -SCEPTICS S EH1 P T IH0 K S -SCEPTRE S EH1 P T ER0 -SCEPTRE S K EH1 P T ER0 -SCEPTRE'S S K EH1 P T ER0 Z -SCEPTRED S K EH1 P T ER0 D -SCEPTRES S K EH1 P T ER0 Z -SCERCE S K EH1 R S -SCERCELY S K EH1 R S L IY0 -SCERNI S K EH1 R N IY0 -SCEVINUS S K EH1 V AH0 N IH0 S -SCH SH -SCHABELITZ SH AE1 B AH0 L IH0 T S -SCHACABAC SH AE1 K AH0 B AE0 K -SCHACH SH AE1 K -SCHADE SH EY1 D -SCHADECK SH AE1 D EH0 K -SCHADOW SH AE1 D OW0 -SCHAEFER SH EY1 F ER0 -SCHAEFER'S SH EY1 F ER0 Z -SCHAEFFER SH EH1 F ER0 -SCHAERTLIN SH EH1 R T L IH0 N -SCHAFF SH AE1 F -SCHAFFER SH EY1 F ER0 -SCHAFFHAUSEN SH AE1 F HH AW2 Z AH0 N -SCHAFFNER SH AE1 F N ER0 -SCHAFROFF SH AE1 F R AO0 F -SCHAH SH AA1 -SCHAHABARIM SH EY1 HH AE2 B ER0 IY1 M -SCHAIBAR S K EY1 B AA2 R -SCHAICK SH AY1 K -SCHAL SH AE1 L -SCHALCKENBERG SH AE1 L K AH0 N B ER0 G -SCHALCKENBERG'S SH AE1 L K AH0 N B ER0 G Z -SCHALISCHIM SH AE1 L IH0 SH IH0 M -SCHALK SH AO1 K -SCHALK'S SH AO1 K S -SCHALKEN SH AO1 K AH0 N -SCHALL SH AO1 L -SCHALLBERG SH AO1 L B ER0 G -SCHALLIBAUM SH AE1 L AH0 B AA2 M -SCHAME SH EY1 M -SCHAMING SH EY1 M IH0 NG -SCHAMIR SH AH0 M IH1 R -SCHAMLE SH AE1 M AH0 L -SCHAMYL SH AE1 M IH0 L -SCHANDAU SH AE1 N D AW0 -SCHANTZE SH AE1 N T Z -SCHANVOCH SH AE1 N V AH0 K -SCHANZES SH AE1 N Z IH0 Z -SCHAPOUR SH EY1 P AO1 R -SCHARLACH SH AA1 R L AH0 K -SCHARNHOFF SH AA1 R N HH AO0 F -SCHARNHORST SH AA1 R N HH AO0 R S T -SCHARWENKA SH AA1 R W EH1 NG K AH0 -SCHATZ SH AE1 T S -SCHATZHAUSER SH AE1 T S HH AW0 Z ER0 -SCHATZLAR SH AE1 T Z L ER0 -SCHAU SH OW1 -SCHAUFFLER SH OW1 F L ER0 -SCHAUMBERG SH OW1 M B ER0 G -SCHAUMBURG SH AW1 M B ER0 G -SCHAUNARD SH AW1 N ER0 D -SCHAUNARD'S SH AW1 N ER0 D Z -SCHE SH -SCHEDULE S K EH1 JH UH0 L -SCHEDULE S K EH1 JH UW0 L -SCHEDULED S K EH1 JH UH0 L D -SCHEDULED S K EH1 JH UW0 L D -SCHEDULES S K EH1 JH UH0 L Z -SCHEDULES S K EH1 JH UW0 L Z -SCHEEDAM SH IY1 D AH0 M -SCHEEL SH IY1 L -SCHEEPERS SH IY1 P ER0 Z -SCHEER SH IH1 R -SCHEERAU SH IH1 R AW1 -SCHEFF SH EH1 F -SCHEFFAUER SH EH1 F AW1 ER0 -SCHEFFEL SH EH1 F AH0 L -SCHEFFER SH EH1 F ER0 -SCHEFFER'S SH EH1 F ER0 Z -SCHEHERAZADE SH EH1 HH ER0 AA1 Z AH0 D -SCHEHEREZADE SH EH1 HH ER0 EY1 Z AH0 D -SCHEIBNER SH AY1 B N ER0 -SCHEICH SH AY0 K -SCHEIDECK SH AY1 D IH0 K -SCHEIDEMANN SH AY1 D M AH0 N -SCHEIH SH AY1 -SCHEIK SH IY1 K -SCHEIMER SH AY1 M ER0 -SCHEIMER'S SH AY1 M ER0 Z -SCHEIN SH AY1 N -SCHELD SH EH1 L D -SCHELDE S K IH0 L D -SCHELDT SH EH1 L T -SCHELL S K EH1 L -SCHELLEN SH EH1 L AH0 N -SCHELLENBERG SH EH1 L AH0 N B ER0 G -SCHELLER S K EH1 L ER0 -SCHELLING S K EH1 L IH0 NG -SCHELLING'S SH EH1 L IH0 NG Z -SCHELM SH EH1 L M -SCHELMS SH EH1 L M Z -SCHEM S K IH0 M -SCHEMA S K IY1 M AH0 -SCHEMATA S K IH0 M AE1 T AH0 -SCHEMATIC S K IH0 M AE1 T IH0 K -SCHEME S K IY1 M -SCHEME'S S K IY1 M Z -SCHEMED S K IY1 M D -SCHEMER S K IY1 M ER0 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S K R IY1 M D -SCREAMER S K R IY1 M ER0 -SCREAMERS S K R IY1 M ER0 Z -SCREAMIN S K R IY1 M IH0 N -SCREAMING S K R IY1 M IH0 NG -SCREAMINGLY S K R IY1 M IH0 NG L IY0 -SCREAMINGS S K R IY1 M IH0 NG Z -SCREAMS S K R IY1 M Z -SCREE S K R IY1 -SCREECH S K R IY1 CH -SCREECHED S K R IY1 CH T -SCREECHER S K R IY1 CH ER0 -SCREECHES S K R IY1 CH IH0 Z -SCREECHIN S K R IY1 CH AH0 N -SCREECHING S K R IY1 CH IH0 NG -SCREECHINGS S K R IY1 CH IH0 NG Z -SCREECHY S K R IY1 CH IY0 -SCREED S K R IY1 D -SCREEDS S K R IY1 D Z -SCREEN S K R IY1 N -SCREEN'D S K R IY1 N D -SCREEN'S S K R IY1 N Z -SCREENED S K R IY1 N D -SCREENING S K R IY1 N IH0 NG -SCREENINGS S K R IY1 N IH0 NG Z -SCREENS S K R IY1 N Z -SCREES S K R IY1 Z -SCREMERSTON S K R IY1 M ER0 S T AH0 N -SCREW S K R UW1 -SCREW'D S K R UW1 D -SCREW'S S K R UW1 Z -SCREWBALL S K R UW1 B AO2 L -SCREWDRIVER S K R UW1 D R AY2 V ER0 -SCREWDRIVERS S K R UW1 D R AY2 V ER0 Z -SCREWED S K R UW1 D -SCREWIN S K R UW1 IH0 N -SCREWING S K R UW1 IH0 NG -SCREWS S K R UW1 Z -SCREWSTOWN S K R UW1 Z T AW2 N -SCREWY S K R UW1 IY0 -SCRIABINE S K R AY1 AH0 B IY1 N -SCRIBBLE S K R IH1 B AH0 L -SCRIBBLED S K R IH1 B AH0 L D -SCRIBBLER S K R IH1 B L ER0 -SCRIBBLERS S K R IH1 B L ER0 Z -SCRIBBLES S K R IH1 B AH0 L Z -SCRIBBLING S K R IH1 B AH0 L IH0 NG -SCRIBBLING S K R IH1 B L IH0 NG -SCRIBBLINGS S K R IH1 B L IH0 NG Z -SCRIBBO S K R IH1 B OW0 -SCRIBE S K R AY1 B -SCRIBE'S S K R AY1 B Z -SCRIBED S K R AY1 B D -SCRIBENDI S K R AY1 B N D IY0 -SCRIBERE S K R AY1 B ER0 -SCRIBES S K R AY1 B Z -SCRIBLERS S K R IH1 B L ER0 Z -SCRIBLERUS S K R IH1 B L ER0 AH0 S -SCRIBNER S K R IH1 B N ER0 -SCRIBNER'S S K R IH1 B N ER0 Z -SCRIBNERS S K R IH1 B N ER0 Z -SCRIDAIN S K R IH1 D EY1 N -SCRIECK S K R IY1 K -SCRIM S K R IH1 M -SCRIMMAGE S K R IH1 M IH0 JH -SCRIMMAGES S K R IH1 M IH0 JH AH0 Z -SCRIMMAGING S K R IH1 M IH0 JH IH0 NG -SCRIMP S K R IH1 M P -SCRIMPED S K R IH1 M P T -SCRIMPING S K R IH1 M P IH0 NG -SCRIMSHAW S K R IH1 M SH AO2 -SCRINGE S K R IH1 N JH -SCRIP S K R 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AA1 G IY0 -SCROGGINS S K R AA1 G IH0 N Z -SCROGGLES S K R AA1 G AH0 L Z -SCROGGS S K R AA1 G Z -SCROGIE S K R AA1 G IY0 -SCROLL S K R OW1 L -SCROLLED S K R OW1 L D -SCROLLS S K R OW1 L Z -SCROLLWORK S K R OW1 L W ER2 K -SCROOBY S K R UW1 B IY0 -SCROOCH S K R UW1 CH -SCROOGE S K R UW1 JH -SCROOGE'S S K R UW1 JH IH0 Z -SCROOGED S K R UW1 JH D -SCROOP S K R UW1 P -SCROOPE S K R UW1 P -SCROOPE'S S K R UW1 P S -SCROPE S K R OW1 P -SCROPE'S S K R AO1 P S -SCROPES S K R OW1 P S -SCROTTON S K R AO1 T AH0 N -SCROTTON'S S K R AO1 T AH0 N Z -SCROTUM S K R AO1 T AH0 M -SCROUGE S K R AW1 JH -SCROUGED S K R AW1 JH D -SCROUGING S K R AW1 JH IH0 NG -SCROUNGE S K R AW1 N JH -SCROUNGING S K R AW1 N JH IH0 NG -SCRUB S K R AH1 B -SCRUB'S S K R AH1 B Z -SCRUBBED S K R AH1 B D -SCRUBBER S K R AH1 B ER0 -SCRUBBERS S K R AH1 B ER0 Z -SCRUBBIN S K R AH1 B IH0 N -SCRUBBING S K R AH1 B IH0 NG -SCRUBBINGS S K R AH1 B IH0 NG Z -SCRUBBS S K R AH1 B Z -SCRUBBY S K R AH1 B IY0 -SCRUBS S K R AH1 B Z -SCRUBWOMAN S K R AH1 B W UH2 M AH0 N -SCRUBWOMEN S K R AH1 B W IH2 M AH0 N -SCRUFF S K R AH1 F -SCRUFF'S S K R AH1 F S -SCRUGGS S K R AH1 G Z -SCRUM S K R AH1 M -SCRUMMAGE S K R AH1 M IH0 JH -SCRUMPTIOUS S K R AH1 M SH AH0 S -SCRUMPYDIKE S K R AH1 M P IY0 D AY2 K -SCRUNCH S K R AH1 N CH -SCRUNCHED S K R AH1 N CH T -SCRUNCHING S K R AH1 N CH IH0 NG -SCRUPLE S K R UW1 P AH0 L -SCRUPLED S K R UW1 P AH0 L D -SCRUPLES S K R UW1 P AH0 L Z -SCRUPLING S K R UW1 P AH0 L IH0 NG -SCRUPULOSITY S K R UW1 P Y AH0 L AA1 S AH0 T IY0 -SCRUPULOUS S K R UW1 P Y AH0 L AH0 S -SCRUPULOUSLY S K R UW1 P Y AH0 L AH0 S L IY0 -SCRUPULOUSNESS S K R UW1 P Y AH0 L AH0 S N AH0 S -SCRUT S K R UW1 T -SCRUTATOR S K R UW1 T AH0 T ER0 -SCRUTINEERS S K R UW1 T AH0 N IH1 R Z -SCRUTINIES S K R UW1 T AH0 N IY0 Z -SCRUTINISE S K R UW1 T AH0 N IY1 S -SCRUTINISED S K R UW1 T AH0 N AY2 Z D -SCRUTINISES S K R UW1 T AH0 N AY2 Z IH0 Z -SCRUTINISING S K R UW1 T AH0 N AY2 Z IH0 NG -SCRUTINISINGLY S K R UW1 T AH0 N AY2 Z IH0 NG L IY0 -SCRUTINIZE S K R UW1 T AH0 N AY2 Z -SCRUTINIZED S K R UW1 T AH0 N AY2 Z D -SCRUTINIZES S K R UW1 T AH0 N AY2 Z IH0 Z -SCRUTINIZING S K R UW1 T AH0 N AY2 Z IH0 NG -SCRUTINIZINGLY S K R UW1 T AH0 N AY2 Z IH0 NG L IY0 -SCRUTINY S K R UW1 T AH0 N IY0 -SCRUTOIRE S K R UW1 T W AA1 R -SCRUTON S K R UW1 T AH0 N -SCRUTS S K R UW1 T S -SCRYMGEOUR S K R IY0 M G AO0 R -SCUBA S K UW1 B AH0 -SCUD S K AH1 D -SCUDAMORE S K UW0 D AA1 M AO0 R -SCUDAMORE'S S K AH1 D AH0 M AO1 R EY0 Z -SCUDAMORES S K AH1 D AH0 M AO2 R Z -SCUDAWAY S K AH1 D AH0 W EY2 -SCUDDED S K AH1 D IH0 D -SCUDDER S K AH1 D ER0 -SCUDDER'S S K AH1 D ER0 Z -SCUDDING S K AH1 D IH0 NG -SCUDDS S K AH1 D Z -SCUDDY S K AH1 D IY0 -SCUDERI S K UW0 D EH1 R IY0 -SCUDERI'S S K UW0 D EH1 R IY0 Z -SCUDERY S K UW0 D EH1 R IY0 -SCUDI S K AH1 D IY0 -SCUDMORE S K AH1 D M AO0 R -SCUDO S K UW1 D OW0 -SCUDS S K AH1 D Z -SCUFF S K AH1 F -SCUFFED S K AH1 F T -SCUFFING S K AH1 F IH0 NG -SCUFFLE S K AH1 F AH0 L -SCUFFLED S K AH1 F AH0 L D -SCUFFLES S K AH1 F AH0 L Z -SCUFFLIN S K AH1 F L IH0 N -SCUFFLING S K AH1 F AH0 L IH0 NG -SCUFFLINGS S K AH1 F AH0 L IH0 NG Z -SCULCO S K AH1 L K OW0 -SCULDUDDERY S K AH1 L D AH1 D ER0 IY0 -SCULL S K AH1 L -SCULLED S K AH1 L D -SCULLER S K AH1 L ER0 -SCULLERIES S K AH1 L ER0 IY0 Z -SCULLERS S K AH1 L ER0 Z -SCULLERY S K AH1 L ER0 IY0 -SCULLERYMAID S K AH1 L ER0 IY0 M EY2 D -SCULLING S K AH1 L IH0 NG -SCULLION S K AH1 L Y AH0 N -SCULLION'S S K AH1 L Y AH0 N Z -SCULLIONS S K AH1 L Y AH0 N Z -SCULLS S K AH1 L Z -SCULLY S K AH1 L IY0 -SCULLY'S S K AH1 L IY0 Z -SCULP S K AH1 L P -SCULPED S K AH1 L P T -SCULPIN S K AH1 L P IH0 N -SCULPING S K AH1 L P IH0 NG -SCULPINS S K AH1 L P IH1 N Z -SCULPS S K AH1 L P S -SCULPTED S K AH1 L P T IH0 D -SCULPTEURS S K AH1 L P CH ER1 Z -SCULPTOR S K AH1 L P T ER0 -SCULPTOR'S S K AH1 L P T ER0 Z -SCULPTORS S K AH1 L P T ER0 Z -SCULPTRESS S K AH1 L P T R IH0 S -SCULPTURAL S K AH1 L P CH ER0 AH0 L -SCULPTURE S K AH1 L P CH ER0 -SCULPTURED S K AH1 L P CH ER0 D -SCULPTURES S K AH1 L P CH ER0 Z 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ER0 N -SENBY S EH1 N B IY0 -SENCE S EH1 N S -SENCES S EH1 N S AH0 Z -SENCI S EH1 N S IY0 -SENCI'S S EH1 N S IY0 Z -SEND S EH1 N D -SENDA S EH1 N D AH0 -SENDAI S EH1 N D AY1 -SENDAL S EH1 N D AH0 L -SENDE S EH1 N D EY0 -SENDED S EH1 N D IH0 D -SENDEN S EH1 N D AH0 N -SENDENNIS S EH0 N D EH1 N IH0 S -SENDER S EH1 N D ER0 -SENDER'S S EH1 N D ER0 Z -SENDERS S EH1 N D ER0 Z -SENDEST S EH0 N D AH0 S T -SENDETH S EH1 N D IH0 TH -SENDIN S EH1 N D IH0 N -SENDING S EH1 N D IH0 NG -SENDLINGEN S EH1 N D L IH0 NG AH0 N -SENDLINGEN'S S EH1 N D L IH0 NG G EH0 N Z -SENDS S EH1 N D Z -SENE S IY2 N -SENECA S EH1 N AH0 K AH0 -SENECA'S S EH1 N AH0 K AH0 Z -SENECAL S EH1 N IH0 K AH0 L -SENECAS S EH1 N AH0 K AH0 Z -SENECHAL S EH1 N IH0 K AH0 L -SENECIO S EH1 N AH0 S IY0 OW0 -SENECTUS S EH1 N IH0 K T AH0 S -SENECTUTE S EH1 N IH0 K CH UW0 T -SENEFERU S EH1 N IH0 F ER0 UW1 -SENEGAL S EH2 N AH0 G AO1 L -SENEGALESE S EH2 N AH0 G AH0 L IY1 Z -SENEGAMBIA S EH2 N AH0 G AE1 M B IY0 AH0 -SENEGAMBIAN S EH2 N AH0 G AE1 M B IY0 AH0 N -SENEKAL S EH1 N AH0 K AH0 L -SENESCENCE S AH0 N EH1 S AH0 N S -SENESCENT S EH0 N EH1 S AH0 N T -SENESCHAL S EH1 N AH0 SH AH0 L -SENESCHAL'S S EH1 N AH0 SH AH0 L Z -SENESCHALS S EH1 N AH0 SH AH0 L Z -SENESINO S EH1 N IH0 S IY1 N OW0 -SENESTRO S EH1 N AH0 S T R OW0 -SENEX S EH1 N AH0 K S -SENEZ S EH0 N EH1 Z -SENF S EH1 N F -SENFFT S EH1 N F T -SENFT S EH1 N F T -SENG S EH1 NG -SENGA S EH1 NG G AH0 -SENGSTACK S EH1 NG S T AE2 K -SENHIME S EH1 N HH AY2 M -SENHOR S IH0 N HH ER0 -SENHORA S EH1 N ER0 AH0 -SENHORA'S S IH0 N HH ER0 AH0 Z -SENHORES S EH1 N HH AO2 R Z -SENHORINA S EH1 N HH AO2 R IY1 N AH0 -SENI S IY1 N IY0 -SENIAS S IY1 N Y AH0 Z -SENILE S IY1 N AY2 L -SENILIS S AH0 N IH1 L AH0 S -SENILITY S AH0 N IH1 L AH0 T IY0 -SENIOR S IY1 N Y ER0 -SENIOR'S S IY1 N Y ER0 Z -SENIORITY S IY0 N Y AO1 R IH0 T IY0 -SENIORS S IY1 N Y ER0 Z -SENIORSHIP S IY1 N Y ER0 SH IH2 P -SENKA S EH1 NG K AH0 -SENLABOR S EH1 N L EY1 B ER0 -SENLAC S EH1 N AH0 L AE2 K -SENLIS S EH1 N AH0 L IH0 S 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EH1 N T AH0 N S -SENTENCED S EH1 N T AH0 N S T -SENTENCES S EH1 N T AH0 N S AH0 Z -SENTENCES S EH1 N T AH0 N S IH0 Z -SENTENCING S EH1 N T AH0 N S IH0 NG -SENTENTIA S EH0 N T EH1 N SH AH0 -SENTENTIAE S EH0 N T EH1 N SH IY0 AH0 -SENTENTIOUS S EH0 N T EH1 N SH AH0 S -SENTENTIOUSLY S EH0 N T EH1 N SH AH0 S L IY0 -SENTENTIOUSNESS S EH0 N T EH1 N SH AH0 S N AH0 S -SENTER S EH1 N T ER0 -SENTER'S S EH1 N T ER0 Z -SENTEST S EH1 N T EH0 S T -SENTI S EH1 N T IY0 -SENTIENCE S EH1 N IY0 AH0 N S -SENTIENCY S EH1 N SH AH0 N S IY0 -SENTIENT S EH1 N SH AH0 N T -SENTIER S EH2 N T IY0 ER0 -SENTIMENS S EH2 N AH0 M AH0 N Z -SENTIMENT S EH1 N AH0 M AH0 N T -SENTIMENT S EH1 N T AH0 M AH0 N T -SENTIMENT'S S EH2 N AH0 M AH0 N T S -SENTIMENTAL S EH2 N AH0 M EH1 N AH0 L -SENTIMENTAL S EH2 N AH0 M EH1 N T AH0 L -SENTIMENTAL S EH2 N T AH0 M EH1 N AH0 L -SENTIMENTAL S EH2 N T AH0 M EH1 N T AH0 L -SENTIMENTALE S EH2 N AH0 M EH1 N T AH0 L -SENTIMENTALISE S EH2 N AH0 M EH1 N AH0 L IY1 S -SENTIMENTALISED S EH2 N AH0 M 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ER0 EY1 SH AH0 N Z -SEPARATISM S EH1 P ER0 AH0 T IH2 Z AH0 M -SEPARATIST S EH1 P ER0 AH0 T IH0 S T -SEPARATISTS S EH1 P ER0 AH0 T IH0 S T S -SEPARATISTS S EH1 P R AH0 T IH0 S T S -SEPARATOR S EH1 P ER0 EY2 T ER0 -SEPARATORS S EH1 P ER0 EY2 T ER0 Z -SEPASTIAN S EH1 P AE2 S CH AH0 N -SEPERATE S EH1 P ER0 AH0 T -SEPERATED S EH1 P ER0 EY2 T IH0 D -SEPERATELY S EH1 P ER0 AH0 T L IY0 -SEPERATES S EH1 P ER0 EY2 T S -SEPET S EY0 P EY1 -SEPH S AH0 F -SEPHAR S IH0 F AA1 R -SEPHAR'S S EH0 F AA2 R Z -SEPHARDI S IH0 F AA1 R D IY0 -SEPHARDIC S AH0 F AA1 R D IH0 K -SEPHARDIM S EH0 F AA2 R D IY1 M -SEPHARDIM S IH0 F AA1 R D IH0 M -SEPHARIAN S IH0 F AA1 R IY0 AH0 N -SEPHARIANS S IH0 F AA1 R IY0 AH0 N Z -SEPHER S EH1 F ER0 -SEPHORA S EH1 F ER0 AH0 -SEPHORAH S EH1 F ER0 AH0 -SEPIA S IY1 P IY0 AH0 -SEPOLCRO S EH1 P AH0 L K R OW0 -SEPOS S IY1 P OW2 Z -SEPOY S EH1 P OY0 -SEPOY'S S EH1 P OY0 Z -SEPOYS S EH1 P OY0 Z -SEPP S EH1 P -SEPPER S EH1 P ER0 -SEPPERL S EH1 P ER0 L -SEPPHORIS S EH0 F AO1 R IH0 S -SEPPI S EH1 P IY0 -SEPPINGS S EH1 P IH0 NG Z -SEPPUKU S EH0 P UW1 K UW0 -SEPPY S EH1 P IY0 -SEPTA S EH1 P T AH0 -SEPTEM S EH0 P T EH1 M -SEPTEMBER S EH0 P T EH1 M B ER0 -SEPTEMBER'S S EH0 P T EH1 M B ER0 Z -SEPTEMBERS S EH0 P T EH1 M B ER0 Z -SEPTEMBRE S EH0 P T EH1 M B R AH0 -SEPTEMBRISTS S EH0 P T EH1 M B R IH0 S T S -SEPTENARY S EH0 P T EH1 N ER0 IY0 -SEPTENNIAL S EH0 P T EH1 N IY0 AH0 L -SEPTENTRIONAL S EH0 P T EH1 N T R IY0 OW1 N AH0 L -SEPTENTRIONALE S EH0 P T EH1 N T R IY0 AH0 N AA1 L IY0 -SEPTIC S EH1 P T IH0 K -SEPTIMA S EH0 P T IY1 M AH0 -SEPTIMINE S EH1 P T IH0 M IY1 N -SEPTIMIUS S EH1 P T IH0 M IY0 IH0 S -SEPTIMIUS'S S EH1 P T IH0 M Y AH0 S IH0 Z -SEPTIMO S EH1 P T IH0 M OW2 -SEPTIMUS S EH1 P T IH0 M IH0 S -SEPTR S EH1 P T ER0 -SEPTS S EH1 P T S -SEPTUAGENARIAN S EH0 P CH UW2 AH0 JH AH0 N EH1 R IY0 AH0 N -SEPTUAGENARIANS S EH0 P CH UW2 AH0 JH AH0 N EH1 R IY0 AH0 N Z -SEPTUAGINT S EH0 P CH UW2 AH0 JH IH1 N T -SEPTUM S EH1 P T AH0 M -SEPULCHER S EY0 P UW0 L CH ER0 -SEPULCHERS S EY0 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AH0 N -SIVINTY S IH1 V AH0 N T IY0 -SIVORI S AY0 V AO1 R IY0 -SIWANOIS S IH1 W AO0 N W AA1 -SIWARD S IH1 W ER0 D -SIWARD'S S IH1 W ER0 D Z -SIWARDS S IH1 W ER0 D Z -SIWASH S IH1 W AA2 SH -SIWASHES S IH1 W AA2 SH AH0 Z -SIX S IH1 K S -SIX'S S IH1 K S IH0 Z -SIXE S IH1 K S -SIXER S IH1 K S ER0 -SIXERS S IH1 K S ER0 Z -SIXES S IH1 K S IH0 Z -SIXFOLD S IH1 K S F OW2 L D -SIXPENCE S IH1 K S P EH1 N S -SIXPENCES S IH1 K S P AH0 N S IH0 Z -SIXPENN'ORTH S IH1 K S P EH2 N S AO2 R TH -SIXPENNY S IH1 K S P EH2 N IY0 -SIXPENNYWORTH S IH1 K S P EH1 N IY0 W ER0 TH -SIXSCORE S IH1 K S K AO1 R -SIXSHOOTER S IH1 K S SH UW2 T ER0 -SIXT S IH1 K S T -SIXTE S IH1 K S T -SIXTEEN S IH0 K S T IY1 N -SIXTEEN S IH1 K S T IY1 N -SIXTEENS S IH1 K S T IY1 N Z -SIXTEENTH S IH0 K S T IY1 N TH -SIXTEENTH S IH1 K S T IY1 N TH -SIXTEENTHS S IH1 K S T IY1 N TH S -SIXTH S IH1 K S TH -SIXTH'S S IH1 K S TH S -SIXTHLY S IH1 K S TH L IY0 -SIXTHS S IH1 K S TH S -SIXTIES S IH1 K S T IY0 Z -SIXTIETH S IH1 K S T IY0 IH0 TH 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T ER0 Z -SKATERS S K EY1 T ER0 Z -SKATES S K EY1 T S -SKATIN S K EY1 T IH0 N -SKATING S K EY1 T IH0 NG -SKAVINSKI S K AH0 V IH1 N S K IY0 -SKAW S K AA1 -SKE S K -SKEAN S K IY1 N -SKEAR S K IY1 R -SKEARED S K IY1 R D -SKEART S K EH1 ER0 T -SKEARY S K IH1 R IY0 -SKEAT S K IY1 T -SKEATS S K IY1 T S -SKEBAWN S K IY1 B AO1 N -SKEDADDLE S K IY1 D AE1 D AH0 L -SKEDADDLED S K IY1 D AE1 D AH0 L D -SKEDADDLING S K IY1 D AE1 D L IH0 NG -SKEE S K IY1 -SKEEL S K IY1 L -SKEELS S K IY1 L Z -SKEELTY S K IY1 L T IY0 -SKEELY S K IY1 L IY0 -SKEENA S K IY1 N AH0 -SKEENY S K IY1 N IY0 -SKEER S K IY1 ER0 -SKEER'D S K IY1 R D -SKEERCE S K IY1 R S -SKEERED S K IY1 R D -SKEERIN S K IY1 R IH0 N -SKEERS S K IY1 ER0 Z -SKEERT S K IY1 R T -SKEERY S K IY1 R IY0 -SKEES S K IY1 Z -SKEESICKS S K IY1 S IH2 K S -SKEET S K IY1 T -SKEET'S S K IY1 T S -SKEETER S K IY1 T ER0 -SKEETER'S S K IY1 T ER0 Z -SKEETERS S K IY1 T ER0 Z -SKEETS S K IY1 T S -SKEEZER S K IY1 Z ER0 -SKEEZERS S K IY1 Z ER0 Z -SKEEZICKS S K IY1 Z IH0 K S 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IY0 Z -SKELWICK S K EH1 L W IH0 K -SKEM S K EH1 M -SKENE S K IY1 N -SKENES S K IY1 N Z -SKENESBOROUGH S K IY1 N Z B ER0 OW0 -SKENY S K IY1 N IY0 -SKEOCH S K IY1 AA0 CH -SKEP S K EH1 P -SKEPS S K EH1 P S -SKEPSEY S K EH1 P S IY0 -SKEPSEY'S S K EH1 P S IY0 Z -SKEPTIC S K EH1 P T IH0 K -SKEPTIC'S S K EH1 P T IH0 K S -SKEPTICAL S K EH1 P T AH0 K AH0 L -SKEPTICAL S K EH1 P T IH0 K AH0 L -SKEPTICALLY S K EH1 P T IH0 K AH0 L IY0 -SKEPTICALLY S K EH1 P T IH0 K L IY0 -SKEPTICISM S K EH1 P T IH0 S IH2 Z AH0 M -SKEPTICS S K EH1 P T IH0 K S -SKERM S K EH1 R M -SKERNFORD S K EH1 R N F ER0 D -SKERRETT S K EH1 R IH0 T -SKERRIES S K EH1 R IY0 Z -SKERRY S K EH1 R IY0 -SKERRYVORE S K EH1 R IY0 V AO2 R -SKERSTYMONE S K EH1 R S T IH0 M OW2 N -SKERT S K ER0 T -SKETCH S K EH1 CH -SKETCHBOOK S K EH1 CH B UH2 K -SKETCHED S K EH1 CH T -SKETCHER S K EH1 CH ER0 -SKETCHER'S S K EH1 CH ER0 Z -SKETCHERS S K EH1 CH ER0 Z -SKETCHES S K EH1 CH AH0 Z -SKETCHES S K EH1 CH IH0 Z -SKETCHILY S K EH1 CH AH0 L IY0 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S K IH1 P -SKIPETARS S K IH1 P EH0 T AA1 R Z -SKIPJACK S K IH1 P JH AE2 K -SKIPP S K IH1 P -SKIPPED S K IH1 P T -SKIPPER S K IH1 P ER0 -SKIPPER'S S K IH1 P ER0 Z -SKIPPERED S K IH1 P ER0 D -SKIPPERING S K IH1 P ER0 IH0 NG -SKIPPERS S K IH1 P ER0 Z -SKIPPIN S K IH1 P IH0 N -SKIPPING S K IH1 P IH0 NG -SKIPPITY S K IH1 P AH0 T IY0 -SKIPPON S K IH1 P AH0 N -SKIPPY S K IH1 P IY0 -SKIPS S K IH1 P S -SKIPT S K IH1 P T -SKIPTON S K IH1 P T AH0 N -SKIPWITH S K IH1 P W IH2 TH -SKIPWORTH S K IH1 P W ER2 TH -SKIRL S K ER1 L -SKIRLED S K ER1 L D -SKIRLIN S K ER1 L IH0 N -SKIRLING S K ER1 L IH0 NG -SKIRLS S K ER1 L Z -SKIRMISH S K ER1 M IH0 SH -SKIRMISHED S K ER1 M IH0 SH T -SKIRMISHER S K ER1 M IH0 SH ER0 -SKIRMISHERS S K ER1 M IH0 SH ER0 Z -SKIRMISHES S K ER1 M IH0 SH IH0 Z -SKIRMISHIN S K ER1 M IH0 SH IH0 N -SKIRMISHING S K ER1 M IH0 SH IH0 NG -SKIRMISHINGS S K ER1 M IH0 SH IH0 NG Z -SKIRNIR S K ER1 N ER0 -SKIRR S K ER1 -SKIRRYWINKS S K ER1 IY0 W IH0 NG K S -SKIRT S K ER1 T -SKIRT'S S K ER1 T S -SKIRTED S K ER1 T IH0 D -SKIRTING S K ER1 T IH0 NG -SKIRTLESS S K ER1 T L AH0 S -SKIRTS S K ER1 T S -SKIRVING S K ER1 V IH0 NG -SKIRWOILLA S K ER1 W UH2 IH1 L AH0 -SKIS S K IY1 Z -SKIT S K IH1 T -SKITE S K AY1 T -SKITS S K IH1 T S -SKITTER S K IH1 T ER0 -SKITTERED S K IH1 T ER0 D -SKITTERING S K IH1 T ER0 IH0 NG -SKITTISH S K IH1 T IH0 SH -SKITTISHLY S K IH1 T IH0 SH L IY0 -SKITTISHNESS S K IH1 T IH0 SH N AH0 S -SKITTLE S K IH1 T AH0 L -SKITTLES S K IH1 T AH0 L Z -SKITZLAND S K IH1 T Z L AH0 N D -SKIVER S K AY1 V ER0 -SKIVERED S K AY1 V ER0 D -SKLARZ S K L AA1 R Z -SKOAL S K OW1 L -SKOBELEFF S K OW1 B AH0 L EH1 F -SKOELL S K OW1 L -SKOFF S K AO1 F -SKOODOOWABSKOOK S K UW1 D UW2 W AO1 B S K UH2 K -SKOODOOWABSKOOKSIS S K UW1 D UW2 W AO1 B S K UW1 K S AH0 S -SKOOKUM S K UW1 K AH0 M -SKOOKUM'S S K UW1 K AH0 M Z -SKOONLY S K UW2 N L IY0 -SKOOT S K UW2 T -SKOP S K AA1 P -SKOPTZI S K AO1 P T Z IY0 -SKORDRAN S K ER0 D R AH0 N -SKORVSKY S K AO1 R V S K IY0 -SKOWHEGAN S K OW1 HH IY2 G AH0 N -SKRAE S K R EY2 -SKRATDJ S K R AE2 T JH -SKREBENSKY S K R EH0 B EH1 N S K IY0 -SKREEN S K R IY0 N -SKREIGH S K R EY1 -SKRIMMAGE S K R IH1 M IH0 JH -SKROELINGAR S K R AA1 AH0 L IH1 NG G ER0 -SKRYMIR S K R IH0 M IH1 R -SKRYMNER S K R IH0 M N ER0 -SKUA S K AH0 -SKUAS S K AH1 AH0 Z -SKULD S K AH0 L D -SKULDA S K AH0 L D AH0 -SKULDUGGERY S K AH0 L D AH1 G ER0 IY0 -SKULE S K AH0 L -SKULI S K AH1 L IY0 -SKULISON S K AH0 L IH0 S AH0 N -SKULK S K AH1 L K -SKULKED S K AH1 L K T -SKULKER S K AH1 L K ER0 -SKULKERS S K AH1 L K ER0 Z -SKULKIN S K AH1 L K IH0 N -SKULKING S K AH1 L K IH0 NG -SKULKS S K AH1 L K S -SKULL S K AH1 L -SKULL'D S K AH1 L D -SKULL'S S K AH1 L Z -SKULLCAP S K AH1 L K AE2 P -SKULLDUGGERY S K AH1 L D AH1 G ER0 IY0 -SKULLED S K AH1 L D -SKULLS S K AH1 L Z -SKULP S K AH1 L P -SKULPIT S K AH1 L P IH0 T -SKUN S K AH1 N -SKUNK S K AH1 NG K -SKUNK'S S K AH1 NG K S -SKUNKED S K AH1 NG K T -SKUNKS S K AH1 NG K S -SKURCE S K ER1 S -SKURGE S K ER1 JH -SKURRIED S K ER1 IY0 D -SKURRY S K ER1 IY0 -SKURRYING S K ER1 IY0 IH0 NG -SKUYTERCLIFF S K AY1 T ER0 K L IH0 F -SKVORTSOV S K V AO1 R T S AA2 V -SKWESS S K W AH0 S -SKY S K AY1 -SKY'S S K AY1 Z -SKYBLUE S K AY1 B L UW2 -SKYBROW S K AY1 B R OW2 -SKYDS S K AY1 D Z -SKYE S K AY1 -SKYED S K AY1 D -SKYEMAN S K AY1 M AH0 N -SKYES S K AY1 Z -SKYEY S K AY1 IY0 -SKYFFINGTON S K AY1 F IH0 NG T AH0 N -SKYING S K AY1 IH0 NG -SKYLAND S K AY1 L AH0 N D -SKYLARK S K AY1 L AA2 R K -SKYLARK'S S K AY1 L AA2 R K S -SKYLARKED S K AY1 L AA2 R K T -SKYLARKIN S K AY1 L AA2 R K IH0 N -SKYLARKING S K AY1 L AA2 R K IH0 NG -SKYLARKS S K AY1 L AA2 R K S -SKYLIGHT S K AY1 L AY2 T -SKYLIGHTS S K AY1 L AY2 T S -SKYLINE S K AY1 L AY2 N -SKYLINES S K AY1 L AY2 N Z -SKYNSES S K IH0 N Z IH0 Z -SKYRMIR S K AY1 R M IH0 R -SKYROCKET S K AY1 R AA2 K AH0 T -SKYROCKETS S K AY1 R AA2 K AH0 T S -SKYSAIL S K AY1 S EY2 L -SKYSAILS S K AY1 S EY1 L Z -SKYSCRAPER S K AY1 S K R EY2 P ER0 -SKYSCRAPERS S K AY1 S K R EY2 P ER0 Z -SKYSTEIN S K AY1 S T IY0 N -SKYWARD S K AY1 W ER0 D -SKYWARDS S K AY1 W ER0 D Z -SL S L -SLAAG S L AA1 G -SLAB S L AE1 B -SLABBED S L AE1 B D -SLABBERING S L AE1 B ER0 IH0 NG -SLABBERT'S S L AE1 B ER0 T S -SLABE S L EY1 B -SLABES S L EY1 B Z -SLABS S L AE1 B Z -SLABSIDES S L AE1 B S AY2 D Z -SLABTOWN S L AE1 B T AW2 N -SLACK S L AE1 K -SLACK'S S L AE1 K S -SLACKED S L AE1 K T -SLACKEN S L AE1 K AH0 N -SLACKEN'D S L AE1 K AH0 N D -SLACKENED S L AE1 K AH0 N D -SLACKENING S L AE1 K AH0 N IH0 NG -SLACKENS S L AE1 K AH0 N Z -SLACKER S L AE1 K ER0 -SLACKERS S L AE1 K ER0 Z -SLACKEST S L AE1 K AH0 S T -SLACKING S L AE1 K IH0 NG -SLACKLY S L AE1 K L IY0 -SLACKNESS S L AE1 K N AH0 S -SLACKS S L AE1 K S -SLADDEN S L AE1 D AH0 N -SLADDER S L AE1 D ER0 -SLADE S L EY1 D -SLADE'S S L AE1 D Z -SLADEN S L EY1 D AH0 N -SLADY S L AE1 D IY0 -SLAFE S L EY1 F -SLAG S L AE1 G -SLAGELSE S L AE1 G AH0 L S -SLAGFID S L AE1 G F IH0 D -SLAGG S L AE1 G -SLAGG'S S L AE1 G Z -SLAGHAMMER S L AE1 HH AE2 M ER0 -SLAIN S L EY1 N -SLAINE S L EY1 N -SLAINS S L EY1 N Z -SLAINTE S L EY1 N T -SLAKE S L EY1 K -SLAKED S L EY1 K T -SLAKES S L EY1 K S -SLAKING S L EY1 K IH0 NG -SLAM S L AE1 M -SLAMMED S L AE1 M D -SLAMMER S L AE1 M ER0 -SLAMMIN S L AE1 M IH0 N -SLAMMING S L AE1 M IH0 NG -SLAMS S L AE1 M Z -SLAN S L AE1 N -SLANDER S L AE1 N D ER0 -SLANDER'S S L AE1 N D ER0 Z -SLANDERED S L AE1 N D ER0 D -SLANDERER S L AE1 N D ER0 ER0 -SLANDERER'S S L AE1 N D ER0 ER0 Z -SLANDERERS S L AE1 N D ER0 ER0 Z -SLANDERIN S L AE1 N D ER0 IH0 N -SLANDERING S L AE1 N D ER0 IH0 NG -SLANDEROUS S L AE1 N D ER0 AH0 S -SLANDEROUSLY S L AE1 N D ER0 AH0 S L IY0 -SLANDERS S L AE1 N D ER0 Z -SLANE S L EY1 N -SLANEY S L EY1 N IY0 -SLANG S L AE1 NG -SLANGED S L AE1 NG D -SLANGILY S L AE1 NG G AH0 L IY0 -SLANGINESS S L AE1 NG IY0 N AH0 S -SLANGING S L AE1 NG IH0 NG -SLANGS S L AE1 NG Z -SLANGY S L EY1 N JH IY0 -SLANK S L AE1 NG K -SLANT S L AE1 N T -SLANTED S L AE1 N AH0 D -SLANTED S L AE1 N IH0 D -SLANTED S L AE1 N T AH0 D -SLANTED S L AE1 N T IH0 D -SLANTIN S L AE1 N T IH0 N -SLANTINDICULAR S L AE1 N T IH0 N D IH1 K Y AH0 L ER0 -SLANTING S L AE1 N T IH0 NG -SLANTINGLY S L AE1 N T IH0 NG L IY0 -SLANTS S L AE1 N T S -SLANTWAYS S L AE1 N T W EY2 Z -SLANTWISE S L AE1 N T W AY2 Z -SLAP S L AE1 P -SLAPDASH S L AE1 P D AE2 SH -SLAPE S L EY1 P -SLAPIN S L AE1 P IH0 N -SLAPJACK S L AE1 P JH AE2 K -SLAPJACKS S L AE1 P JH AE2 K S -SLAPMAN S L AE1 P M AH0 N -SLAPMAN'S S L AE1 P M AH0 N Z -SLAPP S L AE1 P -SLAPPED S L AE1 P T -SLAPPIN S L AE1 P IH0 N -SLAPPING S L AE1 P IH0 NG -SLAPPINGS S L AE1 P IH0 NG Z -SLAPS S L AE1 P S -SLAPSTICK S L AE1 P S T IH2 K -SLASH S L AE1 SH -SLASHAWAY S L AE1 SH AH0 W EY2 -SLASHED S L AE1 SH T -SLASHER S L AE1 SH ER0 -SLASHERS S L AE1 SH ER0 Z -SLASHES S L AE1 SH IH0 Z -SLASHIN S L AE1 SH IH0 N -SLASHING S L AE1 SH IH0 NG -SLASHINGS S L AE1 SH IH0 NG Z -SLAT S L AE1 T -SLATE S L EY1 T -SLATED S L EY1 T IH0 D -SLATEE S L EY1 T IY0 -SLATEES S L EY1 T IY0 Z -SLATER S L EY1 T ER0 -SLATER'S S L EY1 T ER0 Z -SLATERS S L EY1 T ER0 Z -SLATES S L EY1 T S -SLATEY S L EY1 T IY0 -SLATHER S L AE1 DH ER0 -SLATHERED S L AE1 DH ER0 D -SLATHERS S L AE1 DH ER0 Z -SLATIN S L AE1 T AH0 N -SLATING S L EY1 T IH0 NG -SLATOR S L EY1 T ER0 -SLATS S L AE1 T S -SLATTED S L AE1 T AH0 D -SLATTER S L AE1 T ER0 -SLATTERN S L AE1 T ER0 N -SLATTERNLINESS S L AE1 T ER0 N L IY0 N AH0 S -SLATTERNLY S L AE1 T ER0 N L IY0 -SLATTERNS S L AE1 T ER0 N Z -SLATTERY S L AE1 T ER0 IY0 -SLATTIN S L AE1 T IH0 N -SLATTIN'S S L AE1 T IH0 N Z -SLATTING S L AE1 T IH0 NG -SLATTOX S L AE1 T AA2 K S -SLATY S L EY1 T IY0 -SLATZANEK S L AE1 T Z AH0 N IH0 K -SLAUGHTER S L AO1 T ER0 -SLAUGHTER'D S L AO1 T ER0 D -SLAUGHTER'S S L AO1 T ER0 Z -SLAUGHTERED S L AO1 T ER0 D -SLAUGHTERER S L AO1 T ER0 ER0 -SLAUGHTERERS S L AO1 T ER0 ER0 Z -SLAUGHTERHOUSE S L AO1 T ER0 HH AW2 S -SLAUGHTERHOUSES S L AO1 T ER0 HH AW2 S IH0 Z -SLAUGHTERING S L AO1 T ER0 IH0 NG -SLAUGHTERINGS S L AO1 T ER0 IH0 NG Z -SLAUGHTEROUS S L AO1 T ER0 AH0 S -SLAUGHTERS S L AO1 T ER0 Z -SLAV S L AA1 V -SLAV'RY S L AA1 V R IY0 -SLAVA S L AA1 V AH0 -SLAVATA S L AH0 V AA1 T AH0 -SLAVE S L EY1 V -SLAVE'S S L EY1 V Z -SLAVED S L EY1 V D -SLAVEDOM S L EY1 V D AH0 M -SLAVEHOLDER S L EY1 V HH OW2 L D ER0 -SLAVEHOLDER'S S L EY1 V HH OW2 L D ER0 Z -SLAVEHOLDERS S L EY1 V HH OW2 L D ER0 Z -SLAVEHOLDING S L EY1 V HH OW2 L D IH0 NG -SLAVEMASTER S L EY1 V M AE2 S T ER0 -SLAVENA S L AE1 V IH0 N AH0 -SLAVEOWNERS S L EY1 V OW2 N ER0 Z -SLAVER S L EY1 V ER0 -SLAVER'S S L EY1 V ER0 Z -SLAVERED S L EY1 V ER0 D -SLAVERIES S L EY1 V ER0 IY0 Z -SLAVERING S L EY1 V ER0 IH0 NG -SLAVERS S L EY1 V ER0 Z -SLAVERY S L EY1 V ER0 IY0 -SLAVERY'S S L EY1 V ER0 IY0 Z -SLAVES S L EY1 V Z -SLAVEY S L EY1 V IY0 -SLAVEYS S L AH0 V EY1 Z -SLAVI S L AA1 V IY0 -SLAVIA S L AA1 V IY0 AH0 -SLAVIC S L AA1 V IH0 K -SLAVIN S L AE1 V IH0 N -SLAVIN'S S L AE1 V IH0 N Z -SLAVING S L EY1 V IH0 NG -SLAVISH S L EY1 V IH0 SH -SLAVISHLY S L AE1 V IH0 SH L IY0 -SLAVISHNESS S L AE1 V IH0 SH N AH0 S -SLAVISM S L AA1 V IH0 Z AH0 M -SLAVKOVSKY S L AA1 V K AA1 V S K IY0 -SLAVONIA S L AH0 V OW1 N IY0 AH0 -SLAVONIA S L AH0 V OW1 N Y AH0 -SLAVONIAN S L AH0 V OW1 N IY0 AH0 N -SLAVONIANS S L AH0 V OW1 N IY0 AH0 N Z -SLAVONIC S L AH0 V OW1 N IH0 K -SLAVOPHIL S L AA1 V OW0 F IH1 L -SLAVOPHILS S L AA1 V OW0 F IH1 L Z -SLAVOVITCH S L AH0 V OW1 V IH0 CH -SLAVOWITZ S L AE1 V AH0 W IH0 T S -SLAVS S L AA1 V Z -SLAW S L AO1 -SLAWA S L AA0 V AH0 -SLAWSON S L AO1 S AH0 N -SLAY S L EY1 -SLAYBACK S L EY1 B AE2 K -SLAYBACK'S S L EY1 B AE2 K S -SLAYED S L EY1 D -SLAYER S L EY1 ER0 -SLAYER'S S L EY1 ER0 Z -SLAYERS S L EY1 ER0 Z -SLAYEST S L EY1 IH0 S T -SLAYETH S L EY1 IH0 TH -SLAYFORTH S L EY1 F AO2 R TH -SLAYING S L EY1 IH0 NG -SLAYINGS S L EY1 IH0 NG Z -SLAYNE S L EY1 N -SLAYS S L EY1 Z -SLAYTON S L EY1 T AH0 N -SLEAFORD S L IY1 F ER0 D -SLEAFORD'S S L IY1 F ER0 D Z -SLEAFORDS S L IY1 F ER0 D Z -SLEARY S L IH1 R IY0 -SLEAT S L IY1 T -SLEAVE S L IY1 V -SLEAZY S L IY1 Z IY0 -SLED S L EH1 D -SLEDD S L EH1 D -SLEDDED S L EH1 D AH0 D -SLEDDIN S L EH1 D IH0 N -SLEDDING S L EH1 D IH0 NG -SLEDGE S L EH1 JH -SLEDGEHAMMER S L EH1 JH HH AE2 M ER0 -SLEDGEHOUSE S L EH1 JH HH AW2 S -SLEDGEMEN S L EH1 JH M AH0 N -SLEDGES S L EH1 JH IH0 Z -SLEDGING S L EH1 JH IH0 NG -SLEDS S L EH1 D Z -SLEE S L IY1 -SLEE'S S L IY1 Z -SLEECH S L IY1 CH -SLEECH'S S L IY1 CH IH0 Z -SLEEK S L IY1 K -SLEEKED S L IY1 K T -SLEEKER S L IY1 K ER0 -SLEEKEST S L IY1 K AH0 S T -SLEEKING S L IY1 K IH0 NG -SLEEKIT S L IY1 K IH0 T -SLEEKLY S L IY1 K L IY0 -SLEEKNESS S L IY1 K N AH0 S -SLEEMAN S L IY1 M AH0 N -SLEEP S L IY1 P -SLEEP'S S L IY1 P S -SLEEP'ST S L IY1 P S T -SLEEPE S L IY1 P -SLEEPED S L IY1 P T -SLEEPER S L IY1 P ER0 -SLEEPER'S S L IY1 P ER0 Z -SLEEPERS S L IY1 P ER0 Z -SLEEPEST S L IY1 P AH0 S T -SLEEPETH S L IY1 P IH0 TH -SLEEPIER S L IY1 P IY0 ER0 -SLEEPIEST S L IY1 P IY0 AH0 S T -SLEEPILY S L IY1 P AH0 L IY0 -SLEEPIN S L IY1 P IH0 N -SLEEPIN' S L IY1 P IH0 N -SLEEPINBUFF S L IY1 P IY0 N B AH1 F -SLEEPINESS S L IY1 P IY0 N AH0 S -SLEEPING S L IY1 P IH0 NG -SLEEPIT S L IY1 P IH0 T -SLEEPLESS S L IY1 P L AH0 S -SLEEPLESSLY S L IY1 P L AH0 S L IY0 -SLEEPLESSNESS S L IY1 P L AH0 S N AH0 S -SLEEPS S L IY1 P S -SLEEPUM S L IY1 P AH0 M -SLEEPWALKER S L IY1 P W AO2 K ER0 -SLEEPWALKING S L IY1 P W AO2 K IH0 NG -SLEEPY S L IY1 P IY0 -SLEEPYHEAD S L IY1 P IY0 HH EH2 D -SLEEPYHORN S L IY1 P IY0 HH ER0 N -SLEET S L IY1 T -SLEETED S L IY1 T AH0 D -SLEETING S L IY1 T IH0 NG -SLEETS S L IY1 T S -SLEETY S L IY1 T IY0 -SLEEVE S L IY1 V -SLEEVED S L IY1 V D -SLEEVELESS S L IY1 V L IH0 S -SLEEVES S L IY1 V Z -SLEGGE S L EH1 G -SLEGGE'S S L EH1 JH IH0 Z -SLEIGH S L EY1 -SLEIGHBELLS S L EY1 B EH2 L Z -SLEIGHING S L EY1 IH0 NG -SLEIGHRIDE S L AY1 R AY2 D -SLEIGHS S L EY1 Z -SLEIGHT S L AY1 T -SLEIGHTS S L AY1 T S -SLEIPNER S L AY1 P N ER0 -SLEIPNIR S L EY1 P N ER0 -SLEMMER S L EH1 M ER0 -SLEMMICK S L EH1 M IH0 K -SLEMON S L EH1 M AH0 N -SLENCY S L EH1 N S IY0 -SLENDER S L EH1 N D ER0 -SLENDERER S L EH1 N D ER0 ER0 -SLENDEREST S L EH1 N D ER0 IH0 S T -SLENDERLY S L EH1 N D ER0 L IY0 -SLENDERNESS S L EH1 N D ER0 N AH0 S -SLEP S L IY1 P -SLEPT S L EH1 P T -SLESSOR S L EH1 S ER0 -SLESVIG S L EH1 V IH2 G -SLESWICK S L EH1 S W IH0 K -SLETH S L EH1 TH -SLEUT S L UW1 T -SLEUTH S L UW1 TH -SLEUTH'S S L UW1 TH S -SLEUTHHOUND S L UW1 TH HH AW2 N D -SLEUTHHOUNDS S L UW1 TH HH AW2 N D Z -SLEUTHING S L UW1 TH IH0 NG -SLEUTHS S L UW1 TH S -SLEVIN S L EH1 V IH0 N -SLEW S L UW1 -SLEWED S L UW1 D -SLEWEST S L UW1 AH0 S T -SLEWING S L UW1 IH0 NG -SLEWS S L UW1 Z -SLEY S L IY0 -SLEYBELLS S L IY0 B EH2 L Z -SLIAB S L AY1 AH0 B -SLICE S L AY1 S -SLICED S L AY1 S T -SLICER S L AY1 S ER0 -SLICES S L AY1 S AH0 Z -SLICES S L AY1 S IH0 Z -SLICIN S L AY1 S IH0 N -SLICING S L AY1 S IH0 NG -SLICK S L IH1 K -SLICK'S S L IH1 K S -SLICKED S L IH1 K T -SLICKENS S L IH1 K AH0 N Z -SLICKER S L IH1 K ER0 -SLICKERS S L IH1 K ER0 Z -SLICKEST S L IH1 K AH0 S T -SLICKIN S L IH1 K IH0 N -SLICKING S L IH1 K IH0 NG -SLICKLY S L IH1 K L IY0 -SLICKNESS S L IH1 K N AH0 S -SLICKO S L IH1 K OW0 -SLICKS S L IH1 K S -SLICKVILLE S L IH1 K V IH2 L -SLID S L IH1 D -SLIDDEN S L IH1 D AH0 N -SLIDDER S L IH1 D ER0 -SLIDDIM S L IH1 D IH0 M -SLIDE S L AY1 D -SLIDE'S S L AY1 D Z -SLIDED S L AY1 D IH0 D -SLIDELL S L AY1 D AH0 L -SLIDER S L AY1 D ER0 -SLIDERS S L AY1 D ER0 Z -SLIDERSKEW S L AY1 D ER1 S K Y UW1 -SLIDES S L AY1 D Z -SLIDESTAIRS S L AY1 D S T EH1 R Z -SLIDEWALK S L AY1 D W AO2 K -SLIDIN S L AY1 D IH0 N -SLIDING S L AY1 D IH0 NG -SLIDINGS S L AY1 D IH0 NG Z -SLIEU S L Y UH1 -SLIEVE S L IY1 V -SLIFE S L AY1 F -SLIFER S L AY1 F ER0 -SLIFERS S L AY1 F ER0 Z -SLIGH S L AY1 -SLIGHEST S L AY1 AH0 S T -SLIGHT S L AY1 T -SLIGHTED S L AY1 T IH0 D -SLIGHTER S L AY1 T ER0 -SLIGHTEST S L AY1 T AH0 S T -SLIGHTING S L AY1 T IH0 NG -SLIGHTINGLY S L AY1 T IH0 NG L IY0 -SLIGHTLY S L AY1 T L IY0 -SLIGHTLY'S S L AY1 T L IY0 Z -SLIGHTNESS S L AY1 T N AH0 S -SLIGHTS S L AY1 T S -SLIGO S L AY1 G OW2 -SLIKE S L AY1 K -SLIKE'S S L AY1 K S -SLILY S L IH1 L IY0 -SLIM S L IH1 M -SLIM'S S L IH1 M Z -SLIMAK S L IH1 M AH0 K -SLIMAK'S S L AY1 M AE0 K S -SLIMAKOWA S L AY1 M AH0 K OW1 AH0 -SLIMAN S L AY1 M AH0 N -SLIMBROEK S L IH1 M B R OW0 K -SLIME S L AY1 M -SLIMED S L AY1 M D -SLIMEY S L AY1 M IY0 -SLIMILY S L AY1 M AH0 L IY0 -SLIMINESS S L IH1 M IY0 N AH0 S -SLIMLY S L IH1 M L IY0 -SLIMMER S L IH1 M ER0 -SLIMMEST S L IH1 M AH0 S T -SLIMMY S L IH1 M IY0 -SLIMNESS S L IH1 M N AH0 S -SLIMPSY S L IH1 M P S IY0 -SLIMSON S L IH1 M S AH0 N -SLIMY S L AY1 M IY0 -SLING S L IH1 NG -SLINGER S L IH1 NG ER0 -SLINGERS S L IH1 NG ER0 Z -SLINGIN S L IH1 NG AH0 N -SLINGING S L IH1 NG IH0 NG -SLINGS S L IH1 NG Z -SLINGSBY S L IH1 NG Z B IY0 -SLINGSBY'S S L IH1 NG Z B IY0 Z -SLINGSHOT S L IH1 NG SH AA2 T -SLINK S L IH1 NG K -SLINKARD S L IH1 NG K ER0 D -SLINKED S L IH1 NG K T -SLINKIN S L IH1 NG K IH0 N -SLINKING S L IH1 NG K IH0 NG -SLINKS S L IH1 NG K S -SLINKY S L IH1 NG K IY0 -SLIP S L IH1 P -SLIPED S L AY1 P T -SLIPKNOT S L IH1 P N AA2 T -SLIPP'D S L IH1 P D -SLIPP'RY S L IH1 P R IY0 -SLIPPED S L IH1 P T -SLIPPER S L IH1 P ER0 -SLIPPER'S S L IH1 P ER0 Z -SLIPPERED S L IH1 P ER0 D -SLIPPERIEST S L IH1 P IH1 R IY0 AH0 S T -SLIPPERINESS S L IH1 P ER0 IH0 N AH0 S -SLIPPERLESS S L IH1 P ER0 L AH0 S -SLIPPERS S L IH1 P ER0 Z -SLIPPERY S L IH1 P ER0 IY0 -SLIPPERY S L IH1 P R IY0 -SLIPPERY'S S L IH1 P ER0 IY0 Z -SLIPPIN S L IH1 P IH0 N -SLIPPING S L IH1 P IH0 NG -SLIPPIT S L IH1 P IH0 T -SLIPPY S L IH1 P IY0 -SLIPRAILS S L IH1 P R EY2 L Z -SLIPS S L IH1 P S -SLIPSHAW S L IH1 P SH AO2 -SLIPSHOD S L IH1 P SH AA2 D -SLIPSLOP S L IH1 P S L AH0 P -SLIPT S L IH1 P T -SLIPWAY S L IH1 P W EY2 -SLISS S L IH1 S -SLIT S L IH1 T -SLITH S L IH1 DH -SLITHER S L IH1 DH ER0 -SLITHERED S L IH1 DH ER0 D -SLITHERING S L IH1 DH ER0 IH0 NG -SLITHERS S L IH1 DH ER0 Z -SLITHERY S L IH1 DH ER0 IY0 -SLITHY S L IH1 DH IY0 -SLITS S L IH1 T S -SLITTED S L IH1 T IH0 D -SLITTING S L IH1 T IH0 NG -SLIVA S L IY1 V AH0 -SLIVER S L IH1 V ER0 -SLIVERED S L IH1 V ER0 D -SLIVERS S L IH1 V ER0 Z -SLIVERSTONE S L IH1 V ER0 S T OW2 N -SLO S L OW0 -SLOAN S L OW1 N -SLOAN'S S L OW1 N Z -SLOANE S L OW1 N -SLOANE'S S L OW1 N Z -SLOANEHURST S L OW1 N HH ER0 S T -SLOANES S L OW1 N Z -SLOAT S L OW1 T -SLOB S L AA1 B -SLOBBER S L AA1 B ER0 -SLOBBERED S L AA1 B ER0 D -SLOBBERIN S L AA1 B ER0 IH0 N -SLOBBERING S L AA1 B ER0 IH0 NG -SLOBBERS S L AA1 B ER0 Z -SLOBBERY S L AA1 B ER0 IY0 -SLOBS S L AA1 B Z -SLOCOMB S L OW1 K AH0 M -SLOCOMBE S L OW1 K AH0 M -SLOCUM S L OW1 K AH0 M -SLOCUM'S S L OW1 K AH0 M Z -SLOCUMB S L OW1 K AH0 M -SLOCUMS S L OW1 K AH0 M Z -SLOE S L OW1 -SLOES S L OW1 Z -SLOG S L AA1 G -SLOGAN S L OW1 G AH0 N -SLOGANS S L OW1 G AH0 N Z -SLOGGED S L AA1 G D -SLOGGER S L AA1 G ER0 -SLOGGERS S L AA1 G ER0 Z -SLOGGETT S L AA1 G IH0 T -SLOGGING S L AA1 G IH0 NG -SLOGWELL S L AA1 G W EH0 L -SLOICH S L OY1 CH -SLOMAN S L OW1 M AH0 N -SLOMAN'S S L OW1 M AH0 N Z -SLOME S L OW1 M -SLOME'S S L OW1 M Z -SLONE S L OW1 N -SLONE'S S L OW1 N Z -SLOO S L UW1 -SLOOP S L UW1 P -SLOOP'S S L UW1 P S -SLOOPS S L UW1 P S -SLOOT S L UW1 T -SLOOTS S L UW1 T S -SLOP S L AA1 P -SLOPE S L OW1 P -SLOPE'S S L OW1 P S -SLOPED S L OW1 P T -SLOPER S L OW1 P ER0 -SLOPER'S S L OW1 P ER0 Z -SLOPERTON S L OW1 P ER0 T AH0 N -SLOPES S L OW1 P S -SLOPHAM S L AA1 F AH0 M -SLOPHAM'S S L AA1 F AH0 M Z -SLOPIN S L OW1 P IH0 N -SLOPING S L OW1 P IH0 NG -SLOPPED S L AA1 P T -SLOPPER S L AA1 P ER0 -SLOPPILY S L AA1 P AH0 L IY0 -SLOPPIN S L AA1 P IH0 N -SLOPPINESS S L AA1 P IY0 N AH0 S -SLOPPING S L AA1 P IH0 NG -SLOPPY S L AA1 P IY0 -SLOPS S L AA1 P S -SLOSH S L AA1 SH -SLOSHED S L AA1 SH T -SLOSHER S L AA1 SH ER0 -SLOSHES S L AA1 SH IH0 Z -SLOSHING S L AA1 SH IH0 NG -SLOSHY S L AA1 SH IY0 -SLOSSON S L AA1 S AH0 N -SLOSSON'S S L AA1 S AH0 N Z -SLOSSONS S L AA1 S AH0 N Z -SLOT S L AA1 T -SLOTE S L OW1 T IY0 -SLOTH S L OW1 TH -SLOTH'S S L AA1 TH S -SLOTHFUL S L AA1 TH F AH0 L -SLOTHFULLY S L AA1 TH F AH0 L IY0 -SLOTHFULNESS S L AA1 TH F AH0 L N AH0 S -SLOTHS S L AA1 TH S -SLOTMAN S L AA1 T M AH0 N -SLOTMAN'S S L AA1 T M AH0 N Z -SLOTS S L AA1 T S -SLOTT S L AA1 T -SLOTTED S L AA1 T IH0 D -SLOUCH S L AW1 CH -SLOUCHED S L AW1 CH T -SLOUCHES S L AW1 CH IH0 Z -SLOUCHING S L AW1 CH IH0 NG -SLOUCHY S L AW1 CH IY0 -SLOUGH S L AH1 F -SLOUGHED S L AH1 F T -SLOUGHING S L AH1 F IH0 NG -SLOUGHS S L AH1 F S -SLOVAK S L OW1 V AA0 K -SLOVAK S L OW1 V AE0 K -SLOVAKIA S L OW2 V AA1 K IY0 AH0 -SLOVAKIAN S L OW0 V AA1 K IY0 AH0 N -SLOVAKS S L OW1 V AA0 K S -SLOVAKS S L OW1 V AE0 K S -SLOVEN S L AH1 V AH0 N -SLOVENE S L OW2 V IY1 N -SLOVENES S L OW2 V IY1 N Z -SLOVENLINESS S L AH1 V AH0 N L IY0 N AH0 S -SLOVENLY S L AH1 V AH0 N L IY0 -SLOVENS S L AH1 V AH0 N Z -SLOVER S L OW1 V ER0 -SLOW S L OW1 -SLOW'S S L OW1 Z -SLOWBOY S L OW1 B OY2 -SLOWBRIDGE S L OW1 B R IH0 JH -SLOWBURGH S L OW1 B ER0 G -SLOWCOACH S L OW1 K OW2 CH -SLOWE S L OW1 -SLOWED S L OW1 D -SLOWER S L OW1 ER0 -SLOWEST S L OW1 AH0 S T -SLOWFOOT S L OW1 F UH2 T -SLOWIN S L OW1 IH0 N -SLOWING S L OW1 IH0 NG -SLOWLY S L OW1 L IY0 -SLOWNESS S L OW1 N AH0 S -SLOWS S L OW1 Z -SLOY S L OY0 -SLUBBER S L AH1 B ER0 -SLUDGE S L AH1 JH -SLUDGY S L AH1 JH IY0 -SLUE S L UW1 -SLUED S L UW1 D -SLUG S L AH1 G -SLUG'S S L AH1 G Z -SLUGBY S L AH1 G B IY0 -SLUGGARD S L AH1 G ER0 D -SLUGGARD'S S L AH1 G ER0 D Z -SLUGGARDS S L AH1 G ER0 D Z -SLUGGED S L AH1 G D -SLUGGER S L AH1 G ER0 -SLUGGERS S L AH1 G ER0 Z -SLUGGING S L AH1 G IH0 NG -SLUGGISH S L AH1 G IH0 SH -SLUGGISHLY S L AH1 G IH0 SH L IY0 -SLUGGISHNESS S L AH1 G IH0 SH N AH0 S -SLUGS S L AH1 G Z -SLUICE S L UW1 S -SLUICED S L UW1 S T -SLUICES S L UW1 S AH0 Z -SLUICEWAY S L UW1 S W EY2 -SLUICEWAYS S L UW1 S W EY2 Z -SLUICING S L UW1 S IH0 NG -SLUIT S L UW1 T -SLUITS S L UW1 T S -SLUM S L AH1 M -SLUMB'RING S L AH1 M R IH0 NG -SLUMBER S L AH1 M B ER0 -SLUMBER'D S L AH1 M B ER0 D -SLUMBER'S S L AH1 M B ER0 Z -SLUMBERED S L AH1 M B ER0 D -SLUMBERER S L AH1 M B ER0 ER0 -SLUMBERER'S S L AH1 M B ER0 ER0 Z -SLUMBERERS S L AH1 M B ER0 ER0 Z -SLUMBEREST S L AH1 M B ER0 IH0 S T -SLUMBERETH S L AH1 M B ER0 TH -SLUMBERING S L AH1 M B ER0 IH0 NG -SLUMBERINGS S L AH1 M B ER0 IH0 NG Z -SLUMBERLAND S L AH1 M B ER0 L AH0 N D -SLUMBEROUS S L AH1 M B ER0 AH0 S -SLUMBEROUSLY S L AH1 M B ER0 AH0 S L IY0 -SLUMBERS S L AH1 M B ER0 Z -SLUMBROUS S L AH1 M B R AH0 S -SLUMGULLION S L AH1 M G AH1 L Y AH0 N -SLUMKEY S L AH1 M K IY0 -SLUMMER S L AH1 M ER0 -SLUMMERS S L AH1 M ER0 Z -SLUMMING S L AH1 M IH0 NG -SLUMMY S L AH1 M IY0 -SLUMP S L AH1 M P -SLUMP'S S L AH1 M P S -SLUMPED S L AH1 M P T -SLUMPER S L AH1 M P ER0 -SLUMPING S L AH1 M P IH0 NG -SLUMPS S L AH1 M P S -SLUMPY S L AH1 M P IY0 -SLUMS S L AH1 M Z -SLUNG S L AH1 NG -SLUNGSHOT S L AH1 NG SH AA2 T -SLUNK S L AH1 NG K -SLUR S L ER1 -SLURK S L ER1 K -SLURRED S L ER1 D -SLURRING S L ER1 IH0 NG -SLURS S L ER1 Z -SLUS'UHR S L AH1 UH1 R -SLUSH S L AH1 SH -SLUSHED S L AH1 SH T -SLUSHING S L AH1 SH IH0 NG -SLUSHY S L AH1 SH IY0 -SLUSS S L AH1 S -SLUSUHR S L AH1 SH UH1 R -SLUT S L AH1 T -SLUT'S S L AH1 T S -SLUTS S L AH1 T S -SLUTTISH S L AH1 T IH0 SH -SLUTTISHNESS S L AH1 T IH0 SH N AH0 S -SLUYS S L AY1 Z -SLY S L AY1 -SLY'S S L AY1 Z -SLYBOOTS S L AY1 B UW2 T S -SLYCK S L AY1 K -SLYCK'S S L AY1 K S -SLYE'S S L AY1 Z -SLYER S L AY1 ER0 -SLYEST S L AY1 AH0 S T -SLYLY S L AY1 L IY0 -SLYME S L AY1 M -SLYME'S S L AY1 M Z -SLYNE S L AY1 N -SLYNE'S S L AY1 N Z -SLYNESS S L AY1 N AH0 S -SM S M -SMA S M AH0 -SMA'T S M EY1 T -SMACK S M AE1 K -SMACK'S S M AE1 K S -SMACKED S M AE1 K T -SMACKIN S M AE1 K AH0 N -SMACKING S M AE1 K IH0 NG -SMACKS S M AE1 K S -SMACKSMAN S M AE1 K S M AH0 N -SMACKSMEN S M AE1 K S M EH0 N -SMAHT S M AA1 T -SMAIN S M EY1 N -SMAIN'S S M EY1 N Z -SMAITH S M EY1 TH -SMAL S M AE1 L -SMALA S M AA1 L AH0 -SMALAND S M EY1 L AH0 N D -SMALE S M EY1 L -SMALKALDIC S M AE1 L K AH0 L D IH0 K -SMALL S M AO1 L -SMALL'S S M AO1 L Z -SMALLAGE S M AO1 L IH0 JH -SMALLBONES S M AO1 L B OW2 N Z -SMALLCLOTHES S M AO1 L K L OW1 Z -SMALLER S M AO1 L ER0 -SMALLEST S M AO1 L AH0 S T -SMALLEY S M AA1 L IY0 -SMALLEY'S S M AO1 L IY0 Z -SMALLHEAD S M AO1 L HH EH2 D -SMALLING S M AO1 L IH0 NG -SMALLISH S M AO1 L IH0 SH -SMALLNESS S M AO1 L N AH0 S -SMALLNESSES S M AO1 L N AH0 S IH0 Z -SMALLPEACE S M AO1 L P IY0 S -SMALLPOX S M AO1 L P AA2 K S -SMALLS S M AO1 L Z -SMALLSOLE S M AO1 L S AA2 L -SMALLWAYS S M AO1 L W EY2 Z -SMALLWEED S M AO1 L W IY2 D -SMALLWOOD S M AO1 L W UH2 D -SMALT S M AO1 L T -SMANGLE S M AE1 NG G AH0 L -SMARAGD S M AA1 R AE2 G D -SMARLINGHUE S M AA1 R L IH0 NG HH Y UW0 -SMARLINGHUE'S S M AA1 R L IH0 NG HH Y UW0 Z -SMART S M AA1 R T -SMART'S S M AA1 R T S -SMARTED S M AA1 R T AH0 D -SMARTEN S M AA1 R T AH0 N -SMARTENED S M AA1 R T AH0 N D -SMARTENING S M AA1 R T AH0 N IH0 NG -SMARTER S M AA1 R T ER0 -SMARTER'N S M AA1 R T ER0 AH0 N -SMARTEST S M AA1 R T AH0 S T -SMARTIE S M AA1 R T IY0 -SMARTIES S M AA1 R T IY0 Z -SMARTING S M AA1 R T IH0 NG -SMARTISH S M AA1 R T IH0 SH -SMARTLE S M AA1 R T AH0 L -SMARTLY S M AA1 R T L IY0 -SMARTNESS S M AA1 R T N IH0 S -SMARTS S M AA1 R T S -SMARTWEED S M AA1 R T W IY2 D -SMARTY S M AA1 R T IY0 -SMASH S M AE1 SH -SMASH'D S M AE1 SH D -SMASHED S M AE1 SH T -SMASHER S M AE1 SH ER0 -SMASHERS S M AE1 SH ER0 Z -SMASHES S M AE1 SH IH0 Z -SMASHIN S M AE1 SH IH0 N -SMASHING S M AE1 SH IH0 NG -SMASHUP S M AE1 SH AH2 P -SMATCH S M AE1 CH -SMATHE S M AE1 DH -SMATHERS S M AE1 DH ER0 Z -SMATTER S M AE1 T ER0 -SMATTERER S M AE1 T ER0 ER0 -SMATTERERS S M AE1 T ER0 ER0 Z -SMATTERING S M AE1 T ER0 IH0 NG -SMATTERINGS S M AE1 T ER0 IH0 NG Z -SMATTERTON S M AE1 T ER0 T AH0 N -SMAUKER S M AW1 K ER0 -SMAWL S M AO1 L -SMAYLE S M EY1 L -SMEAD S M IY1 D -SMEAR S M IH1 R -SMEAR'D S M IH1 R D -SMEARCASE S M IH1 R K EY2 S -SMEARED S M IH1 R D -SMEARER S M IH1 R ER0 -SMEARING S M IH1 R IH0 NG -SMEARS S M IH1 R Z -SMEARY S M IH1 R IY0 -SMEATH S M IY1 TH -SMEATON S M IY1 T AH0 N -SMEATON'S S M IY1 T AH0 N Z -SMEDDUM S M EH1 D AH0 M -SMEDLEY S M EH1 D L IY0 -SMEDLEY'S S M EH1 D L IY0 Z -SMEE S M IY1 -SMEED S M IY1 D -SMEER S M IY1 ER0 -SMEES S M IY1 Z -SMEET S M IY1 T -SMEETH S M IY1 TH -SMEETON S M IY1 T AH0 N -SMELFUNGUS S M EH1 L F AH1 NG G AH0 S -SMELKOFF S M EH1 L K AO0 F -SMELKOFF'S S M EH1 L K AO0 F S -SMELL S M EH1 L -SMELLED S M EH1 L D -SMELLER S M EH1 L ER0 -SMELLERS S M EH1 L ER0 Z -SMELLETH S M EH1 L IH0 TH -SMELLIE S M EH1 L IY0 -SMELLIE'S S M EH1 L IY0 Z -SMELLIN S M EH1 L IH0 N -SMELLING S M EH1 L IH0 NG -SMELLS S M EH1 L Z -SMELLY S M EH1 L IY0 -SMELT S M EH1 L T -SMELTED S M EH1 L T IH0 D -SMELTER S M EH1 L T ER0 -SMELTERS S M EH1 L T ER0 Z -SMELTING S M EH1 L T IH0 NG -SMELTS S M EH1 L T S -SMERDIS S M EH1 R D IH0 S -SMERDYAKOV S M EH1 R D Y AA1 K AO2 V -SMERDYAKOV'S S M ER1 D AY0 AE1 K AA2 V Z -SMERNOFF S M ER1 N AO0 F -SMERWICK S M ER1 W IH0 K -SMET S M EH1 T -SMETANA S M EH1 T AH0 N AH0 -SMETHURST S M EH1 TH ER0 S T -SMETSE S M EH1 T S -SMIBERT S M IH1 B ER0 T -SMIDDY S M IH1 D IY0 -SMIDGKIN S M IH1 JH K IH0 N -SMIDMORE S M IH1 D M AO2 R -SMIFF S M IH1 F -SMIKE S M AY1 K -SMIKE'S S M AY1 K S -SMIL'D S M IH1 L D -SMILASH S M AY1 L AE2 SH -SMILAX S M AY1 L AE2 K S -SMILE S M AY1 L -SMILE'S S M AY1 L Z -SMILED S M AY1 L D -SMILELESS S M AY1 L L AH0 S -SMILELESSLY S M AY1 L L AH0 S L IY0 -SMILER S M AY1 L ER0 -SMILER'S S M AY1 L ER0 Z -SMILES S M AY1 L Z -SMILES'S S M AY1 L Z IH0 Z -SMILEST S M AY1 L S T -SMILETH S M IH1 L IH0 TH -SMILEY S M AY1 L IY0 -SMILEY'S S M AY1 L IY0 Z -SMILIN S M AY1 L IH0 N -SMILINDA S M IH1 L IY1 N D AH0 -SMILINDA'S S M AY1 L IH0 N D AH0 Z -SMILING S M AY1 L IH0 NG -SMILINGLY S M AY1 L IH0 NG L IY0 -SMILINGS S M AY1 L IH0 NG Z -SMILLIE S M IH1 L IY0 -SMILY S M L IY0 -SMINTHEUS S M IH1 N DH OY0 Z -SMIRCH S M ER1 CH -SMIRCHED S M ER1 CH T -SMIRCHES S M ER1 CH IH0 Z -SMIRCHING S M ER1 CH IH0 NG -SMIRK S M ER1 K -SMIRKE S M ER1 K -SMIRKE'S S M ER1 K S -SMIRKED S M ER1 K T -SMIRKIE S M ER1 K IY0 -SMIRKING S M ER1 K IH0 NG -SMIRKS S M ER1 K S -SMIRNOV S M ER1 N AA0 F -SMIRRE S M ER1 -SMIT S M IH1 T -SMITCH S M IH0 CH -SMITE S M AY2 T -SMITER S M AY1 T ER0 -SMITERS S M AY1 T ER0 Z -SMITES S M AY1 T S -SMITEST S M AY1 T AH0 S T -SMITETH S M IH1 T EH0 TH -SMITH S M IH1 TH -SMITH'S S M IH1 TH S -SMITHE S M IH1 TH -SMITHER S M IH1 DH ER0 -SMITHEREENS S M IH2 DH ER0 IY1 N Z -SMITHERS S M IH1 DH ER0 Z -SMITHERS'S S M IH1 DH ER0 Z IH0 Z -SMITHERTON S M IH1 TH ER0 T AH0 N -SMITHERY S M IH1 TH ER0 IY0 -SMITHFIELD S M IH1 TH F IY0 L D -SMITHICK S M IH1 TH IH0 K -SMITHIE S M IH1 TH IY0 -SMITHIE'S S M IH1 TH IY0 Z -SMITHIES S M IH1 TH IY0 Z -SMITHING S M IH1 TH IH0 NG -SMITHS S M IH1 TH S -SMITHSON S M IH1 TH S AH0 N -SMITHSON'S S M IH1 TH S AH0 N Z -SMITHSONIAN S M IH0 S OW1 N IY0 AH0 N -SMITHSONIAN S M IH0 TH S OW1 N IY0 AH0 N -SMITHSONS S M IH1 TH S AH0 N Z -SMITHTOWN S M IH1 TH T AW2 N -SMITHVILLE S M IH1 TH V IH2 L -SMITHWICK S M IH1 TH W IH2 K -SMITHY S M IH1 TH IY0 -SMITHY'S S M IH1 TH IY0 Z -SMITING S M AH0 T IH0 NG -SMITINGS S M AH0 T IH0 NG Z -SMITTEN S M IH1 T AH0 N -SMITZ S M IH1 T S -SMOAK S M OW1 K -SMOAKED S M OW1 K T -SMOAKING S M OW1 K IH0 NG -SMOAKY S M OW1 K IY0 -SMOCK S M AA1 K -SMOCKED S M AA1 K T -SMOCKFROCK S M AA1 K F R AA2 K -SMOCKS S M AA1 K S -SMOG S M AA1 G -SMOIT S M OY1 T -SMOK'D S M OW1 K D -SMOKE S M OW1 K -SMOKE'S S M OW1 K S -SMOKED S M OW1 K T -SMOKEHOUSE S M OW1 K HH AW2 S -SMOKEING S M OW1 K IH0 NG -SMOKELESS S M OW1 K L AH0 S -SMOKER S M OW1 K ER0 -SMOKER'S S M OW1 K ER0 Z -SMOKERS S M OW1 K ER0 Z -SMOKES S M OW1 K S -SMOKESTACK S M OW1 K S T AE2 K -SMOKESTACKS S M OW1 K S T AE2 K S -SMOKEY S M OW1 K IY0 -SMOKIER S M OW1 K IY0 ER0 -SMOKIES S M OW1 K IY0 Z -SMOKILY S M OW1 K IH0 L IY0 -SMOKIN S M OW1 K IH0 N -SMOKIN' S M OW1 K IH0 N -SMOKINESS S M OW1 K IY0 N AH0 S -SMOKING S M OW1 K IH0 NG -SMOKOVNIKOV S M OW1 K AH0 V N IH0 K AA2 V -SMOKY S M OW1 K IY0 -SMOKY'S S M OW1 K IY0 Z -SMOLDER S M OW1 L D ER0 -SMOLDERED S M OW1 L D ER0 D -SMOLDERING S M OW1 L D ER0 IH0 NG -SMOLENSK S M OW0 L EH1 N S K -SMOLENSKIN S M AH0 L EH1 N S K IH0 N -SMOLENSKIN'S S M AH0 L EH1 N S K IH0 N Z -SMOLENSKO S M AH0 L EH1 N S K OW0 -SMOLICHECK S M AH0 L IH1 CH EH2 K -SMOLIN S M OW1 L IH0 N -SMOLLET S M AA1 L IH0 T -SMOLLETT S M AA1 L IH0 T -SMOLLETT'S S M AA1 L IH0 T S -SMOLLEY S M AA1 L IY0 -SMOLNY S M OW1 L N IY0 -SMOLTS S M OW1 L T S -SMOOCH S M UW1 CH -SMOOK S M UW1 K -SMOORED S M AO1 R D -SMOOT S M UW1 T -SMOOTH S M UW1 DH -SMOOTH'D S M UW1 DH D -SMOOTHBORE S M UW1 DH B AO0 R -SMOOTHE S M UW1 DH -SMOOTHED S M UW1 DH D -SMOOTHER S M UW1 DH ER0 -SMOOTHES S M UW1 DH Z -SMOOTHEST S M UW1 DH AH0 S T -SMOOTHIN S M UW1 DH IH1 N -SMOOTHING S M UW1 DH IH0 NG -SMOOTHLY S M UW1 DH L IY0 -SMOOTHNESS S M UW1 DH N AH0 S -SMOOTHS S M UW1 DH Z -SMOTE S M OW1 T -SMOTH S M AA1 TH -SMOTHE S M AH1 DH -SMOTHER S M AH1 DH ER0 -SMOTHER'D S M AH1 DH ER0 D -SMOTHERED S M AH1 DH ER0 D -SMOTHERIN S M AH1 DH ER0 IH0 N -SMOTHERING S M AH1 DH ER0 IH0 NG -SMOTHERINGLY S M AH1 DH ER0 IH0 NG L IY0 -SMOTHERS S M AH1 DH ER0 Z -SMOTHERY S M AH1 DH ER0 IY0 -SMOUCH S M AW1 CH -SMOUCHED S M AW1 CH T -SMOULDER S M OW1 L D ER0 -SMOULDERED S M OW1 L D ER0 D -SMOULDERING S M OW1 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IH0 T -SNAKY S N EY1 K IY0 -SNANA S N AE1 N AH0 -SNANDULIA S N AE1 N JH AH0 L Y AH0 -SNAP S N AE1 P -SNAP'S S N AE1 P S -SNAPDRAGON S N AE1 P D R AE2 G AH0 N -SNAPDRAGONS S N AE1 P D R AE2 G AH0 N Z -SNAPE S N EY1 P -SNAPHANCE S N AE1 F AH0 N S -SNAPP'D S N AE1 P D -SNAPPED S N AE1 P T -SNAPPER S N AE1 P ER0 -SNAPPERS S N AE1 P ER0 Z -SNAPPIER S N AE1 P IY0 ER0 -SNAPPIEST S N AE1 P IY0 AH0 S T -SNAPPILY S N AE1 P AH0 L IY0 -SNAPPIN S N AE1 P IH0 N -SNAPPING S N AE1 P IH0 NG -SNAPPINGS S N AE1 P IH0 NG Z -SNAPPISH S N AE1 P IH0 SH -SNAPPISHLY S N AE1 P IH0 SH L IY0 -SNAPPISHNESS S N AE1 P IH0 SH N AH0 S -SNAPPS S N AE1 P S -SNAPPY S N AE1 P IY0 -SNAPS S N AE1 P S -SNAPSHOT S N AE1 P SH AA2 T -SNAPSHOTS S N AE1 P SH AA2 T S -SNAPSHOTTED S N AE1 P SH AA2 T IH0 D -SNAPT S N AE1 P T -SNARBI S N AA1 R B IY0 -SNARE S N EH1 R -SNARED S N EH1 R D -SNARER S N EH1 R ER0 -SNARES S N EH1 R Z -SNARING S N EH1 R IH0 NG -SNARK S N AA1 R K -SNARL S N AA1 R L -SNARLED S N AA1 R AH0 L D -SNARLER S N AA1 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S P EY1 SH AH0 S L IY0 -SPACIOUSNESS S P EY1 SH AH0 S N AH0 S -SPAD S P AE0 D -SPADA S P AA1 D AH0 -SPADASSIN S P AA0 D AA0 S IH0 N -SPADASSINS S P AA0 D AA0 S IY0 N Z -SPADDEN S P AE1 D AH0 N -SPADE S P EY1 D -SPADE'S S P EY1 D Z -SPADED S P EY1 D IH0 D -SPADEFUL S P EY1 D F AH0 L -SPADEFULS S P EY1 D F AH0 L Z -SPADES S P EY1 D Z -SPADEVIL S P AA0 D EH1 V AH0 L -SPADILLE S P AA0 D IH1 L -SPADINA S P AA0 D IY1 N AH0 -SPADING S P EY1 D IH0 NG -SPADS S P AA0 D Z -SPAE S P EY1 -SPAEWIFE S P EY1 W AY2 F -SPAFF S P AE1 F -SPAFFORD S P AE1 F ER0 D -SPAFIELDS S P AE1 F IY2 L D Z -SPAGHETTI S P AH0 G EH1 T IY0 -SPAGNA S P AE1 G N AH0 -SPAGNOLETTO S P AA0 G N OW1 L EH1 T OW0 -SPAHI S P AA1 HH IY0 -SPAHIS S P AA1 IH0 S -SPAIGHT S P EY1 T -SPAIN S P EY1 N -SPAIN'S S P EY1 N Z -SPAINS S P EY1 N Z -SPAK S P AE1 K -SPAKE S P EY1 K -SPAKES S P EY1 K S -SPAKEST S P EY1 K AH0 S T -SPAKIN S P AE1 K IH0 N -SPAKING S P EY1 K IH0 NG -SPALATIN S P AE1 L AH0 T AH0 N -SPALATO S P AA0 L AA1 T OW0 -SPALATRO S P AE1 L AH0 T R OW0 -SPALDING S P AO1 L D IH0 NG -SPALDING'S S P AO1 L D IH0 NG Z -SPALL S P AO1 L -SPALLANZANI S P AA0 L AA0 N Z AA1 N IY0 -SPALPANE S P AA0 L P AA1 N EY0 -SPALPEEN S P AA1 L P IY0 N -SPALPEENS S P AA1 L P IY1 N Z -SPALTON S P AO1 L T AH0 N -SPALTON'S S P AO1 L T AH0 N Z -SPAN S P AE1 N -SPAN'S S P AE1 N Z -SPANA S P AE1 N Y AH0 -SPANCIOC S P AE1 NG K IH0 AA0 K -SPANDAU S P AE1 N D AW0 -SPANDRELS S P AE1 N D R AH0 L Z -SPANDRILS S P AE1 D R AH0 L Z -SPANDY S P AE1 N D IY0 -SPANG S P AE1 NG -SPANGENBERG S P AE1 NG AH0 N B ER0 G -SPANGENBERG'S S P AE1 NG AH0 N B ER0 G Z -SPANGLE S P AE1 NG G AH0 L -SPANGLED S P AE1 NG G AH0 L D -SPANGLER S P AE1 NG G AH0 L ER0 -SPANGLER S P AE1 NG G L ER0 -SPANGLER'S S P AE1 NG G AH0 L ER0 Z -SPANGLES S P AE1 NG G AH0 L Z -SPANGLING S P AE1 NG G AH0 L IH0 NG -SPANGLY S P AE1 NG L IY0 -SPANHEIM S P AE1 N HH AY0 M -SPANIARD S P AE1 N Y ER0 D -SPANIARD'S S P AE1 N Y ER0 D Z -SPANIARDS S P AE1 N Y ER0 D Z -SPANIEL S P AE1 N Y AH0 L -SPANIEL'S S P AE1 N Y AH0 L Z -SPANIELS S P AE1 N Y AH0 L Z -SPANISCHE S P AE1 N IH0 SH IY0 -SPANISH S P AE1 N IH0 SH -SPANISHERS S P AE1 N IH0 SH ER0 Z -SPANK S P AE1 NG K -SPANKED S P AE1 NG K T -SPANKER S P AE1 NG K ER0 -SPANKIN S P AE1 NG K IH0 N -SPANKING S P AE1 NG K IH0 NG -SPANKINGS S P AE1 NG K IH0 NG Z -SPANKS S P AE1 NG K S -SPANN'D S P AE1 N D -SPANNED S P AE1 N D -SPANNER S P AE1 N ER0 -SPANNERS S P AE1 N ER0 Z -SPANNESS S P AE1 N AH0 S -SPANNING S P AE1 N IH0 NG -SPANO S P AA1 N OW0 -SPANS S P AE1 N Z -SPANTZ S P AE1 N T S -SPAR S P AA1 R -SPAR'D S P ER0 D -SPARE S P EH1 R -SPARED S P EH1 R D -SPARELY S P EH1 R L IY0 -SPARENECK S P EH1 R AH0 N EH2 K -SPARENESS S P EH1 R N AH0 S -SPARER S P EH1 R ER0 -SPARERIB S P ER0 IH0 R IH1 B -SPARERIBS S P ER0 IH0 R IH1 B Z -SPARES S P EH1 R Z -SPAREST S P ER0 IH0 S T -SPARETH S P AA1 R IH0 TH -SPARFLIGHT S P AA1 R F L AY2 T -SPARGO S P AA1 R G OW0 -SPARGO'S S P AA1 R G OW0 Z -SPARHAWK S P AA1 R HH AO2 K -SPARHAWK'S S P AA1 R HH AO2 K S -SPARICIO S P ER0 IH1 S IY0 OW0 -SPARIN S P ER0 IH0 N -SPARING S P EH1 R IH0 NG -SPARINGLY S P EH1 R IH0 NG L IY0 -SPARK S P AA1 R K -SPARK'S S P AA1 R K S -SPARKE S P AA1 R K -SPARKED S P AA1 R K T -SPARKEION S P AA1 R K IY0 AH0 N -SPARKES S P AA1 R K S -SPARKIN S P AA1 R K IH0 N -SPARKING S P AA1 R K IH0 NG -SPARKINS S P AA1 R K IH0 N Z -SPARKISH S P AA1 R K IH0 SH -SPARKLE S P AA1 R K AH0 L -SPARKLE'S S P AA1 R K AH0 L Z -SPARKLED S P AA1 R K AH0 L D -SPARKLER S P AA1 R K AH0 L ER0 -SPARKLER'S S P AA1 R K AH0 L ER0 Z -SPARKLERS S P AA1 R K AH0 L ER0 Z -SPARKLES S P AA1 R K AH0 L Z -SPARKLET S P AA1 R K L IH0 T -SPARKLIN S P AA1 R K L IH0 N -SPARKLING S P AA1 R K AH0 L IH0 NG -SPARKLING S P AA1 R K L IH0 NG -SPARKLINGLY S P AA1 R K AH0 L IH0 NG L IY0 -SPARKLINGS S P AA1 R K AH0 L IH0 NG Z -SPARKLY S P AA1 R K L IY0 -SPARKS S P AA1 R K S -SPARKS'S S P AA1 R K S IH0 Z -SPARKY S P AA1 R K IY0 -SPARLING S P AA1 R L IH0 NG -SPARMIENTO S P AA1 R M IY0 EH1 N T OW0 -SPARNHEIM S P AA1 R N HH AY0 M -SPARRE S P AA1 R -SPARRED S P AA1 R D -SPARRELL S P EH1 R AH0 L -SPARRER S P AA1 R ER0 -SPARRERS S P AA1 R ER0 Z -SPARRIN S P EH1 R IH0 N -SPARRING S P AA1 R IH0 NG -SPARROW S P EH1 R OW0 -SPARROW'S S P EH1 R OW0 Z -SPARROWGRASS S P EH1 R OW0 G R AE2 S -SPARROWHAWK S P EH1 R OW0 HH AO2 K -SPARROWHAWKS S P EH1 R OW0 HH AO2 K S -SPARROWS S P EH1 R OW0 Z -SPARRY S P EH1 R IY0 -SPARS S P AA1 R Z -SPARSE S P AA1 R S -SPARSELY S P AA1 R S L IY0 -SPARSENESS S P AA1 R S N AH0 S -SPARSER S P AA1 R S ER0 -SPARSHOT S P AA1 R SH AA2 T -SPARSITY S P AA1 R S IH0 T IY0 -SPARTA S P AA1 R T AH0 -SPARTA'S S P AA1 R T AH0 Z -SPARTACUS S P AA1 R T AH0 K AH0 S -SPARTAN S P AA1 R T AH0 N -SPARTAN'S S P AA1 R T AH0 N Z -SPARTANBURG S P AA1 R T AH0 N B ER0 G -SPARTANE S P AA1 R T AH0 N -SPARTANS S P AA1 R T AH0 N Z -SPARTEL S P AA1 R T AH0 L -SPARTIATE S P AA2 R SH IY0 EY2 T -SPARTIVENTO S P AA1 R T AH0 V EY0 N T OW0 -SPAS S P AA1 Z -SPASH S P AE1 SH -SPASM S P AE1 Z AH0 M -SPASMODIC S P AE0 Z M AO1 D IH0 K -SPASMODICALLY S P AE0 Z M AO1 D IH0 K L IY0 -SPASMS S P AE1 Z AH0 M Z -SPASSKII S P AE1 S K IY0 -SPASSO S P AE1 S OW0 -SPAT S P AE1 T -SPATCH S P AE1 CH -SPATE S P EY1 T -SPATES S P EY1 T S -SPATH S P AE1 TH -SPATHE S P EY1 DH -SPATHES S P EY1 DH Z -SPATIAL S P EY1 SH AH0 L -SPATIALLY S P AA1 SH AH0 L IY0 -SPATOLA S P AA0 T OW1 L AH0 -SPATOLINO S P EY1 T OW0 L IY1 N OW0 -SPATS S P AE1 T S -SPATTED S P AE1 T IH0 D -SPATTER S P AE1 T ER0 -SPATTERDASHES S P AE1 T ER0 D AE2 SH AH0 Z -SPATTERED S P AE1 T ER0 D -SPATTERING S P AE1 T ER0 IH0 NG -SPATTERINGS S P AE1 T ER0 IH0 NG Z -SPATTERS S P AE1 T ER0 Z -SPATTING S P AE1 T IH0 NG -SPATULA S P AE1 CH UH0 L AH0 -SPATULATE S P AE1 CH AH0 L EY2 T -SPATZIG S P AE1 T Z IH0 G -SPAULDING S P AO1 L D IH0 NG -SPAULDING'S S P AO1 L D IH0 NG Z -SPAULDINGS S P AO1 L D IH0 NG Z -SPAVIN S P EY1 V IH0 N -SPAVINED S P AE1 V AH0 N D -SPAVINHITCH S P AA0 V IH1 N HH IH1 CH -SPAVINS S P AE1 V IH0 N Z -SPAW S P AO1 -SPAWN S P AA1 N -SPAWN S P AO1 N 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S P EH1 S AH0 M AH0 N -SPECIMENS S P EH1 S AH0 M AH0 N Z -SPECIOSA S P IY1 SH IY0 OW1 S AH0 -SPECIOSUM S P IY1 SH IY0 OW2 Z AH0 M -SPECIOUS S P IY1 SH AH0 S -SPECIOUSLY S P EH1 SH AH0 S L IY0 -SPECIOUSNESS S P EH1 SH AH0 S N AH0 S -SPECIS S P EH1 S AH0 S -SPECK S P EH1 K -SPECKED S P EH1 K T -SPECKLE S P EH1 K AH0 L -SPECKLED S P EH1 K AH0 L D -SPECKLEMS S P EH1 K L AH0 M Z -SPECKLES S P EH1 K AH0 L Z -SPECKLESS S P EH1 K L AH0 S -SPECKLESSLY S P EH1 K L AH0 S L IY0 -SPECKLING S P EH1 K AH0 L IH0 NG -SPECKLY S P EH1 K L IY0 -SPECKPORT S P EH1 K P AO2 R T -SPECKS S P EH1 K S -SPECKSIONEER S P EH1 K S AY2 AH0 N IH1 R -SPECO S P EH1 K OW0 -SPECS S P EH1 K S -SPECT S P EH1 K T -SPECTABILIS S P EH1 K T AH0 B IH1 L AH0 S -SPECTABLE S P EH1 K T AH0 B AH0 L -SPECTACLE S P EH1 K T AH0 K AH0 L -SPECTACLED S P EH1 K T AH0 K AH0 L D -SPECTACLES S P EH1 K T AH0 K AH0 L Z -SPECTACULAR S P EH0 K T AE1 K Y AH0 L ER0 -SPECTACULARLY S P EH0 K T AE1 K Y AH0 L ER0 L IY0 -SPECTATOR S P EH1 K T EY0 T ER0 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EY1 SH AH0 N -SPECULATIONS S P EH2 K Y AH0 L EY1 SH AH0 N Z -SPECULATIST S P EH1 K Y AH0 L AH0 T IH0 S T -SPECULATISTS S P EH1 K Y AH0 L AH0 T IH0 S T S -SPECULATIVE S P EH1 K Y AH0 L AH0 T IH0 V -SPECULATIVELY S P EH1 K Y AH0 L EY2 T IH0 V L IY0 -SPECULATOR S P EH1 K Y AH0 L EY2 T ER0 -SPECULATOR'S S P EH1 K Y AH0 L EY2 T ER0 Z -SPECULATORS S P EH1 K Y AH0 L EY2 T ER0 Z -SPECULUM S P EH1 K Y AH0 L AH0 M -SPED S P EH1 D -SPEDDING S P EH1 D IH0 NG -SPEDDING'S S P EH1 D IH0 NG Z -SPEE S P IY1 -SPEE'S S P IY1 Z -SPEECH S P IY1 CH -SPEECHES S P IY1 CH AH0 Z -SPEECHES S P IY1 CH IH0 Z -SPEECHIFY S P IY1 CH AH0 F AY2 -SPEECHIFYING S P IY1 CH AH0 F AY2 IH0 NG -SPEECHLESS S P IY1 CH L AH0 S -SPEECHLESSLY S P IY1 CH L AH0 S L IY0 -SPEECHLESSNESS S P IY1 CH L AH0 S N AH0 S -SPEECHMAKER S P IY1 CH M EY2 K ER0 -SPEECHMAKING S P IY1 CH M EY2 K IH0 NG -SPEED S P IY1 D -SPEED'S S P IY1 D Z -SPEEDAWAY S P IY1 D AH0 W EY2 -SPEEDBOAT S P IY1 D B OW2 T -SPEEDE S P IY1 D -SPEEDED S P IY1 D IH0 D -SPEEDER S P IY1 D ER0 -SPEEDERS S P IY1 D ER0 Z -SPEEDETH S P IY1 D IH0 TH -SPEEDIER S P IY1 D IY0 ER0 -SPEEDIEST S P IY1 D IY0 IH0 S T -SPEEDILIE S P IY1 D AH0 L IY0 -SPEEDILY S P IY1 D AH0 L IY0 -SPEEDIN S P IY1 D AH0 N -SPEEDINESS S P IY1 D IY0 N AH0 S -SPEEDING S P IY1 D IH0 NG -SPEEDOMETER S P IY0 D AA1 M AH0 T ER0 -SPEEDS S P IY1 D Z -SPEEDSTER S P IY1 D S T ER0 -SPEEDSTERS S P IY1 D S T ER0 Z -SPEEDWAY S P IY1 D W EY2 -SPEEDWELL S P IY1 D W EH2 L -SPEEDWELL'S S P IY1 D W EH2 L Z -SPEEDWELLS S P IY1 D W EH2 L Z -SPEEDY S P IY1 D IY0 -SPEEDY'S S P IY1 D IY0 Z -SPEEK S P IY1 K -SPEEL S P IY1 L -SPEEN S P IY1 N -SPEER S P IH1 R -SPEERED S P IH1 R D -SPEERIN S P IH1 R IH0 N -SPEERING S P IH1 R IH0 NG -SPEERIT S P IY1 R IH0 T -SPEERITS S P IY1 R IH0 T S -SPEERITUAL S P IY1 R IH0 CH AH0 W AH0 L -SPEERS S P IH1 R Z -SPEIDEL S P AY1 D AH0 L -SPEIGHT S P EY1 T -SPEIK S P AY1 K -SPEIR S P IH1 R -SPEIRED S P AY1 ER0 D -SPEIRIN S P IH1 R IH0 N -SPEIRING S P AY1 R IH0 NG -SPEIRS S P IH1 R Z -SPEKE S P IY1 K -SPEKE'S S P EH1 K S -SPELING S P AH0 L IH0 NG -SPELL S P EH1 L -SPELL'S S P EH1 L Z -SPELLBINDER S P EH1 L B AY2 N D ER0 -SPELLBOUND S P EH1 L B AW2 N D -SPELLED S P EH1 L D -SPELLER S P EH1 L ER0 -SPELLERS S P EH1 L ER0 Z -SPELLIN S P EH1 L IH0 N -SPELLIN' S P EH1 L IH0 N -SPELLING S P EH1 L IH0 NG -SPELLINGS S P EH1 L IH0 NG Z -SPELLMAN S P EH1 L M AH0 N -SPELLO S P EH1 L OW0 -SPELLS S P EH1 L Z -SPELMAN S P EH1 L M AH0 N -SPELT S P EH1 L T -SPELTER S P EH1 L T ER0 -SPELTZ S P EH1 L T S -SPELVIN S P EH1 L V IH0 N -SPELW S P IY1 L -SPELWANE S P AH0 L W EY0 N -SPEM S P EH0 M -SPEN S P AH0 N -SPENCE S P EH1 N S -SPENCE'S S P EH1 N S IH0 Z -SPENCER S P EH1 N S ER0 -SPENCER'S S P EH1 N S ER0 Z -SPENCERIAN S P EH1 N S EH1 R IY0 AH0 N -SPENCERS S P EH1 N S ER0 Z -SPENCERVALE S P EH1 N S ER0 V EY2 L -SPEND S P EH1 N D -SPENDALL S P EH1 N D AH0 L -SPENDED S P EH1 N D AH0 D -SPENDER S P EH1 N D ER0 -SPENDERS S P EH1 N D ER0 Z -SPENDETH S P EH1 N D IH0 TH -SPENDIN S P EH1 N D IH0 N 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S P IH1 R IY0 AH0 N S T -SPERIMENT S P EH1 R AH0 M AH0 N T -SPERIT S P EH1 R AH0 T -SPERITED S P EH1 R AH0 T IH0 D -SPERITS S P EH1 R AH0 T S -SPERITUAL S P EH1 R IH0 CH AH0 W AH0 L -SPERLING S P ER1 L IH0 NG -SPERM S P ER1 M -SPERMACETI S P ER1 M AH0 S EH1 T IY0 -SPERMATAZOA S P ER2 M AH0 T AH0 Z OW1 AH0 -SPERMATIC S P ER0 M AE1 T IH0 K -SPERMATOZOA S P ER2 M AH0 T AH0 Z OW1 AH0 -SPERMATOZOON S P ER2 M AH0 T AH0 Z UW2 N -SPERMS S P ER1 M Z -SPERNER S P ER1 N ER0 -SPERO S P EH1 R OW0 -SPERO'S S P EH1 R OW0 Z -SPERONARE S P EH1 R OW0 N AA1 R IY0 -SPERRIT S P EH1 R IH0 T -SPERRITED S P EH1 R IH0 T IH0 D -SPERRITS S P EH1 R IH0 T S -SPERRY S P EH1 R IY0 -SPERRY'S S P EH1 R IY0 Z -SPERVER S P ER1 V ER0 -SPES S P S -SPESHALLY S P EH1 SH AH0 L IY0 -SPESHUL S P EH1 SH UW0 L -SPESSART S P EH1 S ER0 T -SPETTIGEW S P EH1 T IH0 G UW0 -SPEUSIPPUS S P Y UW2 S IH1 P AH0 S -SPEW S P Y UW1 -SPEWED S P Y UW1 D -SPEWING S P Y UW1 IH0 NG -SPEWS S P Y UW1 Z -SPEY S P EY1 -SPEYER S P EY1 ER0 -SPEYK S P IH0 K -SPEZIA S P EH0 Z IY0 AH0 -SPEZZIA S P EH0 T S IY0 AH0 -SPHACTERIA S F AE0 K T IH1 R IY0 AH0 -SPHAGNUM S F AA1 G N AH0 M -SPHENOID S F AH0 N OY2 D -SPHERAL S F ER0 AH0 L -SPHERE S F IH1 R -SPHERE'S S F IH2 R Z -SPHERED S F IH1 R D -SPHERES S F IH1 R Z -SPHERIC S F IH1 R IH0 K -SPHERICAL S F EH1 R IH0 K AH0 L -SPHERICALLY S F IH1 R IH0 K L IY0 -SPHERICITY S F IH1 R IH0 S IH2 T IY0 -SPHEROID S F IH1 R OY2 D -SPHEROIDAL S F IH1 R OY2 D AH0 L -SPHEROIDS S F IH1 R OY2 D Z -SPHEX S F EH1 K S -SPHINCTER S F IH1 NG K T ER0 -SPHINX S F IH1 NG K S -SPHINX'S S F IH1 NG K S IH0 Z -SPHINXES S F IH1 NG K S IH0 Z -SPHINXLIKE S F IH1 NG K S L AY2 K -SPHYGMOGRAPH S F IH1 G M AH0 G R AE1 F -SPHYNX S F IH0 NG K S -SPHYNXES S F IH0 NG K S IH0 Z -SPI S P IY0 -SPIC S P IH1 K -SPICA S P IH1 K AH0 -SPICCA S P IH1 K AH0 -SPICCATO S P IH1 K AA1 T OW0 -SPICE S P AY1 S -SPICED S P AY1 S T -SPICER S P AY1 S ER0 -SPICER'S S P AY1 S ER0 Z -SPICERIES S P AY1 S R IY0 Z -SPICERS S P AY1 S ER0 Z -SPICERY S P AY1 S ER0 IY0 -SPICES S P AY1 S AH0 Z -SPICES S P AY1 S IH0 Z -SPICEWOOD S P AY1 S W UH2 D -SPICIER S P AY1 S IY0 ER0 -SPICIEST S P AY1 S IY0 AH0 S T -SPICILY S P AY1 S IH0 L IY0 -SPICINESS S P AY1 S IY0 N AH0 S -SPICING S P AY1 S IH0 NG -SPICION S P IH1 SH AH0 N -SPICIOUS S P IH1 SH AH0 S -SPICK S P IH1 K -SPICULA S P IH1 K Y AH0 L AH0 -SPICULAE S P IH1 K Y AH0 L EY2 -SPICULES S P IH1 K Y AH0 L IY2 Z -SPICY S P AY1 S IY0 -SPIDEL S P IH1 D AH0 L -SPIDER S P AY1 D ER0 -SPIDER'S S P AY1 D ER0 Z -SPIDERLIKE S P AY1 D ER0 L AY2 K -SPIDERMONKEY S P AY1 D ER0 M AH1 NG K IY0 -SPIDERS S P AY1 D ER0 Z -SPIDERWEB S P AY1 D ER0 W EH2 B -SPIDERWEBS S P AY1 D ER0 W EH2 B Z -SPIDERWITZ S P AY1 D ER1 W IH0 T S -SPIDERWORT S P AY1 D ER0 W ER0 T -SPIDERY S P AY1 D ER0 IY0 -SPIED S P AY1 D -SPIEGEL S P IY1 G AH0 L -SPIEGELBERG S P IY1 G AH0 L B ER0 G -SPIEGELER S P IY1 G AH0 L ER0 -SPIEL S P IY1 L -SPIELBERG S P IY1 L B ER0 G -SPIELEN S P IY1 L AH0 N -SPIELER S P IY1 L ER0 -SPIELERS S P IY1 L ER0 Z -SPIELHAGEN S P IY1 L HH EY2 G AH0 N -SPIELHAGEN'S S P IY1 L HH EY2 G AH0 N Z -SPIELIN S P IY1 L IH0 N -SPIELING S P IY1 L IH0 NG -SPIELMAN'S S P IY1 L M AH0 N Z -SPIELMANN S P IY1 L M AH0 N -SPIER S P AY1 ER0 -SPIERING S P AY1 ER0 IH0 NG -SPIERS S P AY1 ER0 Z -SPIES S P AY1 Z -SPIESZ S P IY1 SH -SPIEWELL S P IY1 W EH0 L -SPIFFING S P IH1 F IH0 NG -SPIFFINGTON S P IH1 F IH0 NG T AH0 N -SPIFFY S P IH1 F IY0 -SPIFFY'S S P IH1 F IY0 Z -SPIFLICATE S P AY1 F L IH0 K EY2 T -SPIFLICATED S P AY1 F L IH0 K EY2 T IH0 D -SPIGG S P IH1 G -SPIGGOT S P IH1 G AH0 T -SPIGHT S P AY1 T -SPIGOT S P IH1 G AH0 T -SPIGOTS S P IH1 G AH0 T S -SPIK S P IH0 K -SPIKE S P AY1 K -SPIKE'S S P AY1 K S -SPIKED S P AY1 K T -SPIKELET S P AY1 K L AH0 T -SPIKELETS S P AY1 K L AH0 T S -SPIKEMAN S P AY1 K M AH0 N -SPIKENARD S P AY1 K AH0 N ER0 D -SPIKER S P AY1 K ER0 -SPIKES S P AY1 K S -SPIKEY S P AY1 K IY0 -SPIKING S P AY1 K IH0 NG -SPIKY S P AY1 K IY0 -SPILE S P AY1 L -SPILED S P AY1 L D -SPILES S P AY1 L Z -SPILETT S P IH1 L IH0 T 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AH0 N -SPINDLEWOOD S P IH1 N D AH0 L W UH2 D -SPINDLIN S P IH1 N D L IH0 N -SPINDLING S P IH1 N D L IH0 NG -SPINDLY S P IH1 N D L IY0 -SPINDRIFT S P IH1 N D R IH0 F T -SPINDRIFTERS S P IH1 N D R IH1 F T ER0 Z -SPINE S P AY1 N -SPINED S P AY1 N D -SPINELESS S P AY1 N L AH0 S -SPINELLOCCIO S P IH0 N EH1 L OW1 CH IY0 OW0 -SPINES S P AY1 N Z -SPINET S P IH1 N IH0 T -SPINETS S P IH0 N EH1 T S -SPINETT S P IH0 N EH1 T -SPINGLE S P IH1 NG G AH0 L -SPINI S P IY1 N IY0 -SPINIFEX S P IH1 N IH0 F EH0 K S -SPINK S P IH1 NG K -SPINK'S S P IH1 NG K S -SPINKS S P IH1 NG K S -SPINKS'S S P IH1 NG K S IH0 Z -SPINKY S P IH1 NG K IY0 -SPINNAKER S P IH1 N AH0 K ER0 -SPINNER S P IH1 N ER0 -SPINNER'S S P IH1 N ER0 Z -SPINNERET S P IH1 N ER0 EY1 -SPINNERETS S P IH1 N ER0 EY1 Z -SPINNERS S P IH1 N ER0 Z -SPINNET S P IH1 N IH0 T -SPINNEY S P IH1 N IY0 -SPINNEY'S S P IH1 N IY0 Z -SPINNEYS S P IH1 N IY0 Z -SPINNIES S P IH1 N IY0 Z -SPINNIN S P IH1 N IH0 N -SPINNIN' S P IH1 N IH0 N -SPINNING S P IH1 N IH0 NG -SPINNY S P IH1 N IY0 -SPINOLA S P IY0 N OW1 L AH0 -SPINOSA S P IY0 N OW1 S AH0 -SPINOUS S P IH1 N AH0 S -SPINOZA S P IH0 N OW1 Z AH0 -SPINOZA'S S P IY0 N OW1 Z AH0 Z -SPINOZISM S P IY0 N OW1 Z IH0 Z AH0 M -SPINS S P IH1 N Z -SPINSTER S P IH1 N S T ER0 -SPINSTER'S S P IH1 N S T ER0 Z -SPINSTERHOOD S P IH1 N S T ER0 HH UH2 D -SPINSTERISH S P IH1 N S T ER0 IH0 SH -SPINSTERS S P IH1 N S T ER0 Z -SPINSTRESS S P IH1 N S T R EH1 S -SPINT S P IH1 N T -SPINVILLE S P IH1 N V IH2 L -SPINY S P AY1 N IY0 -SPION S P IY0 AH0 N -SPIR S P IH1 R -SPIRA S P IH1 R AH0 -SPIRACLE S P IH1 R AH0 K AH0 L -SPIRACLES S P IH1 R AH0 K AH0 L Z -SPIRAEA S P IH1 R IY1 AH0 -SPIRAL S P AY1 R AH0 L -SPIRALED S P AY1 R AH0 L D -SPIRALING S P AY1 R AH0 L IH0 NG -SPIRALLED S P AY1 R AH0 L D -SPIRALLING S P AY1 R AH0 L IH0 NG -SPIRALLY S P AY1 R AH0 L IY0 -SPIRALS S P AY1 R AH0 L Z -SPIRE S P AY1 R -SPIREA S P AY1 R EY1 -SPIREAS S P AY1 R IY1 Z -SPIRED S P AY1 R D -SPIRES S P AY1 R Z -SPIRIDION S P IH1 R IH0 D IY0 AH0 N 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CH AH0 W AH0 L IH0 Z IH0 NG -SPIRITUALISM S P IH1 R IH0 CH AH0 W AH0 L IH0 Z M -SPIRITUALISM S P IH1 R IH0 CH W AH0 L IH2 Z M -SPIRITUALIST S P IH1 R IH0 CH AH0 W AH0 L IH0 S T -SPIRITUALISTIC S P IH1 R IH0 CH W AH0 L IH1 S T IH0 K -SPIRITUALISTS S P IH1 R IH0 CH AH0 W AH0 L IH0 S T S -SPIRITUALITIES S P IH1 R IH0 CH AH0 W AE1 L AH0 T IY0 Z -SPIRITUALITY S P IH2 R IH0 CH AH0 W AE1 L AH0 T IY0 -SPIRITUALIZATION S P IH1 R IH0 CH W AH0 L IH0 Z EY1 SH AH0 N -SPIRITUALIZE S P IH1 R IH0 CH W AH0 L AY2 Z -SPIRITUALIZED S P IH1 R IH0 CH W AH0 L AY2 Z D -SPIRITUALIZES S P IH1 R IH0 CH W AH0 L AY2 Z IH0 Z -SPIRITUALIZING S P IH1 R IH0 CH W AH0 L AY2 Z IH0 NG -SPIRITUALL S P IH1 R IH0 CH AH0 W AH0 L -SPIRITUALLY S P IH1 R IH0 CH AH0 W AH0 L IY0 -SPIRITUALS S P IH1 R IH0 CH AH0 W AH0 L Z -SPIRITUALS S P IH1 R IH0 CH W AH0 L Z -SPIRITUEL S P IH1 R IH0 CH UW0 AH0 L -SPIRITUELLE S P IH1 R IH0 CH W EH1 L -SPIRITUM S P IH1 R IH0 CH AH0 M -SPIRITUOUS S P IH1 R IH0 CH AH0 W AH0 S -SPIRITUS S P IH1 R AH0 T AH0 S -SPIRITY S P IH1 R IH0 T IY0 -SPIRO S P IH1 R OW0 -SPIRT S P IH1 R T -SPIRTED S P IH1 R T IH0 D -SPIRTING S P IH1 R T IH0 NG -SPIRTO S P IH1 R T OW0 -SPIRTS S P IH1 R T S -SPIRY S P AY1 R IY0 -SPISE S P AY1 Z -SPIT S P IH1 T -SPITAL S P IH0 T AH0 L -SPITALFIELDS S P IY0 T AA1 L F IY2 L D Z -SPITBALL S P IH1 T B AO2 L -SPITE S P AY1 T -SPITED S P AY1 T IH0 D -SPITEFUL S P AY1 T F AH0 L -SPITEFULLY S P AY1 T F AH0 L IY0 -SPITEFULNESS S P AY1 T F AH0 L N AH0 S -SPITEHAHN S P IY0 T EH1 HH AH0 N -SPITERI S P IY0 T EH1 R IY0 -SPITES S P AY1 T S -SPITFIRE S P IH1 T F AY2 R -SPITFIRES S P IH1 T F AY2 ER0 Z -SPITHEAD S P IH1 T HH EH2 D -SPITING S P AY1 T IH0 NG -SPITS S P IH1 T S -SPITTA S P IH1 T AH0 -SPITTAL S P IH1 T AH0 L -SPITTED S P IH1 T IH0 D -SPITTER S P IH1 T ER0 -SPITTIN S P IH1 T IH0 N -SPITTING S P IH1 T IH0 NG -SPITTINGS S P IH1 T IH0 NG Z -SPITTLE S P IH1 T AH0 L -SPITTLES S P IH1 T AH0 L Z -SPITTOON S P IH1 T UW1 N -SPITTOONS S P IH1 T UW1 N Z -SPITZ S P IH1 T S -SPITZBERGEN S P IH1 T S B ER0 G AH0 N -SPITZE S P IH1 T Z -SPITZER S P IH1 T Z ER0 -SPITZHASE S P IH1 T S HH EY2 Z -SPITZKOPF S P IH1 T S K AO0 F -SPIVEYCOMB S P IH1 V IY0 K OW2 M -SPIVIN S P IH1 V IH0 N -SPIX S P IH2 K S -SPIZEY S P AY1 Z IY0 -SPIZZA S P IY0 T S AH0 -SPLAIGHTON S P L EY1 T AH0 N -SPLAIN S P L EY1 N -SPLAINED S P L EY1 N D -SPLANN S P L AE1 N -SPLASH S P L AE1 SH -SPLASH'S S P L AE1 SH IH0 Z -SPLASHBOARD S P L AE1 SH B AO2 R D -SPLASHED S P L AE1 SH T -SPLASHER S P L AE1 SH ER0 -SPLASHES S P L AE1 SH AH0 Z -SPLASHES S P L AE1 SH IH0 Z -SPLASHIN S P L AE1 SH IH0 N -SPLASHING S P L AE1 SH IH0 NG -SPLASHINGS S P L AE1 SH IH0 NG Z -SPLASHY S P L AE1 SH IY0 -SPLAT S P L AE1 T -SPLATCHETT'S S P L AE1 CH IH0 T S -SPLATTER S P L AE1 T ER0 -SPLATTERED S P L AE1 T ER0 D -SPLATTERING S P L AE1 T ER0 IH0 NG -SPLAY S P L EY2 -SPLAY'S S P L EY1 Z -SPLAYED S P L EY1 D -SPLAYFOOT S P L EY1 F UH2 T -SPLAYING S P L EY1 IH0 NG -SPLEEN S P L IY1 N -SPLEENFUL S P L IY1 N F AH0 L -SPLEENS S P L IY1 N Z -SPLEENWORT S P L IY1 N W ER0 T -SPLEN S P L EH1 N -SPLENDENS S P L EH1 N D AH0 N Z -SPLENDENT S P L EH1 N D AH0 N T -SPLENDEUR S P L EH1 N D UW1 R -SPLENDEURS S P L EH1 N D ER1 Z -SPLENDIANO S P L EH1 N D IY0 AA1 N OW0 -SPLENDID S P L EH1 N D AH0 D -SPLENDID S P L EH1 N D IH0 D -SPLENDID'S S P L EH1 N D AH0 D Z -SPLENDIDA S P L EH1 N D AH0 D AH0 -SPLENDIDE S P L EH1 N D AH0 D -SPLENDIDEST S P L EH1 N D AH0 D AH0 S T -SPLENDIDLY S P L EH1 N D AH0 D L IY0 -SPLENDIFEROUS S P L EH1 N D IH1 F ER0 AH0 S -SPLENDOR S P L EH1 N D ER0 -SPLENDORS S P L EH1 N D ER0 Z -SPLENDOUR S P L EH1 N D AO2 R -SPLENDOURS S P L EH1 N D AO2 R Z -SPLENETIC S P L EH1 N EH1 T IH0 K -SPLENIC S P L EH1 N IH0 K -SPLENIC S P L IY1 N IH0 K -SPLICE S P L AY1 S -SPLICED S P L AY1 S T -SPLICES S P L AY1 S IH0 Z -SPLICING S P L AY1 S IH0 NG -SPLINT S P L IH1 N T -SPLINTED S P L IH1 N T IH0 D -SPLINTER S P L IH1 N T ER0 -SPLINTER'D S P L IH1 N T R AH0 D -SPLINTERED S P L IH1 N T ER0 D -SPLINTERING S P L IH1 N T ER0 IH0 NG -SPLINTERS S P L IH1 N T ER0 Z -SPLINTERY S P L IH1 N T ER0 IY0 -SPLINTS S P L IH1 N T S -SPLISH S P L IH1 SH -SPLIT S P L IH1 T -SPLITS S P L IH1 T S -SPLITTER S P L IH1 T ER0 -SPLITTERS S P L IH1 T ER0 Z -SPLITTIN S P L IH1 T IH0 N -SPLITTING S P L IH1 T IH0 NG -SPLITTINGS S P L IH1 T IH0 NG Z -SPLORE S P L AO1 R -SPLOSH S P L AA1 SH -SPLOTCH S P L AA1 T CH -SPLOTCHED S P L AA1 T CH T -SPLOTCHES S P L AA1 T CH AH0 Z -SPLOTCHY S P L AA1 T CH IY0 -SPLUEGEN S P L UH1 G AH0 N -SPLUETHNER S P L EH1 TH N ER0 -SPLUGEN S P L G AH0 N -SPLURGE S P L ER1 JH -SPLURGED S P L ER1 JH D -SPLURGES S P L ER1 JH IH0 Z -SPLURGING S P L ER1 JH IH0 NG -SPLUTTER S P L AH1 T ER0 -SPLUTTERED S P L AH1 T ER0 D -SPLUTTERING S P L AH1 T ER0 IH0 NG -SPLUTTERINGS S P L AH1 T ER0 IH0 NG Z -SPLUTTERS S P L AH1 T ER0 Z -SPO'TIN S P OW0 T IH0 N -SPOASE S P AH1 EY2 Z -SPODE S P OW0 D -SPOELBERCH S P OW1 L B ER1 K -SPOFFORD S P AA1 F ER0 D -SPOFFORTH S P AA1 F ER0 TH -SPOHF S P AA1 F -SPOHR S P AA1 R -SPOHR'S S P AA1 R Z -SPOIL S P OY1 L -SPOIL'D S P OY1 L D -SPOILATION S P OY1 L EY1 SH AH0 N -SPOILED S P OY1 L D -SPOILER S P OY1 L ER0 -SPOILER'S S P OY1 L ER0 Z -SPOILERS S P OY1 L ER0 Z -SPOILIN S P OY1 L IH0 N -SPOILING S P OY1 L IH0 NG -SPOILS S P OY1 L Z -SPOILSMAN S P OY1 L Z M AH0 N -SPOILSMEN S P OY1 L Z M IH0 N -SPOILSPORT S P OY1 L S P AO2 R T -SPOILT S P OY1 L T -SPOKANE S P OW0 K AE1 N -SPOKANE S P OW0 K EY1 N -SPOKE S P OW1 K -SPOKED S P OW1 K T -SPOKEN S P OW1 K AH0 N -SPOKENNESS S P OW1 K AH0 N AH0 S -SPOKER S P OW1 K ER0 -SPOKES S P OW1 K S -SPOKESHAVE S P OW1 K SH EY1 V -SPOKESLY S P OW1 K S L IY0 -SPOKESLY'S S P OW1 K S L IY0 Z -SPOKESMAN S P OW1 K S M AH0 N -SPOKESMAN'S S P OW1 K S M AH0 N Z -SPOKESMEN S P OW1 K S M IH0 N -SPOKESWOMAN S P OW1 K S W UH2 M AH0 N -SPOLETO S P OW0 L EY1 T OW0 -SPOLIA S P OW1 L IY0 AH0 -SPOLIATION S P OW0 L IY0 EY1 SH AH0 N -SPOLIATIONS S P OW0 L IY0 EY1 SH AH0 N Z -SPOLIATOR S P OW0 L IY0 EY2 T ER0 -SPOLIATORS S P OW0 L IY0 EY2 T ER0 Z -SPON S P AA0 N -SPONDAIC S P AA1 N D EY1 IH0 K -SPONDE S P AA1 N D -SPONDEE S P AA1 N D IY1 -SPONDEES S P AA1 N D IY1 Z -SPONDULICKS S P AA1 N D Y UW1 L IH0 K S -SPONECK S P AH1 N EH2 K -SPONG S P AO1 NG -SPONGE S P AH1 N JH -SPONGE'S S P AH1 N JH IH0 Z -SPONGECAKE S P AH1 N JH K EY2 K -SPONGED S P AH1 N JH D -SPONGER S P AH1 N JH ER0 -SPONGERS S P AH1 N JH ER0 Z -SPONGES S P AH1 N JH AH0 Z -SPONGING S P AH1 N JH IH0 NG -SPONGY S P AH1 N JH IY0 -SPONSA S P AA1 N S AH0 -SPONSIBILITY S P AA1 N S AH0 B IH1 L AH0 T IY0 -SPONSIBLE S P AA1 N S AH0 B AH0 L -SPONSILIER S P AA1 N S AH0 L IY0 ER0 -SPONSILIER'S S P AA1 N S AH0 L IY0 ER0 Z -SPONSON S P AA1 N S AH0 N -SPONSONS S P AA1 N S AH0 N Z -SPONSOR S P AA1 N S ER0 -SPONSORED S P AA1 N S ER0 D -SPONSORS S P AA1 N S ER0 Z -SPONSORSHIP S P AA1 N S ER0 SH IH2 P -SPONTANEITY S P AA2 N T AH0 N IY1 AH0 T IY0 -SPONTANEOUS S P AA0 N T EY1 N IY0 AH0 S -SPONTANEOUSLY S P AA0 N T EY1 N IY0 AH0 S L IY0 -SPONTANEOUSNESS S P AA0 N T EY1 N IY0 AH0 S N AH0 S -SPONTINI S P AA0 N T IY1 N IY0 -SPONTINI'S S P AA0 N T IY1 N IY0 Z -SPONTOON S P AA0 N T UW1 N -SPOOF S P UW1 F -SPOOF'S S P UW1 F S -SPOOFED S P UW1 F T -SPOOFING S P UW1 F IH0 NG -SPOOK S P UW1 K -SPOOKED S P UW1 K T -SPOOKS S P UW1 K S -SPOOKY S P UW1 K IY0 -SPOOL S P UW1 L -SPOOLING S P UW1 L IH0 NG -SPOOLS S P UW1 L Z -SPOON S P UW1 N -SPOONBILL S P UW1 N B AH0 L -SPOONBILLS S P UW1 N B IH2 L Z -SPOONDRIFT S P UW1 N D R IH2 F T -SPOONED S P UW1 N D -SPOONER S P UW1 N ER0 -SPOONER'S S P UW1 N ER0 Z -SPOONEY S P UW1 N IY0 -SPOONFUL S P UW1 N F UH2 L -SPOONFULS S P UW1 N F UH2 L Z -SPOONIES S P UW1 N IY0 Z -SPOONIN S P UW1 N IH0 N -SPOONING S P UW1 N IH0 NG -SPOONS S P UW1 N Z -SPOONSFUL S P UW1 N Z F AH0 L -SPOONY S P UW1 N IY0 -SPOOR S P UH1 R -SPOOR'EM S P UH1 R EH1 M -SPOORED S P UH1 R D -SPOORING S P UH1 R IH0 NG -SPOORS S P UH1 R Z -SPOR S P ER0 -SPORADIC S P ER0 AE1 D IH0 K -SPORADICALLY S P ER0 AE1 D IH0 K L IY0 -SPORANGIA S P AO1 R AH0 N JH AH0 -SPORE S P AO1 R -SPORES S P AO1 R Z -SPORRAN S P AO1 R AH0 N -SPORRANS S P AO1 ER0 AH0 N Z -SPORT S P AO1 R T -SPORT'S S P AO1 R T S -SPORTED S P AO1 R T IH0 D -SPORTFUL S P AO1 R T F AH0 L -SPORTIN S P AO1 R T IH0 N -SPORTING S P AO1 R T IH0 NG -SPORTINGLY S P AO1 R T IH0 NG L IY0 -SPORTIVE S P AO1 R T IH0 V -SPORTIVELY S P AO1 R T IH0 V L IY0 -SPORTIVENESS S P AO1 R T IH0 V N AH0 S -SPORTMAN S P AO1 R T M AH0 N -SPORTMEN S P AO1 R T M AH0 N -SPORTS S P AO1 R T S -SPORTSMAN S P AO1 R T S M AH0 N -SPORTSMAN'S S P AO1 R T S M AH0 N Z -SPORTSMANLIKE S P AO1 R T S M AH0 N L AY2 K -SPORTSMANSHIP S P AO1 R T S M AH0 N SH IH2 P -SPORTSMEN S P AO1 R T S M IH0 N -SPORTSMEN'S S P AO1 R T S M IH0 N Z -SPORTSWOMAN S P AO1 R T S W UH2 M AH0 N -SPORTY S P AO1 R T IY0 -SPORUS S P AO1 R AH0 S -SPOSA S P OW1 S AH0 -SPOSALIZIO S P OW1 Z AH0 L IH1 Z IY0 OW0 -SPOSE S P OW1 Z -SPOSED S P OW1 Z D -SPOSEN S P OW0 S AH0 N -SPOSI S P OW1 S IY0 -SPOSIN S P OW0 S IH0 N -SPOSO S P OW1 S OW0 -SPOT S P AA1 T -SPOT'S S P AA1 T S -SPOTKIRK S P AA1 T K ER0 K -SPOTLESS S P AA1 T L AH0 S -SPOTLESSLY S P AA1 T L AH0 S L IY0 -SPOTLESSNESS S P AA1 T L AH0 S N AH0 S -SPOTLIGHT S P AA1 T L AY2 T -SPOTLIGHTS S P AA1 T L AY2 T S -SPOTS S P AA1 T S -SPOTSWOOD S P AA1 T S W UH2 D -SPOTSWOOD'S S P AA1 T S W UH2 D Z -SPOTSYLVANIA S P AA1 T S IH0 L V EY1 N Y AH0 -SPOTTED S P AA1 T AH0 D -SPOTTED S P AA1 T IH0 D -SPOTTER S P AA1 T ER0 -SPOTTERS S P AA1 T ER0 Z -SPOTTIN S P AA1 T IH0 N -SPOTTING S P AA1 T IH0 NG -SPOTTISWOODE S P AA1 T AH0 S W UH0 D -SPOTTISWOODE'S S P AA1 T AH0 S W UH0 D Z -SPOTTLEOVER S P AA1 T L OW2 V ER0 -SPOTTLETOE S P AA1 T AH0 L T OW2 -SPOTTS S P AA1 T S -SPOTTSWOOD S P AA1 T S W UH2 D -SPOTTSYLVANIA S P AA1 T S IH0 L V EY1 N Y AH0 -SPOTTY S P AA1 T IY0 -SPOTTY'S S P AA1 T IY0 Z -SPOUSAL S P AW1 Z AH0 L -SPOUSALS S P AW1 Z AH0 L Z -SPOUSE S P AW1 S -SPOUSE'S S P AW1 S IH0 Z -SPOUSELESS S P AW1 S L AH0 S -SPOUSES S P AW1 S AH0 Z -SPOUSES S P AW1 S IH0 Z -SPOUT S P AW1 T -SPOUTED S P AW1 T AH0 D -SPOUTER S P AW1 T ER0 -SPOUTER'S S P AW1 T ER0 Z -SPOUTERS S P AW1 T ER0 Z -SPOUTIN S P AW1 T IH0 N -SPOUTING S P AW1 T IH0 NG -SPOUTINGS S P AW1 T IH0 NG Z -SPOUTS S P AW1 T S -SPOYLE S P OY0 L -SPOZE S P OW1 Z -SPOZED S P OW1 Z D -SPRACH S P R AE1 CH -SPRACHE S P R EY1 K IY0 -SPRADDLE S P R AE1 D AH0 L -SPRADDLED S P R AE1 D AH0 L D -SPRAGG S P R AE1 G -SPRAGG'S S P R AE1 G Z -SPRAGGE S P R AE1 G -SPRAGGINS S P R AE1 G IH0 N Z -SPRAGGON S P R AE1 G AH0 N -SPRAGGS S P R AE1 G Z -SPRAGUE S P R EY1 G -SPRAGUE'S S P R EY1 G Z -SPRAGUES S P R EY1 G Z -SPRAIN S P R EY1 N -SPRAINED S P R EY1 N D -SPRAINING S P R EY1 N IH0 NG -SPRAINS S P R EY1 N Z -SPRANG S P R AE1 NG -SPRAT S P R AE1 T -SPRAT'S S P R AE1 T S -SPRATS S P R AE1 T S -SPRATT S P R AE1 T -SPRATT'S S P R AE1 T S -SPRATTS S P R AE1 T S -SPRAWL S P R AO1 L -SPRAWLED S P R AO1 L D -SPRAWLEY S P R AO1 L IY0 -SPRAWLIN S P R AO1 L IH0 N -SPRAWLING S P R AO1 L IH0 NG -SPRAWLINGLY S P R AO1 L IH0 NG L IY0 -SPRAWLS S P R AO1 L Z -SPRAWLY S P R AO1 L IY0 -SPRAY S P R EY1 -SPRAY'S S P R EY1 Z -SPRAYED S P R EY1 D -SPRAYER S P R EY1 ER0 -SPRAYING S P R EY1 IH0 NG -SPRAYS S P R EY1 Z -SPREAD S P R EH1 D -SPREADEAGLED S P R EH1 D IY1 G AH0 L D -SPREADER S P R EH1 D ER0 -SPREADERS S P R EH1 D ER0 Z -SPREADEST S P R EH1 D AH0 S T -SPREADETH S P R EH1 D IH0 TH -SPREADIN S P R EH1 D IY0 N -SPREADING S P R EH1 D IH0 NG -SPREADS S P R EH1 D Z -SPRECHEN S P R EH1 K AH0 N -SPRECHER S P R EH1 K ER0 -SPRECKDAL S P R EH1 K D AA2 L -SPRECKELS S P R EH1 K AH0 L Z -SPRED S P R D -SPREE S P R IY1 -SPREEING S P R IY1 IH0 NG -SPREES S P R IY1 Z -SPRENGEL S P R EH1 NG G AH0 L -SPRENGER S P R EH1 N JH ER0 -SPRENT S P R AH0 N T -SPRESS S P R EH1 S -SPRETAE S P R IH0 T EY1 -SPREUWS S P R IY1 UW0 Z -SPRIG S P R IH1 G -SPRIGG S P R IH1 G -SPRIGGAN S P R IH1 G AH0 N -SPRIGGANS S P R IH1 G AH0 N Z -SPRIGGED S P R IH1 G D -SPRIGGINS S P R IH1 G IH0 N Z -SPRIGGS S P R IH1 G Z -SPRIGHT S P R AY1 T -SPRIGHTLIER S P R AY1 T L AY2 ER0 -SPRIGHTLIEST S P R AY1 T L IY0 AH0 S T -SPRIGHTLINESS S P R AY1 T L AY2 N AH0 S -SPRIGHTLY S P R AY1 T L IY0 -SPRIGS S P R IH1 G Z -SPRING S P ER0 IH1 NG -SPRING S P R IH1 NG -SPRING'S S P R IH1 NG Z -SPRINGALD S P R IH1 NG G AH0 L D -SPRINGALL S P R IH1 NG G AH0 L -SPRINGBOARD S P R IH1 NG B AO2 R D -SPRINGBOK S P R IH1 NG B AA0 K -SPRINGBOKS S P R IH1 NG B AA0 K S -SPRINGBROOK S P R IH1 NG B R UH2 K -SPRINGBUCK S P R IH1 NG B AH2 K -SPRINGBUCKS S P R IH1 NG B AH2 K S -SPRINGDALE S P R IH1 NG D EY2 L -SPRINGE S P R IH1 NG -SPRINGED S P R IH1 NG D -SPRINGER S P R IH1 NG ER0 -SPRINGER'S S P R IH1 NG ER0 Z -SPRINGERS S P R IH1 NG ER0 Z -SPRINGES S P R IH1 N JH IH0 Z -SPRINGEST S P R IH1 NG AH0 S T -SPRINGETH S P R IH1 NG G IH0 TH -SPRINGETT S P R IH1 NG G IH0 T -SPRINGFIELD S P R IH1 NG F IY2 L D -SPRINGFIELD'S S P R IH1 NG F IY2 L D Z -SPRINGFIELDS S P R IH1 NG F IY2 L D Z -SPRINGHILL S P R IH1 NG HH IH2 L -SPRINGHOUSE S P R IH1 NG HH AW2 S -SPRINGILY S P R IH1 NG AH0 L IY0 -SPRINGIN S P R IH1 NG AH0 N -SPRINGINESS S P R IH1 NG IY0 N AH0 S -SPRINGING S P R IH1 NG IH0 NG -SPRINGLE S P R IH1 NG G AH0 L -SPRINGLESS S P R IH1 NG L AH0 S -SPRINGLIKE S P R IH1 NG L AY2 K -SPRINGS S P ER0 IH1 NG Z -SPRINGS S P R IH1 NG Z -SPRINGTIDE S P R IH1 NG T AY2 D -SPRINGTIDES S P R IH1 NG T AY2 D Z -SPRINGTIME S P R IH1 NG T AY2 M -SPRINGTIME'S S P R IH1 NG T AY2 M Z -SPRINGTIMES S P R IH1 NG T AY2 M Z -SPRINGTON S P R IH1 NG T AH0 N -SPRINGTOWN S P R IH1 NG T AW2 N -SPRINGVALE S P R IH1 NG V EY2 L -SPRINGVILLE S P R IH1 NG V IH2 L -SPRINGWHEAT S P R IH1 NG W IY2 T -SPRINGY S P R IH1 NG IY0 -SPRINKLE S P R IH1 NG K AH0 L -SPRINKLED S P R IH1 NG K AH0 L D -SPRINKLER S P R IH1 NG K AH0 L ER0 -SPRINKLER S P R IH1 NG K L ER0 -SPRINKLERS S P R IH1 NG K AH0 L ER0 Z -SPRINKLERS S P R IH1 NG K L ER0 Z -SPRINKLES S P R IH1 NG K AH0 L Z -SPRINKLIN S P R IH1 NG K L IH0 N -SPRINKLING S P R IH1 NG K AH0 L IH0 NG -SPRINKLING S P R IH1 NG K L IH0 NG -SPRINKLINGS S P R IH1 NG K L IH0 NG Z -SPRINT S P R IH1 N T -SPRINTED S P R IH1 N T IH0 D -SPRINTER S P R IH1 N T ER0 -SPRINTERS S P R IH1 N T ER0 Z -SPRINTING S P R IH1 N T IH0 NG -SPRINTS S P R IH1 N T S -SPRIT S P R IY1 -SPRITE S P R AY1 T -SPRITE'S S P R AY1 T S -SPRITELY S P R AY1 T L IY0 -SPRITES S P R AY1 T S -SPRITS S P R IH1 T S -SPRITSAIL S P R IY1 T S EY2 L -SPROAT S P R OW1 T -SPROAT'S S P R OW1 T S -SPROATLY S P R OW1 T L IY0 -SPROATLY'S S P R OW1 T L IY0 Z -SPROCKET S P R AA1 K AH0 T -SPROCKETS S P R AA1 K AH0 T S -SPROCKETT S P R AA1 K IH0 T -SPROCKETT'S S P R AA1 K AH0 T S -SPRONG S P R AO1 NG -SPROTSFIELD S P R AA1 T S F IY0 L D -SPROTT S P R AA1 T -SPROTTS S P R AA1 T S -SPROUD S P R AW1 D -SPROUL S P R AW1 L -SPROULE S P R UW1 L -SPROUT S P R AW1 T -SPROUTED S P R AW1 T AH0 D -SPROUTED S P R AW1 T IH0 D -SPROUTIN S P R AW1 T IH0 N -SPROUTING S P R AW1 T IH0 NG -SPROUTINGS S P R AW1 T IH0 NG Z -SPROUTS S P R AW1 T S -SPROWL S P R AW1 L -SPROWL'S S P R AW1 L Z -SPROWLE S P R AW1 AH0 L -SPROWLE'S S P R AW1 L Z -SPRUCE S P R UW1 S -SPRUCED S P R UW1 S T -SPRUCELY S P R UW1 S L IY0 -SPRUCENESS S P R UW1 S N AH0 S -SPRUCER S P R UW1 S ER0 -SPRUCES S P R UW1 S IH0 Z -SPRUCH S P R AH1 CH -SPRUDEL S P R UW1 D AH0 L -SPRUGEON S P R UW1 JH IH0 N -SPRUGGINS S P R AH1 G IH0 N Z -SPRUIT S P R UW1 T -SPRUITS S P R UW1 T S -SPRUNG S P R AH1 NG -SPRY S P R AY1 -SPRYER S P R AY1 ER0 -SPRYEST S P R AY1 AH0 S T -SPRYLY S P R AY1 L IY0 -SPRYNESS S P R AY1 N AH0 S -SPUD S P AH1 D -SPUD'S S P AH1 D Z -SPUDD S P AH1 D -SPUDS S P AH1 D Z -SPUE S P Y UW2 -SPUGG S P AH1 G -SPUMANTE S P Y UW0 M AA1 N T IY0 -SPUME S P UW1 M -SPUMING S P UW1 M IH0 NG -SPUMY S P AH1 M IY0 -SPUN S P AH1 N -SPUNE S P AH1 N -SPUNGE S P AH1 N JH -SPUNGING S P AH1 N JH IH0 NG -SPUNGY S P AH1 N JH IY0 -SPUNK S P AH1 NG K -SPUNKED S P AH1 NG K T -SPUNKIE S P AH1 NG K IY0 -SPUNKIES S P AH1 NG K IY0 Z -SPUNKS S P AH1 NG K S -SPUNKY S P AH1 NG K IY0 -SPUNYARN S P AH1 N Y AA1 R N -SPUR S P ER1 -SPUREY S P Y UH1 R IY0 -SPURGE S P ER1 JH -SPURGEON S P ER1 JH IH0 N -SPURGEON'S S P ER1 JH AH0 N Z -SPURGEONS S P ER1 JH AH0 N Z -SPURIOUS S P ER1 IY0 AH0 S -SPURIOUS S P Y UH1 R IY0 AH0 S -SPURIOUSLY S P ER1 IY0 AH0 S L IY0 -SPURIOUSNESS S P ER1 IY0 AH0 S N AH0 S -SPURIUS S P ER1 IY0 IH0 S -SPURLESS S P ER1 L AH0 S -SPURLING S P ER1 L IH0 NG -SPURLOCK S P ER1 L AA2 K -SPURLOCK'S S P ER1 L AA2 K S -SPURN S P ER1 N -SPURN'D S P ER1 N D -SPURNED S P ER1 N D -SPURNING S P ER1 N IH0 NG -SPURNS S P ER1 N Z -SPURR S P ER1 -SPURR'D S P ER1 D -SPURRE S P ER1 -SPURRED S P ER1 D -SPURREL S P ER1 L -SPURRELL S P ER1 EY1 L -SPURRIER S P ER1 IY0 ER0 -SPURRIN S P ER1 IH0 N -SPURRING S P ER1 IH0 NG -SPURS S P ER1 Z -SPURSTOW S P ER1 S T OW0 -SPURT S P ER1 T -SPURTED S P ER1 T IH0 D -SPURTING S P ER1 T IH0 NG -SPURTS S P ER1 T S -SPURZHEIM S P ER1 Z HH AY2 M -SPUT S P AH1 T -SPUTIN S P Y UW1 T AH0 N -SPUTTER S P AH1 T ER0 -SPUTTERED S P AH1 T ER0 D -SPUTTERING S P AH1 T ER0 IH0 NG -SPUTTERINGS S P AH1 T ER0 IH0 NG Z -SPUTTERS S P AH1 T ER0 Z -SPUTUM S P Y UW1 T AH0 M 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AO1 L ER0 -SQUALLERS S K W AO1 L ER0 Z -SQUALLIN S K W AO1 L IH0 N -SQUALLING S K W AO1 L IH0 NG -SQUALLS S K W AO1 L Z -SQUALLY S K W AO1 L IY0 -SQUALOR S K W AA1 L ER0 -SQUALORS S K W AA1 L ER0 Z -SQUALOUR S K W AA1 L ER0 -SQUAM S K W AA1 M -SQUAMISH S K W AA1 M IH0 SH -SQUANDER S K W AA1 N D ER0 -SQUANDER'D S K W AA1 N D ER0 D -SQUANDERED S K W AA1 N D ER0 D -SQUANDERER S K W AA1 N D ER0 ER0 -SQUANDERING S K W AA1 N D ER0 IH0 NG -SQUANDERS S K W AA1 N D ER0 Z -SQUANKO S K W AA1 NG K OW0 -SQUANTO S K W AA1 N T OW0 -SQUANTOWN S K W AO1 N T AW2 N -SQUANTUM S K W AA1 N AH0 M -SQUAR S K W AA1 R -SQUARCI S K W AA0 R CH IY0 -SQUARE S K W EH1 R -SQUARE'S S K W EH1 R Z -SQUARED S K W EH1 R D -SQUAREFACE S K W EH1 R F EY2 S -SQUAREHEAD S K W EH1 R HH EH0 D -SQUARELY S K W EH1 R L IY0 -SQUARENESS S K W EH1 R N AH0 S -SQUARER S K W EH1 R ER0 -SQUARES S K W EH1 R Z -SQUARESAIL S K W EH1 R S EY0 L -SQUAREST S K W EH1 R AH0 S T -SQUARETOES S K W EH1 R IH0 T OW0 Z -SQUARING S K W EH1 R IH0 NG -SQUARISH S K W EH1 R IH0 SH -SQUARS S K W AA1 R Z -SQUASH S K W AA1 SH -SQUASHED S K W AA1 SH T -SQUASHES S K W AA1 SH IH0 Z -SQUASHING S K W AA1 SH IH0 NG -SQUASHY S K W AA1 SH IY0 -SQUAT S K W AA1 T -SQUATS S K W AA1 T S -SQUATTED S K W AA1 T IH0 D -SQUATTER S K W AA1 T ER0 -SQUATTER'S S K W AA1 T ER0 Z -SQUATTERED S K W AA1 T ER0 D -SQUATTERING S K W AA1 T ER0 IH0 NG -SQUATTERS S K W AA1 T ER0 Z -SQUATTIN S K W AA1 T IH0 N -SQUATTING S K W AA1 T IH0 NG -SQUATTY S K W AA1 T IY0 -SQUAW S K W AO1 -SQUAW'S S K W AO1 Z -SQUAWK S K W AO1 K -SQUAWKED S K W AO1 K T -SQUAWKER S K W AO1 K ER0 -SQUAWKIN S K W AO1 K IH0 N -SQUAWKING S K W AO1 K IH0 NG -SQUAWKS S K W AO1 K S -SQUAWMAN S K W AO1 M AH0 N -SQUAWS S K W AO1 Z -SQUAZE S K W AA1 Z IY0 -SQUEAK S K W IY1 K -SQUEAKED S K W IY1 K T -SQUEAKER S K W IY1 K ER0 -SQUEAKERS S K W IY1 K ER0 Z -SQUEAKILY S K W IY1 K AH0 L IY0 -SQUEAKIN S K W IY1 K IH0 N -SQUEAKING S K W IY1 K IH0 NG -SQUEAKINGS S K W IY1 K IH0 NG Z -SQUEAKNIBBLE S K W IY1 K N IH1 B AH0 L -SQUEAKS S K W IY1 K S -SQUEAKY S K W IY1 K IY0 -SQUEAL S K W IY1 L -SQUEALED S K W IY1 L D -SQUEALER S K W IY1 L ER0 -SQUEALERS S K W IY1 L ER0 Z -SQUEALIN S K W IY1 L IH0 N -SQUEALING S K W IY1 L IH0 NG -SQUEALINGS S K W IY1 L IH0 NG Z -SQUEALS S K W IY1 L Z -SQUEAMISH S K W IY1 M IH0 SH -SQUEAMISHLY S K W IY1 M IH0 SH L IY0 -SQUEAMISHNESS S K W IY1 M IH0 SH N AH0 S -SQUEE S K W IY1 -SQUEEGEE S K W IY1 JH IY0 -SQUEERS S K W IH1 R Z -SQUEERS'S S K W IH1 R Z IH0 Z -SQUEEZ'D S K W IY1 Z D -SQUEEZABLE S K W IY1 Z AH0 B AH0 L -SQUEEZE S K W IY1 Z -SQUEEZED S K W IY1 Z D -SQUEEZER S K W IY1 Z ER0 -SQUEEZES S K W IY1 Z IH0 Z -SQUEEZIN S K W IY1 Z IH0 N -SQUEEZING S K W IY1 Z IH0 NG -SQUELCH S K W EH1 L CH -SQUELCHED S K W EH1 L CH T -SQUELCHING S K W EH1 L CH IH0 NG -SQUEM S K W AH0 M -SQUENCH S K W EH1 N CH -SQUENCHED S K W EH1 N CH T -SQUIB S K W IH1 B -SQUIBBER S K W IH1 B ER0 -SQUIBBS S K W IH1 B Z -SQUIBS S K W IH1 B Z -SQUID S K W IH1 D -SQUIDS S K W IH1 D Z -SQUIER S K W AY1 ER0 -SQUIFF S K W IH1 F -SQUIFFY S K W IH1 F IY0 -SQUIGG S K W IH1 G -SQUIGGLES S K W IH1 G AH0 L Z -SQUILL S K W IH1 L -SQUILLACE S K W IH1 L AH0 S -SQUILLS S K W IH1 L Z -SQUINCH S K W IH1 N CH -SQUINCHED S K W IH1 N CH T -SQUINNY S K W IH1 N IY0 -SQUINT S K W IH1 N T -SQUINTED S K W IH1 N T IH0 D -SQUINTIN S K W IH1 N T IH0 N -SQUINTING S K W IH1 N T IH0 NG -SQUINTS S K W IH1 N T S -SQUINTY S K W IH1 N T IY0 -SQUINTY'S S K W IH1 N T IY0 Z -SQUIR S K W ER1 -SQUIRE S K W AY1 R -SQUIRE'LL S K W AY2 R AH0 L -SQUIRE'S S K W AY2 R Z -SQUIREARCHY S K W AY1 ER0 AA2 R K IY0 -SQUIRED S K W AY1 R D -SQUIREDOM S K W AY1 R D AH0 M -SQUIREEN S K W AY1 R IY0 N -SQUIREENS S K W AY1 R IY2 N Z -SQUIREL S K W IH1 R AH0 L -SQUIRELS S K W AY1 ER0 L Z -SQUIRES S K W AY1 ER0 Z -SQUIRESS S K W AY1 ER0 AH0 S -SQUIRING S K W AY1 ER0 IH0 NG -SQUIRL S K W ER1 L -SQUIRM S K W ER1 M -SQUIRMED S K W ER1 M D -SQUIRMIN S K W ER1 M IH0 N -SQUIRMING S K W ER1 M IH0 NG -SQUIRMS S K W ER1 M Z -SQUIRMY S K W ER1 M IY0 -SQUIRRADICAL S K W ER1 AE1 D AH0 K AH0 L -SQUIRREL S K W ER1 AH0 L -SQUIRREL'S S K W ER1 AH0 L Z -SQUIRRELS S K W ER1 AH0 L Z -SQUIRT S K W ER1 T -SQUIRTED S K W ER1 T AH0 D -SQUIRTED S K W ER1 T IH0 D -SQUIRTER S K W ER1 T ER0 -SQUIRTING S K W ER1 T IH0 NG -SQUIRTS S K W ER1 T S -SQUISH S K W IH1 SH -SQUISHING S K W IH1 SH IH0 NG -SQUITTY S K W IH1 T IY0 -SQUOIRE S K W OY2 R -SQUOZE S K W OW1 Z -SR S IH1 S T ER0 -SR S IY1 N Y ER0 -SRAHMANDAZI SH R AA1 M AH0 N D AA1 Z IY0 -SRALIK SH R AA1 L IH0 K -SRAMANA SH R AH0 M AA1 N AH0 -SRANKO SH R AE1 NG K OW0 -SRAVASTI SH R AH0 V AE1 S T IY0 -SREE S R IY1 -SRI EH1 S AA1 R AY1 -SRI S R IY1 -SRI SH R IY1 -SRIA SH R IY1 AH0 -SRIBATSA SH R IY1 B AE1 T S AH0 -SRIJUT SH R IY1 JH AH0 T -SRIKANTHA SH R IY2 K AA1 N DH AH0 -SRINAGAR S R IY1 N AH0 G AA2 R -SRINAGAR SH R IY1 N AH0 G AA2 R -SRINGA SH R IY1 NG G AH0 -SRIPATI S R IY0 P AA1 T IY0 -SRUB S R AH1 B -SRUTI SH R UW1 T IY0 -SS EH1 S EH1 S -SSASSAROR S AE1 S ER0 ER0 -SSEMA S IY1 M AH0 -SSH SH -SSHH SH -SSIDI S IY1 D IY0 -SSS EH1 S EH1 S EH1 S -SSSH S SH -SSSHHH S SH -SSSURI S SH ER0 IY0 -SSSURI'S S SH ER0 AY0 Z -SST S T -SSU EH1 S EH1 S Y UW1 -ST S EY1 N T -ST S T R IY1 T -STA EH1 S T IY1 EY1 -STA S T AH1 -STAAL S T AA1 L -STAARBRUCKER S T AA1 R B R AH0 K ER0 -STAAT S T AA1 T -STAATEN S T AA1 T AH0 N -STAATS S T AA1 T S -STAATSRAETHIN S T AA1 T S R EH0 TH AH0 N -STAATSRAETHIN'S S T AA1 T S R EH0 TH IH0 N Z -STAB S T AE1 B -STABAT S T AE1 B AH0 T -STABB'D S T AE1 B D -STABBED S T AE1 B D -STABBER S T AE1 B ER0 -STABBER'S S T AE1 B ER0 Z -STABBERS S T AE1 B ER0 Z -STABBING S T AE1 B IH0 NG -STABBINGS S T AE1 B IH0 NG Z -STABIAE S T EY1 B IY0 AH0 -STABILITY S T AH0 B IH1 L IH0 T IY0 -STABILIZATION S T EY2 B AH0 L AH0 Z EY1 SH AH0 N -STABILIZE S T EY1 B AH0 L AY2 Z -STABILIZED S T EY1 B AH0 L AY2 Z D -STABILIZER S T EY1 B AH0 L AY2 Z ER0 -STABILIZERS S T EY1 B AH0 L AY2 Z ER0 Z -STABILIZING S T EY1 B AH0 L AY2 Z IH0 NG -STABLE S T EY1 B AH0 L -STABLE'S S T EY1 B AH0 L Z -STABLEBOY S T EY1 B AH0 L B OY2 -STABLED S T EY1 B AH0 L D -STABLEMAN S T EY1 B AH0 L M AH0 N -STABLEMEN S T EY1 B AH0 L M IH0 N -STABLER S T EY1 B AH0 L ER0 -STABLER S T EY1 B L ER0 -STABLES S T EY1 B AH0 L Z -STABLEYARD S T EY1 B AH0 L Y AA2 R D -STABLING S T EY1 B AH0 L IH0 NG -STABLISH S T AE1 B L IH0 SH -STABLISHED S T AE1 B L IH0 SH T -STABLY S T AH0 B L IY0 -STABROEK S T EY1 B R OW0 K -STABS S T AE1 B Z -STABSARZT S T AE1 B Z AA1 R Z T -STACCATO S T AH0 K AA1 T OW2 -STACCATOS S T AH0 K AA1 T OW2 Z -STACE S T EY1 S -STACEY S T EY1 S IY0 -STACEY'S S T EY1 S IY0 Z -STACIA S T AA1 CH AH0 -STACK S T AE1 K -STACKED S T AE1 K T -STACKELBERG S T AE1 K AH0 L B ER0 G -STACKER S T AE1 K ER0 -STACKERS S T AE1 K ER0 Z -STACKHOUSE S T AE1 K HH AW2 S -STACKIN S T AE1 K IH0 N -STACKING S T AE1 K IH0 NG -STACKPOLE S T AE1 K P OW2 L -STACKPOLE'S S T AE1 K P OW2 L Z -STACKPOOLE S T AE1 K P UW2 L -STACKS S T AE1 K S -STACKYARD S T AE1 K Y AA2 R D -STACKYARDS S T AE1 K Y AA2 R D Z -STACPOLE S T AE1 K P OW2 L -STACPOOLE S T AE1 K P UW2 L -STACY S T EY1 S IY0 -STACY'S S T EY1 S IY0 Z -STAD S T AE2 D -STADACONA S T AE1 D AH0 K OW1 N AH0 -STADDLES S T AE1 D AH0 L Z -STADE S T EY1 D -STADES S T EY1 D Z -STADHOLDER S T AE1 D HH OW2 L D ER0 -STADHUIS S T AE1 D HH UW0 IH0 Z -STADIA S T EY1 D IY0 AH0 -STADION S T EY1 D IY0 AH0 N -STADIUM S T EY1 D IY0 AH0 M -STADT S T AE1 D T -STADTHOLDER SH T AE1 T HH OW2 L D ER0 -STADTHOUSE SH T AE1 T HH AW2 S -STAEDEL S T EH1 D AH0 L -STAEL S T EY1 L -STAEL'S S T EY1 L Z -STAEMER S T EY1 M ER0 -STAEMER'S S T EY1 M ER0 Z -STAFF S T AE1 F -STAFF'S S T AE1 F S -STAFFA S T AA1 F AH0 -STAFFE S T AE1 F -STAFFED S T AE1 F T -STAFFELAER S T AE1 F AH0 L IH1 R -STAFFELAER'S S T AE1 F AH0 L IH1 R Z -STAFFER S T AE1 F ER0 -STAFFER'S S T AE1 F ER0 Z -STAFFING S T AE1 F IH0 NG -STAFFORD S T AE1 F ER0 D -STAFFORD'S S T AE1 F ER0 D Z -STAFFORDS S T AE1 F ER0 D Z -STAFFORDSHIRE S T AE1 F ER0 D SH AY2 ER0 -STAFFORDSHIRE S T AE1 F ER0 D SH ER0 -STAFFORTH S T AE1 F ER0 TH -STAFFS S T AE1 F S -STAFFY S T AE1 F IY0 -STAG S T AE1 G -STAG'S S T AE1 G Z -STAGE S T EY1 JH -STAGECOACH S T EY1 JH K OW2 CH -STAGECOACHES S T EY1 JH K OW2 CH IH0 Z -STAGECRAFT S T EY1 JH K R AE2 F T -STAGED S T EY1 JH D -STAGELAND S T EY1 JH L AH0 N D -STAGER S T EY1 JH ER0 -STAGERS S T EY1 JH ER0 Z -STAGES S T EY1 JH AH0 Z -STAGES S T EY1 JH IH0 Z -STAGEY S T EY1 JH IY0 -STAGG S T AE1 G -STAGG'S S T AE1 G Z -STAGGED S T AE1 G D -STAGGER S T AE1 G ER0 -STAGGER'D S T AE1 G ER0 D -STAGGERED S T AE1 G ER0 D -STAGGERER S T AE1 G ER0 ER0 -STAGGERERS S T AE1 G ER0 ER0 Z -STAGGERIN S T AE1 G ER0 IH0 N -STAGGERING S T AE1 G ER0 IH0 NG -STAGGERINGLY S T AE1 G ER0 IH0 NG L IY0 -STAGGERS S T AE1 G ER0 Z -STAGGERY S T AE1 G ER0 IY0 -STAGGS S T AE1 G Z -STAGGS'S S T AE1 G Z IH0 Z -STAGHORN S T AE1 G HH ER0 N -STAGHOUND S T AE1 G HH AW2 N D -STAGHOUNDS S T AE1 G HH AW2 N D Z -STAGHOUSE S T AE1 G HH AW2 S -STAGINESS S T EY1 JH IY0 N AH0 S -STAGING S T EY1 JH IH0 NG -STAGINGS S T EY1 JH IH0 NG Z -STAGIRA S T EY1 JH IH1 R AH0 -STAGIRITE S T EY1 JH ER0 AY2 T -STAGNANCY S T AE1 G N AH0 N S IY0 -STAGNANT S T AE1 G N AH0 N T -STAGNATE S T AE1 G N EY2 T -STAGNATED S T AE1 G N EY2 T IH0 D -STAGNATES S T AE1 G N EY2 T S -STAGNATING S T AE1 G N EY2 T IH0 NG -STAGNATION S T AE0 G N EY1 SH AH0 N -STAGS S T AE1 G Z -STAGY S T EY1 JH IY0 -STAGYRITE S T EY1 JH IY0 R AY2 T -STAH S T AA1 -STAHEL S T AE1 AH0 L -STAHL S T AA1 L -STAHL'S S T AA1 L Z -STAHLECK S T AA1 L IH0 K -STAHOV S T AE1 AA0 V -STAHOVS S T AE1 AO0 F S -STAHT S T AA1 T -STAID S T EY1 D -STAIDER S T EY1 D ER0 -STAIDEST S T EY1 D AH0 S T -STAIDLY S T EY1 D L IY0 -STAIDNESS S T EY1 D N AH0 S -STAIN S T EY1 N -STAIN'D S T EY2 N D -STAINED S T EY1 N D -STAINER S T EY1 N ER0 -STAINERS S T EY1 N ER0 Z -STAINES S T EY1 N Z -STAINES'S S T EY1 N Z IH0 Z -STAINING S T EY1 N IH0 NG -STAINLESS S T EY1 N L AH0 S -STAINS S T EY1 N Z -STAINTON S T EY1 N T AH0 N -STAINTON'S S T EY1 N T AH0 N Z -STAINVILLE S T EY1 N V IH2 L -STAINVILLE'S S T EY1 N V IH2 L Z -STAIR S T EH1 R -STAIR'S S T EH1 R Z -STAIRCASE S T EH1 R K EY2 S -STAIRCASES S T EH1 R K EY2 S IH0 Z -STAIRFOOT S T EH1 R F UH2 T -STAIRHEAD S T EH1 R HH EH2 D -STAIRS S T EH1 R Z -STAIRS'S S T EH1 R Z IH0 Z -STAIRWAY S T EH1 R W EY2 -STAIRWAYS S T EH1 R W EY2 Z -STAIRWELL S T EH1 R W EH2 L -STAITHE S T AY1 DH -STAITHES S T EY1 TH IY0 Z -STAKE S T EY1 K -STAKE'S S T EY1 K S -STAKED S T EY1 K T -STAKEHOLDER S T EY1 K HH OW2 L D ER0 -STAKER S T EY1 K ER0 -STAKERS S T EY1 K ER0 Z -STAKES S T EY1 K S -STAKIN S T EY1 K IH0 N -STAKING S T EY1 K IH0 NG -STALACTICAL S T AH0 L AE1 K T IH0 K AH0 L -STALACTITE S T AH0 L AE1 K T AY2 T -STALACTITES S T AH0 L AE1 K T AY2 T S -STALACTITIC S T AO1 L AE0 K T IH1 T IH0 K -STALAGMITE S T AA0 L AA1 G M AY2 T -STALAGMITES S T AA0 L AA1 G M AY2 T S -STALAGMITIC S T AA0 L AA1 G M IH1 T IH0 K -STALDEN S T AO1 L D AH0 N -STALE S T EY1 L -STALED S T EY1 L D -STALEMATE S T EY1 L M EY2 T -STALENESS S T EY1 L N AH0 S -STALER S T EY1 L ER0 -STALES S T EY1 L Z -STALEST S T EY1 L AH0 S T -STALEY S T EY1 L IY0 -STALHAM S T AO1 L HH AH0 M -STALHEIM S T AE1 L HH AY0 M -STALIN S T AA1 L AH0 N -STALING S T AE1 L IH0 NG -STALK S T AO1 K -STALK'D S T AO1 K D -STALKED S T AO1 K T -STALKENBERG S T AO1 K AH0 N B ER0 G -STALKER S T AO1 K ER0 -STALKER'S S T AO1 K ER0 Z -STALKERS S T AO1 K ER0 Z -STALKIN S T AO1 K IH0 N -STALKING S T AO1 K IH0 NG -STALKS S T AO1 K S -STALKY S T AO1 K IY0 -STALKY'S S T AO1 K IY0 Z -STALL S T AO1 L -STALLED S T AO1 L D -STALLING S T AO1 L IH0 NG -STALLINGS S T AO1 L IH0 NG Z -STALLION S T AE1 L Y AH0 N -STALLION'S S T AE1 L Y AH0 N Z -STALLIONS S T AE1 L Y AH0 N Z -STALLMAN S T AO1 L M AH0 N -STALLS S T AO1 L Z -STALWART S T AO1 L W ER0 T -STALWARTH S T AO1 L W ER0 TH -STALWARTLY S T AO1 L W ER0 T L IY0 -STALWARTS S T AO1 L W ER0 T S -STALWORTH S T AO1 L W ER0 TH -STAM S T AE1 M -STAMACH S T AE1 M AH0 K -STAMBOUL S T AE1 M B UW1 L -STAMBUL S T AE0 M B UW1 L -STAMEN S T EY1 M AH0 N -STAMENS S T EY1 M AH0 N Z -STAMER S T EY1 M ER0 -STAMER'S S T EY1 M ER0 Z -STAMFORD S T AE1 M F ER0 D -STAMFORDHAM S T AE1 M F AO1 R D AH0 M -STAMFORDHAM'S S T AE1 M F AO1 R D AH0 M Z -STAMINA S T AE1 M AH0 N AH0 -STAMINATE S T AE1 M AH0 N EY2 T -STAMMARK S T AE1 M AA2 R K -STAMMER S T AE1 M ER0 -STAMMER'D S T AE1 M ER0 AH0 D -STAMMERED S T AE1 M ER0 D -STAMMERER S T AE1 M ER0 ER0 -STAMMERER'S S T AE1 M ER0 ER0 Z -STAMMERERS S T AE1 M ER0 ER0 Z -STAMMERING S T AE1 M ER0 IH0 NG -STAMMERINGLY S T AE1 M ER0 IH0 NG L IY0 -STAMMERINGS S T AE1 M ER0 IH0 NG Z -STAMMERS S T AE1 M ER0 Z -STAMP S T AE1 M P -STAMP'D S T AE1 M P D -STAMPA S T AE1 M P AH0 -STAMPA'S S T AE1 M P AH0 Z -STAMPED S T AE1 M P T -STAMPEDE S T AE0 M P IY1 D -STAMPEDE'S S T AE0 M P IY1 D Z -STAMPEDED S T AE0 M P IY1 D AH0 D -STAMPEDED S T AE0 M P IY1 D IH0 D -STAMPEDER S T AE0 M P IY1 D ER0 -STAMPEDERS S T AE0 M P IY1 D ER0 Z -STAMPEDES S T AE0 M P IY1 D Z -STAMPEDIN S T AE0 M P IY1 D IH0 N -STAMPEDING S T AE0 M P IY1 D IH0 NG -STAMPER S T AE1 M P ER0 -STAMPERS S T AE1 M P ER0 Z -STAMPIN S T AE1 M P IH0 N -STAMPING S T AE1 M P IH0 NG -STAMPINGS S T AE1 M P IH0 NG Z -STAMPS S T AE1 M P S -STAMPS'S S T AE1 M P S IH0 Z -STAMPT S T AE0 M P T -STAN S T AE1 N -STAN'IN S T AE1 N AE1 N -STAN'S S T AE1 N Z -STANAPAT S T AE1 N AH0 P AE2 T -STANARD S T AE1 N ER0 D -STANBERY S T AE1 N B ER0 IY0 -STANBOROUGH S T AE1 N B ER0 OW0 -STANBURY S T AE1 N B EH2 R IY0 -STANBY S T AE1 N B IY0 -STANCE S T AE1 N S -STANCES S T AE1 N S IH0 Z -STANCH S T AE1 N CH -STANCHED S T AE1 N CH T -STANCHELLS S T AE1 N CH AH0 L Z -STANCHEONS S T AE1 N CH IH0 N Z -STANCHER S T AE1 N CH ER0 -STANCHEST S T AE1 N CH AH0 S T -STANCHESTER S T AE1 N CH EH2 S T ER0 -STANCHING S T AE1 N CH IH0 NG -STANCHION S T AE1 N CH IY1 N -STANCHIONS S T AE1 N CH AH0 N Z -STANCHLY S T AE1 N CH L IY0 -STANCHNESS S T AE1 N CH N AH0 S -STANCHON S T AE1 N CH AH0 N -STANCY S T AE1 N S IY0 -STAND S T AE1 N D -STAND'ST S T AE1 N D IH0 S T -STANDALE S T AE1 N D EY2 L -STANDARD S T AE1 N D ER0 D -STANDARD'S S T AE1 N D ER0 D Z -STANDARDISED S T AE1 N D ER0 D AY2 Z D -STANDARDIZATION S T AE0 N D ER0 D IH0 Z EY1 SH AH0 N -STANDARDIZE S T AE1 N D ER0 D AY2 Z -STANDARDIZED S T AE1 N D ER0 D AY2 Z D -STANDARDIZING S T AE1 N D ER0 D AY2 Z IH0 NG -STANDARDS S T AE1 N D ER0 D Z -STANDBY S T AE1 N D B AY1 -STANDBYS S T AE1 N D B AY2 Z -STANDE S T AE1 N D -STANDEN S T AE1 N D AH0 N -STANDER S T AE1 N D ER0 -STANDERS S T AE1 N D ER0 Z -STANDERTON S T AE1 N D ER0 T AH0 N -STANDERTON'S S T AE1 N D ER0 T AH0 N Z -STANDEST S T AE1 N D AH0 S T -STANDETH S T AE1 N D IH0 TH -STANDFAST S T AE1 N D F AE2 S T -STANDI S T AE1 N D IY0 -STANDIFER S T AE1 N D IH0 F ER0 -STANDIN S T AE1 N D IH0 N -STANDING S T AE1 N D IH0 NG -STANDING'S S T AE1 N D IH0 NG Z -STANDINGS S T AE1 N D IH0 NG Z -STANDISH S T AE1 N D IH0 SH -STANDISH'S S T AE1 N D IH0 SH IH0 Z -STANDISHES S T AE1 N D IH0 SH IH0 Z -STANDOFFISH S T AE1 N D AO2 F IH2 SH -STANDON S T AE1 N D AH0 N -STANDPOINT S T AE1 N D P OY2 N T -STANDPOINTS S T AE1 N D P OY2 N T S -STANDRING S T AE1 N D ER0 IH0 NG -STANDS S T AE1 N D Z -STANDSTILL S T AE1 N D S T IH2 L -STANDUP S T AE1 N D AH2 P -STANE S T AE1 N -STANEHOLME S T AE1 N IH0 HH OW0 L M -STANEHOLME'S S T AE1 N IH0 HH OW0 L M Z -STANES S T EY1 N Z -STANESBY S T EY1 N Z B IY0 -STANFIELD S T AE1 N F IY2 L D -STANFIELD'S S T AE1 N F IY2 L D Z -STANFORD S T AE1 N F ER0 D -STANFORD'S S T AE1 N F ER0 D Z -STANFORDS S T AE1 N F ER0 D Z -STANFORTH S T AE1 N F ER0 TH -STANFORTH'S S T AE1 N F ER0 TH S -STANFORTHS S T AE1 N F ER0 TH S -STANG S T AE1 NG -STANGATE S T AE1 N G EY2 T -STANGEIST S T EY1 NG G AY2 S T -STANGEIST'S S T EY1 NG G AY2 S T S -STANGER S T AE1 NG ER0 -STANGERSON S T EY1 NG G ER0 S AH0 N -STANGERSON'S S T EY1 NG G ER0 S AH0 N Z -STANGO S T AA1 NG G OW0 -STANGRAVE S T AA0 NG G R EY2 V -STANHOPE S T AE1 N AH0 P -STANHOPE'S S T AE1 N AH0 P S -STANHOPES S T AE1 N AH0 P S -STANIEF S T AE1 N IY0 F -STANIEF'S S T AE1 N IY0 EH0 F S -STANIFORD S T AE1 N IH0 F ER0 D -STANIFORD'S S T AE1 N IH0 F ER0 D Z -STANIFORTH S T AE1 N IH0 F AO2 R TH -STANILAND S T AH0 N IH1 L AH0 N D -STANISLAO S T AE1 N IH0 S L AW0 -STANISLAS S T AH0 N IH1 S L AH0 S -STANISLASS S T AE1 N IH0 S L AE2 S -STANISLAUS S T AE1 N IH0 S L AO2 S -STANISLAV S T AE1 N IH0 S L AA0 V -STANISLAVSKY S T AE1 N IH0 S L AH0 V S K IY0 -STANISLAW S T AE1 N IH0 S L AO2 -STANISLAWSKI S T AH0 N IH0 S L AA1 F S K IY0 -STANISLOFF S T AE1 N IH0 S L AO0 F -STANISLOVAS S T AE1 N IH0 S L AA2 V AH0 Z -STANISTREET S T AE1 N IH0 S T R IY2 T -STANISTREET'S S T AE1 N IH0 S T R IY2 T S -STANITSA S T AH0 N IH1 T S AH0 -STANITSAS S T AE1 N IH0 T S AA2 Z -STANK S T AE1 NG K -STANLEIGH S T AE1 N L AH0 -STANLEY S T AE1 N L IY0 -STANLEY'S S T AE1 N L IY0 Z -STANLEYS S T AE1 N L IY0 Z -STANLOCK S T AE1 N L AA2 K -STANLOCK'S S T AE1 N L AA2 K S -STANLY S T AE1 N L IY0 -STANMORE S T AE1 N M AO0 R -STANMORE'S S T AE1 N M AO0 R Z -STANN S T AE1 N -STANNARD S T AE1 N ER0 D -STANNARD'S S T AE1 N ER0 D Z -STANNARY S T AE1 N ER0 IY0 -STANNER S T AE1 N ER0 -STANNIFORD S T AE1 N IH0 F ER0 D -STANNIN S T AE1 N IH0 N -STANNING S T AE1 N IH0 NG -STANNUM S T AE1 N AH0 M -STANNY S T AE1 N IY0 -STANOVOI S T AE1 N AH0 V OY2 -STANS S T AH0 N Z -STANSBURY S T AE1 N S B EH0 R IY0 -STANSBY S T AE1 N S B IY0 -STANSE S T AE1 N S -STANSFELD S T AE1 N S F EH2 L D -STANSFIELD S T AE1 N S F IY0 L D -STANSFORD S T AE1 N S F ER0 D -STANSHY S T AE1 N SH IY0 -STANSHY'S S T AE1 N SH IY0 Z -STANSMORE S T AE1 N S M AO0 R -STANSON S T AE1 N S AH0 N -STANSTEAD S T AE1 N S T EH2 D -STANT S T AE1 N T -STANTILOUP S T AE1 N T AH0 L UW1 P -STANTON S T AE1 N T AH0 N -STANTON'S S T AE1 N T AH0 N Z -STANTONS S T AE1 N T AH0 N Z -STANWAY S T AE1 N W EY2 -STANWELL S T AE1 N W EH2 L -STANWELL'S S T AE1 N W EH2 L Z -STANWICK S T AE1 N W IH0 K -STANWIX S T AE1 N W IH0 K S -STANWOOD S T AE1 N W UH2 D -STANZ S T AE1 N Z -STANZA S T AE1 N Z AH0 -STANZAS S T AE1 N Z AH0 Z -STANZE S T AE1 N Z -STAP S T AE1 P -STAPFER S T AE1 P F ER0 -STAPHYLOCOCCUS S T AE2 F AH0 L AH0 K AO1 K AH0 S -STAPIL S T AE1 P AH0 L -STAPLE S T EY1 P AH0 L -STAPLED S T EY1 P AH0 L D -STAPLEDEAN S T EY1 P AH0 L D EH1 N -STAPLEGROVE S T EY1 P AH0 L G R OW2 V -STAPLEHURST S T EY1 P AH0 L HH ER0 S T -STAPLER S T EY1 P AH0 L ER0 -STAPLER S T EY1 P L ER0 -STAPLES S T EY1 P AH0 L Z -STAPLETON S T EY1 P AH0 L T AH0 N -STAPLETON'S S T EY1 P AH0 L T AH0 N Z -STAPLETONS S T EY1 P AH0 L T AH0 N Z -STAPLEWICK S T EY1 P AH0 L W IH2 K -STAPPIT S T AE1 P IH0 T -STAPS S T AE1 P S -STAPYLTON S T EY1 P AH0 L T AH0 N -STAR S T AA1 R -STAR'D S T AA1 R D -STAR'S S T AA1 R Z -STARBEAM S T AA1 R B IY2 M -STARBEAMS S T AA1 R B IY1 M Z -STARBIRD S T AA1 R B ER2 D -STARBLOSSOM S T AA1 R B L AA1 S AH0 M -STARBOARD S T AA1 R B ER0 D -STARBOARDED S T AA1 R B AO1 R D IH0 D -STARBOTTLE S T AA1 R B AO2 T AH0 L -STARBOTTLE'S S T AA1 R B AO2 T AH0 L Z -STARBRIGHT S T AA1 R B R AY2 T -STARBUCK S T AA1 R B AH2 K -STARBUCK'S S T AA1 R B AH2 K S -STARCH S T AA1 R CH -STARCHED S T AA1 R CH T -STARCHER S T AA1 R CH ER0 -STARCHERS S T AA1 R CH ER0 Z -STARCHES S T AA1 R CH IH0 Z -STARCHING S T AA1 R CH IH0 NG -STARCHY S T AA1 R CH IY0 -STARCK S T AA1 R K -STARCUS S T AA1 R K AH0 S -STARD S T AA1 R D -STARDEN S T AA1 R D AH0 N -STARDI S T AA1 R D IY0 -STARDOM S T AA1 R D AH0 M -STARDUST S T AA1 R D AH2 S T -STARE S T EH1 R -STARED S T EH1 R D -STARELEIGH S T EH1 R L AH0 -STAREMBERG S T AE1 R AH0 M B ER0 G -STARER S T EH1 R ER0 -STARERS S T EH1 R ER0 Z -STARES S T EH1 R Z -STARETS S T AE1 R AH0 T S -STARETTA S T ER0 EH1 T AH0 -STARFALL S T AA1 R F AO2 L -STARFISH S T AA1 R F IH2 SH -STARFISHES S T AA1 R F IH2 SH IH0 Z -STARGAZERS S T AA1 R G EY2 Z ER0 Z -STARGESHINA S T AA1 R G IH0 SH IY1 N AH0 -STARHURST S T AA1 R HH ER0 S T -STARIN S T AE1 R IH0 N -STARING S T EH1 R IH0 NG -STARINGLY S T EH1 R IH0 NG L IY0 -STARK S T AA1 R K -STARK'S S T AA1 R K S -STARKE S T AA1 R K -STARKE'S S T AA1 R K S -STARKEST S T AA1 R K AH0 S T -STARKEY S T AA1 R K IY2 -STARKFIELD S T AA1 R K F IY0 L D -STARKIE S T AA1 R K IY0 -STARKLY S T AA1 R K L IY0 -STARKNESS S T AA1 R K N IH0 S -STARKS S T AA1 R K S -STARKWEATHER S T AA1 R K W EH2 DH ER0 -STARKWEATHER'S S T AA1 R K W EH2 DH ER0 Z -STARLAND S T AA1 R L AH0 N D -STARLEIN S T AA1 R L AY0 N -STARLESS S T AA1 R L IH0 S -STARLETT S T AA1 R L IH0 T -STARLIGHT S T AA1 R L AY2 T -STARLIGHT'S S T AA1 R L AY2 T S -STARLIGHTS S T AA1 R L AY2 T S -STARLIKE S T AA1 R L AY2 K -STARLIN S T AA1 R L IH0 N -STARLING S T AA1 R L IH0 NG -STARLING'S S T AA1 R L IH0 NG Z -STARLINGS S T AA1 R L IH0 NG Z -STARLIT S T AA1 R L IH0 T -STARMAN S T AA1 R M AH0 N -STARMEN S T AA1 R M AH0 N -STARMIDGE S T AA1 R M IH0 JH -STARN S T AA1 R N -STARNA S T AA1 R N AH0 -STARNBERG S T AA1 R N B ER0 G -STARNS S T AA1 R N Z -STARNWORTH S T AA1 R N W ER0 TH -STAROST S T AE1 R AH0 S T -STAROSTA S T AA1 R AO1 S T AH0 -STAROSTA'S S T AA1 R AO1 S T AH0 Z -STAROSTE S T AA0 R OW1 S T -STAROSTINE S T AE1 R AH0 S T IY1 N IY0 -STARPHA S T AA1 R F AH0 -STARR S T AA1 R -STARR'D S T AA1 R D -STARR'S S T AA1 R Z -STARRE S T AA1 R -STARRED S T AA1 R D -STARRES S T AA1 R Z -STARRETT S T AE1 R IH0 T -STARRIE S T AE1 R IY0 -STARRING S T AA1 R IH0 NG -STARRS S T AA1 R Z -STARRY S T AA1 R IY0 -STARS S T AA1 R Z -STARSHINE S T AA1 R SH AY2 N -STARSHIP S T AA1 R SH IH2 P -STARSHIPS S T AA1 R SH IH2 P S -STARSKY S T AA1 R S K IY0 -START S T AA1 R T -STARTED S T AA1 R T AH0 D -STARTED S T AA1 R T IH0 D -STARTER S T AA1 R T ER0 -STARTER'S S T AA1 R T ER0 Z -STARTERS S T AA1 R T ER0 Z -STARTIN S T AA1 R T IH0 N -STARTING S T AA1 R T IH0 NG -STARTINGS S T AA1 R T IH0 NG Z -STARTIT S T AA1 R T IH0 T -STARTLE S T AA1 R T AH0 L -STARTLED S T AA1 R T AH0 L D -STARTLEDLY S T AA1 R T L AH0 D L IY0 -STARTLEMENT S T AA1 R T AH0 L M AH0 N T -STARTLER S T AA1 R T L ER0 -STARTLES S T AA1 R T AH0 L Z -STARTLIN S T AA1 R T L IH0 N -STARTLING S T AA1 R T L IH0 NG -STARTLINGLY S T AA1 R T L IH0 NG L IY0 -STARTOP S T AA1 R T AA2 P -STARTS S T AA1 R T S -STARTSEV S T AA1 R T S EH0 V -STARTUP S T AA1 R T AH2 P -STARTUPS S T AA1 R T AH2 P S -STARV'D S T AA1 R V D -STARVATION S T AA0 R V EY1 SH AH0 N -STARVATIONS S T AA0 R V EY1 SH AH0 N Z -STARVE S T AA1 R V -STARVED S T AA1 R V D -STARVELING S T AA1 R V AH0 L IH0 NG -STARVELINGS S T AA1 R V AH0 L IH0 NG Z -STARVEM S T AA1 R V M -STARVES S T AA1 R V Z -STARVIN S T AA1 R V IH0 N -STARVING S T AA1 R V IH0 NG -STARWARD S T AA1 R W ER0 D -STARWOOD S T AA1 R W UH2 D -STARWORT S T AA1 R W ER0 T -STARY S T EH1 R IY0 -STARZA S T AA1 R Z AH0 -STARZA'S S T AA1 R Z AH0 Z -STARZL S T AA1 R Z AH0 L -STAS S T AH0 S -STASH S T AE1 SH -STASHED S T AE1 SH T -STASHIE S T AE1 SH IY0 -STASHKO S T AA1 SH K OW0 -STASHUN S T AE1 SH AH0 N -STASIEK S T AA1 S IY0 EH0 K -STASIS S T EY1 S AH0 S -STASU S T AA1 S UW0 -STAT S T AE1 T -STATA S T AA1 T AH0 -STATE S T EY1 T -STATE'S S T EY1 T S -STATECRAFT S T EY1 T K R AE2 F T -STATED S T EY1 T AH0 D -STATED S T EY1 T IH0 D -STATEDLY S T EY1 T AH0 D L IY0 -STATEHOOD S T EY1 T HH UH2 D -STATEHOUSE S T EY1 T HH AW2 S -STATELIER S T EY1 T L IY0 ER0 -STATELIEST S T EY1 T L IY0 AH0 S T -STATELILY S T AH0 T EH1 L AH0 L IY0 -STATELINESS S T EY1 T L IY0 N AH0 S -STATELY S T EY1 T L IY0 -STATEMENT S T EY1 T M AH0 N T -STATEMENTS S T EY1 T M AH0 N T S -STATEN S T AE1 T AH0 N -STATER S T EY1 T ER0 -STATEROOM S T EY1 T R UW2 M -STATEROOMS S T EY1 T R UW2 M Z -STATERS S T EY1 T ER0 Z -STATES S T EY1 T S -STATES' S T EY1 T S -STATESMAN S T EY1 T S M AH0 N -STATESMAN'S S T EY1 T S M AH0 N Z -STATESMANLIKE S T AH0 T EH1 S M AH0 N L AY2 K -STATESMANSHIP S T EY1 T S M AH0 N SH IH2 P -STATESMEN S T EY1 T S M IH0 N -STATESMEN'S S T EY1 T S M IH0 N Z -STATHAM S T AE1 TH AH0 M -STATHAM'S S T AE1 TH AH0 M Z -STATIA S T AE1 T IY0 AH0 -STATIC S T AE1 T IH0 K -STATICAL S T AE1 T IH0 K AH0 L -STATICALLY S T AE1 T IH0 K L IY0 -STATICS S T AE1 T IH0 K S -STATILIUS S T EY1 T IH2 L IY0 IH0 S -STATIM S T AH0 T IH1 M -STATIN S T EY1 T IH0 N -STATING S T EY1 T IH0 NG -STATION S T EY1 SH AH0 N -STATION'D S T EY1 SH AH0 N D 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-STENHOUSE S T EH1 N HH AW2 S -STENIO S T IY1 N IY0 OW0 -STENIUS S T IY1 N IY0 AH0 S -STENKA S T EH1 NG K AH0 -STENNIS S T EH1 N IH0 S -STENNIS'S S T EH1 N AH0 S IH0 Z -STENO S T EH1 N OW0 -STENO'S S T EH1 N OW0 Z -STENOG S T EH0 N AH1 G -STENOGRAPHER S T EH0 N AH1 G R AH0 F ER0 -STENOGRAPHER'S S T EH0 N AH1 G R AH0 F ER0 Z -STENOGRAPHERS S T EH0 N AH1 G R AH0 F ER0 Z -STENOGRAPHIC S T EH2 N AH0 G R AE1 F IH0 K -STENOGRAPHY S T EH0 N AH1 G R AH0 F IY0 -STENSON S T EH1 N S AH0 N -STENSON'S S T EH1 N S AH0 N Z -STENSSON S T EH1 N S AH0 N -STENT S T EH1 N T -STENT'S S T EH1 N T S -STENTERELLO S T EH1 N ER0 EH1 L OW0 -STENTOR S T EH1 N T ER0 -STENTORIAN S T EH1 N T AO1 R IY0 AH0 N -STENTORIOUSLY S T EH1 N T AO1 R IY0 AH0 S L IY0 -STENTORS S T EH1 N T ER0 Z -STEP S T EH1 P -STEP'S S T EH1 P S -STEPAN S T EH1 P AH0 N -STEPAN'S S T EH1 P AH0 N Z -STEPANIDA S T EH1 P AH0 N IY1 D AH0 -STEPANITCH S T EH1 P AH0 N IH0 CH -STEPANOFF S T EH1 P AH0 N AO0 F -STEPANOVITCH S T IH0 P AE1 N AH0 V AH0 CH 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N UW0 -STEPHANUS S T EH1 F AH0 N IH0 S -STEPHEN S T IY1 V AH0 N -STEPHEN'S S T IY1 V AH0 N Z -STEPHENS S T EH1 F AH0 N Z -STEPHENS S T IY1 V AH0 N Z -STEPHENS'S S T IY1 V IH0 N Z IH0 Z -STEPHENSON S T IY1 V AH0 N S AH0 N -STEPHENSON'S S T IY1 V AH0 N S AH0 N Z -STEPHENSONS S T IY1 V AH0 N S AH0 N Z -STEPKA S T EH1 P K AH0 -STEPLADDER S T EH1 P L AE2 D ER0 -STEPLADDERS S T EH1 P L AE2 D ER0 Z -STEPMOTHER S T EH1 P M AH2 DH ER0 -STEPMOTHER'S S T IY1 V M AH2 DH ER0 Z -STEPMOTHERS S T IY1 V M AH2 DH ER0 Z -STEPNEY S T EH1 P N IY0 -STEPNEY'S S T EH1 P N IY0 Z -STEPNEYS S T EH1 P N IY0 Z -STEPNIAK S T EH1 P N IY0 AE0 K -STEPP'D S T EH1 P D -STEPPE S T EH1 P -STEPPED S T EH1 P T -STEPPER S T EH1 P ER0 -STEPPERS S T EH1 P ER0 Z -STEPPES S T EH1 P S -STEPPETH S T EH1 P IH0 TH -STEPPIN S T EH1 P IH0 N -STEPPING S T EH1 P IH0 NG -STEPPINGS S T EH1 P IH0 NG Z -STEPS S T EH1 P S -STEPSISTER S T EH1 P S IH2 S T ER0 -STEPSISTERS S T EH1 P S IH2 S T ER0 Z -STEPSON S T EH1 P S AH2 N -STEPSON'S S T EH1 P S AH2 N Z -STEPSONS S T EH1 P S AH2 N Z -STEPT S T EH1 P T -STEPTOE S T EH1 P T OW2 -STEPUNCLE S T EH1 P AH2 NG K AH0 L -STER S T ER0 -STEREO S T EH1 R IY0 OW2 -STEREOGRAPH S T EH2 R IY0 AH0 G R AE2 F -STEREOPTICON S T EH1 R IY0 AA1 P T IH0 K AA2 N -STEREOS S T EH1 R IY0 OW2 Z -STEREOSCOPE S T EH2 R IY0 AH0 S K OW2 P -STEREOSCOPIC S T EH2 R IY0 AH0 S K AA1 P IH0 K -STEREOTYPE S T EH1 R IY0 AH0 T AY2 P -STEREOTYPE S T EH1 R IY0 OW0 T AY2 P -STEREOTYPED S T EH1 R IY0 AH0 T AY2 P T -STEREOTYPED S T EH1 R IY0 OW0 T AY2 P T -STEREOTYPER S T EH1 R IY0 AH0 T AY2 P ER0 -STEREOTYPERS S T EH1 R IY0 AH0 T AY2 P ER0 Z -STEREOTYPES S T EH1 R IY0 AH0 T AY2 P S -STEREOTYPES S T EH1 R IY0 OW0 T AY2 P S -STEREOTYPING S T EH1 R IY0 AH0 T AY2 P IH0 NG -STEREOTYPING S T EH1 R IY0 OW0 T AY2 P IH0 NG -STERILE S T EH1 R AH0 L -STERILISATION S T EH2 R AH0 L IH0 Z EY1 SH AH0 N -STERILISE S T EH2 R AH0 L AH0 S -STERILISED S T EH2 R AH0 L AH0 S T -STERILISING S T EH2 R AH0 L IH0 S IH0 NG -STERILITY S T ER0 IH1 L IH0 T IY0 -STERILIZATION S T EH2 R AH0 L AH0 Z EY1 SH AH0 N -STERILIZATION S T EH2 R AH0 L IH0 Z EY1 SH AH0 N -STERILIZE S T EH1 R AH0 L AY2 Z -STERILIZED S T EH1 R AH0 L AY2 Z D -STERILIZER S T EH1 R AH0 L AY2 Z ER0 -STERILIZING S T EH1 R AH0 L AY2 Z IH0 NG -STERLIN S T ER1 L IH0 N -STERLING S T ER1 L IH0 NG -STERLING'S S T ER1 L IH0 NG Z -STERLINGS S T ER1 L IH0 NG Z -STERN S T ER1 N -STERN'S S T ER1 N Z -STERNA S T ER1 N AH0 -STERNBALD S T ER1 N B AO2 L D -STERNBERG S T ER1 N B ER0 G -STERNBERGER S T ER1 N B ER0 G ER0 -STERNBURG S T ER1 N B ER0 G -STERNDALE S T ER1 N D EY2 L -STERNE S T ER1 N -STERNE'S S T ER1 N Z -STERNED S T ER1 N D -STERNER S T ER1 N ER0 -STERNEST S T ER1 N AH0 S T -STERNFORD S T ER1 N F ER0 D -STERNHEIM S T ER1 N HH AY0 M -STERNHOLD S T ER1 N HH OW0 L D -STERNHOLD'S S T ER1 N HH OW2 L D Z -STERNLY S T ER1 N L IY0 -STERNMOST S T ER0 N M OW2 S T -STERNNESS S T ER1 N AH0 S -STERNNESS S T ER1 N N AH0 S -STERNPOST S T ER1 N P OW2 S T -STERNROYD S T ER1 N R OY2 D 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TH -STEVAN S T EH1 V AH0 N -STEVE S T IY1 V -STEVE'S S T IY1 V Z -STEVEDORE S T IY1 V AH0 D AO2 R -STEVEDORE'S S T IY1 V AH0 D AO2 R Z -STEVEDORES S T IY1 V AH0 D AO2 R Z -STEVEN S T IY1 V AH0 N -STEVEN'S S T IY1 V AH0 N Z -STEVENAGE S T IY1 V AH0 N IH0 JH -STEVENS S T IY1 V AH0 N Z -STEVENS'S S T IY1 V AH0 N Z AH0 Z -STEVENS'S S T IY1 V AH0 N Z IH0 Z -STEVENSBURG S T IY1 V AH0 N Z B ER0 G -STEVENSES S T IY1 V AH0 N S IH0 Z -STEVENSON S T IY1 V AH0 N S AH0 N -STEVENSON'S S T IY1 V AH0 N S AH0 N Z -STEVENSONIAN S T IY1 V AH0 N S OW1 N IY0 AH0 N -STEVENSONS S T IY1 V AH0 N S AH0 N Z -STEVENTON S T IY1 V AH0 N T AH0 N -STEVENYNE S T EH1 V IH0 N AY1 N -STEVEY S T IY1 V IY0 -STEVIE S T IY1 V IY0 -STEVIE'S S T IY1 V IY0 Z -STEVY S T EH1 V IY0 -STEW S T UW1 -STEW'D S T UW1 D -STEW'S S T UW1 Z -STEWARD S T UW1 ER0 D -STEWARD'S S T UW1 ER0 D Z -STEWARDESS S T UW1 ER0 D AH0 S -STEWARDESSES S T UW1 ER0 D AH0 S IH0 Z -STEWARDS S T UW1 ER0 D Z -STEWARDSHIP S T UW1 ER0 D SH IH2 P -STEWART S T UW1 ER0 T -STEWART'S S T UW1 ER0 T S -STEWARTS S T UW1 ER0 T S -STEWED S T UW1 D -STEWIN S T UW1 IH0 N -STEWING S T UW1 IH0 NG -STEWPAN S T UW1 P AH0 N -STEWPANS S T UW1 P AE2 N Z -STEWPOT S T UW1 P AA2 T -STEWS S T UW1 Z -STEY S T IY0 -STEYN S T EY1 N -STEYN'S S T EY1 N Z -STEYNE S T EY1 N -STEYNE'S S T EY1 N Z -STEYNHAM S T EY1 N AH0 M -STEYNING S T EY1 N IH0 NG -STHAND S T HH AE1 N D -STHANTO S T HH AE1 N T OW0 -STHENELUS S TH IY1 N AH0 L AH0 S -STHETIC S TH EH1 T IH0 K -STHOP S TH AA1 P -STHOR S TH ER0 -STHRAIGHT S TH R EY2 T -STHRAME S TH R EY2 M -STHRANGE S TH R EY2 N JH -STHRANGER S TH R EY1 N JH ER0 -STHRANGERS S TH R EY1 N JH ER0 Z -STHRAW S TH R AO1 -STHREET S TH R IY1 T -STHRIKE S TH R AY2 K -STHROKE S TH R OW2 K -STHRONG S TH R AO1 NG -STHRUCK S TH R AH0 K -STI S T AY1 -STIBBS S T IH1 B Z -STICH S T IH1 CH -STICK S T IH1 K -STICK'S S T IH1 K S -STICKATIT S T IH1 K AH0 T IH2 T -STICKCHI S T IH1 K CH IY0 -STICKED S T IH1 K T -STICKEEN S T IH1 K IY2 N -STICKELBACK S T IH1 K AH0 L B AE2 K -STICKEN S T IH1 K AH0 N -STICKER S T IH1 K ER0 -STICKERS S T IH1 K ER0 Z -STICKERY S T IH1 K ER0 IY0 -STICKETH S T IH1 K IH0 TH -STICKIER S T IH1 K IY0 ER0 -STICKIEST S T IH1 K IY0 AH0 S T -STICKILY S T IH1 K AH0 L IY0 -STICKIN S T IH1 K IH0 N -STICKINESS S T IH1 K IY0 N AH0 S -STICKING S T IH1 K IH0 NG -STICKINGS S T IH1 K IH0 NG Z -STICKIT S T IH1 K IH0 T -STICKLE S T IH1 K AH0 L -STICKLEBACK S T IH1 K AH0 L B AE2 K -STICKLEBACKS S T IH1 K AH0 L B AE2 K S -STICKLEFORD S T IH1 K AH0 L F ER0 D -STICKLER S T IH1 K AH0 L ER0 -STICKLER S T IH1 K L ER0 -STICKLERS S T IH1 K L ER0 Z -STICKLES S T IH1 K AH0 L Z -STICKLING S T IH1 K L IH0 NG -STICKLY S T IH1 K L IY0 -STICKNEY S T IH1 K N IY0 -STICKNEY'S S T IH1 K N IY0 Z -STICKPIN S T IH1 K P AE1 N -STICKS S T IH1 K S -STICKY S T IH1 K IY0 -STID S T IH1 D -STIDDIER S T IH1 D IY0 ER0 -STIDDY S T IH1 D IY0 -STIDMANN S T IH1 D M AH0 N -STIEFBACH S T IY1 F B AA0 K -STIEFBACH'S S T IY1 F B AA2 K S -STIEFEL S T IY1 F AH0 L -STIEGLITZ S T IY1 G L IH0 T S -STIELOW S T IY1 L OW0 -STIEPAN S T IY1 P AH0 N -STIER S T AY1 ER0 -STIES S T AY1 Z -STIETENCRON S T IY1 T IH0 N K R AA2 N -STIF S T AY1 F -STIFF S T IH1 F -STIFF'S S T IH1 F S -STIFFE S T IH1 F -STIFFED S T IH1 F T -STIFFELKIND S T IH1 F IH0 L K AY1 N D -STIFFEN S T IH1 F AH0 N -STIFFEN'D S T IH1 F AH0 N D -STIFFENED S T IH1 F AH0 N D -STIFFENER S T IH1 F AH0 N ER0 -STIFFENIN S T IH1 F AH0 N IH0 N -STIFFENING S T IH1 F AH0 N IH0 NG -STIFFENING S T IH1 F N IH0 NG -STIFFENS S T IH1 F AH0 N Z -STIFFER S T IH1 F ER0 -STIFFEST S T IH1 F AH0 S T -STIFFISH S T IH1 F IH0 SH -STIFFLEG S T IH1 F L IH0 G -STIFFLY S T IH1 F L IY0 -STIFFNECKED S T IH1 F N EH0 K T -STIFFNER S T IH1 F N ER0 -STIFFNER'S S T IH1 F N ER0 Z -STIFFNESS S T IH1 F N AH0 S -STIFFORD S T IH1 F ER0 D -STIFFS S T IH1 F S -STIFFSON S T IH1 F S AH0 N -STIFFSON'S S T IH1 F S AH0 N Z -STIFFY S T IH1 F IY0 -STIFLE S T AY1 F AH0 L -STIFLED S T AY1 F AH0 L D -STIFLES S T AY1 F AH0 L Z -STIFLING S T AY1 F AH0 L IH0 NG -STIFLING S T AY1 F L IH0 NG -STIFLINGLY S T AY1 F L IH0 NG L IY0 -STIG S T IH1 G -STIG'S S T IH0 G Z -STIGAND S T IH1 G AH0 N D -STIGANDI S T IH1 G AH0 N D IY0 -STIGE S T IH1 JH -STIGGINS S T IH1 G IH0 N Z -STIGGS S T IH1 G Z -STIGMA S T IH1 G M AH0 -STIGMAS S T IH1 G M AH0 Z -STIGMATA S T IH0 G M AA1 T AH0 -STIGMATISE S T IH1 G M AH0 T AY2 Z -STIGMATISED S T IH1 G M AH0 T AY2 Z D -STIGMATISES S T IH1 G M AH0 T AY2 Z IH0 Z -STIGMATISING S T IH1 G M AH0 T AY2 Z IH0 NG -STIGMATIZATION S T IH1 G M AH0 T AH0 Z EY1 SH AH0 N -STIGMATIZE S T IH1 G M AH0 T AY2 Z -STIGMATIZED S T IH1 G M AH0 T AY2 Z D -STIGMATIZES S T IH1 G M AH0 T AY2 Z IH0 Z -STIGMATIZING S T IH1 G M AH0 T AY2 Z IH0 NG -STIKEEN S T IH1 K IY2 N -STIKINE S T IH1 K IY0 N -STIL S T IH1 L -STILE S T AY1 L -STILED S T AY1 L D -STILES S T AY1 L Z -STILES'S S T AY1 L Z IH0 Z -STILETTO S T AH0 L EH1 T OW0 -STILETTOES S T AH0 L EH1 T OW0 Z -STILETTOS S T AH0 L EH1 T OW0 Z -STILICHO S T IH1 L AH0 K OW0 -STILL S T IH1 L -STILL'D S T IH1 L D -STILL'S S T IH1 L Z -STILLBORN S T IH1 L B AO1 R N -STILLBOTTLE S T IH1 L B AO2 T AH0 L -STILLBROOK S T IH1 L B R UH0 K -STILLBURY S T IH1 L B ER0 IY0 -STILLE S T IH1 L -STILLED S T IH1 L D -STILLER S T IH1 L ER0 -STILLEST S T IH1 L IH0 S T -STILLING S T IH1 L IH0 NG -STILLINGER S T IH1 L IH0 NG ER0 -STILLINGFLEET S T IH1 L IH0 NG F L IY2 T -STILLINGHAST S T IH1 L IH0 NG HH AE2 S T -STILLINGHAST'S S T IH1 L IH0 NG HH S T S -STILLINGS S T IH1 L IH0 NG Z -STILLMAN S T IH1 L M AH0 N -STILLMAN'S S T IH1 L M AH0 N Z -STILLNESS S T IH1 L N AH0 S -STILLNESSES S T IH1 L N AH0 S IH0 Z -STILLROOM S T IH1 L R UW2 M -STILLS S T IH1 L Z -STILLSON S T IH1 L S AH0 N -STILLWATER S T IH1 L W AO2 T ER0 -STILLWELL S T IH1 L W EH2 L -STILLWELL'S S T IH1 L W EH2 L Z -STILLWOOD S T IH1 L W UH2 D -STILLY S T IH1 L IY0 -STILMAN S T IH1 L M AH0 N -STILO S T AY1 L OW0 -STILPO S T IH1 L P OW0 -STILT S T IH1 L T -STILTED S T IH1 L T IH0 D -STILTEDNESS S T IH1 L T IH0 D N AH0 S -STILTON S T IH1 L T AH0 N -STILTS S T IH1 L T S -STILTSTALKING S T IH1 L T S T AO1 K IH0 NG -STILUS S T AH0 L AH0 S -STILWELL S T IH1 L W EH2 L -STILWELL'S S T IH1 L W EH2 L Z -STIM S T IH1 M -STIMCOE S T IH1 M K OW2 -STIMCOE'S S T IH1 M K OW2 Z -STIMPCETT S T IH1 M P S IH0 T -STIMPSON S T IH1 M P S AH0 N -STIMPSON'S S T IH1 M P S AH0 N Z -STIMSON S T IH1 M S AH0 N -STIMSON'S S T IH1 M S AH0 N Z -STIMULANT S T IH1 M Y AH0 L AH0 N T -STIMULANTS S T IH1 M Y AH0 L AH0 N T S -STIMULATE S T IH1 M Y AH0 L EY2 T -STIMULATED S T IH1 M Y AH0 L EY2 T AH0 D -STIMULATED S T IH1 M Y AH0 L EY2 T IH0 D -STIMULATES S T IH1 M Y AH0 L EY2 T S -STIMULATING S T IH1 M Y AH0 L EY2 T IH0 NG -STIMULATION S T IH2 M Y AH0 L EY1 SH AH0 N -STIMULATIONS S T IH1 M Y AH0 L EY1 SH AH0 N Z -STIMULATIVE S T IH1 M Y AH0 L EY2 T IH0 V -STIMULATOR S T IH1 M Y AH0 L EY2 T ER0 -STIMULI S T IH1 M Y AH0 L AY2 -STIMULUS S T IH1 M Y AH0 L AH0 S -STINA S T IY1 N AH0 -STINCHE S T IH1 N CH -STINE S T AY1 N -STING S T IH1 NG -STINGAREE S T IH1 NG G ER0 IY1 -STINGAREE'S S T IH1 NG G ER0 IY1 Z -STINGAREES S T IH1 NG G ER0 IY1 Z -STINGER S T IH1 NG ER0 -STINGERS S T IH1 NG ER0 Z -STINGETH S T IH1 NG G IH0 TH -STINGEY S T IH1 NG G IY0 -STINGIER S T IH1 N JH IY0 ER0 -STINGIEST S T IH1 N JH IY0 AH0 S T -STINGILY S T IH1 NG G AH0 L IY0 -STINGIN S T IH1 NG G AH0 N -STINGINESS S T IH1 N JH IY0 N IH0 S -STINGING S T IH1 NG IH0 NG -STINGINGLY S T IH1 NG IH0 NG L IY0 -STINGLESS S T IH1 NG L AH0 S -STINGO S T IH1 NG G OW0 -STINGO'S S T IH1 NG G OW0 Z -STINGS S T IH1 NG Z -STINGWING S T IH1 NG G W IH2 NG -STINGY S T IH1 N JH IY0 -STINK S T IH1 NG K -STINKARD S T IH1 NG K ER0 D -STINKCAT S T IH1 NG K K AE2 T -STINKER S T IH1 NG K ER0 -STINKERS S T IH1 NG K ER0 Z -STINKETH S T IH1 NG K IH0 TH -STINKFOOT S T IH1 NG K F UH2 T -STINKFOOTS S T IH1 NG K F UH1 T S -STINKIN S T IH1 NG K IH0 N -STINKING S T IH1 NG K IH0 NG -STINKS S T IH1 NG K S -STINKY S T IH1 NG K IY0 -STINSON S T IH1 N S AH0 N -STINT S T IH1 N T -STINTED S T IH1 N T IH0 D -STINTING S T IH1 N T IH0 NG -STINTS S T IH1 N T S -STIORNA S T Y AO1 R N AH0 -STIOSHKA S T AY1 OW0 SH K AH0 -STIP S T IH2 P -STIPEND S T AY1 P AH0 N D -STIPENDIARIES S T AY1 P AH0 N D IY0 EH2 R IY0 Z -STIPENDIARY S T AY1 P AH0 N D IY0 EH0 R IY0 -STIPENDS S T AY1 P AH0 N D Z -STIPP S T IH1 P -STIPPLE S T IH1 P AH0 L -STIPPLED S T IH1 P AH0 L D -STIPPLING S T IH1 P AH0 L IH0 NG -STIPULATE S T IH1 P Y AH0 L EY2 T -STIPULATED S T IH1 P Y AH0 L EY2 T IH0 D -STIPULATES S T IH1 P Y AH0 L EY2 T S -STIPULATING S T IH1 P Y AH0 L EY2 T IH0 NG -STIPULATION S T IH2 P Y AH0 L EY1 SH AH0 N -STIPULATIONS S T IH2 P Y AH0 L EY1 SH AH0 N Z -STIR S T ER1 -STIRABOUT S T ER1 AH0 B AW2 T -STIRBACKS S T ER1 B AE2 K S -STIRES S T AY1 R Z -STIRHAMPTON S T ER1 HH AE1 M P T AH0 N -STIRK S T ER1 K -STIRKS S T ER1 K S -STIRLESS S T ER1 L AH0 S -STIRLING S T ER1 L IH0 NG -STIRLING'S S T ER1 L IH0 NG Z -STIRLINGSHIRE S T ER1 L IH0 NG SH AY2 R -STIRMINGHAM S T ER1 M IH0 NG HH AE2 M -STIRN S T ER1 N -STIRNER S T ER1 N ER0 -STIRR'D S T ER1 D -STIRRE S T ER1 -STIRRED S T ER1 D -STIRRER S T ER1 ER0 -STIRRERS S T ER1 R Z -STIRREST S T ER1 AH0 S T -STIRRETH S T ER1 AH0 TH -STIRRIN S T ER1 IH0 N -STIRRING S T ER1 IH0 NG -STIRRINGLY S T ER1 IH0 NG L IY0 -STIRRINGS S T ER1 IH0 NG Z -STIRRUP S T ER1 AH0 P -STIRRUPED S T ER1 AH0 P T -STIRRUPS S T ER1 AH0 P S -STIRS S T ER1 Z -STISTED S T IH1 S T IH0 D -STISTICK S T IH1 S T IH0 K -STITCH S T IH1 CH -STITCHED S T IH1 CH T -STITCHER S T IH1 CH ER0 -STITCHERY S T IH1 CH ER0 IY0 -STITCHES S T IH1 CH IH0 Z -STITCHIN S T IH1 CH IH0 N -STITCHING S T IH1 CH IH0 NG -STITCHINGS S T IH1 CH IH0 NG Z -STITCHLEY S T IH1 CH L IY0 -STITCHLEY'S S T IH1 CH L IY0 Z -STITCHWORT S T IH1 CH W ER0 T -STITH S T IH1 TH -STITHY S T IH1 TH IY0 -STITT S T IH1 T -STITTS S T IH1 T S -STITZ S T IH1 T S -STIVED S T AY1 V D -STIVER S T AY1 V ER0 -STIVERS S T AY1 V ER0 Z -STIXON S T IH1 K S AH0 N -STIZUS S T IH1 Z AH0 S -STJERNHOEK S EY1 N T JH ER1 N HH OW2 K -STLONG S T L AO0 NG -STO S T OW0 -STOA S T OW1 AH0 -STOAT S T OW1 T -STOATS S T OW1 T S -STOB S T AA1 B -STOBAEUS S T AA1 B EY1 AH0 S -STOBALT S T OW1 B AO2 L T -STOBART S T AA1 B AA2 R T -STOBART'S S T AA1 B ER0 T S -STOBB S T AA1 B -STOBBS S T AA1 B Z -STOBELL S T OW1 B AH0 L -STOBELL'S S T OW1 B AH0 L Z -STOBO S T OW1 B OW0 -STOCK S T AA1 K -STOCK'D S T AA1 K D -STOCK'S S T AA1 K S -STOCKADE S T AA1 K AH0 D -STOCKADED S T AA1 K EY1 D IH0 D -STOCKADER S T AA1 K EY1 D ER0 -STOCKADERS S T AA1 K EY1 D ER0 Z -STOCKADES S T AA1 K EY1 D Z -STOCKARD S T AA1 K ER0 D -STOCKBRIDGE S T AA1 K B R IH0 JH -STOCKBROKER S T AA1 K B R OW2 K ER0 -STOCKBROKER'S S T AA1 K B R OW2 K ER0 Z -STOCKBROKERS S T AA1 K B R OW2 K ER0 Z -STOCKBROKING S T AA1 K B R OW2 K IH0 NG -STOCKDALE S T AA1 K D EY2 L -STOCKDALE'S S T AA1 K D EY2 L Z -STOCKDOVE S T AA1 K D AH0 V -STOCKED S T AA1 K T -STOCKENS S T AA1 K AH0 N Z -STOCKFISH S T AA1 K F IH2 SH -STOCKHAM S T AA1 K HH AH0 M -STOCKHAUSEN S T AA1 K HH AW2 Z AH0 N -STOCKHEATH S T AA1 K HH IY2 TH -STOCKHOLDER S T AA1 K HH OW2 L D ER0 -STOCKHOLDERS S T AA1 K HH OW2 L D ER0 Z -STOCKHOLM S T AA1 K HH OW2 L M -STOCKIE S T AA1 K IY0 -STOCKIER S T AA1 K IY0 ER0 -STOCKILY S T AA1 K AH0 L IY0 -STOCKIN S T AA1 K IH0 N -STOCKIN' S T AA1 K IH0 N -STOCKIN'S S T AA1 K IH0 N Z -STOCKINET S T AA1 K IH0 N EH2 T -STOCKING S T AA1 K IH0 NG -STOCKINGED S T AA1 K IH0 NG D -STOCKINGLESS S T AA1 K IH0 NG L AH0 S -STOCKINGS S T AA1 K IH0 NG Z -STOCKINGTON S T AA1 K IH0 NG T AH0 N -STOCKINGTONIANS S T AA1 K IH0 NG T OW1 N IY0 AH0 N Z -STOCKINS S T AA1 K IH0 N Z -STOCKJOBBER S T AA1 K JH AA1 B ER0 -STOCKJOBBING S T AA1 K JH AA1 B IH0 NG -STOCKLEY S T AA1 K L IY0 -STOCKMAN S T AA1 K M AH0 N -STOCKMAN'S S T AA1 K M AE2 N Z -STOCKMAR S T AA1 K M AA2 R -STOCKMAR'S S T AA1 K M AA2 R Z -STOCKMEN S T AA1 K M EH2 N -STOCKMEN'S S T AA1 K M EH0 N Z -STOCKPILE S T AA1 K P AY2 L -STOCKPORT S T AA1 K P AO2 R T -STOCKRIDER S T AA1 K R AY2 D ER0 -STOCKROOM S T AA1 K R UW2 M -STOCKS S T AA1 K S -STOCKSTILL S T AA1 K S T IH2 L -STOCKTAKING S T AA1 K T EY2 K IH0 NG -STOCKTON S T AA1 K T AH0 N -STOCKTON'S S T AA1 K T AH0 N Z -STOCKWELL S T AA1 K W EH2 L -STOCKWHIP S T AA1 K W IH2 P -STOCKWHIPS S T AA1 K W IH2 P S -STOCKY S T AA1 K IY0 -STOCKYARD S T AA1 K Y AA2 R D -STOCKYARDS S T AA1 K Y AA2 R D Z -STODDARD S T AA1 D ER0 D -STODDARD'S S T AA1 D ER0 D Z -STODDARDS S T AA1 D ER0 D Z -STODDART S T AA1 D AA0 R T -STODDART'S S T AA1 D ER0 T S -STODE S T OW0 D -STODGE S T AA1 JH -STODGER S T AA1 JH ER0 -STODGER'S S T AA1 JH ER0 Z -STODGINESS S T AA1 JH IY0 N AH0 S -STODGY S T AA1 JH IY0 -STOEFFI S T OW1 F IY0 -STOELL'S S T OW1 L Z -STOEP S T OW1 P -STOEPS S T OW1 P S -STOFF S T AO1 F -STOFFEL S T AA1 F AH0 L -STOFFLES S T AA1 F AH0 L Z -STOFFLET S T AO1 F L IH0 T -STOGBUCHNER S T AA1 G B K N ER0 -STOGIE S T AA1 G IY0 -STOGIES S T AA1 G IY0 Z -STOGY S T AA1 G IY0 -STOHL S T OW1 L -STOIC S T OW1 IH0 K -STOIC'S S T OW1 IH0 K S -STOICAL S T OW1 IH0 K AH0 L -STOICALLY S T OW1 IH0 K L IY0 -STOICISM S T OW1 AH0 S IH2 Z AH0 M -STOICK S T OW1 IH0 K -STOICS S T OW1 IH0 K S -STOK S T AA1 K -STOKE S T OW1 K -STOKED S T OW1 K T -STOKEHOLD S T OW1 K HH OW2 L D -STOKEHOLE S T OW1 K HH OW2 L -STOKELY S T OW1 K L IY0 -STOKER S T OW1 K ER0 -STOKER'S S T OW1 K ER0 Z -STOKERS S T OW1 K ER0 Z -STOKES S T OW1 K S -STOKES'S S T OW1 K S IH0 Z -STOKESLEY S T OW1 K S L IY0 -STOKH S T AA0 K -STOKIMATIS S T OW1 K IY0 M AA1 T IH0 S -STOKING S T OW1 K IH0 NG -STOKOWSKI S T OW1 K AO1 F S K IY0 -STOKTON S T AA1 K T AH0 N -STOL'N S T OW1 L AH0 N -STOLA S T OW1 L AH0 -STOLATZ S T OW1 L AH0 T S -STOLBERG S T OW1 L B ER0 G -STOLD S T OW1 L D -STOLE S T OW1 L -STOLED S T OW1 L D -STOLEN S T OW1 L AH0 N -STOLES S T OW1 L Z -STOLGOLAND S T OW1 L G OW0 L AH0 N D -STOLID S T AA1 L AH0 D -STOLIDITY S T AH0 L IH1 D AH0 T IY0 -STOLIDLY S T AA1 L AH0 D L IY0 -STOLIDNESS S T OW1 L AH0 D N AH0 S -STOLIKER S T OW2 L IH0 K ER0 -STOLKJAERRE S T OW1 L K JH EH1 R -STOLL S T OW1 L -STOLLEN S T AA1 L AH0 N -STOLLER S T OW1 L ER0 -STOLLER'S S T AA1 L ER0 Z -STOLO S T OW1 L OW0 -STOLONS S T OW1 L AH0 N Z -STOLPE S T OW1 L P -STOLPEN S T OW1 L P EH2 N -STOLPHE S T AA1 L F IY0 -STOLT S T OW1 L T -STOLTOR S T OW1 L T ER0 -STOLYPIN S T AA1 L IH0 P AE1 N -STOLZ S T OW1 L Z -STOLZEN S T OW1 L Z AH0 N -STOLZENBERG S T OW1 L Z AH0 N B ER0 G -STOLZENFELS S T OW1 L Z IH0 N F AH0 L Z -STOMACH S T AH1 M AH0 K -STOMACH'S S T AH1 M AH0 K S -STOMACHACHE S T AH1 M AH0 K EY2 K -STOMACHED S T AH1 M AH0 K T -STOMACHER S T AH1 M AH0 K ER0 -STOMACHERS S T AH1 M AH0 K ER0 Z -STOMACHFUL S T AH1 M AH0 K F AH0 L -STOMACHIC S T AH1 M AH0 K IH0 K -STOMACHS S T AH1 M AH0 K S -STOMACK S T AH1 M AH0 K -STOMACKE S T AH1 M AH0 K -STOMICK S T AA1 M IH0 K -STOMMICK S T AA1 M IH0 K -STOMP S T AA1 M P -STOMPED S T AA1 M P T -STOMPIN S T AA1 M P IH0 N -STOMPING S T AA1 M P IH0 NG -STON S T AH0 N -STOND S T AA1 N D -STONE S T OW1 N -STONE'S S T OW1 N Z -STONEBREAKER S T OW1 N B R EY2 K ER0 -STONEBRIDGE S T OW1 N B R IH2 JH -STONECHAT S T AA1 N IH0 K AH0 T -STONECROP S T OW1 N K R AA2 P -STONECROSS S T OW1 N K R AO2 S -STONECUTTER S T OW1 N K AH2 T ER0 -STONECUTTER'S S T OW1 N K AH2 T ER0 Z -STONECUTTERS S T OW1 N K AH2 T ER0 Z -STONED S T OW1 N D -STONEGATE S T AA1 N IH0 G EY1 T -STONEHAM S T OW1 N AH0 M -STONEHAVEN S T OW1 N HH EY2 V AH0 N -STONEHEART S T OW1 N HH AA0 R T -STONEHENGE S T OW1 N HH EH2 N JH -STONEHOUSE S T OW1 N HH AW2 S -STONELAND S T OW1 N L AH0 N D -STONELEIGH S T OW1 N L AH0 -STONELESS S T OW1 N L AH0 S -STONELIKE S T OW1 N L AY2 K -STONEMAN S T OW1 N M AH0 N -STONEMAN'S S T OW1 N M AH0 N Z -STONEMASON S T OW1 N M EY1 S AH0 N -STONEMASON'S S T OW1 N M EY1 S AH0 N Z -STONEMASONS S T OW1 N M EY1 S AH0 N Z -STONER S T OW1 N ER0 -STONER'S S T OW1 N ER0 Z -STONES S T OW1 N Z -STONEST S T OW1 N S T -STONESTUDY S T OW1 N S T AH2 D IY0 -STONEWALL S T OW1 N W AO2 L -STONEWALL'S S T OW1 N W AO2 L Z -STONEWARE S T OW1 N W EH2 R -STONEWELL S T OW1 N W EH2 L -STONEWELL'S S T OW1 N W EH2 L Z -STONEWORK S T OW1 N W ER2 K -STONEY S T OW1 N IY0 -STONHAWON S T AA1 N HH AH0 W AA0 N -STONIE S T OW1 N IY0 -STONIER S T OW1 N IY0 ER0 -STONIES S T OW1 N IY0 Z -STONIEST S T OW1 N IY0 AH0 S T -STONILY S T AA1 N AH0 L IY0 -STONINESS S T OW1 N IY0 N AH0 S -STONING S T OW1 N IH0 NG -STONINGTON S T OW1 N IH0 NG T AH0 N -STONISH S T AA1 N IH0 SH -STONISHED S T AA1 N IH0 SH T -STONISHING S T AA1 N IH0 SH IH0 NG -STONO S T OW1 N OW0 -STONOR S T AA1 N ER0 -STONOR'S S T AA1 N ER0 Z -STONY S T OW1 N IY0 -STONYHURST S T OW1 N IY0 HH ER0 S T -STOO S T UW1 -STOOARD S T UW1 AA0 R D -STOOD S T UH1 D -STOOD'ST S T UW1 D S T -STOODE S T UW1 D -STOODEST S T UH1 D AH0 S T -STOODLES S T UW1 D AH0 L Z -STOODST S T UH1 D S T -STOOF S T UW1 F -STOOGE S T UW1 JH -STOOK S T UH1 K -STOOKED S T UW1 K T -STOOKING S T UW1 K IH0 NG -STOOKS S T UW1 K S -STOOL S T UW1 L -STOOLS S T UW1 L Z -STOOM S T UW1 M -STOOP S T UW1 P -STOOP'D S T UW1 P D -STOOPED S T UW1 P T -STOOPID S T UW1 P IH0 D -STOOPIN S T UW1 P IH0 N -STOOPING S T UW1 P IH0 NG -STOOPS S T UW1 P S -STOOPT S T UW1 P T -STOOPY S T UW1 P IY0 -STOORWORM S T UW1 R W ER0 M -STOOT S T UW1 T -STOP S T AA1 P -STOP'D S T AA1 P D -STOPCHASE S T AA1 P CH EY2 S -STOPCOCK S T AA1 P K AA2 K -STOPE S T OW1 P -STOPED S T OW1 P T -STOPES S T OW1 P S -STOPFOOT S T AA1 P F UH2 T -STOPFORD S T AA1 P F ER0 D -STOPGAP S T AA1 P G AE2 P -STOPING S T OW1 P IH0 NG -STOPOVER S T AA1 P OW2 V ER0 -STOPP'D S T AA1 P D -STOPPA S T AA1 P AH0 -STOPPAGE S T AA1 P IH0 JH -STOPPAGES S T AA1 P IH0 JH IH0 Z -STOPPED S T AA1 P T -STOPPER S T AA1 P ER0 -STOPPERED S T AA1 P ER0 D -STOPPERS S T AA1 P ER0 Z -STOPPETH S T AA1 P IH0 TH -STOPPIN S T AA1 P IH0 N -STOPPING S T AA1 P IH0 NG -STOPPINGS S T AA1 P IH0 NG Z -STOPPIT S T AA1 P IH0 T -STOPPLE S T AA1 P AH0 L -STOPPUM S T AA1 P AH0 M -STOPS S T AA1 P S -STOPT S T AA1 P T -STOPWATCH S T AA1 P W AA2 CH -STOR S T AO1 R -STOR'D S T AA1 D -STORACE S T AO1 R AH0 S -STORAGE S T AO1 R AH0 JH -STORAGE S T AO1 R IH0 JH -STORAX S T AO1 R AE2 K S -STORBORG S T AO1 R B AO0 R G -STORBUK S T AO1 R B UH2 K -STORBY S T AO1 R B IY0 -STORCK S T AO1 R K -STORDALEN S T AO1 R D AA0 L EY1 N -STORE S T AO1 R -STORE'S S T AO1 R Z -STORED S T AO1 R D -STOREFIELD S T AO1 R F IY2 L D -STOREHOUSE S T AO1 R HH AW2 S -STOREHOUSES S T AO1 R HH AW2 Z AH0 Z -STOREKEEPER S T AO1 R K IY2 P ER0 -STOREKEEPER'S S T AO1 R K IY2 P ER0 Z -STOREKEEPERS S T AO1 R K IY2 P ER0 Z -STOREKEEPING S T AO1 R K IY2 P IH0 NG -STOREMAN S T AO1 R M AH0 N -STORER S T AO1 R ER0 -STOREROOM S T AO1 R R UW2 M -STOREROOMS S T AO1 R R UW2 M Z -STORES S T AO1 R Z -STORESHIP S T AO1 R SH IH2 P -STORESHIPS S T AO1 R SH IH2 P S -STOREY S T AO1 R IY0 -STOREY'S S T AO1 R IY0 Z -STOREYED S T AO1 R IY0 D -STOREYS S T AO1 R IY0 Z -STORIA S T AO1 R IY0 AH0 -STORIE S T AO1 R IY0 -STORIED S T AO1 R IY0 D -STORIES S T AO1 R IY0 Z -STORIETTES S T AO1 R IY0 EH1 T S -STORING S T AO1 R IH0 NG -STORISENDE S T AO1 R IH0 S EH1 N D EY0 -STORK S T AO1 R K -STORK'S S T AO1 R K S -STORKS S T AO1 R K S -STORM S T AO1 R M -STORM'D S T AO1 R M D -STORM'S S T AO1 R M Z -STORMBERG S T AO1 R M B ER0 G -STORMBOUND S T AO1 R M B AW2 N D -STORMCOCK S T AO1 R M K AA2 K -STORME S T AO1 R M -STORMED S T AO1 R M D -STORMER S T AO1 R M ER0 -STORMERS S T AO1 R M ER0 Z -STORMES S T AO1 R M Z -STORMFIELD S T AO1 R M F IY2 L D -STORMFIELD'S S T AO1 R M F IY2 L D Z -STORMFUL S T AO1 R M F AH0 L -STORMIER S T AO1 R M IY0 ER0 -STORMIEST S T AO1 R M IY0 IH0 S T -STORMILY S T AO1 R M AH0 L IY0 -STORMIN S T AO1 R M IY0 N -STORMING S T AO1 R M IH0 NG -STORMINGS S T AO2 R M IH0 NG Z -STORMLESS S T AO1 R M L AH0 S -STORMONT S T AO1 R M OW0 N T -STORMONT'S S T AO1 R M AA2 N T S -STORMONTH S T AO1 R M AH0 N TH -STORMPOINT S T AO1 R M P OY2 N T -STORMS S T AO1 R M Z -STORMWAYS S T AO1 R M W EY2 Z -STORMY S T AO1 R M IY0 -STORMY'S S T AO1 R M IY0 Z -STORNAWAY S T AO1 R N AH0 W EY2 -STORNDALE S T AO1 R N D EY2 L -STORNELLI S T AO1 R N EH1 L IY0 -STORNHAM S T AO1 R N HH AH0 M -STORNOWAY S T AO1 R N OW0 W EY2 -STORR S T AO1 R -STORRE S T AO1 R IY0 -STORRINGTON S T AO1 R IH0 NG T AH0 N -STORROW S T AA1 R OW0 -STORRS S T AO1 R Z -STORTHING S T AO1 R DH IH0 NG -STORY S T AO1 R IY0 -STORY'S S T AO1 R IY0 Z -STORYBOOK S T AO1 R IY0 B UH2 K -STORYBOOKS S T AO1 R IY0 B UH2 K S -STORYS S T AO1 R IY0 Z -STORYTELLER S T AO1 R IY0 T EH2 L ER0 -STORYTELLERS S T AO1 R IY0 T EH2 L ER0 Z -STORYTELLING S T AO1 R IY0 T EH2 L IH0 NG -STOSS S T AA1 S -STOT S T AA1 T -STOTFORD S T AA1 T F ER0 D -STOTHARD S T AA1 TH ER0 D -STOTS S T AA1 T S -STOTT S T AA1 T -STOTT'S S T AA1 T S -STOTTS S T AA1 T S -STOTZ S T AA1 T S -STOUGHTON S T OW1 T AH0 N -STOUGHTON'S S T OW1 T AH0 N Z -STOUND S T AW1 N D -STOUP S T UW1 P -STOUPS S T UW1 P S -STOUR S T AW1 R -STOUR'S S T UH1 R Z -STOURBRIDGE S T OW1 ER1 B R IH2 JH -STOUSH S T AW1 SH -STOUT S T AW1 T -STOUT'S S T AW1 T S -STOUTENBURGH S T AW1 T AH0 N B ER0 G -STOUTENBURGH'S S T AW1 T AH0 N B ER0 G Z -STOUTER S T AW1 T ER0 -STOUTEST S T AW1 T AH0 S T -STOUTHEART S T AW1 T HH AA2 R T -STOUTHEARTED S T AW1 T HH AA2 R T IH0 D -STOUTISH S T AW1 T IH0 SH -STOUTLY S T AW1 T L IY0 -STOUTNESS S T AW1 T N AH0 S -STOUTS S T AW1 T S -STOVE S T OW1 V -STOVE'S S T OW1 V Z -STOVED S T OW1 V D -STOVEPIPE S T OW1 V P AY2 P -STOVEPIPES S T OW1 V P AY2 P S -STOVER S T OW1 V ER0 -STOVER'S S T OW1 V ER0 Z -STOVES S T OW1 V Z -STOVEY S T OW1 V IY0 -STOVIK S T OW1 V IH0 K -STOVIN S T OW1 V IH0 N -STOW S T OW1 -STOW'D S T AW1 D -STOW'S S T OW1 Z -STOWAGE S T OW1 AH0 JH -STOWAWAY S T OW1 AH0 W EY2 -STOWAWAYS S T OW1 AH0 W EY2 Z -STOWBODY S T OW1 B AA2 D IY0 -STOWE S T OW1 -STOWE'S S T OW1 Z -STOWED S T OW1 D -STOWEL S T AW1 AH0 L -STOWELL S T AA1 W EH0 L -STOWER S T OW1 ER0 -STOWER'S S T OW1 ER0 Z -STOWEY S T OW1 IY0 -STOWIN S T OW1 IH0 N -STOWING S T OW1 IH0 NG -STOWMARKET S T OW1 M AA2 R K IH0 T -STOWN S T AW2 N -STOWORTH S T AA1 W ER0 TH -STOWS S T OW1 Z -STOWSHER S T OW1 SH ER0 -STOWTE S T OW1 T -STOWTING S T OW1 T IH0 NG -STOY S T OY1 -STOYAN S T OY1 AH0 N -STOYANOVITCH S T OY1 AH0 N AA1 V IH0 CH -STR S T R -STRA S T R AH0 -STRAANGE S T R AA1 EY0 N JH -STRAANGER S T R AA1 EY0 N JH ER0 -STRAAT S T R AA1 T -STRABANE S T R AH0 B EY2 N -STRABISMUS S T R AH0 B IH1 Z M AH0 S -STRABO S T R AA1 B OW0 -STRABO'S S T R AA1 B OW0 Z -STRACCHINO S T R AE1 K IY1 N OW0 -STRACHAN S T R AE1 CH AH0 N -STRACHEY S T R AE1 CH IY0 -STRACHEY'S S T R AE1 CH IY0 Z -STRACHINSKY S T R AE1 CH IH1 N S K IY0 -STRACHINSKY'S S T R AE1 CH IH1 N S K IY0 Z -STRACTED S T R AE1 K T IH0 D -STRAD S T R AE2 D -STRADA S T R AA1 D AH0 -STRADDLE S T R AE1 D AH0 L -STRADDLED S T R AE1 D AH0 L D -STRADDLES S T R AE1 D AH0 L Z -STRADDLING S T R AE1 D L IH0 NG -STRADELLA S T R AE1 D EH1 L AH0 -STRADELLA'S S T R AE1 D EH1 L AH0 Z -STRADIOTES S T R EY1 D IY0 OW2 T S -STRADIOTS S T R AE1 D IY0 AH0 T S -STRADIVARI S T R AE2 D IH0 V EH1 R IY0 -STRADIVARIUS S T R AE2 D IH0 V EH1 R IY0 AH0 S -STRADLING S T R AE1 D L IH0 NG -STRAE S T R EY1 -STRAET S T R EH0 T -STRAFE S T R EY1 F -STRAFED S T R EY1 F T -STRAFFORD S T R AE1 F ER0 D -STRAFFORD'S S T R AE1 F ER0 D Z -STRAFING S T R EY1 F IH0 NG -STRAGGALLS S T R AE1 G AO2 L Z -STRAGGLE S T R AE1 G AH0 L -STRAGGLED S T R AE1 G AH0 L D -STRAGGLER S T R AE1 G L ER0 -STRAGGLERS S T R AE1 G L ER0 Z -STRAGGLES S T R AE1 G AH0 L Z -STRAGGLING S T R AE1 G AH0 L IH0 NG -STRAGGLY S T R AE1 G AH0 L IY0 -STRAHAN S T R AE1 HH AH0 N -STRAHAN'S S T R AE1 HH AH0 N Z -STRAHL S T R AA1 L -STRAIGHT S T R EY1 T -STRAIGHT'S S T R EY1 T S -STRAIGHTAWAY S T R EY1 T AH0 W EY2 -STRAIGHTEN S T R EY1 T AH0 N -STRAIGHTENED S T R EY1 T AH0 N D -STRAIGHTENERS S T R EY1 T AH0 N ER0 Z -STRAIGHTENIN S T R EY1 T N IH0 N -STRAIGHTENING S T R EY1 T AH0 N IH0 NG -STRAIGHTENING S T R EY1 T N IH0 NG -STRAIGHTENS S T R EY1 T AH0 N Z -STRAIGHTER S T R EY1 T ER0 -STRAIGHTEST S T R EY1 T AH0 S T -STRAIGHTFORWARD S T R EY1 T F AO1 R W ER0 D -STRAIGHTFORWARDLY S T R EY2 T F AO1 R W ER0 D L IY0 -STRAIGHTFORWARDNESS S T R EY1 T F AO1 R W ER0 D N AH0 S -STRAIGHTLY S T R EY1 T L IY0 -STRAIGHTNESS S T R EY1 T N AH0 S -STRAIGHTS S T R EY1 T S -STRAIGHTWAY S T R EY1 T W EY2 -STRAIGHTWAYS S T R EY1 T W EY2 Z -STRAIK S T R EY1 K -STRAIN S T R EY1 N -STRAIN'D S T R EY1 N D -STRAIN'S S T R EY1 N Z -STRAINED S T R EY1 N D -STRAINER S T R EY1 N ER0 -STRAINERS S T R EY1 N ER0 Z -STRAININ S T R EY1 N IH0 N -STRAINING S T R EY1 N IH0 NG -STRAININGLY S T R EY1 N IH0 NG L IY0 -STRAININGS S T R EY1 N IH0 NG Z -STRAINS S T R EY1 N Z -STRAIT S T R EY1 T -STRAITEN S T R EY1 T AH0 N -STRAITENED S T R EY1 T AH0 N D -STRAITENING S T R EY1 T N IH0 NG -STRAITER S T R EY1 T ER0 -STRAITEST S T R EY1 T AH0 S T -STRAITHWAITE S T R EY1 TH W EY2 T -STRAITJACKET S T R EY1 T JH AE2 K AH0 T -STRAITLACED S T R EY1 T L AH0 S T -STRAITLY S T R EY1 T L IY0 -STRAITNESS S T R EY1 T N AH0 S -STRAITS S T R EY1 T S -STRAKE S T R EY1 K -STRAKED S T R EY1 K T -STRAKENCZ S T R AA1 K IH0 N CH -STRAKER S T R EY1 K ER0 -STRAKES S T R EY1 K S -STRAKOSCH S T R AA1 K AO1 SH -STRALEY S T R AE1 L IY0 -STRALSUND S T R AE1 L S AH0 N D -STRAMASH S T R AE1 M AE2 SH -STRAME S T R EY1 M -STRAMMFEST S T R AE1 M F EH2 S T -STRAMONIUM S T R AH0 M OW1 N IY0 AH0 M -STRAN S T R AE1 N -STRAND S T R AE1 N D -STRANDED S T R AE1 N D AH0 D -STRANDED S T R AE1 N D IH0 D -STRANDING S T R AE1 N D IH0 NG -STRANDS S T R AE1 N D Z -STRANDVIK S T R AE1 N D V IH0 K -STRANG S T R AE1 NG -STRANG'S S T R AE1 NG Z -STRANGE S T R EY1 N JH -STRANGE'S S T R EY1 N JH IH0 Z -STRANGELY S T R EY1 N JH L IY0 -STRANGENESS S T R EY1 N JH N AH0 S -STRANGENESSES S T R EY1 N JH N AH0 S IH0 Z -STRANGER S T R EY1 N JH ER0 -STRANGER'S S T R EY1 N JH ER0 Z -STRANGERS S T R EY1 N JH ER0 Z -STRANGERSHIP S T R EY1 N JH ER0 SH IH2 P -STRANGES S T R EY1 N JH IH0 Z -STRANGEST S T R EY1 N JH IH0 S T -STRANGEWAYS S T R EY1 N JH W EY2 Z -STRANGFORD S T R AE1 NG F ER0 D -STRANGLE S T R AE1 NG G AH0 L -STRANGLED S T R AE1 NG G AH0 L D -STRANGLEHOLD S T R AE1 NG G AH0 L HH OW2 L D -STRANGLER S T R AE1 NG G L ER0 -STRANGLER'S S T R AE1 NG G L ER0 Z -STRANGLERS S T R AE1 NG G L ER0 Z -STRANGLES S T R AE1 NG G AH0 L Z -STRANGLING S T R AE1 NG G AH0 L IH0 NG -STRANGLING S T R AE1 NG G L IH0 NG -STRANGMAN S T R AE1 NG M AH0 N -STRANGULATED S T R AE2 NG G Y AH0 L EY2 T IH0 D -STRANGULATION S T R AE2 NG G Y AH0 L EY1 SH AH0 N -STRANGWAY S T R AE1 NG W EY2 -STRANGWAYS S T R AE1 NG W EY2 Z -STRANGWISE S T R AE1 NG W AY2 Z -STRANGWISE'S S T R AE1 NG W AY2 Z IH0 Z -STRANGWYN S T R AE1 NG W IH0 N -STRANN S T R AE1 N -STRANNGER S T R AE1 N JH ER0 -STRANOR S T R AE1 N ER0 -STRANRAER S T R AE1 N R EY1 R -STRANSOM S T R AE1 N S AH0 M -STRAP S T R AE1 P -STRAP'S S T R AE1 P S -STRAPPADO S T R AE1 P AA1 D OW0 -STRAPPED S T R AE1 P T -STRAPPER S T R AE1 P ER0 -STRAPPERS S T R AE1 P ER0 Z -STRAPPIN S T R AE1 P IH0 N -STRAPPING S T R AE1 P IH0 NG -STRAPPINGS S T R AE1 P IH0 NG Z -STRAPS S T R AE1 P S -STRASBOURG S T R AE1 S B AO2 R G -STRASBURG S T R AE1 S B ER0 G -STRASBURGH S T R AE1 S B ER0 G -STRASOLDA S T R AE1 S OW1 L D AH0 -STRASS S T R AE1 S -STRASSBURG S T R AE1 S B ER0 G -STRASSE S T R AE1 S -STRATA S T R AE1 T AH0 -STRATAGEM S T R AE1 T AH0 JH AH0 M -STRATAGEMS S T R AE1 T AH0 JH AH0 M Z -STRATAS S T R AE1 T AH0 Z -STRATE S T R EY1 T -STRATEGEM S T R AE1 T IH0 JH EH0 M -STRATEGETIC S T R AE1 T IH0 JH EH1 T IH0 K -STRATEGI S T R AE1 T IH0 JH IY0 -STRATEGIAN S T R AE1 T IH0 JH IY0 AH0 N -STRATEGIANS S T R AH0 T IY1 JH AH0 N Z -STRATEGIC S T R AH0 T IY1 JH IH0 K -STRATEGICAL S T R AH0 T IY1 JH IH0 K AH0 L -STRATEGICALLY S T R AH0 T IY1 JH IH0 K L IY0 -STRATEGIES S T R AE1 T AH0 JH IY0 Z -STRATEGIST S T R AE1 T IH0 JH IH0 S T -STRATEGISTS S T R AE1 T IH0 JH IH0 S T S -STRATEGUS S T R AE1 T IH0 JH IH0 S -STRATEGY S T R AE1 T AH0 JH IY0 -STRATEMEYER S T R AE1 T IH0 M AY0 ER0 -STRATES S T R EY1 T S -STRATFIELD S T R AE1 T F IY2 L D -STRATFORD S T R AE1 T F ER0 D -STRATFORD'S S T R AE1 T F ER0 D Z -STRATFORDTOWN S T R AE1 T F AO2 R D T AW2 N -STRATH S T R AE1 TH -STRATHAIRLIE S T R AE1 T HH EH1 R L IY0 -STRATHAIVRON S T R AE1 TH EY1 V R AH0 N -STRATHBOGIE S T R AE1 TH B UH1 G IY0 -STRATHBOGY S T R AE1 TH B OW2 JH IY0 -STRATHCLYDE S T R AE1 TH K L AY1 D -STRATHCONA S T R AE1 TH K OW1 N AH0 -STRATHCONA'S S T R AE1 TH K AH0 N AH0 Z -STRATHDENE S T R AE1 TH D IY0 N -STRATHEARN S T R AE1 TH ER0 N -STRATHEDEN S T R AE1 TH IY1 D AH0 N -STRATHFIELDSAYE S T R AE1 TH F IY2 L D Z EY0 -STRATHGOVAN S T R AE1 TH G OW1 V AH0 N -STRATHMOIRA S T R AE1 TH M OY1 R AH0 -STRATHMORE S T R AE1 TH M AO2 R -STRATHMUIR S T R AE1 TH M Y UH1 R -STRATHORAN S T R AE1 TH AO0 R AE1 N -STRATHPEFFER S T R AE1 TH P EH1 F ER0 -STRATHS S T R AE1 TH S -STRATHSAY S T R AE1 TH S EY0 -STRATHSAY'S S T R AE1 TH S EY2 Z -STRATHSEY S T R AE1 TH S IY0 -STRATHSPEY S T R AE1 TH S P IY0 -STRATHSPEYS S T R AE1 TH S P EY1 Z -STRATHYRE S T R AE1 TH AY1 R -STRATIFICATION S T R AE1 T AH0 F AH0 K EY1 SH AH0 N -STRATIFICATIONS S T R AE1 T AH0 F AH0 K EY1 SH AH0 N Z -STRATIFIED S T R AE1 T AH0 F AY2 D -STRATIMOJEFF S T R AE1 T AH0 M AH0 JH EH1 F -STRATO S T R AE1 T OW0 -STRATON S T R AE1 T AH0 N -STRATONICE S T R AE1 T AH0 N IH0 S -STRATOSPHERE S T R AE1 T AH0 S F IH2 R -STRATTON S T R AE1 T AH0 N -STRATTON'S S T R AE1 T AH0 N Z -STRATUM S T R AE1 T AH0 M -STRATUS S T R AE1 T AH0 S -STRAUCHT S T R AW1 K T -STRAUDENHEIM S T R AW1 D IH0 N HH AY0 M -STRAUGHT S T R AO1 T -STRAUMEY S T R AW1 M IY0 -STRAUNGE S T R AW1 N JH -STRAUNGER S T R AW1 NG ER0 -STRAUS S T R AW1 S -STRAUSS S T R AW1 S -STRAUSS'S S T R AW1 S IH0 Z -STRAVINSKY S T R AH0 V IH1 N S K IY0 -STRAW S T R AO1 -STRAW'S S T R AO1 Z -STRAWBERRIES S T R AO1 B EH2 R IY0 Z -STRAWBERRY S T R AO1 B EH2 R IY0 -STRAWED S T R AO1 D -STRAWINSKY S T R AA1 V IH1 N S K IY0 -STRAWN S T R AO1 N -STRAWN'S S T R AO1 N Z -STRAWS S T R AO1 Z -STRAWSTACK S T R AO1 S T AE2 K -STRAWY S T R AO1 IY0 -STRAWYERS S T R AO1 Y ER0 Z -STRAY S T R EY1 -STRAY'D S T R EY1 D -STRAYED S T R EY1 D -STRAYER S T R EY1 ER0 -STRAYHORN S T R EY1 HH ER0 N -STRAYIN S T R EY1 IH0 N -STRAYING S T R EY1 IH0 NG -STRAYINGS S T R EY1 IH0 NG Z -STRAYS S T R EY1 Z -STREAK S T R IY1 K -STREAK'D S T R IY1 K D -STREAKED S T R IY1 K T -STREAKIN S T R IY1 K IH0 N -STREAKING S T R IY1 K IH0 NG -STREAKINGS S T R IY1 K IH0 NG Z -STREAKS S T R IY1 K S -STREAKY S T R IY1 K IY0 -STREAM S T R IY1 M -STREAM'D S T R IY2 M D -STREAM'S S T R IY2 M Z -STREAMED S T R IY1 M D -STREAMER S T R IY1 M ER0 -STREAMERS S T R IY1 M ER0 Z -STREAMETH S T R IY2 M IH0 TH -STREAMIN S T R IY2 M IH0 N -STREAMING S T R IY1 M IH0 NG -STREAMLET S T R IY1 M L AH0 T -STREAMLET'S S T R IY1 M L AH0 T S -STREAMLETS S T R IY1 M L AH0 T S -STREAMLINE S T R IY1 M L AY2 N -STREAMLINED S T R IY1 M L AY2 N D -STREAMS S T R IY1 M Z -STREAMSIDE S T R IY2 M S AY2 D -STREAMY S T R IY1 M IY0 -STREATFEILD S T R IY1 T F IY1 L D -STREATFIELD S T R IY1 T F IY2 L D -STREATHAM S T R IY1 TH AH0 M -STREATHMERE S T R IY1 TH M IH2 R -STREATLEY S T R IY1 T L IY0 -STREATOR S T R IY1 T ER0 -STREBELOW S T R EH1 B AH0 L OW0 -STREBER S T R IY1 B ER0 -STRECKFUSS S T R EH1 K F AH0 S -STRECKMANN S T R EH1 K M AH0 N -STREEK S T R IY2 K -STREESES S T R IY1 Z IH0 Z -STREET S T R IY1 T -STREET'S S T R IY1 T S -STREETCAR S T R IY1 T K AA2 R -STREETCARS S T R IY1 T K AA2 R Z -STREETE S T R IY1 T -STREETED S T R IY1 T IH0 D -STREETER S T R IY1 T ER0 -STREETER'S S T R IY1 T ER0 Z -STREETS S T R IY1 T S -STREETWALKER S T R IY1 T W AO2 K ER0 -STREFF S T R EH1 F -STREFFORD S T R EH1 F ER0 D -STREFFORD'S S T R EH1 F ER0 D Z -STREFFY S T R EH1 F IY0 -STREGA S T R IY1 G AH0 -STREHLA S T R EH1 L AH0 -STREIGHT S T R EY1 T -STREIGHT'S S T R AY1 T S -STREIGHTS S T R AY1 T S -STRELINSKI S T R EH0 L IH1 N S K IY0 -STRELITSKI S T R EH1 L IH1 T S K IY0 -STRELITSKI'S S T R EH1 L IH1 T S K IY0 Z -STRELITZ S T R EH1 L IH0 T S -STRELLENHAUS S T R EH1 L AH0 N HH AW2 S -STRELLEY S T R EH1 L IY0 -STRELSA S T R EH1 L S AH0 -STRELSA'S S T R EH1 L S AH0 Z -STRELSAU S T R EH1 L S AO0 -STRELTSI S T R EH1 L T S IY0 -STREN'TH S T R IY1 N TH -STRENG S T R EH1 NG -STRENGTH S T R EH1 NG K TH -STRENGTH S T R EH1 NG TH -STRENGTH'S S T R EH1 NG TH S -STRENGTHEN S T R EH1 NG TH AH0 N -STRENGTHEN'D S T R EH1 NG TH AH0 N D -STRENGTHENED S T R EH1 NG TH AH0 N D -STRENGTHENER S T R EH1 NG TH AH0 N ER0 -STRENGTHENETH S T R EH1 NG TH AH0 N EH1 TH -STRENGTHENING S T R EH1 NG TH AH0 N IH0 NG -STRENGTHENS S T R EH1 NG TH AH0 N Z -STRENGTHLESS S T R EH1 NG TH L AH0 S -STRENGTHS S T R EH1 NG K TH S -STRENGTHS S T R EH1 NG TH S -STRENUOSITY S T R EH1 N Y UW0 S AH0 T IY0 -STRENUOUS S T R EH1 N Y UW0 AH0 S -STRENUOUSLY S T R EH1 N Y UW0 AH0 S L IY0 -STRENUOUSNESS S T R EH1 N Y UW0 AH0 S N AH0 S -STREPHON S T R EH1 F AH0 N -STREPHON'S S T R EH1 F AH0 N Z -STREPSIADES S T R EH1 P S IY2 EY0 D Z -STREPSICEROS S T R EH1 P S IH0 S EH1 R OW0 Z -STRESA S T R EH1 S AH0 -STRESS S T R EH1 S -STRESSED S T R EH1 S T -STRESSES S T R EH1 S AH0 Z -STRESSES S T R EH1 S IH0 Z -STRESSFUL S T R EH1 S F AH0 L -STRESSING S T R EH1 S IH0 NG -STRET S T R EH1 T -STRETCH S T R EH1 CH -STRETCH'D S T R EH1 CH D -STRETCHED S T R EH1 CH T -STRETCHER S T R EH1 CH ER0 -STRETCHERS S T R EH1 CH ER0 Z -STRETCHES S T R EH1 CH AH0 Z -STRETCHES S T R EH1 CH IH0 Z -STRETCHEST S T R EH1 CH AH0 S T -STRETCHETH S T R EH1 CH IH0 TH -STRETCHIN S T R EH1 CH IH0 N -STRETCHING S T R EH1 CH IH0 NG -STRETCHINGS S T R EH1 CH IH0 NG Z -STRETCHT S T R EH1 CH T -STRETCHY S T R EH1 CH IY0 -STRETHER S T R EH1 DH ER0 -STRETHER'S S T R EH1 DH ER0 Z -STRETT S T R EH1 T -STRETTA S T R EH1 T AH0 -STRETTI S T R EH1 T IY0 -STRETTO S T R EH1 T OW2 -STRETTON S T R EH1 T AH0 N -STRETTON'S S T R EH1 T AH0 N Z -STRETTS S T R EH1 T S -STREUSS S T R UW1 S -STREVA S T R EH1 V AH0 -STREW S T R UW1 -STREW'D S T R UW1 D -STREWED S T R UW1 D -STREWING S T R UW1 IH0 NG -STREWN S T R UW1 N -STREWS S T R UW1 Z -STRIAE S T R AY1 IY0 -STRIATA S T R IY0 AA1 T AH0 -STRIATED S T R IY0 EY2 T IH0 D -STRIATIONS S T R IY0 EY1 SH AH0 N Z -STRIBBLE S T R IH1 B AH0 L -STRIBLING S T ER1 AH0 B AH0 L IH0 NG -STRIBLING S T R IH1 B L IH0 NG -STRIC'LY S T R IH1 K AH0 L IY0 -STRICK S T R IH1 K -STRICKEN S T R IH1 K AH0 N -STRICKENLY S T R IH1 K AH0 N L IY0 -STRICKLAND S T R IH1 K L AH0 N D -STRICKLAND'S S T R IH1 K L AH0 N D Z -STRICKLANDS S T R IH1 K L AH0 N D Z -STRICKLY S T R IH1 K L IY0 -STRICT S T R IH1 K T -STRICTER S T R IH1 K T ER0 -STRICTEST S T R IH1 K T AH0 S T -STRICTLY S T R IH1 K T L IY0 -STRICTNESS S T R IH1 K T N AH0 S -STRICTURE S T R IH1 K CH ER0 -STRICTURES S T R IH1 K CH ER0 Z -STRID S T R IH1 D -STRIDDEN S T R IH1 D AH0 N -STRIDE S T R AY1 D -STRIDE'S S T R AY1 D Z -STRIDED S T R AY1 D IH0 D -STRIDENCY S T R AY1 D AH0 N S IY0 -STRIDENT S T R AY1 D AH0 N T -STRIDENTLY S T R AY1 D AH0 N T L IY0 -STRIDER S T R AY1 D ER0 -STRIDERS S T R AY1 D ER0 Z -STRIDES S T R AY1 D Z -STRIDING S T R AY1 D IH0 NG -STRIDOR S T R AY1 D ER0 -STRIDULATION S T R IH1 JH AH0 L EY1 SH AH0 N -STRIDULATIONS S T R IH1 JH AH0 L EY1 SH AH0 N Z -STRIDULOUS S T R IH1 JH AH0 L AH0 S -STRIEM S T R IY1 M -STRIFE S T R AY1 F -STRIFE'S S T R AY1 F S -STRIFES S T R AY1 F S -STRIK'ST S T R AY1 K S T -STRIKE S T R AY1 K -STRIKE'S S T R AY1 K S -STRIKEBREAKERS S T R AY1 K B R EY2 K ER0 Z -STRIKEN S T R AY1 K AH0 N -STRIKER S T R AY1 K ER0 -STRIKER'S S T R AY1 K ER0 Z -STRIKERS S T R AY1 K ER0 Z -STRIKES S T R AY1 K S -STRIKEST S T R AY1 K AH0 S T -STRIKETH S T R AY1 K TH -STRIKIN S T R AY1 K IH0 N -STRIKING S T R AY1 K IH0 NG -STRIKINGLY S T R AY1 K IH0 NG L IY0 -STRINDBERG S T R AY1 N D B ER0 G -STRINDBERG'S S T R AY1 N D B ER0 G Z -STRING S T R IH1 NG -STRING'S S T R IH1 NG Z -STRINGED S T R IH1 NG D -STRINGENCY S T R IH1 N JH AH0 N S IY0 -STRINGENT S T R IH1 N JH AH0 N T -STRINGENTLY S T R IH1 N JH AH0 N T L IY0 -STRINGER S T R IH1 NG ER0 -STRINGER'S S T R IH1 NG ER0 Z -STRINGERS S T R IH1 NG ER0 Z -STRINGFELLOW S T R IH1 NG F EH2 L OW0 -STRINGHAM S T R IH1 NG HH AE2 M -STRINGHAM'S S T R IH1 NG AH0 M Z -STRINGIN S T R IH1 NG AH0 N -STRINGING S T R IH1 NG IH0 NG -STRINGLESS S T R IH1 NG L AH0 S -STRINGPIECE S T R IH1 NG P IY2 S -STRINGS S T R IH1 NG Z -STRINGY S T R IH1 NG IY0 -STRINT S T R IH1 N T -STRIP S T R IH1 P -STRIPE S T R AY1 P -STRIPED S T R AY1 P T -STRIPER S T R AY1 P ER0 -STRIPES S T R AY1 P S -STRIPEY S T R AY1 P IY0 -STRIPING S T R AY1 P IH0 NG -STRIPINGS S T R AY1 P IH0 NG Z -STRIPLING S T R IH1 P L IH0 NG -STRIPLING'S S T R IH1 P L IH0 NG Z -STRIPLINGS S T R IH1 P L IH0 NG Z -STRIPP'D S T R IH1 P D -STRIPPED S T R IH1 P T -STRIPPER S T R IH1 P ER0 -STRIPPERS S T R IH1 P ER0 Z -STRIPPING S T R IH1 P IH0 NG -STRIPPINGS S T R IH1 P IH0 NG Z -STRIPS S T R IH1 P S -STRIPT S T R IH1 P T -STRIPY S T R IH1 P IY0 -STRITE S T R AY1 T -STRIVE S T R AY1 V -STRIVED S T R AY1 V D -STRIVEN S T R IH1 V AH0 N -STRIVER S T R AY1 V ER0 -STRIVERS S T R AY1 V ER0 Z -STRIVES S T R AY1 V Z -STRIVEST S T R AY1 V EH2 S T -STRIVETH S T R AY1 V EH2 TH -STRIVIN S T R AY1 V IH0 N -STRIVING S T R AY1 V IH0 NG -STRIVINGS S T R AY1 V IH0 NG Z -STRIX S T R IH1 K S -STROBELL S T R OW1 B AH0 L -STROBIK S T R OW1 B IH0 K -STROBRIDGE S T R AA1 B R IH0 JH -STROBRIDGE'S S T R AA1 B R IH0 JH AH0 Z -STROCK S T R AA1 K -STRODE S T R OW1 D -STROEBEL S T R OW1 B AH0 L -STROEBEL'S S T R OW1 B AH0 L Z -STROEHEN S T R OW1 AH0 N -STROEM S T R OW1 M -STROEVE S T R OW1 V -STROEVE'S S T R OW1 V Z -STROGANOFF S T R OW1 G AH0 N AO2 F -STROGANOFFS S T R OW1 G AH0 N AO2 F S -STROGOFF S T R AA1 G AO0 F -STROGUE S T R OW1 G -STROICI S T R OY1 CH IY0 -STROIKE S T R OY1 K -STROKE S T R OW1 K -STROKE'S S T R OW2 K S -STROKED S T R OW1 K T -STROKES S T R OW1 K S -STROKHR S T R OW1 K R -STROKING S T R OW1 K IH0 NG -STROKINGS S T R OW1 K IH0 NG Z -STROKOR S T R OW1 K ER0 -STROLL S T R OW1 L -STROLL'D S T R OW1 L D -STROLLED S T R OW1 L D -STROLLER S T R OW1 L ER0 -STROLLER'S S T R OW1 L ER0 Z -STROLLERS S T R OW1 L ER0 Z -STROLLIN S T R AA1 L IH0 N -STROLLING S T R OW1 L IH0 NG -STROLLS S T R OW1 L Z -STROM S T R AA1 M -STROMA S T R OW1 M AH0 -STROMBERG S T R AA1 M B ER0 G -STROMBERG'S S T R AA1 M B ER0 G Z -STROMBICHIDES S T R AA1 M B IH1 CH AY1 D Z -STROMBOLI S T R AA2 M B OW1 L IY0 -STROMER S T R OW1 M ER0 -STROMLING S T R AA1 M L IH0 NG -STROMNESS S T R AA1 M N AH0 S -STROMSTAD S T R AA1 M S T AE2 D -STRONE S T R OW1 N IY0 -STRONG S T R AO1 NG -STRONG'S S T R AO1 NG Z -STRONGARA S T R AO1 NG G AA1 R AH0 -STRONGBOW S T R AO1 NG B OW2 -STRONGBOW'S S T R AO1 NG B OW2 Z -STRONGBOX S T R AO1 NG B AA2 K S -STRONGE S T R AO1 NG -STRONGER S T R AO1 NG ER0 -STRONGER S T R AO1 NG G ER0 -STRONGER'N S T R AO1 NG ER0 AH0 N -STRONGEST S T R AO1 NG G AH0 S T -STRONGHEART S T R AO1 NG HH AA2 R T -STRONGHOLD S T R AO1 NG HH OW2 L D -STRONGHOLDS S T R AO1 NG HH OW2 L D Z -STRONGISH S T R AO1 NG IH0 SH -STRONGITHARM S T R AO1 NG G IH0 TH AA1 R M -STRONGLY S T R AO1 NG L IY0 -STRONGNESS S T R AO1 NG N AH0 S -STRONGROOM S T R AO1 NG R UW2 M -STRONGS S T R AO1 NG Z -STRONGTHARM S T R AO1 NG TH AA1 R M -STRONTIUM S T R AA1 N T IY0 AH0 M -STROOD S T R UW1 D -STROOK S T R UH1 K -STROOM S T R UW2 M -STROP S T R AA1 P -STROPHE S T R AA1 F AH0 -STROPHES S T R AA1 F AH0 S -STROPHIUS S T R OW1 F IY0 IH0 S -STROPPED S T R AA1 P T -STROPPING S T R AA1 P IH0 NG -STROPS S T R AA1 P S -STRORLEY S T R AO1 R L IY0 -STROTHER S T R AA1 DH ER0 -STROTHERS S T R AH1 DH ER0 Z -STROUD S T R AW1 D -STROUDS S T R AW1 D Z -STROUDSBURG S T R AW1 D Z B ER0 G -STROUSS S T R AW1 S -STROUT S T R AW1 T -STROUT'S S T R AW1 T S -STROVE S T R OW1 V -STROW S T R OW1 -STROW'D S T R AW1 D -STROWED S T R OW1 D -STROWN S T R OW1 N -STROWS S T R OW1 Z -STROYAN S T R OY1 AH0 N -STROYER S T R OY1 ER0 -STROZZA S T R AA1 Z AH0 -STROZZI S T R AA1 Z IY0 -STRUBBER S T R AH1 B ER0 -STRUBLE S T R UW1 B AH0 L -STRUCK S T R AH1 K -STRUCKEN S T R AH1 K AH0 N -STRUCTION S T R AH1 K SH AH0 N -STRUCTURAL S T R AH1 K CH ER0 AH0 L -STRUCTURALLY S T R AH1 K CH ER0 AH0 L IY0 -STRUCTURE S T R AH1 K CH ER0 -STRUCTURE'S S T R AH1 K CH ER0 Z -STRUCTURED S T R AH1 K CH ER0 D -STRUCTURELESS S T R AH1 K CH ER0 L AH0 S -STRUCTURES S T R AH1 K CH ER0 Z -STRUDWICK S T R AH1 D W IH0 K -STRUENSEE S T R UH1 N S IY1 -STRUGGLE S T R AH1 G AH0 L -STRUGGLE'S S T R AH1 G AH0 L Z -STRUGGLED S T R AH1 G AH0 L D -STRUGGLER S T R AH1 G AH0 L ER0 -STRUGGLERS S T R AH1 G L ER0 Z -STRUGGLES S T R AH1 G AH0 L Z -STRUGGLIN S T R AH1 G AH0 L IH0 N -STRUGGLING S T R AH1 G AH0 L IH0 NG -STRUGGLING S T R AH1 G L IH0 NG -STRUGGLINGS S T R AH1 G AH0 L IH0 NG Z -STRULDBRUGS S T R AH1 L D B R AH1 G Z -STRULL S T R AH1 L -STRUM S T R AH1 M -STRUMLEY S T R AH1 M L IY0 -STRUMMED S T R AH1 M D -STRUMMERY S T R AH1 M ER0 IY0 -STRUMMIN S T R AH1 M IH0 N -STRUMMING S T R AH1 M IH0 NG -STRUMP S T R AH1 M P -STRUMPET S T R AH1 M P AH0 T -STRUMPETS S T R AH1 M P AH0 T S -STRUMS S T R AH1 M Z -STRUNG S T R AH1 NG -STRUNSKY S T R AH1 N S K IY0 -STRUT S T R AH1 T -STRUTABOUT S T R AH1 T AH0 B AW2 T -STRUTH S T R UW1 TH -STRUTHERS S T R AH1 DH ER0 Z -STRUTHERS'S S T R AH1 DH ER0 Z IH0 Z -STRUTHIO S T R UW1 TH IY0 OW0 -STRUTS S T R AH1 T S -STRUTT S T R AH1 T -STRUTT'S S T R AH1 T S -STRUTTED S T R AH1 T IH0 D -STRUTTER S T R AH1 T ER0 -STRUTTIN S T R AH1 T IH0 N -STRUTTING S T R AH1 T IH0 NG -STRUTTINGS S T R AH1 T IH0 NG Z -STRUTWELL S T R AH1 T W EH2 L -STRUVE S T R UW1 V -STRUVE'S S T R AH1 V Z -STRUVELIUS S T R UW1 V L IY0 IH0 S -STRUWWELPETER S T R UW1 W W EH1 L P IY0 T ER0 -STRYCHNIA S T R IH1 K N IY0 AH0 -STRYCHNINE S T R IH1 K N AY2 N -STRYCHNOS S T R IH1 K N OW0 S -STRYKER S T R AY1 K ER0 -STRYKER'S S T R AY1 K ER0 Z -STRYMON S T R IH1 M AH0 N -STRYPE S T R IH1 P -STRYPE'S S T R IH1 P S -STRYVER S T R AY1 V ER0 -STS S T S -STU S T UW1 -STUART S T AO1 R T -STUART S T UW1 ER0 T -STUART S T Y UW1 ER0 T -STUART'S S T AO1 R T S -STUART'S S T UW1 ER0 T S -STUART'S S T Y UW1 ER0 T S -STUARTS S T UW1 ER0 T S -STUB S T AH1 B -STUBB S T AH1 B -STUBB'S S T AH1 B Z -STUBBED S T AH1 B D -STUBBER S T AH1 B ER0 -STUBBERD S T AH1 B ER0 D -STUBBES S T AH1 B Z -STUBBING S T AH1 B IH0 NG -STUBBINGS S T AH1 B IH0 NG Z -STUBBINS S T AH1 B IH0 N Z -STUBBLE S T AH1 B AH0 L -STUBBLED S T AH1 B AH0 L D -STUBBLEFIELD S T AH1 B AH0 L F IY2 L D -STUBBLES S T AH1 B AH0 L Z -STUBBLY S T AH1 B L IY0 -STUBBORN S T AH1 B ER0 N -STUBBORNER S T AH1 B ER0 N ER0 -STUBBORNESS S T AH1 B ER0 N AH0 S -STUBBORNEST S T AH1 B ER0 N AH0 S T -STUBBORNLY S T AH1 B ER0 N L IY0 -STUBBORNNESS S T AH1 B ER0 N AH0 S -STUBBORNNESS S T AH1 B ER0 N N AH0 S -STUBBS S T AH1 B Z -STUBBS'S S T AH1 B Z IH0 Z -STUBBY S T AH1 B IY0 -STUBBY'S S T AH1 B IY0 Z -STUBE S T UW1 B -STUBEN S T UW1 B IH0 N -STUBLEY S T AH1 B L IY0 -STUBS S T AH1 B Z -STUBTAIL S T AH1 T EY2 L -STUCCO S T AH1 K OW0 -STUCCOED S T AH1 K OW0 D -STUCK S T AH1 K -STUCK'S S T AH1 K S -STUCKER S T AH1 K ER0 -STUCKUP S T AH1 K AH2 P -STUCKY S T AH1 K IY0 -STUD S T AH1 D -STUDD S T AH1 D -STUDDED S T AH1 D IH0 D -STUDDENHAM S T AH1 D IH0 N HH AH0 M -STUDDING S T AH1 D IH0 NG -STUDDINGSAIL S T AH1 D IH0 NG Z EY2 L -STUDDINGSAILS S T AH1 D IH0 N S EY1 L Z -STUDDY S T AH1 D IY0 -STUDE S T UW1 D -STUDEBAKER S T UW1 D AH0 B EY2 K ER0 -STUDENT S T UW1 D AH0 N T -STUDENT'S S T UW1 D AH0 N T S -STUDENTS S T UW1 D AH0 N T S -STUDENTSHIP S T UW1 D AH0 N T SH IH2 P -STUDHOLME S T AH1 D HH OW2 L M -STUDIA S T UW1 D IY0 AH0 -STUDIE S T AH1 D IY0 -STUDIED S T AH1 D IY0 D -STUDIEDLY S T AH1 D IY0 D L IY0 -STUDIEN S T AH1 D IY0 N -STUDIES S T AH1 D IY0 Z -STUDII S T UW1 D IY0 IY2 -STUDIIS S T AH1 D IY2 Z -STUDIO S T UW1 D IY0 OW2 -STUDIORUM S T UW1 D IY0 AO1 R AH0 M -STUDIOS S T UW1 D IY0 OW2 Z -STUDIOSUS S T UW1 D IY0 OW2 Z AH0 S -STUDIOUS S T UW1 D IY0 AH0 S -STUDIOUSLY S T UW1 D IY0 AH0 S L IY0 -STUDIOUSNESS S T UW1 D IY0 AH0 S N AH0 S -STUDIUM S T UW1 D IY0 AH0 M -STUDLAND S T AH1 D L AH0 N D -STUDLEIGH S T AH1 D L AH0 -STUDLEIGHS S T AH1 D L IY0 Z -STUDLEY S T AH1 D L IY0 -STUDS S T AH1 D Z -STUDWICK S T AH1 D W IH0 K -STUDWICK'S S T AH1 D W IH0 K S -STUDY S T AH1 D IY0 -STUDY'S S T AH1 D IY0 Z -STUDYIN S T AH1 D IY0 IH0 N -STUDYING S T AH1 D IY0 IH0 NG -STUDZIANKA S T AH0 JH IY0 AA1 NG K AH0 -STUEBEL S T UH1 B AH0 L -STUECK S T UW1 K -STUERMER S T UW1 ER1 M ER0 -STUERMER'S S T UW1 ER0 M ER0 Z -STUF S T AH1 F -STUFF S T AH1 F -STUFF'D S T AH1 F D -STUFF'LL S T AH1 F L -STUFF'S S T AH1 F S -STUFFE S T AH1 F -STUFFED S T AH1 F T -STUFFER S T AH1 F ER0 -STUFFERS S T AH1 F ER0 Z -STUFFIER S T AH1 F IY0 ER0 -STUFFILY S T AH1 F AH0 L IY0 -STUFFIN S T AH1 F IH0 N -STUFFINESS S T AH1 F IH0 N IH0 S -STUFFING S T AH1 F IH0 NG -STUFFINGS S T AH1 F IH0 NG Z -STUFFS S T AH1 F S -STUFFY S T AH1 F IY0 -STUFFY'S S T AH1 F IY0 Z -STUFT S T AH1 F T -STUHK S T UW1 K -STUKA S T UW1 K AH0 -STUKAS S T UW1 K AH0 Z -STUKELEY S T UW1 K AH0 L IY0 -STUKELEY'S S T UW1 K AH0 L IY0 Z -STUKELY S T AH1 K L IY0 -STUKELY'S S T AH1 K L IY0 Z -STUKEY S T AH1 K IY0 -STULPNAGEL S T AH1 L P N AH0 G AH0 L -STULTA S T AH1 L T AH0 -STULTIFICATION S T AH1 L T AH0 F AH0 K EY1 SH AH0 N -STULTIFIED S T AH1 L T AH0 F AY2 D -STULTIFIES S T AH1 L T AH0 F AY2 Z -STULTIFY S T AH1 L T AH0 F AY2 -STULTIFYING S T AH1 L T AH0 F AY2 IH0 NG -STULTITIA S T AH1 L T AH0 T Y AH0 -STULTS S T AH1 L T S -STULTUS S T AH1 L T AH0 S -STULTZ S T AH1 L T S -STUMBLE S T AH1 M B AH0 L -STUMBLED S T AH1 M B AH0 L D -STUMBLER S T AH1 M B L ER0 -STUMBLES S T AH1 M B AH0 L Z -STUMBLETH S T AH1 M B AH0 L TH -STUMBLIN S T AH1 M B L IH0 N -STUMBLIN' S T AH1 M B L IH0 N -STUMBLING S T AH1 M B AH0 L IH0 NG -STUMBLING S T AH1 M B L IH0 NG -STUMBLINGLY S T AH1 M B L IH0 NG L IY0 -STUMBLINGS S T AH1 M B L IH0 NG Z -STUMFOLD S T AH1 M F OW2 L D -STUMFOLD'S S T AH1 M F OW2 L D Z -STUMFOLDIAN S T AH1 M F OW2 L D IY0 AH0 N -STUMM S T AH1 M -STUMM'S S T AH1 M Z -STUMMICK S T AH1 M IH0 K -STUMMICKS S T AH1 M IH0 K S -STUMMIK S T AH1 M IH0 K -STUMP S T AH1 M P -STUMP'S S T AH1 M P S -STUMPAGE S T AH1 M P IH0 JH -STUMPED S T AH1 M P T -STUMPER S T AH1 M P ER0 -STUMPF S T AH1 M P F -STUMPH S T AH1 M F -STUMPIN S T AH1 M P IH0 N -STUMPING S T AH1 M P IH0 NG -STUMPINGHAME S T AH1 M P IH0 NG AH0 M -STUMPS S T AH1 M P S -STUMPTAIL S T AH1 M P T EY2 L -STUMPTON S T AH1 M P T AH0 N -STUMPY S T AH1 M P IY0 -STUMPY'S S T AH1 M P IY0 Z -STUN S T AH1 N -STUN' S T AH1 N -STUNDEN S T AH1 N D AH0 N -STUNG S T AH1 NG -STUNK S T AH1 NG K -STUNN'D S T AH1 N D -STUNNED S T AH1 N D -STUNNENBERG S T AH1 N AH0 N B ER0 G -STUNNER S T AH1 N ER0 -STUNNERS S T AH1 N ER0 Z -STUNNIN S T AH1 N IH0 N -STUNNING S T AH1 N IH0 NG -STUNNINGLY S T AH1 N IH0 NG L IY0 -STUNS S T AH1 N Z -STUNSAIL S T AH1 N S EY2 L -STUNSAILS S T AH1 N S EY2 L Z -STUNT S T AH1 N T -STUNTED S T AH1 N T IH0 D -STUNTING S T AH1 N T IH0 NG -STUNTS S T AH1 N T S -STUPE S T UW1 P -STUPEFACTION S T UW1 P AH0 F AE1 K SH AH0 N -STUPEFIED S T UW1 P F AY2 D -STUPEFIES S T UW1 P F AY2 Z -STUPEFY S T UW0 P IY1 F IY0 -STUPEFYING S T UW0 P EH1 F AY2 IH0 NG -STUPENDIOUS S T UW0 P EH1 N D IY0 AH0 S -STUPENDOUS S T UW0 P EH1 N D AH0 S -STUPENDOUSLY S T UW0 P EH1 N D AH0 S L IY0 -STUPENDOUSNESS S T UW0 P EH1 N D AH0 S N AH0 S -STUPID S T UW1 P AH0 D -STUPID S T UW1 P IH0 D -STUPIDE S T UW1 P IH0 D -STUPIDER S T UW1 P IH0 D ER0 -STUPIDEST S T UW1 P IH0 D AH0 S T -STUPIDITIES S T UW0 P IH1 D IH0 T IY0 Z -STUPIDITY S T UW0 P IH1 D IH0 T IY0 -STUPIDLY S T UW1 P AH0 D L IY0 -STUPIDNESS S T UW0 P IH1 D N AH0 S -STUPIDS S T UW1 P IH0 D Z -STUPIFIED S T UW1 P AH0 F AY2 D -STUPIFY S T UW1 P AH0 F AY2 -STUPIFYING S T UW1 P AH0 F AY2 IH0 NG -STUPOR S T UW1 P ER0 -STUPORS S T UW1 P ER0 Z -STUPOUR S T UW1 P AO1 R -STUPPENY S T AH1 P IH0 N IY0 -STURATZBERG S T ER1 AH0 T S B ER0 G -STURB S T ER1 B -STURCH S T ER1 K -STURDEE S T ER1 D IY0 -STURDEVANT S T ER1 D IH0 V AH0 N T -STURDIER S T ER1 D IY0 ER0 -STURDIEST S T ER1 D IY0 AH0 S T -STURDILY S T ER1 D AH0 L IY0 -STURDINESS S T ER1 D IY0 N AH0 S -STURDY S T ER1 D IY0 -STURE S T UH1 R -STURE'S S CH ER0 Z -STURGE S T ER1 JH -STURGEON S T ER1 JH AH0 N -STURGEON S T ER1 JH IH0 N -STURGEON'S S T ER1 JH AH0 N Z -STURGEONS S T ER1 JH AH0 N Z -STURGES S T ER1 JH IH0 Z -STURGESS S T ER1 G IH0 S -STURGIS S T ER1 JH IH0 S -STURGIS'S S T ER1 JH IH0 S IH0 Z -STURGISES S T ER1 G AH0 S AH0 Z -STURK S T ER1 K -STURK'S S T ER2 K S -STURLA S T ER1 L AH0 -STURLUSON S T ER1 L AH0 S AH0 N 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AH0 -SWEARE S W EH1 R -SWEARED S W IH1 R D -SWEARENGEN S W IH1 R IH0 NG AH0 N -SWEARER S W IH1 R ER0 -SWEARERS S W EH1 R ER0 Z -SWEAREST S W EH1 R EH2 S T -SWEARETH S W EH1 R IH0 TH -SWEARIN S W IH1 R IH0 N -SWEARING S W EH1 R IH0 NG -SWEARINGEN S W EH1 R IH0 NG AH0 N -SWEARS S W EH1 R Z -SWEAT S W EH1 T -SWEATBAND S W IY1 T B AE2 N D -SWEATED S W EH1 T IH0 D -SWEATER S W EH1 T ER0 -SWEATER'S S W EH1 T ER0 Z -SWEATERS S W EH1 T ER0 Z -SWEATIN S W EH1 T IH0 N -SWEATING S W EH1 T IH0 NG -SWEATINGS S W EH1 T IH0 NG Z -SWEATS S W EH1 T S -SWEATSHOP S W EH1 T SH AA2 P -SWEATSHOPS S W EH1 T SH AA2 P S -SWEATY S W EH1 T IY0 -SWEDE S W IY1 D -SWEDE'S S W IY1 D Z -SWEDEN S W IY1 D AH0 N -SWEDEN'S S W IY1 D AH0 N Z -SWEDENBERG S W IY1 D AH0 N B ER0 G -SWEDENBORG S W IY1 D AH0 N B AO0 R G -SWEDENBORG'S S W IY1 D AH0 N B AO0 R G Z -SWEDENBORGIAN S W IY1 D AH0 N B AO1 R JH AH0 N -SWEDENBORGIANISM S W IY1 D AH0 N B AO1 R JH AH0 N IH2 Z AH0 M -SWEDENBORGIANS S W IY1 D AH0 N B AO1 R JH AH0 N Z -SWEDES 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T AH0 N -TALLINGTON'S T AO1 L IH0 NG T AH0 N Z -TALLIS T AE1 L IH0 S -TALLISH T AE1 L IH0 SH -TALLMADGE T AE1 L M AE0 JH -TALLMAN T AO1 L M AH0 N -TALLMAN'S T AO1 L M AH0 N Z -TALLNESS T AO1 L N AH0 S -TALLONS T AE1 L AH0 N Z -TALLOW T AE1 L OW0 -TALLOWAX T AE2 L AH0 W AE2 K S -TALLOWED T AE1 L OW0 D -TALLOWY T AE2 L AH0 W IY0 -TALLUSHATCHEE T AE2 L UW1 SH AE2 CH IY0 -TALLY T AE1 L IY0 -TALLY'S T AE1 L IY0 Z -TALLYHO T AE2 L IY0 HH OW1 -TALLYING T AE1 L IY0 IH0 NG -TALLYMAN T AE2 L IY0 M AE2 N -TALMA T AE1 L M AH0 -TALMA'S T AE1 L M AH0 Z -TALMADGE T AE1 L M AE0 JH -TALMAGE T AE1 L M IH0 JH -TALMAGE'S T AE1 L M IH0 JH IH0 Z -TALMASH T AE1 L M AE0 SH -TALMONT T AE1 L M AA2 N T -TALMUD T AE1 L M AH0 D -TALMUDIC T AE1 L M UW1 D IH0 K -TALMUDICAL T AE1 L M UW1 D AH0 K AH0 L -TALMUDIST T AE1 L M UW1 D AH0 S T -TALMUDISTS T AE1 L M UW1 D AH0 S T S -TALMUDS T AE1 L M AH1 D Z -TALOFA T AE1 L AH0 F AH0 -TALOLO T AE1 L AH0 L OW0 -TALON T AE1 L AH0 N -TALON'S T AE1 L AH0 N Z -TALONED T AE1 L AH0 N D -TALONS T AE1 L AH0 N Z -TALOO T AH0 L UW2 -TALOUEL T AE1 L UW1 AH0 L -TALPERS T AE1 L P ER0 Z -TALPERS'S T AE1 L P ER0 Z IH0 Z -TALS T AH0 L Z -TALSO T AA1 L S OW0 -TALSONIAN T AA1 L S OW1 N IY0 AH0 N -TALT T AO1 L T -TALTAVULL T AA1 L T AH0 V AH0 L -TALTHYBIUS T AE1 L TH IH0 B IY0 IH0 S -TALTON T AE1 L T AH0 N -TALU T AA1 L UW0 -TALUS T AE1 L IH0 S -TALUSES T AE1 L AH0 S IH0 Z -TALUTA T AA0 L UW1 T AH0 -TALVIG T AE1 L V IH0 G -TAM T AE1 M -TAM'D T AE2 M D -TAM'S T AE2 M Z -TAMA T AA1 M AH0 -TAMADA T AH0 M AA1 D AH0 -TAMADA'S T AH0 M AA1 D AH0 Z -TAMAHAY T AA0 M AA0 HH EY0 -TAMAHNAWUS T AA0 M AA0 N AO1 IH0 S -TAMAHNOUS T AA0 M AA0 N AH0 S -TAMAHU T AA0 M AA1 HH UW0 -TAMAI T AA0 M AA1 IY0 -TAMAITI T AA0 M AA0 IY1 T IY0 -TAMALA T AH0 M AA1 L AH0 -TAMALE T AH0 M AA1 L IY0 -TAMALES T AH0 M AA1 L IY0 Z -TAMALPAIS T AH0 M AA1 L P EY1 -TAMAN T EY1 M AH0 N -TAMANAC T AE1 M AH0 N AE2 K -TAMANACS T AE1 M AH0 N AE2 K S -TAMANDUA T AA0 M AA0 N D UW0 AH0 -TAMANES T AH0 M AA1 N IY0 Z -TAMANOIR 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M P L AH0 M AO2 R Z -TEMPLES T EH1 M P AH0 L Z -TEMPLESTOWE T EH1 M P L IH0 S T OW1 -TEMPLETON T EH1 M P AH0 L T AH0 N -TEMPLETON'S T EH1 M P AH0 L T AH0 N Z -TEMPLEWOOD T EH1 M P AH0 L W UH2 D -TEMPLO T EH1 M P L OW0 -TEMPLUM T EH1 M P L AH0 M -TEMPO T EH1 M P OW2 -TEMPORA T EH1 M P ER0 AH0 -TEMPORAL T EH1 M P ER0 AH0 L -TEMPORALITIES T EH1 M P ER0 AE1 L IH0 T IY0 Z -TEMPORALITY T EH1 M P ER0 AE1 L AH0 T IY0 -TEMPORALLY T EH1 M P ER0 AH0 L IY0 -TEMPORALS T EH1 M P ER0 AH0 L Z -TEMPORARILY T EH2 M P ER0 EH1 R AH0 L IY0 -TEMPORARY T EH1 M P ER0 EH2 R IY0 -TEMPORE T EH1 M P AO0 R -TEMPORE T EH2 M P AO1 R IY0 -TEMPORIBUS T EH2 M P AO1 R AH0 B AH0 S -TEMPORIS T EH1 M P ER0 IH0 S -TEMPORISE T EH1 M P ER0 AY1 Z -TEMPORISED T EH1 M P ER0 AY1 Z D -TEMPORISING T EH1 M P ER0 AY1 Z IH0 NG -TEMPORIZE T EH1 M P ER0 AY2 Z -TEMPORIZED T EH1 M P ER0 AY2 Z D -TEMPORIZING T EH1 M P ER0 AY2 Z IH0 NG -TEMPORUM T EH2 M P AO1 R AH0 M -TEMPORY T EH1 M P ER0 IY0 -TEMPS T EH1 M P S -TEMPT T EH1 M P T 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AH0 N T S -TENANTABLE T EH1 N AH0 N T AH0 B AH0 L -TENANTED T EH1 N AH0 N T IH0 D -TENANTING T EH1 N AH0 N T IH0 NG -TENANTLESS T EH1 N AH0 N T L AH0 S -TENANTRY T EH1 N AH0 N T R IY0 -TENANTS T EH1 N AH0 N T S -TENAS T EH1 N AH0 Z -TENASSERIM T AH0 N AE1 S ER0 AH0 M -TENAWA T EH0 N AA1 W AH0 -TENAWAS T EH0 N AA1 W AH0 Z -TENAX T EH1 N AE0 K S -TENAYA T EH0 N AA1 Y AH0 -TENBRUGGEN T EH1 N B R UW1 G AH0 N -TENBRUGGEN'S T EH1 N B R UW1 G AH0 N Z -TENBY T EH1 N B IY0 -TENCH T EH1 N CH -TENCIN T EH1 N S IH0 N -TENCZYN T EH1 N CH IH0 N -TEND T EH1 N D -TEND'REST T EH1 N R AH0 S T -TENDA T EH1 N D AH0 -TENDANCE T EH1 N D AH0 N S -TENDE T EH1 N D -TENDED T EH1 N D AH0 D -TENDED T EH1 N D IH0 D -TENDENCIES T EH1 N D AH0 N S IY0 Z -TENDENCY T EH1 N D AH0 N S IY0 -TENDER T EH1 N D ER0 -TENDER'S T EH1 N D ER0 Z -TENDERED T EH1 N D ER0 D -TENDERER T EH1 N D ER0 ER0 -TENDEREST T EH1 N D ER0 IH0 S T -TENDERFEET T EH1 N D ER0 F IY2 T -TENDERFOOT T EH1 N D ER0 F UH2 T -TENDERFOOT'S T EH1 N D ER0 F UH2 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AH0 N T S -TENENS T EH1 N AH0 N Z -TENENTE T EH1 N AH0 N T -TENER T EH1 N ER0 -TENERE T EH1 N ER0 -TENERIFE T EH1 N AH0 R AY2 F -TENERIFFE T EH1 N ER0 IH0 F -TENESCO T EH0 N EH1 S K OW0 -TENET T EH1 N AH0 T -TENETS T EH1 N AH0 T S -TENEZ T EH0 N EH1 Z -TENFOLD T EH1 N F OW2 L D -TENGA T EH1 NG G AH0 -TENGELYI T EH1 NG G AH0 L Y IY0 -TENGELYI'S T EH1 NG G AH0 L Y IY1 Z -TENGGA T EH1 NG G AH0 -TENGGA'S T EH1 NG G AH0 Z -TENGGER T EH1 NG G ER0 -TENGO T EH1 NG G OW0 -TENGU T EH1 NG UW1 -TENGYUEH T EH1 NG Y W EH1 -TENHAM T EH1 N AH0 M -TENIE T EH1 N IY0 -TENIENTE T EH1 N IY0 EH1 N T EY0 -TENIERS T IY1 N IY0 ER0 Z -TENIET T EH1 N IY0 AH0 T -TENIR T AH0 N IH1 R -TENISE T EH1 N AY0 Z -TENISON T EH1 N IH0 S AH0 N -TENISON'S T EH1 N IH0 S AH0 N Z -TENJIN T EH1 N JH IH0 N -TENLOW T EH1 N L OW0 -TENN T EH1 N -TENNANT T EH1 N AH0 N T -TENNANT'S T EH1 N AH0 N T S -TENNANTS T EH1 N AH0 N T S -TENNE T EH1 N -TENNELLY T EH1 N AH0 L IY0 -TENNELLY'S T EH1 N AH0 L IY0 Z -TENNENT T EH1 N AH0 N T -TENNER T EH1 N ER0 -TENNESSEAN T EH2 N IH0 S IY1 AH0 N -TENNESSEANS T EH2 N IH0 S IY1 AH0 N Z -TENNESSEE T EH2 N AH0 S IY1 -TENNESSEE'S T EH2 N AH0 S IY1 Z -TENNESSEEAN T EH2 N AH0 S IY1 AH0 N -TENNESSEEANS T EH2 N IH0 S IY0 AH0 N Z -TENNEY T EH1 N IY0 -TENNEY'S T EH1 N IY0 Z -TENNIEL T EH1 N IY0 L -TENNIEL'S T EH1 N IY0 AH0 L Z -TENNIN T EH1 N IH0 N -TENNINGTON T EH1 N IH0 NG T AH0 N -TENNIS T EH1 N AH0 S -TENNIS T EH1 N IH0 S -TENNISON T EH1 N IH0 S AH0 N -TENNO T EH1 N OW0 -TENNY T EH1 N IY0 -TENNY'S T EH1 N IY0 Z -TENNYRATE T EH1 N IH0 R EY0 T -TENNYSON T EH1 N IH0 S AH0 N -TENNYSON'S T EH1 N IH0 S AH0 N Z -TENNYSONIAN T EH1 N IH0 S OW1 N IY0 AH0 N -TENNYSONS T EH1 N IH0 S AH0 N Z -TENOCHTITLAN T EH1 N AA1 K T IY2 T L AH0 N -TENON T EH1 N AH0 N -TENONS T EH1 N AH0 N Z -TENOR T EH1 N ER0 -TENOR'S T EH1 N ER0 Z -TENORE T EH1 N AO2 R -TENORINA T EH1 N AO2 R IY1 N AH0 -TENORIO T EH0 N AO1 R IY0 OW0 -TENORS T EH1 N ER0 Z -TENOS T IY1 N OW0 Z -TENOUR T EH1 N ER0 -TENPENCE T EH1 N P EH1 N S 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EH1 N T AH0 T IH0 V N AH0 S -TENTATIVES T EH1 N T AH0 T IH0 V Z -TENTE T EH1 N T EY0 -TENTED T EH1 N T IH0 D -TENTER T EH1 N T ER0 -TENTERDEN T EH1 N T ER0 D AH0 N -TENTERHOOKS T EH1 N T ER0 HH UH2 K S -TENTERS T EH1 N T ER0 Z -TENTH T EH1 N TH -TENTHLY T EH1 N TH L IY0 -TENTHOLM T EH1 N TH AH0 M -TENTHS T EH1 N TH S -TENTING T EH1 N T IH0 NG -TENTION T EH1 N SH AH0 N -TENTLESS T EH1 N T L AH0 S -TENTMAKER T EH1 N T M EY2 K ER0 -TENTMATE T EH1 N T M EY2 T -TENTORIUM T EH1 N T AO1 R IY0 AH0 M -TENTS T EH1 N T S -TENU T EH0 N UW1 -TENUE T EH1 N Y UW0 -TENUES T EH1 N Y UW0 Z -TENUI T EH0 N UW1 IY0 -TENUITY T EH0 N UW1 AH0 T IY0 -TENUOUS T EH1 N Y AH0 W AH0 S -TENURE T EH1 N Y ER0 -TENURES T EH1 N Y ER0 Z -TENWAY T EH1 N W EY2 -TEO T EY1 OW0 -TEOCALI T IY1 AH0 K AA1 L IY0 -TEOCALLI T EY1 OW0 K AE1 L IY0 -TEOCALLIS T EY1 OW0 K AE1 L IH0 S -TEODORA T IY2 AH0 D AO1 R AH0 -TEODORO T IY2 OW0 D AO1 R OW0 -TEODORO'S T IY2 AH0 D AO1 R OW0 Z -TEOFAN T EY1 OW0 F AE2 N -TEOS T IY1 OW0 Z -TEOTUCTLI T IY1 OW0 T AH2 K T L IY0 -TEP T IH0 P -TEPE T IY1 P -TEPEACA T IY1 P IY1 K AH0 -TEPEE T IY1 P IY1 -TEPEES T IY1 P IY0 Z -TEPELEN T IY1 P L AH0 N -TEPELENI T EH1 P AH0 L IY1 N IY0 -TEPELENTI T IY1 P IH0 L EH1 N T IY0 -TEPELENTI'S T IY1 P IH0 L EH1 N T IY0 Z -TEPENACASCO T EH2 P EH2 N AH0 K AE1 S K OW0 -TEPETL T IY1 P T L -TEPHANY T EH1 F AH0 N IY0 -TEPI T EH1 P IY0 -TEPIC T EH1 P IH0 K -TEPID T EH1 P IH0 D -TEPIDARIUM T EH1 P IH0 D EH1 R IY0 AH0 M -TEPIDITY T IH0 P IH1 D IH0 T IY0 -TEPIDLY T EH1 P IH0 D L IY0 -TEPL T EH1 P AH0 L -TEPLITZ T EH1 P L IH0 T S -TEPPING T EH1 P IH0 NG -TEPUS T EH1 P AH0 S -TEQUILA T AH0 K IY1 L AH0 -TER T ER1 -TER'BLE T ER1 B AH0 L -TER'S T ER0 Z -TERA T EH1 R AH0 -TERAH T EH2 R AH0 -TERAI T EH0 R AA1 IY0 -TERAIN T ER0 EY1 N -TERALBAY T ER0 AA1 L B EY2 -TERAMACHI T EH1 R AH0 M AA1 CH IY0 -TERAMORE T EH0 R AA1 M AO0 R -TERAPHIM T ER0 AA0 F IY1 M -TERAUCHI T EH1 R AA0 UW1 CH IY0 -TERBACCER T ER1 B AE0 K S ER0 -TERBACCO T ER1 B AA1 K OW0 -TERBACKER T ER0 B AE2 K 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AH0 N -TORT T AO1 R T -TORTA T AO1 R T AH0 -TORTE T AO1 R T -TORTEVAL T AO0 R T IY1 V AH0 L -TORTHA T AO1 R TH AH0 -TORTILLA T AO0 R T IY1 AH0 -TORTILLARD T AO0 R T IY1 L ER0 D -TORTILLAS T AO2 R T IY1 AH0 Z -TORTO T AO0 R T OW0 -TORTOISE T AO1 R T AH0 S -TORTOISE'S T AO1 R T AH0 S IH0 Z -TORTOISES T AO1 R T AH0 S AH0 Z -TORTOISESHELL T AO1 R T AH0 S SH AH0 L -TORTONA T AO1 R T AH0 N AH0 -TORTONI T AO0 R T AO1 N IY0 -TORTONI'S T AO0 R T AO1 N IY0 Z -TORTOSA T AO0 R T OW1 S AH0 -TORTOSHELL T AO0 R T AO1 SH AH0 L -TORTS T AO1 R T S -TORTUE T AO1 R CH UW2 -TORTUGA T AO1 R CH AH0 G AH0 -TORTUGAS T AO1 R CH AH0 G AH0 Z -TORTUOSITIES T AO1 R CH UW0 AA1 S IH0 T IY0 Z -TORTUOSITY T AO1 R CH UW0 AA1 S IH0 T IY0 -TORTUOUS T AO1 R CH AH0 W AH0 S -TORTUOUSLY T AO1 R CH W AH0 S L IY0 -TORTUR'D T AO1 R CH UW2 D -TORTURE T AO1 R CH ER0 -TORTURED T AO1 R CH ER0 D -TORTURER T AO1 R CH ER0 ER0 -TORTURER'S T AO1 R CH ER0 ER0 Z -TORTURERS T AO1 R CH ER0 ER0 Z -TORTURES T AO1 R CH ER0 Z -TORTURIN T AO1 R 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T AA1 K S AH0 N Z -TOY T OY1 -TOY'S T OY1 Z -TOYAH T OY1 AH0 -TOYATTE T OY1 AH0 T -TOYE T OY1 -TOYE'S T OY1 Z -TOYED T OY1 D -TOYING T OY1 IH0 NG -TOYLAND T OY1 L AH0 N D -TOYLE T OY1 L -TOYLIKE T OY1 L AY2 K -TOYMAKER T OY1 M EY2 K ER0 -TOYMAN T OY1 M AH0 N -TOYMAN'S T OY1 M AH0 N Z -TOYNBEE T OY1 N B IY0 -TOYNBEE'S T OY1 N B IY0 Z -TOYO T OW1 Y OW0 -TOYON T OY1 AH0 N -TOYS T OY1 Z -TOYSHOP T OY1 SH AA2 P -TOYSHOPS T OY1 SH AA2 P S -TOZA T OW1 Z AH0 -TOZER T OW1 Z ER0 -TOZER'S T OW1 Z ER0 Z -TR T R -TR'EN T ER0 N -TRA T R AH0 -TRABB T R AE1 B -TRABB'S T R AE1 B Z -TRABUCOS T R AA0 B UW1 K OW0 Z -TRABUE T R AA1 B W EH0 -TRAC T R AE1 K -TRAC'D T R EY1 K D -TRACADIE T R EY1 K AE1 D IY0 -TRACASSIER T R EY1 K AH0 S IY0 ER0 -TRACE T R EY1 S -TRACEABLE T R EY1 S AH0 B AH0 L -TRACED T R EY1 S T -TRACELESS T R EY1 S L AH0 S -TRACER T R EY1 S ER0 -TRACERIED T R EY1 S R IY0 D -TRACERIES T R EY1 S ER0 IY0 Z -TRACERS T R EY1 S ER0 Z -TRACERY T R EY1 S ER0 IY0 -TRACES T R EY1 S AH0 Z -TRACES T R EY1 S IH0 Z -TRACEY T R EY1 S IY0 -TRACEY'S T R EY1 S IY0 Z -TRACHEA T R EY1 K IY0 AH0 -TRACHEOTOMY T R EY1 K IY0 AH0 T OW2 M IY0 -TRACHIS T R EY1 K IY0 Z -TRACHOMA T R EY1 K OW1 M AH0 -TRACHONITIS T R EY1 K AH0 N AY1 T IH0 S -TRACHYTE T R EY1 K AY2 T -TRACHYTIC T R EY1 K IH1 T IH0 K -TRACING T R EY1 S IH0 NG -TRACINGS T R EY1 S IH0 NG Z -TRACK T R AE1 K -TRACK'D T R AE1 K D -TRACK'S T R AE1 K S -TRACKED T R AE1 K T -TRACKER T R AE1 K ER0 -TRACKERS T R AE1 K ER0 Z -TRACKIN T R AE1 K IH0 N -TRACKING T R AE1 K IH0 NG -TRACKLESS T R AE1 K L AH0 S -TRACKMEN T R AE1 K M EH2 N -TRACKS T R AE1 K S -TRACKSIDE T R AE1 K S AY2 D -TRACKWAY T R AE1 K W EY2 -TRACKWAYS T R AE1 K W EY2 Z -TRACT T R AE1 K T -TRACTABILITY T R AE1 K T AH0 B IH1 L IH0 T IY0 -TRACTABLE T R AE1 K T AH0 B AH0 L -TRACTARIAN T R AE1 K T EH1 R IY0 AH0 N -TRACTARIANISM T R AE1 K T EH1 R IY0 AH0 N IH2 Z AH0 M -TRACTARIANS T R AE1 K T EH1 R IY0 AH0 N Z -TRACTATE T R AE1 K T EY2 T -TRACTATES T R AE1 K T EY2 T S -TRACTATUS T R AE1 K T EY1 T AH0 S -TRACTION T R AE1 K SH AH0 N -TRACTIONS T R AE1 K SH AH0 N Z -TRACTIVE T R AE1 K T IH0 V -TRACTOR T R AE1 K T ER0 -TRACTORS T R AE1 K T ER0 Z -TRACTS T R AE1 K T S -TRACY T R EY1 S IY0 -TRACY'S T R EY1 S IY0 Z -TRACYS T R EY1 S IY0 Z -TRADDLES T R AE1 D AH0 L Z -TRADE T R EY1 D -TRADE'S T R EY1 D Z -TRADED T R EY1 D IH0 D -TRADEING T R EY1 D IH0 NG -TRADEMARK T R EY1 D M AA2 R K -TRADEMARKS T R EY1 D M AA2 R K S -TRADER T R EY1 D ER0 -TRADER'S T R EY1 D ER0 Z -TRADERS T R EY1 D ER0 Z -TRADES T R EY1 D Z -TRADESFOLK T R EY1 D Z F OW2 K -TRADESMAN T R EY1 D Z M AH0 N -TRADESMAN'S T R EY1 D Z M AH0 N Z -TRADESMEN T R EY1 D Z M AH0 N -TRADESMEN'S T R EY1 D Z M AH0 N Z -TRADESPEOPLE T R EY1 D Z P IY2 P AH0 L -TRADESWOMAN T R EY1 D S W UH2 M AH0 N -TRADEWINDS T R EY1 D W IH2 N D Z -TRADGEDY T R AE0 JH AH0 D IY0 -TRADGIC T R AE1 JH IH0 K -TRADIN T R EY1 D IH0 N -TRADING T R EY1 D IH0 NG -TRADITION T R AH0 D IH1 SH AH0 N -TRADITION'S T R AH0 D IH1 SH AH0 N Z -TRADITIONAL T R 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L IH0 N -TRAILING T R EY1 L IH0 NG -TRAILL T R EY1 L -TRAILL'S T R EY1 L Z -TRAILLES T R EY1 L Z -TRAILLESS T R EY1 L AH0 S -TRAILMEN T R EY1 L M AH0 N -TRAILMEN'S T R EY1 L M AH0 N Z -TRAILS T R EY1 L Z -TRAIN T R EY1 N -TRAIN'D T R EY1 N D -TRAIN'S T R EY1 N Z -TRAINARD T R EY1 N ER0 D -TRAINBANDS T R EY1 N B AE2 N D Z -TRAINE T R EY1 N -TRAINED T R EY1 N D -TRAINEE T R EY1 N IY1 -TRAINEES T R EY1 N IY1 Z -TRAINER T R EY1 N ER0 -TRAINER'S T R EY1 N ER0 Z -TRAINERS T R EY1 N ER0 Z -TRAINFUL T R EY1 N F AH0 L -TRAININ T R EY1 N IH0 N -TRAINING T R EY1 N IH0 NG -TRAININGS T R EY1 N IH0 NG Z -TRAINLOAD T R EY1 N L OW2 D -TRAINLOADS T R EY1 N L OW2 D Z -TRAINMAN T R EY1 N M AH0 N -TRAINMASTER T R EY1 N M AE2 S T ER0 -TRAINMASTER'S T R EY1 N M AE2 S T ER0 Z -TRAINMEN T R EY1 N M AH0 N -TRAINOR T R EY1 N ER0 -TRAINOR'S T R EY1 N ER0 Z -TRAINORS T R EY1 N ER0 Z -TRAINS T R EY1 N Z -TRAIPSE T R EY1 P S -TRAIPSED T R EY1 P S T -TRAIPSIN T R EY1 P S IH0 N -TRAIPSING T R EY1 P S IH0 NG -TRAIT T R EY1 T -TRAIT'ROUS T R EY1 T R AH0 S -TRAITE T R EY1 T -TRAITER T R EY1 T ER0 -TRAITEUR T R EY1 T UW1 R -TRAITI T R EY1 T IY0 -TRAITOR T R EY1 T ER0 -TRAITOR'S T R EY1 T ER0 Z -TRAITORESS T R EY1 T ER0 EH1 S -TRAITOROUS T R EY1 T ER0 AH0 S -TRAITOROUSLY T R EY1 T ER0 AH0 S L IY0 -TRAITORS T R EY1 T ER0 Z -TRAITRE T R EY1 T ER0 -TRAITRESS T R EY1 T R AH0 S -TRAITS T R EY1 T S -TRAJAN T R EY1 JH AH0 N -TRAJAN'S T R EY1 JH AH0 N Z -TRAJECTORIES T R AH0 JH EH1 K T ER0 IY0 Z -TRAJECTORY T R AH0 JH EH1 K T ER0 IY0 -TRAKTIR T R AE1 K T ER0 -TRALALA T R AA0 L AA1 L AH0 -TRALEE T R AE1 L IY0 -TRALEIGH T R EY1 L IY0 -TRAM T R AE1 M -TRAMCAR T R AE1 M K AA2 R -TRAMCARS T R AE1 M K AA2 R Z -TRAMLINE T R AE1 M L AY2 N -TRAMLINES T R AE1 M L AY2 N Z -TRAMMEL T R AE1 M AH0 L -TRAMMELED T R AE1 M AH0 L D -TRAMMELLED T R AE1 M AH0 L D -TRAMMELLING T R AE1 M AH0 L IH0 NG -TRAMMELS T R AE1 M AH0 L Z -TRAMONTANA T R AA0 M OW0 N T AE1 N AH0 -TRAMONTANE T R AA0 M OW1 N T EY2 N -TRAMP T R AE1 M P -TRAMP'S T R AE1 M P S -TRAMPAS T R AE1 M P AH0 Z -TRAMPED T R AE1 M P T -TRAMPER T R AE1 M P ER0 -TRAMPERS T R AE1 M P ER0 Z -TRAMPIN T R AE1 M P IH0 N -TRAMPING T R AE1 M P IH0 NG -TRAMPINGS T R AE1 M P IH0 NG Z -TRAMPISH T R AE1 M P IH0 SH -TRAMPLE T R AE1 M P AH0 L -TRAMPLEASURE T R AE1 M P L EH1 ZH ER0 -TRAMPLED T R AE1 M P AH0 L D -TRAMPLER T R AE1 M P L ER0 -TRAMPLES T R AE1 M P AH0 L Z -TRAMPLIN T R AE1 M P L IH0 N -TRAMPLING T R AE1 M P L IH0 NG -TRAMPLINGS T R AE1 M P L IH0 NG Z -TRAMPS T R AE1 M P Z -TRAMS T R AE1 M Z -TRAMTRIST T R AE1 M T R IH0 S T -TRAMU T R AA0 M UW1 -TRAMWAY T R AE1 M W EY2 -TRAMWAYS T R AE1 M W EY2 Z -TRAMWORTH T R AE1 M W ER0 TH -TRANA T R AE1 N AH0 -TRANCE T R AE1 N S -TRANCED T R AE1 N S T -TRANCELIKE T R AE1 N S L AY2 K -TRANCES T R AE1 N S IH0 Z -TRANCHANT T R AE1 N CH AH0 N T -TRANCHE T R AE1 N CH -TRANCHEES T R AE1 N CH IY2 Z -TRANDAFIR T R AE1 N D AH0 F ER0 -TRANEST T R AE2 N IH0 S T -TRANGOIL T R AE1 NG G OY2 L -TRANI T R AA1 N IY0 -TRANK T R AE1 NG K 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AH0 N D EH1 N AH0 L IH0 S T -TRANSCENDENTALISTS T R AE2 N S AH0 N D EH1 N AH0 L IH0 S T S -TRANSCENDENTALLY T R AE2 N S AH0 N D EH1 N AH0 L IY0 -TRANSCENDENTLY T R AE0 N S EH1 N D AH0 N T L IY0 -TRANSCENDETH T R AE0 N S EH1 N D IH0 TH -TRANSCENDING T R AE0 N S EH1 N D IH0 NG -TRANSCENDS T R AE0 N S EH1 N D Z -TRANSCONTINENTAL T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L -TRANSCONTINENTAL T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L -TRANSCONTINENTAL T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L -TRANSCONTINENTAL T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L -TRANSCRIBE T R AE0 N S K R AY1 B -TRANSCRIBED T R AE0 N S K R AY1 B D -TRANSCRIBER T R AE0 N S K R AY1 B ER0 -TRANSCRIBER'S T R AE0 N S K R AY1 B ER0 Z -TRANSCRIBERS T R AE0 N S K R AY1 B ER0 Z -TRANSCRIBES T R AE0 N S K R AY1 B Z -TRANSCRIBING T R AE0 N S K R AY1 B IH0 NG -TRANSCRIPT T R AE1 N S K R IH2 P T -TRANSCRIPTION T R AE2 N S K R IH1 P SH AH0 N -TRANSCRIPTIONS T R AE2 N S K R IH1 P SH AH0 N Z -TRANSCRIPTS T R AE1 N S K R IH2 P T S -TRANSDUCERS T R AE0 N S D UW1 S ER0 Z -TRANSEPT T R AE0 N S EH1 P T -TRANSEPTS T R AE1 N S EH2 P T S -TRANSFER T R AE0 N S F ER1 -TRANSFER T R AE1 N S F ER0 -TRANSFERABLE T R AE0 N S F ER1 AH0 B AH0 L -TRANSFERENCE T R AE0 N S F ER1 AH0 N S -TRANSFERMENT T R AE0 N S F ER1 M AH0 N T -TRANSFERR'D T R AE0 N S F ER1 D -TRANSFERRED T R AE0 N S F ER1 D -TRANSFERRED T R AE1 N S F ER0 D -TRANSFERRENCE T R AE0 N S F ER1 AH0 N S -TRANSFERRING T R AE0 N S F ER1 IH0 NG -TRANSFERS T R AE0 N S F ER1 Z -TRANSFERS T R AE1 N S F ER0 Z -TRANSFIGURATION T R AE0 N S F IH1 G Y ER0 EY1 SH AH0 N -TRANSFIGURATIONS T R AE0 N S F IH1 G Y ER0 EY1 SH AH0 N Z -TRANSFIGURE T R AE0 N S F IH1 G Y ER0 -TRANSFIGURED T R AE0 N S F IH1 G Y ER0 D -TRANSFIGURES T R AE0 N S F IH1 G Y ER0 Z -TRANSFIGURING T R AE0 N S F IH1 G Y ER0 IH0 NG -TRANSFIX T R AE0 N S F IH1 K S -TRANSFIX'D T R AE0 N S F IH1 K S D -TRANSFIXED T R AE0 N S F IH1 K S T -TRANSFIXES T R AE0 N S F IH1 K S IH0 Z -TRANSFIXING T R AE0 N S F IH1 K S IH0 NG -TRANSFORM T R AE0 N S F AO1 R M -TRANSFORM T R AE1 N S F AO0 R M -TRANSFORM'D T R AE0 N S F AO1 R M D -TRANSFORMATION T R AE2 N S F ER0 M EY1 SH AH0 N -TRANSFORMATIONS T R AE2 N S F ER0 M EY1 SH AH0 N Z -TRANSFORMED T R AE0 N S F AO1 R M D -TRANSFORMER T R AE0 N S F AO1 R M ER0 -TRANSFORMERS T R AE0 N S F AO1 R M ER0 Z -TRANSFORMING T R AE0 N S F AO1 R M IH0 NG -TRANSFORMISM T R AE0 N S F AO1 R M IH0 Z AH0 M -TRANSFORMS T R AE0 N S F AO1 R M Z -TRANSFUSE T R AE0 N S F Y UW1 Z -TRANSFUSED T R AE0 N S F Y UW1 Z D -TRANSFUSING T R AE0 N S F Y UW1 Z IH0 NG -TRANSFUSION T R AE0 N S F Y UW1 ZH AH0 N -TRANSGRESS T R AE0 N Z G R EH1 S -TRANSGRESSED T R AE0 N Z G R EH1 S T -TRANSGRESSES T R AE0 N Z G R EH1 S IH0 S -TRANSGRESSING T R AE0 N Z G R EH1 S IH0 NG -TRANSGRESSION T R AE0 N Z G R EH1 SH AH0 N -TRANSGRESSIONS T R AE0 N Z G R EH1 SH AH0 N Z -TRANSGRESSOR T R AE0 N Z G R EH1 S ER0 -TRANSGRESSORS T R AE0 N Z G R EH1 S ER0 Z -TRANSHIP T R AE1 N SH IH2 P -TRANSHIPMENT T R AE1 N SH IH1 P M AH0 N T -TRANSHIPPED T R AE1 N ZH IH1 P T -TRANSHIPPING T R AE1 N ZH IH1 P IH0 NG -TRANSIENCE T R AE1 N Z IY0 AH0 N S -TRANSIENCY T R AE1 N Z IY0 AH0 N S IY0 -TRANSIENT T R AE1 N ZH AH0 N T -TRANSIENTLY T R AE1 N ZH AH0 N T L IY0 -TRANSIENTS T R AE1 N Z IY0 AH0 N T S -TRANSISTOR T R AE0 N Z IH1 S T ER0 -TRANSISTORS T R AE0 N Z IH1 S T ER0 Z -TRANSIT T R AE1 N Z IH0 T -TRANSITA T R AE0 N S IY1 T AH0 -TRANSITION T R AE0 N Z IH1 SH AH0 N -TRANSITIONAL T R AE0 N S IH1 SH AH0 N AH0 L -TRANSITIONAL T R AE0 N Z IH1 SH AH0 N AH0 L -TRANSITIONS T R AE0 N Z IH1 SH AH0 N Z -TRANSITIVE T R AE1 N Z IH0 T IH0 V -TRANSITORILY T R AE1 N Z AH0 T AO2 R AH0 L IY0 -TRANSITORINESS T R AE1 N Z AH0 T AO2 R IY0 N AH0 S -TRANSITORY T R AE1 N Z AH0 T AO2 R IY0 -TRANSITS T R AE1 N Z IH0 T S -TRANSITU T R AE1 N Z IH0 T UW0 -TRANSKEI T R AE1 N Z K EY2 -TRANSL T R AE0 N S L -TRANSLATABLE T R AE0 N Z L EY1 T AH0 B AH0 L -TRANSLATE T R AE0 N S L EY1 T -TRANSLATE T R AE0 N Z L EY1 T -TRANSLATED T R AE0 N S L EY1 T IH0 D 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AE0 N Z M AH0 G R EY2 T IH0 D -TRANSMIGRATES T R AE0 N Z M AH0 G R EY2 T S -TRANSMIGRATION T R AE0 N Z M AH0 G R EY1 SH AH0 N -TRANSMIGRATIONS T R AE0 N Z M AH0 G R EY1 SH AH0 N Z -TRANSMISSIBLE T R AE0 N S M IH1 S AH0 B AH0 L -TRANSMISSION T R AE0 N S M IH1 SH AH0 N -TRANSMISSION T R AE0 N Z M IH1 SH AH0 N -TRANSMISSIONS T R AE0 N Z M IH1 SH AH0 N Z -TRANSMIT T R AE0 N Z M IH1 T -TRANSMITS T R AE0 N Z M IH1 T S -TRANSMITTABLE T R AE0 N Z M IH1 T AH0 B AH0 L -TRANSMITTAL T R AE0 N S M IH1 T AH0 L -TRANSMITTED T R AE0 N S M IH1 T IH0 D -TRANSMITTED T R AE0 N Z M IH1 T AH0 D -TRANSMITTER T R AE0 N S M IH1 T ER0 -TRANSMITTERS T R AE0 N S M IH1 T ER0 Z -TRANSMITTING T R AE0 N S M IH1 T IH0 NG -TRANSMOGRIFICATION T R AE0 N Z M OW0 G R IH0 F IH0 K EY1 SH AH0 N -TRANSMOGRIFIED T R AE0 N Z M OW0 G R AH0 F AY2 D -TRANSMUTABLE T R AE0 N S M Y UW1 T AH0 B AH0 L -TRANSMUTATION T R AE0 N S M Y UW0 T EY1 SH AH0 N -TRANSMUTATIONS T R AE0 N S M Y UW0 T EY1 SH AH0 N Z -TRANSMUTE T R AE0 N S M Y UW2 T 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T R AE0 N S P L AE1 N T IH0 NG -TRANSPLANTS T R AE0 N S P L AE1 N T S -TRANSPONTINE T R AE0 N S P AA1 N T IY2 N -TRANSPORT T R AE0 N S P AO1 R T -TRANSPORT T R AE1 N S P AO0 R T -TRANSPORT'S T R AE0 N S P AO1 R T S -TRANSPORTABLE T R AE0 N S P AO1 R T AH0 B AH0 L -TRANSPORTATION T R AE2 N S P ER0 T EY1 SH AH0 N -TRANSPORTATIONS T R AE2 N S P ER0 T EY1 SH AH0 N Z -TRANSPORTED T R AE0 N S P AO1 R T AH0 D -TRANSPORTER T R AE0 N S P AO1 R T ER0 -TRANSPORTING T R AE0 N S P AO1 R T IH0 NG -TRANSPORTS T R AE0 N S P AO1 R T S -TRANSPORTS T R AE1 N S P AO0 R T S -TRANSPOSE T R AE0 N S P OW1 Z -TRANSPOSED T R AE0 N S P OW1 Z D -TRANSPOSES T R AE0 N S P OW1 Z IH0 Z -TRANSPOSING T R AE0 N S P OW1 Z IH0 NG -TRANSPOSITION T R AE0 N S P AH0 Z IH1 SH AH0 N -TRANSPOSITIONS T R AE0 N S P AH0 Z IH1 SH AH0 N Z -TRANSSHIP T R AE0 N SH IH2 P -TRANSSHIPMENT T R AE0 N SH IH1 P M AH0 N T -TRANSSHIPPED T R AE0 N SH IH1 P T -TRANSUBSTANTIATION T R AE0 N S AH0 B S T AE2 N SH IY0 EY1 SH AH0 N -TRANSVAAL T R AE0 N Z V AA1 L -TRANSVAALERS T R AE0 N Z V AA1 L ER0 Z -TRANSVALUATION T R AE0 N Z V AE2 L Y UW0 EY1 SH AH0 N -TRANSVERSAL T R AE0 N Z V ER1 S AH0 L -TRANSVERSE T R AE0 N Z V ER1 S -TRANSVERSELY T R AE0 N Z V ER1 S L IY0 -TRANSVERSLY T R AE0 N Z V ER1 S L IY0 -TRANSYLVANIA T R AE2 N S IH0 L V EY1 N Y AH0 -TRANSYLVANIA T R AE2 N Z IY0 L V EY1 N IY0 AH0 -TRANSYLVANIAN T R AE2 N S IH0 L V EY1 N IY0 AH0 N -TRANSYLVANIANS T R AE2 N S IH0 L V EY1 N IY0 AH0 N Z -TRANT T R AE1 N T -TRANT'S T R AE1 N T S -TRANTA T R AE1 N T AH0 -TRANTER T R AE1 N T ER0 -TRANTER'S T R AE1 N T ER0 Z -TRANTHAM T R AE1 N TH AH0 M -TRANTO T R AE1 N T OW0 -TRANTOW T R AE1 N T OW0 -TRANTOWITZ T R AE1 N T AH0 W IH0 T S -TRANTRIDGE T R AE1 N T R IH0 JH -TRANYON T R AE1 N Y AH0 N -TRAP T R AE1 P -TRAP'S T R AE1 P S -TRAPANI T R AA0 P AA1 N IY0 -TRAPBOIS T R AE1 P B W AA1 -TRAPDOOR T R AE1 P D AO2 R -TRAPDOORS T R AE1 P D AO2 R Z -TRAPESIN T R AA1 P EH0 S IH0 N -TRAPESING T R AH0 P EH1 Z IH0 NG -TRAPEZE T R AH0 P IY1 Z 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AW1 B AH0 L -TRAUE T R AW1 -TRAUGOTT T R AW1 G AH0 T -TRAUM T R AO1 M -TRAUMA T R AO1 M AH0 -TRAUMATIC T R AO0 M AE1 T IH0 K -TRAUMEREI T R AO1 M EH0 R EY2 -TRAUN T R AO1 N -TRAUNBERG T R AO1 N B ER0 G -TRAUT T R AO1 T -TRAUTTENBERG T R AW1 T AH0 N B ER0 G -TRAUTVETTER T R AW1 T V EH1 T ER0 -TRAV'LLER T R AA0 V L ER0 -TRAVAIL T R AH0 V EY1 L -TRAVAILE T R AH0 V EY1 L -TRAVAILED T R AH0 V EY1 L D -TRAVAILETH T R AH0 V EY1 L EH2 TH -TRAVAILING T R AH0 V EY1 L IH0 NG -TRAVAILLE T R AH0 V EY1 L -TRAVAILLER T R AH0 V EY1 L ER0 -TRAVAILLEURS T R AH0 V EY1 L ER1 Z -TRAVAILS T R AH0 V EY1 L Z -TRAVANCORE T R AA0 V AA1 N K AO2 R -TRAVANN T R AH0 V AE1 N -TRAVAUX T R AH0 V OW1 -TRAVE T R AE1 V -TRAVEL T R AE1 V AH0 L -TRAVEL'D T R AE1 V AH0 L D -TRAVEL'S T R AE1 V AH0 L Z -TRAVELED T R AE1 V AH0 L D -TRAVELER T R AE1 V AH0 L ER0 -TRAVELER T R AE1 V L ER0 -TRAVELER'S T R AE1 V AH0 L ER0 Z -TRAVELERS T R AE1 V AH0 L ER0 Z -TRAVELERS T R AE1 V L ER0 Z -TRAVELIN T R AE1 V AH0 L IH0 N -TRAVELING T R AE1 V AH0 L IH0 NG -TRAVELING T R AE1 V L IH0 NG -TRAVELL T R AE1 V AH0 L -TRAVELL'D T R AE1 V AH0 L D -TRAVELLED T R AE1 V AH0 L D -TRAVELLER T R AE1 V AH0 L ER0 -TRAVELLER'S T R AE1 V AH0 L ER0 Z -TRAVELLERS T R AE1 V AH0 L ER0 Z -TRAVELLIN T R AE1 V AH0 L IH0 N -TRAVELLING T R AE1 V AH0 L IH0 NG -TRAVELLING T R AE1 V L IH0 NG -TRAVELLINGS T R AE1 V L IH0 NG Z -TRAVELOGUE T R AE1 V AH0 L AO2 G -TRAVELS T R AE1 V AH0 L Z -TRAVENION T R AE1 V AH0 N Y AH0 N -TRAVENION'S T R AA0 V EH1 N IY0 AH0 N Z -TRAVERS T R AE1 V ER0 Z -TRAVERS'S T R AE1 V ER0 Z IH0 Z -TRAVERSABLE T R AH0 V ER1 S AH0 B AH0 L -TRAVERSE T R AE1 V ER0 S -TRAVERSE T R AH0 V ER1 S -TRAVERSE'S T R AH0 V ER1 S IH0 Z -TRAVERSED T R AE1 V ER0 S T -TRAVERSED T R AH0 V ER1 S T -TRAVERSER T R AH0 V ER1 S ER0 -TRAVERSERS T R AH0 V ER1 S ER0 Z -TRAVERSES T R AH0 V ER1 S IH0 Z -TRAVERSING T R AH0 V ER1 S IH0 NG -TRAVERTINE T R AE1 V ER0 T AY2 N -TRAVESIA T R AE1 V AH0 S IY0 AH0 -TRAVESTIE T R AE1 V AH0 S T IY0 -TRAVESTIED T R AE1 V AH0 S T IY0 D -TRAVESTIES T R AE1 V AH0 S T IY0 Z -TRAVESTY T R AE1 V AH0 S T IY0 -TRAVESTYING T R AE1 V AH0 S T IY0 IH0 NG -TRAVIATA T R AA0 V IY0 AA1 T AH0 -TRAVICE T R AE1 V AH0 S -TRAVILLA T R AA0 V IH1 L AH0 -TRAVILLA'S T R AA0 V IH1 L AH0 Z -TRAVILLAS T R AA0 V IH1 L AH0 Z -TRAVINO T R AA0 V IY1 N OW0 -TRAVIS T R AE1 V IH0 S -TRAVIS'S T R AE1 V IH0 S IH0 Z -TRAVNIK T R AE1 V N IH0 K -TRAVOIS T R AH0 V W AA1 -TRAVOY T R AH0 V OY1 -TRAWL T R AO1 L -TRAWLER T R AO1 L ER0 -TRAWLER'S T R AO1 L ER0 Z -TRAWLERS T R AO1 L ER0 Z -TRAWLING T R AO1 L IH0 NG -TRAWLS T R AO1 L Z -TRAXALLA T R AE1 K S AH0 L AH0 -TRAY T R EY1 -TRAY'S T R EY1 Z -TRAYFUL T R EY1 F AH0 L -TRAYLOR T R EY1 L ER0 -TRAYLORS T R EY1 L ER0 Z -TRAYNE T R EY1 N -TRAYNOR T R EY1 N ER0 -TRAYNOR'S T R EY1 N ER0 Z -TRAYS T R EY1 Z -TRAYTOR T R EY1 T ER0 -TRE T R EY1 -TREACH'ROUS T R EH1 CH ER0 AH0 S -TREACHERIES T R EH1 CH ER0 IY0 Z -TREACHEROUS T R EH1 CH ER0 AH0 S -TREACHEROUSLY T R EH1 CH ER0 AH0 S L IY0 -TREACHERY T R EH1 CH ER0 IY0 -TREACLE T R IY1 K AH0 L -TREACLY T R EH1 CH L IY0 -TREAD T R EH1 D -TREAD'ST T R EH1 D S T -TREADED T R EH1 D IH0 D -TREADER T R EH1 D ER0 -TREADERS T R EH1 D ER0 Z -TREADEST T R EH1 D AH0 S T -TREADETH T R EH1 D IH0 TH -TREADIN T R EH1 D IY0 N -TREADING T R EH1 D IH0 NG -TREADLE T R EH1 D AH0 L -TREADLES T R EH1 D AH0 L Z -TREADMAN T R EH1 D M AH0 N -TREADMILL T R EH1 D M IH2 L -TREADMILLS T R EH1 D M IH2 L Z -TREADS T R EH1 D Z -TREADWAY T R EH1 D W EY2 -TREADWELL T R EH1 D W EH2 L -TREADWELL'S T R EH1 D W EH2 L Z -TREADWELLS T R EH1 D W EH2 L Z -TREADWYN T R EH1 D W IH0 N -TREAS T R IY1 Z -TREASON T R IY1 Z AH0 N -TREASON'S T R IY1 Z AH0 N Z -TREASONABLE T R IY1 Z AH0 N AH0 B AH0 L -TREASONABLY T R IY1 Z AH0 N AH0 B L IY0 -TREASONOUS T R IY1 Z AH0 N AH0 S -TREASONS T R IY1 Z AH0 N Z -TREASUR'D T R EH1 ZH ER0 D -TREASURE T R EH1 ZH ER0 -TREASURE'S T R EH1 ZH ER0 Z -TREASURED T R EH1 ZH ER0 D -TREASURER T R EH1 ZH ER0 ER0 -TREASURER'S T R EH1 ZH ER0 ER0 Z -TREASURERS T R EH1 ZH ER0 ER0 Z -TREASURERSHIP T R EH1 ZH ER0 ER0 SH IH2 P -TREASURES T R EH1 ZH ER0 Z -TREASURIES T R EH1 ZH ER0 IY0 Z -TREASURING T R EH1 ZH ER0 IH0 NG -TREASURY T R EH1 ZH ER0 IY0 -TREASURY'S T R EH1 ZH ER0 IY0 Z -TREAT T R IY1 T -TREAT'S T R IY1 T S -TREATED T R IY1 T AH0 D -TREATED T R IY1 T IH0 D -TREATETH T R IY1 T IH0 TH -TREATIES T R IY1 T IY0 Z -TREATIN T R IY1 T AH0 N -TREATING T R IY1 T IH0 NG -TREATISE T R IY1 T AH0 S -TREATISES T R IY1 T AH0 S AH0 Z -TREATMENT T R IY1 T M AH0 N T -TREATMENTS T R IY1 T M AH0 N T S -TREATS T R IY1 T S -TREATY T R IY1 T IY0 -TREATY'S T R IY1 T IY0 Z -TREB T R EH1 B -TREBASI T R EH0 B AA1 S IY0 -TREBASSOF T R EH0 B AA1 S AH0 F -TREBASSOF'S T R EH0 B AA1 S OW0 F S -TREBATIUS T R EH0 B EY1 SH AH0 S -TREBELLIUS T R EH0 B EH1 L Y AH0 S -TREBERON T R EH0 B EH1 R AH0 N -TREBIA T R EH0 B IY0 AH0 -TREBINJE T R IH0 B IH1 N JH -TREBISKEN T R IH0 B IH1 S K AH0 N -TREBIZOND T R EH1 B IH0 Z AA1 N D -TREBIZONDE T R EH1 B IH0 Z AA1 N D -TREBLE T R EH1 B AH0 L -TREBLE'S T R EH1 B AH0 L Z -TREBLED T R EH1 B AH0 L D -TREBLES T R EH1 B AH0 L Z -TREBLING T R EH1 B AH0 L IH0 NG -TREBLY T R EH0 B L IY0 -TREBON T R EH1 B AH0 N -TREBONIUS T R EH0 B OW1 N IY0 AH0 S -TREBOOZE T R IH0 B UW1 Z -TREBY T R B AY1 -TRECARREL T R IH0 K EH1 R AH0 L -TRED T R D -TREDEGAR T R EH1 D IH0 G ER0 -TREDEGAR'S T R EH1 D IH0 G ER0 Z -TREDGOLD T R EH1 D G OW2 L D -TREDGOLD'S T R EH1 D G OW2 L D Z -TREDNOKE T R EH1 N OW1 K -TREDOWEN T R EH1 D AW1 AH0 N -TREDS T R EH1 D Z -TREDWAY T R EH1 D W EY2 -TREDWELL T R EH1 D W EH2 L -TREDWORTH T R EH1 D W ER2 TH -TREE T R IY1 -TREE'D T R IY2 D -TREE'S T R IY2 Z -TREED T R IY1 D -TREEING T R IY1 IH0 NG -TREELESS T R IY1 L AH0 S -TREEN T R IY1 N -TREES T R IY1 Z -TREETON T R IY1 T AH0 N -TREETOP T R IY1 T AO2 P -TREETOPS T R IY1 T AO2 P S -TREETRUNKS T R IY1 T R AH1 NG K S -TREFETHAN T R EH1 F IH0 TH AH0 N -TREFETHEN T R EH1 F IH0 TH AH0 N -TREFETHEN'S T R EH1 F IH0 TH AH0 N Z -TREFF T R EH1 F -TREFFINGER T R EH1 F IH0 NG ER0 -TREFFINGER'S T R EH1 F IH0 NG ER0 Z -TREFFRY T R EH1 F R IY0 -TREFFRY'S T R EH1 F R IY0 Z -TREFFY T R EH1 F IY0 -TREFFY'S T R EH1 F IY0 Z -TREFOIL T R EH1 F OY2 L -TREFOIL'S T R EH1 F OY2 L Z -TREFOILS T R EH1 F OY2 L Z -TREFOYLE T R EH1 F OY0 L -TREFUSIS T R EH1 F Y UW0 S IH0 S -TREFUSIS'S T R EH1 F Y UW0 S IH0 S IH0 Z -TREGANNON T R EH1 G AE1 N AH0 N -TREGAR T R EH1 G ER0 -TREGARRICK T R EH1 G EH1 R IH0 K -TREGARS T R EH1 G ER0 Z -TREGARTHEN T R EH1 G AA2 R TH AH0 N -TREGARTHEN'S T R EH1 G AA2 R TH AH0 N Z -TREGEAGLE T R EH1 G IY1 G AH0 L -TREGEAR T R EH1 G IH2 R -TREGEAR'S T R EH1 G IH1 R Z -TREGELLIS T R EH1 G IH0 L IH0 S -TREGENNA T R EH0 G EH1 N AH0 -TREGENNA'S T R EH0 G EH1 N AH0 Z -TREGENNAS T R EH0 G EH1 N AH0 Z -TREGERAINT T R EH1 G ER0 EY1 N T -TREGETHNER T R EH1 G IH0 T N ER0 -TREGO T R EH1 G OW0 -TREGO'S T R EH1 G OW0 Z -TREGONNING T R EH1 G AH0 N IH0 NG -TREGONY T R EH1 G AH0 N IY0 -TREGONYS T R EH1 G AH0 N IY0 Z -TREGUIER T R EY1 G W IY0 ER0 -TREGUNC T R EH1 G AH2 NG K -TREGUNTER T R EH1 G AH1 N T ER0 -TREHARNE T R EH1 HH AA0 R N -TREHARNE'S T R EH1 HH AA0 R N Z -TREHAWKE T R EH1 HH AO2 K -TREHEARN T R IH0 HH ER1 N -TREHEARNE T R IH0 HH ER1 N -TREHERNE T R EH1 HH ER1 N -TREHERNE'S T R EH1 HH ER0 N Z -TREIG T R IY1 G -TREILHARD T R AY1 L HH AA2 R D -TREILLE T R AY1 L -TREISURE T R EH1 ZH ER0 -TREITSCHKE T R AY1 CH K -TREIZE T R AY1 Z -TREJAGO T R EY1 Y AA1 G OW0 -TREK T R EH1 K -TREKED T R EH1 K T -TREKKED T R EH1 K T -TREKKERS T R EH1 K ER0 Z -TREKKING T R EH1 K IH0 NG -TREKS T R EH1 K S -TREKSCHUIT T R EH1 K S CH UW0 IH0 T -TREKSCHUYT T R EH1 K S K AY1 T -TRELASKE T R EH1 L EY1 S K -TRELAWNEY T R EH1 L AO1 N IY0 -TRELAWNEY'S T R EH1 L AW1 N IY0 Z -TRELAWNY T R EH1 L AO2 N IY0 -TRELAWNY'S T R EH1 L AO2 N IY0 Z -TRELEAVEN T R EH0 L EH1 V AH0 N -TRELISKEY T R IH0 L IH1 S K IY0 -TRELISS T R EH1 L IH0 S -TRELITZ T R EH1 L IH0 T S -TRELLA T R EH1 L AH0 -TRELLICE T R EH1 L AH0 S -TRELLIS T R EH1 L AH0 S -TRELLISED T R EH1 L AH0 S T -TRELLISES T R EH1 L AH0 S AH0 Z -TRELLISWORK T R EH1 L AH0 S W ER2 K -TRELOAR T R EH1 L AO0 R -TRELUAN T R EH1 L UW0 AA1 N -TRELYON T R EH1 L IY0 AA0 N -TREM T R EH1 M -TREMADOC T R EH1 M AE1 D AH0 K -TREMAIL T R EH1 M EY2 L -TREMAIN T R EH1 M AY0 N -TREMAIN'S T R IH0 M EY1 N Z -TREMAINE T R IH0 M EY1 N -TREMAINE'S T R IH0 M EY1 N Z -TREMAYNE T R EH1 M EY0 N -TREMAZAN T R EH1 M AA0 Z AA1 N -TREMBATH T R EH1 M B AH0 TH -TREMBLANT T R EH1 M B L AH0 N T -TREMBLAY T R EH1 M B L EY0 -TREMBLAYE T R EH1 M B L EY1 -TREMBLE T R EH1 M B AH0 L -TREMBLED T R EH1 M B AH0 L D -TREMBLEMENT T R EH1 M B AH0 L M AH0 N T -TREMBLER T R EH1 M B L ER0 -TREMBLERS T R EH1 M B L ER0 Z -TREMBLES T R EH1 M B AH0 L Z -TREMBLEST T R EH1 M B AH0 L AH0 S T -TREMBLETH T R EH1 M B AH0 L TH -TREMBLETON T R EH1 M B AH0 L T AH0 N -TREMBLEY T R EH1 M B L IY0 -TREMBLIN T R EH1 M B L IH0 N -TREMBLING T R EH1 M B AH0 L IH0 NG -TREMBLING T R EH1 M B L IH0 NG -TREMBLINGLY T R EH1 M B AH0 L IH0 NG L IY0 -TREMBLINGS T R EH1 M B AH0 L IH0 NG Z -TREMBLY T R EH1 M B L IY0 -TREMEAU T R EH1 M OW1 -TREMENDIOUS T R AH0 M EH1 N D IY0 AH0 S -TREMENDOUS T R AH0 M EH1 N D AH0 S -TREMENDOUS T R IH0 M EH1 N D AH0 S -TREMENDOUSLY T R AH0 M EH1 N D AH0 S L IY0 -TREMENDOUSLY T R IH0 M EH1 N D AH0 S L IY0 -TREMENDOUSNESS T R AH0 M EH1 N D AH0 S N AH0 S -TREMENHERE T R EH1 M AH0 N HH IH1 R -TREMENHERE'S T R EH1 M AH0 N HH IH1 R Z -TREMENJOUS T R AH0 M EH1 N JH AH0 S -TREMENJUS T R AH0 M EH1 N JH AH0 S -TREMENS T R EH1 M AH0 N Z -TREMERE T R EH1 M ER0 -TREMLETT T R EH1 M AH0 L IH0 T -TREMNELL T R EH1 M N AH0 L -TREMOILLE T R EH1 M AH0 L -TREMOLINO T R EH1 M OW0 L IY1 N OW0 -TREMOLO T R EH1 M OW0 L OW0 -TREMOLOS T R EH1 M AH0 L OW0 Z -TREMONCEAU T R EY1 M AA0 N S OW1 -TREMONT T R EH1 M AH0 N T -TREMONT'S T R EH1 M AA2 N T S -TREMONTES T R EY1 M AA0 N T EH0 S -TREMONTS T R EY1 M AA0 N T S -TREMOR T R EH1 M ER0 -TREMOREL T R EH1 M ER0 EH1 L -TREMOREL'S T R EH1 M ER0 EH1 L Z -TREMORS T R EH1 M ER0 Z -TREMOUILLE T R EH1 M UW1 IY0 -TREMOUR T R EH1 M UH1 R -TREMOURS T R EH1 M ER0 Z -TREMP T R EH1 M P -TREMPE T R EH1 M P -TREMUKJI T R IH0 M UW0 K JH IY0 -TREMULO T R EH1 M Y AH0 L OW0 -TREMULOUS T R EH1 M Y AH0 L AH0 S -TREMULOUSLY T R EH1 M Y AH0 L AH0 S L IY0 -TREMULOUSNESS T R EH1 M Y AH0 L AH0 S N AH0 S -TRENAILS T R EH1 N EH0 L Z -TRENBY T R EH1 N B IY0 -TRENCH T R EH1 N CH -TRENCH'S T R EH1 N CH IH0 Z -TRENCHANCY T R EH1 N CH AE1 N S IY0 -TRENCHANT T R EH1 N CH AH0 N T -TRENCHANTLY T R EH1 N CH AH0 N T L IY0 -TRENCHARD T R EH1 NG K ER0 D -TRENCHARD'S T R EH1 N CH ER0 D Z -TRENCHARDS T R EH1 N CH ER0 D Z -TRENCHED T R EH1 N CH T -TRENCHER T R EH1 N CH ER0 -TRENCHERMAN T R EH1 N CH ER0 M AH0 N -TRENCHERMEN T R EH1 N CH ER0 M IH0 N -TRENCHERS T R EH1 N CH ER0 Z -TRENCHES T R EH1 N CH IH0 Z -TRENCHING T R EH1 N CH IH0 NG -TRENCHON T R EH1 N CH AH0 N -TRENCHS T R EH1 N CH S -TRENCK T R EH1 NG K -TRENCK'S T R EH1 NG K S -TREND T R EH1 N D -TRENDALL T R EH1 N D AH0 L -TRENDED T R EH1 N D IH0 D -TRENDELLSOHN T R EH1 N D AH0 L S AH0 N 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EH1 S P AE2 S IH0 N -TRESPASSING T R EH1 S P AE2 S IH0 NG -TRESPASSING T R EH1 S P AH0 S IH0 NG -TRESQU T R EH1 S K -TRESS T R EH1 S -TRESSA T R EH1 S AH0 -TRESSA'S T R EH1 S AH0 Z -TRESSADY T R EH1 S AH0 D IY0 -TRESSADY'S T R EH1 S AH0 D IY0 Z -TRESSAMER T R EH1 S AH0 M ER0 -TRESSAMER'S T R EH1 S AH0 M ER0 Z -TRESSAN T R EH1 S AH0 N -TRESSED T R EH1 S T -TRESSEL T R EH1 S AH0 L -TRESSELS T R EH1 S AH0 L Z -TRESSES T R EH1 S IH0 Z -TRESSIDAR T R EH1 S IH0 D AA1 R -TRESSIDY T R EH1 S IH0 D IY0 -TRESSILIAN T R EH0 S IH1 L Y AH0 N -TRESSILIAN'S T R EH0 S IH1 L Y AH0 N Z -TRESSITER T R EH1 S IH0 T ER0 -TRESTLE T R EH1 S AH0 L -TRESTLES T R EH1 S AH0 L Z -TRESUMPSCOTT T R IH0 Z AH1 M P S K AH0 T -TRET T R EH1 T -TRETA T R EY1 T AH0 -TRETHAWAY T R EH1 TH AH0 W EY2 -TRETHERICK T R EH1 DH ER0 IH0 K -TRETHERICK'S T R EH1 DH ER0 IH0 K S -TRETHEWAY T R EH1 DH W EY0 -TRETHICK T R EH1 TH IH0 K -TRETHICK'S T R EH1 TH IH0 K S -TRETHOWEN T R EH1 TH AW1 AH0 N -TRETS T R AH0 T S -TRETTON T R EH1 T AH0 N 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Z -TREVES'S T R AH0 V Z IH0 Z -TREVET T R AH0 V EH1 T -TREVETHICK T R AH0 V EH1 TH IH0 K -TREVETHICK'S T R AH0 V EH1 TH IH0 K S -TREVETHLAN T R AH0 V EH1 TH L AH0 N -TREVETHLAN'S T R AH0 V EH1 TH L AH0 N Z -TREVI T R EH0 V IY1 -TREVIGNAC T R AH0 V IH1 G N AH0 K -TREVILIAN T R AH0 V IH1 L Y AH0 N -TREVILLE T R AH0 V IH2 L -TREVILLE'S T R AH0 V IH2 L Z -TREVILLIAN T R EH0 V IH1 L Y AH0 N -TREVINO T R AH0 V IY1 N OW0 -TREVIS T R EH1 V IH0 S -TREVISA T R EH0 V IY1 S AH0 -TREVISA'S T R AH0 V IY1 Z AH0 Z -TREVISE T R EH0 V IY1 S -TREVISE'S T R EH0 V IY1 S IH0 Z -TREVISO T R EH0 V IY1 S OW0 -TREVISON T R EH1 V IH0 S AH0 N -TREVISON'S T R EH1 V IH0 S AH0 N Z -TREVITHICK T R EH0 V IY1 TH IH0 K -TREVITT T R EH1 V IH0 T -TREVLYN T R EH1 V L AH0 N -TREVLYN'S T R EH1 V L AH0 N Z -TREVLYNS T R EH1 V L AH0 N Z -TREVNA T R AH0 V N AH0 -TREVOR T R EH1 V ER0 -TREVOR'S T R EH1 V ER0 Z -TREVORS T R EH1 V ER0 Z -TREVORS'S T R EH1 V ER0 Z IH0 Z -TREVOSE T R EY1 V OW0 S -TREVOUX T R EH1 V UW1 -TREVYLLIAN T R AH0 V IH0 L IY0 AH0 N -TREW T R UW1 -TREWE T R UW1 -TREWHELLA T R IY1 HH W EH1 L AH0 -TREWIN T R UW1 IH0 N -TREWINION T R IY1 W AY0 N Y AH0 N -TREWINNARD T R UW1 IH0 N ER0 D -TREWITT T R UW1 IH0 T -TREWLY T R UW1 L IY0 -TREWS T R UW1 Z -TREWTH T R UW1 TH -TREY T R EY1 -TREYF T R EY1 F -TREYMES T R EY1 M Z -TREYS T R EY1 Z -TREYSA T R EY1 S AH0 -TREZAC T R EH1 Z AH0 K -TREZE T R EH1 Z -TRI T R AY1 -TRIA T R IY0 AH0 -TRIAD T R AY1 AE2 D -TRIADS T R AY1 AE2 D Z -TRIAL T R AY1 AH0 L -TRIAL T R AY1 L -TRIAL'S T R AY1 AH0 L Z -TRIALL T R AY1 L -TRIALS T R AY1 AH0 L Z -TRIALS T R AY1 L Z -TRIAMOND T R AY1 M AH0 N D -TRIANA T R IY0 AE1 N AH0 -TRIANGLE T R AY1 AE2 NG G AH0 L -TRIANGLES T R AY1 AE2 NG G AH0 L Z -TRIANGULAR T R AY0 AE1 NG G Y AH0 L ER0 -TRIANGULARLY T R AY0 AE1 NG G Y AH0 L ER0 L IY0 -TRIANGULATION T R AY0 AE2 NG G Y UW0 L EY1 SH AH0 N -TRIANON T R AY2 AE1 N AH0 N -TRIANONS T R AY1 AE2 N AH0 N Z -TRIARII T R AY2 AE1 R IY0 -TRIAS T R IY1 AH0 Z -TRIASSIC T R AY2 AE1 S IH2 K -TRIB T R IH1 B -TRIBAL T R AY1 B AH0 L -TRIBB T R IH1 B -TRIBBLE T R IH1 B AH0 L -TRIBBYLATION T R IH1 B AH0 L EY1 SH AH0 N -TRIBE T R AY1 B -TRIBE'S T R AY1 B Z -TRIBEFOLK T R IH0 B EH1 F OW2 K -TRIBELESS T R AY1 B L AH0 S -TRIBES T R AY1 B Z -TRIBESFOLK T R AY1 B Z F OW2 K -TRIBESMAN T R AY1 B Z M AE0 N -TRIBESMEN T R AY1 B Z M IH0 N -TRIBONIAN T R IH2 B AH0 N IY1 AH0 N -TRIBOULET T R AY1 B UW0 L EH1 T -TRIBOULET'S T R AY1 B UW0 L EH1 T S -TRIBU T R IH1 B Y UW0 -TRIBULATION T R IH2 B Y AH0 L EY1 SH AH0 N -TRIBULATIONS T R IH2 B Y AH0 L EY1 SH AH0 N Z -TRIBUNA T R AY2 B Y UW1 N AH0 -TRIBUNAL T R AH0 B Y UW1 N AH0 L -TRIBUNALS T R AY2 B Y UW1 N AH0 L Z -TRIBUNATE T R IH1 B Y UW0 N AH0 T -TRIBUNAUX T R IH1 B Y UW0 N OW1 -TRIBUNE T R IH1 B Y UW0 N -TRIBUNE'S T R IH1 B Y UW0 N Z -TRIBUNES T R IH1 B Y UW0 N Z -TRIBUNESHIP T R IH1 B Y UW0 N SH IH2 P -TRIBUS T R IH1 B AH0 S -TRIBUTA T R IH1 B Y AH0 T AH0 -TRIBUTARIES T R IH1 B Y AH0 T EH2 R IY0 Z -TRIBUTARIOS T R IH1 B Y AH0 T EH2 R IY0 OW0 Z -TRIBUTARY T R IH1 B Y AH0 T EH2 R IY0 -TRIBUTE T R IH1 B Y UW0 T -TRIBUTES T R IH1 B Y UW0 T S -TRIC T R IH0 K -TRICASSE T R IY0 K AA0 S EY1 -TRICE T R AY1 S -TRICED T R AY1 S T -TRICEPS T R AY1 S EH2 P S -TRICERATOPS T R AY2 S EH1 R AH0 T AO2 P S -TRICHET T R IH1 CH IH0 T -TRICHINA T R IH1 CH AY1 N AH0 -TRICHINOPOLI T R IH1 CH IH2 N OW0 P OW1 L IY0 -TRICHINOPOLY T R IH1 CH IH0 N AA1 P AH0 L IY0 -TRICHINOSIS T R IH1 CH IH0 N OW1 S AH0 S -TRICHY T R IH1 CH IY0 -TRICING T R AY1 S IH0 NG -TRICK T R IH1 K -TRICK'D T R IH1 K D -TRICK'S T R IH1 K S -TRICKED T R IH1 K T -TRICKERIES T R IH1 K ER0 IY0 Z -TRICKERY T R IH1 K ER0 IY0 -TRICKEY T R IH1 K IY0 -TRICKIER T R IH1 K IY0 ER0 -TRICKIEST T R IH1 K IY0 AH0 S T -TRICKINESS T R IH1 K IY0 N AH0 S -TRICKING T R IH1 K IH0 NG -TRICKLE T R IH1 K AH0 L -TRICKLED T R IH1 K AH0 L D -TRICKLES T R IH1 K AH0 L Z -TRICKLIN T R IH1 K L IH0 N -TRICKLING T R IH1 K L IH0 NG -TRICKLINGS T R IH1 K L IH0 NG Z -TRICKS T R IH1 K S -TRICKSEY T R IH1 K S IY0 -TRICKSOME T R IH1 K S AH0 M -TRICKSTER T R IH1 K S T ER0 -TRICKSTER'S T R IH1 K S T ER0 Z -TRICKSTERS T R IH1 K S T ER0 Z -TRICKSY T R IH1 K S IY0 -TRICKSY'S T R IH1 K S IY0 Z -TRICKY T R IH1 K IY0 -TRICLINIUM T R AY0 K L IH1 N IY0 AH0 M -TRICOLOR T R AY0 K AH2 L ER0 -TRICOLORED T R IY0 K AH1 L ER0 D -TRICOLOUR T R IY0 K AH1 L ER0 -TRICOLOURED T R IY0 K AH1 L ER0 D -TRICON T R IH1 K AH0 N -TRICORNE T R AY0 K AO2 R N -TRICOT T R AY1 K AA0 T -TRICOTEUSES T R AY0 K AH0 T UW1 Z IH0 Z -TRICOTRIN T R AY0 K AA1 T R IH0 N -TRICYCLE T R IH1 S IH0 K AH0 L -TRICYCLES T R IH1 S IH0 K AH0 L Z -TRIDACNA T R AY0 D AE1 K N AH0 -TRIDDELFITZ T R IH1 D AH0 L F IH0 T S -TRIDDELSITZ T R IH1 D AH0 L S IH0 T S -TRIDE T R AY0 D -TRIDENT T R AY1 D AH0 N T -TRIDENTATA T R AY1 D AH0 N T AA1 T AH0 -TRIDENTINE T R AY1 D AH0 N T AY2 N -TRIDENTS T R AY1 D AH0 N T S -TRIDGE T R IH1 JH -TRIDON T R AY1 D AH0 N -TRIE T R AY1 -TRIED T R AY1 D -TRIEL T R IY0 AH0 L -TRIENNIAL T R AY0 EH1 N IY0 AH0 L -TRIENT T R IY0 AH0 N T -TRIENTJE T R AY1 N T JH -TRIER T R AY1 ER0 -TRIERARCH T R AY1 ER0 AA2 R K -TRIERMAIN T R AY1 ER0 M EY1 N -TRIERS T R AY1 ER0 Z -TRIES T R AY1 Z -TRIEST T R IY0 AH0 S T -TRIESTE T R IY1 S T -TRIETH T R AY1 IH0 TH -TRIEVE T R IY1 V -TRIFFITT T R IH1 F IH0 T -TRIFFITT'S T R IH1 F IH0 T S -TRIFINA T R AY0 F IY1 N AH0 -TRIFLE T R AY1 F AH0 L -TRIFLED T R AY1 F AH0 L D -TRIFLER T R AY1 F AH0 L ER0 -TRIFLERS T R AY1 F AH0 L ER0 Z -TRIFLES T R AY1 F AH0 L Z -TRIFLIN T R AY1 F L IH0 N -TRIFLING T R AY1 F L IH0 NG -TRIFLINGLY T R AY1 F L IH0 NG L IY0 -TRIFLINGS T R AY1 F L IH0 NG Z -TRIFOLIUM T R AY1 F OW2 L IY0 AH0 M -TRIFON T R AY1 F AO0 N -TRIFORIUM T R AY1 F AO2 R IY0 AH0 M -TRIFORMIS T R AY1 F AO1 R M AH0 S -TRIG T R IH1 G -TRIGAMY T R AY0 G AH0 M IY0 -TRIGARTAS T R AY0 G AA0 R T AH0 S -TRIGEDGO T R AY0 G EH1 D G OW2 -TRIGEMINAL T R AY0 JH EH1 M AH0 N AH0 L -TRIGEMINUS T R AY0 G EH0 M IY1 N AH0 S -TRIGG T R IH1 G -TRIGGE T R IH1 G -TRIGGED T R IH1 G D -TRIGGER T R IH1 G ER0 -TRIGGER'S T R IH1 G ER0 Z -TRIGGERED T R IH1 G ER0 D -TRIGGERS T R IH1 G ER0 Z -TRIGGS T R IH1 G Z -TRIGGS'S T R IH1 G Z IH0 Z -TRIGILLGUS T R IH1 JH AH0 L G AH0 S -TRIGLYPHS T R AY0 G L IH1 F S -TRIGONOCEPHALUS T R IY0 G OW1 N OW0 S EH2 F AH0 L AH0 S -TRIGONOMETRICAL T R AY0 G AA2 N AH0 M EH1 T R IH0 K AH0 L -TRIGONOMETRY T R IH1 G AH0 N AA1 M AH0 T R IY0 -TRIGORIN T R AY0 G AO1 R IH0 N -TRIKE T R AY2 K -TRILBY T R IH1 L B IY0 -TRILBY'S T R IH1 L B IY0 Z -TRILE T R AY0 L -TRILITHON T R AY0 L IH1 TH AH0 N -TRILITHONS T R AY0 L IH1 TH AA2 N Z -TRILL T R IH1 L -TRILLED T R IH1 L D -TRILLER T R IH1 L ER0 -TRILLING T R IH1 L IH0 NG -TRILLION T R IH1 L Y AH0 N -TRILLIONS T R IH1 L Y AH0 N Z -TRILLIUM T R IH1 L IY0 AH0 M -TRILLIUMS T R IH1 L IY0 AH0 M Z -TRILLS T R IH1 L Z -TRILOBITE T R IH1 L AH0 B AY2 T -TRILOBITES T R IH1 L AH0 B AY2 T S -TRILOGIES T R IH1 L AH0 JH IY0 Z -TRILOGY T R IH1 L AH0 JH IY0 -TRIM T R IH1 M -TRIM'S T R IH1 M Z -TRIMALCHIO T R IY0 M AA1 L K IY0 OW0 -TRIMALCHIO'S T R IY0 M AA1 L K IY0 OW2 Z -TRIMBLE T R IH1 M B AH0 L -TRIMBLE'S T R IH1 M B AH0 L Z -TRIMETER T R IH1 M AH0 T ER0 -TRIMLEY T R IH1 M L IY0 -TRIMLY T R IH1 M L IY0 -TRIMM T R IH1 M -TRIMM'D T R IH1 M D -TRIMM'S T R IH1 M Z -TRIMMED T R IH1 M D -TRIMMER T R IH1 M ER0 -TRIMMER'S T R IH1 M ER0 Z -TRIMMERS T R IH1 M ER0 Z -TRIMMEST T R IH1 M AH0 S T -TRIMMIN T R IH1 M IH0 N -TRIMMIN'S T R IH1 M IH0 N Z -TRIMMING T R IH1 M IH0 NG -TRIMMINGS T R IH1 M IH0 NG Z -TRIMMINS T R IH1 M IH0 N Z -TRIMMLE T R IH1 M L -TRIMMY T R IH1 M IY0 -TRIMNESS T R IH1 M N AH0 S -TRIMOUILLE T R IH1 M UW1 IY0 -TRIMS T R IH1 M Z -TRIMURTI T R IY0 M UH1 R T IY0 -TRIN T R IH0 N -TRINA T R IY1 N AH0 -TRINA'S T R IY1 N AH0 Z -TRINACRIA T R AY2 N AE1 K R IY0 AH0 -TRINACRIAN T R AY2 N AE1 K R IY0 AH0 N -TRINAL T R AH0 N AH0 L -TRINC T R IH0 NG K -TRINCALO T R IH0 NG K AA1 L OW0 -TRINCOMALEE T R IH0 NG K OW1 M AH0 L IY0 -TRINCULO T R IH0 NG K UW1 L OW0 -TRINDER T R IH1 N D ER0 -TRINE T R AY1 N -TRING T R IH0 NG -TRINGHAM T R IH1 NG HH AE2 M -TRINGLE T R IH1 NG G AH0 L -TRINGLE'S T R IH1 NG L Z -TRINGLES T R IH1 NG G AH0 L Z -TRINGLO T R IH1 NG L OW0 -TRINIDAD T R IH1 N IH0 D AE2 D -TRINIDAD'S T R IH1 N IH0 D AE2 D Z -TRINIDADA T R IH1 N IH0 D AE2 D AH0 -TRINIDADO T R IH1 N IH0 D AE2 D OW0 -TRINITA T R IY0 N IY1 T AH0 -TRINITARIAN T R IH1 N AH0 T EH1 R IY0 AH0 N -TRINITARIANISM T R IH1 N AH0 T EH1 R IY0 AH0 N IH2 Z AH0 M -TRINITARIANS T R IH1 N AH0 T EH1 R IY0 AH0 N Z -TRINITATIS T R IH1 N IH0 T EY1 T AH0 S -TRINITE T R IH1 N IH0 T -TRINITIES T R IH1 N IH0 T IY0 Z -TRINITY T R IH1 N AH0 T IY0 -TRINITY T R IH1 N IH0 T IY0 -TRINITY'S T R IH1 N IH0 T IY0 Z -TRINK T R IH1 NG K -TRINKEN T R IH1 NG K AH0 N -TRINKET T R IH1 NG K AH0 T -TRINKETRY T R IH1 NG K AH0 T R IY0 -TRINKETS T R IH1 NG K AH0 T S -TRINKETT T R IH1 NG K IH0 T -TRINKGELD T R IH1 NG K G EH2 L D -TRINOBANTES T R AY2 N OW1 B AE1 N T S -TRINQUIER T R IH1 N K W IH1 R -TRIO T R IY1 OW2 -TRIOLET T R IY0 OW1 L IH0 T -TRIOLETS T R IY0 OW0 L EY1 Z -TRIOMED T R IY0 OW1 M D -TRIOMPHANTE T R IY0 OW1 M F AE2 N T -TRIOMPHE T R IY0 OW1 M F -TRIONAL T R IY0 OW1 N AH0 L -TRIONFO T R IY0 OW1 N F OW0 -TRIOS T R IY0 OW0 Z -TRIP T R IH1 P -TRIP'S T R IH1 P S -TRIPANG T R AY0 P AA1 NG -TRIPARTITE T R AY0 P AA1 R T AY2 T -TRIPATALY T R AY0 P AA1 T AH0 L IY0 -TRIPE T R AY1 P -TRIPEAUD T R AY1 P OW1 D -TRIPES T R AY1 P S -TRIPHAMMER T R IH1 P HH AE2 M ER0 -TRIPLANE T R IH1 P L EY2 N -TRIPLANES T R IH1 P L EY2 N Z -TRIPLANETARY T R IH1 P L AE1 N AH0 T EH2 R IY0 -TRIPLANETARY'S T R IH1 P L AE1 N AH0 T EH2 R IY0 Z -TRIPLE T R IH1 P AH0 L -TRIPLED T R IH1 P AH0 L D -TRIPLES T R IH1 P AH0 L Z -TRIPLET T R IH1 P L AH0 T -TRIPLET'S T R IH1 P L AH0 T S -TRIPLETS T R IH1 P L AH0 T S -TRIPLETT T R IH1 P L IH0 T -TRIPLEX T R IH0 P L EH1 K S -TRIPLICATE T R IH1 P L IH0 K AH0 T -TRIPLICITY T R IH1 P L IH1 S AH0 T IY0 -TRIPLING T R IH1 P AH0 L IH0 NG -TRIPLING T R IH1 P L IH0 NG -TRIPLY T R IH1 P L IY0 -TRIPOD T R AY1 P AA2 D -TRIPODS T R AY1 P AA2 D Z -TRIPOLI T R IH1 P AH0 L IY0 -TRIPOLIS T R IH1 P AH0 L IH0 S -TRIPOLITAN T R IH1 P AH0 L IH1 T AH0 N -TRIPOLITANS T R IH1 P AH0 L IH1 T AH0 N Z -TRIPOS T R IY1 P OW0 Z -TRIPP T R IH1 P -TRIPP'D T R IH1 P D -TRIPP'S T R IH1 P S -TRIPPE T R IH1 P -TRIPPED T R IH1 P T -TRIPPELLI T R IH1 P EH1 L IY0 -TRIPPER T R IH1 P ER0 -TRIPPERS T R IH1 P ER0 Z -TRIPPET T R IH1 P AH0 T -TRIPPIN T R IH1 P IH0 N -TRIPPING T R IH1 P IH0 NG -TRIPPINGLY T R IH1 P IH0 NG L IY0 -TRIPPINGS T R IH1 P IH0 NG Z -TRIPPLE T R IH1 P AH0 L -TRIPPS T R IH1 P S -TRIPS T R IH1 P S -TRIPT T R IH1 P T -TRIPTOLEMUS T R IH1 P T AA1 L AH0 M AH0 S -TRIPTON T R IH1 P T AH0 N -TRIPTYCH T R IH1 P T IH0 K -TRIREME T R AY1 R IY2 M -TRIREMES T R AY1 R IY2 M Z -TRIRODOV T R IH1 R AH0 D AO2 V -TRIRODOV'S T R IH1 R AH0 D AA0 V Z -TRIS T R IH1 S -TRISAGION T R IH1 Z AH0 JH AH0 N -TRISCOE T R IH1 S K OW0 -TRISCOE'S T R IH1 S K OW0 Z -TRISCOES T R IH1 S K OW0 Z -TRISMEGISTUS T R IH1 S M IH0 JH IY1 S T AH0 S -TRISMUS T R IH1 Z M AH0 S -TRISSOTIN T R IH0 S OW1 T IH0 N -TRIST T R AY1 S T -TRISTAM T R IH1 S T AH0 M -TRISTAN T R IH1 S T AE2 N -TRISTAN'S T R IH1 S T AH0 N Z -TRISTE T R AY1 S T IY0 -TRISTEMENT T R IH1 S T AH0 M AH0 N T -TRISTES T R AY1 S T IY0 Z -TRISTESSE T R AY1 S T EH0 S -TRISTEZA T R AY1 S T EY1 Z AH0 -TRISTFUL T R IH1 S T F AH0 L -TRISTIA T R IH1 S T IY0 AH0 -TRISTIS T R IH1 S T IH0 S -TRISTRAM T R IH1 S T R AH0 M -TRISTRAM'S T R IH1 S T R AH0 M Z -TRISTRAMS T R IH1 S T R AH0 M Z -TRISTRAN T R IH1 S T R AH0 N -TRISTREM T R IH1 S T R IH0 M -TRISTREM'S T R IH1 S T R AH0 M Z -TRISYLLABIC T R AY1 S IH0 L AE1 B IH0 K -TRISYLLABLE T R AY1 S IH1 L AH0 B AH0 L -TRISYSTEM T R AY1 S IH2 S T AH0 M -TRIT T R IH1 T -TRITE T R AY1 T -TRITELY T R AY1 T L IY0 -TRITENESS T R AY1 T N AH0 S -TRITEST T R AY1 T IH0 S T -TRITILL T R IH1 T AH0 L -TRITON T R AY1 T AH0 N -TRITON'S T R AY1 T AH0 N Z -TRITONIA T R AY1 T AH0 N IY0 AH0 -TRITONS T R AY1 T AH0 N Z -TRITTON T R IH1 T AH0 N -TRITU T R IY1 CH UW0 -TRITURATE T R IH1 CH ER0 AH0 T -TRITURATED T R IY1 CH ER0 EY2 T IH0 D -TRITURATION T R IH0 CH UH0 R EY1 SH AH0 N -TRIUM T R AY1 AH0 M -TRIUMPH T R AY1 AH0 M F -TRIUMPH'D T R AY1 AH0 M F D -TRIUMPH'S T R AY1 AH0 M F S -TRIUMPHAL T R AY0 AH1 M F AH0 L -TRIUMPHANS T R AY0 AH1 M F AH0 N Z -TRIUMPHANT T R AY0 AH1 M F AH0 N T -TRIUMPHANTLY T R AY0 AH1 M F AH0 N T L IY0 -TRIUMPHATOR T R AY1 AH0 M F EY2 T ER0 -TRIUMPHE T R AY1 AH0 M F -TRIUMPHED T R AY1 AH0 M F T -TRIUMPHER T R AY1 AH0 M F ER0 -TRIUMPHING T R AY1 AH0 M F IH0 NG -TRIUMPHS T R AY1 AH0 M F S -TRIUMVIR T R AY0 AH1 M V ER0 -TRIUMVIRATE T R AY0 AH1 M V ER0 AH0 T -TRIUMVIRI T R AY0 AH1 M V ER0 IY0 -TRIUMVIRS T R AY0 AH1 M V ER0 Z -TRIUNE T R AY1 AH0 N -TRIUS T R AY1 AH0 S -TRIVET T R IH1 V AH0 T -TRIVETT T R IH1 V IH0 T -TRIVETT'S T R IH0 V EH1 T S -TRIVIA T R IH1 V IY0 AH0 -TRIVIAL T R IH1 V IY0 AH0 L -TRIVIALITIES T R IH1 V IY0 AE1 L IH0 T IY0 Z -TRIVIALITY T R IH2 V IY0 AE1 L AH0 T IY0 -TRIVIALLY T R IH1 V IY0 AH0 L IY0 -TRIVIUM T R IH1 V IY0 AH0 M -TRIVULCE T R IH1 V AH1 L S -TRIX T R IH1 K S -TRIX'S T R IH1 K S IH0 Z -TRIXIE T R IH1 K S IY0 -TRIXIE'S T R IH1 K S IY0 Z -TRIXTON T R IH1 K S T AH0 N -TRIXY T R IH1 K S IY0 -TRIXY'S T R IH1 K S IY0 Z -TRO T R OW0 -TROAD T R OW1 D -TROAS T R OW1 AH0 Z -TROAT T R OW1 T -TROATS T R OW1 T S -TROBE T R OW1 B -TROCADERO T R AA1 K AH0 D EH1 R OW0 -TROCERO T R OW0 CH EH1 R OW0 -TROCHA T R AA1 CH AH0 -TROCHAIC T R AA1 CH EY1 IH0 K -TROCHANTER T R AA1 CH AE1 N T ER0 -TROCHEE T R AA1 CH IY0 -TROCHEES T R AA1 CH IY2 Z -TROCHES T R AA1 CH IH0 Z -TROCHILUS T R AA1 CH IY1 L UW0 Z -TROCHU T R OW1 K UW0 -TROCHU'S T R AA1 CH UW2 Z -TROCHUS T R AA1 CH AH0 S -TROD T R AA1 D -TRODDEN T R AA1 D AH0 N -TRODE T R OW1 D -TROEZEN T R OW1 Z AH0 N -TROEZENE T R OW1 Z IY0 N -TROFAST T R OW0 F AE2 S T -TROFFATER T R AA1 F EY2 T ER0 -TROFIMOV T R AH0 F IY1 M AA0 V -TROGLODYTE T R AA1 G L AH0 D AY2 T -TROGLODYTES T R AA1 G L AH0 D AY2 T S -TROGLODYTIC T R AA1 G L AH0 D IH1 T IH0 K -TROGNON T R AA1 G N AH0 N -TROGON T R AA1 G AO0 N -TROGONS T R AA1 G AH0 N Z -TROGZMONDOFF T R OW1 Z M OW0 N D AO1 F -TROIA T R OW1 Y AH0 -TROIKA T R OY1 K AH0 -TROIKAS T R OY1 K AH0 Z -TROIL T R OY1 L -TROILUS T R OY1 L AH0 S -TROIS T W AA1 -TROISIEME T R OY1 S IY1 M -TROISVILLE T R OY1 Z V IH2 L -TROISVILLES T R OY1 Z V IH2 L Z -TROITZ T R OY1 T S -TROJA T R OW1 JH AH0 -TROJAN T R OW1 JH AH0 N -TROJAN'S T R OW1 JH AH0 N Z -TROJANS T R OW1 JH AH0 N Z -TROKOFF T R OW1 K AO0 F -TROLD T R OW2 L D -TROLDS T R OW2 L D Z -TROLL T R OW1 L -TROLL'S T R OW1 L Z -TROLLE T R OW1 L -TROLLED T R OW1 L D -TROLLER T R OW1 L ER0 -TROLLERS T R OW1 L ER0 Z -TROLLEY T R AA1 L IY0 -TROLLEYS T R AA1 L IY0 Z -TROLLHAETTA T R OW1 L HH EH1 T AH0 -TROLLIES T R AA1 L IY0 Z -TROLLING T R OW1 L IH0 NG -TROLLOLOP T R AA1 L AH0 L AA1 P -TROLLOP T R AA1 L AH0 P -TROLLOPE T R OW1 L OW2 P -TROLLOPE'S T R AA1 L AH0 P S -TROLLOPING T R AA1 L AH0 P IH0 NG -TROLLOPS T R AA1 L AH0 P S -TROLLS T R OW1 L Z -TROLLY T R AA1 L IY0 -TROLOO T R OW0 L UW2 -TROMBA T R AA1 M B AH0 -TROMBONE T R AA0 M B OW1 N -TROMBONES T R AA0 M B OW1 N Z -TROMP T R AA1 M P -TROMP'S T R AA1 M P S -TROMPE T R AA1 M P IY0 -TROMPED T R AA1 M P T -TROMPERE T R AA1 M P IY1 R -TROMPETTE T R AA1 M P EH2 T -TROMPIN T R AA1 M P IH0 N -TROMPLE T R AA1 M P AH0 L -TROMPLED T R AA1 M P AH0 L D -TROMPS T R AA1 M P S -TROMSO T R AA1 M S OW0 -TROMSOE T R AA1 M S OW0 -TROMSOEE T R AA1 M S OW0 IY0 -TRON T R AA1 N -TRONCHET T R OW0 N K IH0 T -TRONCHIN T R OW0 N K IH0 N -TRONCKH T R AA1 NG K -TROND T R AA1 N D -TRONDHJEM T R OW0 N D JH AH0 M -TRONE T R OW1 N -TRONEG T R OW0 N EH1 G -TRONES T R OW1 N Z -TRONG T R AO0 NG -TRONK T R AA1 NG K -TRONKA T R AA1 NG K AH0 -TRONYEM T R AA1 N Y AH0 M -TROO T R UW1 -TROOBLE T R UW1 B AH0 L -TROODOS T R UW1 D OW0 Z -TROOF T R UW1 F -TROOL T R UW1 L -TROOLY T R UW0 L IY0 -TROON T R UW1 N -TROOP T R UW1 P -TROOP'S T R UW1 P S -TROOPED T R UW1 P T -TROOPER T R UW1 P ER0 -TROOPER'S T R UW1 P ER0 Z -TROOPERS T R UW1 P ER0 Z -TROOPIN T R UW1 P IH0 N -TROOPING T R UW1 P IH0 NG -TROOPS T R UW1 P S -TROOPSHIP T R UW1 P SH IH2 P -TROOPSHIPS T R UW1 P SH IH2 P S -TROOST T R UW1 S T -TROOT T R UW1 T -TROOTH T R UW1 TH -TROOZ T R UW1 Z -TROP T R AA2 P -TROPAEOLUM T R AA1 P IY0 OW1 L AH0 M -TROPE T R OW2 P -TROPES T R OW2 P S -TROPHIC T R OW1 F IH0 K -TROPHIED T R OW1 F IY0 D -TROPHIES T R OW1 F IY0 Z -TROPHIME T R OW1 F AY2 M -TROPHIMUS T R OW1 F IH0 M IH0 S -TROPHONIUS T R OW1 F AO1 N IY0 IH0 S -TROPHY T R OW1 F IY0 -TROPIC T R AA1 P IH0 K -TROPICAL T R AA1 P IH0 K AH0 L -TROPICALLY T R AA1 P IH0 K L IY0 -TROPICK T R AA1 P IH0 K -TROPICS T R AA1 P IH0 K S -TROPININ T R AA2 P IH0 N IH0 N -TROPISM T R AA2 P IH0 Z AH0 M -TROPLONG T R AA1 P L AO0 NG -TROPPO T R AA1 P OW0 -TROS T R OW0 Z -TROSACHS T R AA1 S AE1 K S -TROSSACHS T R AA1 S AE1 K S -TROSTLE'S T R AA1 S AH0 L Z -TROT T R AA1 T -TROT'S T R AA1 T S -TROTH T R AO1 TH -TROTH T R OW1 TH -TROTHS T R AO1 TH S -TROTKINS T R AA1 T K IH0 N Z -TROTMAN T R AA1 T M AH0 N -TROTS T R AA1 T S -TROTSKY T R AA1 T S K IY2 -TROTSKY'S T R AA1 T S K IY0 Z -TROTT T R AA1 T -TROTT'S T R AA1 T S -TROTTED T R AA1 T AH0 D -TROTTED T R AA1 T IH0 D -TROTTER T R AA1 T ER0 -TROTTER'S T R AA1 T ER0 Z -TROTTERS T R AA1 T ER0 Z -TROTTIE T R AA1 T IY0 -TROTTIN T R AA1 T IH0 N -TROTTING T R AA1 T IH0 NG -TROTTLE T R AA1 T AH0 L -TROTTLE'S T R AA1 T AH0 L Z -TROTTMAN T R AA1 T M AH0 N -TROTTOIR T R AA1 T W AA2 R -TROTTOIRS T R AA1 T W AA2 R Z -TROTTY T R AA1 T IY0 -TROTTY'S T R AA1 T IY0 Z -TROTWOOD T R AA1 T W UH2 D -TROTZKY T R AA1 T S K IY0 -TROU T R UW1 -TROUB T R AH1 B -TROUBADOR T R UW1 B AH0 D ER0 -TROUBADOUR T R UW1 B AH0 D AO2 R -TROUBADOUR'S T R UW1 B AH0 D UH1 R Z -TROUBADOURS T R UW1 B AH0 D AO2 R Z -TROUBERT T R UW1 B ER0 T -TROUBETZKOY T R AH1 B EH1 T S K OY1 -TROUBLE T R AH1 B AH0 L -TROUBLE'S T R AH1 B AH0 L Z -TROUBLED T R AH1 B AH0 L D -TROUBLEMAKER T R AH1 B AH0 L M EY2 K ER0 -TROUBLER T R AH1 B AH0 L ER0 -TROUBLERS T R AH1 B AH0 L ER0 Z -TROUBLES T R AH1 B AH0 L Z -TROUBLESOM T R AH1 B AH0 L S AH0 M -TROUBLESOME T R AH1 B AH0 L S AH0 M -TROUBLESOMELY T R AH1 B AH0 L S AH0 M L IY0 -TROUBLESOMENESS T R AH1 B AH0 L S AH0 M N AH0 S -TROUBLETH T R AH1 B AH0 L TH -TROUBLIN T R AH1 B L IH0 N -TROUBLING T R AH1 B AH0 L IH0 NG -TROUBLING T R AH1 B L IH0 NG -TROUBLOUS T R AH1 B L AH0 S -TROUBRIDGE T R AH1 B R IH2 JH -TROUBRIDGE'S T R AH1 B R IH2 JH IH0 Z -TROUFFLE T R AH1 F AH0 L -TROUGH T R AO1 F -TROUGHS T R AO1 F S -TROUILLEFOU T R UW1 IY0 F UW0 -TROUIN T R W IY1 N -TROUKHATCHEVSKY T R AW1 K AE1 CH IH0 V S K IY0 -TROUNCE T R AW1 N S -TROUNCED T R AW1 N S T -TROUNCING T R AW1 N S IH0 NG -TROUP T R UW1 P -TROUP'S T R UW1 P S -TROUPE T R UW1 P -TROUPER T R UW1 P ER0 -TROUPES T R UW1 P S -TROUSER T R AW1 Z ER0 -TROUSER'S T R AW1 Z ER0 Z -TROUSERED T R AW1 Z ER0 D -TROUSERINGS T R AW1 S R IH0 NG Z -TROUSERLESS T R AW1 Z ER0 L AH0 S -TROUSERS T R AW1 Z ER0 Z -TROUSIES T ER1 AH0 S IY0 Z -TROUSSEAU T R AW1 S OW1 -TROUSSEAUX T R AW1 S OW1 -TROUT T R AW1 T -TROUT'S T R AW1 T S -TROUTBECK T R AW1 T B EH2 K -TROUTENAU T R AW1 T IH0 N OW0 -TROUTH T R AW1 TH -TROUTING T R AW1 T IH0 NG -TROUTLINGS T R AW1 T L IH0 NG Z -TROUTS T R AW1 T S -TROUTT T R AW1 T -TROUVA T R UW1 V AH0 -TROUVAILLE T R UW1 V EY1 L -TROUVE T R UW1 V -TROUVENT T R UW2 V AH0 N T -TROUVER T R UW1 V ER0 -TROUVERA T R UW1 V ER0 AH0 -TROUVERE T R UW1 V ER0 -TROUVERES T R UW1 V ER0 Z -TROUVEREZ T R UW2 V EH1 R EH0 Z -TROUVES T R AW1 V Z -TROUVEZ T R UW1 V EH0 Z -TROUVILLE T R UW1 V IH2 L -TROVAR T R OW0 V AA1 R -TROVATO T R OW0 V AA1 T OW0 -TROVATORE T R OW1 V AH0 T AO2 R -TROVE T R OW1 V -TROVER T R OW1 V ER0 -TROVES T R OW1 V Z -TROVO T R OW1 V OW0 -TROW T R OW1 -TROW'S T R OW1 Z -TROWBRIDGE T R OW1 B R IH0 JH -TROWBRIDGE'S T R OW1 B R IH0 JH AH0 Z -TROWBRIDGES T R OW1 B R IH0 JH IH0 Z -TROWED T R AW1 D -TROWEL T R AW1 W EH0 L -TROWELFUL T R AA1 W EH0 L F AH0 L -TROWELS T R AW1 L Z -TROWER T R AW1 ER0 -TROWL T R AW1 L -TROWS T R OW1 Z -TROWSER T R AW1 Z ER0 -TROWSERS T R AW1 Z ER0 Z -TROWSIS T R AW1 S IH0 S -TROWTH T R OW1 TH -TROY T R OY1 -TROY'S T R OY1 Z -TROYANO'S T R OY2 AA1 N OW0 Z -TROYAS T R OY2 AA1 Z -TROYES T R OY1 Z -TROYLAND T R OY1 L AH0 N D -TROYON T R OY1 AH0 N -TROYON'S T R OY1 AH0 N Z -TRR T R -TRU T R UW0 -TRUAGE T R UW1 AH0 JH -TRUAGH T R UW1 AH0 -TRUANCIES T R UW1 AH0 N S IY0 Z -TRUANCY T R UW1 AH0 N S IY0 -TRUANT T R UW1 AH0 N T -TRUANT'S T R UW1 AH0 N T S -TRUANTRY T R UW1 AH0 N T R IY0 -TRUANTS T R UW1 AH0 N T S -TRUAX T R UW1 AE0 K S -TRUBA T R UW1 B AH0 -TRUBBLE T R AH1 B AH0 L -TRUBERT T R UW1 B ER0 T -TRUBLET T R AH1 B L EH2 T -TRUBNER T R AH1 B N ER0 -TRUBY T R UW1 B IY0 -TRUC T R AH1 K -TRUCE T R UW1 S -TRUCELESS T R UW1 S L AH0 S -TRUCES T R UW1 S IH0 Z -TRUCHSES T R UW1 K S AH0 Z -TRUCHSESS T R AH1 CH AH0 S -TRUCHSZ T R UW1 SH -TRUCK T R AH1 K -TRUCK'S T R AH1 K S -TRUCKED T R AH1 K T -TRUCKEE T R AH1 K IY0 -TRUCKER T R AH1 K ER0 -TRUCKERS T R AH1 K ER0 Z -TRUCKING T R AH1 K IH0 NG -TRUCKLE T R AH1 K AH0 L -TRUCKLED T R AH1 K AH0 L D -TRUCKLEFORD T R AH1 K L IH0 F ER0 D -TRUCKLEFORD'S T R AH1 K L IH0 F ER0 D Z -TRUCKLES T R AH1 K AH0 L Z -TRUCKLING T R AH1 K L IH0 NG -TRUCKLOAD T R AH1 K L OW2 D -TRUCKLOADS T R AH1 K L OW2 D Z -TRUCKMAN T R AH1 K M AH0 N -TRUCKMEN T R AH1 K M EH2 N -TRUCKS T R AH1 K S -TRUCULENCE T R AH1 K Y AH0 L AH0 N S -TRUCULENT T R AH1 K Y AH0 L AH0 N T -TRUCULENTLY T R AH1 K Y AH0 L AH0 N T L IY0 -TRUD T R AH1 D -TRUDA T R UW1 D AH0 -TRUDAINE T R UW1 D EY0 N -TRUDAINE'S T R UW1 D EY1 N Z -TRUDCHEN T R UW1 D CH AH0 N -TRUDE T R UW1 D -TRUDE'S T R UW1 D Z -TRUDEAU T R UW0 D OW1 -TRUDEL T R UW1 D AH0 L -TRUDEL'S T R UW1 D AH0 L Z -TRUDER T R UW1 D ER0 -TRUDGE T R AH1 JH -TRUDGED T R AH1 JH D -TRUDGEON T R AH1 JH AH0 N -TRUDGES T R AH1 JH IH0 Z -TRUDGIN T R AH1 JH IH0 N -TRUDGING T R AH1 JH IH0 NG -TRUDI T R UW1 D IY0 -TRUDI'S T R UW1 D IY0 Z -TRUDOLYUBOV T R UW1 D AA2 L Y UW1 B AO2 V -TRUDY T R UW1 D IY0 -TRUDY'S T R UW1 D IY0 Z -TRUE T R UW1 -TRUE'S T R UW1 Z -TRUEBA T R UH1 B AH0 -TRUEBLOOD T R UW1 B L AH2 D -TRUEBNER T R UW1 B N ER0 -TRUEBOY T R UH1 B OY2 -TRUEFIT T R UW1 F IH2 T -TRUEFITT T R UW1 AH0 F IH0 T -TRUEFITT'S T R UW1 AH0 F IH0 T S -TRUEHEART T R UW1 HH AA0 R T -TRUEHEARTED T R UW1 HH AA0 R T IH0 D -TRUELLA T R UW2 EH1 L AH0 -TRUELOVE T R UW1 L AH2 V -TRUELOVE'S T R UW1 L AH2 V Z -TRUELY T R UW1 L IY0 -TRUEMAN T R UH1 M AH0 N -TRUEMAN'S T R UH1 M AH0 N Z -TRUENESS T R UW1 N AH0 S -TRUEPENNY T R UW1 EH2 P EH2 N IY0 -TRUER T R UW1 ER0 -TRUESDALE T R UW1 Z D EY2 L -TRUESDALE'S T R UW1 Z D EY2 L Z -TRUEST T R UW1 IH0 S T -TRUETT T R UW1 T -TRUEWIT T R UW1 W IH0 T -TRUEY T R UW1 IY0 -TRUF T R AH1 F -TRUFALDIN T R UW0 F AE1 L D IH0 N -TRUFE T R UW1 F -TRUFF T R AH1 F -TRUFFES T R UW0 F S -TRUFFLE T R AH1 F AH0 L -TRUFFLED T R AH1 F AH0 L D -TRUFFLES T R AH1 F AH0 L Z -TRUG T R AH1 G -TRUIDE T R UW1 IH0 D -TRUIE T R UW0 IY1 -TRUISM T R UW1 IH0 Z AH0 M -TRUISMS T R UW1 IH0 Z AH0 M Z -TRUITJE T R UW1 T JH -TRUJANO T R UW0 Y AA1 N OW0 -TRUJILLO T R UW0 HH IY1 OW0 -TRULL T R AH1 L -TRULLIBER T R AH1 L IH0 B ER0 -TRULLS T R AH1 L Z -TRULS T R AH1 L Z -TRULY T R UW1 L IY0 -TRUM T R AH1 M -TRUMAN T R UW1 M AH0 N -TRUMAN'S T R UW1 M AH0 N Z -TRUMBLE T R AH1 M B AH0 L -TRUMBULL T R AH1 M B AH0 L -TRUMBULL'S T R AH1 M B AH0 L Z -TRUMBULLSES T R AH1 M B AH0 L S IH0 Z -TRUMEAU T R UW1 M OW1 -TRUMELLE T R UW0 M EH1 L -TRUMENCE T R UW1 M AH0 N S -TRUMET T R AH1 M IH0 T -TRUMILLY T R UW1 M IH0 L IY0 -TRUMKARD T R AH1 M K ER0 D -TRUMP T R AH1 M P -TRUMPED T R AH1 M P T -TRUMPER T R AH1 M P ER0 -TRUMPERIES T R AH1 M P ER0 IY0 Z -TRUMPERY T R AH1 M P ER0 IY0 -TRUMPET T R AH1 M P AH0 T -TRUMPET'S T R AH1 M P AH0 T S -TRUMPETED T R AH1 M P AH0 T IH0 D -TRUMPETER T R AH1 M P AH0 T ER0 -TRUMPETER'S T R AH1 M P AH0 T ER0 Z -TRUMPETERS T R AH1 M P AH0 T ER0 Z -TRUMPETING T R AH1 M P AH0 T IH0 NG -TRUMPETINGS T R AH1 M P AH0 T IH0 NG Z -TRUMPETS T R AH1 M P AH0 T S -TRUMPING T R AH1 M P IH0 NG -TRUMPINGTON T R AH1 M P IH0 NG T AH0 N -TRUMPLER T R AH1 M P AH0 L ER0 -TRUMPS T R AH1 M P S -TRUNCATED T R AH1 NG K EY2 T IH0 D -TRUNCHEON T R AH1 N CH IH0 N -TRUNCHEONS T R AH1 N CH IH0 N Z -TRUNDLE T R AH1 N D AH0 L -TRUNDLE'S T R AH1 N D AH0 L Z -TRUNDLEBEN T R AH1 N D AH0 L B AH0 N -TRUNDLED T R AH1 N D AH0 L D -TRUNDLES T R AH1 N D AH0 L Z -TRUNDLING T R AH1 N D AH0 L IH0 NG -TRUNELLA T R UW2 N EH1 L AH0 -TRUNION T R AH1 N Y AH0 N -TRUNK T R AH1 NG K -TRUNK'S T R AH1 NG K S -TRUNKED T R AH1 NG K T -TRUNKFUL T R AH1 NG K F AH0 L -TRUNKFULS T R AH1 NG K F AH0 L Z -TRUNKLESS T R AH1 NG K L AH0 S -TRUNKMAKER T R AH1 NG K M EY2 K ER0 -TRUNKS T R AH1 NG K S -TRUNNELL T R AH1 N AH0 L -TRUNNELL'S T R AH1 N AH0 L Z -TRUNNION T R AH1 N Y AH0 N -TRUNNION'S T R AH1 N Y AH0 N Z -TRUNNIONS T R AH1 N Y AH0 N Z -TRURIE T R UH1 R IY0 -TRURO T R UW1 R OW0 -TRURO'S T R UH1 R OW0 Z -TRUS T R AH1 S -TRUSCOMB T R AH1 S K AH0 M -TRUSCOMB'S T R AH1 S K AH0 M Z -TRUSCOTT T R AH1 S K AH0 T -TRUSCOTT'S T R AH1 S K AH0 T S -TRUSIA T R UW1 ZH AH0 -TRUSIA'S T R UW1 ZH AH0 Z -TRUSLOVE T R AH1 S L AH2 V -TRUSLOW T R AH1 S L OW0 -TRUSOTSKY T R AH1 S AA0 T S K IY0 -TRUSS T R AH1 S -TRUSSED T R AH1 S T -TRUSSER T R AH1 S ER0 -TRUSSES T R AH1 S IH0 Z -TRUSSING T R AH1 S IH0 NG -TRUSSIT T R AH1 S IH0 T -TRUST T R AH1 S T -TRUST'S T R AH1 S T S -TRUSTAFORD T R AH1 S T AH0 F ER0 D -TRUSTCOTT T R AH1 S T K AA2 T -TRUSTED T R AH1 S T IH0 D -TRUSTEE T R AH2 S T IY1 -TRUSTEE'S T R AH2 S T IY1 Z -TRUSTEES T R AH2 S T IY1 Z -TRUSTEESHIP T R AH2 S T IY1 SH IH2 P -TRUSTEN T R AH1 S T AH0 N -TRUSTERS T R AH1 S T ER0 Z -TRUSTEST T R AH1 S T AH0 S T -TRUSTETH T R AH1 S T IH0 TH -TRUSTFUL T R AH1 S T F AH0 L -TRUSTFULLY T R AH1 S T F AH0 L IY0 -TRUSTFULNESS T R AH1 S T F AH0 L N AH0 S -TRUSTIER T R AH1 S T IY0 ER0 -TRUSTIES T R AH1 S T IY0 Z -TRUSTIEST T R AH1 S T IY0 AH0 S T -TRUSTIN T R AH1 S T IH0 N -TRUSTING T R AH1 S T IH0 NG -TRUSTINGLY T R AH1 S T IH0 NG L IY0 -TRUSTLESS T R AH1 S T L AH0 S -TRUSTRAM T R AH1 S T R AH0 M -TRUSTS T R AH1 S T S -TRUSTWORTHINESS T R AH1 S T W ER2 DH IY0 N AH0 S -TRUSTWORTHY T R 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K S F ER0 D -TUXPAM T AH1 K S P AE0 M -TUXTON T AH1 K S T AH0 N -TUY T UW1 -TUYL T UW1 L -TUYN T UW1 IH0 N -TUYN'S T UW1 IH0 N Z -TUZENBACH T UW1 Z IH0 N B AA0 K -TVENTY T V EH1 N T IY0 -TVER T V ER0 -TW T -TWA T W AH0 -TWA'N'T T W AH1 AH0 N T -TWACHTMAN T W AA0 K T M AH0 N -TWAD T W AA1 D -TWADDLE T W AA1 D AH0 L -TWADDLER T W AA1 D AH0 L ER0 -TWADDLING T W AA1 D AH0 L IH0 NG -TWAE T W IY0 -TWAIN T W EY1 N -TWAIN'S T W EY1 N Z -TWAL T W AH0 L -TWALA T W AA1 L AH0 -TWALA'S T W AO1 L AH0 Z -TWALMONTH T W AA1 M AH0 N TH -TWAN'T T W AA1 N T -TWANG T W AA1 NG -TWANGED T W AA1 N JH D -TWANGING T W AA1 N JH IH0 NG -TWANGLING T W AA1 NG L IH0 NG -TWANGS T W AA1 NG Z -TWANGY T W AA1 N JH IY0 -TWANKAY T W AA1 NG K EY1 -TWANT T W AO1 N T -TWAR T W AO1 R -TWARDOWSKI T W ER0 D AW1 S K IY0 -TWARDOWSKY T W ER0 D AW1 S K IY0 -TWARN'T T W AO1 R N T -TWAS T W AH1 Z -TWASN'T T W AA1 Z AH0 N T -TWATT T W AA2 T -TWAY T W EY2 -TWEAK T W IY1 K -TWEAKED T W IY1 K T -TWEAKING T W IY1 K IH0 NG -TWEAKS T W IY1 K S -TWEATY T W IY1 T IY0 -TWEDDLE T W EH1 D AH0 L -TWEE T W IY1 -TWEED T W IY1 D -TWEED'S T W IY1 D Z -TWEEDALE T W IY1 D EY2 L -TWEEDDALE T W IY1 D D EY2 L -TWEEDIE T W IY1 D IY0 -TWEEDIE'S T W IY1 D IY0 Z -TWEEDIES T W IY1 D IY0 Z -TWEEDLE T W IY1 D AH0 L -TWEEDLEDEE T W IY1 D AH0 L D IY0 -TWEEDLEDUM T W IY1 D AH0 L D AH0 M -TWEEDLES T W IY1 D AH0 L Z -TWEEDMOUTH T W IY1 D M AH0 TH -TWEEDS T W IY1 D Z -TWEEDSIDE T W IY1 D S AY2 D -TWEEDY T W IY1 D IY0 -TWEEN T W IY1 N -TWEENDECKS T W IY1 N D IH0 K S -TWEENY T W IY1 N IY0 -TWEET T W IY1 T -TWEET'S T W IY1 T S -TWEETY T W IY1 T IY0 -TWEEZERS T W IY1 Z ER0 Z -TWEEZY T W IY1 Z IY0 -TWEEZY'S T W IY1 Z IY0 Z -TWEL T W EH1 L -TWELF T W EH1 L F -TWELFTH T W EH1 L F TH -TWELFTHS T W EH1 L F TH S -TWELL T W EH2 L -TWELLS T W EH1 L Z -TWELVE T W EH1 L V -TWELVEFOLD T W EH1 L V F OW2 L D -TWELVEMONTH T W EH1 L V M AH0 N TH -TWELVEMONTH'S T W EH1 L V M AH0 N TH S -TWELVEMONTHS T W EH1 L V M AH0 N TH S -TWELVEMOUGH T W EH1 L V M OW0 -TWELVEPENCE T W EH1 L V EH0 P EH1 N S -TWELVES T W EH1 L V Z -TWELVETREES T W EH1 L V AH0 T R IY1 Z -TWEMLOW T W EH1 M L OW0 -TWEMLOW'S T W EH1 M L OW2 Z -TWEN T W EH1 N -TWENT T W EH1 N T -TWENTIE T W EH1 N T IY0 -TWENTIES T W EH1 N IY0 Z -TWENTIES T W EH1 N T IY0 Z -TWENTIETH T W EH1 N IY0 AH0 TH -TWENTIETH T W EH1 N IY0 IH0 TH -TWENTIETH T W EH1 N T IY0 AH0 TH -TWENTIETH T W EH1 N T IY0 IH0 TH -TWENTIETHS T W EH1 N IY0 AH0 TH S -TWENTY T W EH1 N IY0 -TWENTY T W EH1 N T IY0 -TWENTY'S T W EH1 N IY0 Z -TWENTY'S T W EH1 N T IY0 Z -TWENTYFIVE T W EH2 N IY0 F AY1 V -TWENTYFOLD T W EH2 N IY0 F OW2 L D -TWENTYFOUR T W EH2 N IY0 F AO0 R -TWENTYMAN T W EH2 N IY0 M AE2 N -TWENTYMAN'S T W EH2 N IY0 M AH0 N Z -TWENTYTWO T W EH2 N IY0 T UW1 -TWER T W ER0 -TWERE T W ER0 -TWEREN'T T W ER1 AH0 N T -TWETTENHAM T W EH1 T IH0 N HH AH0 M -TWETTENHAM'S T W EH1 T AH0 N AH0 M Z -TWEXBY T W EH2 K S B IY0 -TWI T W AY1 -TWICE T W AY1 S -TWICED T W AY1 S T -TWICET T W AY1 S AH0 T -TWICHELL T W IH1 CH AH0 L -TWICHELL'S T W IH1 CH AH0 L Z -TWICK T W IH2 K -TWICKENHAM T W IH1 K AH0 N HH AE2 M -TWICT T W IH1 K T -TWIDDEL T W IH1 D AH0 L -TWIDDLE T W IH1 D AH0 L -TWIDDLED T W IH1 D AH0 L D -TWIDDLER T W IH1 D L ER0 -TWIDDLES T W IH1 D AH0 L Z -TWIDDLING T W IH1 D L IH0 NG -TWIFORD'S T W IH1 F ER0 D Z -TWIG T W IH1 G -TWIG'S T W IH1 G Z -TWIGG T W IH1 G -TWIGGED T W IH1 G D -TWIGGER T W IH1 G ER0 -TWIGGING T W IH1 G IH0 NG -TWIGGS T W IH1 G Z -TWIGGY T W IH1 G IY0 -TWIGS T W IH1 G Z -TWILIGHT T W AY1 L AY2 T -TWILIGHT'S T W AY1 L AY2 T S -TWILIGHTED T W AY1 L AY2 T IH0 D -TWILIGHTS T W AY1 L AY2 T S -TWILIT T W AY1 L IH2 T -TWILL T W IH1 L -TWILLED T W IH1 L D -TWILLER T W IH1 L ER0 -TWILLINGATE T W IH1 L IH0 NG G EY2 T -TWILLY T W IH1 L IY0 -TWIN T W IH1 N -TWIN'D T W IH1 N D -TWIN'S T W IH1 N Z -TWINE T W AY1 N -TWINE'S T W AY1 N Z -TWINEALL T W AY1 N IY1 L -TWINED T W AY1 N D -TWINES T W AY1 N Z -TWINETH T W IH1 N IH0 TH -TWINGE T W IH1 N JH -TWINGED T W IH1 N JH D -TWINGES T W IH1 N JH IH0 Z -TWINGING T W 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AH0 N AO1 TH ER0 AY2 Z D -UNAUTHORIZED AH0 N AO1 TH ER0 AY2 Z D -UNAVAILABLE AH2 N AH0 V EY1 L AH0 B AH0 L -UNAVAILING AH2 N AH0 V EY1 L IH0 NG -UNAVAILINGLY AH2 N AH0 V EY1 L IH0 NG L IY0 -UNAVELLA AH2 N AH0 V EH1 L AH0 -UNAVENGED AH2 N AH0 V EH1 N JH D -UNAVOIDABLE AH2 N AH0 V OY1 D AH0 B AH0 L -UNAVOIDABLY AH2 N AH0 V OY1 D AH0 B L IY0 -UNAVOWABLE AH2 N AH0 V AW1 AH0 B AH0 L -UNAVOWED AH2 N AH0 V AW1 D -UNAWAKENED AH2 N AH0 W EY1 K AH0 N D -UNAWARE AH2 N AH0 W EH1 R -UNAWARENESS AH2 N AH0 W EH1 R N AH0 S -UNAWARES AH2 N AH0 W EH1 R Z -UNAWED AH0 N AO1 D -UNBACKED AH0 N B AE2 K T -UNBAFFLED AH0 N B AE1 F AH0 L D -UNBAITED AH0 N B EY1 T IH0 D -UNBAKED AH0 N B AH0 K T -UNBALANCE AH0 N B AE1 L AH0 N S -UNBALANCED AH0 N B AE1 L AH0 N S T -UNBALANCING AH0 N B AE1 L AH0 N S IH0 NG -UNBALLASTED AH0 N B AE1 L AH0 S T IH0 D -UNBANDAGED AH0 N B AE1 N D IH0 JH D -UNBAPTISED AH0 N B AE1 P T AH0 S T -UNBAPTIZED AH0 N B AE1 P T AY2 Z D -UNBAR AH0 N B AA0 R -UNBARED AH0 N B EH1 R D -UNBARKED AH0 N 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-UNBLAMEABLE AH0 N B L AA1 M IY0 AH0 B AH0 L -UNBLAMED AH0 N B L EY1 M D -UNBLANCHED AH0 N B L AE1 N CH T -UNBLEACHED AH0 N B L IY1 CH T -UNBLEMISH'D AH0 N B L EH1 M IH0 SH D -UNBLEMISHED AH0 N B L EH1 M IH0 SH T -UNBLENCHING AH0 N B L EH1 N CH IH0 NG -UNBLENDED AH0 N B L EH1 N D IH0 D -UNBLESS'D AH0 N B L EH1 S AH0 D -UNBLESSED AH0 N B L EH1 S T -UNBLEST AH0 N B L EH1 S T -UNBLIGHTED AH0 N B L AY1 T IH0 D -UNBLINDED AH0 N B L AY1 N D IH0 D -UNBLINKING AH0 N B L IH1 NG K IH0 NG -UNBLINKINGLY AH0 N B L IH1 NG K IH0 NG L IY0 -UNBLOCKED AH0 N B L AA2 K T -UNBLOODY AH0 N B L AH1 D IY0 -UNBLOTTED AH0 N B L AA1 T IH0 D -UNBLOWN AH0 N B L AW1 N -UNBLUNTED AH0 N B L AH1 N T AH0 D -UNBLURRED AH0 N B L ER1 D -UNBLUSHING AH0 N B L AH1 SH IH0 NG -UNBLUSHINGLY AH0 N B L AH1 SH IH0 NG L IY0 -UNBOASTFUL AH1 N B OW2 S T F AH0 L -UNBODIED AH0 N B AA1 D IY0 D -UNBOILED AH0 N B OY1 L D -UNBOLT AH1 N B OW2 L T -UNBOLTED AH1 N B OW2 L T IH0 D -UNBOLTING AH1 N B OW2 L T IH0 NG -UNBONNETED AH0 N B AA2 N AH0 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AH0 N B R AY1 D AH0 L D -UNBRIGHTENED AH0 N B R AY1 T AH0 N D -UNBROACHED AH0 N B R OW1 CH T -UNBROKE AH0 N B R OW1 K -UNBROKEN AH0 N B R OW1 K AH0 N -UNBROKENLY AH0 N B R OW1 K AH0 N L IY0 -UNBROTHERLY AH0 N B R AH1 DH ER0 L IY0 -UNBRUISED AH0 N B R UW1 Z D -UNBRUSHED AH0 N B R AH2 SH T -UNBUCKLE AH0 N B AH0 K AH0 L -UNBUCKLED AH0 N B AH0 K AH0 L D -UNBUCKLING AH0 N B AH1 K L IH0 NG -UNBUILD AH0 N B IH1 L D -UNBUILDING AH0 N B IH1 L D IH0 NG -UNBUILT AH2 N B IH1 L T -UNBURDEN AH0 N B ER1 D AH0 N -UNBURDENED AH0 N B ER1 D AH0 N D -UNBURDENING AH0 N B ER1 D AH0 N IH0 NG -UNBURDENS AH0 N B ER1 D AH0 N Z -UNBURIED AH0 N B ER1 IY0 D -UNBURNED AH0 N B ER1 N D -UNBURNISHED AH0 N B ER1 N IH0 SH T -UNBURNT AH0 N B ER1 N T -UNBURTHEN AH0 N B ER1 TH AH0 N -UNBURTHENED AH0 N B ER1 TH AH0 N D -UNBURTHENING AH0 N B ER1 TH AH0 N IH0 NG -UNBUSINESS AH0 N B IH1 Z N IH0 S -UNBUSINESSLIKE AH0 N B IH1 Z N IH0 S L AY2 K -UNBUTTERED AH0 N B AH1 T ER0 D -UNBUTTON AH0 N B AH1 T AH0 N -UNBUTTONED AH0 N B AH1 T AH0 N D -UNBUTTONING AH0 N B AH1 T AH0 N IH0 NG -UNBUTTONS AH0 N B AH1 T AH0 N Z -UNC AH1 NG K -UNCAGED AH0 N K EY1 JH D -UNCALCULATED AH0 N K AE1 L K Y AH0 L EY2 T IH0 D -UNCALCULATING AH0 N K AE1 L K Y AH0 L EY2 T IH0 NG -UNCALLED AH0 N K AO1 L D -UNCANCELLED AH0 N K AE1 N S AH0 L D -UNCANDID AH0 N K AE1 N D AH0 D -UNCANDOR AH0 N K AE1 N D ER0 -UNCANNILY AH0 N K AE1 N AH0 L IY0 -UNCANNINESS AH0 N K AE1 N IY0 N AH0 S -UNCANNON AH0 N K AE1 N AH0 N -UNCANNY AH0 N K AE1 N IY0 -UNCANONICAL AH1 N K AH0 N AA1 N AH0 K AH0 L -UNCAPABLE AH0 N K EY1 P AH0 B AH0 L -UNCAPAPA AH0 N K AE1 P AA1 P AH0 -UNCAPAPAS AH0 N K AE1 P AA1 P AH0 Z -UNCAPPED AH0 N K AE1 P T -UNCAPTURABLE AH0 N K AE1 P CH ER0 AH0 B AH0 L -UNCAPTURED AH0 N K AE1 P CH ER0 D -UNCARED AH0 N K EH1 R D -UNCARING AH0 N K EH1 R IH0 NG -UNCARPETED AH0 N K AA1 R P AH0 T IH0 D -UNCARVED AH0 N K AA2 R V D -UNCAS AH1 N K AH0 Z -UNCASE AH0 N K EY1 S -UNCASED AH0 N K EY1 S T -UNCATALOGUED AH0 N K AE1 T AH0 L AO2 G D -UNCAUGHT AH0 N K AO1 T -UNCAUSED AH0 N K AA1 Z D -UNCEASING AH0 N S IY1 S IH0 NG -UNCEASINGLY AH0 N S IY1 S IH0 NG L IY0 -UNCEILED AH0 N S IY1 L D -UNCELEBRATED AH2 N S EH2 L AH0 B R EY2 T AH0 D -UNCEMENTED AH0 N S M AH0 N T IH0 D -UNCENSORED AH0 N S EH1 N S ER0 D -UNCENSURED AH0 N S EH1 N SH ER0 D -UNCEREMONIOUS AH2 N S EH2 R AH0 M OW1 N IY0 AH0 S -UNCEREMONIOUSLY AH2 N S EH2 R AH0 M OW1 N IY0 AH0 S L IY0 -UNCEREMONIOUSNESS AH2 N S EH2 R AH0 M OW1 N IY0 AH0 S N AH0 S -UNCERTAIN AH0 N S ER1 T AH0 N -UNCERTAINLY AH0 N S ER1 T AH0 N L IY0 -UNCERTAINTIES AH0 N S ER1 T AH0 N T IY0 Z -UNCERTAINTY AH0 N S ER1 T AH0 N T IY0 -UNCERTIFICATED AH0 N S ER1 T AH0 F AH0 K EY2 T IH0 D -UNCERTIFIED AH0 N S ER1 T AH0 F AY2 D -UNCHAIN AH2 N K AY0 N -UNCHAINED AH0 N CH EY1 N D -UNCHAINING AH0 N CH EY1 N IH0 NG -UNCHALLENGEABLE AH0 N CH AE1 L AH0 N JH AH0 B AH0 L -UNCHALLENGED AH0 N CH AE1 L IH0 N JH D -UNCHANCY AH0 N CH AE1 N S IY0 -UNCHANG'D AH0 N CH AA1 NG D -UNCHANGEABLE AH0 N CH EY1 N JH AH0 B AH0 L -UNCHANGEABLENESS AH0 N CH EY1 N JH AH0 B AH0 L N AH0 S -UNCHANGEABLY AH0 N CH EY1 N JH AH0 B L IY0 -UNCHANGED AH0 N CH EY1 N JH D -UNCHANGING AH0 N CH EY1 N JH IH0 NG -UNCHANGINGLY AH0 N CH EY1 N JH IH0 NG L IY0 -UNCHAPERONED AH0 N CH AE1 P ER0 OW2 N D -UNCHARACTERISTIC AH2 N K EH2 R IH0 K T ER0 IH1 S T IH0 K -UNCHARACTERISTICALLY AH2 N K EH2 R IH0 K T ER0 IH1 S T IH0 K L IY0 -UNCHARGED AH0 N CH AA1 R JH D -UNCHARITABLE AH2 N K EH2 R AH0 T AH0 B AH0 L -UNCHARITABLENESS AH2 N K EH2 R AH0 T AH0 B AH0 L N AH0 S -UNCHARITABLY AH2 N K EH2 R IH0 T AH0 B L IY0 -UNCHARITY AH2 N K EH2 R IH0 T IY0 -UNCHARTED AH0 N CH AA1 R T IH0 D -UNCHARTERED AH0 N CH AA1 R T ER0 D -UNCHASTE AH0 N CH EY1 S T -UNCHASTENED AH0 N CH EY1 S AH0 N D -UNCHASTISED AH0 N CH AE0 S T AY1 Z D -UNCHASTITY AH0 N CH AE1 S T AH0 T IY0 -UNCHECK'D AH0 N CH EH1 K D -UNCHECKED AH0 N CH EH1 K T -UNCHECKERED AH0 N CH EH1 K ER0 D -UNCHEEDAH AH0 N CH IY2 D AH0 -UNCHEERED AH2 N K IH1 R D -UNCHEERFUL AH2 N K IH1 R F AH0 L -UNCHEERING AH2 N K IH1 R IH0 NG -UNCHEQUERED AH0 N CH EH2 K ER0 D -UNCHERISHED AH0 N CH EH1 R IH0 SH T -UNCHIDDEN AH0 N CH IH1 D AH0 N -UNCHILDISH AH0 N CH AY1 L D IH0 SH -UNCHILDLIKE AH0 N CH AY1 L D L AY2 K -UNCHILLED AH0 N CH IH1 L D -UNCHINKED AH0 N CH IH1 NG K T -UNCHIPPED AH0 N CH IH1 P T -UNCHIVALROUS AH2 N K AY1 V AH0 L R AH0 S -UNCHOKED AH0 N CH OW2 K T -UNCHOSEN AH0 N CH OW1 Z AH0 N -UNCHPIN AH0 N CH P IH2 N -UNCHRISTENED AH2 N K R IH1 S AH0 N D -UNCHRISTIAN AH2 N K R IH1 S CH AH0 N -UNCHRISTIANLIKE AH2 N K R IH1 S CH AH0 N L AY2 K -UNCHRONICLED AH2 N K R AA1 N IH0 K AH0 L D -UNCHURCHED AH0 N CH ER1 CH T -UNCIAL AH0 N SH AH0 L -UNCIRCUMCISED AH0 N S ER1 K AH0 M S AY2 Z D -UNCIRCUMCISION AH0 N S ER1 K AH0 M S IH1 ZH AH0 N -UNCIVIL AH0 N S IH1 V AH0 L -UNCIVILISED AH0 N S IH1 V AH0 L AY0 Z D -UNCIVILIZED AH0 N S IH1 V AH0 L AY0 Z D -UNCIVILLY AH0 N S IH1 V AH0 L IY0 -UNCLAD AH0 N K L AE2 D -UNCLAIMED AH0 N K L EY1 M D -UNCLASP AH0 N K L AE1 S P -UNCLASPED AH0 N K L AE1 S P T -UNCLASPING AH0 N K L AE1 S P IH0 NG -UNCLASPS AH0 N K L AE1 S P S -UNCLASSED AH0 N K L AE1 S T -UNCLASSIC AH0 N K L AE1 S IH0 K -UNCLASSICAL AH0 N K L AE1 S IH0 K AH0 L -UNCLASSIFIABLE AH0 N K L AE1 S AH0 F AY2 AH0 B AH0 L -UNCLASSIFIED AH0 N K L AE1 S IH0 F AY2 D -UNCLE AH1 NG K AH0 L -UNCLE'S AH1 NG K AH0 L Z -UNCLEAN AH0 N K L IY1 N -UNCLEANED AH0 N K L IY1 N D -UNCLEANLINESS AH0 N K L IY1 N L IY0 N AH0 S -UNCLEANLY AH0 N K L IY1 N L IY0 -UNCLEANNESS AH0 N K L IY1 N IH0 S -UNCLEANSED AH0 N K L EH1 N Z D -UNCLEAR AH0 N K L IH1 R -UNCLEARED AH0 N K L IH1 R D -UNCLENCH AH0 N K L EH1 N CH -UNCLENCHED AH0 N K L EH1 N CH T -UNCLENCHING AH0 N K L EH1 N CH IH0 NG -UNCLERICAL AH0 N K L EH1 R IH0 K AH0 L -UNCLES AH1 NG K AH0 L Z -UNCLIMBABLE AH0 N K L IH1 M B AH0 B AH0 L -UNCLINCHED AH0 N K L IH1 N CH T -UNCLIPPED AH0 N K L IH1 P T -UNCLOAKED AH0 N K L OW1 K T -UNCLOGGED AH0 N K L AO1 G D -UNCLOSE AH0 N K L OW1 Z -UNCLOSED AH0 N K L OW1 Z D -UNCLOSES AH0 N K L OW1 Z IH0 Z -UNCLOSING AH0 N K L OW1 Z IH0 NG -UNCLOTHED AH0 N K L AO2 TH T -UNCLOUD AH0 N K L AW1 D -UNCLOUDED AH0 N K L AW1 D IH0 D -UNCLOYING AH0 N K L OY1 IH0 NG -UNCO AH0 N K OW0 -UNCOCKED AH0 N K AA1 K T -UNCOCKING AH0 N K AA1 K IH0 NG -UNCOFFINED AH0 N K AA1 F AY1 N D -UNCOIL AH2 N K OY1 L -UNCOILED AH2 N K OY1 L D -UNCOILING AH2 N K OY1 L IH0 NG -UNCOILS AH2 N K OY1 L Z -UNCOINED AH2 N K OY1 N D -UNCOLLECTED AH0 N K AH0 L EH1 K T IH0 D -UNCOLORED AH0 N K AH1 L ER0 D -UNCOLOURED AH0 N K AH1 L ER0 D -UNCOMBED AH2 N K AH0 M D -UNCOMBINED AH2 N K AH0 M B AY1 N D -UNCOMELINESS AH0 N K AH1 M L IY0 N AH0 S -UNCOMELY AH0 N K AH1 M L IY0 -UNCOMFORTABLE AH0 N K AH1 M F ER0 T AH0 B AH0 L -UNCOMFORTABLENESS AH0 N K AH1 M F ER0 T AH0 B AH0 L N AH0 S -UNCOMFORTABLY AH0 N K AH1 M F T AH0 B L IY0 -UNCOMFORTED AH0 N K AH1 M F ER0 T IH0 D -UNCOMFORTING AH0 N K AH1 M F ER0 T IH0 NG -UNCOMFY AH0 N K AH1 M F IY0 -UNCOMMANDED AH2 N K AH0 M AE1 N D IH0 D -UNCOMMENTED AH0 N K AA1 M EH0 N T IH0 D -UNCOMMERCIAL AH2 N K AH0 M ER1 SH AH0 L -UNCOMMITTED AH2 N K AH0 M IH1 T IH0 D -UNCOMMON AH0 N K AA1 M AH0 N -UNCOMMONLY AH2 N K AA1 M AH0 N L IY0 -UNCOMMONNESS AH0 N K AA1 M AH0 N N AH0 S -UNCOMMUNICATED AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 D -UNCOMMUNICATIVE AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V -UNCOMMUNICATIVENESS AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V N AH0 S -UNCOMPAHGRE AH0 N K AH1 M P AH0 HH G ER0 -UNCOMPANIONABLE AH2 N K AH0 M P AE1 N Y AH0 N AH0 B AH0 L -UNCOMPANIONED AH2 N K AH0 M P AE1 N Y AH0 N D -UNCOMPELLED AH2 N K AH0 M P EH1 L D -UNCOMPENSATED AH0 N K AA1 M P AH0 N S EY2 T IH0 D -UNCOMPLAINING AH2 N K AH0 M P L EY1 N IH0 NG -UNCOMPLAININGLY AH2 N K AH0 M P L EY1 N IH0 NG L IY0 -UNCOMPLETED AH2 N K AH0 M P L IY1 T IH0 D -UNCOMPLEX AH0 N K AA1 M P L EH0 K S -UNCOMPLICATED AH0 N K AA1 M P L AH0 K EY2 T IH0 D -UNCOMPLIMENTARY AH0 N K AA1 M P L AH0 M EH1 N ER0 IY0 -UNCOMPOUNDED AH2 N K AH0 M P AW1 N D IH0 D -UNCOMPREHENDED AH2 N K AA2 M P R IH0 HH EH1 N D IH0 D -UNCOMPREHENDING AH2 N K AA2 M P R IH0 HH EH1 N D IH0 NG -UNCOMPREHENDINGLY 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T AH0 M AH0 T -UNDERESTIMATE AH1 N D ER0 EH1 S T AH0 M EY2 T -UNDERESTIMATED AH1 N D ER0 EH1 S T AH0 M EY2 T IH0 D -UNDERESTIMATES AH2 N D ER0 EH1 S T IH0 M EY0 T S -UNDERESTIMATES AH2 N D ER0 EH1 S T IH0 M IH0 T S -UNDERESTIMATING AH2 N D ER0 EH1 S T IH0 M EY2 T IH0 NG -UNDERESTIMATION AH1 N D ER0 EH1 S T AH0 M EY1 SH AH0 N -UNDERFED AH1 N D ER0 F T -UNDERFEEDING AH1 N D ER0 F IY1 D IH0 NG -UNDERFLOW AH1 N D ER0 F L OW2 -UNDERFOOT AH2 N D ER0 F UH1 T -UNDERFOREMAN AH1 N D ER0 F AO1 R M AH0 N -UNDERGARMENT AH1 N D ER0 G AA2 R M AH0 N T -UNDERGARMENTS AH1 N D ER0 G AA2 R M AH0 N T S -UNDERGLOW AH1 N D ER0 G L OW2 -UNDERGO AH2 N D ER0 G OW1 -UNDERGOES AH1 N D ER0 G OW2 Z -UNDERGOING AH2 N D ER0 G OW1 IH0 NG -UNDERGONE AH2 N D ER0 G AO1 N -UNDERGRAD AH1 N D ER0 G R AE2 D -UNDERGRADS AH1 N D ER0 G R AE2 D Z -UNDERGRADUATE AH2 N D ER0 G R AE1 JH AH0 W AH0 T -UNDERGRADUATE'S AH1 N D ER0 G R AE1 JH AH0 W AH0 T S -UNDERGRADUATES AH2 N D ER0 G R AE1 JH AH0 W AH0 T S -UNDERGROUND AH1 N D ER0 G R AW2 N D -UNDERGROWN AH1 N D ER0 G R OW1 N -UNDERGROWTH AH1 N D ER0 G R OW2 TH -UNDERGROWTHS AH1 N D ER0 G R OW1 TH S -UNDERHAM AH1 N D ER0 HH AE2 M -UNDERHAND AH1 N D ER0 HH AE2 N D -UNDERHANDED AH1 N D ER0 HH AE1 N D IH0 D -UNDERHANDEDLY AH1 N D ER0 HH AE1 N D IH0 D L IY0 -UNDERHANDEDNESS AH1 N D ER0 HH AE1 N D IH0 D N AH0 S -UNDERHILL AH1 N D ER0 HH IH2 L -UNDERHILL'S AH1 N D ER0 HH IH2 L Z -UNDERHILLS AH1 N D ER0 HH IH2 L Z -UNDERHUNG AH1 N D ER0 HH AH1 NG -UNDERIVED AH1 N D ER0 AY1 V D -UNDERJAW AH1 N D ER0 JH AO1 -UNDERLAID AH1 N D ER0 L EY2 D -UNDERLAIN AH1 N D ER0 L AH0 N -UNDERLAND AH1 N D ER0 L AH0 N D -UNDERLAY AH1 N D ER0 L EY2 -UNDERLET AH1 N D ER0 L IH0 T -UNDERLIDS AH1 N D ER0 L IH2 D Z -UNDERLIE AH2 N D ER0 L AY1 -UNDERLIES AH2 N D ER0 L AY1 Z -UNDERLINE AH1 N D ER0 L AY2 N -UNDERLINED AH1 N D ER0 L AY2 N D -UNDERLINEN AH1 N D ER0 L IH2 N AH0 N -UNDERLINES AH1 N D ER0 L AY2 N Z -UNDERLING AH1 N D ER0 L IH0 NG -UNDERLINGS AH1 N D ER0 L IH0 NG Z -UNDERLINING AH1 N D ER0 L 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AH0 N S EH1 P ER0 EY2 T IH0 D -UNSER AH1 N S ER0 -UNSERE AH0 N S EH1 R -UNSERIOUS AH0 N S EH1 R IY0 AH0 S -UNSERN AH0 N S EH1 R N -UNSERVED AH0 N S ER1 V D -UNSERVICEABLE AH0 N S ER1 V AH0 S AH0 B AH0 L -UNSERVICEABLE AH1 N S ER1 V AH0 S AH0 B AH0 L -UNSET AH0 N S EH1 T -UNSET AH1 N S EH1 T -UNSETTING AH0 N S EH1 T IH0 NG -UNSETTLE AH0 N S EH1 T AH0 L -UNSETTLED AH0 N S EH1 T AH0 L D -UNSETTLEDNESS AH0 N S EH1 T L AH0 D N AH0 S -UNSETTLEMENT AH0 N S EH1 T AH0 L M AH0 N T -UNSETTLES AH0 N S EH1 T AH0 L Z -UNSETTLING AH0 N S EH1 T AH0 L IH0 NG -UNSETTLING AH0 N S EH1 T L IH0 NG -UNSEVERED AH0 N S AH0 V IH1 R D -UNSEWED AH0 N S OW1 D -UNSEWN AH0 N S OW1 N -UNSEX AH0 N S EH2 K S -UNSEXED AH0 N S EH1 K S T -UNSEXING AH0 N S EH1 K S IH0 NG -UNSHACKLE AH0 N SH AE1 K AH0 L -UNSHACKLED AH0 N SH AE1 K AH0 L D -UNSHADED AH0 N SH EY1 D IH0 D -UNSHADOWED AH0 N SH AE1 D OW0 D -UNSHAKABLE AH0 N SH EY1 K AH0 B AH0 L -UNSHAKABLY AH0 N SH EY1 K AH0 B L IY0 -UNSHAKEABLE AH0 N SH EY1 K AH0 B AH0 L 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-UNSUSPECTED AH2 N S AH0 S P EH1 K T IH0 D -UNSUSPECTEDLY AH2 N S AH0 S P EH1 K T IH0 D L IY0 -UNSUSPECTIN AH2 N S AH0 S P EH1 K T IH0 N -UNSUSPECTING AH2 N S AH0 S P EH1 K T IH0 NG -UNSUSPECTINGLY AH2 N S AH0 S P EH1 K T IH0 NG L IY0 -UNSUSPICION AH2 N S AH0 S P IH1 SH AH0 N -UNSUSPICIOUS AH2 N S AH0 S P IH1 SH AH0 S -UNSUSPICIOUSLY AH2 N S AH0 S P IH1 SH AH0 S L IY0 -UNSUSPICIOUSNESS AH2 N S AH0 S P IH1 SH AH0 S N AH0 S -UNSUSTAINABLE AH2 N S AH0 S T EY1 N AH0 B AH0 L -UNSUSTAINED AH2 N S AH0 S T EY1 N D -UNSWALLOWED AH0 N S W AA1 L OW0 D -UNSWATHED AH0 N S W EY1 DH D -UNSWAYED AH0 N S W EY1 D -UNSWEET AH0 N S W IY2 T -UNSWEETENED AH0 N S W IY1 T AH0 N D -UNSWEPT AH0 N S W EH2 P T -UNSWERVING AH0 N S W ER1 V IH0 NG -UNSWERVINGLY AH0 N S W ER1 V IH0 NG L IY0 -UNSWORN AH0 N S W AO1 R N -UNSWORTH AH1 N S W ER0 TH -UNSYLLABLED AH0 N S IH1 L AH0 B AH0 L D -UNSYMMETRICAL AH0 N S AH0 M EH1 T R IH0 K AH0 L -UNSYMPATHETIC AH0 N S IH2 M P AH0 TH EH1 T IH0 K -UNSYMPATHETICALLY AH0 N S IH2 M P AH0 TH EH1 T IH0 K L IY0 -UNSYMPATHISING AH0 N S IH2 M P AH0 TH AY2 Z IH0 NG -UNSYMPATHIZING AH0 N S IH2 M P AH0 TH AY2 Z IH0 NG -UNSYSTEMATIC AH0 N S IH2 S T AH0 M AE1 T IH0 K -UNT AH0 N T -UNTACTFUL AH0 N T AE1 K T F AH0 L -UNTAINTED AH0 N T EY1 N T IH0 D -UNTAKEN AH0 N T EY1 K AH0 N -UNTALKATIVE AH0 N T AO1 K AH0 T IH0 V -UNTAMABLE AH0 N T AE1 M AH0 B AH0 L -UNTAMEABLE AH0 N T AE1 M IY0 AH0 B AH0 L -UNTAMED AH0 N T EY1 M D -UNTAMO AH0 N T AA1 M OW0 -UNTAMOINEN AH0 N T AE1 M OY1 N AH0 N -UNTAMPERED AH0 N T AE1 M P ER0 D -UNTANGLE AH0 N T AE1 NG G AH0 L -UNTANGLED AH0 N T AE1 NG G AH0 L D -UNTANGLING AH0 N T AE1 NG L IH0 NG -UNTANNED AH0 N T AE1 N D -UNTAPPED AH0 N T AE1 P T -UNTARNISHED AH0 N T AA1 R N IH0 SH T -UNTASTED AH0 N T AE1 S T IH0 D -UNTAUGHT AH0 N T AO1 T -UNTAXED AH0 N T AE1 K S T -UNTEACH AH0 N T IY1 CH -UNTEACHABLE AH0 N T IY1 CH AH0 B AH0 L -UNTECHNICAL AH0 N T EH1 K N IH0 K AH0 L -UNTELLABLE AH0 N T EH1 L AH0 B AH0 L -UNTEMPERED AH0 N T EH1 M P ER0 D -UNTEMPTED AH0 N T EH1 M P T IH0 D -UNTEMPTING AH0 N T EH1 M P T IH0 NG -UNTENABILITY AH0 N T EH1 N AH0 B IH1 L IH0 T IY0 -UNTENABLE AH0 N T EH1 N AH0 B AH0 L -UNTENANTED AH0 N T EH1 N AH0 N T IH0 D -UNTENDED AH0 N T EH1 N D IH0 D -UNTENDER AH0 N T EH1 N D ER0 -UNTENDERLY AH0 N T EH1 N D ER0 L IY0 -UNTER AH1 N T ER0 -UNTERMEYER AH1 N T ER0 M AY2 R -UNTEROFFIZIER AH1 N T ER0 AO1 F AH0 Z IY0 ER0 -UNTERRIFIED AH0 N T EH1 R AH0 F AY2 D -UNTERSEE AH1 N T ER0 S IY2 -UNTERSEEBOOT AH1 N T ER0 S IY1 B UW2 T -UNTERWALD AH1 N T ER0 W AO0 L D -UNTERWALDEN AH1 N T ER0 W AO2 L D AH0 N -UNTESTED AH0 N T EH1 S T IH0 D -UNTETHERED AH0 N T EH1 DH ER0 D -UNTHANK AH0 N TH AE2 NG K -UNTHANKE'S AH0 N TH AE2 NG K S -UNTHANKED AH0 N TH AE1 NG K T -UNTHANKFUL AH0 N TH AE1 NG K F AH0 L -UNTHANKFULLY AH0 N TH AE1 NG K F AH0 L IY0 -UNTHANKFULNESS AH0 N TH AE1 NG K F AH0 L N AH0 S -UNTHATCHED AH0 N TH AE1 CH T -UNTHEATRICAL AH0 N TH IY0 AE1 T R IH0 K AH0 L -UNTHEOLOGICAL AH0 N TH IY0 AH0 L AA1 JH IH0 K AH0 L -UNTHINK AH0 N TH IH1 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UW1 -WHERETO W EH0 R EH1 T OW0 -WHEREUNDER W EH1 R AH0 N D ER0 -WHEREUNTO W EH1 R AH0 N T OW0 -WHEREUPON HH W EH1 R AH0 P AA1 N -WHEREUPON W EH1 R AH0 P AA1 N -WHEREVER HH W EH0 R EH1 V ER0 -WHEREVER W EH0 R EH1 V ER0 -WHEREWITH W EH1 R W IH0 DH -WHEREWITHAL HH W EH1 R W IH0 DH AO2 L -WHEREWITHAL W EH1 R W IH0 DH AO2 L -WHERFORE W ER1 F AO2 R -WHERIVER W EH0 R AY2 V ER0 -WHERNSIDE W ER1 N S AY2 D -WHERO HH W EH1 R OW0 -WHERO'S HH W IH1 R OW0 Z -WHEROF W EH0 R AH0 F -WHERRIES HH W EH1 R IY0 Z -WHERRIT HH W EH1 R IH0 T -WHERRY HH W EH1 R IY0 -WHERRY W EH1 R IY0 -WHERRY'S HH W EH1 R IY0 Z -WHERWELL W ER1 W EH0 L -WHET HH W EH1 T -WHET W EH1 T -WHETHEN HH W EH1 TH AH0 N -WHETHER HH W EH1 DH ER0 -WHETHER W EH1 DH ER0 -WHETS HH W EH1 T S -WHETSTONE HH W EH1 T S T OW2 N -WHETSTONE W EH1 T S T OW2 N -WHETSTONES HH W EH1 T S T OW2 N Z -WHETTED HH W EH1 T IH0 D -WHETTED W EH1 T IH0 D -WHETTING HH W EH1 T IH0 NG -WHETZEL HH W EH1 T Z AH0 L -WHETZEL W EH1 T Z AH0 L -WHEUGH HH W -WHEW HH W UW1 -WHEW HH Y UW1 -WHEW W UW1 -WHEWELL HH W EH1 W EH0 L -WHEWELL'S HH W UW1 AH0 L Z -WHEY HH W EY1 -WHEY W EY1 -WHI HH W AY1 -WHIBLEY HH W IH1 B L IY0 -WHIBLEY'S HH W IH1 B L IY0 Z -WHICH HH W IH1 CH -WHICH W IH1 CH -WHICH'LL HH W IH1 CH L -WHICH'S HH W IH1 CH IH0 Z -WHICH'S W IH1 CH IH0 Z -WHICHE HH W IH1 CH -WHICHEHALSE HH W IH1 CH EH0 HH AE1 L S -WHICHELLO HH W IH0 CH EH1 L OW0 -WHICHELLO'S HH W IH0 CH EH1 L OW0 Z -WHICHELO HH W IH1 CH IH0 L OW0 -WHICHEVER HH W IH0 CH EH1 V ER0 -WHICHEVER W IH0 CH EH1 V ER0 -WHICHSOEVER HH W IH1 CH S OW0 EH1 V ER0 -WHICK HH W IH1 K -WHICKER HH W IH1 K ER0 -WHICKER W IH1 K ER0 -WHICKERED HH W IH1 K ER0 D -WHIDAH HH W IH1 D AH0 -WHIDBY HH W IH1 D B IY0 -WHIDBY W IH1 D B IY0 -WHIDDEN HH W IH1 D AH0 N -WHIDDEN W IH1 D AH0 N -WHIDDON HH W IH1 D AH0 N -WHIDDON W IH1 D AH0 N -WHIFF HH W IH1 F -WHIFF W IH1 F -WHIFFED HH W IH1 F T -WHIFFET HH W IH1 F IH0 T -WHIFFING HH W IH1 F IH0 NG -WHIFFLE HH W IH1 F AH0 L -WHIFFLER HH W IH1 F L ER0 -WHIFFLETIT HH W IH1 F L IH0 T IH1 T -WHIFFLETREE HH W IH1 F AH0 L T R IY2 -WHIFFLETREES HH W IH1 F AH0 L T R IY2 Z -WHIFFLING HH W IH1 F L IH0 NG -WHIFFS HH W IH1 F S -WHIG HH W IH1 G -WHIG W IH1 G -WHIGGERY HH W IH1 G ER0 IY0 -WHIGGISH HH W IH1 G IH0 SH -WHIGGISM HH W IH1 G IH0 Z AH0 M -WHIGS HH W IH1 G Z -WHIGS W IH1 G Z -WHIL'ST W AY1 L S T -WHILDEN HH W AY1 L D AH0 N -WHILDEN W AY1 L D AH0 N -WHILE HH W AY1 L -WHILE W AY1 L -WHILE'S W AY1 L Z -WHILED W AY1 L D -WHILERE W AY1 L ER0 -WHILES HH W AY1 L Z -WHILES W AY1 L Z -WHILEST W AY1 L AH0 S T -WHILING W AY1 L IH0 NG -WHILK HH W IH1 L K -WHILOM W AY1 L AH0 M -WHILOME W AY1 L OW1 M -WHILSE W AY1 L S -WHILST W AY1 L S T -WHIM HH W IH1 M -WHIM W IH1 M -WHIMPER HH W IH1 M P ER0 -WHIMPER W IH1 M P ER0 -WHIMPERED HH W IH1 M P ER0 D -WHIMPERIN HH W IH1 M P ER0 IH0 N -WHIMPERING HH W IH1 M P ER0 IH0 NG -WHIMPERING W IH1 M P ER0 IH0 NG -WHIMPERINGLY HH W IH1 M P ER0 IH0 NG L IY0 -WHIMPERINGS HH W IH1 M P ER0 IH0 NG Z -WHIMPERS HH W IH1 M P ER0 Z -WHIMPLE HH W IH1 M P AH0 L -WHIMPLE'S HH W IH1 M P AH0 L Z -WHIMPOLE HH W IH1 M P OW2 L -WHIMS HH W IH1 M Z -WHIMS W IH1 M Z -WHIMSEY HH W IH1 M Z IY0 -WHIMSEYS HH W IH1 M Z IY0 Z -WHIMSICAL HH W IH1 M Z IH0 K AH0 L -WHIMSICAL W IH1 M Z IH0 K AH0 L -WHIMSICALITIES HH W IH1 M Z IH0 K AE1 L AH0 T IY0 Z -WHIMSICALITY HH W IH1 M Z IH0 K AE1 L AH0 T IY0 -WHIMSICALLY HH W IH1 M Z IH0 K L IY0 -WHIMSICALLY W IH1 M Z IH0 K L IY0 -WHIMSIES HH W IH1 M Z IY0 Z -WHIMSY HH W IH1 M S IY0 -WHIMSY W IH1 M S IY0 -WHIN HH W AY1 N -WHINBOROUGH HH W IH1 N B ER0 OW0 -WHINCHER HH W IH1 N CH ER0 -WHINDALE HH W AY1 N D EY2 L -WHINE HH W AY1 N -WHINE W AY1 N -WHINED HH W AY1 N D -WHINER HH W AY1 N ER0 -WHINER W AY1 N ER0 -WHINERS HH W AY1 N ER0 Z -WHINERS W AY1 N ER0 Z -WHINES HH W AY1 N Z -WHINES W AY1 N Z -WHINEY HH W AY1 N IY0 -WHINFIELD HH W IH1 N F IY2 L D -WHING HH W IH1 NG -WHINGER HH W IH1 NG ER0 -WHINGS HH W IH1 NG Z -WHININ HH W AY1 N IH0 N -WHINING HH W AY1 N IH0 NG -WHINING W AY1 N IH0 NG -WHININGLY HH W AY1 N IH0 NG L IY0 -WHININGS HH W AY1 N IH0 NG Z -WHINIVER HH W AY1 N IH0 V ER0 -WHINNER W IH1 N ER0 -WHINNEY HH W IH1 N IY0 -WHINNEY W IH1 N IY0 -WHINNEYED W IH1 N IY0 D -WHINNEYING W IH1 N IY0 IH0 NG -WHINNIE W IH1 N IY0 -WHINNIED W IH1 N IY0 D -WHINNIES W IH1 N IY0 Z -WHINNY W IH1 N IY0 -WHINNYFOLD W IH1 N IY0 F OW2 L D -WHINNYING W IH1 N IY0 IH0 NG -WHINS HH W IH1 N Z -WHINSTANE HH W IH1 N S T AH0 N -WHINSTONE HH W IH1 N S T OW2 N -WHINTHORPE HH W AY1 N TH AO0 R P -WHINY HH W AY1 N IY0 -WHINY W AY1 N IY0 -WHIP HH W IH1 P -WHIP W IH1 P -WHIP'S HH W IH1 P S -WHIPCORD HH W IH1 P K AO0 R D -WHIPCORDS HH W IH1 P K AO1 R D Z -WHIPHAND HH W IH1 P HH AE2 N D -WHIPLASH HH W IH1 P L AE2 SH -WHIPLASH W IH1 P L AE2 SH -WHIPLASHES HH W IH1 P L AE2 SH AH0 Z -WHIPLASHES W IH1 P L AE2 SH AH0 Z -WHIPLIKE W IH1 P L AY2 K -WHIPP HH W IH1 P -WHIPP W IH1 P -WHIPP'D HH W IH1 P D -WHIPPED HH W IH1 P T -WHIPPED W IH1 P T -WHIPPER HH W IH1 P ER0 -WHIPPERS HH W IH1 P ER0 Z -WHIPPERSNAPPER HH W IH1 P ER0 S N AE1 P ER0 -WHIPPERSNAPPERS HH W IH1 P ER0 S N AE1 P ER0 Z -WHIPPET HH W IH1 P AH0 T -WHIPPETS HH W IH1 P IH0 T S -WHIPPIN HH W IH1 P IH0 N -WHIPPING HH W IH1 P IH0 NG -WHIPPING W IH1 P IH0 NG -WHIPPINGS HH W IH1 P IH0 NG Z -WHIPPLE HH W IH1 P AH0 L -WHIPPLE W IH1 P AH0 L -WHIPPLE'S HH W IH1 P AH0 L Z -WHIPPLE'S W IH1 P AH0 L Z -WHIPPLEBY HH W IH1 P AH0 L B IY0 -WHIPPLETON HH W IH1 P AH0 L T AH0 N -WHIPPLETON'S HH W IH1 P AH0 L T AH0 N Z -WHIPPOORWILL HH W IH1 P ER0 W IH2 L -WHIPPOORWILL'S HH W IH1 P ER0 W IH2 L Z -WHIPPOORWILLS HH W IH1 P ER0 W IH2 L Z -WHIPPOORWILLS W IH1 P ER0 W IH2 L Z -WHIPS HH W IH1 P S -WHIPS W IH1 P S -WHIPSAW HH W IH1 P S AO2 -WHIPSAW W IH1 P S AO2 -WHIPSAWED HH W IH1 P S AO2 D -WHIPSAWED W IH1 P S AO2 D -WHIPSTAFF HH W IH1 P S T AE2 F -WHIPSTER HH W IH1 P S T ER0 -WHIPSTOCK HH W IH1 P S T AA2 K -WHIPT HH W IH1 P T -WHIR W ER1 -WHIRK W ER1 K -WHIRL HH W ER1 L -WHIRL W ER1 L -WHIRL'D HH W ER1 L D -WHIRLED HH W ER1 L D -WHIRLED W ER1 L D -WHIRLER HH W ER1 L ER0 -WHIRLIGIG HH W ER1 L IH0 JH IH0 G -WHIRLIGIGS HH W ER1 L IH0 JH IH1 G Z -WHIRLIN HH W ER1 L IH0 N -WHIRLING HH W ER1 L IH0 NG -WHIRLING W ER1 L IH0 NG -WHIRLINGS HH W ER1 L IH0 NG Z -WHIRLPOOL HH W ER1 L P UW2 L -WHIRLPOOL W ER1 L P UW2 L -WHIRLPOOL'S HH W ER1 L P UW2 L Z -WHIRLPOOL'S W ER1 L P UW2 L Z -WHIRLPOOLERS HH W ER1 L P UW2 L ER0 Z -WHIRLPOOLS HH W ER1 L P UW2 L Z -WHIRLPOOLS W ER1 L P UW2 L Z -WHIRLS HH W ER1 L Z -WHIRLWIND HH W ER1 L W IH2 N D -WHIRLWIND W ER1 L W IH2 N D -WHIRLWIND'S HH W ER1 L W IH2 N D Z -WHIRLWINDS HH W ER1 L W IH2 N D Z -WHIRLY HH W ER1 L IY0 -WHIRR W ER1 -WHIRR'S W ER1 Z -WHIRRED W ER1 D -WHIRRING HH W ER1 IH0 NG -WHIRRING W ER1 IH0 NG -WHIRRINGS W ER1 IH0 NG Z -WHIRRS W ER1 Z -WHIRS W ER1 Z -WHIRTER W ER1 T ER0 -WHIS W IH1 S -WHISH HH W IH1 SH -WHISHAW HH W IH1 SH AO2 -WHISHING HH W IH1 SH IH0 NG -WHISHT HH W IH1 SH T -WHISK HH W IH1 S K -WHISK W IH1 S K -WHISKED HH W IH1 S K T -WHISKED W IH1 S K T -WHISKER HH W IH1 S K ER0 -WHISKER W IH1 S K ER0 -WHISKERED W IH1 S K ER0 D -WHISKERLESS W IH1 S K ER0 L AH0 S -WHISKERS HH W IH1 S K ER0 Z -WHISKERS W IH1 S K ER0 Z -WHISKERY W IH1 S K ER0 IY0 -WHISKEY HH W IH1 S K IY0 -WHISKEY W IH1 S K IY0 -WHISKEY'S W IH1 S K IY0 Z -WHISKEYS HH W IH1 S K IY0 Z -WHISKEYS W IH1 S K IY0 Z -WHISKIES W IH1 S K IY0 Z -WHISKING W IH1 S K IH0 NG -WHISKS HH W IH1 S K S -WHISKS W IH1 S K S -WHISKY HH W IH1 S K IY0 -WHISKY W IH1 S K IY0 -WHISKY'S W IH1 S K IY0 Z -WHISP W IH1 S P -WHISP'RING W IH1 S P R IH0 NG -WHISPER HH W IH1 S P ER0 -WHISPER W IH1 S P ER0 -WHISPER'D W IH1 S P ER0 D -WHISPERED HH W IH1 S P ER0 D -WHISPERED W IH1 S P ER0 D -WHISPERER W IH1 S P ER0 ER0 -WHISPERERS W IH1 S P ER0 ER0 Z -WHISPERETH W IH1 S P ER0 IH0 TH -WHISPERFOOT W IH1 S P ER1 F UH0 T -WHISPERIN W IH1 S P ER0 IH0 N -WHISPERING HH W IH1 S P ER0 IH0 NG -WHISPERING W IH1 S P ER0 IH0 NG -WHISPERINGLY W IH1 S P ER0 IH0 NG L IY0 -WHISPERINGS W IH1 S P ER0 IH0 NG Z -WHISPERS HH W IH1 S P ER0 Z -WHISPERS W IH1 S P ER0 Z -WHISPERY W IH1 S P ER0 IY0 -WHISPS W IH1 S P S -WHIST W IH1 S T -WHISTLE HH W IH1 S AH0 L -WHISTLE W IH1 S AH0 L -WHISTLE'S HH W IH1 S AH0 L Z -WHISTLEBINKIE HH W IH1 S AH0 L B IH0 NG K IY0 -WHISTLED HH W IH1 S AH0 L D -WHISTLED W IH1 S AH0 L D -WHISTLEING HH W IH1 S AH0 L IH0 NG -WHISTLER HH W IH1 S L ER0 -WHISTLER W IH1 S L ER0 -WHISTLER'S W IH1 S L ER0 Z -WHISTLERIAN W IH1 S L ER0 IY1 AH0 N -WHISTLERS HH W IH1 S L ER0 Z -WHISTLERS W IH1 S L ER0 Z -WHISTLES HH W IH1 S AH0 L Z -WHISTLES W IH1 S AH0 L Z -WHISTLIN W IH1 S L IH0 N -WHISTLING HH W IH1 S L IH0 NG -WHISTLING W IH1 S L IH0 NG -WHISTLINGS W IH1 S L IH0 NG Z -WHISTON HH W IH1 S T AH0 N -WHISTON W IH1 S T AH0 N -WHISTON'S W IH1 S T AH0 N Z -WHIT HH W IH1 T -WHIT W IH1 T -WHITADDER HH W IH1 T AH0 D ER0 -WHITAKER HH W IH1 T AH0 K ER0 -WHITAKER W IH1 T AH0 K ER0 -WHITAKER'S HH W IH1 T AH0 K ER0 Z -WHITALL HH W IH1 T AH0 L -WHITBREAD HH W IH1 T B R EH2 D -WHITBREAD W IH1 T B R EH2 D -WHITBURN HH W IH1 T B ER2 N -WHITBURN'S HH W IH1 T B ER0 N Z -WHITBURY HH W IH1 T B EH2 R IY0 -WHITBY HH W IH1 T B IY0 -WHITBY W IH1 T B IY0 -WHITBY'S HH W IH1 T B IY0 Z -WHITCHURCH HH W IH1 T CH ER2 CH -WHITCHURCH W IH1 T CH ER2 CH -WHITCLIFFE HH W IH1 T K L IH0 F -WHITCOMB HH W IH1 T K AH0 M -WHITCOMB W IH1 T K AH0 M -WHITCOMB'S HH W IH1 T K AH0 M Z -WHITCOMBE HH W IH1 T K AH0 M -WHITE HH W AY1 T -WHITE W AY1 T -WHITE'S HH W AY1 T S -WHITE'S W AY1 T S -WHITEAWAY HH W IH1 T AH0 W EY2 -WHITEBAIT HH W AY1 T B EY1 T -WHITEBEAR HH W AY1 T B EH2 R -WHITEBECK HH W AY1 T B EH0 K -WHITEBOY HH W AY1 T B OY2 -WHITEBOYS HH W AY1 T B OY2 Z -WHITECAP HH W AY1 T K AE2 P -WHITECAPPED HH W AY1 T K AE2 P T -WHITECAPS HH W AY1 T K AE2 P S -WHITECHAPEL HH W AY1 T K AE1 P AH0 L -WHITECHOKER HH W AY1 T K AH0 K ER0 -WHITECLETT HH W AY1 T K L IH0 T -WHITECLIFF HH W AY1 T K L IH0 F -WHITECLIFFE HH W AY1 T K L IH0 F -WHITECRAIGS HH W AY1 T K R EY1 G Z -WHITECROSS HH W AY1 T K R AO2 S -WHITED HH W AY1 T IH0 D -WHITED W AY1 T IH0 D -WHITEFACE HH W AY1 T F EY2 S -WHITEFACED HH W AY1 T F AH0 S T -WHITEFIELD HH W AY1 T F IY2 L D -WHITEFIELD W AY1 T F IY2 L D -WHITEFIELD'S HH W AY1 T F IY2 L D Z -WHITEFIRE HH W AY1 T F AY2 R -WHITEFISH HH W AY1 T F IH2 SH -WHITEFISH W AY1 T F IH2 SH -WHITEFOOT HH W AY1 T F UH2 T -WHITEFRIARS HH W AY1 T F R AY1 ER0 Z -WHITEHAIRED HH W AY1 T HH EH1 R D -WHITEHALL HH W AY1 T HH AO2 L -WHITEHALL W AY1 T HH AO2 L -WHITEHALL'S HH W AY1 T HH AO2 L Z -WHITEHALL'S W AY1 T HH AO2 L Z -WHITEHAVEN HH W AY1 T HH EY2 V AH0 N -WHITEHEAD HH W AY1 T HH EH2 D -WHITEHEAD W AY1 T HH EH2 D -WHITEHEAD'S HH W AY1 T HH EH2 D Z -WHITEHEAD'S W AY1 T HH EH2 D Z -WHITEHEADED HH W AY1 T HH EH2 D IH0 D -WHITEHILL HH W AY1 T HH IH2 L -WHITEHILL W AY1 T HH IH2 L -WHITEHORSE HH W AY1 T HH AO2 R S -WHITEHORSE W AY1 T HH AO2 R S -WHITEHOUSE HH W AY1 T HH AW2 S -WHITEHOUSE W AY1 T HH AW2 S -WHITEISH HH W AY1 T IH0 SH -WHITELADIES HH W AY1 T EY2 D IY0 Z -WHITELAND HH W AY1 T L AH0 N D -WHITELAW HH W AY1 T L AO2 -WHITELAW W AY1 T L AO2 -WHITELAW'S HH W AY1 T L AO2 Z 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AY1 T S AY2 D Z -WHITESIDES W AY1 T S AY2 D Z -WHITEST HH W AY1 T IH0 S T -WHITEST W AY1 T IH0 S T -WHITESTONE HH W AY1 T S T OW2 N -WHITESTONE W AY1 T S T OW2 N -WHITETAIL HH W AY1 T EY2 L -WHITETAIL HH W AY1 T T EY2 L -WHITETAIL W AY1 T T EY2 L -WHITETHORN HH W AY1 T T HH ER0 N -WHITETHROAT HH W AY1 T IH0 TH R OW1 T -WHITETHROATS HH W AY1 T IH0 TH R OW1 T S -WHITEWASH HH W AY1 T W AA2 SH -WHITEWASH W AY1 T W AA2 SH -WHITEWASHED HH W AY1 T W AA2 SH T -WHITEWASHED W AY1 T W AA2 SH T -WHITEWASHER HH W AY1 T W AA2 SH ER0 -WHITEWASHERS HH W AY1 T W AA2 SH ER0 Z -WHITEWASHES HH W AY1 T W AA2 SH AH0 Z -WHITEWASHIN HH W AY1 T W AA2 SH IH0 N -WHITEWASHING HH W AY1 T W AA2 SH IH0 NG -WHITEWATER HH W AY1 T W AO2 T ER0 -WHITEWATER W AY1 T W AO2 T ER0 -WHITEWAYS HH W AY1 T W EY2 Z -WHITEWEBS HH W AY1 T W EH2 B Z -WHITEWING HH W AY1 T W IH2 NG -WHITEWOOD HH W AY1 T W UH2 D -WHITEY HH W AY1 T IY0 -WHITEY HH W AY1 T IY2 -WHITEY W AY1 T IY2 -WHITEY'S HH W AY1 T IY2 Z -WHITFIELD HH W IH1 T F IY0 L D 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k u5 rr -kuar1 k ua1 rr -kuar2 k ua2 rr -kuar3 k ua3 rr -kuar4 k ua4 rr -kuar5 k ua5 rr -kuair1 k uai1 rr -kuair2 k uai2 rr -kuair3 k uai3 rr -kuair4 k uai4 rr -kuair5 k uai5 rr -kuanr1 k uan1 rr -kuanr2 k uan2 rr -kuanr3 k uan3 rr -kuanr4 k uan4 rr -kuanr5 k uan5 rr -kuangr1 k uang1 rr -kuangr2 k uang2 rr -kuangr3 k uang3 rr -kuangr4 k uang4 rr -kuangr5 k uang5 rr -kuir1 k uei1 rr -kuir2 k uei2 rr -kuir3 k uei3 rr -kuir4 k uei4 rr -kuir5 k uei5 rr -kunr1 k uen1 rr -kunr2 k uen2 rr -kunr3 k uen3 rr -kunr4 k uen4 rr -kunr5 k uen5 rr -kuor1 k uo1 rr -kuor2 k uo2 rr -kuor3 k uo3 rr -kuor4 k uo4 rr -kuor5 k uo5 rr -lar1 l a1 rr -lar2 l a2 rr -lar3 l a3 rr -lar4 l a4 rr -lar5 l a5 rr -lair1 l ai1 rr -lair2 l ai2 rr -lair3 l ai3 rr -lair4 l ai4 rr -lair5 l ai5 rr -lanr1 l an1 rr -lanr2 l an2 rr -lanr3 l an3 rr -lanr4 l an4 rr -lanr5 l an5 rr -langr1 l ang1 rr -langr2 l ang2 rr -langr3 l ang3 rr -langr4 l ang4 rr -langr5 l ang5 rr -laor1 l ao1 rr -laor2 l ao2 rr -laor3 l ao3 rr -laor4 l ao4 rr -laor5 l ao5 rr -ler1 l e1 rr -ler2 l e2 rr -ler3 l e3 rr -ler4 l e4 rr -ler5 l e5 rr -leir1 l ei1 rr -leir2 l ei2 rr -leir3 l ei3 rr -leir4 l ei4 rr -leir5 l ei5 rr -lengr1 l eng1 rr -lengr2 l eng2 rr -lengr3 l eng3 rr -lengr4 l eng4 rr -lengr5 l eng5 rr -lir1 l i1 rr -lir2 l i2 rr -lir3 l i3 rr -lir4 l i4 rr -lir5 l i5 rr -liar1 l ia1 rr -liar2 l ia2 rr -liar3 l ia3 rr -liar4 l ia4 rr -liar5 l ia5 rr -lianr1 l ian1 rr -lianr2 l ian2 rr -lianr3 l ian3 rr -lianr4 l ian4 rr -lianr5 l ian5 rr -liangr1 l iang1 rr -liangr2 l iang2 rr -liangr3 l iang3 rr -liangr4 l iang4 rr -liangr5 l iang5 rr -liaor1 l iao1 rr -liaor2 l iao2 rr -liaor3 l iao3 rr -liaor4 l iao4 rr -liaor5 l iao5 rr -lier1 l ie1 rr -lier2 l ie2 rr -lier3 l ie3 rr -lier4 l ie4 rr -lier5 l ie5 rr -linr1 l in1 rr -linr2 l in2 rr -linr3 l in3 rr -linr4 l in4 rr -linr5 l in5 rr -lingr1 l ing1 rr -lingr2 l ing2 rr -lingr3 l ing3 rr -lingr4 l ing4 rr -lingr5 l ing5 rr -liur1 l iou1 rr -liur2 l iou2 rr -liur3 l iou3 rr -liur4 l iou4 rr -liur5 l iou5 rr -lor1 l o1 rr -lor2 l o2 rr -lor3 l o3 rr -lor4 l o4 rr -lor5 l o5 rr -longr1 l ong1 rr -longr2 l ong2 rr -longr3 l ong3 rr -longr4 l ong4 rr -longr5 l ong5 rr -lour1 l ou1 rr -lour2 l ou2 rr -lour3 l ou3 rr -lour4 l ou4 rr -lour5 l ou5 rr -lur1 l u1 rr -lur2 l u2 rr -lur3 l u3 rr -lur4 l u4 rr -lur5 l u5 rr -luanr1 l uan1 rr -luanr2 l uan2 rr -luanr3 l uan3 rr -luanr4 l uan4 rr -luanr5 l uan5 rr -luer1 l ve1 rr -luer2 l ve2 rr -luer3 l ve3 rr -luer4 l ve4 rr -luer5 l ve5 rr -lver1 l ve1 rr -lver2 l ve2 rr -lver3 l ve3 rr -lver4 l ve4 rr -lver5 l ve5 rr -lunr1 l uen1 rr -lunr2 l uen2 rr -lunr3 l uen3 rr -lunr4 l uen4 rr -lunr5 l uen5 rr -luor1 l uo1 rr -luor2 l uo2 rr -luor3 l uo3 rr -luor4 l uo4 rr -luor5 l uo5 rr -lvr1 l v1 rr -lvr2 l v2 rr -lvr3 l v3 rr -lvr4 l v4 rr -lvr5 l v5 rr -mar1 m a1 rr -mar2 m a2 rr -mar3 m a3 rr -mar4 m a4 rr -mar5 m a5 rr -mair1 m ai1 rr -mair2 m ai2 rr -mair3 m ai3 rr -mair4 m ai4 rr -mair5 m ai5 rr -manr1 m an1 rr -manr2 m an2 rr -manr3 m an3 rr -manr4 m an4 rr -manr5 m an5 rr -mangr1 m ang1 rr -mangr2 m ang2 rr -mangr3 m ang3 rr -mangr4 m ang4 rr -mangr5 m ang5 rr -maor1 m ao1 rr -maor2 m ao2 rr -maor3 m ao3 rr -maor4 m ao4 rr -maor5 m ao5 rr -mer1 m e1 rr -mer2 m e2 rr -mer3 m e3 rr -mer4 m e4 rr -mer5 m e5 rr -meir1 m ei1 rr -meir2 m ei2 rr -meir3 m ei3 rr -meir4 m ei4 rr -meir5 m ei5 rr -menr1 m en1 rr -menr2 m en2 rr -menr3 m en3 rr -menr4 m en4 rr -menr5 m en5 rr -mengr1 m eng1 rr -mengr2 m eng2 rr -mengr3 m eng3 rr -mengr4 m eng4 rr -mengr5 m eng5 rr -mir1 m i1 rr -mir2 m i2 rr -mir3 m i3 rr -mir4 m i4 rr -mir5 m i5 rr -mianr1 m ian1 rr -mianr2 m ian2 rr -mianr3 m ian3 rr -mianr4 m ian4 rr -mianr5 m ian5 rr -miaor1 m iao1 rr -miaor2 m iao2 rr -miaor3 m iao3 rr -miaor4 m iao4 rr -miaor5 m iao5 rr -mier1 m ie1 rr -mier2 m ie2 rr -mier3 m ie3 rr -mier4 m ie4 rr -mier5 m ie5 rr -minr1 m in1 rr -minr2 m in2 rr -minr3 m in3 rr -minr4 m in4 rr -minr5 m in5 rr -mingr1 m ing1 rr -mingr2 m ing2 rr -mingr3 m ing3 rr -mingr4 m ing4 rr -mingr5 m ing5 rr -miur1 m iou1 rr -miur2 m iou2 rr -miur3 m iou3 rr -miur4 m iou4 rr -miur5 m iou5 rr -mor1 m o1 rr -mor2 m o2 rr -mor3 m o3 rr -mor4 m o4 rr -mor5 m o5 rr -mour1 m ou1 rr -mour2 m ou2 rr -mour3 m ou3 rr -mour4 m ou4 rr -mour5 m ou5 rr -mur1 m u1 rr -mur2 m u2 rr -mur3 m u3 rr -mur4 m u4 rr -mur5 m u5 rr -nar1 n a1 rr -nar2 n a2 rr -nar3 n a3 rr -nar4 n a4 rr -nar5 n a5 rr -nair1 n ai1 rr -nair2 n ai2 rr -nair3 n ai3 rr -nair4 n ai4 rr -nair5 n ai5 rr -nanr1 n an1 rr -nanr2 n an2 rr -nanr3 n an3 rr -nanr4 n an4 rr -nanr5 n an5 rr -nangr1 n ang1 rr -nangr2 n ang2 rr -nangr3 n ang3 rr -nangr4 n ang4 rr -nangr5 n ang5 rr -naor1 n ao1 rr -naor2 n ao2 rr -naor3 n ao3 rr -naor4 n ao4 rr -naor5 n ao5 rr -ner1 n e1 rr -ner2 n e2 rr -ner3 n e3 rr -ner4 n e4 rr -ner5 n e5 rr -neir1 n ei1 rr -neir2 n ei2 rr -neir3 n ei3 rr -neir4 n ei4 rr -neir5 n ei5 rr -nenr1 n en1 rr -nenr2 n en2 rr -nenr3 n en3 rr -nenr4 n en4 rr -nenr5 n en5 rr -nengr1 n eng1 rr -nengr2 n eng2 rr -nengr3 n eng3 rr -nengr4 n eng4 rr -nengr5 n eng5 rr -nir1 n i1 rr -nir2 n i2 rr -nir3 n i3 rr -nir4 n i4 rr -nir5 n i5 rr -nianr1 n ian1 rr -nianr2 n ian2 rr -nianr3 n ian3 rr -nianr4 n ian4 rr -nianr5 n ian5 rr -niangr1 n iang1 rr -niangr2 n iang2 rr -niangr3 n iang3 rr -niangr4 n iang4 rr -niangr5 n iang5 rr -niaor1 n iao1 rr -niaor2 n iao2 rr -niaor3 n iao3 rr -niaor4 n iao4 rr -niaor5 n iao5 rr -nier1 n ie1 rr -nier2 n ie2 rr -nier3 n ie3 rr -nier4 n ie4 rr -nier5 n ie5 rr -ninr1 n in1 rr -ninr2 n in2 rr -ninr3 n in3 rr -ninr4 n in4 rr -ninr5 n in5 rr -ningr1 n ing1 rr -ningr2 n ing2 rr -ningr3 n ing3 rr -ningr4 n ing4 rr -ningr5 n ing5 rr -niur1 n iou1 rr -niur2 n iou2 rr -niur3 n iou3 rr -niur4 n iou4 rr -niur5 n iou5 rr -nongr1 n ong1 rr -nongr2 n ong2 rr -nongr3 n ong3 rr -nongr4 n ong4 rr -nongr5 n ong5 rr -nour1 n ou1 rr -nour2 n ou2 rr -nour3 n ou3 rr -nour4 n ou4 rr -nour5 n ou5 rr -nur1 n u1 rr -nur2 n u2 rr -nur3 n u3 rr -nur4 n u4 rr -nur5 n u5 rr -nuanr1 n uan1 rr -nuanr2 n uan2 rr -nuanr3 n uan3 rr -nuanr4 n uan4 rr -nuanr5 n uan5 rr -nuer1 n ve1 rr -nuer2 n ve2 rr -nuer3 n ve3 rr -nuer4 n ve4 rr -nuer5 n ve5 rr -nver1 n ve1 rr -nver2 n ve2 rr -nver3 n ve3 rr -nver4 n ve4 rr -nver5 n ve5 rr -nuor1 n uo1 rr -nuor2 n uo2 rr -nuor3 n uo3 rr -nuor4 n uo4 rr -nuor5 n uo5 rr -nvr1 n v1 rr -nvr2 n v2 rr -nvr3 n v3 rr -nvr4 n v4 rr -nvr5 n v5 rr -or1 o1 rr -or2 o2 rr -or3 o3 rr -or4 o4 rr -or5 o5 rr -our1 ou1 rr -our2 ou2 rr -our3 ou3 rr -our4 ou4 rr -our5 ou5 rr -par1 p a1 rr -par2 p a2 rr -par3 p a3 rr -par4 p a4 rr -par5 p a5 rr -pair1 p ai1 rr -pair2 p ai2 rr -pair3 p ai3 rr -pair4 p ai4 rr -pair5 p ai5 rr -panr1 p an1 rr -panr2 p an2 rr -panr3 p an3 rr -panr4 p an4 rr -panr5 p an5 rr -pangr1 p ang1 rr -pangr2 p ang2 rr -pangr3 p ang3 rr -pangr4 p ang4 rr -pangr5 p ang5 rr -paor1 p ao1 rr -paor2 p ao2 rr -paor3 p ao3 rr -paor4 p ao4 rr -paor5 p ao5 rr -peir1 p ei1 rr -peir2 p ei2 rr -peir3 p ei3 rr -peir4 p ei4 rr -peir5 p ei5 rr -penr1 p en1 rr -penr2 p en2 rr -penr3 p en3 rr -penr4 p en4 rr -penr5 p en5 rr -pengr1 p eng1 rr -pengr2 p eng2 rr -pengr3 p eng3 rr -pengr4 p eng4 rr -pengr5 p eng5 rr -pir1 p i1 rr -pir2 p i2 rr -pir3 p i3 rr -pir4 p i4 rr -pir5 p i5 rr -pianr1 p ian1 rr -pianr2 p ian2 rr -pianr3 p ian3 rr -pianr4 p ian4 rr -pianr5 p ian5 rr -piaor1 p iao1 rr -piaor2 p iao2 rr -piaor3 p iao3 rr -piaor4 p iao4 rr -piaor5 p iao5 rr -pier1 p ie1 rr -pier2 p ie2 rr -pier3 p ie3 rr -pier4 p ie4 rr -pier5 p ie5 rr -pinr1 p in1 rr -pinr2 p in2 rr -pinr3 p in3 rr -pinr4 p in4 rr -pinr5 p in5 rr -pingr1 p ing1 rr -pingr2 p ing2 rr -pingr3 p ing3 rr -pingr4 p ing4 rr -pingr5 p ing5 rr -por1 p o1 rr -por2 p o2 rr -por3 p o3 rr -por4 p o4 rr -por5 p o5 rr -pour1 p ou1 rr -pour2 p ou2 rr -pour3 p ou3 rr -pour4 p ou4 rr -pour5 p ou5 rr -pur1 p u1 rr -pur2 p u2 rr -pur3 p u3 rr -pur4 p u4 rr -pur5 p u5 rr -qir1 q i1 rr -qir2 q i2 rr -qir3 q i3 rr -qir4 q i4 rr -qir5 q i5 rr -qiar1 q ia1 rr -qiar2 q ia2 rr -qiar3 q ia3 rr -qiar4 q ia4 rr -qiar5 q ia5 rr -qianr1 q ian1 rr -qianr2 q ian2 rr -qianr3 q ian3 rr -qianr4 q ian4 rr -qianr5 q ian5 rr -qiangr1 q iang1 rr -qiangr2 q iang2 rr -qiangr3 q iang3 rr -qiangr4 q iang4 rr -qiangr5 q iang5 rr -qiaor1 q iao1 rr -qiaor2 q iao2 rr -qiaor3 q iao3 rr -qiaor4 q iao4 rr -qiaor5 q iao5 rr -qier1 q ie1 rr -qier2 q ie2 rr -qier3 q ie3 rr -qier4 q ie4 rr -qier5 q ie5 rr -qinr1 q in1 rr -qinr2 q in2 rr -qinr3 q in3 rr -qinr4 q in4 rr -qinr5 q in5 rr -qingr1 q ing1 rr -qingr2 q ing2 rr -qingr3 q ing3 rr -qingr4 q ing4 rr -qingr5 q ing5 rr -qiongr1 q iong1 rr -qiongr2 q iong2 rr -qiongr3 q iong3 rr -qiongr4 q iong4 rr -qiongr5 q iong5 rr -qiur1 q iou1 rr -qiur2 q iou2 rr -qiur3 q iou3 rr -qiur4 q iou4 rr -qiur5 q iou5 rr -qur1 q v1 rr -qur2 q v2 rr -qur3 q v3 rr -qur4 q v4 rr -qur5 q v5 rr -quanr1 q van1 rr -quanr2 q van2 rr -quanr3 q van3 rr -quanr4 q van4 rr -quanr5 q van5 rr -quer1 q ve1 rr -quer2 q ve2 rr -quer3 q ve3 rr -quer4 q ve4 rr -quer5 q ve5 rr -qunr1 q vn1 rr -qunr2 q vn2 rr -qunr3 q vn3 rr -qunr4 q vn4 rr -qunr5 q vn5 rr -ranr1 r an1 rr -ranr2 r an2 rr -ranr3 r an3 rr -ranr4 r an4 rr -ranr5 r an5 rr -rangr1 r ang1 rr -rangr2 r ang2 rr -rangr3 r ang3 rr -rangr4 r ang4 rr -rangr5 r ang5 rr -raor1 r ao1 rr -raor2 r ao2 rr -raor3 r ao3 rr -raor4 r ao4 rr -raor5 r ao5 rr -rer1 r e1 rr -rer2 r e2 rr -rer3 r e3 rr -rer4 r e4 rr -rer5 r e5 rr -renr1 r en1 rr -renr2 r en2 rr -renr3 r en3 rr -renr4 r en4 rr -renr5 r en5 rr -rengr1 r eng1 rr -rengr2 r eng2 rr -rengr3 r eng3 rr -rengr4 r eng4 rr -rengr5 r eng5 rr -rir1 r iii1 rr -rir2 r iii2 rr -rir3 r iii3 rr -rir4 r iii4 rr -rir5 r iii5 rr -rongr1 r ong1 rr -rongr2 r ong2 rr -rongr3 r ong3 rr -rongr4 r ong4 rr -rongr5 r ong5 rr -rour1 r ou1 rr -rour2 r ou2 rr -rour3 r ou3 rr -rour4 r ou4 rr -rour5 r ou5 rr -rur1 r u1 rr -rur2 r u2 rr -rur3 r u3 rr -rur4 r u4 rr -rur5 r u5 rr -ruar1 r ua1 rr -ruar2 r ua2 rr -ruar3 r ua3 rr -ruar4 r ua4 rr -ruar5 r ua5 rr -ruanr1 r uan1 rr -ruanr2 r uan2 rr 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u2 rr -zur3 z u3 rr -zur4 z u4 rr -zur5 z u5 rr -zuanr1 z uan1 rr -zuanr2 z uan2 rr -zuanr3 z uan3 rr -zuanr4 z uan4 rr -zuanr5 z uan5 rr -zuir1 z uei1 rr -zuir2 z uei2 rr -zuir3 z uei3 rr -zuir4 z uei4 rr -zuir5 z uei5 rr -zunr1 z uen1 rr -zunr2 z uen2 rr -zunr3 z uen3 rr -zunr4 z uen4 rr -zunr5 z uen5 rr -zuor1 z uo1 rr -zuor2 z uo2 rr -zuor3 z uo3 rr -zuor4 z uo4 rr -zuor5 z uo5 rr diff --git a/text/numbers.py b/text/numbers.py deleted file mode 100644 index 660a2964b10a273c0c0b7e8998c8111d7e4aa3be..0000000000000000000000000000000000000000 --- a/text/numbers.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" This code is modified from https://github.com/keithito/tacotron """ - -import inflect -import re - -_inflect = inflect.engine() -_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") -_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") -_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") -_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") -_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") -_number_re = re.compile(r"[0-9]+") - - -def _remove_commas(m): - return m.group(1).replace(",", "") - - -def _expand_decimal_point(m): - return m.group(1).replace(".", " point ") - - -def _expand_dollars(m): - match = m.group(1) - parts = match.split(".") - if len(parts) > 2: - return match + " dollars" # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = "dollar" if dollars == 1 else "dollars" - cent_unit = "cent" if cents == 1 else "cents" - return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = "dollar" if dollars == 1 else "dollars" - return "%s %s" % (dollars, dollar_unit) - elif cents: - cent_unit = "cent" if cents == 1 else "cents" - return "%s %s" % (cents, cent_unit) - else: - return "zero dollars" - - -def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) - - -def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return "two thousand" - elif num > 2000 and num < 2010: - return "two thousand " + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + " hundred" - else: - return _inflect.number_to_words( - num, andword="", zero="oh", group=2 - ).replace(", ", " ") - else: - return _inflect.number_to_words(num, andword="") - - -def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r"\1 pounds", text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text diff --git a/text/pinyin.py b/text/pinyin.py deleted file mode 100644 index bb4b41b5c3990d0c9026beb288a60c1edc86528e..0000000000000000000000000000000000000000 --- a/text/pinyin.py +++ /dev/null @@ -1,218 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -initials = [ - "b", - "c", - "ch", - "d", - "f", - "g", - "h", - "j", - "k", - "l", - "m", - "n", - "p", - "q", - "r", - "s", - "sh", - "t", - "w", - "x", - "y", - "z", - "zh", -] -finals = [ - "a1", - "a2", - "a3", - "a4", - "a5", - "ai1", - "ai2", - "ai3", - "ai4", - "ai5", - "an1", - "an2", - "an3", - "an4", - "an5", - "ang1", - "ang2", - "ang3", - "ang4", - "ang5", - "ao1", - "ao2", - "ao3", - "ao4", - "ao5", - "e1", - "e2", - "e3", - "e4", - "e5", - "ei1", - "ei2", - "ei3", - "ei4", - "ei5", - "en1", - "en2", - "en3", - "en4", - "en5", - "eng1", - "eng2", - "eng3", - "eng4", - "eng5", - "er1", - "er2", - "er3", - "er4", - "er5", - "i1", - "i2", - "i3", - "i4", - "i5", - "ia1", - "ia2", - "ia3", - "ia4", - "ia5", - "ian1", - "ian2", - "ian3", - "ian4", - "ian5", - "iang1", - "iang2", - "iang3", - "iang4", - "iang5", - "iao1", - "iao2", - "iao3", - "iao4", - "iao5", - "ie1", - "ie2", - "ie3", - "ie4", - "ie5", - "ii1", - "ii2", - "ii3", - "ii4", - "ii5", - "iii1", - "iii2", - "iii3", - "iii4", - "iii5", - "in1", - "in2", - "in3", - "in4", - "in5", - "ing1", - "ing2", - "ing3", - "ing4", - "ing5", - "iong1", - "iong2", - "iong3", - "iong4", - "iong5", - "iou1", - "iou2", - "iou3", - "iou4", - "iou5", - "o1", - "o2", - "o3", - "o4", - "o5", - "ong1", - "ong2", - "ong3", - "ong4", - "ong5", - "ou1", - "ou2", - "ou3", - "ou4", - "ou5", - "u1", - "u2", - "u3", - "u4", - "u5", - "ua1", - "ua2", - "ua3", - "ua4", - "ua5", - "uai1", - "uai2", - "uai3", - "uai4", - "uai5", - "uan1", - "uan2", - "uan3", - "uan4", - "uan5", - "uang1", - "uang2", - "uang3", - "uang4", - "uang5", - "uei1", - "uei2", - "uei3", - "uei4", - "uei5", - "uen1", - "uen2", - "uen3", - "uen4", - "uen5", - "uo1", - "uo2", - "uo3", - "uo4", - "uo5", - "v1", - "v2", - "v3", - "v4", - "v5", - "van1", - "van2", - "van3", - "van4", - "van5", - "ve1", - "ve2", - "ve3", - "ve4", - "ve5", - "vn1", - "vn2", - "vn3", - "vn4", - "vn5", -] -valid_symbols = initials + finals + ["rr"] diff --git a/text/symbol_table.py b/text/symbol_table.py deleted file mode 100644 index 8b3945510ea8f029aa2b567d35ce05240c02112c..0000000000000000000000000000000000000000 --- a/text/symbol_table.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright 2020 Mobvoi Inc. (authors: Fangjun Kuang) -# -# See ../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from dataclasses import dataclass -from dataclasses import field -from typing import Dict -from typing import Generic -from typing import List -from typing import Optional -from typing import TypeVar -from typing import Union - -Symbol = TypeVar('Symbol') - -# SymbolTable is copied from -# https://github.com/k2-fsa/k2/blob/master/k2/python/k2/symbol_table.py - -''' - SymbolTable: map symbol to id -''' -@dataclass(repr=False) -class SymbolTable(Generic[Symbol]): - '''SymbolTable that maps symbol IDs, found on the FSA arcs to - actual objects. These objects can be arbitrary Python objects - that can serve as keys in a dictionary (i.e. they need to be - hashable and immutable). - - The SymbolTable can only be read to/written from disk if the - symbols are strings. - ''' - _id2sym: Dict[int, Symbol] = field(default_factory=dict) - '''Map an integer to a symbol. - ''' - - _sym2id: Dict[Symbol, int] = field(default_factory=dict) - '''Map a symbol to an integer. - ''' - - _next_available_id: int = 1 - '''A helper internal field that helps adding new symbols - to the table efficiently. - ''' - - eps: Symbol = '' - '''Null symbol, always mapped to index 0. - ''' - - def __post_init__(self): - assert all(self._sym2id[sym] == idx for idx, sym in self._id2sym.items()) - assert all(self._id2sym[idx] == sym for sym, idx in self._sym2id.items()) - assert 0 not in self._id2sym or self._id2sym[0] == self.eps - - self._next_available_id = max(self._id2sym, default=0) + 1 - self._id2sym.setdefault(0, self.eps) - self._sym2id.setdefault(self.eps, 0) - - - @staticmethod - def from_str(s: str) -> 'SymbolTable': - '''Build a symbol table from a string. - - The string consists of lines. Every line has two fields separated - by space(s), tab(s) or both. The first field is the symbol and the - second the integer id of the symbol. - - Args: - s: - The input string with the format described above. - Returns: - An instance of :class:`SymbolTable`. - ''' - id2sym: Dict[int, str] = dict() - sym2id: Dict[str, int] = dict() - - for line in s.split('\n'): - fields = line.split() - if len(fields) == 0: - continue # skip empty lines - assert len(fields) == 2, \ - f'Expect a line with 2 fields. Given: {len(fields)}' - sym, idx = fields[0], int(fields[1]) - assert sym not in sym2id, f'Duplicated symbol {sym}' - assert idx not in id2sym, f'Duplicated id {idx}' - id2sym[idx] = sym - sym2id[sym] = idx - - eps = id2sym.get(0, '') - - return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps) - - @staticmethod - def from_file(filename: str) -> 'SymbolTable': - '''Build a symbol table from file. - - Every line in the symbol table file has two fields separated by - space(s), tab(s) or both. The following is an example file: - - .. code-block:: - - 0 - a 1 - b 2 - c 3 - - Args: - filename: - Name of the symbol table file. Its format is documented above. - - Returns: - An instance of :class:`SymbolTable`. - - ''' - with open(filename, 'r', encoding='utf-8') as f: - return SymbolTable.from_str(f.read().strip()) - - def to_str(self) -> str: - ''' - Returns: - Return a string representation of this object. You can pass - it to the method ``from_str`` to recreate an identical object. - ''' - s = '' - for idx, symbol in sorted(self._id2sym.items()): - s += f'{symbol} {idx}\n' - return s - - def to_file(self, filename: str): - '''Serialize the SymbolTable to a file. - - Every line in the symbol table file has two fields separated by - space(s), tab(s) or both. The following is an example file: - - .. code-block:: - - 0 - a 1 - b 2 - c 3 - - Args: - filename: - Name of the symbol table file. Its format is documented above. - ''' - with open(filename, 'w') as f: - for idx, symbol in sorted(self._id2sym.items()): - print(symbol, idx, file=f) - - def add(self, symbol: Symbol, index: Optional[int] = None) -> int: - '''Add a new symbol to the SymbolTable. - - Args: - symbol: - The symbol to be added. - index: - Optional int id to which the symbol should be assigned. - If it is not available, a ValueError will be raised. - - Returns: - The int id to which the symbol has been assigned. - ''' - # Already in the table? Return its ID. - if symbol in self._sym2id: - return self._sym2id[symbol] - # Specific ID not provided - use next available. - if index is None: - index = self._next_available_id - # Specific ID provided but not available. - if index in self._id2sym: - raise ValueError(f"Cannot assign id '{index}' to '{symbol}' - " - f"already occupied by {self._id2sym[index]}") - self._sym2id[symbol] = index - self._id2sym[index] = symbol - - # Update next available ID if needed - if self._next_available_id <= index: - self._next_available_id = index + 1 - - return index - - def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]: - '''Get a symbol for an id or get an id for a symbol - - Args: - k: - If it is an id, it tries to find the symbol corresponding - to the id; if it is a symbol, it tries to find the id - corresponding to the symbol. - - Returns: - An id or a symbol depending on the given `k`. - ''' - if isinstance(k, int): - return self._id2sym[k] - else: - return self._sym2id[k] - - def merge(self, other: 'SymbolTable') -> 'SymbolTable': - '''Create a union of two SymbolTables. - Raises an AssertionError if the same IDs are occupied by - different symbols. - - Args: - other: - A symbol table to merge with ``self``. - - Returns: - A new symbol table. - ''' - self._check_compatible(other) - return SymbolTable( - _id2sym={**self._id2sym, **other._id2sym}, - _sym2id={**self._sym2id, **other._sym2id}, - eps=self.eps - ) - - def _check_compatible(self, other: 'SymbolTable') -> None: - # Epsilon compatibility - assert self.eps == other.eps, f'Mismatched epsilon symbol: ' \ - f'{self.eps} != {other.eps}' - # IDs compatibility - common_ids = set(self._id2sym).intersection(other._id2sym) - for idx in common_ids: - assert self[idx] == other[idx], f'ID conflict for id: {idx}, ' \ - f'self[idx] = "{self[idx]}", ' \ - f'other[idx] = "{other[idx]}"' - # Symbols compatibility - common_symbols = set(self._sym2id).intersection(other._sym2id) - for sym in common_symbols: - assert self[sym] == other[sym], f'ID conflict for id: {sym}, ' \ - f'self[sym] = "{self[sym]}", ' \ - f'other[sym] = "{other[sym]}"' - - def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]: - return self.get(item) - - def __contains__(self, item: Union[int, Symbol]) -> bool: - if isinstance(item, int): - return item in self._id2sym - else: - return item in self._sym2id - - def __len__(self) -> int: - return len(self._id2sym) - - def __eq__(self, other: 'SymbolTable') -> bool: - if len(self) != len(other): - return False - - for s in self.symbols: - if self[s] != other[s]: - return False - - return True - - @property - def ids(self) -> List[int]: - '''Returns a list of integer IDs corresponding to the symbols. - ''' - ans = list(self._id2sym.keys()) - ans.sort() - return ans - - @property - def symbols(self) -> List[Symbol]: - '''Returns a list of symbols (e.g., strings) corresponding to - the integer IDs. - ''' - ans = list(self._sym2id.keys()) - ans.sort() - return ans - diff --git a/text/symbols.py b/text/symbols.py deleted file mode 100644 index a75cebd57c090c0827284a3e8885e17b42831ed2..0000000000000000000000000000000000000000 --- a/text/symbols.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" This code is modified from https://github.com/keithito/tacotron """ - -""" -Defines the set of symbols used in text input to the model. - -The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. """ - -from text import cmudict, pinyin - -_pad = "_" -_punctuation = "!'(),.:;? " -_special = "-" -_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" -_silences = ["@sp", "@spn", "@sil"] - -# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters): -_arpabet = ["@" + s for s in cmudict.valid_symbols] -_pinyin = ["@" + s for s in pinyin.valid_symbols] - -# Export all symbols: -symbols = ( - [_pad] - + list(_special) - + list(_punctuation) - + list(_letters) - + _arpabet - + _silences - # + _pinyin # for chinese -) diff --git a/text/text_token_collation.py b/text/text_token_collation.py deleted file mode 100644 index b64a93fa0189070a6f9e6a3b97a94a4083c1d40c..0000000000000000000000000000000000000000 --- a/text/text_token_collation.py +++ /dev/null @@ -1,131 +0,0 @@ - # Copyright (c) 2023 Amphion. - - # This source code is licensed under the MIT license found in the - # LICENSE file in the root directory of this source tree. - -from pathlib import Path -from typing import List, Tuple -import os -import numpy as np -import torch -from text.symbol_table import SymbolTable -from text import text_to_sequence - - -''' - TextToken: map text to id -''' -# TextTokenCollator is modified from -# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/collation.py -class TextTokenCollator: - def __init__( - self, - text_tokens: List[str], - add_eos: bool = True, - add_bos: bool = True, - pad_symbol: str = "", - bos_symbol: str = "", - eos_symbol: str = "", - ): - self.pad_symbol = pad_symbol - self.add_eos = add_eos - self.add_bos = add_bos - self.bos_symbol = bos_symbol - self.eos_symbol = eos_symbol - - unique_tokens = [pad_symbol] - if add_bos: - unique_tokens.append(bos_symbol) - if add_eos: - unique_tokens.append(eos_symbol) - unique_tokens.extend(sorted(text_tokens)) - - self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)} - self.idx2token = unique_tokens - - def index( - self, tokens_list: List[str] - ) -> Tuple[torch.Tensor, torch.Tensor]: - seqs, seq_lens = [], [] - for tokens in tokens_list: - assert ( - all([True if s in self.token2idx else False for s in tokens]) - is True - ) - seq = ( - ([self.bos_symbol] if self.add_bos else []) - + list(tokens) - + ([self.eos_symbol] if self.add_eos else []) - ) - seqs.append(seq) - seq_lens.append(len(seq)) - - max_len = max(seq_lens) - for k, (seq, seq_len) in enumerate(zip(seqs, seq_lens)): - seq.extend([self.pad_symbol] * (max_len - seq_len)) - - tokens = torch.from_numpy( - np.array( - [[self.token2idx[token] for token in seq] for seq in seqs], - dtype=np.int64, - ) - ) - tokens_lens = torch.IntTensor(seq_lens) - - return tokens, tokens_lens - - def __call__(self, text): - tokens_seq = [p for p in text] - seq = ( - ([self.bos_symbol] if self.add_bos else []) - + tokens_seq - + ([self.eos_symbol] if self.add_eos else []) - ) - - token_ids = [self.token2idx[token] for token in seq] - token_lens = len(tokens_seq) + self.add_eos + self.add_bos - - return token_ids, token_lens - - -def get_text_token_collater(text_tokens_file: str) -> TextTokenCollator: - text_tokens_path = Path(text_tokens_file) - unique_tokens = SymbolTable.from_file(text_tokens_path) - collater = TextTokenCollator( - unique_tokens.symbols, add_bos=True, add_eos=True - ) - token2idx = collater.token2idx - return collater, token2idx - - -class phoneIDCollation: - def __init__(self, cfg, dataset=None, symbols_dict_file=None) -> None: - - if cfg.preprocess.phone_extractor != 'lexicon': - ### get text token collator - if symbols_dict_file is None: - assert dataset is not None - symbols_dict_file = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.symbols_dict - ) - self.text_token_colloator, token2idx = get_text_token_collater(symbols_dict_file) - # # unique_tokens = SymbolTable.from_file(symbols_dict_path) - # # text_tokenizer = TextToken(unique_tokens.symbols, add_bos=True, add_eos=True) - - # # update phone symbols dict file with pad_symbol or optional tokens (add_bos and add_eos) in TextTokenCollator - # phone_symbol_dict = SymbolTable() - # for s in sorted(list(set(token2idx.keys()))): - # phone_symbol_dict.add(s) - # phone_symbol_dict.to_file(symbols_dict_file) - - def get_phone_id_sequence(self, cfg, phones_seq): - - if cfg.preprocess.phone_extractor == 'lexicon': - phones_seq = ' '.join(phones_seq) - sequence = text_to_sequence(phones_seq, cfg.preprocess.text_cleaners) - else: - sequence, seq_len = self.text_token_colloator(phones_seq) - return sequence - \ No newline at end of file diff --git a/utils/HyperParams/__init__.py b/utils/HyperParams/__init__.py deleted file mode 100644 index 706e31b1c499f11548d08d38e1c3091aeb2dadaa..0000000000000000000000000000000000000000 --- a/utils/HyperParams/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .hps import HyperParams diff --git a/utils/HyperParams/hps.py b/utils/HyperParams/hps.py deleted file mode 100644 index cc6f474c4e28d092ea8ba2cdcf7233322c5ff281..0000000000000000000000000000000000000000 --- a/utils/HyperParams/hps.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -class HyperParams: - """The class to store hyperparameters. The key is case-insensitive. - - Args: - *args: a list of dict or HyperParams. - **kwargs: a list of key-value pairs. - """ - - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HyperParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/utils/__init__.py b/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/utils/audio.py b/utils/audio.py deleted file mode 100644 index 374d50915cafa106a3035e77b99adc96e8484f0b..0000000000000000000000000000000000000000 --- a/utils/audio.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import numpy as np -from numpy import linalg as LA -import librosa -import soundfile as sf -import librosa.filters - - -def load_audio_torch(wave_file, fs): - """Load audio data into torch tensor - - Args: - wave_file (str): path to wave file - fs (int): sample rate - - Returns: - audio (tensor): audio data in tensor - fs (int): sample rate - """ - - audio, sample_rate = librosa.load(wave_file, sr=fs, mono=True) - # audio: (T,) - assert len(audio) > 2 - - # Check the audio type (for soundfile loading backbone) - float, 8bit or 16bit - if np.issubdtype(audio.dtype, np.integer): - max_mag = -np.iinfo(audio.dtype).min - else: - max_mag = max(np.amax(audio), -np.amin(audio)) - max_mag = ( - (2**31) + 1 - if max_mag > (2**15) - else ((2**15) + 1 if max_mag > 1.01 else 1.0) - ) - - # Normalize the audio - audio = torch.FloatTensor(audio.astype(np.float32)) / max_mag - - if (torch.isnan(audio) | torch.isinf(audio)).any(): - return [], sample_rate or fs or 48000 - - # Resample the audio to our target samplerate - if fs is not None and fs != sample_rate: - audio = torch.from_numpy( - librosa.core.resample(audio.numpy(), orig_sr=sample_rate, target_sr=fs) - ) - sample_rate = fs - - return audio, fs - - -def _stft(y, cfg): - return librosa.stft( - y=y, n_fft=cfg.n_fft, hop_length=cfg.hop_size, win_length=cfg.win_size - ) - - -def energy(wav, cfg): - D = _stft(wav, cfg) - magnitudes = np.abs(D).T # [F, T] - return LA.norm(magnitudes, axis=1) - - -def get_energy_from_tacotron(audio, _stft): - audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1) - audio = torch.autograd.Variable(audio, requires_grad=False) - mel, energy = _stft.mel_spectrogram(audio) - energy = torch.squeeze(energy, 0).numpy().astype(np.float32) - return mel, energy diff --git a/utils/audio_slicer.py b/utils/audio_slicer.py deleted file mode 100644 index 28474596b42c8f8215b878a80967112960d0c9e0..0000000000000000000000000000000000000000 --- a/utils/audio_slicer.py +++ /dev/null @@ -1,476 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import json -import numpy as np -from tqdm import tqdm -import torch -import torchaudio - -from utils.io import save_audio -from utils.audio import load_audio_torch - - -# This function is obtained from librosa. -def get_rms( - y, - *, - frame_length=2048, - hop_length=512, - pad_mode="constant", -): - padding = (int(frame_length // 2), int(frame_length // 2)) - y = np.pad(y, padding, mode=pad_mode) - - axis = -1 - # put our new within-frame axis at the end for now - out_strides = y.strides + tuple([y.strides[axis]]) - # Reduce the shape on the framing axis - x_shape_trimmed = list(y.shape) - x_shape_trimmed[axis] -= frame_length - 1 - out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) - xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides) - if axis < 0: - target_axis = axis - 1 - else: - target_axis = axis + 1 - xw = np.moveaxis(xw, -1, target_axis) - # Downsample along the target axis - slices = [slice(None)] * xw.ndim - slices[axis] = slice(0, None, hop_length) - x = xw[tuple(slices)] - - # Calculate power - power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) - - return np.sqrt(power) - - -class Slicer: - """ - Copy from: https://github.com/openvpi/audio-slicer/blob/main/slicer2.py - """ - - def __init__( - self, - sr: int, - threshold: float = -40.0, - min_length: int = 5000, - min_interval: int = 300, - hop_size: int = 10, - max_sil_kept: int = 5000, - ): - if not min_length >= min_interval >= hop_size: - raise ValueError( - "The following condition must be satisfied: min_length >= min_interval >= hop_size" - ) - if not max_sil_kept >= hop_size: - raise ValueError( - "The following condition must be satisfied: max_sil_kept >= hop_size" - ) - min_interval = sr * min_interval / 1000 - self.threshold = 10 ** (threshold / 20.0) - self.hop_size = round(sr * hop_size / 1000) - self.win_size = min(round(min_interval), 4 * self.hop_size) - self.min_length = round(sr * min_length / 1000 / self.hop_size) - self.min_interval = round(min_interval / self.hop_size) - self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) - - def _apply_slice(self, waveform, begin, end): - begin = begin * self.hop_size - if len(waveform.shape) > 1: - end = min(waveform.shape[1], end * self.hop_size) - return waveform[:, begin:end], begin, end - else: - end = min(waveform.shape[0], end * self.hop_size) - return waveform[begin:end], begin, end - - # @timeit - def slice(self, waveform, return_chunks_positions=False): - if len(waveform.shape) > 1: - # (#channle, wave_len) -> (wave_len) - samples = waveform.mean(axis=0) - else: - samples = waveform - if samples.shape[0] <= self.min_length: - return [waveform] - rms_list = get_rms( - y=samples, frame_length=self.win_size, hop_length=self.hop_size - ).squeeze(0) - sil_tags = [] - silence_start = None - clip_start = 0 - for i, rms in enumerate(rms_list): - # Keep looping while frame is silent. - if rms < self.threshold: - # Record start of silent frames. - if silence_start is None: - silence_start = i - continue - # Keep looping while frame is not silent and silence start has not been recorded. - if silence_start is None: - continue - # Clear recorded silence start if interval is not enough or clip is too short - is_leading_silence = silence_start == 0 and i > self.max_sil_kept - need_slice_middle = ( - i - silence_start >= self.min_interval - and i - clip_start >= self.min_length - ) - if not is_leading_silence and not need_slice_middle: - silence_start = None - continue - # Need slicing. Record the range of silent frames to be removed. - if i - silence_start <= self.max_sil_kept: - pos = rms_list[silence_start : i + 1].argmin() + silence_start - if silence_start == 0: - sil_tags.append((0, pos)) - else: - sil_tags.append((pos, pos)) - clip_start = pos - elif i - silence_start <= self.max_sil_kept * 2: - pos = rms_list[ - i - self.max_sil_kept : silence_start + self.max_sil_kept + 1 - ].argmin() - pos += i - self.max_sil_kept - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - clip_start = pos_r - else: - sil_tags.append((min(pos_l, pos), max(pos_r, pos))) - clip_start = max(pos_r, pos) - else: - pos_l = ( - rms_list[ - silence_start : silence_start + self.max_sil_kept + 1 - ].argmin() - + silence_start - ) - pos_r = ( - rms_list[i - self.max_sil_kept : i + 1].argmin() - + i - - self.max_sil_kept - ) - if silence_start == 0: - sil_tags.append((0, pos_r)) - else: - sil_tags.append((pos_l, pos_r)) - clip_start = pos_r - silence_start = None - # Deal with trailing silence. - total_frames = rms_list.shape[0] - if ( - silence_start is not None - and total_frames - silence_start >= self.min_interval - ): - silence_end = min(total_frames, silence_start + self.max_sil_kept) - pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start - sil_tags.append((pos, total_frames + 1)) - # Apply and return slices. - if len(sil_tags) == 0: - return [waveform] - else: - chunks = [] - chunks_pos_of_waveform = [] - - if sil_tags[0][0] > 0: - chunk, begin, end = self._apply_slice(waveform, 0, sil_tags[0][0]) - chunks.append(chunk) - chunks_pos_of_waveform.append((begin, end)) - - for i in range(len(sil_tags) - 1): - chunk, begin, end = self._apply_slice( - waveform, sil_tags[i][1], sil_tags[i + 1][0] - ) - chunks.append(chunk) - chunks_pos_of_waveform.append((begin, end)) - - if sil_tags[-1][1] < total_frames: - chunk, begin, end = self._apply_slice( - waveform, sil_tags[-1][1], total_frames - ) - chunks.append(chunk) - chunks_pos_of_waveform.append((begin, end)) - - return ( - chunks - if not return_chunks_positions - else ( - chunks, - chunks_pos_of_waveform, - ) - ) - - -def split_utterances_from_audio( - wav_file, - output_dir, - max_duration_of_utterance=10.0, - min_interval=300, - db_threshold=-40, -): - """ - Split a long audio into utterances accoring to the silence (VAD). - - max_duration_of_utterance (second): - The maximum duration of every utterance (seconds) - min_interval (millisecond): - The smaller min_interval is, the more sliced audio clips this script is likely to generate. - """ - print("File:", wav_file.split("/")[-1]) - waveform, fs = torchaudio.load(wav_file) - - slicer = Slicer(sr=fs, min_interval=min_interval, threshold=db_threshold) - chunks, positions = slicer.slice(waveform, return_chunks_positions=True) - - durations = [(end - begin) / fs for begin, end in positions] - print( - "Slicer's min silence part is {}ms, min and max duration of sliced utterances is {}s and {}s".format( - min_interval, min(durations), max(durations) - ) - ) - - res_chunks, res_positions = [], [] - for i, chunk in enumerate(chunks): - if len(chunk.shape) == 1: - chunk = chunk[None, :] - - begin, end = positions[i] - assert end - begin == chunk.shape[-1] - - max_wav_len = max_duration_of_utterance * fs - if chunk.shape[-1] <= max_wav_len: - res_chunks.append(chunk) - res_positions.append(positions[i]) - else: - # TODO: to reserve overlapping and conduct fade-in, fade-out - - # Get segments number - number = 2 - while chunk.shape[-1] // number >= max_wav_len: - number += 1 - seg_len = chunk.shape[-1] // number - - # Split - for num in range(number): - s = seg_len * num - t = min(s + seg_len, chunk.shape[-1]) - - seg_begin = begin + s - seg_end = begin + t - - res_chunks.append(chunk[:, s:t]) - res_positions.append((seg_begin, seg_end)) - - # Save utterances - os.makedirs(output_dir, exist_ok=True) - res = {"fs": int(fs)} - for i, chunk in enumerate(res_chunks): - filename = "{:04d}.wav".format(i) - res[filename] = [int(p) for p in res_positions[i]] - save_audio(os.path.join(output_dir, filename), chunk, fs) - - # Save positions - with open(os.path.join(output_dir, "positions.json"), "w") as f: - json.dump(res, f, indent=4, ensure_ascii=False) - return res - - -def is_silence( - wavform, - fs, - threshold=-40.0, - min_interval=300, - hop_size=10, - min_length=5000, -): - """ - Detect whether the given wavform is a silence - - wavform: (T, ) - """ - threshold = 10 ** (threshold / 20.0) - - hop_size = round(fs * hop_size / 1000) - win_size = min(round(min_interval), 4 * hop_size) - min_length = round(fs * min_length / 1000 / hop_size) - - if wavform.shape[0] <= min_length: - return True - - # (#Frame,) - rms_array = get_rms(y=wavform, frame_length=win_size, hop_length=hop_size).squeeze( - 0 - ) - return (rms_array < threshold).all() - - -def split_audio( - wav_file, target_sr, output_dir, max_duration_of_segment=10.0, overlap_duration=1.0 -): - """ - Split a long audio into segments. - - target_sr: - The target sampling rate to save the segments. - max_duration_of_utterance (second): - The maximum duration of every utterance (second) - overlap_duraion: - Each segment has "overlap duration" (second) overlap with its previous and next segment - """ - # (#channel, T) -> (T,) - waveform, fs = torchaudio.load(wav_file) - waveform = torchaudio.functional.resample( - waveform, orig_freq=fs, new_freq=target_sr - ) - waveform = torch.mean(waveform, dim=0) - - # waveform, _ = load_audio_torch(wav_file, target_sr) - assert len(waveform.shape) == 1 - - assert overlap_duration < max_duration_of_segment - length = int(max_duration_of_segment * target_sr) - stride = int((max_duration_of_segment - overlap_duration) * target_sr) - chunks = [] - for i in range(0, len(waveform), stride): - # (length,) - chunks.append(waveform[i : i + length]) - if i + length >= len(waveform): - break - - # Save segments - os.makedirs(output_dir, exist_ok=True) - results = [] - for i, chunk in enumerate(chunks): - uid = "{:04d}".format(i) - filename = os.path.join(output_dir, "{}.wav".format(uid)) - results.append( - {"Uid": uid, "Path": filename, "Duration": len(chunk) / target_sr} - ) - save_audio( - filename, - chunk, - target_sr, - turn_up=not is_silence(chunk, target_sr), - add_silence=False, - ) - - return results - - -def merge_segments_torchaudio(wav_files, fs, output_path, overlap_duration=1.0): - """Merge the given wav_files (may have overlaps) into a long audio - - fs: - The sampling rate of the wav files. - output_path: - The output path to save the merged audio. - overlap_duration (float, optional): - Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0. - """ - - waveforms = [] - for file in wav_files: - # (T,) - waveform, _ = load_audio_torch(file, fs) - waveforms.append(waveform) - - if len(waveforms) == 1: - save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False) - return - - overlap_len = int(overlap_duration * fs) - fade_out = torchaudio.transforms.Fade(fade_out_len=overlap_len) - fade_in = torchaudio.transforms.Fade(fade_in_len=overlap_len) - fade_in_and_out = torchaudio.transforms.Fade(fade_out_len=overlap_len) - - segments_lens = [len(wav) for wav in waveforms] - merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1) - merged_waveform = torch.zeros(merged_waveform_len) - - start = 0 - for index, wav in enumerate( - tqdm(waveforms, desc="Merge for {}".format(output_path)) - ): - wav_len = len(wav) - - if index == 0: - wav = fade_out(wav) - elif index == len(waveforms) - 1: - wav = fade_in(wav) - else: - wav = fade_in_and_out(wav) - - merged_waveform[start : start + wav_len] = wav - start += wav_len - overlap_len - - save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True) - - -def merge_segments_encodec(wav_files, fs, output_path, overlap_duration=1.0): - """Merge the given wav_files (may have overlaps) into a long audio - - fs: - The sampling rate of the wav files. - output_path: - The output path to save the merged audio. - overlap_duration (float, optional): - Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0. - """ - - waveforms = [] - for file in wav_files: - # (T,) - waveform, _ = load_audio_torch(file, fs) - waveforms.append(waveform) - - if len(waveforms) == 1: - save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False) - return - - device = waveforms[0].device - dtype = waveforms[0].dtype - shape = waveforms[0].shape[:-1] - - overlap_len = int(overlap_duration * fs) - segments_lens = [len(wav) for wav in waveforms] - merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1) - - sum_weight = torch.zeros(merged_waveform_len, device=device, dtype=dtype) - out = torch.zeros(*shape, merged_waveform_len, device=device, dtype=dtype) - offset = 0 - - for frame in waveforms: - frame_length = frame.size(-1) - t = torch.linspace(0, 1, frame_length + 2, device=device, dtype=torch.float32)[ - 1:-1 - ] - weight = 0.5 - (t - 0.5).abs() - weighted_frame = frame * weight - - cur = out[..., offset : offset + frame_length] - cur += weighted_frame[..., : cur.size(-1)] - out[..., offset : offset + frame_length] = cur - - cur = sum_weight[offset : offset + frame_length] - cur += weight[..., : cur.size(-1)] - sum_weight[offset : offset + frame_length] = cur - - offset += frame_length - overlap_len - - assert sum_weight.min() > 0 - merged_waveform = out / sum_weight - save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True) diff --git a/utils/data_utils.py b/utils/data_utils.py deleted file mode 100644 index 7976d050f01990c8a98a37d48be67bc68695a6c3..0000000000000000000000000000000000000000 --- a/utils/data_utils.py +++ /dev/null @@ -1,575 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os - -import numpy as np -from scipy.interpolate import interp1d -from tqdm import tqdm -from sklearn.preprocessing import StandardScaler - - -def load_content_feature_path(meta_data, processed_dir, feat_dir): - utt2feat_path = {} - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - feat_path = os.path.join( - processed_dir, utt_info["Dataset"], feat_dir, f'{utt_info["Uid"]}.npy' - ) - utt2feat_path[utt] = feat_path - - return utt2feat_path - - -def load_source_content_feature_path(meta_data, feat_dir): - utt2feat_path = {} - for utt in meta_data: - feat_path = os.path.join(feat_dir, f"{utt}.npy") - utt2feat_path[utt] = feat_path - - return utt2feat_path - - -def get_spk_map(spk2id_path, utt2spk_path): - utt2spk = {} - with open(spk2id_path, "r") as spk2id_file: - spk2id = json.load(spk2id_file) - with open(utt2spk_path, encoding="utf-8") as f: - for line in f.readlines(): - utt, spk = line.strip().split("\t") - utt2spk[utt] = spk - return spk2id, utt2spk - - -def get_target_f0_median(f0_dir): - total_f0 = [] - for utt in os.listdir(f0_dir): - if not utt.endswith(".npy"): - continue - f0_feat_path = os.path.join(f0_dir, utt) - f0 = np.load(f0_feat_path) - total_f0 += f0.tolist() - - total_f0 = np.array(total_f0) - voiced_position = np.where(total_f0 != 0) - return np.median(total_f0[voiced_position]) - - -def get_conversion_f0_factor(source_f0, target_median, source_median=None): - """Align the median between source f0 and target f0 - - Note: Here we use multiplication, whose factor is target_median/source_median - - Reference: Frequency and pitch interval - http://blog.ccyg.studio/article/be12c2ee-d47c-4098-9782-ca76da3035e4/ - """ - if source_median is None: - voiced_position = np.where(source_f0 != 0) - source_median = np.median(source_f0[voiced_position]) - factor = target_median / source_median - return source_median, factor - - -def transpose_key(frame_pitch, trans_key): - # Transpose by user's argument - print("Transpose key = {} ...\n".format(trans_key)) - - transed_pitch = frame_pitch * 2 ** (trans_key / 12) - return transed_pitch - - -def pitch_shift_to_target(frame_pitch, target_pitch_median, source_pitch_median=None): - # Loading F0 Base (median) and shift - source_pitch_median, factor = get_conversion_f0_factor( - frame_pitch, target_pitch_median, source_pitch_median - ) - print( - "Auto transposing: source f0 median = {:.1f}, target f0 median = {:.1f}, factor = {:.2f}".format( - source_pitch_median, target_pitch_median, factor - ) - ) - transed_pitch = frame_pitch * factor - return transed_pitch - - -def load_frame_pitch( - meta_data, - processed_dir, - pitch_dir, - use_log_scale=False, - return_norm=False, - interoperate=False, - utt2spk=None, -): - utt2pitch = {} - utt2uv = {} - if utt2spk is None: - pitch_scaler = StandardScaler() - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - pitch_path = os.path.join( - processed_dir, utt_info["Dataset"], pitch_dir, f'{utt_info["Uid"]}.npy' - ) - pitch = np.load(pitch_path) - assert len(pitch) > 0 - uv = pitch != 0 - utt2uv[utt] = uv - if use_log_scale: - nonzero_idxes = np.where(pitch != 0)[0] - pitch[nonzero_idxes] = np.log(pitch[nonzero_idxes]) - utt2pitch[utt] = pitch - pitch_scaler.partial_fit(pitch.reshape(-1, 1)) - - mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] - if return_norm: - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - pitch = utt2pitch[utt] - normalized_pitch = (pitch - mean) / std - utt2pitch[utt] = normalized_pitch - pitch_statistic = {"mean": mean, "std": std} - else: - spk2utt = {} - pitch_statistic = [] - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - if not utt2spk[utt] in spk2utt: - spk2utt[utt2spk[utt]] = [] - spk2utt[utt2spk[utt]].append(utt) - - for spk in spk2utt: - pitch_scaler = StandardScaler() - for utt in spk2utt[spk]: - dataset = utt.split("_")[0] - uid = "_".join(utt.split("_")[1:]) - pitch_path = os.path.join( - processed_dir, dataset, pitch_dir, f"{uid}.npy" - ) - pitch = np.load(pitch_path) - assert len(pitch) > 0 - uv = pitch != 0 - utt2uv[utt] = uv - if use_log_scale: - nonzero_idxes = np.where(pitch != 0)[0] - pitch[nonzero_idxes] = np.log(pitch[nonzero_idxes]) - utt2pitch[utt] = pitch - pitch_scaler.partial_fit(pitch.reshape(-1, 1)) - - mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] - if return_norm: - for utt in spk2utt[spk]: - pitch = utt2pitch[utt] - normalized_pitch = (pitch - mean) / std - utt2pitch[utt] = normalized_pitch - pitch_statistic.append({"spk": spk, "mean": mean, "std": std}) - - return utt2pitch, utt2uv, pitch_statistic - - -# discard -def load_phone_pitch( - meta_data, - processed_dir, - pitch_dir, - utt2dur, - use_log_scale=False, - return_norm=False, - interoperate=True, - utt2spk=None, -): - print("Load Phone Pitch") - utt2pitch = {} - utt2uv = {} - if utt2spk is None: - pitch_scaler = StandardScaler() - for utt_info in tqdm(meta_data): - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - pitch_path = os.path.join( - processed_dir, utt_info["Dataset"], pitch_dir, f'{utt_info["Uid"]}.npy' - ) - frame_pitch = np.load(pitch_path) - assert len(frame_pitch) > 0 - uv = frame_pitch != 0 - utt2uv[utt] = uv - phone_pitch = phone_average_pitch(frame_pitch, utt2dur[utt], interoperate) - if use_log_scale: - nonzero_idxes = np.where(phone_pitch != 0)[0] - phone_pitch[nonzero_idxes] = np.log(phone_pitch[nonzero_idxes]) - utt2pitch[utt] = phone_pitch - pitch_scaler.partial_fit(remove_outlier(phone_pitch).reshape(-1, 1)) - - mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] - max_value = np.finfo(np.float64).min - min_value = np.finfo(np.float64).max - if return_norm: - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - pitch = utt2pitch[utt] - normalized_pitch = (pitch - mean) / std - max_value = max(max_value, max(normalized_pitch)) - min_value = min(min_value, min(normalized_pitch)) - utt2pitch[utt] = normalized_pitch - phone_normalized_pitch_path = os.path.join( - processed_dir, - utt_info["Dataset"], - "phone_level_" + pitch_dir, - f'{utt_info["Uid"]}.npy', - ) - pitch_statistic = { - "mean": mean, - "std": std, - "min_value": min_value, - "max_value": max_value, - } - else: - spk2utt = {} - pitch_statistic = [] - for utt_info in tqdm(meta_data): - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - if not utt2spk[utt] in spk2utt: - spk2utt[utt2spk[utt]] = [] - spk2utt[utt2spk[utt]].append(utt) - - for spk in spk2utt: - pitch_scaler = StandardScaler() - for utt in spk2utt[spk]: - dataset = utt.split("_")[0] - uid = "_".join(utt.split("_")[1:]) - pitch_path = os.path.join( - processed_dir, dataset, pitch_dir, f"{uid}.npy" - ) - frame_pitch = np.load(pitch_path) - assert len(frame_pitch) > 0 - uv = frame_pitch != 0 - utt2uv[utt] = uv - phone_pitch = phone_average_pitch( - frame_pitch, utt2dur[utt], interoperate - ) - if use_log_scale: - nonzero_idxes = np.where(phone_pitch != 0)[0] - phone_pitch[nonzero_idxes] = np.log(phone_pitch[nonzero_idxes]) - utt2pitch[utt] = phone_pitch - pitch_scaler.partial_fit(remove_outlier(phone_pitch).reshape(-1, 1)) - - mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0] - max_value = np.finfo(np.float64).min - min_value = np.finfo(np.float64).max - - if return_norm: - for utt in spk2utt[spk]: - pitch = utt2pitch[utt] - normalized_pitch = (pitch - mean) / std - max_value = max(max_value, max(normalized_pitch)) - min_value = min(min_value, min(normalized_pitch)) - utt2pitch[utt] = normalized_pitch - pitch_statistic.append( - { - "spk": spk, - "mean": mean, - "std": std, - "min_value": min_value, - "max_value": max_value, - } - ) - - return utt2pitch, utt2uv, pitch_statistic - - -def phone_average_pitch(pitch, dur, interoperate=False): - pos = 0 - - if interoperate: - nonzero_ids = np.where(pitch != 0)[0] - interp_fn = interp1d( - nonzero_ids, - pitch[nonzero_ids], - fill_value=(pitch[nonzero_ids[0]], pitch[nonzero_ids[-1]]), - bounds_error=False, - ) - pitch = interp_fn(np.arange(0, len(pitch))) - phone_pitch = np.zeros(len(dur)) - - for i, d in enumerate(dur): - d = int(d) - if d > 0 and pos < len(pitch): - phone_pitch[i] = np.mean(pitch[pos : pos + d]) - else: - phone_pitch[i] = 0 - pos += d - return phone_pitch - - -def load_energy( - meta_data, - processed_dir, - energy_dir, - use_log_scale=False, - return_norm=False, - utt2spk=None, -): - utt2energy = {} - if utt2spk is None: - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - energy_path = os.path.join( - processed_dir, utt_info["Dataset"], energy_dir, f'{utt_info["Uid"]}.npy' - ) - if not os.path.exists(energy_path): - continue - energy = np.load(energy_path) - assert len(energy) > 0 - - if use_log_scale: - nonzero_idxes = np.where(energy != 0)[0] - energy[nonzero_idxes] = np.log(energy[nonzero_idxes]) - utt2energy[utt] = energy - - if return_norm: - with open( - os.path.join( - processed_dir, utt_info["Dataset"], energy_dir, "statistics.json" - ) - ) as f: - stats = json.load(f) - mean, std = ( - stats[utt_info["Dataset"] + "_" + utt_info["Singer"]][ - "voiced_positions" - ]["mean"], - stats["LJSpeech_LJSpeech"]["voiced_positions"]["std"], - ) - for utt in utt2energy.keys(): - energy = utt2energy[utt] - normalized_energy = (energy - mean) / std - utt2energy[utt] = normalized_energy - - energy_statistic = {"mean": mean, "std": std} - else: - spk2utt = {} - energy_statistic = [] - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - if not utt2spk[utt] in spk2utt: - spk2utt[utt2spk[utt]] = [] - spk2utt[utt2spk[utt]].append(utt) - - for spk in spk2utt: - energy_scaler = StandardScaler() - for utt in spk2utt[spk]: - dataset = utt.split("_")[0] - uid = "_".join(utt.split("_")[1:]) - energy_path = os.path.join( - processed_dir, dataset, energy_dir, f"{uid}.npy" - ) - if not os.path.exists(energy_path): - continue - frame_energy = np.load(energy_path) - assert len(frame_energy) > 0 - - if use_log_scale: - nonzero_idxes = np.where(frame_energy != 0)[0] - frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes]) - utt2energy[utt] = frame_energy - energy_scaler.partial_fit(frame_energy.reshape(-1, 1)) - - mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0] - if return_norm: - for utt in spk2utt[spk]: - energy = utt2energy[utt] - normalized_energy = (energy - mean) / std - utt2energy[utt] = normalized_energy - energy_statistic.append({"spk": spk, "mean": mean, "std": std}) - - return utt2energy, energy_statistic - - -def load_frame_energy( - meta_data, - processed_dir, - energy_dir, - use_log_scale=False, - return_norm=False, - interoperate=False, - utt2spk=None, -): - utt2energy = {} - if utt2spk is None: - energy_scaler = StandardScaler() - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - energy_path = os.path.join( - processed_dir, utt_info["Dataset"], energy_dir, f'{utt_info["Uid"]}.npy' - ) - frame_energy = np.load(energy_path) - assert len(frame_energy) > 0 - - if use_log_scale: - nonzero_idxes = np.where(frame_energy != 0)[0] - frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes]) - utt2energy[utt] = frame_energy - energy_scaler.partial_fit(frame_energy.reshape(-1, 1)) - - mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0] - if return_norm: - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - energy = utt2energy[utt] - normalized_energy = (energy - mean) / std - utt2energy[utt] = normalized_energy - energy_statistic = {"mean": mean, "std": std} - - else: - spk2utt = {} - energy_statistic = [] - for utt_info in meta_data: - utt = utt_info["Dataset"] + "_" + utt_info["Uid"] - if not utt2spk[utt] in spk2utt: - spk2utt[utt2spk[utt]] = [] - spk2utt[utt2spk[utt]].append(utt) - - for spk in spk2utt: - energy_scaler = StandardScaler() - for utt in spk2utt[spk]: - dataset = utt.split("_")[0] - uid = "_".join(utt.split("_")[1:]) - energy_path = os.path.join( - processed_dir, dataset, energy_dir, f"{uid}.npy" - ) - frame_energy = np.load(energy_path) - assert len(frame_energy) > 0 - - if use_log_scale: - nonzero_idxes = np.where(frame_energy != 0)[0] - frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes]) - utt2energy[utt] = frame_energy - energy_scaler.partial_fit(frame_energy.reshape(-1, 1)) - - mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0] - if return_norm: - for utt in spk2utt[spk]: - energy = utt2energy[utt] - normalized_energy = (energy - mean) / std - utt2energy[utt] = normalized_energy - energy_statistic.append({"spk": spk, "mean": mean, "std": std}) - - return utt2energy, energy_statistic - - -def align_length(feature, target_len, pad_value=0.0): - feature_len = feature.shape[-1] - dim = len(feature.shape) - # align 1-D data - if dim == 2: - if target_len > feature_len: - feature = np.pad( - feature, - ((0, 0), (0, target_len - feature_len)), - constant_values=pad_value, - ) - else: - feature = feature[:, :target_len] - # align 2-D data - elif dim == 1: - if target_len > feature_len: - feature = np.pad( - feature, (0, target_len - feature_len), constant_values=pad_value - ) - else: - feature = feature[:target_len] - else: - raise NotImplementedError - return feature - - -def align_whisper_feauture_length( - feature, target_len, fast_mapping=True, source_hop=320, target_hop=256 -): - factor = np.gcd(source_hop, target_hop) - source_hop //= factor - target_hop //= factor - # print( - # "Mapping source's {} frames => target's {} frames".format( - # target_hop, source_hop - # ) - # ) - - max_source_len = 1500 - target_len = min(target_len, max_source_len * source_hop // target_hop) - - width = feature.shape[-1] - - if fast_mapping: - source_len = target_len * target_hop // source_hop + 1 - feature = feature[:source_len] - - else: - source_len = max_source_len - - # const ~= target_len * target_hop - const = source_len * source_hop // target_hop * target_hop - - # (source_len * source_hop, dim) - up_sampling_feats = np.repeat(feature, source_hop, axis=0) - # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) - down_sampling_feats = np.average( - up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 - ) - assert len(down_sampling_feats) >= target_len - - # (target_len, dim) - feat = down_sampling_feats[:target_len] - - return feat - - -def align_content_feature_length(feature, target_len, source_hop=320, target_hop=256): - factor = np.gcd(source_hop, target_hop) - source_hop //= factor - target_hop //= factor - # print( - # "Mapping source's {} frames => target's {} frames".format( - # target_hop, source_hop - # ) - # ) - - # (source_len, 256) - source_len, width = feature.shape - - # const ~= target_len * target_hop - const = source_len * source_hop // target_hop * target_hop - - # (source_len * source_hop, dim) - up_sampling_feats = np.repeat(feature, source_hop, axis=0) - # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) - down_sampling_feats = np.average( - up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 - ) - - err = abs(target_len - len(down_sampling_feats)) - if err > 4: ## why 4 not 3? - print("target_len:", target_len) - print("raw feature:", feature.shape) - print("up_sampling:", up_sampling_feats.shape) - print("down_sampling_feats:", down_sampling_feats.shape) - exit() - if len(down_sampling_feats) < target_len: - # (1, dim) -> (err, dim) - end = down_sampling_feats[-1][None, :].repeat(err, axis=0) - down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) - - # (target_len, dim) - feat = down_sampling_feats[:target_len] - - return feat - - -def remove_outlier(values): - values = np.array(values) - p25 = np.percentile(values, 25) - p75 = np.percentile(values, 75) - lower = p25 - 1.5 * (p75 - p25) - upper = p75 + 1.5 * (p75 - p25) - normal_indices = np.logical_and(values > lower, values < upper) - return values[normal_indices] diff --git a/utils/distribution.py b/utils/distribution.py deleted file mode 100644 index de3000e99194f7e848712d8b4cb77c988f098fd2..0000000000000000000000000000000000000000 --- a/utils/distribution.py +++ /dev/null @@ -1,270 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -import torch.nn.functional as F - -from torch.distributions import Normal - - -def log_sum_exp(x): - """numerically stable log_sum_exp implementation that prevents overflow""" - # TF ordering - axis = len(x.size()) - 1 - m, _ = torch.max(x, dim=axis) - m2, _ = torch.max(x, dim=axis, keepdim=True) - return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis)) - - -def discretized_mix_logistic_loss( - y_hat, y, num_classes=256, log_scale_min=-7.0, reduce=True -): - """Discretized mixture of logistic distributions loss - - Note that it is assumed that input is scaled to [-1, 1]. - - Args: - y_hat (Tensor): Predicted output (B x C x T) - y (Tensor): Target (B x T x 1). - num_classes (int): Number of classes - log_scale_min (float): Log scale minimum value - reduce (bool): If True, the losses are averaged or summed for each - minibatch. - - Returns - Tensor: loss - """ - assert y_hat.dim() == 3 - assert y_hat.size(1) % 3 == 0 - nr_mix = y_hat.size(1) // 3 - - # (B x T x C) - y_hat = y_hat.transpose(1, 2) - - # unpack parameters. (B, T, num_mixtures) x 3 - logit_probs = y_hat[:, :, :nr_mix] - means = y_hat[:, :, nr_mix : 2 * nr_mix] - log_scales = torch.clamp(y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min) - - # B x T x 1 -> B x T x num_mixtures - y = y.expand_as(means) - - centered_y = y - means - inv_stdv = torch.exp(-log_scales) - plus_in = inv_stdv * (centered_y + 1.0 / (num_classes - 1)) - cdf_plus = torch.sigmoid(plus_in) - min_in = inv_stdv * (centered_y - 1.0 / (num_classes - 1)) - cdf_min = torch.sigmoid(min_in) - - # log probability for edge case of 0 (before scaling) - # equivalent: torch.log(torch.sigmoid(plus_in)) - log_cdf_plus = plus_in - F.softplus(plus_in) - - # log probability for edge case of 255 (before scaling) - # equivalent: (1 - torch.sigmoid(min_in)).log() - log_one_minus_cdf_min = -F.softplus(min_in) - - # probability for all other cases - cdf_delta = cdf_plus - cdf_min - - mid_in = inv_stdv * centered_y - # log probability in the center of the bin, to be used in extreme cases - # (not actually used in our code) - log_pdf_mid = mid_in - log_scales - 2.0 * F.softplus(mid_in) - - # tf equivalent - """ - log_probs = tf.where(x < -0.999, log_cdf_plus, - tf.where(x > 0.999, log_one_minus_cdf_min, - tf.where(cdf_delta > 1e-5, - tf.log(tf.maximum(cdf_delta, 1e-12)), - log_pdf_mid - np.log(127.5)))) - """ - # TODO: cdf_delta <= 1e-5 actually can happen. How can we choose the value - # for num_classes=65536 case? 1e-7? not sure.. - inner_inner_cond = (cdf_delta > 1e-5).float() - - inner_inner_out = inner_inner_cond * torch.log( - torch.clamp(cdf_delta, min=1e-12) - ) + (1.0 - inner_inner_cond) * (log_pdf_mid - np.log((num_classes - 1) / 2)) - inner_cond = (y > 0.999).float() - inner_out = ( - inner_cond * log_one_minus_cdf_min + (1.0 - inner_cond) * inner_inner_out - ) - cond = (y < -0.999).float() - log_probs = cond * log_cdf_plus + (1.0 - cond) * inner_out - - log_probs = log_probs + F.log_softmax(logit_probs, -1) - - if reduce: - return -torch.sum(log_sum_exp(log_probs)) - else: - return -log_sum_exp(log_probs).unsqueeze(-1) - - -def to_one_hot(tensor, n, fill_with=1.0): - # we perform one hot encore with respect to the last axis - one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_() - if tensor.is_cuda: - one_hot = one_hot.cuda() - one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with) - return one_hot - - -def sample_from_discretized_mix_logistic(y, log_scale_min=-7.0, clamp_log_scale=False): - """ - Sample from discretized mixture of logistic distributions - - Args: - y (Tensor): B x C x T - log_scale_min (float): Log scale minimum value - - Returns: - Tensor: sample in range of [-1, 1]. - """ - assert y.size(1) % 3 == 0 - nr_mix = y.size(1) // 3 - - # B x T x C - y = y.transpose(1, 2) - logit_probs = y[:, :, :nr_mix] - - # sample mixture indicator from softmax - temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5) - temp = logit_probs.data - torch.log(-torch.log(temp)) - _, argmax = temp.max(dim=-1) - - # (B, T) -> (B, T, nr_mix) - one_hot = to_one_hot(argmax, nr_mix) - # select logistic parameters - means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1) - log_scales = torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1) - if clamp_log_scale: - log_scales = torch.clamp(log_scales, min=log_scale_min) - # sample from logistic & clip to interval - # we don't actually round to the nearest 8bit value when sampling - u = means.data.new(means.size()).uniform_(1e-5, 1.0 - 1e-5) - x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1.0 - u)) - - x = torch.clamp(torch.clamp(x, min=-1.0), max=1.0) - - return x - - -# we can easily define discretized version of the gaussian loss, however, -# use continuous version as same as the https://clarinet-demo.github.io/ -def mix_gaussian_loss(y_hat, y, log_scale_min=-7.0, reduce=True): - """Mixture of continuous gaussian distributions loss - - Note that it is assumed that input is scaled to [-1, 1]. - - Args: - y_hat (Tensor): Predicted output (B x C x T) - y (Tensor): Target (B x T x 1). - log_scale_min (float): Log scale minimum value - reduce (bool): If True, the losses are averaged or summed for each - minibatch. - Returns - Tensor: loss - """ - assert y_hat.dim() == 3 - C = y_hat.size(1) - if C == 2: - nr_mix = 1 - else: - assert y_hat.size(1) % 3 == 0 - nr_mix = y_hat.size(1) // 3 - - # (B x T x C) - y_hat = y_hat.transpose(1, 2) - - # unpack parameters. - if C == 2: - # special case for C == 2, just for compatibility - logit_probs = None - means = y_hat[:, :, 0:1] - log_scales = torch.clamp(y_hat[:, :, 1:2], min=log_scale_min) - else: - # (B, T, num_mixtures) x 3 - logit_probs = y_hat[:, :, :nr_mix] - means = y_hat[:, :, nr_mix : 2 * nr_mix] - log_scales = torch.clamp( - y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min - ) - - # B x T x 1 -> B x T x num_mixtures - y = y.expand_as(means) - - centered_y = y - means - dist = Normal(loc=0.0, scale=torch.exp(log_scales)) - # do we need to add a trick to avoid log(0)? - log_probs = dist.log_prob(centered_y) - - if nr_mix > 1: - log_probs = log_probs + F.log_softmax(logit_probs, -1) - - if reduce: - if nr_mix == 1: - return -torch.sum(log_probs) - else: - return -torch.sum(log_sum_exp(log_probs)) - else: - if nr_mix == 1: - return -log_probs - else: - return -log_sum_exp(log_probs).unsqueeze(-1) - - -def sample_from_mix_gaussian(y, log_scale_min=-7.0): - """ - Sample from (discretized) mixture of gaussian distributions - Args: - y (Tensor): B x C x T - log_scale_min (float): Log scale minimum value - Returns: - Tensor: sample in range of [-1, 1]. - """ - C = y.size(1) - if C == 2: - nr_mix = 1 - else: - assert y.size(1) % 3 == 0 - nr_mix = y.size(1) // 3 - - # B x T x C - y = y.transpose(1, 2) - - if C == 2: - logit_probs = None - else: - logit_probs = y[:, :, :nr_mix] - - if nr_mix > 1: - # sample mixture indicator from softmax - temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5) - temp = logit_probs.data - torch.log(-torch.log(temp)) - _, argmax = temp.max(dim=-1) - - # (B, T) -> (B, T, nr_mix) - one_hot = to_one_hot(argmax, nr_mix) - - # Select means and log scales - means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1) - log_scales = torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1) - else: - if C == 2: - means, log_scales = y[:, :, 0], y[:, :, 1] - elif C == 3: - means, log_scales = y[:, :, 1], y[:, :, 2] - else: - assert False, "shouldn't happen" - - scales = torch.exp(log_scales) - dist = Normal(loc=means, scale=scales) - x = dist.sample() - - x = torch.clamp(x, min=-1.0, max=1.0) - return x diff --git a/utils/dsp.py b/utils/dsp.py deleted file mode 100644 index 18f9466f6b12e5539ce221f86030f5114ccdb503..0000000000000000000000000000000000000000 --- a/utils/dsp.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch - -# ZERO = 1e-12 - - -def gaussian_normalize_mel_channel(mel, mu, sigma): - """ - Shift to Standorm Normal Distribution - - Args: - mel: (n_mels, frame_len) - mu: (n_mels,), mean value - sigma: (n_mels,), sd value - Return: - Tensor like mel - """ - mu = np.expand_dims(mu, -1) - sigma = np.expand_dims(sigma, -1) - return (mel - mu) / sigma - - -def de_gaussian_normalize_mel_channel(mel, mu, sigma): - """ - - Args: - mel: (n_mels, frame_len) - mu: (n_mels,), mean value - sigma: (n_mels,), sd value - Return: - Tensor like mel - """ - mu = np.expand_dims(mu, -1) - sigma = np.expand_dims(sigma, -1) - return sigma * mel + mu - - -def decompress(audio_compressed, bits): - mu = 2**bits - 1 - audio = np.sign(audio_compressed) / mu * ((1 + mu) ** np.abs(audio_compressed) - 1) - return audio - - -def compress(audio, bits): - mu = 2**bits - 1 - audio_compressed = np.sign(audio) * np.log(1 + mu * np.abs(audio)) / np.log(mu + 1) - return audio_compressed - - -def label_to_audio(quant, bits): - classes = 2**bits - audio = 2 * quant / (classes - 1.0) - 1.0 - return audio - - -def audio_to_label(audio, bits): - """Normalized audio data tensor to digit array - - Args: - audio (tensor): audio data - bits (int): data bits - - Returns: - array: digit array of audio data - """ - classes = 2**bits - # initialize an increasing array with values from -1 to 1 - bins = np.linspace(-1, 1, classes) - # change value in audio tensor to digits - quant = np.digitize(audio, bins) - 1 - return quant - - -def label_to_onehot(x, bits): - """Converts a class vector (integers) to binary class matrix. - Args: - x: class vector to be converted into a matrix - (integers from 0 to num_classes). - num_classes: total number of classes. - Returns: - A binary matrix representation of the input. The classes axis - is placed last. - """ - classes = 2**bits - - result = torch.zeros((x.shape[0], classes), dtype=torch.float32) - for i in range(x.shape[0]): - result[i, x[i]] = 1 - - output_shape = x.shape + (classes,) - output = torch.reshape(result, output_shape) - return output diff --git a/utils/duration.py b/utils/duration.py deleted file mode 100644 index c9544b40b88c68b4e1df33ab1c81a6196a43111e..0000000000000000000000000000000000000000 --- a/utils/duration.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import os -import tgt - - -def get_alignment(tier, cfg): - sample_rate = cfg["sample_rate"] - hop_size = cfg["hop_size"] - - sil_phones = ["sil", "sp", "spn"] - - phones = [] - durations = [] - start_time = 0 - end_time = 0 - end_idx = 0 - - for t in tier._objects: - s, e, p = t.start_time, t.end_time, t.text - - # Trim leading silences - if phones == []: - if p in sil_phones: - continue - else: - start_time = s - - if p not in sil_phones: - # For ordinary phones - phones.append(p) - end_time = e - end_idx = len(phones) - else: - # For silent phones - phones.append(p) - - durations.append( - int( - np.round(e * sample_rate / hop_size) - - np.round(s * sample_rate / hop_size) - ) - ) - - # Trim tailing silences - phones = phones[:end_idx] - durations = durations[:end_idx] - - return phones, durations, start_time, end_time - - -def get_duration(utt, wav, cfg): - speaker = utt["Singer"] - basename = utt["Uid"] - dataset = utt["Dataset"] - sample_rate = cfg["sample_rate"] - - # print(cfg.processed_dir, dataset, speaker, basename) - wav_path = os.path.join( - cfg.processed_dir, dataset, "raw_data", speaker, "{}.wav".format(basename) - ) - text_path = os.path.join( - cfg.processed_dir, dataset, "raw_data", speaker, "{}.lab".format(basename) - ) - tg_path = os.path.join( - cfg.processed_dir, dataset, "TextGrid", speaker, "{}.TextGrid".format(basename) - ) - - # Read raw text - with open(text_path, "r") as f: - raw_text = f.readline().strip("\n") - - # Get alignments - textgrid = tgt.io.read_textgrid(tg_path) - phone, duration, start, end = get_alignment( - textgrid.get_tier_by_name("phones"), cfg - ) - text = "{" + " ".join(phone) + "}" - if start >= end: - return None - - return duration, text, int(sample_rate * start), int(sample_rate * end) diff --git a/utils/f0.py b/utils/f0.py deleted file mode 100644 index 4a8fd414d108c0b9af1bf5ca50291087d9608c52..0000000000000000000000000000000000000000 --- a/utils/f0.py +++ /dev/null @@ -1,320 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import librosa -import numpy as np -import torch -import parselmouth -import torchcrepe -import pyworld as pw - - -def get_bin_index(f0, m, M, n_bins, use_log_scale): - """ - WARNING: to abandon! - - Args: - raw_f0: tensor whose shpae is (N, frame_len) - Returns: - index: tensor whose shape is same to f0 - """ - raw_f0 = f0.clone() - raw_m, raw_M = m, M - - if use_log_scale: - f0[torch.where(f0 == 0)] = 1 - f0 = torch.log(f0) - m, M = float(np.log(m)), float(np.log(M)) - - # Set normal index in [1, n_bins - 1] - width = (M + 1e-7 - m) / (n_bins - 1) - index = (f0 - m) // width + 1 - # Set unvoiced frames as 0, Therefore, the vocabulary is [0, n_bins- 1], whose size is n_bins - index[torch.where(f0 == 0)] = 0 - - # TODO: Boundary check (special: to judge whether 0 for unvoiced) - if torch.any(raw_f0 > raw_M): - print("F0 Warning: too high f0: {}".format(raw_f0[torch.where(raw_f0 > raw_M)])) - index[torch.where(raw_f0 > raw_M)] = n_bins - 1 - if torch.any(raw_f0 < raw_m): - print("F0 Warning: too low f0: {}".format(raw_f0[torch.where(f0 < m)])) - index[torch.where(f0 < m)] = 0 - - return torch.as_tensor(index, dtype=torch.long, device=f0.device) - - -def f0_to_coarse(f0, pitch_bin, pitch_min, pitch_max): - ## TODO: Figure out the detail of this function - - f0_mel_min = 1127 * np.log(1 + pitch_min / 700) - f0_mel_max = 1127 * np.log(1 + pitch_max / 700) - - is_torch = isinstance(f0, torch.Tensor) - f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (pitch_bin - 2) / ( - f0_mel_max - f0_mel_min - ) + 1 - - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > pitch_bin - 1] = pitch_bin - 1 - f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int32) - assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, ( - f0_coarse.max(), - f0_coarse.min(), - ) - return f0_coarse - - -def interpolate(f0): - """Interpolate the unvoiced part. Thus the f0 can be passed to a subtractive synthesizer. - Args: - f0: A numpy array of shape (seq_len,) - Returns: - f0: Interpolated f0 of shape (seq_len,) - uv: Unvoiced part of shape (seq_len,) - """ - uv = f0 == 0 - if len(f0[~uv]) > 0: - # interpolate the unvoiced f0 - f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) - uv = uv.astype("float") - uv = np.min(np.array([uv[:-2], uv[1:-1], uv[2:]]), axis=0) - uv = np.pad(uv, (1, 1)) - return f0, uv - - -def get_log_f0(f0): - f0[np.where(f0 == 0)] = 1 - log_f0 = np.log(f0) - return log_f0 - - -# ========== Methods ========== - - -def get_f0_features_using_pyin(audio, cfg): - """Using pyin to extract the f0 feature. - Args: - audio - fs - win_length - hop_length - f0_min - f0_max - Returns: - f0: numpy array of shape (frame_len,) - """ - f0, voiced_flag, voiced_probs = librosa.pyin( - y=audio, - fmin=cfg.f0_min, - fmax=cfg.f0_max, - sr=cfg.sample_rate, - win_length=cfg.win_size, - hop_length=cfg.hop_size, - ) - # Set nan to 0 - f0[voiced_flag == False] = 0 - return f0 - - -def get_f0_features_using_parselmouth(audio, cfg, speed=1): - """Using parselmouth to extract the f0 feature. - Args: - audio - mel_len - hop_length - fs - f0_min - f0_max - speed(default=1) - Returns: - f0: numpy array of shape (frame_len,) - pitch_coarse: numpy array of shape (frame_len,) - """ - hop_size = int(np.round(cfg.hop_size * speed)) - - # Calculate the time step for pitch extraction - time_step = hop_size / cfg.sample_rate * 1000 - - f0 = ( - parselmouth.Sound(audio, cfg.sample_rate) - .to_pitch_ac( - time_step=time_step / 1000, - voicing_threshold=0.6, - pitch_floor=cfg.f0_min, - pitch_ceiling=cfg.f0_max, - ) - .selected_array["frequency"] - ) - - # Pad the pitch to the mel_len - # pad_size = (int(len(audio) // hop_size) - len(f0) + 1) // 2 - # f0 = np.pad(f0, [[pad_size, mel_len - len(f0) - pad_size]], mode="constant") - - # Get the coarse part - pitch_coarse = f0_to_coarse(f0, cfg.pitch_bin, cfg.f0_min, cfg.f0_max) - return f0, pitch_coarse - - -def get_f0_features_using_dio(audio, cfg): - """Using dio to extract the f0 feature. - Args: - audio - mel_len - fs - hop_length - f0_min - f0_max - Returns: - f0: numpy array of shape (frame_len,) - """ - # Get the raw f0 - _f0, t = pw.dio( - audio.astype("double"), - cfg.sample_rate, - f0_floor=cfg.f0_min, - f0_ceil=cfg.f0_max, - channels_in_octave=2, - frame_period=(1000 * cfg.hop_size / cfg.sample_rate), - ) - # Get the f0 - f0 = pw.stonemask(audio.astype("double"), _f0, t, cfg.sample_rate) - return f0 - - -def get_f0_features_using_harvest(audio, mel_len, fs, hop_length, f0_min, f0_max): - """Using harvest to extract the f0 feature. - Args: - audio - mel_len - fs - hop_length - f0_min - f0_max - Returns: - f0: numpy array of shape (frame_len,) - """ - f0, _ = pw.harvest( - audio.astype("double"), - fs, - f0_floor=f0_min, - f0_ceil=f0_max, - frame_period=(1000 * hop_length / fs), - ) - f0 = f0.astype("float")[:mel_len] - return f0 - - -def get_f0_features_using_crepe_legacy( - audio, mel_len, fs, hop_length, hop_length_new, f0_min, f0_max, threshold=0.3 -): - """Using torchcrepe to extract the f0 feature. - Args: - audio - mel_len - fs - hop_length - hop_length_new - f0_min - f0_max - threshold(default=0.3) - Returns: - f0: numpy array of shape (frame_len,) - """ - # Currently, crepe only supports 16khz audio - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - audio_16k = librosa.resample(audio, orig_sr=fs, target_sr=16000) - audio_16k_torch = torch.FloatTensor(audio_16k).unsqueeze(0).to(device) - - # Get the raw pitch - f0, pd = torchcrepe.predict( - audio_16k_torch, - 16000, - hop_length_new, - f0_min, - f0_max, - pad=True, - model="full", - batch_size=1024, - device=device, - return_periodicity=True, - ) - - # Filter, de-silence, set up threshold for unvoiced part - pd = torchcrepe.filter.median(pd, 3) - pd = torchcrepe.threshold.Silence(-60.0)(pd, audio_16k_torch, 16000, hop_length_new) - f0 = torchcrepe.threshold.At(threshold)(f0, pd) - f0 = torchcrepe.filter.mean(f0, 3) - - # Convert unvoiced part to 0hz - f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0) - - # Interpolate f0 - nzindex = torch.nonzero(f0[0]).squeeze() - f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy() - time_org = 0.005 * nzindex.cpu().numpy() - time_frame = np.arange(mel_len) * hop_length / fs - f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1]) - return f0 - -def get_f0_features_using_crepe(audio, cfg): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - audio_torch = torch.FloatTensor(audio).unsqueeze(0).to(device) - - crepe_pitch, pd = torchcrepe.predict(audio_torch, cfg.sample_rate, cfg.hop_size, fmin=cfg.f0_min, fmax=cfg.f0_max, return_periodicity=True) - - threshold = 0.3 - - # Filter, de-silence, set up threshold for unvoiced part - pd = torchcrepe.filter.median(pd, 3) - pd = torchcrepe.threshold.Silence(-60.0)(pd, audio_torch, cfg.sample_rate, 256) - crepe_pitch = torchcrepe.threshold.At(threshold)(crepe_pitch, pd) - crepe_pitch = torchcrepe.filter.mean(crepe_pitch, 3) - - # Convert unvoiced part to 0hz - crepe_pitch = torch.where(torch.isnan(crepe_pitch), torch.full_like(crepe_pitch, 0), crepe_pitch) - - return crepe_pitch[0].cpu().numpy() - - -def get_f0(audio, cfg): - if cfg.pitch_extractor == "dio": - f0 = get_f0_features_using_dio(audio, cfg) - elif cfg.pitch_extractor == "pyin": - f0 = get_f0_features_using_pyin(audio, cfg) - elif cfg.pitch_extractor == "parselmouth": - f0, _ = get_f0_features_using_parselmouth(audio, cfg) - elif cfg.pitch_extractor == "crepe": - f0 = get_f0_features_using_crepe(audio, cfg) - # elif cfg.data.f0_extractor == 'cwt': # todo - - return f0 - - -def get_cents(f0_hz): - """ - F_{cent} = 1200 * log2 (F/440) - - Reference: - APSIPA'17, Perceptual Evaluation of Singing Quality - """ - voiced_f0 = f0_hz[f0_hz != 0] - return 1200 * np.log2(voiced_f0 / 440) - - -def get_pitch_derivatives(f0_hz): - """ - f0_hz: (,T) - """ - f0_cent = get_cents(f0_hz) - return f0_cent[1:] - f0_cent[:-1] - - -def get_pitch_sub_median(f0_hz): - """ - f0_hz: (,T) - """ - f0_cent = get_cents(f0_hz) - return f0_cent - np.median(f0_cent) diff --git a/utils/hparam.py b/utils/hparam.py deleted file mode 100644 index c5dd35c6a3158b0aaf8d936dba139a030a48bc62..0000000000000000000000000000000000000000 --- a/utils/hparam.py +++ /dev/null @@ -1,659 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/training/python/training/hparam.py pylint: disable=line-too-long -"""Hyperparameter values.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import json -import numbers -import re -import six - -# Define the regular expression for parsing a single clause of the input -# (delimited by commas). A legal clause looks like: -# []? = -# where is either a single token or [] enclosed list of tokens. -# For example: "var[1] = a" or "x = [1,2,3]" -PARAM_RE = re.compile( - r""" - (?P[a-zA-Z][\w\.]*) # variable name: "var" or "x" - (\[\s*(?P\d+)\s*\])? # (optional) index: "1" or None - \s*=\s* - ((?P[^,\[]*) # single value: "a" or None - | - \[(?P[^\]]*)\]) # list of values: None or "1,2,3" - ($|,\s*)""", - re.VERBOSE, -) - - -def _parse_fail(name, var_type, value, values): - """Helper function for raising a value error for bad assignment.""" - raise ValueError( - "Could not parse hparam '%s' of type '%s' with value '%s' in %s" - % (name, var_type.__name__, value, values) - ) - - -def _reuse_fail(name, values): - """Helper function for raising a value error for reuse of name.""" - raise ValueError("Multiple assignments to variable '%s' in %s" % (name, values)) - - -def _process_scalar_value(name, parse_fn, var_type, m_dict, values, results_dictionary): - """Update results_dictionary with a scalar value. - - Used to update the results_dictionary to be returned by parse_values when - encountering a clause with a scalar RHS (e.g. "s=5" or "arr[0]=5".) - - Mutates results_dictionary. - - Args: - name: Name of variable in assignment ("s" or "arr"). - parse_fn: Function for parsing the actual value. - var_type: Type of named variable. - m_dict: Dictionary constructed from regex parsing. - m_dict['val']: RHS value (scalar) - m_dict['index']: List index value (or None) - values: Full expression being parsed - results_dictionary: The dictionary being updated for return by the parsing - function. - - Raises: - ValueError: If the name has already been used. - """ - try: - parsed_value = parse_fn(m_dict["val"]) - except ValueError: - _parse_fail(name, var_type, m_dict["val"], values) - - # If no index is provided - if not m_dict["index"]: - if name in results_dictionary: - _reuse_fail(name, values) - results_dictionary[name] = parsed_value - else: - if name in results_dictionary: - # The name has already been used as a scalar, then it - # will be in this dictionary and map to a non-dictionary. - if not isinstance(results_dictionary.get(name), dict): - _reuse_fail(name, values) - else: - results_dictionary[name] = {} - - index = int(m_dict["index"]) - # Make sure the index position hasn't already been assigned a value. - if index in results_dictionary[name]: - _reuse_fail("{}[{}]".format(name, index), values) - results_dictionary[name][index] = parsed_value - - -def _process_list_value(name, parse_fn, var_type, m_dict, values, results_dictionary): - """Update results_dictionary from a list of values. - - Used to update results_dictionary to be returned by parse_values when - encountering a clause with a list RHS (e.g. "arr=[1,2,3]".) - - Mutates results_dictionary. - - Args: - name: Name of variable in assignment ("arr"). - parse_fn: Function for parsing individual values. - var_type: Type of named variable. - m_dict: Dictionary constructed from regex parsing. - m_dict['val']: RHS value (scalar) - values: Full expression being parsed - results_dictionary: The dictionary being updated for return by the parsing - function. - - Raises: - ValueError: If the name has an index or the values cannot be parsed. - """ - if m_dict["index"] is not None: - raise ValueError("Assignment of a list to a list index.") - elements = filter(None, re.split("[ ,]", m_dict["vals"])) - # Make sure the name hasn't already been assigned a value - if name in results_dictionary: - raise _reuse_fail(name, values) - try: - results_dictionary[name] = [parse_fn(e) for e in elements] - except ValueError: - _parse_fail(name, var_type, m_dict["vals"], values) - - -def _cast_to_type_if_compatible(name, param_type, value): - """Cast hparam to the provided type, if compatible. - - Args: - name: Name of the hparam to be cast. - param_type: The type of the hparam. - value: The value to be cast, if compatible. - - Returns: - The result of casting `value` to `param_type`. - - Raises: - ValueError: If the type of `value` is not compatible with param_type. - * If `param_type` is a string type, but `value` is not. - * If `param_type` is a boolean, but `value` is not, or vice versa. - * If `param_type` is an integer type, but `value` is not. - * If `param_type` is a float type, but `value` is not a numeric type. - """ - fail_msg = "Could not cast hparam '%s' of type '%s' from value %r" % ( - name, - param_type, - value, - ) - - # Some callers use None, for which we can't do any casting/checking. :( - if issubclass(param_type, type(None)): - return value - - # Avoid converting a non-string type to a string. - if issubclass(param_type, (six.string_types, six.binary_type)) and not isinstance( - value, (six.string_types, six.binary_type) - ): - raise ValueError(fail_msg) - - # Avoid converting a number or string type to a boolean or vice versa. - if issubclass(param_type, bool) != isinstance(value, bool): - raise ValueError(fail_msg) - - # Avoid converting float to an integer (the reverse is fine). - if issubclass(param_type, numbers.Integral) and not isinstance( - value, numbers.Integral - ): - raise ValueError(fail_msg) - - # Avoid converting a non-numeric type to a numeric type. - if issubclass(param_type, numbers.Number) and not isinstance(value, numbers.Number): - raise ValueError(fail_msg) - - return param_type(value) - - -def parse_values(values, type_map, ignore_unknown=False): - """Parses hyperparameter values from a string into a python map. - - `values` is a string containing comma-separated `name=value` pairs. - For each pair, the value of the hyperparameter named `name` is set to - `value`. - - If a hyperparameter name appears multiple times in `values`, a ValueError - is raised (e.g. 'a=1,a=2', 'a[1]=1,a[1]=2'). - - If a hyperparameter name in both an index assignment and scalar assignment, - a ValueError is raised. (e.g. 'a=[1,2,3],a[0] = 1'). - - The hyperparameter name may contain '.' symbols, which will result in an - attribute name that is only accessible through the getattr and setattr - functions. (And must be first explicit added through add_hparam.) - - WARNING: Use of '.' in your variable names is allowed, but is not well - supported and not recommended. - - The `value` in `name=value` must follows the syntax according to the - type of the parameter: - - * Scalar integer: A Python-parsable integer point value. E.g.: 1, - 100, -12. - * Scalar float: A Python-parsable floating point value. E.g.: 1.0, - -.54e89. - * Boolean: Either true or false. - * Scalar string: A non-empty sequence of characters, excluding comma, - spaces, and square brackets. E.g.: foo, bar_1. - * List: A comma separated list of scalar values of the parameter type - enclosed in square brackets. E.g.: [1,2,3], [1.0,1e-12], [high,low]. - - When index assignment is used, the corresponding type_map key should be the - list name. E.g. for "arr[1]=0" the type_map must have the key "arr" (not - "arr[1]"). - - Args: - values: String. Comma separated list of `name=value` pairs where - 'value' must follow the syntax described above. - type_map: A dictionary mapping hyperparameter names to types. Note every - parameter name in values must be a key in type_map. The values must - conform to the types indicated, where a value V is said to conform to a - type T if either V has type T, or V is a list of elements of type T. - Hence, for a multidimensional parameter 'x' taking float values, - 'x=[0.1,0.2]' will parse successfully if type_map['x'] = float. - ignore_unknown: Bool. Whether values that are missing a type in type_map - should be ignored. If set to True, a ValueError will not be raised for - unknown hyperparameter type. - - Returns: - A python map mapping each name to either: - * A scalar value. - * A list of scalar values. - * A dictionary mapping index numbers to scalar values. - (e.g. "x=5,L=[1,2],arr[1]=3" results in {'x':5,'L':[1,2],'arr':{1:3}}") - - Raises: - ValueError: If there is a problem with input. - * If `values` cannot be parsed. - * If a list is assigned to a list index (e.g. 'a[1] = [1,2,3]'). - * If the same rvalue is assigned two different values (e.g. 'a=1,a=2', - 'a[1]=1,a[1]=2', or 'a=1,a=[1]') - """ - results_dictionary = {} - pos = 0 - while pos < len(values): - m = PARAM_RE.match(values, pos) - if not m: - raise ValueError("Malformed hyperparameter value: %s" % values[pos:]) - # Check that there is a comma between parameters and move past it. - pos = m.end() - # Parse the values. - m_dict = m.groupdict() - name = m_dict["name"] - if name not in type_map: - if ignore_unknown: - continue - raise ValueError("Unknown hyperparameter type for %s" % name) - type_ = type_map[name] - - # Set up correct parsing function (depending on whether type_ is a bool) - if type_ == bool: - - def parse_bool(value): - if value in ["true", "True"]: - return True - elif value in ["false", "False"]: - return False - else: - try: - return bool(int(value)) - except ValueError: - _parse_fail(name, type_, value, values) - - parse = parse_bool - else: - parse = type_ - - # If a singe value is provided - if m_dict["val"] is not None: - _process_scalar_value( - name, parse, type_, m_dict, values, results_dictionary - ) - - # If the assigned value is a list: - elif m_dict["vals"] is not None: - _process_list_value(name, parse, type_, m_dict, values, results_dictionary) - - else: # Not assigned a list or value - _parse_fail(name, type_, "", values) - - return results_dictionary - - -class HParams(object): - """Class to hold a set of hyperparameters as name-value pairs. - - A `HParams` object holds hyperparameters used to build and train a model, - such as the number of hidden units in a neural net layer or the learning rate - to use when training. - - You first create a `HParams` object by specifying the names and values of the - hyperparameters. - - To make them easily accessible the parameter names are added as direct - attributes of the class. A typical usage is as follows: - - ```python - # Create a HParams object specifying names and values of the model - # hyperparameters: - hparams = HParams(learning_rate=0.1, num_hidden_units=100) - - # The hyperparameter are available as attributes of the HParams object: - hparams.learning_rate ==> 0.1 - hparams.num_hidden_units ==> 100 - ``` - - Hyperparameters have type, which is inferred from the type of their value - passed at construction type. The currently supported types are: integer, - float, boolean, string, and list of integer, float, boolean, or string. - - You can override hyperparameter values by calling the - [`parse()`](#HParams.parse) method, passing a string of comma separated - `name=value` pairs. This is intended to make it possible to override - any hyperparameter values from a single command-line flag to which - the user passes 'hyper-param=value' pairs. It avoids having to define - one flag for each hyperparameter. - - The syntax expected for each value depends on the type of the parameter. - See `parse()` for a description of the syntax. - - Example: - - ```python - # Define a command line flag to pass name=value pairs. - # For example using argparse: - import argparse - parser = argparse.ArgumentParser(description='Train my model.') - parser.add_argument('--hparams', type=str, - help='Comma separated list of "name=value" pairs.') - args = parser.parse_args() - ... - def my_program(): - # Create a HParams object specifying the names and values of the - # model hyperparameters: - hparams = tf.HParams(learning_rate=0.1, num_hidden_units=100, - activations=['relu', 'tanh']) - - # Override hyperparameters values by parsing the command line - hparams.parse(args.hparams) - - # If the user passed `--hparams=learning_rate=0.3` on the command line - # then 'hparams' has the following attributes: - hparams.learning_rate ==> 0.3 - hparams.num_hidden_units ==> 100 - hparams.activations ==> ['relu', 'tanh'] - - # If the hyperparameters are in json format use parse_json: - hparams.parse_json('{"learning_rate": 0.3, "activations": "relu"}') - ``` - """ - - _HAS_DYNAMIC_ATTRIBUTES = True # Required for pytype checks. - - def __init__(self, model_structure=None, **kwargs): - """Create an instance of `HParams` from keyword arguments. - - The keyword arguments specify name-values pairs for the hyperparameters. - The parameter types are inferred from the type of the values passed. - - The parameter names are added as attributes of `HParams` object, so they - can be accessed directly with the dot notation `hparams._name_`. - - Example: - - ```python - # Define 3 hyperparameters: 'learning_rate' is a float parameter, - # 'num_hidden_units' an integer parameter, and 'activation' a string - # parameter. - hparams = tf.HParams( - learning_rate=0.1, num_hidden_units=100, activation='relu') - - hparams.activation ==> 'relu' - ``` - - Note that a few names are reserved and cannot be used as hyperparameter - names. If you use one of the reserved name the constructor raises a - `ValueError`. - - Args: - model_structure: An instance of ModelStructure, defining the feature - crosses to be used in the Trial. - **kwargs: Key-value pairs where the key is the hyperparameter name and - the value is the value for the parameter. - - Raises: - ValueError: If both `hparam_def` and initialization values are provided, - or if one of the arguments is invalid. - - """ - # Register the hyperparameters and their type in _hparam_types. - # This simplifies the implementation of parse(). - # _hparam_types maps the parameter name to a tuple (type, bool). - # The type value is the type of the parameter for scalar hyperparameters, - # or the type of the list elements for multidimensional hyperparameters. - # The bool value is True if the value is a list, False otherwise. - self._hparam_types = {} - self._model_structure = model_structure - for name, value in six.iteritems(kwargs): - self.add_hparam(name, value) - - def add_hparam(self, name, value): - """Adds {name, value} pair to hyperparameters. - - Args: - name: Name of the hyperparameter. - value: Value of the hyperparameter. Can be one of the following types: - int, float, string, int list, float list, or string list. - - Raises: - ValueError: if one of the arguments is invalid. - """ - # Keys in kwargs are unique, but 'name' could the name of a pre-existing - # attribute of this object. In that case we refuse to use it as a - # hyperparameter name. - if getattr(self, name, None) is not None: - raise ValueError("Hyperparameter name is reserved: %s" % name) - if isinstance(value, (list, tuple)): - if not value: - raise ValueError( - "Multi-valued hyperparameters cannot be empty: %s" % name - ) - self._hparam_types[name] = (type(value[0]), True) - else: - self._hparam_types[name] = (type(value), False) - setattr(self, name, value) - - def set_hparam(self, name, value): - """Set the value of an existing hyperparameter. - - This function verifies that the type of the value matches the type of the - existing hyperparameter. - - Args: - name: Name of the hyperparameter. - value: New value of the hyperparameter. - - Raises: - KeyError: If the hyperparameter doesn't exist. - ValueError: If there is a type mismatch. - """ - param_type, is_list = self._hparam_types[name] - if isinstance(value, list): - if not is_list: - raise ValueError( - "Must not pass a list for single-valued parameter: %s" % name - ) - setattr( - self, - name, - [_cast_to_type_if_compatible(name, param_type, v) for v in value], - ) - else: - if is_list: - raise ValueError( - "Must pass a list for multi-valued parameter: %s." % name - ) - setattr(self, name, _cast_to_type_if_compatible(name, param_type, value)) - - def del_hparam(self, name): - """Removes the hyperparameter with key 'name'. - - Does nothing if it isn't present. - - Args: - name: Name of the hyperparameter. - """ - if hasattr(self, name): - delattr(self, name) - del self._hparam_types[name] - - def parse(self, values): - """Override existing hyperparameter values, parsing new values from a string. - - See parse_values for more detail on the allowed format for values. - - Args: - values: String. Comma separated list of `name=value` pairs where 'value' - must follow the syntax described above. - - Returns: - The `HParams` instance. - - Raises: - ValueError: If `values` cannot be parsed or a hyperparameter in `values` - doesn't exist. - """ - type_map = {} - for name, t in self._hparam_types.items(): - param_type, _ = t - type_map[name] = param_type - - values_map = parse_values(values, type_map) - return self.override_from_dict(values_map) - - def override_from_dict(self, values_dict): - """Override existing hyperparameter values, parsing new values from a dictionary. - - Args: - values_dict: Dictionary of name:value pairs. - - Returns: - The `HParams` instance. - - Raises: - KeyError: If a hyperparameter in `values_dict` doesn't exist. - ValueError: If `values_dict` cannot be parsed. - """ - for name, value in values_dict.items(): - self.set_hparam(name, value) - return self - - def set_model_structure(self, model_structure): - self._model_structure = model_structure - - def get_model_structure(self): - return self._model_structure - - def to_json(self, indent=None, separators=None, sort_keys=False): - """Serializes the hyperparameters into JSON. - - Args: - indent: If a non-negative integer, JSON array elements and object members - will be pretty-printed with that indent level. An indent level of 0, or - negative, will only insert newlines. `None` (the default) selects the - most compact representation. - separators: Optional `(item_separator, key_separator)` tuple. Default is - `(', ', ': ')`. - sort_keys: If `True`, the output dictionaries will be sorted by key. - - Returns: - A JSON string. - """ - - def remove_callables(x): - """Omit callable elements from input with arbitrary nesting.""" - if isinstance(x, dict): - return { - k: remove_callables(v) - for k, v in six.iteritems(x) - if not callable(v) - } - elif isinstance(x, list): - return [remove_callables(i) for i in x if not callable(i)] - return x - - return json.dumps( - remove_callables(self.values()), - indent=indent, - separators=separators, - sort_keys=sort_keys, - ) - - def parse_json(self, values_json): - """Override existing hyperparameter values, parsing new values from a json object. - - Args: - values_json: String containing a json object of name:value pairs. - - Returns: - The `HParams` instance. - - Raises: - KeyError: If a hyperparameter in `values_json` doesn't exist. - ValueError: If `values_json` cannot be parsed. - """ - values_map = json.loads(values_json) - return self.override_from_dict(values_map) - - def values(self): - """Return the hyperparameter values as a Python dictionary. - - Returns: - A dictionary with hyperparameter names as keys. The values are the - hyperparameter values. - """ - return {n: getattr(self, n) for n in self._hparam_types.keys()} - - def get(self, key, default=None): - """Returns the value of `key` if it exists, else `default`.""" - if key in self._hparam_types: - # Ensure that default is compatible with the parameter type. - if default is not None: - param_type, is_param_list = self._hparam_types[key] - type_str = "list<%s>" % param_type if is_param_list else str(param_type) - fail_msg = ( - "Hparam '%s' of type '%s' is incompatible with " - "default=%s" % (key, type_str, default) - ) - - is_default_list = isinstance(default, list) - if is_param_list != is_default_list: - raise ValueError(fail_msg) - - try: - if is_default_list: - for value in default: - _cast_to_type_if_compatible(key, param_type, value) - else: - _cast_to_type_if_compatible(key, param_type, default) - except ValueError as e: - raise ValueError("%s. %s" % (fail_msg, e)) - - return getattr(self, key) - - return default - - def __contains__(self, key): - return key in self._hparam_types - - def __str__(self): - return str(sorted(self.values().items())) - - def __repr__(self): - return "%s(%s)" % (type(self).__name__, self.__str__()) - - @staticmethod - def _get_kind_name(param_type, is_list): - """Returns the field name given parameter type and is_list. - - Args: - param_type: Data type of the hparam. - is_list: Whether this is a list. - - Returns: - A string representation of the field name. - - Raises: - ValueError: If parameter type is not recognized. - """ - if issubclass(param_type, bool): - # This check must happen before issubclass(param_type, six.integer_types), - # since Python considers bool to be a subclass of int. - typename = "bool" - elif issubclass(param_type, six.integer_types): - # Setting 'int' and 'long' types to be 'int64' to ensure the type is - # compatible with both Python2 and Python3. - typename = "int64" - elif issubclass(param_type, (six.string_types, six.binary_type)): - # Setting 'string' and 'bytes' types to be 'bytes' to ensure the type is - # compatible with both Python2 and Python3. - typename = "bytes" - elif issubclass(param_type, float): - typename = "float" - else: - raise ValueError("Unsupported parameter type: %s" % str(param_type)) - - suffix = "list" if is_list else "value" - return "_".join([typename, suffix]) diff --git a/utils/hubert.py b/utils/hubert.py deleted file mode 100644 index 84b509fb9fde8485cfb504a675e5d3b7d27622ff..0000000000000000000000000000000000000000 --- a/utils/hubert.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.0/preprocess_hubert_f0.py - -import os -import librosa -import torch -import numpy as np -from fairseq import checkpoint_utils -from tqdm import tqdm -import torch - - -def load_hubert_model(hps): - # Load model - ckpt_path = hps.hubert_file - print("Load Hubert Model...") - - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - [ckpt_path], - suffix="", - ) - model = models[0] - model.eval() - - if torch.cuda.is_available(): - model = model.cuda() - - return model - - -def get_hubert_content(hmodel, wav_16k_tensor): - feats = wav_16k_tensor - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - padding_mask = torch.BoolTensor(feats.shape).fill_(False) - inputs = { - "source": feats.to(wav_16k_tensor.device), - "padding_mask": padding_mask.to(wav_16k_tensor.device), - "output_layer": 9, # layer 9 - } - with torch.no_grad(): - logits = hmodel.extract_features(**inputs) - feats = hmodel.final_proj(logits[0]).squeeze(0) - - return feats - - -def content_vector_encoder(model, audio_path, default_sampling_rate=16000): - """ - # content vector default sr: 16000 - """ - - wav16k, sr = librosa.load(audio_path, sr=default_sampling_rate) - device = next(model.parameters()).device - wav16k = torch.from_numpy(wav16k).to(device) - - # (1, 256, frame_len) - content_feature = get_hubert_content(model, wav_16k_tensor=wav16k) - - return content_feature.cpu().detach().numpy() - - -def repeat_expand_2d(content, target_len): - """ - content : [hubert_dim(256), src_len] - target: [hubert_dim(256), target_len] - """ - src_len = content.shape[-1] - target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to( - content.device - ) - temp = torch.arange(src_len + 1) * target_len / src_len - current_pos = 0 - for i in range(target_len): - if i < temp[current_pos + 1]: - target[:, i] = content[:, current_pos] - else: - current_pos += 1 - target[:, i] = content[:, current_pos] - - return target - - -def get_mapped_features(raw_content_features, mapping_features): - """ - Content Vector: frameshift = 20ms, hop_size = 480 in 24k - - Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms) - """ - source_hop = 480 - target_hop = 256 - - factor = np.gcd(source_hop, target_hop) - source_hop //= factor - target_hop //= factor - print( - "Mapping source's {} frames => target's {} frames".format( - target_hop, source_hop - ) - ) - - results = [] - for index, mapping_feat in enumerate(tqdm(mapping_features)): - # mappping_feat: (mels_frame_len, n_mels) - target_len = len(mapping_feat) - - # (source_len, 256) - raw_feats = raw_content_features[index][0].cpu().numpy().T - source_len, width = raw_feats.shape - - # const ~= target_len * target_hop - const = source_len * source_hop // target_hop * target_hop - - # (source_len * source_hop, dim) - up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0) - # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) - down_sampling_feats = np.average( - up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 - ) - - err = abs(target_len - len(down_sampling_feats)) - if err > 3: - print("index:", index) - print("mels:", mapping_feat.shape) - print("raw content vector:", raw_feats.shape) - print("up_sampling:", up_sampling_feats.shape) - print("down_sampling_feats:", down_sampling_feats.shape) - exit() - if len(down_sampling_feats) < target_len: - # (1, dim) -> (err, dim) - end = down_sampling_feats[-1][None, :].repeat(err, axis=0) - down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) - - # (target_len, dim) - feats = down_sampling_feats[:target_len] - results.append(feats) - - return results - - -def extract_hubert_features_of_dataset(datasets, model, out_dir): - for utt in tqdm(datasets): - uid = utt["Uid"] - audio_path = utt["Path"] - - content_vector_feature = content_vector_encoder(model, audio_path) # (T, 256) - - save_path = os.path.join(out_dir, uid + ".npy") - np.save(save_path, content_vector_feature) diff --git a/utils/io.py b/utils/io.py deleted file mode 100644 index e6e31d2fc6ebf3ca888f58dbce96bb3d6c7c2905..0000000000000000000000000000000000000000 --- a/utils/io.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import numpy as np -import torch -import torchaudio - - -def save_feature(process_dir, feature_dir, item, feature, overrides=True): - """Save features to path - - Args: - process_dir (str): directory to store features - feature_dir (_type_): directory to store one type of features (mel, energy, ...) - item (str): uid - feature (tensor): feature tensor - overrides (bool, optional): whether to override existing files. Defaults to True. - """ - process_dir = os.path.join(process_dir, feature_dir) - os.makedirs(process_dir, exist_ok=True) - out_path = os.path.join(process_dir, item + ".npy") - - if os.path.exists(out_path): - if overrides: - np.save(out_path, feature) - else: - np.save(out_path, feature) - - -def save_txt(process_dir, feature_dir, item, feature, overrides=True): - process_dir = os.path.join(process_dir, feature_dir) - os.makedirs(process_dir, exist_ok=True) - out_path = os.path.join(process_dir, item + ".txt") - - if os.path.exists(out_path): - if overrides: - f = open(out_path, "w") - f.writelines(feature) - f.close() - else: - f = open(out_path, "w") - f.writelines(feature) - f.close() - - -def save_audio(path, waveform, fs, add_silence=False, turn_up=False, volume_peak=0.9): - if turn_up: - # continue to turn up to volume_peak - ratio = volume_peak / max(waveform.max(), abs(waveform.min())) - waveform = waveform * ratio - - if add_silence: - silence_len = fs // 20 - silence = np.zeros((silence_len,), dtype=waveform.dtype) - result = np.concatenate([silence, waveform, silence]) - waveform = result - - waveform = torch.as_tensor(waveform, dtype=torch.float32, device="cpu") - if len(waveform.size()) == 1: - waveform = waveform[None, :] - elif waveform.size(0) != 1: - # Stereo to mono - waveform = torch.mean(waveform, dim=0, keepdim=True) - torchaudio.save(path, waveform, fs, encoding="PCM_S", bits_per_sample=16) - - -async def async_load_audio(path, sample_rate: int = 24000): - r""" - Args: - path: The source loading path. - sample_rate: The target sample rate, will automatically resample if necessary. - - Returns: - waveform: The waveform object. Should be [1 x sequence_len]. - """ - - async def use_torchaudio_load(path): - return torchaudio.load(path) - - waveform, sr = await use_torchaudio_load(path) - waveform = torch.mean(waveform, dim=0, keepdim=True) - - if sr != sample_rate: - waveform = torchaudio.functional.resample(waveform, sr, sample_rate) - - if torch.any(torch.isnan(waveform) or torch.isinf(waveform)): - raise ValueError("NaN or Inf found in waveform.") - return waveform - - -async def async_save_audio( - path, - waveform, - sample_rate: int = 24000, - add_silence: bool = False, - volume_peak: float = 0.9, -): - r""" - Args: - path: The target saving path. - waveform: The waveform object. Should be [n_channel x sequence_len]. - sample_rate: Sample rate. - add_silence: If ``true``, concat 0.05s silence to beginning and end. - volume_peak: Turn up volume for larger number, vice versa. - """ - - async def use_torchaudio_save(path, waveform, sample_rate): - torchaudio.save( - path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16 - ) - - waveform = torch.as_tensor(waveform, device="cpu", dtype=torch.float32) - shape = waveform.size()[:-1] - - ratio = abs(volume_peak) / max(waveform.max(), abs(waveform.min())) - waveform = waveform * ratio - - if add_silence: - silence_len = sample_rate // 20 - silence = torch.zeros((*shape, silence_len), dtype=waveform.type()) - waveform = torch.concatenate((silence, waveform, silence), dim=-1) - - if waveform.dim() == 1: - waveform = waveform[None] - - await use_torchaudio_save(path, waveform, sample_rate) - - -def load_mel_extrema(cfg, dataset_name, split): - dataset_dir = os.path.join( - cfg.OUTPUT_PATH, - "preprocess/{}_version".format(cfg.data.process_version), - dataset_name, - ) - - min_file = os.path.join( - dataset_dir, - "mel_min_max", - split.split("_")[-1], - "mel_min.npy", - ) - max_file = os.path.join( - dataset_dir, - "mel_min_max", - split.split("_")[-1], - "mel_max.npy", - ) - mel_min = np.load(min_file) - mel_max = np.load(max_file) - return mel_min, mel_max diff --git a/utils/io_optim.py b/utils/io_optim.py deleted file mode 100644 index 942619d625d4c8e2d00ae1255421ceaf6ab39986..0000000000000000000000000000000000000000 --- a/utils/io_optim.py +++ /dev/null @@ -1,123 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torchaudio -import json -import os -import numpy as np -import librosa -from torch.nn.utils.rnn import pad_sequence -from modules import whisper_extractor as whisper - - -class TorchaudioDataset(torch.utils.data.Dataset): - def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None): - """ - Args: - cfg: config - dataset: dataset name - - """ - assert isinstance(dataset, str) - - self.sr = sr - self.cfg = cfg - - if metadata is None: - self.train_metadata_path = os.path.join( - cfg.preprocess.processed_dir, dataset, cfg.preprocess.train_file - ) - self.valid_metadata_path = os.path.join( - cfg.preprocess.processed_dir, dataset, cfg.preprocess.valid_file - ) - self.metadata = self.get_metadata() - else: - self.metadata = metadata - - if accelerator is not None: - self.device = accelerator.device - elif torch.cuda.is_available(): - self.device = torch.device("cuda") - else: - self.device = torch.device("cpu") - - def get_metadata(self): - metadata = [] - with open(self.train_metadata_path, "r", encoding="utf-8") as t: - metadata.extend(json.load(t)) - with open(self.valid_metadata_path, "r", encoding="utf-8") as v: - metadata.extend(json.load(v)) - return metadata - - def __len__(self): - return len(self.metadata) - - def __getitem__(self, index): - utt_info = self.metadata[index] - wav_path = utt_info["Path"] - - wav, sr = torchaudio.load(wav_path) - - # resample - if sr != self.sr: - wav = torchaudio.functional.resample(wav, sr, self.sr) - # downmixing - if wav.shape[0] > 1: - wav = torch.mean(wav, dim=0, keepdim=True) - assert wav.shape[0] == 1 - wav = wav.squeeze(0) - # record the length of wav without padding - length = wav.shape[0] - # wav: (T) - return utt_info, wav, length - - -class LibrosaDataset(TorchaudioDataset): - def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None): - super().__init__(cfg, dataset, sr, accelerator, metadata) - - def __getitem__(self, index): - utt_info = self.metadata[index] - wav_path = utt_info["Path"] - - wav, _ = librosa.load(wav_path, sr=self.sr) - # wav: (T) - wav = torch.from_numpy(wav) - - # record the length of wav without padding - length = wav.shape[0] - return utt_info, wav, length - - -class FFmpegDataset(TorchaudioDataset): - def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None): - super().__init__(cfg, dataset, sr, accelerator, metadata) - - def __getitem__(self, index): - utt_info = self.metadata[index] - wav_path = utt_info["Path"] - - # wav: (T,) - wav = whisper.load_audio(wav_path) # sr = 16000 - # convert to torch tensor - wav = torch.from_numpy(wav) - # record the length of wav without padding - length = wav.shape[0] - - return utt_info, wav, length - - -def collate_batch(batch_list): - """ - Args: - batch_list: list of (metadata, wav, length) - """ - metadata = [item[0] for item in batch_list] - # wavs: (B, T) - wavs = pad_sequence([item[1] for item in batch_list], batch_first=True) - lens = [item[2] for item in batch_list] - - return metadata, wavs, lens diff --git a/utils/mel.py b/utils/mel.py deleted file mode 100644 index d32d38226dfe6c0162527b3136a392b9168c7f06..0000000000000000000000000000000000000000 --- a/utils/mel.py +++ /dev/null @@ -1,283 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from librosa.filters import mel as librosa_mel_fn - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def extract_linear_features(y, cfg, center=False): - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global hann_window - hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - # complex tensor as default, then use view_as_real for future pytorch compatibility - spec = torch.stft( - y, - cfg.n_fft, - hop_length=cfg.hop_size, - win_length=cfg.win_size, - window=hann_window[str(y.device)], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) - spec = torch.view_as_real(spec) - spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) - spec = torch.squeeze(spec, 0) - return spec - - -def mel_spectrogram_torch(y, cfg, center=False): - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global mel_basis, hann_window - if cfg.fmax not in mel_basis: - mel = librosa_mel_fn( - sr=cfg.sample_rate, - n_fft=cfg.n_fft, - n_mels=cfg.n_mel, - fmin=cfg.fmin, - fmax=cfg.fmax, - ) - mel_basis[str(cfg.fmax) + "_" + str(y.device)] = ( - torch.from_numpy(mel).float().to(y.device) - ) - hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - spec = torch.stft( - y, - cfg.n_fft, - hop_length=cfg.hop_size, - win_length=cfg.win_size, - window=hann_window[str(y.device)], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) - - spec = torch.view_as_real(spec) - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) - spec = spectral_normalize_torch(spec) - - return spec - - -mel_basis = {} -hann_window = {} - - -def extract_mel_features( - y, - cfg, - center=False - # n_fft, n_mel, sampling_rate, hop_size, win_size, fmin, fmax, center=False -): - """Extract mel features - - Args: - y (tensor): audio data in tensor - cfg (dict): configuration in cfg.preprocess - center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False. - - Returns: - tensor: a tensor containing the mel feature calculated based on STFT result - """ - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global mel_basis, hann_window - if cfg.fmax not in mel_basis: - mel = librosa_mel_fn( - sr=cfg.sample_rate, - n_fft=cfg.n_fft, - n_mels=cfg.n_mel, - fmin=cfg.fmin, - fmax=cfg.fmax, - ) - mel_basis[str(cfg.fmax) + "_" + str(y.device)] = ( - torch.from_numpy(mel).float().to(y.device) - ) - hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - # complex tensor as default, then use view_as_real for future pytorch compatibility - spec = torch.stft( - y, - cfg.n_fft, - hop_length=cfg.hop_size, - win_length=cfg.win_size, - window=hann_window[str(y.device)], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) - spec = torch.view_as_real(spec) - spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) - - spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) - spec = spectral_normalize_torch(spec) - - return spec.squeeze(0) - - -def extract_mel_features_tts( - y, - cfg, - center=False, - taco=False, - _stft=None, -): - """Extract mel features - - Args: - y (tensor): audio data in tensor - cfg (dict): configuration in cfg.preprocess - center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False. - taco: use tacotron mel - - Returns: - tensor: a tensor containing the mel feature calculated based on STFT result - """ - if not taco: - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) - - global mel_basis, hann_window - if cfg.fmax not in mel_basis: - mel = librosa_mel_fn( - sr=cfg.sample_rate, - n_fft=cfg.n_fft, - n_mels=cfg.n_mel, - fmin=cfg.fmin, - fmax=cfg.fmax, - ) - mel_basis[str(cfg.fmax) + "_" + str(y.device)] = ( - torch.from_numpy(mel).float().to(y.device) - ) - hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - # complex tensor as default, then use view_as_real for future pytorch compatibility - spec = torch.stft( - y, - cfg.n_fft, - hop_length=cfg.hop_size, - win_length=cfg.win_size, - window=hann_window[str(y.device)], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) - spec = torch.view_as_real(spec) - spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) - - spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) - spec = spectral_normalize_torch(spec) - spec = spec.squeeze(0) - else: - audio = torch.clip(y, -1, 1) - audio = torch.autograd.Variable(audio, requires_grad=False) - spec, energy = _stft.mel_spectrogram(audio) - spec = torch.squeeze(spec, 0) - - spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec) - spec = spectral_normalize_torch(spec) - - return spec.squeeze(0) - - -def amplitude_phase_spectrum(y, cfg): - hann_window = torch.hann_window(cfg.win_size).to(y.device) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - - stft_spec = torch.stft( - y, - cfg.n_fft, - hop_length=cfg.hop_size, - win_length=cfg.win_size, - window=hann_window, - center=False, - return_complex=True, - ) - - stft_spec = torch.view_as_real(stft_spec) - if stft_spec.size()[0] == 1: - stft_spec = stft_spec.squeeze(0) - - if len(list(stft_spec.size())) == 4: - rea = stft_spec[:, :, :, 0] # [batch_size, n_fft//2+1, frames] - imag = stft_spec[:, :, :, 1] # [batch_size, n_fft//2+1, frames] - else: - rea = stft_spec[:, :, 0] # [n_fft//2+1, frames] - imag = stft_spec[:, :, 1] # [n_fft//2+1, frames] - - log_amplitude = torch.log( - torch.abs(torch.sqrt(torch.pow(rea, 2) + torch.pow(imag, 2))) + 1e-5 - ) # [n_fft//2+1, frames] - phase = torch.atan2(imag, rea) # [n_fft//2+1, frames] - - return log_amplitude, phase, rea, imag diff --git a/utils/mert.py b/utils/mert.py deleted file mode 100644 index 4181429feb36f5013bafafff9505b0b9571485b4..0000000000000000000000000000000000000000 --- a/utils/mert.py +++ /dev/null @@ -1,139 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://huggingface.co/m-a-p/MERT-v1-330M - -import torch -from tqdm import tqdm -import numpy as np - -from transformers import Wav2Vec2FeatureExtractor -from transformers import AutoModel -import torchaudio -import torchaudio.transforms as T -from sklearn.preprocessing import StandardScaler - - -def mert_encoder(model, processor, audio_path, hps): - """ - # mert default sr: 24000 - """ - with torch.no_grad(): - resample_rate = processor.sampling_rate - device = next(model.parameters()).device - - input_audio, sampling_rate = torchaudio.load(audio_path) - input_audio = input_audio.squeeze() - - if sampling_rate != resample_rate: - resampler = T.Resample(sampling_rate, resample_rate) - input_audio = resampler(input_audio) - - inputs = processor( - input_audio, sampling_rate=resample_rate, return_tensors="pt" - ).to( - device - ) # {input_values: tensor, attention_mask: tensor} - - outputs = model(**inputs, output_hidden_states=True) # list: len is 25 - - # [25 layer, Time steps, 1024 feature_dim] - # all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() - # mert_features.append(all_layer_hidden_states) - - feature = outputs.hidden_states[ - hps.mert_feature_layer - ].squeeze() # [1, frame len, 1024] -> [frame len, 1024] - - return feature.cpu().detach().numpy() - - -def mert_features_normalization(raw_mert_features): - normalized_mert_features = list() - - mert_features = np.array(raw_mert_features) - scaler = StandardScaler().fit(mert_features) - for raw_mert_feature in raw_mert_feature: - normalized_mert_feature = scaler.transform(raw_mert_feature) - normalized_mert_features.append(normalized_mert_feature) - return normalized_mert_features - - -def get_mapped_mert_features(raw_mert_features, mapping_features, fast_mapping=True): - source_hop = 320 - target_hop = 256 - - factor = np.gcd(source_hop, target_hop) - source_hop //= factor - target_hop //= factor - print( - "Mapping source's {} frames => target's {} frames".format( - target_hop, source_hop - ) - ) - - mert_features = [] - for index, mapping_feat in enumerate(tqdm(mapping_features)): - # mapping_feat: (mels_frame_len, n_mels) - target_len = mapping_feat.shape[0] - - # (frame_len, 1024) - raw_feats = raw_mert_features[index].cpu().numpy() - source_len, width = raw_feats.shape - - # const ~= target_len * target_hop - const = source_len * source_hop // target_hop * target_hop - - # (source_len * source_hop, dim) - up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0) - # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) - down_sampling_feats = np.average( - up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 - ) - - err = abs(target_len - len(down_sampling_feats)) - if err > 3: - print("index:", index) - print("mels:", mapping_feat.shape) - print("raw mert vector:", raw_feats.shape) - print("up_sampling:", up_sampling_feats.shape) - print("const:", const) - print("down_sampling_feats:", down_sampling_feats.shape) - exit() - if len(down_sampling_feats) < target_len: - # (1, dim) -> (err, dim) - end = down_sampling_feats[-1][None, :].repeat(err, axis=0) - down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0) - - # (target_len, dim) - feats = down_sampling_feats[:target_len] - mert_features.append(feats) - - return mert_features - - -def load_mert_model(hps): - print("Loading MERT Model: ", hps.mert_model) - - # Load model - model_name = hps.mert_model - model = AutoModel.from_pretrained(model_name, trust_remote_code=True) - - if torch.cuda.is_available(): - model = model.cuda() - - # model = model.eval() - - preprocessor = Wav2Vec2FeatureExtractor.from_pretrained( - model_name, trust_remote_code=True - ) - return model, preprocessor - - -# loading the corresponding preprocessor config -# def load_preprocessor (model_name="m-a-p/MERT-v1-330M"): -# print('load_preprocessor...') -# preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(model_name,trust_remote_code=True) -# return preprocessor diff --git a/utils/model_summary.py b/utils/model_summary.py deleted file mode 100644 index ec72b0d17869dc0886a9eb665efedb70bb307bbf..0000000000000000000000000000000000000000 --- a/utils/model_summary.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import humanfriendly -import numpy as np -import torch - - -def get_human_readable_count(number: int) -> str: - """Return human_readable_count - - Originated from: - https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/memory.py - - Abbreviates an integer number with K, M, B, T for thousands, millions, - billions and trillions, respectively. - Examples: - >>> get_human_readable_count(123) - '123 ' - >>> get_human_readable_count(1234) # (one thousand) - '1 K' - >>> get_human_readable_count(2e6) # (two million) - '2 M' - >>> get_human_readable_count(3e9) # (three billion) - '3 B' - >>> get_human_readable_count(4e12) # (four trillion) - '4 T' - >>> get_human_readable_count(5e15) # (more than trillion) - '5,000 T' - Args: - number: a positive integer number - Return: - A string formatted according to the pattern described above. - """ - assert number >= 0 - labels = [" ", "K", "M", "B", "T"] - num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1) - num_groups = int(np.ceil(num_digits / 3)) - num_groups = min(num_groups, len(labels)) - shift = -3 * (num_groups - 1) - number = number * (10**shift) - index = num_groups - 1 - return f"{number:.2f} {labels[index]}" - - -def to_bytes(dtype) -> int: - return int(str(dtype)[-2:]) // 8 - - -def model_summary(model: torch.nn.Module) -> str: - message = "Model structure:\n" - message += str(model) - tot_params = sum(p.numel() for p in model.parameters()) - num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) - percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params) - tot_params = get_human_readable_count(tot_params) - num_params = get_human_readable_count(num_params) - message += "\n\nModel summary:\n" - message += f" Class Name: {model.__class__.__name__}\n" - message += f" Total Number of model parameters: {tot_params}\n" - message += ( - f" Number of trainable parameters: {num_params} ({percent_trainable}%)\n" - ) - num_bytes = humanfriendly.format_size( - sum( - p.numel() * to_bytes(p.dtype) for p in model.parameters() if p.requires_grad - ) - ) - message += f" Size: {num_bytes}\n" - dtype = next(iter(model.parameters())).dtype - message += f" Type: {dtype}" - return message diff --git a/utils/prompt_preparer.py b/utils/prompt_preparer.py deleted file mode 100644 index dba833ee72cd661bbc178ffc4735e05a4e4949c9..0000000000000000000000000000000000000000 --- a/utils/prompt_preparer.py +++ /dev/null @@ -1,73 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -class PromptPreparer: - def prepare_prompts(self, y, y_lens, codes, nar_stage, y_prompts_codes): - if self.prefix_mode == 0: - y_emb, prefix_len = self._handle_prefix_mode_0(y, codes, nar_stage) - elif self.prefix_mode == 1: - y_emb, prefix_len = self._handle_prefix_mode_1(y, y_lens, codes, nar_stage) - elif self.prefix_mode in [2, 4]: - y_emb, prefix_len = self._handle_prefix_mode_2_4(y, y_lens, codes, nar_stage, y_prompts_codes) - else: - raise ValueError("Invalid prefix mode") - - return y_emb, prefix_len - - def _handle_prefix_mode_0(self, y, codes, nar_stage): - prefix_len = 0 - y_emb = self.nar_audio_embeddings[0](y) - for j in range(1, nar_stage): - y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j]) - return y_emb, 0 - - def _handle_prefix_mode_1(self, y, y_lens, codes, nar_stage): - int_low = (0.25 * y_lens.min()).type(torch.int64).item() - prefix_len = torch.randint(int_low, int_low * 2, size=()).item() - prefix_len = min(prefix_len, 225) - - y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len]) - y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:]) - for j in range(1, self.num_quantizers): - y_prompts += self.nar_audio_embeddings[j]( - codes[:, :prefix_len, j] - ) - if j < nar_stage: - y_emb += self.nar_audio_embeddings[j]( - codes[:, prefix_len:, j] - ) - y_emb = torch.concat([y_prompts, y_emb], axis=1) - return y_emb, prefix_len - - def _handle_prefix_mode_2_4(self, y, y_lens, codes, nar_stage, y_prompts_codes): - if self.prefix_mode == 2: - prefix_len = min(225, int(0.25 * y_lens.min().item())) - - y_prompts_codes = [] - for b in range(codes.shape[0]): - start = self.rng.randint(0, y_lens[b].item() - prefix_len) - y_prompts_codes.append( - torch.clone(codes[b, start : start + prefix_len]) - ) - codes[ - b, start : start + prefix_len, nar_stage - ] = self.audio_token_num - y_prompts_codes = torch.stack(y_prompts_codes, dim=0) - else: - prefix_len = y_prompts_codes.shape[1] - - y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0]) - y_emb = self.nar_audio_embeddings[0](y) - for j in range(1, self.num_quantizers): - y_prompts += self.nar_audio_embeddings[j]( - y_prompts_codes[..., j] - ) - if j < nar_stage: - y_emb += self.nar_audio_embeddings[j](codes[..., j]) - y_emb = torch.concat([y_prompts, y_emb], axis=1) - - return y_emb, prefix_len diff --git a/utils/ssim.py b/utils/ssim.py deleted file mode 100644 index a0b95007b2c225f3cae869f0653a733d1d92043a..0000000000000000000000000000000000000000 --- a/utils/ssim.py +++ /dev/null @@ -1,80 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from https://github.com/Po-Hsun-Su/pytorch-ssim - -import torch -import torch.nn.functional as F -from torch.autograd import Variable -from math import exp - - -def gaussian(window_size, sigma): - gauss = torch.Tensor( - [ - exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) - for x in range(window_size) - ] - ) - return gauss / gauss.sum() - - -def create_window(window_size, channel): - _1D_window = gaussian(window_size, 1.5).unsqueeze(1) - _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) - window = Variable( - _2D_window.expand(channel, 1, window_size, window_size).contiguous() - ) - return window - - -def _ssim(img1, img2, window, window_size, channel, size_average=True): - mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) - mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) - - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1 * mu2 - - sigma1_sq = ( - F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq - ) - sigma2_sq = ( - F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq - ) - sigma12 = ( - F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - - mu1_mu2 - ) - - C1 = 0.01**2 - C2 = 0.03**2 - - ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( - (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) - ) - - if size_average: - return ssim_map.mean() - else: - return ssim_map.mean(1) - - -class SSIM(torch.nn.Module): - def __init__(self, window_size=11, size_average=True): - super(SSIM, self).__init__() - self.window_size = window_size - self.size_average = size_average - self.channel = 1 - self.window = create_window(window_size, self.channel) - - def forward(self, fake, real, bias=6.0): - fake = fake[:, None, :, :] + bias # [B, 1, T, n_mels] - real = real[:, None, :, :] + bias # [B, 1, T, n_mels] - self.window = self.window.to(dtype=fake.dtype, device=fake.device) - loss = 1 - _ssim( - fake, real, self.window, self.window_size, self.channel, self.size_average - ) - return loss diff --git a/utils/stft.py b/utils/stft.py deleted file mode 100644 index bcec4c84ace0cc40d65361316222b090428cc391..0000000000000000000000000000000000000000 --- a/utils/stft.py +++ /dev/null @@ -1,278 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import torch.nn.functional as F -import numpy as np -from scipy.signal import get_window -from librosa.util import pad_center, tiny -from librosa.filters import mel as librosa_mel_fn - -import torch -import numpy as np -import librosa.util as librosa_util -from scipy.signal import get_window - - -def window_sumsquare( - window, - n_frames, - hop_length, - win_length, - n_fft, - dtype=np.float32, - norm=None, -): - """ - # from librosa 0.6 - Compute the sum-square envelope of a window function at a given hop length. - - This is used to estimate modulation effects induced by windowing - observations in short-time fourier transforms. - - Parameters - ---------- - window : string, tuple, number, callable, or list-like - Window specification, as in `get_window` - - n_frames : int > 0 - The number of analysis frames - - hop_length : int > 0 - The number of samples to advance between frames - - win_length : [optional] - The length of the window function. By default, this matches `n_fft`. - - n_fft : int > 0 - The length of each analysis frame. - - dtype : np.dtype - The data type of the output - - Returns - ------- - wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` - The sum-squared envelope of the window function - """ - if win_length is None: - win_length = n_fft - - n = n_fft + hop_length * (n_frames - 1) - x = np.zeros(n, dtype=dtype) - - # Compute the squared window at the desired length - win_sq = get_window(window, win_length, fftbins=True) - win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2 - win_sq = librosa_util.pad_center(win_sq, n_fft) - - # Fill the envelope - for i in range(n_frames): - sample = i * hop_length - x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))] - return x - - -def griffin_lim(magnitudes, stft_fn, n_iters=30): - """ - PARAMS - ------ - magnitudes: spectrogram magnitudes - stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods - """ - - angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size()))) - angles = angles.astype(np.float32) - angles = torch.autograd.Variable(torch.from_numpy(angles)) - signal = stft_fn.inverse(magnitudes, angles).squeeze(1) - - for i in range(n_iters): - _, angles = stft_fn.transform(signal) - signal = stft_fn.inverse(magnitudes, angles).squeeze(1) - return signal - - -def dynamic_range_compression(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -class STFT(torch.nn.Module): - """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" - - def __init__(self, filter_length, hop_length, win_length, window="hann"): - super(STFT, self).__init__() - self.filter_length = filter_length - self.hop_length = hop_length - self.win_length = win_length - self.window = window - self.forward_transform = None - scale = self.filter_length / self.hop_length - fourier_basis = np.fft.fft(np.eye(self.filter_length)) - - cutoff = int((self.filter_length / 2 + 1)) - fourier_basis = np.vstack( - [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])] - ) - - forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) - inverse_basis = torch.FloatTensor( - np.linalg.pinv(scale * fourier_basis).T[:, None, :] - ) - - if window is not None: - assert filter_length >= win_length - # get window and zero center pad it to filter_length - fft_window = get_window(window, win_length, fftbins=True) - fft_window = pad_center(fft_window, filter_length) - fft_window = torch.from_numpy(fft_window).float() - - # window the bases - forward_basis *= fft_window - inverse_basis *= fft_window - - self.register_buffer("forward_basis", forward_basis.float()) - self.register_buffer("inverse_basis", inverse_basis.float()) - - def transform(self, input_data): - num_batches = input_data.size(0) - num_samples = input_data.size(1) - - self.num_samples = num_samples - - # similar to librosa, reflect-pad the input - input_data = input_data.view(num_batches, 1, num_samples) - input_data = F.pad( - input_data.unsqueeze(1), - (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), - mode="reflect", - ) - input_data = input_data.squeeze(1) - - forward_transform = F.conv1d( - input_data.cuda(), - torch.autograd.Variable(self.forward_basis, requires_grad=False).cuda(), - stride=self.hop_length, - padding=0, - ).cpu() - - cutoff = int((self.filter_length / 2) + 1) - real_part = forward_transform[:, :cutoff, :] - imag_part = forward_transform[:, cutoff:, :] - - magnitude = torch.sqrt(real_part**2 + imag_part**2) - phase = torch.autograd.Variable(torch.atan2(imag_part.data, real_part.data)) - - return magnitude, phase - - def inverse(self, magnitude, phase): - recombine_magnitude_phase = torch.cat( - [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 - ) - - inverse_transform = F.conv_transpose1d( - recombine_magnitude_phase, - torch.autograd.Variable(self.inverse_basis, requires_grad=False), - stride=self.hop_length, - padding=0, - ) - - if self.window is not None: - window_sum = window_sumsquare( - self.window, - magnitude.size(-1), - hop_length=self.hop_length, - win_length=self.win_length, - n_fft=self.filter_length, - dtype=np.float32, - ) - # remove modulation effects - approx_nonzero_indices = torch.from_numpy( - np.where(window_sum > tiny(window_sum))[0] - ) - window_sum = torch.autograd.Variable( - torch.from_numpy(window_sum), requires_grad=False - ) - window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum - inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ - approx_nonzero_indices - ] - - # scale by hop ratio - inverse_transform *= float(self.filter_length) / self.hop_length - - inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :] - inverse_transform = inverse_transform[:, :, : -int(self.filter_length / 2) :] - - return inverse_transform - - def forward(self, input_data): - self.magnitude, self.phase = self.transform(input_data) - reconstruction = self.inverse(self.magnitude, self.phase) - return reconstruction - - -class TacotronSTFT(torch.nn.Module): - def __init__( - self, - filter_length, - hop_length, - win_length, - n_mel_channels, - sampling_rate, - mel_fmin, - mel_fmax, - ): - super(TacotronSTFT, self).__init__() - self.n_mel_channels = n_mel_channels - self.sampling_rate = sampling_rate - self.stft_fn = STFT(filter_length, hop_length, win_length) - mel_basis = librosa_mel_fn( - sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax - ) - mel_basis = torch.from_numpy(mel_basis).float() - self.register_buffer("mel_basis", mel_basis) - - def spectral_normalize(self, magnitudes): - output = dynamic_range_compression(magnitudes) - return output - - def spectral_de_normalize(self, magnitudes): - output = dynamic_range_decompression(magnitudes) - return output - - def mel_spectrogram(self, y): - """Computes mel-spectrograms from a batch of waves - PARAMS - ------ - y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] - - RETURNS - ------- - mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) - """ - assert torch.min(y.data) >= -1 - assert torch.max(y.data) <= 1 - - magnitudes, phases = self.stft_fn.transform(y) - magnitudes = magnitudes.data - mel_output = torch.matmul(self.mel_basis, magnitudes) - mel_output = self.spectral_normalize(mel_output) - energy = torch.norm(magnitudes, dim=1) - - return mel_output, energy diff --git a/utils/symbol_table.py b/utils/symbol_table.py deleted file mode 100644 index 730ffe7a8018f80f662e260542a859a6f6f74a47..0000000000000000000000000000000000000000 --- a/utils/symbol_table.py +++ /dev/null @@ -1,313 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from -# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/utils/symbol_table.py - -from dataclasses import dataclass -from dataclasses import field -from typing import Dict -from typing import Generic -from typing import List -from typing import Optional -from typing import TypeVar -from typing import Union - -Symbol = TypeVar('Symbol') - - -@dataclass(repr=False) -class SymbolTable(Generic[Symbol]): - '''SymbolTable that maps symbol IDs, found on the FSA arcs to - actual objects. These objects can be arbitrary Python objects - that can serve as keys in a dictionary (i.e. they need to be - hashable and immutable). - - The SymbolTable can only be read to/written from disk if the - symbols are strings. - ''' - _id2sym: Dict[int, Symbol] = field(default_factory=dict) - '''Map an integer to a symbol. - ''' - - _sym2id: Dict[Symbol, int] = field(default_factory=dict) - '''Map a symbol to an integer. - ''' - - _next_available_id: int = 1 - '''A helper internal field that helps adding new symbols - to the table efficiently. - ''' - - eps: Symbol = '' - '''Null symbol, always mapped to index 0. - ''' - - def __post_init__(self): - assert all(self._sym2id[sym] == idx for idx, sym in self._id2sym.items()) - assert all(self._id2sym[idx] == sym for sym, idx in self._sym2id.items()) - assert 0 not in self._id2sym or self._id2sym[0] == self.eps - - self._next_available_id = max(self._id2sym, default=0) + 1 - self._id2sym.setdefault(0, self.eps) - self._sym2id.setdefault(self.eps, 0) - - - @staticmethod - def from_str(s: str) -> 'SymbolTable': - '''Build a symbol table from a string. - - The string consists of lines. Every line has two fields separated - by space(s), tab(s) or both. The first field is the symbol and the - second the integer id of the symbol. - - Args: - s: - The input string with the format described above. - Returns: - An instance of :class:`SymbolTable`. - ''' - id2sym: Dict[int, str] = dict() - sym2id: Dict[str, int] = dict() - - for line in s.split('\n'): - fields = line.split() - if len(fields) == 0: - continue # skip empty lines - assert len(fields) == 2, \ - f'Expect a line with 2 fields. Given: {len(fields)}' - sym, idx = fields[0], int(fields[1]) - assert sym not in sym2id, f'Duplicated symbol {sym}' - assert idx not in id2sym, f'Duplicated id {idx}' - id2sym[idx] = sym - sym2id[sym] = idx - - eps = id2sym.get(0, '') - - return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps) - - @staticmethod - def from_file(filename: str) -> 'SymbolTable': - '''Build a symbol table from file. - - Every line in the symbol table file has two fields separated by - space(s), tab(s) or both. The following is an example file: - - .. code-block:: - - 0 - a 1 - b 2 - c 3 - - Args: - filename: - Name of the symbol table file. Its format is documented above. - - Returns: - An instance of :class:`SymbolTable`. - - ''' - with open(filename, 'r', encoding='utf-8') as f: - return SymbolTable.from_str(f.read().strip()) - - def to_str(self) -> str: - ''' - Returns: - Return a string representation of this object. You can pass - it to the method ``from_str`` to recreate an identical object. - ''' - s = '' - for idx, symbol in sorted(self._id2sym.items()): - s += f'{symbol} {idx}\n' - return s - - def to_file(self, filename: str): - '''Serialize the SymbolTable to a file. - - Every line in the symbol table file has two fields separated by - space(s), tab(s) or both. The following is an example file: - - .. code-block:: - - 0 - a 1 - b 2 - c 3 - - Args: - filename: - Name of the symbol table file. Its format is documented above. - ''' - with open(filename, 'w') as f: - for idx, symbol in sorted(self._id2sym.items()): - print(symbol, idx, file=f) - - def add(self, symbol: Symbol, index: Optional[int] = None) -> int: - '''Add a new symbol to the SymbolTable. - - Args: - symbol: - The symbol to be added. - index: - Optional int id to which the symbol should be assigned. - If it is not available, a ValueError will be raised. - - Returns: - The int id to which the symbol has been assigned. - ''' - # Already in the table? Return its ID. - if symbol in self._sym2id: - return self._sym2id[symbol] - # Specific ID not provided - use next available. - if index is None: - index = self._next_available_id - # Specific ID provided but not available. - if index in self._id2sym: - raise ValueError(f"Cannot assign id '{index}' to '{symbol}' - " - f"already occupied by {self._id2sym[index]}") - self._sym2id[symbol] = index - self._id2sym[index] = symbol - - # Update next available ID if needed - if self._next_available_id <= index: - self._next_available_id = index + 1 - - return index - - def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]: - '''Get a symbol for an id or get an id for a symbol - - Args: - k: - If it is an id, it tries to find the symbol corresponding - to the id; if it is a symbol, it tries to find the id - corresponding to the symbol. - - Returns: - An id or a symbol depending on the given `k`. - ''' - if isinstance(k, int): - return self._id2sym[k] - else: - return self._sym2id[k] - - def merge(self, other: 'SymbolTable') -> 'SymbolTable': - '''Create a union of two SymbolTables. - Raises an AssertionError if the same IDs are occupied by - different symbols. - - Args: - other: - A symbol table to merge with ``self``. - - Returns: - A new symbol table. - ''' - self._check_compatible(other) - return SymbolTable( - _id2sym={**self._id2sym, **other._id2sym}, - _sym2id={**self._sym2id, **other._sym2id}, - eps=self.eps - ) - - def _check_compatible(self, other: 'SymbolTable') -> None: - # Epsilon compatibility - assert self.eps == other.eps, f'Mismatched epsilon symbol: ' \ - f'{self.eps} != {other.eps}' - # IDs compatibility - common_ids = set(self._id2sym).intersection(other._id2sym) - for idx in common_ids: - assert self[idx] == other[idx], f'ID conflict for id: {idx}, ' \ - f'self[idx] = "{self[idx]}", ' \ - f'other[idx] = "{other[idx]}"' - # Symbols compatibility - common_symbols = set(self._sym2id).intersection(other._sym2id) - for sym in common_symbols: - assert self[sym] == other[sym], f'ID conflict for id: {sym}, ' \ - f'self[sym] = "{self[sym]}", ' \ - f'other[sym] = "{other[sym]}"' - - def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]: - return self.get(item) - - def __contains__(self, item: Union[int, Symbol]) -> bool: - if isinstance(item, int): - return item in self._id2sym - else: - return item in self._sym2id - - def __len__(self) -> int: - return len(self._id2sym) - - def __eq__(self, other: 'SymbolTable') -> bool: - if len(self) != len(other): - return False - - for s in self.symbols: - if self[s] != other[s]: - return False - - return True - - @property - def ids(self) -> List[int]: - '''Returns a list of integer IDs corresponding to the symbols. - ''' - ans = list(self._id2sym.keys()) - ans.sort() - return ans - - @property - def symbols(self) -> List[Symbol]: - '''Returns a list of symbols (e.g., strings) corresponding to - the integer IDs. - ''' - ans = list(self._sym2id.keys()) - ans.sort() - return ans - - -class TextToken: - def __init__( - self, - text_tokens: List[str], - add_eos: bool = True, - add_bos: bool = True, - pad_symbol: str = "", - bos_symbol: str = "", - eos_symbol: str = "", - ): - self.pad_symbol = pad_symbol - self.add_eos = add_eos - self.add_bos = add_bos - self.bos_symbol = bos_symbol - self.eos_symbol = eos_symbol - - unique_tokens = [pad_symbol] - if add_bos: - unique_tokens.append(bos_symbol) - if add_eos: - unique_tokens.append(eos_symbol) - unique_tokens.extend(sorted(text_tokens)) - - self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)} - self.idx2token = unique_tokens - - - def get_token_id_seq(self, text): - tokens_seq = [p for p in text] - seq = ( - ([self.bos_symbol] if self.add_bos else []) - + tokens_seq - + ([self.eos_symbol] if self.add_eos else []) - ) - - token_ids = [self.token2idx[token] for token in seq] - token_lens = len(tokens_seq) + self.add_eos + self.add_bos - - return token_ids, token_lens - - \ No newline at end of file diff --git a/utils/tokenizer.py b/utils/tokenizer.py deleted file mode 100644 index 8971432bdcc2f3f2920775bb3397c90f2a91f8b8..0000000000000000000000000000000000000000 --- a/utils/tokenizer.py +++ /dev/null @@ -1,151 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# This code is modified from -# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py - -import re -from typing import Any, Dict, List, Optional, Pattern, Union - -import torch -import torchaudio -from encodec import EncodecModel -from encodec.utils import convert_audio - - - -class AudioTokenizer: - """EnCodec audio tokenizer for encoding and decoding audio. - - Attributes: - device: The device on which the codec model is loaded. - codec: The pretrained EnCodec model. - sample_rate: Sample rate of the model. - channels: Number of audio channels in the model. - """ - - def __init__(self, device: Any = None) -> None: - model = EncodecModel.encodec_model_24khz() - model.set_target_bandwidth(6.0) - remove_encodec_weight_norm(model) - - if not device: - device = torch.device("cpu") - if torch.cuda.is_available(): - device = torch.device("cuda:0") - - self._device = device - - self.codec = model.to(device) - self.sample_rate = model.sample_rate - self.channels = model.channels - - @property - def device(self): - return self._device - - def encode(self, wav: torch.Tensor) -> torch.Tensor: - """Encode the audio waveform. - - Args: - wav: A tensor representing the audio waveform. - - Returns: - A tensor representing the encoded audio. - """ - return self.codec.encode(wav.to(self.device)) - - def decode(self, frames: torch.Tensor) -> torch.Tensor: - """Decode the encoded audio frames. - - Args: - frames: A tensor representing the encoded audio frames. - - Returns: - A tensor representing the decoded audio waveform. - """ - return self.codec.decode(frames) - - - -def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str): - """ - Tokenize the audio waveform using the given AudioTokenizer. - - Args: - tokenizer: An instance of AudioTokenizer. - audio_path: Path to the audio file. - - Returns: - A tensor of encoded frames from the audio. - - Raises: - FileNotFoundError: If the audio file is not found. - RuntimeError: If there's an error processing the audio data. - """ - # try: - # Load and preprocess the audio waveform - wav, sr = torchaudio.load(audio_path) - wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels) - wav = wav.unsqueeze(0) - - # Extract discrete codes from EnCodec - with torch.no_grad(): - encoded_frames = tokenizer.encode(wav) - return encoded_frames - - # except FileNotFoundError: - # raise FileNotFoundError(f"Audio file not found at {audio_path}") - # except Exception as e: - # raise RuntimeError(f"Error processing audio data: {e}") - - - -def remove_encodec_weight_norm(model): - from encodec.modules import SConv1d - from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock - from torch.nn.utils import remove_weight_norm - - encoder = model.encoder.model - for key in encoder._modules: - if isinstance(encoder._modules[key], SEANetResnetBlock): - remove_weight_norm(encoder._modules[key].shortcut.conv.conv) - block_modules = encoder._modules[key].block._modules - for skey in block_modules: - if isinstance(block_modules[skey], SConv1d): - remove_weight_norm(block_modules[skey].conv.conv) - elif isinstance(encoder._modules[key], SConv1d): - remove_weight_norm(encoder._modules[key].conv.conv) - - decoder = model.decoder.model - for key in decoder._modules: - if isinstance(decoder._modules[key], SEANetResnetBlock): - remove_weight_norm(decoder._modules[key].shortcut.conv.conv) - block_modules = decoder._modules[key].block._modules - for skey in block_modules: - if isinstance(block_modules[skey], SConv1d): - remove_weight_norm(block_modules[skey].conv.conv) - elif isinstance(decoder._modules[key], SConvTranspose1d): - remove_weight_norm(decoder._modules[key].convtr.convtr) - elif isinstance(decoder._modules[key], SConv1d): - remove_weight_norm(decoder._modules[key].conv.conv) - - -def extract_encodec_token(wav_path): - model = EncodecModel.encodec_model_24khz() - model.set_target_bandwidth(6.0) - - wav, sr = torchaudio.load(wav_path) - wav = convert_audio(wav, sr, model.sample_rate, model.channels) - wav = wav.unsqueeze(0) - if torch.cuda.is_available(): - model = model.cuda() - wav = wav.cuda() - with torch.no_grad(): - encoded_frames = model.encode(wav) - codes_ = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1) # [B, n_q, T] - codes = codes_.cpu().numpy()[0,:,:].T # [T, 8] - - return codes \ No newline at end of file diff --git a/utils/topk_sampling.py b/utils/topk_sampling.py deleted file mode 100644 index 03f405c15506609bbd571818123cf7f0db7f1f4b..0000000000000000000000000000000000000000 --- a/utils/topk_sampling.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import torch -import torch.nn.functional as F - - -# This function is modified from https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py -def top_k_top_p_filtering( - logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1 -): - """ - Filter a distribution of logits using top-k and/or nucleus (top-p) filtering. - - Args: - logits (torch.Tensor): Logits distribution with shape (batch size, vocabulary size). - top_k (int, optional): Keep only top k tokens with highest probability (top-k filtering). - Set to 0 to disable. Defaults to 0. - top_p (float, optional): Keep the top tokens with a cumulative probability >= top_p (nucleus filtering). - Must be between 0 and 1, inclusive. Defaults to 1.0. - filter_value (float, optional): The value to assign to filtered logits. Defaults to -float('Inf'). - min_tokens_to_keep (int, optional): Ensure that at least this number of tokens are kept per batch example. - Defaults to 1. - - Returns: - torch.Tensor: The filtered logits. - """ - """ - Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) - Make sure we keep at least min_tokens_to_keep per batch example in the output - From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 - """ - if top_k > 0: - # Apply top-k filtering - top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) - indices_to_remove = logits < torch.topk(logits, top_k).values[..., -1, None] - logits[indices_to_remove] = filter_value - - if top_p < 1.0: - # Apply top-p filtering - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) - - # Create a mask to remove tokens with cumulative probability above the top_p threshold - sorted_indices_to_remove = cumulative_probs > top_p - if min_tokens_to_keep > 1: - sorted_indices_to_remove[..., :min_tokens_to_keep] = 0 - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() - sorted_indices_to_remove[..., 0] = 0 - - # Scatter sorted tensors back to original indexing - indices_to_remove = sorted_indices.scatter(1, sorted_indices, sorted_indices_to_remove) - logits[indices_to_remove] = filter_value - - return logits - - -def topk_sampling(logits, top_k=50, top_p=1.0, temperature=1.0): - """ - Perform top-k and top-p sampling on logits. - - Args: - logits (torch.Tensor): The logits to sample from. - top_k (int, optional): The number of highest probability tokens to keep for top-k filtering. - Must be a positive integer. Defaults to 50. - top_p (float, optional): The cumulative probability threshold for nucleus sampling. - Must be between 0 and 1. Defaults to 1.0. - temperature (float, optional): The scaling factor to adjust the logits distribution. - Must be strictly positive. Defaults to 1.0. - - Returns: - torch.Tensor: The sampled token. - """ - - # Adjust logits using temperature - if temperature != 1.0: - logits = logits / temperature - - # Top-p/top-k filtering - logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) - - # Sample from the filtered distribution - token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) - return token diff --git a/utils/trainer_utils.py b/utils/trainer_utils.py deleted file mode 100644 index e5d9ad794864aa3ee0c49a86e9be293f69442886..0000000000000000000000000000000000000000 --- a/utils/trainer_utils.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - - -def check_nan(logger, loss, y_pred, y_gt): - if torch.any(torch.isnan(loss)): - logger.info("out has nan: ", torch.any(torch.isnan(y_pred))) - logger.info("y_gt has nan: ", torch.any(torch.isnan(y_gt))) - logger.info("out: ", y_pred) - logger.info("y_gt: ", y_gt) - logger.info("loss = {:.4f}\n".format(loss.item())) - exit() diff --git a/utils/util.py b/utils/util.py deleted file mode 100644 index 096d9129cd0dab4798f205ab7fdc690303ef5d37..0000000000000000000000000000000000000000 --- a/utils/util.py +++ /dev/null @@ -1,688 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - - -import collections -import glob -import os -import random -import time -import argparse -from collections import OrderedDict - -import json5 -import numpy as np -import glob -from torch.nn import functional as F - - -try: - from ruamel.yaml import YAML as yaml -except: - from ruamel_yaml import YAML as yaml - -import torch - -from utils.hparam import HParams -import logging -from logging import handlers - - -def str2bool(v): - """Used in argparse.ArgumentParser.add_argument to indicate - that a type is a bool type and user can enter - - - yes, true, t, y, 1, to represent True - - no, false, f, n, 0, to represent False - - See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse # noqa - """ - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def find_checkpoint_of_mapper(mapper_ckpt_dir): - mapper_ckpts = glob.glob(os.path.join(mapper_ckpt_dir, "ckpts/*.pt")) - - # Select the max steps - mapper_ckpts.sort() - mapper_weights_file = mapper_ckpts[-1] - return mapper_weights_file - - -def pad_f0_to_tensors(f0s, batched=None): - # Initialize - tensors = [] - - if batched == None: - # Get the max frame for padding - size = -1 - for f0 in f0s: - size = max(size, f0.shape[-1]) - - tensor = torch.zeros(len(f0s), size) - - for i, f0 in enumerate(f0s): - tensor[i, : f0.shape[-1]] = f0[:] - - tensors.append(tensor) - else: - start = 0 - while start + batched - 1 < len(f0s): - end = start + batched - 1 - - # Get the max frame for padding - size = -1 - for i in range(start, end + 1): - size = max(size, f0s[i].shape[-1]) - - tensor = torch.zeros(batched, size) - - for i in range(start, end + 1): - tensor[i - start, : f0s[i].shape[-1]] = f0s[i][:] - - tensors.append(tensor) - - start = start + batched - - if start != len(f0s): - end = len(f0s) - - # Get the max frame for padding - size = -1 - for i in range(start, end): - size = max(size, f0s[i].shape[-1]) - - tensor = torch.zeros(len(f0s) - start, size) - - for i in range(start, end): - tensor[i - start, : f0s[i].shape[-1]] = f0s[i][:] - - tensors.append(tensor) - - return tensors - - -def pad_mels_to_tensors(mels, batched=None): - """ - Args: - mels: A list of mel-specs - Returns: - tensors: A list of tensors containing the batched mel-specs - mel_frames: A list of tensors containing the frames of the original mel-specs - """ - # Initialize - tensors = [] - mel_frames = [] - - # Split mel-specs into batches to avoid cuda memory exceed - if batched == None: - # Get the max frame for padding - size = -1 - for mel in mels: - size = max(size, mel.shape[-1]) - - tensor = torch.zeros(len(mels), mels[0].shape[0], size) - mel_frame = torch.zeros(len(mels), dtype=torch.int32) - - for i, mel in enumerate(mels): - tensor[i, :, : mel.shape[-1]] = mel[:] - mel_frame[i] = mel.shape[-1] - - tensors.append(tensor) - mel_frames.append(mel_frame) - else: - start = 0 - while start + batched - 1 < len(mels): - end = start + batched - 1 - - # Get the max frame for padding - size = -1 - for i in range(start, end + 1): - size = max(size, mels[i].shape[-1]) - - tensor = torch.zeros(batched, mels[0].shape[0], size) - mel_frame = torch.zeros(batched, dtype=torch.int32) - - for i in range(start, end + 1): - tensor[i - start, :, : mels[i].shape[-1]] = mels[i][:] - mel_frame[i - start] = mels[i].shape[-1] - - tensors.append(tensor) - mel_frames.append(mel_frame) - - start = start + batched - - if start != len(mels): - end = len(mels) - - # Get the max frame for padding - size = -1 - for i in range(start, end): - size = max(size, mels[i].shape[-1]) - - tensor = torch.zeros(len(mels) - start, mels[0].shape[0], size) - mel_frame = torch.zeros(len(mels) - start, dtype=torch.int32) - - for i in range(start, end): - tensor[i - start, :, : mels[i].shape[-1]] = mels[i][:] - mel_frame[i - start] = mels[i].shape[-1] - - tensors.append(tensor) - mel_frames.append(mel_frame) - - return tensors, mel_frames - - -def load_model_config(args): - """Load model configurations (in args.json under checkpoint directory) - - Args: - args (ArgumentParser): arguments to run bins/preprocess.py - - Returns: - dict: dictionary that stores model configurations - """ - if args.checkpoint_dir is None: - assert args.checkpoint_file is not None - checkpoint_dir = os.path.split(args.checkpoint_file)[0] - else: - checkpoint_dir = args.checkpoint_dir - config_path = os.path.join(checkpoint_dir, "args.json") - print("config_path: ", config_path) - - config = load_config(config_path) - return config - - -def remove_and_create(dir): - if os.path.exists(dir): - os.system("rm -r {}".format(dir)) - os.makedirs(dir, exist_ok=True) - - -def has_existed(path, warning=False): - if not warning: - return os.path.exists(path) - - if os.path.exists(path): - answer = input( - "The path {} has existed. \nInput 'y' (or hit Enter) to skip it, and input 'n' to re-write it [y/n]\n".format( - path - ) - ) - if not answer == "n": - return True - - return False - - -def remove_older_ckpt(saved_model_name, checkpoint_dir, max_to_keep=5): - if os.path.exists(os.path.join(checkpoint_dir, "checkpoint")): - with open(os.path.join(checkpoint_dir, "checkpoint"), "r") as f: - ckpts = [x.strip() for x in f.readlines()] - else: - ckpts = [] - ckpts.append(saved_model_name) - for item in ckpts[:-max_to_keep]: - if os.path.exists(os.path.join(checkpoint_dir, item)): - os.remove(os.path.join(checkpoint_dir, item)) - with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as f: - for item in ckpts[-max_to_keep:]: - f.write("{}\n".format(item)) - - -def set_all_random_seed(seed: int): - random.seed(seed) - np.random.seed(seed) - torch.random.manual_seed(seed) - - -def save_checkpoint( - args, - generator, - g_optimizer, - step, - discriminator=None, - d_optimizer=None, - max_to_keep=5, -): - saved_model_name = "model.ckpt-{}.pt".format(step) - checkpoint_path = os.path.join(args.checkpoint_dir, saved_model_name) - - if discriminator and d_optimizer: - torch.save( - { - "generator": generator.state_dict(), - "discriminator": discriminator.state_dict(), - "g_optimizer": g_optimizer.state_dict(), - "d_optimizer": d_optimizer.state_dict(), - "global_step": step, - }, - checkpoint_path, - ) - else: - torch.save( - { - "generator": generator.state_dict(), - "g_optimizer": g_optimizer.state_dict(), - "global_step": step, - }, - checkpoint_path, - ) - - print("Saved checkpoint: {}".format(checkpoint_path)) - - if os.path.exists(os.path.join(args.checkpoint_dir, "checkpoint")): - with open(os.path.join(args.checkpoint_dir, "checkpoint"), "r") as f: - ckpts = [x.strip() for x in f.readlines()] - else: - ckpts = [] - ckpts.append(saved_model_name) - for item in ckpts[:-max_to_keep]: - if os.path.exists(os.path.join(args.checkpoint_dir, item)): - os.remove(os.path.join(args.checkpoint_dir, item)) - with open(os.path.join(args.checkpoint_dir, "checkpoint"), "w") as f: - for item in ckpts[-max_to_keep:]: - f.write("{}\n".format(item)) - - -def attempt_to_restore( - generator, g_optimizer, checkpoint_dir, discriminator=None, d_optimizer=None -): - checkpoint_list = os.path.join(checkpoint_dir, "checkpoint") - if os.path.exists(checkpoint_list): - checkpoint_filename = open(checkpoint_list).readlines()[-1].strip() - checkpoint_path = os.path.join(checkpoint_dir, "{}".format(checkpoint_filename)) - print("Restore from {}".format(checkpoint_path)) - checkpoint = torch.load(checkpoint_path, map_location="cpu") - if generator: - if not list(generator.state_dict().keys())[0].startswith("module."): - raw_dict = checkpoint["generator"] - clean_dict = OrderedDict() - for k, v in raw_dict.items(): - if k.startswith("module."): - clean_dict[k[7:]] = v - else: - clean_dict[k] = v - generator.load_state_dict(clean_dict) - else: - generator.load_state_dict(checkpoint["generator"]) - if g_optimizer: - g_optimizer.load_state_dict(checkpoint["g_optimizer"]) - global_step = 100000 - if discriminator and "discriminator" in checkpoint.keys(): - discriminator.load_state_dict(checkpoint["discriminator"]) - global_step = checkpoint["global_step"] - print("restore discriminator") - if d_optimizer and "d_optimizer" in checkpoint.keys(): - d_optimizer.load_state_dict(checkpoint["d_optimizer"]) - print("restore d_optimizer...") - else: - global_step = 0 - return global_step - - -class ExponentialMovingAverage(object): - def __init__(self, decay): - self.decay = decay - self.shadow = {} - - def register(self, name, val): - self.shadow[name] = val.clone() - - def update(self, name, x): - assert name in self.shadow - update_delta = self.shadow[name] - x - self.shadow[name] -= (1.0 - self.decay) * update_delta - - -def apply_moving_average(model, ema): - for name, param in model.named_parameters(): - if name in ema.shadow: - ema.update(name, param.data) - - -def register_model_to_ema(model, ema): - for name, param in model.named_parameters(): - if param.requires_grad: - ema.register(name, param.data) - - -class YParams(HParams): - def __init__(self, yaml_file): - if not os.path.exists(yaml_file): - raise IOError("yaml file: {} is not existed".format(yaml_file)) - super().__init__() - self.d = collections.OrderedDict() - with open(yaml_file) as fp: - for _, v in yaml().load(fp).items(): - for k1, v1 in v.items(): - try: - if self.get(k1): - self.set_hparam(k1, v1) - else: - self.add_hparam(k1, v1) - self.d[k1] = v1 - except Exception: - import traceback - - print(traceback.format_exc()) - - # @property - def get_elements(self): - return self.d.items() - - -def override_config(base_config, new_config): - """Update new configurations in the original dict with the new dict - - Args: - base_config (dict): original dict to be overridden - new_config (dict): dict with new configurations - - Returns: - dict: updated configuration dict - """ - for k, v in new_config.items(): - if type(v) == dict: - if k not in base_config.keys(): - base_config[k] = {} - base_config[k] = override_config(base_config[k], v) - else: - base_config[k] = v - return base_config - - -def get_lowercase_keys_config(cfg): - """Change all keys in cfg to lower case - - Args: - cfg (dict): dictionary that stores configurations - - Returns: - dict: dictionary that stores configurations - """ - updated_cfg = dict() - for k, v in cfg.items(): - if type(v) == dict: - v = get_lowercase_keys_config(v) - updated_cfg[k.lower()] = v - return updated_cfg - - -def _load_config(config_fn, lowercase=False): - """Load configurations into a dictionary - - Args: - config_fn (str): path to configuration file - lowercase (bool, optional): whether changing keys to lower case. Defaults to False. - - Returns: - dict: dictionary that stores configurations - """ - with open(config_fn, "r") as f: - data = f.read() - config_ = json5.loads(data) - if "base_config" in config_: - # load configurations from new path - p_config_path = os.path.join(os.getenv("WORK_DIR"), config_["base_config"]) - p_config_ = _load_config(p_config_path) - config_ = override_config(p_config_, config_) - if lowercase: - # change keys in config_ to lower case - config_ = get_lowercase_keys_config(config_) - return config_ - - -def load_config(config_fn, lowercase=False): - """Load configurations into a dictionary - - Args: - config_fn (str): path to configuration file - lowercase (bool, optional): _description_. Defaults to False. - - Returns: - JsonHParams: an object that stores configurations - """ - config_ = _load_config(config_fn, lowercase=lowercase) - # create an JsonHParams object with configuration dict - cfg = JsonHParams(**config_) - return cfg - - -def save_config(save_path, cfg): - """Save configurations into a json file - - Args: - save_path (str): path to save configurations - cfg (dict): dictionary that stores configurations - """ - with open(save_path, "w") as f: - json5.dump( - cfg, f, ensure_ascii=False, indent=4, quote_keys=True, sort_keys=True - ) - - -class JsonHParams: - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = JsonHParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() - - -class ValueWindow: - def __init__(self, window_size=100): - self._window_size = window_size - self._values = [] - - def append(self, x): - self._values = self._values[-(self._window_size - 1) :] + [x] - - @property - def sum(self): - return sum(self._values) - - @property - def count(self): - return len(self._values) - - @property - def average(self): - return self.sum / max(1, self.count) - - def reset(self): - self._values = [] - - -class Logger(object): - def __init__( - self, - filename, - level="info", - when="D", - backCount=10, - fmt="%(asctime)s : %(message)s", - ): - self.level_relations = { - "debug": logging.DEBUG, - "info": logging.INFO, - "warning": logging.WARNING, - "error": logging.ERROR, - "crit": logging.CRITICAL, - } - if level == "debug": - fmt = "%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s" - self.logger = logging.getLogger(filename) - format_str = logging.Formatter(fmt) - self.logger.setLevel(self.level_relations.get(level)) - sh = logging.StreamHandler() - sh.setFormatter(format_str) - th = handlers.TimedRotatingFileHandler( - filename=filename, when=when, backupCount=backCount, encoding="utf-8" - ) - th.setFormatter(format_str) - self.logger.addHandler(sh) - self.logger.addHandler(th) - self.logger.info( - "==========================New Starting Here==============================" - ) - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2, 3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1.0 / norm_type) - return total_norm - - -def get_current_time(): - pass - - -def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: - """ - Args: - lengths: - A 1-D tensor containing sentence lengths. - max_len: - The length of masks. - Returns: - Return a 2-D bool tensor, where masked positions - are filled with `True` and non-masked positions are - filled with `False`. - - >>> lengths = torch.tensor([1, 3, 2, 5]) - >>> make_pad_mask(lengths) - tensor([[False, True, True, True, True], - [False, False, False, True, True], - [False, False, True, True, True], - [False, False, False, False, False]]) - """ - assert lengths.ndim == 1, lengths.ndim - max_len = max(max_len, lengths.max()) - n = lengths.size(0) - seq_range = torch.arange(0, max_len, device=lengths.device) - expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len) - - return expaned_lengths >= lengths.unsqueeze(-1) - diff --git a/utils/whisper.py b/utils/whisper.py deleted file mode 100644 index 16462c7e84f2ce71d4fd5e57832d705fd07b95ca..0000000000000000000000000000000000000000 --- a/utils/whisper.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import os -import pickle -from tqdm import tqdm -import numpy as np - -from modules import whisper_extractor as whisper - - -def whisper_encoder_batch(model, audio_paths): - batch = len(audio_paths) - batch_mel = torch.zeros((batch, 80, 3000), dtype=torch.float32, device=model.device) - - for i, audio_path in enumerate(audio_paths): - # (48000,) - audio = whisper.load_audio(str(audio_path)) - audio = whisper.pad_or_trim(audio) - - # (80, 3000) - mel = whisper.log_mel_spectrogram(audio).to(model.device) - batch_mel[i] = mel - - with torch.no_grad(): - # (batch, 1500, 1024) - features = model.embed_audio(batch_mel) - - return features.cpu().detach().numpy() - - -def whisper_encoder(model, audio_path): - audio = whisper.load_audio(str(audio_path)) - audio = whisper.pad_or_trim(audio) - - # (80, 3000) - mel = whisper.log_mel_spectrogram(audio).to(model.device).unsqueeze(0) - - with torch.no_grad(): - # (1, 1500, 1024) -> # (1500, 1024) - features = model.embed_audio(mel).squeeze(0) - - return features.cpu().detach().numpy() - - -def get_mapped_whisper_features( - raw_whisper_features, mapping_features, fast_mapping=True -): - """ - Whisper: frameshift = 20ms (30s audio -> 1500 frames), hop_size = 480 in 24k - # Ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/model.py#L136 - - Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms) - """ - source_hop = 480 - target_hop = 256 - - factor = np.gcd(source_hop, target_hop) - source_hop //= factor - target_hop //= factor - print( - "Mapping source's {} frames => target's {} frames".format( - target_hop, source_hop - ) - ) - - max_source_len = 1500 - whisper_features = [] - for index, mapping_feat in enumerate(tqdm(mapping_features)): - # mapping_feat: (mels_frame_len, n_mels) - target_len = mapping_feat.shape[0] - # The max target_len is 2812 - target_len = min(target_len, max_source_len * source_hop // target_hop) - - # (1500, dim) - raw_feats = raw_whisper_features[index] - width = raw_feats.shape[-1] - - if fast_mapping: - source_len = target_len * target_hop // source_hop + 1 - raw_feats = raw_feats[:source_len] - else: - source_len = max_source_len - - # const ~= target_len * target_hop - const = source_len * source_hop // target_hop * target_hop - - # (source_len * source_hop, dim) - up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0) - # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim) - down_sampling_feats = np.average( - up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1 - ) - assert len(down_sampling_feats) >= target_len - - # (target_len, dim) - feats = down_sampling_feats[:target_len] - whisper_features.append(feats) - - return whisper_features - - -def load_whisper_model(hps): - print("Loading Whisper Model: ", hps.whisper_model) - model = whisper.load_model(hps.whisper_model) - if torch.cuda.is_available(): - model = model.cuda() - - model = model.eval() - return model - - -def load_target_acoustic_features( - output_path, dataset, acoustic_features_name, acoustic_features_fs, dataset_type -): - mapping_dir = os.path.join( - output_path, - dataset, - "{}/{}".format(acoustic_features_name, acoustic_features_fs), - ) - with open(os.path.join(mapping_dir, "{}.pkl".format(dataset_type)), "rb") as f: - mapping_features = pickle.load(f) - - # Mels: (n_mels, frame_len) -> (frame_len, n_mels) - if acoustic_features_name == "mels": - print("Transposing mel features...") - mapping_features = [feat.T for feat in mapping_features] - - print( - "Mapping to the acoustic features {}, #sz = {}, feats[0] is {}".format( - acoustic_features_name, len(mapping_features), mapping_features[0].shape - ) - ) - return mapping_features - - -def extract_whisper_features_of_dataset( - datasets, - model, - batch_size, - out_dir, -): - audio_paths = [utt["Path"] for utt in datasets] - if len(audio_paths) < batch_size: - batch_size = len(audio_paths) - - start, end = 0, 0 - while end < len(audio_paths): - # Raw features: (batch_size, 1500, dim) - start = end - end = start + batch_size - tmp_raw_whisper_features = whisper_encoder_batch(model, audio_paths[start:end]) - - # Mapping to acoustic features' lengths - for index, utt in enumerate(tqdm(datasets[start:end])): - uid = utt["Uid"] - raw_whisper_feature = tmp_raw_whisper_features[index] - - save_path = os.path.join(out_dir, uid + ".npy") - np.save(save_path, raw_whisper_feature) - - print("{}/{} Done...".format(end, len(audio_paths))) diff --git a/utils/world.py b/utils/world.py deleted file mode 100644 index ce5f61bd9b571607fd83da6b22283757e67201da..0000000000000000000000000000000000000000 --- a/utils/world.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -# 1. Extract WORLD features including F0, AP, SP -# 2. Transform between SP and MCEP -import torchaudio -import pyworld as pw -import numpy as np -import torch -import diffsptk -import os -from tqdm import tqdm -import pickle -import torchaudio - - -def get_mcep_params(fs): - """Hyperparameters of transformation between SP and MCEP - - Reference: - https://github.com/CSTR-Edinburgh/merlin/blob/master/misc/scripts/vocoder/world_v2/copy_synthesis.sh - - """ - if fs in [44100, 48000]: - fft_size = 2048 - alpha = 0.77 - if fs in [16000]: - fft_size = 1024 - alpha = 0.58 - return fft_size, alpha - - -def extract_world_features(waveform, frameshift=10): - # waveform: (1, seq) - # x: (seq,) - x = np.array(waveform, dtype=np.double) - - _f0, t = pw.dio(x, fs, frame_period=frameshift) # raw pitch extractor - f0 = pw.stonemask(x, _f0, t, fs) # pitch refinement - sp = pw.cheaptrick(x, f0, t, fs) # extract smoothed spectrogram - ap = pw.d4c(x, f0, t, fs) # extract aperiodicity - - return f0, sp, ap, fs - - -def sp2mcep(x, mcsize, fs): - fft_size, alpha = get_mcep_params(fs) - x = torch.as_tensor(x, dtype=torch.float) - - tmp = diffsptk.ScalarOperation("SquareRoot")(x) - tmp = diffsptk.ScalarOperation("Multiplication", 32768.0)(tmp) - mgc = diffsptk.MelCepstralAnalysis( - cep_order=mcsize - 1, fft_length=fft_size, alpha=alpha, n_iter=1 - )(tmp) - return mgc.numpy() - - -def mcep2sp(x, mcsize, fs): - fft_size, alpha = get_mcep_params(fs) - x = torch.as_tensor(x, dtype=torch.float) - - tmp = diffsptk.MelGeneralizedCepstrumToSpectrum( - alpha=alpha, - cep_order=mcsize - 1, - fft_length=fft_size, - )(x) - tmp = diffsptk.ScalarOperation("Division", 32768.0)(tmp) - sp = diffsptk.ScalarOperation("Power", 2)(tmp) - return sp.double().numpy() - - -def f0_statistics(f0_features, path): - print("\nF0 statistics...") - - total_f0 = [] - for f0 in tqdm(f0_features): - total_f0 += [f for f in f0 if f != 0] - - mean = sum(total_f0) / len(total_f0) - print("Min = {}, Max = {}, Mean = {}".format(min(total_f0), max(total_f0), mean)) - - with open(path, "wb") as f: - pickle.dump([mean, total_f0], f) - - -def world_synthesis(f0, sp, ap, fs, frameshift): - y = pw.synthesize( - f0, sp, ap, fs, frame_period=frameshift - ) # synthesize an utterance using the parameters - return y