test

ckpts/svc/vocalist_l1_contentvec+whisper/args.json

@@ -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,
-}

ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/optimizer.bin

@@ -1,3 +0,0 @@
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-size 249512389

ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/pytorch_model.bin

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ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/random_states_0.pkl

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ckpts/svc/vocalist_l1_contentvec+whisper/checkpoint/epoch-6852_step-0678447_loss-1.946773/singers.json

@@ -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
-}

ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.0

@@ -1,3 +0,0 @@
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ckpts/svc/vocalist_l1_contentvec+whisper/log/vocalist_l1_contentvec+whisper/events.out.tfevents.1696052302.mmnewyardnodesz63219.120.1

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ckpts/svc/vocalist_l1_contentvec+whisper/singers.json

@@ -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
-}

config/audioldm.json

@@ -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
-    }
-  }
-}

config/autoencoderkl.json

@@ -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
-    }
-  }
-}

config/base.json

@@ -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",
-    ],
-  },
-}

config/comosvc.json

@@ -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
-        }
-    }
-}

config/diffusion.json

@@ -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
-            }
-        }
-    }
-}

config/fs2.json

@@ -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
-      },
-    }
-
-}

config/transformer.json

@@ -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
-        }
-    }
-}

config/tts.json

@@ -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,
-  }
-
-}

config/valle.json

@@ -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 <BOS> 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
-        ]
-    }
-}

config/vits.json

@@ -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,
-        }
-    }
-}

config/vocoder.json

@@ -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"
-    ],
-  }
-}

egs/svc/MultipleContentsSVC/README.md

@@ -1,153 +0,0 @@
-# Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion
-
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2310.11160)
-[![demo](https://img.shields.io/badge/SVC-Demo-red)](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html)
-
-<br>
-<div align="center">
-<img src="../../../imgs/svc/MultipleContentsSVC.png" width="85%">
-</div>
-<br>
-
-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}
-}
-```

egs/svc/MultipleContentsSVC/exp_config.json

@@ -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
-        }
-    }
-}

egs/svc/MultipleContentsSVC/run.sh

@@ -1 +0,0 @@
-../_template/run.sh

egs/svc/README.md

@@ -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):
-
-<br>
-<div align="center">
-  <img src="../../imgs/svc/pipeline.png" width="70%">
-</div>
-<br>
-
-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).

egs/svc/_template/run.sh

@@ -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

egs/vocoder/README.md

@@ -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)

egs/vocoder/gan/README.md

@@ -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:
-
-<br>
-<div align="center">
-  <img src="../../../imgs/vocoder/gan/pipeline.png" width="40%">
-</div>
-<br>
-
-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 \
-```

egs/vocoder/gan/_template/run.sh

@@ -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

egs/vocoder/gan/apnet/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/apnet/run.sh

@@ -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

egs/vocoder/gan/bigvgan/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/bigvgan/run.sh

@@ -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

egs/vocoder/gan/bigvgan_large/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/bigvgan_large/run.sh

@@ -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

egs/vocoder/gan/exp_config_base.json

@@ -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
-    },
-  }
-}

egs/vocoder/gan/hifigan/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/hifigan/run.sh

@@ -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

egs/vocoder/gan/melgan/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/melgan/run.sh

@@ -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

egs/vocoder/gan/nsfhifigan/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/nsfhifigan/run.sh

@@ -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

egs/vocoder/gan/tfr_enhanced_hifigan/README.md

@@ -1,185 +0,0 @@
-# Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fedility Vocoder
-
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2311.14957)
-[![demo](https://img.shields.io/badge/Vocoder-Demo-red)](https://vocodexelysium.github.io/MS-SB-CQTD/)
-
-<br>
-<div align="center">
-<img src="../../../../imgs/vocoder/gan/MSSBCQTD.png" width="80%">
-</div>
-<br>
-
-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}
-}
-```

egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json

@@ -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,
-  }
-}

egs/vocoder/gan/tfr_enhanced_hifigan/run.sh

@@ -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

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