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best_model.pth

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best_model_188159.pth

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checkpoint_209000.pth

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checkpoint_210000.pth

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config.json

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+{
+    "output_path": "yourtts_luo",
+    "logger_uri": null,
+    "run_name": "YourTTS-Luo",
+    "project_name": "YourTTS",
+    "run_description": "\n            - YourTTS trained using the Luo OpenBible dataset.\n        ",
+    "print_step": 50,
+    "plot_step": 100,
+    "model_param_stats": false,
+    "wandb_entity": null,
+    "dashboard_logger": "tensorboard",
+    "save_on_interrupt": true,
+    "log_model_step": 1000,
+    "save_step": 1000,
+    "save_n_checkpoints": 2,
+    "save_checkpoints": true,
+    "save_all_best": false,
+    "save_best_after": 0,
+    "target_loss": "loss_1",
+    "print_eval": true,
+    "test_delay_epochs": 0,
+    "run_eval": true,
+    "run_eval_steps": null,
+    "distributed_backend": "nccl",
+    "distributed_url": "tcp://localhost:54321",
+    "mixed_precision": true,
+    "precision": "fp16",
+    "epochs": 1000,
+    "batch_size": 20,
+    "eval_batch_size": 20,
+    "grad_clip": [
+        1000,
+        1000
+    ],
+    "scheduler_after_epoch": true,
+    "lr": 0.001,
+    "optimizer": "AdamW",
+    "optimizer_params": {
+        "betas": [
+            0.8,
+            0.99
+        ],
+        "eps": 1e-09,
+        "weight_decay": 0.01
+    },
+    "lr_scheduler": null,
+    "lr_scheduler_params": null,
+    "use_grad_scaler": false,
+    "allow_tf32": false,
+    "cudnn_enable": true,
+    "cudnn_deterministic": false,
+    "cudnn_benchmark": false,
+    "training_seed": 54321,
+    "model": "vits",
+    "num_loader_workers": 8,
+    "num_eval_loader_workers": 0,
+    "use_noise_augment": false,
+    "audio": {
+        "fft_size": 1024,
+        "sample_rate": 24000,
+        "win_length": 1024,
+        "hop_length": 256,
+        "num_mels": 80,
+        "mel_fmin": 0.0,
+        "mel_fmax": null
+    },
+    "use_phonemes": false,
+    "phonemizer": null,
+    "phoneme_language": null,
+    "compute_input_seq_cache": true,
+    "text_cleaner": "no_cleaners",
+    "enable_eos_bos_chars": false,
+    "test_sentences_file": "",
+    "phoneme_cache_path": null,
+    "characters": {
+        "characters_class": "TTS.tts.models.vits.VitsCharacters",
+        "vocab_dict": null,
+        "pad": "_",
+        "eos": "&",
+        "bos": "*",
+        "blank": null,
+        "characters": "abcdefghijklmnoprstuvwyz\u02bc",
+        "punctuations": " !',.:;?\u2019",
+        "phonemes": null,
+        "is_unique": true,
+        "is_sorted": true
+    },
+    "add_blank": true,
+    "batch_group_size": 4,
+    "loss_masking": null,
+    "min_audio_len": 19200,
+    "max_audio_len": 288000,
+    "min_text_len": 1,
+    "max_text_len": Infinity,
+    "compute_f0": false,
+    "compute_energy": false,
+    "compute_linear_spec": true,
+    "precompute_num_workers": 12,
+    "start_by_longest": true,
+    "shuffle": false,
+    "drop_last": false,
+    "datasets": [
+        {
+            "formatter": "",
+            "dataset_name": "luo_openbible",
+            "path": "data",
+            "meta_file_train": "manifest_train.jsonl",
+            "ignored_speakers": null,
+            "language": "luo",
+            "phonemizer": "",
+            "meta_file_val": "manifest_dev.jsonl",
+            "meta_file_attn_mask": ""
+        }
+    ],
+    "test_sentences": [
+        [
+            "jo kolosai achiel.",
+            "one",
+            null,
+            "luo"
+        ],
+        [
+            "magoyo erokamano ni wuoro ka un gi mor.",
+            "one",
+            null,
+            "luo"
+        ],
+        [
+            "epafra bende nonyisowa kuom hera ma roho maler osemiyou.",
+            "one",
+            null,
+            "luo"
+        ]
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+    "eval_split_size": 0.01,
+    "use_speaker_weighted_sampler": false,
+    "speaker_weighted_sampler_alpha": 1.0,
+    "use_language_weighted_sampler": false,
+    "language_weighted_sampler_alpha": 1.0,
+    "use_length_weighted_sampler": false,
+    "length_weighted_sampler_alpha": 1.0,
+    "model_args": {
+        "num_chars": 36,
+        "out_channels": 513,
+        "spec_segment_size": 62,
+        "hidden_channels": 192,
+        "hidden_channels_ffn_text_encoder": 768,
+        "num_heads_text_encoder": 2,
+        "num_layers_text_encoder": 10,
+        "kernel_size_text_encoder": 3,
+        "dropout_p_text_encoder": 0.1,
+        "dropout_p_duration_predictor": 0.5,
+        "kernel_size_posterior_encoder": 5,
+        "dilation_rate_posterior_encoder": 1,
+        "num_layers_posterior_encoder": 16,
+        "kernel_size_flow": 5,
+        "dilation_rate_flow": 1,
+        "num_layers_flow": 4,
+        "resblock_type_decoder": "2",
+        "resblock_kernel_sizes_decoder": [
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+                5
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+                5
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+        "upsample_kernel_sizes_decoder": [
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+            4,
+            4
+        ],
+        "periods_multi_period_discriminator": [
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+        "use_sdp": true,
+        "noise_scale": 1.0,
+        "inference_noise_scale": 0.667,
+        "length_scale": 1,
+        "noise_scale_dp": 1.0,
+        "inference_noise_scale_dp": 1.0,
+        "max_inference_len": null,
+        "init_discriminator": true,
+        "use_spectral_norm_disriminator": false,
+        "use_speaker_embedding": false,
+        "num_speakers": 0,
+        "speakers_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/speakers.pth",
+        "d_vector_file": [
+            "data/speakers.pth"
+        ],
+        "speaker_embedding_channels": 256,
+        "use_d_vector_file": true,
+        "d_vector_dim": 512,
+        "detach_dp_input": true,
+        "use_language_embedding": true,
+        "embedded_language_dim": 4,
+        "num_languages": 0,
+        "language_ids_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/language_ids.json",
+        "use_speaker_encoder_as_loss": false,
+        "speaker_encoder_config_path": "yourtts_luo/checkpoints_yourtts_cml_tts_dataset/config_se.json",
+        "speaker_encoder_model_path": "yourtts_luo/checkpoints_yourtts_cml_tts_dataset/model_se.pth",
+        "condition_dp_on_speaker": true,
+        "freeze_encoder": false,
+        "freeze_DP": false,
+        "freeze_PE": false,
+        "freeze_flow_decoder": false,
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+        "encoder_sample_rate": null,
+        "interpolate_z": true,
+        "reinit_DP": false,
+        "reinit_text_encoder": false
+    },
+    "lr_gen": 0.0002,
+    "lr_disc": 0.0002,
+    "lr_scheduler_gen": "ExponentialLR",
+    "lr_scheduler_gen_params": {
+        "gamma": 0.999875,
+        "last_epoch": -1
+    },
+    "lr_scheduler_disc": "ExponentialLR",
+    "lr_scheduler_disc_params": {
+        "gamma": 0.999875,
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+    },
+    "kl_loss_alpha": 1.0,
+    "disc_loss_alpha": 1.0,
+    "gen_loss_alpha": 1.0,
+    "feat_loss_alpha": 1.0,
+    "mel_loss_alpha": 45.0,
+    "dur_loss_alpha": 1.0,
+    "speaker_encoder_loss_alpha": 9.0,
+    "return_wav": true,
+    "use_weighted_sampler": false,
+    "weighted_sampler_attrs": null,
+    "weighted_sampler_multipliers": null,
+    "r": 1,
+    "num_speakers": 0,
+    "use_speaker_embedding": false,
+    "speakers_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/speakers.pth",
+    "speaker_embedding_channels": 256,
+    "language_ids_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/language_ids.json",
+    "use_language_embedding": true,
+    "use_d_vector_file": true,
+    "d_vector_file": [
+        "data/speakers.pth"
+    ],
+    "d_vector_dim": 512
+}

config.json.tmp

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+{
+    "output_path": "yourtts_luo",
+    "logger_uri": null,
+    "run_name": "YourTTS-Luo",
+    "project_name": "YourTTS",
+    "run_description": "\n            - YourTTS trained using the Luo OpenBible dataset.\n        ",
+    "print_step": 50,
+    "plot_step": 100,
+    "model_param_stats": false,
+    "wandb_entity": null,
+    "dashboard_logger": "tensorboard",
+    "save_on_interrupt": true,
+    "log_model_step": 1000,
+    "save_step": 1000,
+    "save_n_checkpoints": 2,
+    "save_checkpoints": true,
+    "save_all_best": false,
+    "save_best_after": 0,
+    "target_loss": "loss_1",
+    "print_eval": true,
+    "test_delay_epochs": 0,
+    "run_eval": true,
+    "run_eval_steps": null,
+    "distributed_backend": "nccl",
+    "distributed_url": "tcp://localhost:54321",
+    "mixed_precision": true,
+    "precision": "fp16",
+    "epochs": 1000,
+    "batch_size": 20,
+    "eval_batch_size": 20,
+    "grad_clip": [
+        1000,
+        1000
+    ],
+    "scheduler_after_epoch": true,
+    "lr": 0.001,
+    "optimizer": "AdamW",
+    "optimizer_params": {
+        "betas": [
+            0.8,
+            0.99
+        ],
+        "eps": 1e-09,
+        "weight_decay": 0.01
+    },
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+    "lr_scheduler_params": null,
+    "use_grad_scaler": false,
+    "allow_tf32": false,
+    "cudnn_enable": true,
+    "cudnn_deterministic": false,
+    "cudnn_benchmark": false,
+    "training_seed": 54321,
+    "model": "vits",
+    "num_loader_workers": 8,
+    "num_eval_loader_workers": 0,
+    "use_noise_augment": false,
+    "audio": {
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+        "sample_rate": 24000,
+        "win_length": 1024,
+        "hop_length": 256,
+        "num_mels": 80,
+        "mel_fmin": 0.0,
+        "mel_fmax": null
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+    "use_phonemes": false,
+    "phonemizer": null,
+    "phoneme_language": null,
+    "compute_input_seq_cache": true,
+    "text_cleaner": "no_cleaners",
+    "enable_eos_bos_chars": false,
+    "test_sentences_file": "",
+    "phoneme_cache_path": null,
+    "characters": {
+        "characters_class": "TTS.tts.models.vits.VitsCharacters",
+        "vocab_dict": null,
+        "pad": "_",
+        "eos": "&",
+        "bos": "*",
+        "blank": null,
+        "characters": "abcdefghijklmnoprstuvwyz\u02bc",
+        "punctuations": " !',.:;?\u2019",
+        "phonemes": null,
+        "is_unique": true,
+        "is_sorted": true
+    },
+    "add_blank": true,
+    "batch_group_size": 4,
+    "loss_masking": null,
+    "min_audio_len": 19200,
+    "max_audio_len": 288000,
+    "min_text_len": 1,
+    "max_text_len": Infinity,
+    "compute_f0": false,
+    "compute_energy": false,
+    "compute_linear_spec": true,
+    "precompute_num_workers": 12,
+    "start_by_longest": true,
+    "shuffle": false,
+    "drop_last": false,
+    "datasets": [
+        {
+            "formatter": "",
+            "dataset_name": "luo_openbible",
+            "path": "data",
+            "meta_file_train": "manifest_train.jsonl",
+            "ignored_speakers": null,
+            "language": "luo",
+            "phonemizer": "",
+            "meta_file_val": "manifest_dev.jsonl",
+            "meta_file_attn_mask": ""
+        }
+    ],
+    "test_sentences": [
+        [
+            "jo kolosai achiel.",
+            "one",
+            null,
+            "luo"
+        ],
+        [
+            "magoyo erokamano ni wuoro ka un gi mor.",
+            "one",
+            null,
+            "luo"
+        ],
+        [
+            "epafra bende nonyisowa kuom hera ma roho maler osemiyou.",
+            "one",
+            null,
+            "luo"
+        ]
+    ],
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+    "length_weighted_sampler_alpha": 1.0,
+    "model_args": {
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+        "out_channels": 513,
+        "spec_segment_size": 62,
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+        "resblock_type_decoder": "2",
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+                5
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+                3,
+                5
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+                3,
+                5
+            ]
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+        "upsample_kernel_sizes_decoder": [
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+        "use_speaker_embedding": false,
+        "num_speakers": 0,
+        "speakers_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/speakers.pth",
+        "d_vector_file": [
+            "data/speakers.pth"
+        ],
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+        "use_d_vector_file": true,
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+        "detach_dp_input": true,
+        "use_language_embedding": true,
+        "embedded_language_dim": 4,
+        "num_languages": 0,
+        "language_ids_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/language_ids.json",
+        "use_speaker_encoder_as_loss": false,
+        "speaker_encoder_config_path": "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json",
+        "speaker_encoder_model_path": "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar",
+        "condition_dp_on_speaker": true,
+        "freeze_encoder": false,
+        "freeze_DP": false,
+        "freeze_PE": false,
+        "freeze_flow_decoder": false,
+        "freeze_waveform_decoder": false,
+        "encoder_sample_rate": null,
+        "interpolate_z": true,
+        "reinit_DP": false,
+        "reinit_text_encoder": false
+    },
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+    "lr_disc": 0.0002,
+    "lr_scheduler_gen": "ExponentialLR",
+    "lr_scheduler_gen_params": {
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+        "last_epoch": -1
+    },
+    "lr_scheduler_disc": "ExponentialLR",
+    "lr_scheduler_disc_params": {
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+        "last_epoch": -1
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+    "gen_loss_alpha": 1.0,
+    "feat_loss_alpha": 1.0,
+    "mel_loss_alpha": 45.0,
+    "dur_loss_alpha": 1.0,
+    "speaker_encoder_loss_alpha": 9.0,
+    "return_wav": true,
+    "use_weighted_sampler": false,
+    "weighted_sampler_attrs": null,
+    "weighted_sampler_multipliers": null,
+    "r": 1,
+    "num_speakers": 0,
+    "use_speaker_embedding": false,
+    "speakers_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/speakers.pth",
+    "speaker_embedding_channels": 256,
+    "language_ids_file": "yourtts_luo/YourTTS-Luo-February-19-2025_07+16PM-0000000/language_ids.json",
+    "use_language_embedding": true,
+    "use_d_vector_file": true,
+    "d_vector_file": [
+        "data/speakers.pth"
+    ],
+    "d_vector_dim": 512
+}

events.out.tfevents.1739992596.fbcaa2f54084.30.0

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+oid sha256:50bf1402dd27ee6fd4af7928671be7317711d417a1954bc14300fc5f33fa591b
+size 183689712

language_ids.json

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+{
+    "luo": 0
+}

speakers.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:13c9d3177b5b8d0e597035fb1a5080a8765823ece136f0a760cd855bdd98e3b8
+size 864

train_yourtts.py

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+import os
+
+import torch
+from trainer import Trainer, TrainerArgs
+
+from TTS.config import load_config
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.vits_config import VitsConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.vits import CharactersConfig, Vits, VitsArgs, VitsAudioConfig
+from TTS.tts.utils.speakers import SpeakerManager
+from TTS.tts.utils.managers import save_file
+from tqdm import tqdm
+import json
+import gdown
+import tarfile
+
+torch.set_num_threads(24)
+
+
+def nemo(root_path, meta_file, **kwargs):
+    """
+    Normalizes NeMo-style json manifest files to TTS format
+    """
+    meta_path = os.path.join(root_path, meta_file)
+    items = []
+    with open(meta_path, "r", encoding="utf-8") as ttf:
+        for line in ttf:
+            cols = json.loads(line)
+            wav_file = cols["audio_filepath"]
+            text = cols["text"]
+            speaker_name = cols["speaker_name"] if "speaker_name" in cols else "one"
+            language = cols["language"] if "language" in cols else ""
+            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "language": language, "root_path": root_path})
+    return items
+
+
+def compute_embeddings(
+    model_path,
+    config_path,
+    output_path,
+    old_speakers_file=None,
+    old_append=False,
+    config_dataset_path=None,
+    formatter=None,
+    dataset_name=None,
+    dataset_path=None,
+    meta_file_train=None,
+    meta_file_val=None,
+    disable_cuda=False,
+    no_eval=False,
+):
+    use_cuda = torch.cuda.is_available() and not disable_cuda
+
+    if config_dataset_path is not None:
+        c_dataset = load_config(config_dataset_path)
+        meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not no_eval)
+    else:
+        c_dataset = BaseDatasetConfig()
+        c_dataset.dataset_name = dataset_name
+        c_dataset.path = dataset_path
+        if meta_file_train is not None:
+            c_dataset.meta_file_train = meta_file_train
+        if meta_file_val is not None:
+            c_dataset.meta_file_val = meta_file_val
+        meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not no_eval, formatter=formatter)
+
+    if meta_data_eval is None:
+        samples = meta_data_train
+    else:
+        samples = meta_data_train + meta_data_eval
+
+    encoder_manager = SpeakerManager(
+        encoder_model_path=model_path,
+        encoder_config_path=config_path,
+        d_vectors_file_path=old_speakers_file,
+        use_cuda=use_cuda,
+    )
+
+    class_name_key = encoder_manager.encoder_config.class_name_key
+
+    # compute speaker embeddings
+    if old_speakers_file is not None and old_append:
+        speaker_mapping = encoder_manager.embeddings
+    else:
+        speaker_mapping = {}
+
+    for fields in tqdm(samples):
+        class_name = fields[class_name_key]
+        audio_file = fields["audio_file"]
+        embedding_key = fields["audio_unique_name"]
+
+        # Only update the speaker name when the embedding is already in the old file.
+        if embedding_key in speaker_mapping:
+            speaker_mapping[embedding_key]["name"] = class_name
+            continue
+
+        if old_speakers_file is not None and embedding_key in encoder_manager.clip_ids:
+            # get the embedding from the old file
+            embedd = encoder_manager.get_embedding_by_clip(embedding_key)
+        else:
+            # extract the embedding
+            embedd = encoder_manager.compute_embedding_from_clip(audio_file)
+
+        # create speaker_mapping if target dataset is defined
+        speaker_mapping[embedding_key] = {}
+        speaker_mapping[embedding_key]["name"] = class_name
+        speaker_mapping[embedding_key]["embedding"] = embedd
+
+    if speaker_mapping:
+        # save speaker_mapping if target dataset is defined
+        if os.path.isdir(output_path):
+            mapping_file_path = os.path.join(output_path, "speakers.pth")
+        else:
+            mapping_file_path = output_path
+
+        if os.path.dirname(mapping_file_path) != "":
+            os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
+
+        save_file(speaker_mapping, mapping_file_path)
+        print("Speaker embeddings saved at:", mapping_file_path)
+
+
+# Name of the run for the Trainer
+RUN_NAME = "YourTTS-Luo"
+
+# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
+OUT_PATH = "yourtts_luo"
+
+# If you want to do transfer learning and speedup your training you can set here the path to the CML-TTS available checkpoint that can be downloaded here:  https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p
+RESTORE_PATH = os.path.join(OUT_PATH, "checkpoints_yourtts_cml_tts_dataset/best_model.pth")
+
+URL = "https://drive.google.com/u/2/uc?id=1yDCSJ1pFZQTHhL09GMbOrdjcPULApa0p"
+OUTPUT_CHECKPOINTS_FILEPATH = os.path.join(OUT_PATH, "checkpoints_yourtts_cml_tts_dataset.tar.bz")
+
+# Download the CML-TTS checkpoint if it does not exist
+if not os.path.exists(RESTORE_PATH):
+    print(f"Downloading the CML-TTS checkpoint from {URL}")
+    gdown.download(url=URL, output=OUTPUT_CHECKPOINTS_FILEPATH, quiet=False, fuzzy=True)
+    with tarfile.open(OUTPUT_CHECKPOINTS_FILEPATH, "r:bz2") as tar:
+        tar.extractall(OUT_PATH)
+else:
+    print(f"Checkpoint already exists at {RESTORE_PATH}")
+
+# This paramter is useful to debug, it skips the training epochs and just do the evaluation  and produce the test sentences
+SKIP_TRAIN_EPOCH = False
+
+# Set here the batch size to be used in training and evaluation
+BATCH_SIZE = 4
+
+# Note: If you add new datasets, please make sure that the dataset sampling rate and this parameter are matching, otherwise resample your audios
+SAMPLE_RATE = 24000
+
+# Max audio length in seconds to be used in training
+MAX_AUDIO_LEN_IN_SECONDS = 12
+# Min audio length in seconds to be used in training
+MIN_AUDIO_LEN_IN_SECONDS = 0.8
+
+dataset_conf = BaseDatasetConfig(
+    dataset_name="luo_openbible",
+    meta_file_train="manifest_train.jsonl",
+    meta_file_val="manifest_dev.jsonl",
+    language="luo",
+    path="data"
+)
+
+### Extract speaker embeddings
+SPEAKER_ENCODER_CHECKPOINT_PATH = (
+    "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
+)
+SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
+
+D_VECTOR_FILES = []  # List of speaker embeddings/d-vectors to be used during the training
+
+# Checks if the speakers embeddings are already computated, if not compute it
+embeddings_file = os.path.join(dataset_conf.path, "speakers.pth")
+if not os.path.isfile(embeddings_file):
+    print(f">>> Computing the speaker embeddings for the {dataset_conf.dataset_name} dataset")
+    compute_embeddings(
+        SPEAKER_ENCODER_CHECKPOINT_PATH,
+        SPEAKER_ENCODER_CONFIG_PATH,
+        embeddings_file,
+        formatter=nemo,
+        dataset_name=dataset_conf.dataset_name,
+        dataset_path=dataset_conf.path,
+        meta_file_train=dataset_conf.meta_file_train,
+        meta_file_val=dataset_conf.meta_file_val,
+    )
+D_VECTOR_FILES.append(embeddings_file)
+
+# Audio config used in training.
+audio_config = VitsAudioConfig(
+    sample_rate=SAMPLE_RATE,
+    hop_length=256,
+    win_length=1024,
+    fft_size=1024,
+    mel_fmin=0.0,
+    mel_fmax=None,
+    num_mels=80,
+)
+
+# Init VITSArgs setting the arguments that are needed for the YourTTS model
+model_args = VitsArgs(
+    spec_segment_size=62,
+    hidden_channels=192,
+    hidden_channels_ffn_text_encoder=768,
+    num_heads_text_encoder=2,
+    num_layers_text_encoder=10,
+    kernel_size_text_encoder=3,
+    dropout_p_text_encoder=0.1,
+    d_vector_file=D_VECTOR_FILES,
+    use_d_vector_file=True,
+    d_vector_dim=512,
+    speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
+    speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
+    resblock_type_decoder="2",  # In the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
+    # Useful parameters to enable the Speaker Consistency Loss (SCL) described in the paper
+    use_speaker_encoder_as_loss=False,
+    # Useful parameters to enable multilingual training
+    use_language_embedding=True,
+    embedded_language_dim=4,
+)
+
+CHARS = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ʼ']
+PUNCT = [' ', '!', "'", ',', '.', ':', ';', '?', '’']
+
+# General training config, here you can change the batch size and others useful parameters
+config = VitsConfig(
+    output_path=OUT_PATH,
+    model_args=model_args,
+    run_name=RUN_NAME,
+    project_name="YourTTS",
+    run_description="""
+            - YourTTS trained using the Luo OpenBible dataset.
+        """,
+    dashboard_logger="tensorboard",
+    logger_uri=None,
+    audio=audio_config,
+    batch_size=BATCH_SIZE,
+    batch_group_size=4,
+    eval_batch_size=BATCH_SIZE,
+    num_loader_workers=8,
+    # eval_split_max_size=256,
+    print_step=50,
+    plot_step=100,
+    # log_model_step=1000,
+    save_step=1000,
+    save_n_checkpoints=2,
+    save_checkpoints=True,
+    target_loss="loss_1",
+    print_eval=True,
+    compute_input_seq_cache=True,
+    add_blank=True,
+    text_cleaner="no_cleaners",
+    characters=CharactersConfig(
+        characters_class="TTS.tts.models.vits.VitsCharacters",
+        pad="_",
+        eos="&",
+        bos="*",
+        blank=None,
+        characters="".join(CHARS),
+        punctuations="".join(PUNCT),
+    ),
+    phoneme_cache_path=None,
+    precompute_num_workers=12,
+    start_by_longest=True,
+    datasets=[dataset_conf],
+    cudnn_benchmark=False,
+    min_audio_len=int(SAMPLE_RATE * MIN_AUDIO_LEN_IN_SECONDS),
+    max_audio_len=SAMPLE_RATE * MAX_AUDIO_LEN_IN_SECONDS,
+    mixed_precision=True,
+    test_sentences=[
+        ["jo kolosai achiel.", "one", None, "luo"],
+        ["magoyo erokamano ni wuoro ka un gi mor.", "one", None, "luo"],
+        ["epafra bende nonyisowa kuom hera ma roho maler osemiyou.", "one", None, "luo"],
+    ],
+    # Enable the weighted sampler
+    # use_weighted_sampler=True,
+    # Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
+    # weighted_sampler_attrs={"language": 1.0, "speaker_name": 1.0},
+    # weighted_sampler_attrs={"language": 1.0},
+    # weighted_sampler_multipliers={
+    #     # "speaker_name": {
+    #     # you can force the batching scheme to give a higher weight to a certain speaker and then this speaker will appears more frequently on the batch.
+    #     # It will speedup the speaker adaptation process. Considering the CML train dataset and "new_speaker" as the speaker name of the speaker that you want to adapt.
+    #     # The line above will make the balancer consider the "new_speaker" as 106 speakers so 1/4 of the number of speakers present on CML dataset.
+    #     # 'new_speaker': 106, # (CML tot. train speaker)/4 = (424/4) = 106
+    #     # }
+    # },
+    # It defines the Speaker Consistency Loss (SCL) α to 9 like the YourTTS paper
+    speaker_encoder_loss_alpha=9.0,
+)
+
+# Load all the datasets samples and split traning and evaluation sets
+train_samples, eval_samples = load_tts_samples(
+    config.datasets,
+    eval_split=True,
+    formatter=nemo,
+    eval_split_max_size=config.eval_split_max_size,
+    eval_split_size=config.eval_split_size,
+)
+
+# Init the model
+model = Vits.init_from_config(config)
+
+# Init the trainer and 🚀
+trainer = Trainer(
+    TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH),
+    config,
+    output_path=OUT_PATH,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+)
+trainer.fit()