train_gpt_xtts.py

import os
import torch

# 🔒 Permitir deserialización segura de configuraciones XTTS
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import XttsAudioConfig
torch.serialization.add_safe_globals([XttsConfig, XttsAudioConfig])

from trainer import Trainer, TrainerArgs

from TTS.config.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
from TTS.utils.manage import ModelManager

class StopTraining(Exception):
    pass


class EpochLimitTrainer(Trainer):
    def __init__(self, max_epochs, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.max_epochs = max_epochs
        self.current_epoch = 0

    def train_epoch(self):
        if self.current_epoch >= self.max_epochs:
            print(f"✅ Entrenamiento detenido tras {self.max_epochs} epochs.")
            raise StopTraining
        print(f"👉 Epoch {self.current_epoch+1}/{self.max_epochs}")
        self.current_epoch += 1
        return super().train_epoch()

    def fit(self):
        try:
            super().fit()
        except StopTraining:
            print("🏁 Entrenamiento finalizado de forma controlada.")
            # 🔒 Guardar modelo final aunque no toque save_step
            final_ckpt = os.path.join(self.output_path, "final_model.pth")
            print(f"💾 Guardando checkpoint final en {final_ckpt}")
            self.save_checkpoint(final_ckpt)


# Logging parameters
RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT"
PROJECT_NAME = "XTTS_trainer"
DASHBOARD_LOGGER = "tensorboard"
LOGGER_URI = None

# Set here the path that the checkpoints will be saved. Default: ./run/training/
OUT_PATH = "/tmp/output_model/run/training"


# Training Parameters
OPTIMIZER_WD_ONLY_ON_WEIGHTS = True  # for multi-gpu training please make it False
START_WITH_EVAL = True  # if True it will star with evaluation
BATCH_SIZE = 3  # set here the batch size
GRAD_ACUMM_STEPS = 84  # set here the grad accumulation steps
# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly.

# Define here the dataset that you want to use for the fine-tuning on.
config_dataset = BaseDatasetConfig(
    formatter="vctk",
    dataset_name="voxpopuli",
    path="/tmp/dataset",
#    meta_file_train="metadata.csv",
#    meta_file_val="metadata.csv",  # importante si también lo usas para validación
    language="es",
)

# Add here the configs of the datasets
DATASETS_CONFIG_LIST = [config_dataset]

# Define the path where XTTS v2.0.1 files will be downloaded
CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)


# DVAE files
DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"

# Set the path to the downloaded files
DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(DVAE_CHECKPOINT_LINK))
MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(MEL_NORM_LINK))

# download DVAE files if needed
if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
    print(" > Downloading DVAE files!")
    ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)


# Download XTTS v2.0 checkpoint if needed
TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"
XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/model.pth"

# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(TOKENIZER_FILE_LINK))  # vocab.json file
XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, os.path.basename(XTTS_CHECKPOINT_LINK))  # model.pth file

# download XTTS v2.0 files if needed
if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT):
    print(" > Downloading XTTS v2.0 files!")
    ModelManager._download_model_files(
        [TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
    )


# Training sentences generations
SPEAKER_REFERENCE = [
    "./tests/data/ljspeech/wavs/LJ001-0002.wav"  # speaker reference to be used in training test sentences
]
LANGUAGE = config_dataset.language


def main():
    # init args and config
    model_args = GPTArgs(
        max_conditioning_length=132300,  # 6 secs
        min_conditioning_length=66150,  # 3 secs
        debug_loading_failures=False,
        max_wav_length=255995,  # ~11.6 seconds
        max_text_length=239,
        mel_norm_file=MEL_NORM_FILE,
        dvae_checkpoint=DVAE_CHECKPOINT,
        xtts_checkpoint=XTTS_CHECKPOINT,  # checkpoint path of the model that you want to fine-tune
        tokenizer_file=TOKENIZER_FILE,
        gpt_num_audio_tokens=1026,
        gpt_start_audio_token=1024,
        gpt_stop_audio_token=1025,
        gpt_use_masking_gt_prompt_approach=True,
        gpt_use_perceiver_resampler=True,
    )
    # define audio config
    audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
    # training parameters config
    config = GPTTrainerConfig(
        output_path=OUT_PATH,
        model_args=model_args,
        run_name=RUN_NAME,
        project_name=PROJECT_NAME,
        run_description="""
            GPT XTTS training
            """,
        dashboard_logger=DASHBOARD_LOGGER,
        logger_uri=LOGGER_URI,
        audio=audio_config,
        batch_size=BATCH_SIZE,
        batch_group_size=48,
        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=10000,
        save_n_checkpoints=1,
        save_checkpoints=True,
        # target_loss="loss",
        print_eval=False,
        # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
        optimizer="AdamW",
        optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
        optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
        lr=5e-06,  # learning rate
        lr_scheduler="MultiStepLR",
        # it was adjusted accordly for the new step scheme
        lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
        #test_sentences=[
        #    {
        #        "text": "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
        #        "speaker_wav": SPEAKER_REFERENCE,
        #        "language": LANGUAGE,
        #    },
        #    {
        #        "text": "This cake is great. It's so delicious and moist.",
        #        "speaker_wav": SPEAKER_REFERENCE,
        #        "language": LANGUAGE,
        #    },
        #],
        epochs=150,
    )

    # init the model from config
    model = GPTTrainer.init_from_config(config)

    # load training samples
    train_samples, eval_samples = load_tts_samples(
        DATASETS_CONFIG_LIST,
        eval_split=True,
        eval_split_max_size=config.eval_split_max_size,
        eval_split_size=config.eval_split_size,
    )
    
    # init the trainer and 🚀
    #trainer = Trainer(
    #    TrainerArgs(
    #        restore_path=None,  # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
    #        skip_train_epoch=False,
    #        start_with_eval=START_WITH_EVAL,
    #        grad_accum_steps=GRAD_ACUMM_STEPS,
    #    ),
    #    config,
    #    output_path=OUT_PATH,
    #    model=model,
    #    train_samples=train_samples,
    #    eval_samples=eval_samples,
    #)

    trainer = EpochLimitTrainer(
        max_epochs=150,
        args=TrainerArgs(
            restore_path=None,
            skip_train_epoch=False,
            start_with_eval=START_WITH_EVAL,
            grad_accum_steps=GRAD_ACUMM_STEPS,
        ),
        config=config,
        output_path=OUT_PATH,
        model=model,
        train_samples=train_samples,
        eval_samples=eval_samples,
    )
    
    trainer.fit()
    #trainer.init_training()
    #max_epochs = 5
    #for epoch in range(max_epochs):
    #    trainer.train_epoch()   # entrena una época
    #    trainer.eval_epoch()    # evalúa



if __name__ == "__main__":
    main()