train_glowtts.py

import os
import inspect
from trainer import Trainer, TrainerArgs
from TTS.tts.configs.glow_tts_config import GlowTTSConfig
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor

def main():
    output_path = os.path.dirname(os.path.abspath(__file__))

    dataset_config = BaseDatasetConfig(
        formatter="ljspeech",
        meta_file_train="metadata.csv",
        path=os.path.join(output_path, "LJSpeech-1.1/")
    )

    config = GlowTTSConfig(
        batch_size=256,
        eval_batch_size=128,
        num_loader_workers=4,
        num_eval_loader_workers=2,
        run_eval=True,
        test_delay_epochs=-1,
        epochs=600,
        text_cleaner="phoneme_cleaners",
        use_phonemes=True,
        phoneme_language="en-us",
        phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
        print_step=25,
        print_eval=False,
        mixed_precision=True,
        output_path=output_path,
        datasets=[dataset_config],
	    max_audio_len=22050 * 10,
        min_audio_len=22050 * 1,
    )

    ap = AudioProcessor(config=config.audio)

    tokenizer, config = TTSTokenizer.init_from_config(config)

    train_samples, eval_samples = load_tts_samples(
        config,
        eval_split=True,
        eval_split_max_size=20,
    )

    model = GlowTTS(config, ap, tokenizer=tokenizer, speaker_manager=None)

    trainer = Trainer(
        TrainerArgs(),
        config,
        output_path,
        model=model,
        train_samples=train_samples,
        eval_samples=eval_samples,
        training_assets={'audio_processor': ap},
    )

    if getattr(trainer, "best_loss", None) is None:
        trainer.best_loss = {"train_loss": float("inf")}
    elif isinstance(trainer.best_loss, dict) and trainer.best_loss.get("train_loss") is None:
        trainer.best_loss["train_loss"] = float("inf")

    trainer.fit()

if __name__ == "__main__":
    main()