File size: 2,095 Bytes
f717040 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | 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()
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