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import os
from trainer import Trainer, TrainerArgs
from TTS.utils.audio import AudioProcessor
from TTS.vocoder.configs import WavegradConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.wavegrad import Wavegrad
output_path = os.path.dirname(os.path.abspath(__file__))
config = WavegradConfig(
batch_size=32,
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
seq_len=6144,
pad_short=2000,
use_noise_augment=True,
eval_split_size=50,
print_step=50,
print_eval=True,
mixed_precision=False,
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
# init model
model = Wavegrad(config)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
trainer.fit()
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