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import os
from coqpit import Coqpit
from trainer import Trainer, TrainerArgs
from TTS.tts.configs.shared_configs import BaseAudioConfig
from TTS.utils.audio import AudioProcessor
from TTS.vocoder.configs.hifigan_config import *
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.gan import GAN
output_path = "/storage/output-hifigan/"
audio_config = BaseAudioConfig(
mel_fmin=50,
mel_fmax=8000,
hop_length=256,
stats_path="/storage/TTS/scale_stats.npy",
)
config = HifiganConfig(
batch_size=74,
eval_batch_size=16,
num_loader_workers=8,
num_eval_loader_workers=8,
lr_disc=0.0002,
lr_gen=0.0002,
run_eval=True,
test_delay_epochs=5,
epochs=1000,
use_noise_augment=True,
seq_len=8192,
pad_short=2000,
save_step=5000,
print_step=50,
print_eval=True,
mixed_precision=False,
eval_split_size=30,
save_n_checkpoints=2,
save_best_after=5000,
data_path="/storage/filtered_dataset",
output_path=output_path,
audio=audio_config,
)
# init audio processor
ap = AudioProcessor.init_from_config(config)
# load training samples
print("config.eval_split_size = ", config.eval_split_size)
eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
# init model
model = GAN(config, ap)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
trainer.fit()
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