mirror of https://github.com/coqui-ai/TTS.git
76 lines
1.9 KiB
Python
76 lines
1.9 KiB
Python
import os
|
|
|
|
from TTS.config.shared_configs import BaseAudioConfig
|
|
from TTS.trainer import Trainer, TrainingArgs
|
|
from TTS.tts.configs.shared_configs import BaseDatasetConfig
|
|
from TTS.tts.configs.vits_config import VitsConfig
|
|
from TTS.tts.datasets import load_tts_samples
|
|
from TTS.tts.models.vits import Vits
|
|
from TTS.utils.audio import AudioProcessor
|
|
|
|
output_path = os.path.dirname(os.path.abspath(__file__))
|
|
dataset_config = BaseDatasetConfig(
|
|
name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
|
|
)
|
|
audio_config = BaseAudioConfig(
|
|
sample_rate=22050,
|
|
win_length=1024,
|
|
hop_length=256,
|
|
num_mels=80,
|
|
preemphasis=0.0,
|
|
ref_level_db=20,
|
|
log_func="np.log",
|
|
do_trim_silence=True,
|
|
trim_db=45,
|
|
mel_fmin=0,
|
|
mel_fmax=None,
|
|
spec_gain=1.0,
|
|
signal_norm=False,
|
|
do_amp_to_db_linear=False,
|
|
)
|
|
|
|
config = VitsConfig(
|
|
audio=audio_config,
|
|
run_name="vits_ljspeech",
|
|
batch_size=48,
|
|
eval_batch_size=16,
|
|
batch_group_size=5,
|
|
num_loader_workers=4,
|
|
num_eval_loader_workers=4,
|
|
run_eval=True,
|
|
test_delay_epochs=-1,
|
|
epochs=1000,
|
|
text_cleaner="english_cleaners",
|
|
use_phonemes=True,
|
|
phoneme_language="en-us",
|
|
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
|
|
compute_input_seq_cache=True,
|
|
print_step=25,
|
|
print_eval=True,
|
|
mixed_precision=True,
|
|
max_seq_len=500000,
|
|
output_path=output_path,
|
|
datasets=[dataset_config],
|
|
)
|
|
|
|
# init audio processor
|
|
ap = AudioProcessor(**config.audio.to_dict())
|
|
|
|
# load training samples
|
|
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
|
|
|
|
# init model
|
|
model = Vits(config)
|
|
|
|
# init the trainer and 🚀
|
|
trainer = Trainer(
|
|
TrainingArgs(),
|
|
config,
|
|
output_path,
|
|
model=model,
|
|
train_samples=train_samples,
|
|
eval_samples=eval_samples,
|
|
training_assets={"audio_processor": ap},
|
|
)
|
|
trainer.fit()
|