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import os
from trainer import Trainer, TrainerArgs
from TTS.config.shared_configs import BaseDatasetConfig
from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.delightful_tts import DelightfulTTS, DelightfulTtsArgs, VocoderConfig
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio.processor import AudioProcessor
data_path = ""
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(
dataset_name="ljspeech", formatter="ljspeech", meta_file_train="metadata.csv", path=data_path
)
audio_config = DelightfulTtsAudioConfig()
model_args = DelightfulTtsArgs()
vocoder_config = VocoderConfig()
delightful_tts_config = DelightfulTTSConfig(
run_name="delightful_tts_ljspeech",
run_description="Train like in delightful tts paper.",
model_args=model_args,
audio=audio_config,
vocoder=vocoder_config,
batch_size=32,
eval_batch_size=16,
num_loader_workers=10,
num_eval_loader_workers=10,
precompute_num_workers=10,
batch_group_size=2,
compute_input_seq_cache=True,
compute_f0=True,
f0_cache_path=os.path.join(output_path, "f0_cache"),
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"),
print_step=50,
print_eval=False,
mixed_precision=True,
output_path=output_path,
datasets=[dataset_config],
start_by_longest=False,
eval_split_size=0.1,
binary_align_loss_alpha=0.0,
use_attn_priors=False,
lr_gen=4e-1,
lr=4e-1,
lr_disc=4e-1,
max_text_len=130,
)
tokenizer, config = TTSTokenizer.init_from_config(delightful_tts_config)
ap = AudioProcessor.init_from_config(config)
train_samples, eval_samples = load_tts_samples(
dataset_config,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
model = DelightfulTTS(ap=ap, config=config, tokenizer=tokenizer, speaker_manager=None)
trainer = Trainer(
TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit()
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