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import os
from trainer import Trainer, TrainerArgs
from TTS.config.shared_configs import BaseAudioConfig
from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
audio_config = BaseAudioConfig(
sample_rate=22050,
resample=False, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
do_trim_silence=True,
trim_db=23.0,
signal_norm=False,
mel_fmin=0.0,
mel_fmax=8000,
spec_gain=1.0,
log_func="np.log",
preemphasis=0.0,
)
config = Tacotron2Config( # This is the config that is saved for the future use
audio=audio_config,
batch_size=32,
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=-1,
r=2,
# gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
double_decoder_consistency=False,
epochs=1000,
text_cleaner="phoneme_cleaners",
use_phonemes=True,
phoneme_language="en-us",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
print_step=150,
print_eval=False,
mixed_precision=True,
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=44000 * 10,
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True, # set this to enable multi-sepeaker training
decoder_ssim_alpha=0.0, # disable ssim losses that causes NaN for some runs.
postnet_ssim_alpha=0.0,
postnet_diff_spec_alpha=0.0,
decoder_diff_spec_alpha=0.0,
attention_norm="softmax",
optimizer="Adam",
lr_scheduler=None,
lr=3e-5,
)
## INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
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,
)
# init speaker manager for multi-speaker training
# it mainly handles speaker-id to speaker-name for the model and the data-loader
speaker_manager = SpeakerManager()
speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
# init model
model = Tacotron2(config, ap, tokenizer, speaker_manager)
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()
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