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import os
from glob import glob
from trainer import Trainer, TrainerArgs
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, VitsArgs, VitsAudioConfig
from TTS.tts.utils.languages import LanguageManager
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = "/media/julian/Workdisk/train"
mailabs_path = "/home/julian/workspace/mailabs/**"
dataset_paths = glob(mailabs_path)
dataset_config = [
BaseDatasetConfig(
formatter="mailabs",
meta_file_train=None,
path=path,
language=path.split("/")[-1], # language code is the folder name
)
for path in dataset_paths
]
audio_config = VitsAudioConfig(
sample_rate=16000,
win_length=1024,
hop_length=256,
num_mels=80,
mel_fmin=0,
mel_fmax=None,
)
vitsArgs = VitsArgs(
use_language_embedding=True,
embedded_language_dim=4,
use_speaker_embedding=True,
use_sdp=False,
)
config = VitsConfig(
model_args=vitsArgs,
audio=audio_config,
run_name="vits_vctk",
use_speaker_embedding=True,
batch_size=32,
eval_batch_size=16,
batch_group_size=0,
num_loader_workers=12,
num_eval_loader_workers=12,
precompute_num_workers=12,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
text_cleaner="multilingual_cleaners",
use_phonemes=True,
phoneme_language=None,
phonemizer="multi_phonemizer",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
compute_input_seq_cache=True,
print_step=25,
use_language_weighted_sampler=True,
print_eval=False,
mixed_precision=False,
min_audio_len=audio_config.sample_rate,
max_audio_len=audio_config.sample_rate * 10,
output_path=output_path,
datasets=dataset_config,
test_sentences=[
[
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
"mary_ann",
None,
"en-us",
],
[
"Il m'a fallu beaucoup de temps pour d\u00e9velopper une voix, et maintenant que je l'ai, je ne vais pas me taire.",
"ezwa",
None,
"fr-fr",
],
["Ich finde, dieses Startup ist wirklich unglaublich.", "eva_k", None, "de-de"],
["Я думаю, что этот стартап действительно удивительный.", "nikolaev", None, "ru"],
],
)
# force the convertion of the custom characters to a config attribute
config.from_dict(config.to_dict())
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
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 maps speaker-id to speaker-name in the model and data-loader
speaker_manager = SpeakerManager()
speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
config.model_args.num_speakers = speaker_manager.num_speakers
language_manager = LanguageManager(config=config)
config.model_args.num_languages = language_manager.num_languages
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# config is updated with the default characters if not defined in the config.
tokenizer, config = TTSTokenizer.init_from_config(config)
# init model
model = Vits(config, ap, tokenizer, speaker_manager, language_manager)
# 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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