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import os
from trainer import Trainer, TrainerArgs
from TTS.tts.configs.shared_configs import BaseDatasetConfig , CharactersConfig
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.vits import Vits, VitsAudioConfig
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
# DEFINE DATASET CONFIG
TRAIN_PATH = "/home/aymen/Tamazight-NLP/Speech/media_ipsapps/shi_tls/cut_22kHz_offset/manifests/"
dataset_config = BaseDatasetConfig(
formatter="nemo", meta_file_train="manifest_processed.json", path=TRAIN_PATH
)
audio_config = VitsAudioConfig(
sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
)
character_config = CharactersConfig(
characters_class= "TTS.tts.models.vits.VitsCharacters",
characters= "ⴰⴱⴳⴷⴹⴻⴼⴽⵀⵃⵄⵅⵇⵉⵊⵍⵎⵏⵓⵔⵕⵖⵙⵚⵛⵜⵟⵡⵢⵣⵥⵯ",
punctuations=" !,.:?",
pad= "<PAD>",
eos= "<EOS>",
bos= "<BOS>",
blank= "<BLNK>",
)
config = VitsConfig(
audio=audio_config,
characters=character_config,
run_name="vits_shi_male",
batch_size=16,
eval_batch_size=4,
batch_group_size=5,
num_loader_workers=4,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
save_step=5000,
text_cleaner="no_cleaners",
use_phonemes=False,
compute_input_seq_cache=True,
print_step=25,
print_eval=True,
mixed_precision=True,
output_path=output_path,
datasets=[dataset_config],
cudnn_benchmark=False,
test_sentences=[
["ⴰⵣⵓⵍ. ⵎⴰⵏⵣⴰⴽⵉⵏ?"],
["ⵡⴰ ⵜⴰⵎⵖⴰⵔⵜ ⵎⴰ ⴷ ⵓⴽⴰⵏ ⵜⵙⴽⵔⵜ?"],
["ⴳⵏ! ⴰⴷ ⴰⴽ ⵉⵙⵙⴳⵏ ⵕⴱⴱⵉ ⵉⵜⵜⵓ ⴽ."],
["ⴰⵔⵔⴰⵡ ⵏ ⵍⵀⵎⵎ ⵢⵓⴽⵔ ⴰⵖ ⵉⵀⴷⵓⵎⵏ ⵏⵏⵖ!"]
],
)
# 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.
# config is updated with the default characters if not defined in 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 model
model = Vits(config, ap, tokenizer, speaker_manager=None)
# 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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