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import os
from trainer import Trainer, TrainerArgs
from TTS.tts.configs.shared_configs import BaseDatasetConfig , CharactersConfig
from TTS.config.shared_configs import BaseAudioConfig
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
from TTS.tts.utils.speakers import SpeakerManager
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_names={
"persian-tts-dataset-famale":"dilara",
"persian-tts-dataset":"changiz",
"persian-tts-dataset-male":"farid"
}
def mozilla_with_speaker(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes Mozilla meta data files to TTS format"""
txt_file = os.path.join(root_path, meta_file)
items = []
speaker_name = dataset_names[os.path.basename(root_path)]
print(speaker_name)
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = cols[1].strip()
text = cols[0].strip()
wav_file = os.path.join(root_path, "wavs", wav_file)
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
return items
dataset_config1 = BaseDatasetConfig(
formatter="mozilla" ,meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset"
)
dataset_config2 = BaseDatasetConfig(
formatter="mozilla" ,meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset-famale"
)
dataset_config3 = BaseDatasetConfig(
formatter="mozilla" ,meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset-male"
)
audio_config = BaseAudioConfig(
sample_rate=22050,
do_trim_silence=False,
resample=False,
mel_fmin=0,
mel_fmax=None
)
character_config=CharactersConfig(
characters='ءابتثجحخدذرزسشصضطظعغفقلمنهويِپچژکگیآأؤإئًَُّ',
punctuations='!(),-.:;? ̠،؛؟<>',
phonemes='ˈˌːˑpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟaegiouwyɪʊ̩æɑɔəɚɛɝɨ̃ʉʌʍ0123456789"#$%*+/=ABCDEFGHIJKLMNOPRSTUVWXYZ[]^_{}',
pad="<PAD>",
eos="<EOS>",
bos="<BOS>",
blank="<BLNK>",
characters_class="TTS.tts.utils.text.characters.IPAPhonemes",
)
config = VitsConfig(
audio=audio_config,
run_name="vits_fa_female",
batch_size=8,
eval_batch_size=4,
batch_group_size=5,
num_loader_workers=0,
num_eval_loader_workers=2,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
save_step=1000,
text_cleaner="basic_cleaners",
use_phonemes=True,
phoneme_language="fa",
characters=character_config,
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
compute_input_seq_cache=True,
print_step=25,
print_eval=True,
mixed_precision=False,
test_sentences=[
["سلطان محمود در زمستانی سخت به طلخک گفت که: با این جامه ی یک لا در این سرما چه می کنی "],
["مردی نزد بقالی آمد و گفت پیاز هم ده تا دهان بدان خو شبوی سازم."],
["از مال خود پاره ای گوشت بستان و زیره بایی معطّر بساز"],
["یک بار هم از جهنم بگویید."],
["یکی اسبی به عاریت خواست"]
],
output_path=output_path,
datasets=[dataset_config1,dataset_config2,dataset_config3],
)
# 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(
config.datasets,
formatter=mozilla_with_speaker,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
speaker_manager = SpeakerManager()
speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
config.num_speakers = speaker_manager.num_speakers
print("\n"*10)
print("#>"*10)
print(speaker_manager.speaker_names)
print("\n"*10)
# init model
model = Vits(config, ap, tokenizer, speaker_manager=speaker_manager)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit() |