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import os
from trainer import Trainer, TrainerArgs
from TTS.tts.configs.shared_configs import BaseDatasetConfig,BaseAudioConfig,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__))
RESTORE_PATH = '/home/azureuser/BanglaTTS/nctb-vits-single-female-1/checkpoint.pth'
SPEAKER_ID = 9
SPEAKER_GENDER = 'male'
meta_file = f"/home/azureuser/BanglaTTS/nctb-audiobook-no-numbers/{SPEAKER_GENDER}/SP_{SPEAKER_ID}/metadata.txt"
root_path = f"/home/azureuser/BanglaTTS/nctb-audiobook-no-numbers/{SPEAKER_GENDER}/SP_{SPEAKER_ID}"
def formatter(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
"""Normalizes the LJSpeech meta data file to TTS format
https://keithito.com/LJ-Speech-Dataset/"""
txt_file = meta_file
items = []
speaker_name = f"nctb_{SPEAKER_GENDER}_{SPEAKER_ID}"
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
cols = line.split("|")
wav_file = os.path.join(root_path,'audio', cols[0])
try:
text = cols[1]
except:
print("not found")
items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
return items
dataset_config = BaseDatasetConfig(
meta_file_train=meta_file, path=os.path.join(root_path, "")
)
characters_config = CharactersConfig(
pad = '<PAD>',
eos = '<EOS>', #'<EOS>', #'।',
bos = '<BOS>',# None,
blank = '<BLNK>',
phonemes = None,
characters = "abcdefghijklmnopqrstuvwxyz0123456789+=/*√তট৫ভিঐঋখঊড়ইজমএেঘঙসীঢ়হঞ‘ঈকণ৬ঁৗশঢঠ\u200c১্২৮দৃঔগও—ছউংবৈঝাযফ\u200dচরষঅৌৎথড়৪ধ০ুূ৩আঃপয়’'”^নলো_…ৰ",
#characters = "তট৫ভিঐঋখঊড়ইজমএেঘঙসীঢ়হঞ‘ঈকণ৬ঁৗশঢঠ\u200c১্২৮দৃঔগও—ছউংবৈঝাযফ\u200dচরষঅৌৎথড়৪ধ০ুূ৩আঃপয়’নলোˌamɾʃˈonbŋlitjʰɔdkpeɟːfɡuhrʈæsʒɖwəc",
punctuations = "-–:;!,|.?॥। “",
)
#ণ´0ুয)wCছ=ক'স_{rMথd“ো+W।চঋ৷ঔ…’Eৰওঢxoঝূৎ5iটআইSyAc—ড√ল8ঁিk়াYVzফLbD-শlপ য়–গ(রঐ্ঊ‘অGঈষgভ!:n;ীO?vড়aq/tRঘবএঠpধ
#ংখJঙঢ়]ৃউNহত,”নৗIfBৈmP॥sueঃৌhFমজদঞT.*েHj[
audio_config = VitsAudioConfig(
sample_rate=16000, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
)
# VitsConfig: all model related values for training, validating and testing.
config = VitsConfig(
audio=audio_config,
run_name="vits-ft-nctb",
batch_size=48,
eval_batch_size=8,
batch_group_size=5,
num_loader_workers=8,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=35, # testing
# phonemizer="bn_phonemizer",# multi_phonemizer
text_cleaner='multilingual_cleaners',#'multilingual_cleaners', #"collapse_whitespace" phoneme_cleaners multilingual_cleaners
use_phonemes=False,
# phoneme_language="bn",
# phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
compute_input_seq_cache=True,
add_blank=True,
use_language_weighted_sampler = True,
print_step=500,
print_eval=False,
mixed_precision=True,
output_path=output_path,
datasets=[dataset_config],
characters = characters_config,
save_step=1000,
cudnn_benchmark=True,
# dashboard_logger = 'wandb',
test_sentences = [
["আমরা বাংলায় ওয়েব ডেভেলপমেন্ট নিয়ে কাজ করতে গিয়ে প্রথম যে সমস্যাটার মুখোমুখি হই, সেটা হলো, বাংলা ডেমো টেক্সট"],
["আমি বাঙালি ভাষায় কথা বলতে পারি।"],
["আমরা প্রকৃতি কে ভালোবাসি।"],
["আপনি কেমন আছেন?"],
]
)
# 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,
formatter=formatter,
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(restore_path = RESTORE_PATH),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit() |