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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 CharactersConfig, Vits, VitsArgs, 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_config = BaseDatasetConfig(
formatter="mozilla_with_speaker",
dataset_name="multi_persian",
meta_file_train="metadata.csv",
language="fa",
phonemizer="espeak",
path="/kaggle/input",
)
audio_config = BaseAudioConfig(
sample_rate=22050,
do_trim_silence=False,
resample=False,
)
### Extract speaker embeddings
SPEAKER_ENCODER_CHECKPOINT_PATH = (
"https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar"
)
SPEAKER_ENCODER_CONFIG_PATH = "https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json"
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",
)
model_args = VitsArgs(
d_vector_file=['/kaggle/working/speakers.pth'],
use_d_vector_file=True,
d_vector_dim=512,
num_layers_text_encoder=10,
speaker_encoder_model_path=SPEAKER_ENCODER_CHECKPOINT_PATH,
speaker_encoder_config_path=SPEAKER_ENCODER_CONFIG_PATH,
# resblock_type_decoder="2", # On the paper, we accidentally trained the YourTTS using ResNet blocks type 2, if you like you can use the ResNet blocks type 1 like the VITS model
# Usefull parameters to enable the Speaker Consistency Loss (SCL) discribed in the paper
# use_speaker_encoder_as_loss=True,
# Usefull parameters to the enable multilingual training
# use_language_embedding=True,
# embedded_language_dim=4,
)
config = VitsConfig(
audio=audio_config,
run_name="vits_fa_female",
model_args=model_args,
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","dilara",None,"fa"],
[" با این جامه ی یک لا در این سرما چه می کنی ","farid",None,"fa"],
["مردی نزد بقالی آمد و گفت پیاز هم ده تا دهان بدان خو شبوی سازم.","farid",None,"fa"],
["از مال خود پاره ای گوشت بستان و زیره بایی معطّر بساز","dilara",None,"fa"],
["یک بار هم از جهنم بگویید.","changiz",None,"fa"],
["یکی اسبی به عاریت خواست","changiz",None,"fa"]
],
output_path=output_path,
datasets=[audio_config],
# Enable the weighted sampler
use_weighted_sampler=True,
# Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
weighted_sampler_attrs={"speaker_name": 1.0},
weighted_sampler_multipliers={},
# It defines the Speaker Consistency Loss (SCL) α to 9 like the paper
speaker_encoder_loss_alpha=9.0,
)
# 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.
# Load all the datasets samples and split traning and evaluation sets
train_samples, eval_samples = load_tts_samples(
config.datasets,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# Init the model
model = Vits.init_from_config(config,ap, tokenizer)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit() |