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="", eos="", bos="", blank="", 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()