import os from trainer import Trainer, TrainerArgs from TTS.tts.configs.align_tts_config import AlignTTSConfig from TTS.tts.configs.shared_configs import BaseDatasetConfig from TTS.tts.datasets import load_tts_samples from TTS.tts.models.align_tts import AlignTTS from TTS.tts.utils.text.tokenizer import TTSTokenizer from TTS.utils.audio import AudioProcessor output_path = os.path.dirname(os.path.abspath(__file__)) # init configs dataset_config = BaseDatasetConfig( formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") ) config = AlignTTSConfig( batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="english_cleaners", use_phonemes=False, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), print_step=25, print_eval=True, mixed_precision=False, output_path=output_path, datasets=[dataset_config], ) # 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. # If characters are not defined in the config, default characters are passed to 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 = AlignTTS(config, ap, tokenizer) # INITIALIZE THE TRAINER # Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training, # distributed training, etc. trainer = Trainer( TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples ) # AND... 3,2,1... 🚀 trainer.fit()