import os from trainer import Trainer, TrainerArgs from TTS.config.shared_configs import BaseAudioConfig from TTS.tts.configs.overflow_config import OverflowConfig from TTS.tts.configs.shared_configs import BaseDatasetConfig from TTS.tts.datasets import load_tts_samples from TTS.tts.models.overflow import Overflow 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("data", "LJSpeech-1.1/") ) audio_config = BaseAudioConfig( sample_rate=22050, do_trim_silence=True, trim_db=60.0, signal_norm=False, mel_fmin=0.0, mel_fmax=8000, spec_gain=1.0, log_func="np.log", ref_level_db=20, preemphasis=0.0, ) config = OverflowConfig( # This is the config that is saved for the future use run_name="overflow_ljspeech", audio=audio_config, batch_size=30, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="phoneme_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), precompute_num_workers=8, mel_statistics_parameter_path=os.path.join(output_path, "lj_parameters.pt"), force_generate_statistics=False, print_step=1, print_eval=True, mixed_precision=True, 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, ) # INITIALIZE THE MODEL # Models take a config object and a speaker manager as input # Config defines the details of the model like the number of layers, the size of the embedding, etc. # Speaker manager is used by multi-speaker models. model = Overflow(config, ap, tokenizer) # init the trainer and 🚀 trainer = Trainer( TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples, gpu=1, ) trainer.fit()