import os from trainer import Trainer, TrainerArgs from TTS.config.shared_configs import BaseAudioConfig from TTS.tts.configs.shared_configs import BaseDatasetConfig from TTS.tts.configs.tacotron_config import TacotronConfig from TTS.tts.datasets import load_tts_samples from TTS.tts.models.tacotron import Tacotron from TTS.tts.utils.speakers import SpeakerManager from TTS.tts.utils.text.tokenizer import TTSTokenizer from TTS.utils.audio import AudioProcessor output_path = os.path.dirname(os.path.abspath(__file__)) dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) audio_config = BaseAudioConfig( sample_rate=22050, resample=True, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training. do_trim_silence=True, trim_db=23.0, 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 = TacotronConfig( # This is the config that is saved for the future use audio=audio_config, batch_size=48, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, precompute_num_workers=4, run_eval=True, test_delay_epochs=-1, r=6, gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]], double_decoder_consistency=True, epochs=1000, text_cleaner="phoneme_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), print_step=25, print_eval=False, mixed_precision=True, min_text_len=0, max_text_len=500, min_audio_len=0, max_audio_len=44000 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio output_path=output_path, datasets=[dataset_config], use_speaker_embedding=True, # set this to enable multi-sepeaker training ) ## 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 speaker manager for multi-speaker training # it mainly handles speaker-id to speaker-name for the model and the data-loader speaker_manager = SpeakerManager() speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name") # init model model = Tacotron(config, ap, tokenizer, speaker_manager) # 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()