import os from TTS.config.shared_configs import BaseAudioConfig from TTS.trainer import Trainer, TrainingArgs from TTS.tts.configs.shared_configs import BaseDatasetConfig from TTS.tts.configs.vits_config import VitsConfig from TTS.tts.datasets import load_tts_samples from TTS.tts.models.vits import Vits from TTS.tts.utils.speakers import SpeakerManager from TTS.utils.audio import AudioProcessor output_path = os.path.dirname(os.path.abspath(__file__)) dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/")) audio_config = BaseAudioConfig( sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, preemphasis=0.0, ref_level_db=20, log_func="np.log", do_trim_silence=True, trim_db=23.0, mel_fmin=0, mel_fmax=None, spec_gain=1.0, signal_norm=False, do_amp_to_db_linear=False, resample=True, ) config = VitsConfig( audio=audio_config, run_name="vits_vctk", use_speaker_embedding=True, batch_size=32, eval_batch_size=16, batch_group_size=5, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="english_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), compute_input_seq_cache=True, print_step=25, print_eval=False, mixed_precision=True, sort_by_audio_len=True, min_seq_len=32 * 256 * 4, max_seq_len=1500000, output_path=output_path, datasets=[dataset_config], ) # init audio processor ap = AudioProcessor(**config.audio.to_dict()) # load training samples train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader speaker_manager = SpeakerManager() speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples) config.model_args.num_speakers = speaker_manager.num_speakers # init model model = Vits(config, speaker_manager) # init the trainer and 🚀 trainer = Trainer( TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples, training_assets={"audio_processor": ap}, ) trainer.fit()