import os from trainer import Trainer, TrainerArgs from TTS.utils.audio import AudioProcessor from TTS.utils.downloaders import download_thorsten_de from TTS.vocoder.configs import WavegradConfig from TTS.vocoder.datasets.preprocess import load_wav_data from TTS.vocoder.models.wavegrad import Wavegrad output_path = os.path.dirname(os.path.abspath(__file__)) config = WavegradConfig( 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, seq_len=6144, pad_short=2000, use_noise_augment=True, eval_split_size=50, print_step=50, print_eval=True, mixed_precision=False, data_path=os.path.join(output_path, "../thorsten-de/wavs/"), output_path=output_path, ) # download dataset if not already present if not os.path.exists(config.data_path): print("Downloading dataset") download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../")) download_thorsten_de(download_path) # init audio processor ap = AudioProcessor(**config.audio.to_dict()) # load training samples eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size) # init model model = Wavegrad(config) # init the trainer and 🚀 trainer = Trainer( TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples, training_assets={"audio_processor": ap}, ) trainer.fit()