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| from synthesizer.preprocess import create_embeddings | |
| from utils.argutils import print_args | |
| from pathlib import Path | |
| import argparse | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser( | |
| description="Creates embeddings for the synthesizer from the LibriSpeech utterances.", | |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter | |
| ) | |
| parser.add_argument("synthesizer_root", type=Path, help=\ | |
| "Path to the synthesizer training data that contains the audios and the train.txt file. " | |
| "If you let everything as default, it should be <datasets_root>/SV2TTS/synthesizer/.") | |
| parser.add_argument("-e", "--encoder_model_fpath", type=Path, | |
| default="encoder/saved_models/pretrained.pt", help=\ | |
| "Path your trained encoder model.") | |
| parser.add_argument("-n", "--n_processes", type=int, default=4, help= \ | |
| "Number of parallel processes. An encoder is created for each, so you may need to lower " | |
| "this value on GPUs with low memory. Set it to 1 if CUDA is unhappy.") | |
| args = parser.parse_args() | |
| # Preprocess the dataset | |
| print_args(args, parser) | |
| create_embeddings(**vars(args)) | |