synthesizer_preprocess_embeds.py

from synthesizer.models.tacotron.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="saved_models/default/encoder.pt", help="Path your trained encoder model.")
    parser.add_argument("-n", "--n_processes", type=int, default=2, 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 a lower value if CUDA is unhappy.")
    args = parser.parse_args()

    # Preprocess the dataset
    print_args(args, parser)
    create_embeddings(**vars(args))