27 lines
1.3 KiB
Python
27 lines
1.3 KiB
Python
from models.synthesizer.preprocess import create_embeddings
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from utils.argutils import print_args
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from pathlib import Path
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import argparse
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if __name__ == "__main__":
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print("This method is deprecaded and will not be longer supported, please use 'pre.py'")
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parser = argparse.ArgumentParser(
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description="Creates embeddings for the synthesizer from the LibriSpeech utterances.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument("synthesizer_root", type=Path, help=\
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"Path to the synthesizer training data that contains the audios and the train.txt file. "
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"If you let everything as default, it should be <datasets_root>/SV2TTS/synthesizer/.")
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parser.add_argument("-e", "--encoder_model_fpath", type=Path,
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default="data/ckpt/encoder/pretrained.pt", help=\
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"Path your trained encoder model.")
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parser.add_argument("-n", "--n_processes", type=int, default=4, help= \
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"Number of parallel processes. An encoder is created for each, so you may need to lower "
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"this value on GPUs with low memory. Set it to 1 if CUDA is unhappy.")
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args = parser.parse_args()
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# Preprocess the dataset
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print_args(args, parser)
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create_embeddings(**vars(args))
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