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47 lines
2.3 KiB
Python
47 lines
2.3 KiB
Python
from utils.argutils import print_args
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from encoder.train import train
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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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parser = argparse.ArgumentParser(
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description="Trains the speaker encoder. You must have run encoder_preprocess.py first.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument("run_id", type=str, help= \
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"Name for this model instance. If a model state from the same run ID was previously "
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"saved, the training will restart from there. Pass -f to overwrite saved states and "
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"restart from scratch.")
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parser.add_argument("clean_data_root", type=Path, help= \
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"Path to the output directory of encoder_preprocess.py. If you left the default "
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"output directory when preprocessing, it should be <datasets_root>/SV2TTS/encoder/.")
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parser.add_argument("-m", "--models_dir", type=Path, default="encoder/saved_models/", help=\
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"Path to the output directory that will contain the saved model weights, as well as "
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"backups of those weights and plots generated during training.")
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parser.add_argument("-v", "--vis_every", type=int, default=10, help= \
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"Number of steps between updates of the loss and the plots.")
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parser.add_argument("-u", "--umap_every", type=int, default=100, help= \
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"Number of steps between updates of the umap projection. Set to 0 to never update the "
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"projections.")
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parser.add_argument("-s", "--save_every", type=int, default=500, help= \
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"Number of steps between updates of the model on the disk. Set to 0 to never save the "
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"model.")
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parser.add_argument("-b", "--backup_every", type=int, default=7500, help= \
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"Number of steps between backups of the model. Set to 0 to never make backups of the "
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"model.")
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parser.add_argument("-f", "--force_restart", action="store_true", help= \
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"Do not load any saved model.")
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parser.add_argument("--visdom_server", type=str, default="http://localhost")
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parser.add_argument("--no_visdom", action="store_true", help= \
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"Disable visdom.")
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args = parser.parse_args()
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# Process the arguments
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args.models_dir.mkdir(exist_ok=True)
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# Run the training
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print_args(args, parser)
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train(**vars(args))
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