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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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args.models_dir.mkdir(exist_ok=True) |
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print_args(args, parser) |
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train(**vars(args)) |
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