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36 lines
1.7 KiB
Python
36 lines
1.7 KiB
Python
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from synthesizer.hparams import hparams
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from synthesizer.train import train
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from utils.argutils import print_args
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import argparse
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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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("syn_dir", type=str, default=argparse.SUPPRESS, help= \
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"Path to the synthesizer directory that contains the ground truth mel spectrograms, "
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"the wavs and the embeds.")
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parser.add_argument("-m", "--models_dir", type=str, default="synthesizer/saved_models/", help=\
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"Path to the output directory that will contain the saved model weights and the logs.")
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parser.add_argument("-s", "--save_every", type=int, default=1000, 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=25000, 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 and restart from scratch.")
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parser.add_argument("--hparams", default="",
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help="Hyperparameter overrides as a comma-separated list of name=value "
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"pairs")
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args = parser.parse_args()
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print_args(args, parser)
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args.hparams = hparams.parse(args.hparams)
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# Run the training
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train(**vars(args))
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