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from synthesizer.hparams import hparams
from synthesizer.train import train
from utils.argutils import print_args
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("run_id", type=str, help= \
"Name for this model instance. If a model state from the same run ID was previously "
"saved, the training will restart from there. Pass -f to overwrite saved states and "
"restart from scratch.")
parser.add_argument("syn_dir", type=str, default=argparse.SUPPRESS, help= \
"Path to the synthesizer directory that contains the ground truth mel spectrograms, "
"the wavs and the embeds.")
parser.add_argument("-m", "--models_dir", type=str, default="synthesizer/saved_models/", help=\
"Path to the output directory that will contain the saved model weights and the logs.")
parser.add_argument("-s", "--save_every", type=int, default=1000, help= \
"Number of steps between updates of the model on the disk. Set to 0 to never save the "
"model.")
parser.add_argument("-b", "--backup_every", type=int, default=25000, help= \
"Number of steps between backups of the model. Set to 0 to never make backups of the "
"model.")
parser.add_argument("-l", "--log_every", type=int, default=200, help= \
"Number of steps between summary the training info in tensorboard")
parser.add_argument("-f", "--force_restart", action="store_true", help= \
"Do not load any saved model and restart from scratch.")
parser.add_argument("--hparams", default="",
help="Hyperparameter overrides as a comma-separated list of name=value "
"pairs")
args = parser.parse_args()
print_args(args, parser)
args.hparams = hparams.parse(args.hparams)
# Run the training
train(**vars(args))
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