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from utils.argutils import print_args
from encoder.train import train
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Trains the speaker encoder. You must have run encoder_preprocess.py first.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
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("clean_data_root", type=Path, help= \
"Path to the output directory of encoder_preprocess.py. If you left the default "
"output directory when preprocessing, it should be <datasets_root>/SV2TTS/encoder/.")
parser.add_argument("-m", "--models_dir", type=Path, default="encoder/saved_models/", help=\
"Path to the output directory that will contain the saved model weights, as well as "
"backups of those weights and plots generated during training.")
parser.add_argument("-v", "--vis_every", type=int, default=10, help= \
"Number of steps between updates of the loss and the plots.")
parser.add_argument("-u", "--umap_every", type=int, default=100, help= \
"Number of steps between updates of the umap projection. Set to 0 to never update the "
"projections.")
parser.add_argument("-s", "--save_every", type=int, default=500, 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=7500, help= \
"Number of steps between backups of the model. Set to 0 to never make backups of the "
"model.")
parser.add_argument("-f", "--force_restart", action="store_true", help= \
"Do not load any saved model.")
parser.add_argument("--visdom_server", type=str, default="http://localhost")
parser.add_argument("--no_visdom", action="store_true", help= \
"Disable visdom.")
args = parser.parse_args()
# Process the arguments
args.models_dir.mkdir(exist_ok=True)
# Run the training
print_args(args, parser)
train(**vars(args))
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