encoder_train.py

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, default='1', 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("--train_data_root", type=Path, required=True, help="Path to the output "
                        "directory of train data. If you left the default output "
                        "directory when preprocessing, it should be <datasets_root>/SV2TTS/encoder_train/.")
    parser.add_argument("--test_data_root", type=Path, required=True, help="Path to the output "
                        "directory of test data. it should be <datasets_root>/SV2TTS/encoder_test/.")
    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=100,
                        help="Number of steps between updates of the loss and the plots.")
    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=10000, 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",default=False,  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))