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, 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("--v_port", type=int, default=8097, help= \
        "Port on which Visdom Server is running")
    parser.add_argument("--no_visdom", action="store_true", help= \
        "Disable visdom.")
    parser.add_argument("-gpuid", "--gpu_id", type=str, default='0', help= \
        "Select the GPU to run the code")
    parser.add_argument("--module_name", type=str, default="model_baseline")
    args = parser.parse_args()

    # Process the arguments
    args.models_dir.mkdir(exist_ok=True)

    # Run the training
    print_args(args, parser)
    train(**vars(args))