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. By default, training outputs will be stored to saved_models//. 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 /SV2TTS/encoder/.") parser.add_argument("-m", "--models_dir", type=Path, default="saved_models", help=\ "Path to the root directory that contains all models. A directory will be created under this root." "It 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() # Run the training print_args(args, parser) train(**vars(args))