required=False, help='checkpoint path') parser.add_argument('--rank', type=str, default="0", required=False, help='rank of current gpu') parser.add_argument('--load_checkpoint', type=bool, default=False, required=False) parser.add_argument('--hp_config', type=str, required=True, help='hparams configs') args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = args.rank ## hyperparamerter hp = create_hparams(f"hp_config/{args.hp_config}") ## create logger logger = prepare_directories_and_logger(Logger, output_directory = f'output/{args.output_directory}') ''' ## DataLoader Part The directory of the hp.training_files like this: - hp.training_files - aaa.mp3 - aaa1.mp3 - aa2.mp3 . . . every mp3 is clip to the same length (hp.training_files: ../clips.5s/ ) '''
required=True, help='channel number in VQVC+') parser.add_argument( '-t', '--trainer', type=str, required=True, help='which trainer do you want? (rhythm, mean_std, normal)') parser.add_argument('--load_checkpoint', type=bool, default=False, required=False) args = parser.parse_args() logger = prepare_directories_and_logger( Logger, output_directory= f'output/{args.model}_n{args.n_embed}_ch{args.channel}_{args.trainer}') import importlib trainer = importlib.import_module(f'trainer.{args.trainer}') train_ = getattr(trainer, 'train_') model = importlib.import_module(f'model.{args.model}.vq_model') model = getattr(model, 'VC_MODEL') ''' Dataset and loader ''' def make_inf_iterator(data_iterator):