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):
