def get_loader(data_path='data/vctk',
max_seq_len=1000,
batch_size=64,
nspk=22):
dataset = NpzFolder(data_path + '/numpy_features_valid', nspk == 1)
loader = NpzLoader(dataset,
max_seq_len=max_seq_len,
batch_size=batch_size,
num_workers=4,
pin_memory=True)
return loader
def get_loaders(data_path='data/vctk',
max_seq_len=1000,
batch_size=64,
nspk=22):
# wrap train dataset
train_dataset = NpzFolder(data_path + '/numpy_features', nspk == 1)
train_loader = NpzLoader(train_dataset,
max_seq_len=max_seq_len,
batch_size=batch_size,
num_workers=4,
pin_memory=True,
shuffle=True)
# wrap validation dataset
valid_dataset = NpzFolder(data_path + '/numpy_features_valid', nspk == 1)
valid_loader = NpzLoader(valid_dataset,
max_seq_len=max_seq_len,
batch_size=batch_size,
num_workers=4,
pin_memory=True)
return train_loader, valid_loader
def main():
parser = argparse.ArgumentParser(description='PyTorch Loop')
# Env options:
parser.add_argument('--epochs', type=int, default=92, metavar='N',
help='number of epochs to train (default: 92)')
parser.add_argument('--seed', type=int, default=10, metavar='S',
help='random seed (default: 3)')
parser.add_argument('--expName', type=str, default='vctk', metavar='E',
help='Experiment name')
parser.add_argument('--data', default='data/vctk',
metavar='D', type=str, help='Data path')
parser.add_argument('--checkpoint', default='',
metavar='C', type=str, help='Checkpoint path')
parser.add_argument('--gpu', default=0,
metavar='G', type=int, help='GPU device ID')
# Data options
parser.add_argument('--max-seq-len', type=int, default=1000,
help='Max sequence length for tbptt')
parser.add_argument('--batch-size', type=int, default=64,
help='Batch size')
# Model options
parser.add_argument('--nspk', type=int, default=22,
help='Number of speakers')
# init
args = parser.parse_args()
args.expName = os.path.join('checkpoints', args.expName)
torch.cuda.set_device(args.gpu)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
logging = create_output_dir(args)
# data
valid_dataset = NpzFolder(args.data + '/numpy_features_valid', args.nspk == 1)
valid_loader = NpzLoader(valid_dataset,
max_seq_len=args.max_seq_len,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True)
# load model
model, norm = model_def(args.checkpoint, gpu=args.gpu, valid_loader=valid_loader)
# Begin!
eval_loss = evaluate(model, norm, valid_loader, logging)
# init
args = parser.parse_args()
args.expName = os.path.join('checkpoints', args.expName)
torch.cuda.set_device(args.gpu)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
logging = create_output_dir(args)
vis = visdom.Visdom(env=args.expName)
# data
logging.info("Building dataset.")
train_dataset = NpzFolder(args.data + '/numpy_features', args.nspk == 1)
train_loader = NpzLoader(train_dataset,
max_seq_len=args.max_seq_len,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True,
shuffle=True)
valid_dataset = NpzFolder(args.data + '/numpy_features_valid', args.nspk == 1)
valid_loader = NpzLoader(valid_dataset,
max_seq_len=args.max_seq_len,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True)
logging.info("Dataset ready!")
def train(model, criterion, optimizer, epoch, train_losses):
total = 0 # Reset every plot_every