Exemple #1
0
def load_dataset(args):
    train_dataset = LJspeechDataset(args.data_path, True, 0.1)
    test_dataset = LJspeechDataset(args.data_path, False, 0.1)
    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn,
                              num_workers=args.num_workers, pin_memory=True)
    test_loader = DataLoader(test_dataset, batch_size=args.batch_size, collate_fn=collate_fn,
                             num_workers=args.num_workers, pin_memory=True)
    synth_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn_synthesize,
                              num_workers=args.num_workers, pin_memory=True)

    return train_loader, test_loader, synth_loader
def load_dataset(args):
    test_dataset = LJspeechDataset(args.data_path, False, 0.1)
    synth_loader = DataLoader(test_dataset,
                              batch_size=1,
                              collate_fn=collate_fn_synthesize,
                              num_workers=args.num_workers,
                              pin_memory=True)

    return synth_loader
Exemple #3
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if not os.path.isdir(args.save):
    os.makedirs(args.save)
if not os.path.isdir(args.loss):
    os.makedirs(args.loss)
if not os.path.isdir(args.sample_path):
    os.makedirs(args.sample_path)
if not os.path.isdir(os.path.join(args.sample_path, args.model_name)):
    os.makedirs(os.path.join(args.sample_path, args.model_name))
if not os.path.isdir(os.path.join(args.save, args.model_name)):
    os.makedirs(os.path.join(args.save, args.model_name))

use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# LOAD DATASETS
train_dataset = LJspeechDataset(args.data_path, True, 0.1)
test_dataset = LJspeechDataset(args.data_path, False, 0.1)
train_loader = DataLoader(train_dataset,
                          batch_size=args.batch_size,
                          shuffle=True,
                          collate_fn=collate_fn,
                          num_workers=args.num_workers,
                          pin_memory=True)
test_loader = DataLoader(test_dataset,
                         batch_size=args.batch_size,
                         collate_fn=collate_fn,
                         num_workers=args.num_workers,
                         pin_memory=True)
synth_loader = DataLoader(test_dataset,
                          batch_size=1,
                          collate_fn=collate_fn_synthesize,
Exemple #4
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                    default=0,
                    help='Number of workers')

parser.add_argument('--log', type=str, default='./log', help='Log folder.')
args = parser.parse_args()

if not os.path.isdir(args.sample_path):
    os.makedirs(args.sample_path)
if not os.path.isdir(os.path.join(args.sample_path, args.model_name)):
    os.makedirs(os.path.join(args.sample_path, args.model_name))

use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# LOAD DATASETS
test_dataset = LJspeechDataset(args.data_path, False, 0.1)
synth_loader = DataLoader(test_dataset,
                          batch_size=1,
                          collate_fn=collate_fn_synthesize,
                          num_workers=args.num_workers,
                          pin_memory=True)


def build_model():
    causality = True if args.causal == 'yes' else False
    model = Flowavenet(in_channel=1,
                       cin_channel=args.cin_channels,
                       n_block=args.n_block,
                       n_flow=args.n_flow,
                       n_layer=args.n_layer,
                       affine=True,