def data_loader(split_from, split_to, eval):
if (params['dataset'] == "toy_sin_wave"):
dataset = FolderDataset(toy_sin_wave=True)
else:
dataset = FolderDataset(path=path,
overlap_len=overlap_len,
q_levels=params['q_levels'],
ratio_min=split_from,
ratio_max=split_to)
return DataLoader(dataset,
batch_size=params['batch_size'],
seq_len=params['seq_len'],
overlap_len=overlap_len,
shuffle=(not eval),
drop_last=(not eval))
def data_loader(partition):
dataset = FolderDataset(params['datasets_path'], path, cond_path, overlap_len, params['q_levels'],
params['ulaw'], params['seq_len'], params['batch_size'], params['cond_dim'],
params['cond_len'], params['norm_ind'], params['static_spk'],
params['look_ahead'], partition)
return DataLoader(dataset, batch_size=params['batch_size'], shuffle=False, drop_last=True, num_workers=2)
def data_loader(split_from, split_to, eval):
dataset = FolderDataset(path, overlap_len, params['q_levels'],
split_from, split_to)
return DataLoader(dataset,
batch_size=params['batch_size'],
seq_len=params['seq_len'],
overlap_len=overlap_len,
shuffle=(not eval),
drop_last=(not eval))
def data_loader(split_from, split_to, eval):
dataset = FolderDataset(
path, overlap_len, params['q_levels'], split_from, split_to
)
l = dataset.__len__()
dataset_filenames = []
for i in range(0, l):
# print(dataset.get_filename(i))
dataset_filenames.append(dataset.get_filename(i))
dataloader = DataLoader(
dataset,
batch_size=params['batch_size'],
seq_len=params['seq_len'],
overlap_len=overlap_len,
shuffle=(not eval),
drop_last=(not eval)
)
return dataloader, dataset_filenames
def data_loader(split_from, split_to, eval):
dataset = FolderDataset(path_wav, path_spec, params['hindsight'],
params['q_levels'],
split_from, split_to
)
return DataLoader(
dataset,
batch_size = params['batch_size'],
seq_len = params['seq_len']
hindsight = params['hindsight'],
shuffle=(not eval),
drop_last=(not eval)
)