How to use the asteroid.data.wham_dataset.WhamDataset function in asteroid

def main(conf):
    # from asteroid.data.toy_data import WavSet
    # train_set = WavSet(n_ex=1000, n_src=2, ex_len=32000)
    # val_set = WavSet(n_ex=1000, n_src=2, ex_len=32000)
    # Define data pipeline
    train_set = WhamDataset(conf['data']['train_dir'], conf['data']['task'],
                            sample_rate=conf['data']['sample_rate'],
                            nondefault_nsrc=conf['data']['nondefault_nsrc'])
    val_set = WhamDataset(conf['data']['valid_dir'], conf['data']['task'],
                          sample_rate=conf['data']['sample_rate'],
                          nondefault_nsrc=conf['data']['nondefault_nsrc'])

    train_loader = DataLoader(train_set, shuffle=True,
                              batch_size=conf['training']['batch_size'],
                              num_workers=conf['training']['num_workers'])
    val_loader = DataLoader(val_set, shuffle=False,
                            batch_size=conf['training']['batch_size'],
                            num_workers=conf['training']['num_workers'])
    conf['masknet'].update({'n_src': train_set.n_src})

    # Define model and optimizer in a local function (defined in the recipe).
    # Two advantages to this : re-instantiating the model and optimizer
    # for retraining and evaluating is straight-forward.
    model, optimizer = make_model_and_optimizer(conf)

def main(conf):
    train_set = WhamDataset(conf['data']['train_dir'], conf['data']['task'],
                            sample_rate=conf['data']['sample_rate'], segment=conf['data']['segment'],
                            nondefault_nsrc=conf['data']['nondefault_nsrc'])
    val_set = WhamDataset(conf['data']['valid_dir'], conf['data']['task'],
                          sample_rate=conf['data']['sample_rate'],
                          nondefault_nsrc=conf['data']['nondefault_nsrc'])

    train_loader = DataLoader(train_set, shuffle=True,
                              batch_size=conf['training']['batch_size'],
                              num_workers=conf['training']['num_workers'],
                              drop_last=True)
    val_loader = DataLoader(val_set, shuffle=True,
                            batch_size=conf['training']['batch_size'],
                            num_workers=conf['training']['num_workers'],
                            drop_last=True)
    # Update number of source values (It depends on the task)
    conf['masknet'].update({'n_src': train_set.n_src})

def get_data_loaders(conf, train_part='filterbank'):
    train_set = WhamDataset(conf['data']['train_dir'], conf['data']['task'],
                            sample_rate=conf['data']['sample_rate'],
                            nondefault_nsrc=conf['data']['nondefault_nsrc'],
                            normalize_audio=True)
    val_set = WhamDataset(conf['data']['valid_dir'], conf['data']['task'],
                          sample_rate=conf['data']['sample_rate'],
                          nondefault_nsrc=conf['data']['nondefault_nsrc'],
                          normalize_audio=True)

    if train_part not in ['filterbank', 'separator']:
        raise ValueError('Part to train: {} is not available.'.format(
            train_part))

    train_loader = DataLoader(train_set, shuffle=True, drop_last=True,
                              batch_size=conf[train_part + '_training'][
                                  train_part[0] + '_batch_size'],
                              num_workers=conf[train_part + '_training'][
                                  train_part[0] + '_num_workers'])
    val_loader = DataLoader(val_set, shuffle=False, drop_last=True,
                            batch_size=conf[train_part + '_training'][
                                train_part[0] + '_batch_size'],

def main(conf):
    train_set = WhamDataset(conf['data']['train_dir'], conf['data']['task'],
                            sample_rate=conf['data']['sample_rate'], segment=conf['data']['segment'],
                            nondefault_nsrc=conf['data']['nondefault_nsrc'])
    val_set = WhamDataset(conf['data']['valid_dir'], conf['data']['task'],
                          sample_rate=conf['data']['sample_rate'],
                          nondefault_nsrc=conf['data']['nondefault_nsrc'])

    train_loader = DataLoader(train_set, shuffle=True,
                              batch_size=conf['training']['batch_size'],
                              num_workers=conf['training']['num_workers'],
                              drop_last=True)
    val_loader = DataLoader(val_set, shuffle=True,
                            batch_size=conf['training']['batch_size'],
                            num_workers=conf['training']['num_workers'],
                            drop_last=True)
    # Update number of source values (It depends on the task)
    conf['masknet'].update({'n_src': train_set.n_src})

    # Define model and optimizer in a local function (defined in the recipe).
    # Two advantages to this : re-instantiating the model and optimizer

def get_data_loaders(conf, train_part='filterbank'):
    train_set = WhamDataset(conf['data']['train_dir'], conf['data']['task'],
                            sample_rate=conf['data']['sample_rate'],
                            nondefault_nsrc=conf['data']['nondefault_nsrc'],
                            normalize_audio=True)
    val_set = WhamDataset(conf['data']['valid_dir'], conf['data']['task'],
                          sample_rate=conf['data']['sample_rate'],
                          nondefault_nsrc=conf['data']['nondefault_nsrc'],
                          normalize_audio=True)

    if train_part not in ['filterbank', 'separator']:
        raise ValueError('Part to train: {} is not available.'.format(
            train_part))

    train_loader = DataLoader(train_set, shuffle=True, drop_last=True,
                              batch_size=conf[train_part + '_training'][
                                  train_part[0] + '_batch_size'],
                              num_workers=conf[train_part + '_training'][

def main(conf):
    model = load_best_separator_if_available(conf['train_conf'])
    # Handle device placement
    if conf['use_gpu']:
        model.cuda()
    model_device = next(model.parameters()).device
    test_set = WhamDataset(conf['test_dir'], conf['task'],
                           sample_rate=conf['sample_rate'],
                           nondefault_nsrc=model.separator.n_sources,
                           segment=None, normalize_audio=True)
    # Used to reorder sources only
    loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')

    # Randomly choose the indexes of sentences to save.
    ex_save_dir = os.path.join(conf['exp_dir'], 'examples/')
    if conf['n_save_ex'] == -1:
        conf['n_save_ex'] = len(test_set)
    save_idx = random.sample(range(len(test_set)), conf['n_save_ex'])
    series_list = []
    torch.no_grad().__enter__()
    cnt = 0
    for idx in tqdm(range(len(test_set))):
        # Forward the network on the mixture.