examples/lstm-chime.py
def batch_at(features, labels, seq_begins, seq_lengths):
    '''Extract a single batch of data to pass to the model being trained.

    Parameters
    ----------
    features, labels : ndarray
        Arrays of the input features and target labels.
    seq_begins : ndarray
        Array of the start offsets of the speech segments to include.
    seq_lengths : ndarray
        Array of the lengths of the speech segments to include in the batch.

    Returns
    -------
    features, labels, mask : ndarrays
        A triple of arrays for training a network. The first element contains
        input features, the second contains target labels, and the third
        contains a "mask" consisting of ones where there is valid data and zeros
        everywhere else.
    '''
    length = seq_lengths.max()
    feat = np.zeros((BATCH_SIZE, length, features.shape[-1]), 'f')
    labl = np.zeros((BATCH_SIZE, length), 'i')
    mask = np.zeros((BATCH_SIZE, length), 'f')
    for b, (begin, length) in enumerate(zip(seq_begins, seq_lengths)):
        feat[b, :length] = features[begin:begin+length]
        labl[b, :length] = labels[begin:begin+length]
        mask[b, :length] = 1
    return [feat, labl, mask]
def batch_at(features, labels, seq_begins, seq_lengths):
    '''Extract a single batch of data to pass to the model being trained.

    Parameters
    ----------
    features, labels : ndarray
        Arrays of the input features and target labels.
    seq_begins : ndarray
        Array of the start offsets of the speech segments to include.
    seq_lengths : ndarray
        Array of the lengths of the speech segments to include in the batch.

    Returns
    -------
    features, labels, mask : ndarrays
        A triple of arrays for training a network. The first element contains
        input features, the second contains target labels, and the third
        contains a "mask" consisting of ones where there is valid data and zeros
        everywhere else.
    '''
    length = seq_lengths.max()
    feat = np.zeros((BATCH_SIZE, length, features.shape[-1]), 'f')
    labl = np.zeros((BATCH_SIZE, length), 'i')
    mask = np.zeros((BATCH_SIZE, length), 'f')
    for b, (begin, length) in enumerate(zip(seq_begins, seq_lengths)):
        feat[b, :length] = features[begin:begin+length]
        labl[b, :length] = labels[begin:begin+length]
        mask[b, :length] = 1
    return [feat, labl, mask]
def batch_at ( features , labels , seq_begins , seq_lengths ) : length = seq_lengths . max ( ) feat = np . zeros ( ( BATCH_SIZE , length , features . shape [ - 1 ] ) , 'f' ) labl = np . zeros ( ( BATCH_SIZE , length ) , 'i' ) mask = np . zeros ( ( BATCH_SIZE , length ) , 'f' ) for b , ( begin , length ) in enumerate ( zip ( seq_begins , seq_lengths ) ) : feat [ b , : length ] = features [ begin : begin + length ] labl [ b , : length ] = labels [ begin : begin + length ] mask [ b , : length ] = 1 return [ feat , labl , mask ]
Extract a single batch of data to pass to the model being trained.

    Parameters
    ----------
    features, labels : ndarray
        Arrays of the input features and target labels.
    seq_begins : ndarray
        Array of the start offsets of the speech segments to include.
    seq_lengths : ndarray
        Array of the lengths of the speech segments to include in the batch.

    Returns
    -------
    features, labels, mask : ndarrays
        A triple of arrays for training a network. The first element contains
        input features, the second contains target labels, and the third
        contains a "mask" consisting of ones where there is valid data and zeros
        everywhere else.
Extract a single batch of data to pass to the model being trained .