paddlespeech.s2t.io.utility module

paddlespeech.s2t.io.utility.feat_type(filepath)

paddlespeech.s2t.io.utility.pad_list(sequences: List[ndarray], padding_value: float = 0.0) → ndarray

paddlespeech.s2t.io.utility.pad_sequence(sequences: List[ndarray], batch_first: bool = True, padding_value: float = 0.0) → ndarray

Pad a list of variable length Tensors with padding_value

pad_sequence stacks a list of Tensors along a new dimension, and pads them to equal length. For example, if the input is list of sequences with size L x * and if batch_first is False, and T x B x * otherwise.

B is batch size. It is equal to the number of elements in sequences. T is length of the longest sequence. L is length of the sequence. * is any number of trailing dimensions, including none.

Example:

>>> a = np.ones([25, 300])
>>> b = np.ones([22, 300])
>>> c = np.ones([15, 300])
>>> pad_sequence([a, b, c]).shape
[25, 3, 300]

Note:

This function returns a np.ndarray of size T x B x * or B x T x * where T is the length of the longest sequence. This function assumes trailing dimensions and type of all the Tensors in sequences are same.

Args:

sequences (list[np.ndarray]): list of variable length sequences. batch_first (bool, optional): output will be in B x T x * if True, or in

T x B x * otherwise

padding_value (float, optional): value for padded elements. Default: 0.

Returns:

np.ndarray of size T x B x * if batch_first is False. np.ndarray of size B x T x * otherwise