# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from io import BytesIO
from typing import List
import numpy as np
from paddlespeech.s2t.utils.log import Log
__all__ = ["pad_list", "pad_sequence", "feat_type"]
logger = Log(__name__).getlog()
[docs]def pad_list(sequences: List[np.ndarray],
padding_value: float=0.0) -> np.ndarray:
return pad_sequence(sequences, True, padding_value)
[docs]def pad_sequence(sequences: List[np.ndarray],
batch_first: bool=True,
padding_value: float=0.0) -> np.ndarray:
r"""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 :attr:`batch_first` is ``False``.
np.ndarray of size ``B x T x *`` otherwise
"""
# assuming trailing dimensions and type of all the Tensors
# in sequences are same and fetching those from sequences[0]
max_size = sequences[0].shape
trailing_dims = max_size[1:]
max_len = max([s.shape[0] for s in sequences])
if batch_first:
out_dims = (len(sequences), max_len) + trailing_dims
else:
out_dims = (max_len, len(sequences)) + trailing_dims
out_tensor = np.full(out_dims, padding_value, dtype=sequences[0].dtype)
for i, tensor in enumerate(sequences):
length = tensor.shape[0]
# use index notation to prevent duplicate references to the tensor
if batch_first:
out_tensor[i, :length, ...] = tensor
else:
out_tensor[:length, i, ...] = tensor
return out_tensor
[docs]def feat_type(filepath):
# deal with Byteio type for paddlespeech server
if isinstance(filepath, BytesIO):
return 'sound'
suffix = filepath.split(":")[0].split('.')[-1].lower()
if suffix == 'ark':
return 'mat'
elif suffix == 'scp':
return 'scp'
elif suffix == 'npy':
return 'npy'
elif suffix == 'npz':
return 'npz'
elif suffix in ['wav', 'flac']:
# PCM16
return 'sound'
else:
raise ValueError(f"Not support filetype: {suffix}")