You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
82 lines
2.8 KiB
82 lines
2.8 KiB
# 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.
|
|
import numpy as np
|
|
|
|
from paddlespeech.s2t.io.utility import pad_list
|
|
from paddlespeech.s2t.utils.log import Log
|
|
|
|
__all__ = ["CustomConverter"]
|
|
|
|
logger = Log(__name__).getlog()
|
|
|
|
|
|
class CustomConverter():
|
|
"""Custom batch converter.
|
|
|
|
Args:
|
|
subsampling_factor (int): The subsampling factor.
|
|
dtype (np.dtype): Data type to convert.
|
|
|
|
"""
|
|
|
|
def __init__(self, subsampling_factor=1, dtype=np.float32):
|
|
"""Construct a CustomConverter object."""
|
|
self.subsampling_factor = subsampling_factor
|
|
self.ignore_id = -1
|
|
self.dtype = dtype
|
|
|
|
def __call__(self, batch):
|
|
"""Transform a batch and send it to a device.
|
|
|
|
Args:
|
|
batch (list): The batch to transform.
|
|
|
|
Returns:
|
|
tuple(np.ndarray, nn.ndarray, nn.ndarray)
|
|
|
|
"""
|
|
# batch should be located in list
|
|
assert len(batch) == 1
|
|
(xs, ys), utts = batch[0]
|
|
assert xs[0] is not None, "please check Reader and Augmentation impl."
|
|
|
|
# perform subsampling
|
|
if self.subsampling_factor > 1:
|
|
xs = [x[::self.subsampling_factor, :] for x in xs]
|
|
|
|
# get batch of lengths of input sequences
|
|
ilens = np.array([x.shape[0] for x in xs])
|
|
|
|
# perform padding and convert to tensor
|
|
# currently only support real number
|
|
if xs[0].dtype.kind == "c":
|
|
xs_pad_real = pad_list([x.real for x in xs], 0).astype(self.dtype)
|
|
xs_pad_imag = pad_list([x.imag for x in xs], 0).astype(self.dtype)
|
|
# Note(kamo):
|
|
# {'real': ..., 'imag': ...} will be changed to ComplexTensor in E2E.
|
|
# Don't create ComplexTensor and give it E2E here
|
|
# because torch.nn.DataParellel can't handle it.
|
|
xs_pad = {"real": xs_pad_real, "imag": xs_pad_imag}
|
|
else:
|
|
xs_pad = pad_list(xs, 0).astype(self.dtype)
|
|
|
|
# NOTE: this is for multi-output (e.g., speech translation)
|
|
ys_pad = pad_list(
|
|
[np.array(y[0][:]) if isinstance(y, tuple) else y for y in ys],
|
|
self.ignore_id)
|
|
|
|
olens = np.array(
|
|
[y[0].shape[0] if isinstance(y, tuple) else y.shape[0] for y in ys])
|
|
return utts, xs_pad, ilens, ys_pad, olens
|