# 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.
# Modified from espnet(https://github.com/espnet/espnet)
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()
[docs]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,
load_aux_input=False,
load_aux_output=False):
"""Construct a CustomConverter object."""
self.subsampling_factor = subsampling_factor
self.ignore_id = -1
self.dtype = dtype
self.load_aux_input = load_aux_input
self.load_aux_output = load_aux_output
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
data, utts = batch[0]
xs_data, ys_data = [], []
for ud in data:
if ud[0].ndim > 1:
# speech data (input): (speech_len, feat_dim)
xs_data.append(ud)
else:
# text data (output): (text_len, )
ys_data.append(ud)
assert xs_data[0][
0] is not None, "please check Reader and Augmentation impl."
xs_pad, ilens = [], []
for xs in xs_data:
# perform subsampling
if self.subsampling_factor > 1:
xs = [x[::self.subsampling_factor, :] for x in xs]
# get batch of lengths of input sequences
ilens.append(np.array([x.shape[0] for x in xs]))
# perform padding and convert to tensor
# currently only support real number
xs_pad.append(pad_list(xs, 0).astype(self.dtype))
if not self.load_aux_input:
xs_pad, ilens = xs_pad[0], ilens[0]
break
# NOTE: this is for multi-output (e.g., speech translation)
ys_pad, olens = [], []
for ys in ys_data:
ys_pad.append(
pad_list([
np.array(y[0][:]) if isinstance(y, tuple) else y for y in ys
], self.ignore_id))
olens.append(
np.array([
y[0].shape[0] if isinstance(y, tuple) else y.shape[0]
for y in ys
]))
if not self.load_aux_output:
ys_pad, olens = ys_pad[0], olens[0]
break
return utts, xs_pad, ilens, ys_pad, olens