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108 lines
3.4 KiB
108 lines
3.4 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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import numpy as np
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from paddlespeech.s2t.io.utility import pad_list
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from paddlespeech.s2t.utils.log import Log
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__all__ = ["CustomConverter"]
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logger = Log(__name__).getlog()
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class CustomConverter():
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"""Custom batch converter.
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Args:
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subsampling_factor (int): The subsampling factor.
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dtype (np.dtype): Data type to convert.
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"""
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def __init__(self,
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subsampling_factor=1,
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dtype=np.float32,
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load_aux_input=False,
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load_aux_output=False):
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"""Construct a CustomConverter object."""
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self.subsampling_factor = subsampling_factor
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self.ignore_id = -1
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self.dtype = dtype
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self.load_aux_input = load_aux_input
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self.load_aux_output = load_aux_output
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def __call__(self, batch):
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"""Transform a batch and send it to a device.
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Args:
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batch (list): The batch to transform.
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Returns:
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tuple(np.ndarray, nn.ndarray, nn.ndarray)
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"""
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# batch should be located in list
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assert len(batch) == 1
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data, utts = batch[0]
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xs_data, ys_data = [], []
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for ud in data:
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if ud[0].ndim > 1:
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# speech data (input): (speech_len, feat_dim)
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xs_data.append(ud)
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else:
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# text data (output): (text_len, )
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ys_data.append(ud)
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assert xs_data[0][
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0] is not None, "please check Reader and Augmentation impl."
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xs_pad, ilens = [], []
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for xs in xs_data:
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# perform subsampling
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if self.subsampling_factor > 1:
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xs = [x[::self.subsampling_factor, :] for x in xs]
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# get batch of lengths of input sequences
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ilens.append(np.array([x.shape[0] for x in xs]))
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# perform padding and convert to tensor
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# currently only support real number
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xs_pad.append(pad_list(xs, 0).astype(self.dtype))
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if not self.load_aux_input:
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xs_pad, ilens = xs_pad[0], ilens[0]
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break
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# NOTE: this is for multi-output (e.g., speech translation)
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ys_pad, olens = [], []
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for ys in ys_data:
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ys_pad.append(
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pad_list([
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np.array(y[0][:]) if isinstance(y, tuple) else y for y in ys
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], self.ignore_id))
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olens.append(
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np.array([
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y[0].shape[0] if isinstance(y, tuple) else y.shape[0]
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for y in ys
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]))
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if not self.load_aux_output:
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ys_pad, olens = ys_pad[0], olens[0]
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break
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return utts, xs_pad, ilens, ys_pad, olens
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