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157 lines
5.8 KiB
157 lines
5.8 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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import unittest
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import paddle
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import paddlespeech.s2t # noqa: F401
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from paddlespeech.audio.utils.tensor_utils import add_sos_eos
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from paddlespeech.audio.utils.tensor_utils import pad_sequence
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# from paddlespeech.audio.utils.tensor_utils import reverse_pad_list
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def reverse_pad_list(ys_pad: paddle.Tensor,
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ys_lens: paddle.Tensor,
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pad_value: float=-1.0) -> paddle.Tensor:
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"""Reverse padding for the list of tensors.
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Args:
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ys_pad (tensor): The padded tensor (B, Tokenmax).
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ys_lens (tensor): The lens of token seqs (B)
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pad_value (int): Value for padding.
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Returns:
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Tensor: Padded tensor (B, Tokenmax).
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Examples:
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>>> x
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tensor([[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]])
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>>> pad_list(x, 0)
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tensor([[4, 3, 2, 1],
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[7, 6, 5, 0],
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[9, 8, 0, 0]])
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"""
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r_ys_pad = pad_sequence([(paddle.flip(y[:i], [0]))
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for y, i in zip(ys_pad, ys_lens)], True, pad_value)
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return r_ys_pad
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def naive_reverse_pad_list_with_sos_eos(r_hyps,
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r_hyps_lens,
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sos=5000,
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eos=5000,
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ignore_id=-1):
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r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(ignore_id))
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r_hyps, _ = add_sos_eos(r_hyps, sos, eos, ignore_id)
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return r_hyps
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def reverse_pad_list_with_sos_eos(r_hyps,
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r_hyps_lens,
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sos=5000,
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eos=5000,
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ignore_id=-1):
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# >>> r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(self.ignore_id))
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# >>> r_hyps, _ = add_sos_eos(r_hyps, self.sos, self.eos, self.ignore_id)
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max_len = paddle.max(r_hyps_lens)
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index_range = paddle.arange(0, max_len, 1)
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seq_len_expand = r_hyps_lens.unsqueeze(1)
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seq_mask = seq_len_expand > index_range # (beam, max_len)
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index = (seq_len_expand - 1) - index_range # (beam, max_len)
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# >>> index
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# >>> tensor([[ 2, 1, 0],
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# >>> [ 2, 1, 0],
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# >>> [ 0, -1, -2]])
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index = index * seq_mask
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# >>> index
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# >>> tensor([[2, 1, 0],
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# >>> [2, 1, 0],
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# >>> [0, 0, 0]])
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def paddle_gather(x, dim, index):
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index_shape = index.shape
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index_flatten = index.flatten()
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if dim < 0:
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dim = len(x.shape) + dim
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nd_index = []
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for k in range(len(x.shape)):
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if k == dim:
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nd_index.append(index_flatten)
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else:
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reshape_shape = [1] * len(x.shape)
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reshape_shape[k] = x.shape[k]
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x_arange = paddle.arange(x.shape[k], dtype=index.dtype)
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x_arange = x_arange.reshape(reshape_shape)
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dim_index = paddle.expand(x_arange, index_shape).flatten()
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nd_index.append(dim_index)
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ind2 = paddle.transpose(paddle.stack(nd_index), [1, 0]).astype("int64")
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paddle_out = paddle.gather_nd(x, ind2).reshape(index_shape)
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return paddle_out
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r_hyps = paddle_gather(r_hyps, 1, index)
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# >>> r_hyps
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# >>> tensor([[3, 2, 1],
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# >>> [4, 8, 9],
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# >>> [2, 2, 2]])
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r_hyps = paddle.where(seq_mask, r_hyps, eos)
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# >>> r_hyps
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# >>> tensor([[3, 2, 1],
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# >>> [4, 8, 9],
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# >>> [2, eos, eos]])
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B = r_hyps.shape[0]
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_sos = paddle.ones([B, 1], dtype=r_hyps.dtype) * sos
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# r_hyps = paddle.concat([hyps[:, 0:1], r_hyps], axis=1)
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r_hyps = paddle.concat([_sos, r_hyps], axis=1)
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# >>> r_hyps
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# >>> tensor([[sos, 3, 2, 1],
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# >>> [sos, 4, 8, 9],
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# >>> [sos, 2, eos, eos]])
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return r_hyps
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class TestU2Model(unittest.TestCase):
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def setUp(self):
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paddle.set_device('cpu')
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self.sos = 5000
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self.eos = 5000
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self.ignore_id = -1
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self.reverse_hyps = paddle.to_tensor([[4, 3, 2, 1, -1],
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[5, 4, 3, 2, 1]])
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self.reverse_hyps_sos_eos = paddle.to_tensor(
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[[self.sos, 4, 3, 2, 1, self.eos], [self.sos, 5, 4, 3, 2, 1]])
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self.hyps = paddle.to_tensor([[1, 2, 3, 4, -1], [1, 2, 3, 4, 5]])
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self.hyps_lens = paddle.to_tensor([4, 5], paddle.int32)
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def test_reverse_pad_list(self):
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r_hyps = reverse_pad_list(self.hyps, self.hyps_lens)
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self.assertSequenceEqual(r_hyps.tolist(), self.reverse_hyps.tolist())
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def test_naive_reverse_pad_list_with_sos_eos(self):
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r_hyps_sos_eos = naive_reverse_pad_list_with_sos_eos(self.hyps,
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self.hyps_lens)
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self.assertSequenceEqual(r_hyps_sos_eos.tolist(),
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self.reverse_hyps_sos_eos.tolist())
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def test_static_reverse_pad_list_with_sos_eos(self):
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r_hyps_sos_eos_static = reverse_pad_list_with_sos_eos(self.hyps,
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self.hyps_lens)
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self.assertSequenceEqual(r_hyps_sos_eos_static.tolist(),
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self.reverse_hyps_sos_eos.tolist())
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if __name__ == '__main__':
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unittest.main()
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