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728 lines
27 KiB
728 lines
27 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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"""Tacotron2 decoder related modules."""
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddlespeech.t2s.modules.tacotron2.attentions import AttForwardTA
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class Prenet(nn.Layer):
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"""Prenet module for decoder of Spectrogram prediction network.
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This is a module of Prenet in the decoder of Spectrogram prediction network,
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which described in `Natural TTS
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Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`_.
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The Prenet preforms nonlinear conversion
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of inputs before input to auto-regressive lstm,
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which helps to learn diagonal attentions.
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Notes
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----------
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This module alway applies dropout even in evaluation.
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See the detail in `Natural TTS Synthesis by
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Conditioning WaveNet on Mel Spectrogram Predictions`_.
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.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
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https://arxiv.org/abs/1712.05884
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"""
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def __init__(self, idim, n_layers=2, n_units=256, dropout_rate=0.5):
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"""Initialize prenet module.
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Args:
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idim (int):
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Dimension of the inputs.
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odim (int):
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Dimension of the outputs.
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n_layers (int, optional):
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The number of prenet layers.
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n_units (int, optional):
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The number of prenet units.
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"""
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super().__init__()
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self.dropout_rate = dropout_rate
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self.prenet = nn.LayerList()
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for layer in range(n_layers):
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n_inputs = idim if layer == 0 else n_units
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self.prenet.append(
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nn.Sequential(nn.Linear(n_inputs, n_units), nn.ReLU()))
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor):
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Batch of input tensors (B, ..., idim).
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Returns:
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Tensor: Batch of output tensors (B, ..., odim).
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"""
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for i in range(len(self.prenet)):
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# F.dropout 引入了随机, tacotron2 的 dropout 是不能去掉的
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x = F.dropout(self.prenet[i](x))
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return x
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class Postnet(nn.Layer):
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"""Postnet module for Spectrogram prediction network.
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This is a module of Postnet in Spectrogram prediction network,
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which described in `Natural TTS Synthesis by
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Conditioning WaveNet on Mel Spectrogram Predictions`_.
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The Postnet predicts refines the predicted
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Mel-filterbank of the decoder,
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which helps to compensate the detail sturcture of spectrogram.
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.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
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https://arxiv.org/abs/1712.05884
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"""
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def __init__(
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self,
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idim,
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odim,
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n_layers=5,
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n_chans=512,
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n_filts=5,
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dropout_rate=0.5,
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use_batch_norm=True, ):
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"""Initialize postnet module.
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Args:
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idim (int): Dimension of the inputs.
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odim (int): Dimension of the outputs.
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n_layers (int, optional): The number of layers.
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n_filts (int, optional): The number of filter size.
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n_units (int, optional): The number of filter channels.
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use_batch_norm (bool, optional): Whether to use batch normalization..
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dropout_rate (float, optional): Dropout rate..
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"""
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super().__init__()
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self.postnet = nn.LayerList()
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for layer in range(n_layers - 1):
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ichans = odim if layer == 0 else n_chans
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ochans = odim if layer == n_layers - 1 else n_chans
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if use_batch_norm:
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self.postnet.append(
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nn.Sequential(
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nn.Conv1D(
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ichans,
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ochans,
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n_filts,
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stride=1,
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padding=(n_filts - 1) // 2,
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bias_attr=False, ),
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nn.BatchNorm1D(ochans),
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nn.Tanh(),
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nn.Dropout(dropout_rate), ))
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else:
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self.postnet.append(
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nn.Sequential(
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nn.Conv1D(
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ichans,
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ochans,
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n_filts,
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stride=1,
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padding=(n_filts - 1) // 2,
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bias_attr=False, ),
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nn.Tanh(),
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nn.Dropout(dropout_rate), ))
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ichans = n_chans if n_layers != 1 else odim
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if use_batch_norm:
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self.postnet.append(
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nn.Sequential(
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nn.Conv1D(
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ichans,
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odim,
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n_filts,
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stride=1,
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padding=(n_filts - 1) // 2,
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bias_attr=False, ),
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nn.BatchNorm1D(odim),
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nn.Dropout(dropout_rate), ))
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else:
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self.postnet.append(
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nn.Sequential(
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nn.Conv1D(
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ichans,
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odim,
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n_filts,
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stride=1,
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padding=(n_filts - 1) // 2,
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bias_attr=False, ),
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nn.Dropout(dropout_rate), ))
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def forward(self, xs):
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"""Calculate forward propagation.
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Args:
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xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
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Returns:
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Tensor: Batch of padded output tensor. (B, odim, Tmax).
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"""
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for i in range(len(self.postnet)):
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xs = self.postnet[i](xs)
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return xs
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class ZoneOutCell(nn.Layer):
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"""ZoneOut Cell module.
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This is a module of zoneout described in
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`Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations`_.
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This code is modified from `eladhoffer/seq2seq.pytorch`_.
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Examples
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----------
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>>> lstm = paddle.nn.LSTMCell(16, 32)
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>>> lstm = ZoneOutCell(lstm, 0.5)
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.. _`Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations`:
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https://arxiv.org/abs/1606.01305
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.. _`eladhoffer/seq2seq.pytorch`:
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https://github.com/eladhoffer/seq2seq.pytorch
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"""
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def __init__(self, cell, zoneout_rate=0.1):
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"""Initialize zone out cell module.
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Args:
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cell (nn.Layer): Paddle recurrent cell module
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e.g. `paddle.nn.LSTMCell`.
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zoneout_rate (float, optional): Probability of zoneout from 0.0 to 1.0.
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"""
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super().__init__()
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self.cell = cell
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self.hidden_size = cell.hidden_size
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self.zoneout_rate = zoneout_rate
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if zoneout_rate > 1.0 or zoneout_rate < 0.0:
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raise ValueError(
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"zoneout probability must be in the range from 0.0 to 1.0.")
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def forward(self, inputs, hidden):
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"""Calculate forward propagation.
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Args:
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inputs (Tensor):
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Batch of input tensor (B, input_size).
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hidden (tuple):
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- Tensor: Batch of initial hidden states (B, hidden_size).
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- Tensor: Batch of initial cell states (B, hidden_size).
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Returns:
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Tensor:
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Batch of next hidden states (B, hidden_size).
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tuple:
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- Tensor: Batch of next hidden states (B, hidden_size).
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- Tensor: Batch of next cell states (B, hidden_size).
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"""
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# we only use the second output of LSTMCell in paddle
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_, next_hidden = self.cell(inputs, hidden)
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next_hidden = self._zoneout(hidden, next_hidden, self.zoneout_rate)
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# to have the same output format with LSTMCell in paddle
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return next_hidden[0], next_hidden
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def _zoneout(self, h, next_h, prob):
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# apply recursively
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if isinstance(h, tuple):
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num_h = len(h)
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if not isinstance(prob, tuple):
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prob = tuple([prob] * num_h)
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return tuple(
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[self._zoneout(h[i], next_h[i], prob[i]) for i in range(num_h)])
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if self.training:
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mask = paddle.bernoulli(paddle.ones([*paddle.shape(h)]) * prob)
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return mask * h + (1 - mask) * next_h
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else:
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return prob * h + (1 - prob) * next_h
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class Decoder(nn.Layer):
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"""Decoder module of Spectrogram prediction network.
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This is a module of decoder of Spectrogram prediction network in Tacotron2,
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which described in `Natural TTS
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Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`_.
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The decoder generates the sequence of
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features from the sequence of the hidden states.
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.. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
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https://arxiv.org/abs/1712.05884
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"""
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def __init__(
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self,
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idim,
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odim,
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att,
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dlayers=2,
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dunits=1024,
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prenet_layers=2,
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prenet_units=256,
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postnet_layers=5,
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postnet_chans=512,
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postnet_filts=5,
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output_activation_fn=None,
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cumulate_att_w=True,
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use_batch_norm=True,
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use_concate=True,
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dropout_rate=0.5,
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zoneout_rate=0.1,
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reduction_factor=1, ):
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"""Initialize Tacotron2 decoder module.
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Args:
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idim (int):
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Dimension of the inputs.
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odim (int):
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Dimension of the outputs.
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att (nn.Layer):
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Instance of attention class.
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dlayers (int, optional):
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The number of decoder lstm layers.
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dunits (int, optional):
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The number of decoder lstm units.
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prenet_layers (int, optional):
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The number of prenet layers.
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prenet_units (int, optional):
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The number of prenet units.
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postnet_layers (int, optional):
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The number of postnet layers.
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postnet_filts (int, optional):
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The number of postnet filter size.
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postnet_chans (int, optional):
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The number of postnet filter channels.
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output_activation_fn (nn.Layer, optional):
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Activation function for outputs.
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cumulate_att_w (bool, optional):
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Whether to cumulate previous attention weight.
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use_batch_norm (bool, optional):
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Whether to use batch normalization.
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use_concate (bool, optional):
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Whether to concatenate encoder embedding with decoder lstm outputs.
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dropout_rate (float, optional):
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Dropout rate.
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zoneout_rate (float, optional):
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Zoneout rate.
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reduction_factor (int, optional):
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Reduction factor.
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"""
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super().__init__()
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# store the hyperparameters
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self.idim = idim
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self.odim = odim
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self.att = att
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self.output_activation_fn = output_activation_fn
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self.cumulate_att_w = cumulate_att_w
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self.use_concate = use_concate
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self.reduction_factor = reduction_factor
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# check attention type
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if isinstance(self.att, AttForwardTA):
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self.use_att_extra_inputs = True
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else:
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self.use_att_extra_inputs = False
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# define lstm network
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prenet_units = prenet_units if prenet_layers != 0 else odim
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self.lstm = nn.LayerList()
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for layer in range(dlayers):
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iunits = idim + prenet_units if layer == 0 else dunits
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lstm = nn.LSTMCell(iunits, dunits)
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if zoneout_rate > 0.0:
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lstm = ZoneOutCell(lstm, zoneout_rate)
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self.lstm.append(lstm)
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# define prenet
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if prenet_layers > 0:
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self.prenet = Prenet(
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idim=odim,
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n_layers=prenet_layers,
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n_units=prenet_units,
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dropout_rate=dropout_rate, )
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else:
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self.prenet = None
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# define postnet
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if postnet_layers > 0:
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self.postnet = Postnet(
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idim=idim,
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odim=odim,
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n_layers=postnet_layers,
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n_chans=postnet_chans,
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n_filts=postnet_filts,
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use_batch_norm=use_batch_norm,
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dropout_rate=dropout_rate, )
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else:
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self.postnet = None
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# define projection layers
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iunits = idim + dunits if use_concate else dunits
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self.feat_out = nn.Linear(
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iunits, odim * reduction_factor, bias_attr=False)
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self.prob_out = nn.Linear(iunits, reduction_factor)
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def _zero_state(self, hs):
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init_hs = paddle.zeros([paddle.shape(hs)[0], self.lstm[0].hidden_size])
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return init_hs
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def forward(self, hs, hlens, ys):
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"""Calculate forward propagation.
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Args:
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hs (Tensor):
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Batch of the sequences of padded hidden states (B, Tmax, idim).
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hlens (Tensor(int64) padded):
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Batch of lengths of each input batch (B,).
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ys (Tensor):
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Batch of the sequences of padded target features (B, Lmax, odim).
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Returns:
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Tensor:
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Batch of output tensors after postnet (B, Lmax, odim).
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Tensor:
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Batch of output tensors before postnet (B, Lmax, odim).
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Tensor:
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Batch of logits of stop prediction (B, Lmax).
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Tensor:
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Batch of attention weights (B, Lmax, Tmax).
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Note:
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This computation is performed in teacher-forcing manner.
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"""
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# thin out frames (B, Lmax, odim) -> (B, Lmax/r, odim)
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if self.reduction_factor > 1:
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ys = ys[:, self.reduction_factor - 1::self.reduction_factor]
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# length list should be list of int
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# hlens = list(map(int, hlens))
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# initialize hidden states of decoder
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c_list = [self._zero_state(hs)]
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z_list = [self._zero_state(hs)]
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for _ in range(1, len(self.lstm)):
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c_list.append(self._zero_state(hs))
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z_list.append(self._zero_state(hs))
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prev_out = paddle.zeros([paddle.shape(hs)[0], self.odim])
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# initialize attention
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prev_att_ws = []
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prev_att_w = paddle.zeros(paddle.shape(hlens))
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prev_att_ws.append(prev_att_w)
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self.att.reset()
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# loop for an output sequence
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outs, logits, att_ws = [], [], []
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for y in ys.transpose([1, 0, 2]):
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if self.use_att_extra_inputs:
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att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_ws[-1],
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prev_out)
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else:
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att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_ws[-1])
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prenet_out = self.prenet(
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prev_out) if self.prenet is not None else prev_out
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xs = paddle.concat([att_c, prenet_out], axis=1)
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# we only use the second output of LSTMCell in paddle
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_, next_hidden = self.lstm[0](xs, (z_list[0], c_list[0]))
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z_list[0], c_list[0] = next_hidden
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for i in range(1, len(self.lstm)):
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# we only use the second output of LSTMCell in paddle
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_, next_hidden = self.lstm[i](z_list[i - 1],
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(z_list[i], c_list[i]))
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z_list[i], c_list[i] = next_hidden
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zcs = (paddle.concat([z_list[-1], att_c], axis=1)
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if self.use_concate else z_list[-1])
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outs.append(
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self.feat_out(zcs).reshape([paddle.shape(hs)[0], self.odim, -1
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]))
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logits.append(self.prob_out(zcs))
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att_ws.append(att_w)
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# teacher forcing
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prev_out = y
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if self.cumulate_att_w and paddle.sum(prev_att_w) != 0:
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prev_att_w = prev_att_w + att_w # Note: error when use +=
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else:
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prev_att_w = att_w
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prev_att_ws.append(prev_att_w)
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# (B, Lmax)
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logits = paddle.concat(logits, axis=1)
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# (B, odim, Lmax)
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before_outs = paddle.concat(outs, axis=2)
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# (B, Lmax, Tmax)
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att_ws = paddle.stack(att_ws, axis=1)
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if self.reduction_factor > 1:
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# (B, odim, Lmax)
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before_outs = before_outs.reshape(
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[paddle.shape(before_outs)[0], self.odim, -1])
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if self.postnet is not None:
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# (B, odim, Lmax)
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after_outs = before_outs + self.postnet(before_outs)
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else:
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after_outs = before_outs
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# (B, Lmax, odim)
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before_outs = before_outs.transpose([0, 2, 1])
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# (B, Lmax, odim)
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after_outs = after_outs.transpose([0, 2, 1])
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logits = logits
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# apply activation function for scaling
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if self.output_activation_fn is not None:
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before_outs = self.output_activation_fn(before_outs)
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after_outs = self.output_activation_fn(after_outs)
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return after_outs, before_outs, logits, att_ws
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def inference(
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self,
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h,
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threshold=0.5,
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minlenratio=0.0,
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maxlenratio=10.0,
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use_att_constraint=False,
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backward_window=None,
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forward_window=None, ):
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"""Generate the sequence of features given the sequences of characters.
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Args:
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|
h(Tensor):
|
|
Input sequence of encoder hidden states (T, C).
|
|
threshold(float, optional, optional):
|
|
Threshold to stop generation. (Default value = 0.5)
|
|
minlenratio(float, optional, optional):
|
|
Minimum length ratio. If set to 1.0 and the length of input is 10,
|
|
the minimum length of outputs will be 10 * 1 = 10. (Default value = 0.0)
|
|
maxlenratio(float, optional, optional):
|
|
Minimum length ratio. If set to 10 and the length of input is 10,
|
|
the maximum length of outputs will be 10 * 10 = 100. (Default value = 0.0)
|
|
use_att_constraint(bool, optional):
|
|
Whether to apply attention constraint introduced in `Deep Voice 3`_. (Default value = False)
|
|
backward_window(int, optional):
|
|
Backward window size in attention constraint. (Default value = None)
|
|
forward_window(int, optional):
|
|
(Default value = None)
|
|
|
|
Returns:
|
|
Tensor:
|
|
Output sequence of features (L, odim).
|
|
Tensor:
|
|
Output sequence of stop probabilities (L,).
|
|
Tensor:
|
|
Attention weights (L, T).
|
|
|
|
Note:
|
|
This computation is performed in auto-regressive manner.
|
|
.. _`Deep Voice 3`: https://arxiv.org/abs/1710.07654
|
|
"""
|
|
# setup
|
|
|
|
assert len(paddle.shape(h)) == 2
|
|
hs = h.unsqueeze(0)
|
|
ilens = paddle.shape(h)[0]
|
|
# 本来 maxlen 和 minlen 外面有 int()，防止动转静的问题此处删除
|
|
maxlen = paddle.shape(h)[0] * maxlenratio
|
|
minlen = paddle.shape(h)[0] * minlenratio
|
|
# 本来是直接使用 threshold 的，此处为了防止动转静的问题把 threshold 转成 tensor
|
|
threshold = paddle.ones([1]) * threshold
|
|
|
|
# initialize hidden states of decoder
|
|
c_list = [self._zero_state(hs)]
|
|
z_list = [self._zero_state(hs)]
|
|
for _ in range(1, len(self.lstm)):
|
|
c_list.append(self._zero_state(hs))
|
|
z_list.append(self._zero_state(hs))
|
|
prev_out = paddle.zeros([1, self.odim])
|
|
|
|
# initialize attention
|
|
prev_att_ws = []
|
|
prev_att_w = paddle.zeros([ilens])
|
|
prev_att_ws.append(prev_att_w)
|
|
|
|
self.att.reset()
|
|
|
|
# setup for attention constraint
|
|
if use_att_constraint:
|
|
last_attended_idx = 0
|
|
else:
|
|
last_attended_idx = -1
|
|
|
|
# loop for an output sequence
|
|
idx = 0
|
|
outs, att_ws, probs = [], [], []
|
|
prob = paddle.zeros([1])
|
|
while paddle.to_tensor(True):
|
|
z_list = z_list
|
|
c_list = c_list
|
|
# updated index
|
|
idx += self.reduction_factor
|
|
|
|
# decoder calculation
|
|
if self.use_att_extra_inputs:
|
|
att_c, att_w = self.att(
|
|
hs,
|
|
ilens,
|
|
z_list[0],
|
|
prev_att_ws[-1],
|
|
prev_out,
|
|
last_attended_idx=last_attended_idx,
|
|
backward_window=backward_window,
|
|
forward_window=forward_window, )
|
|
else:
|
|
att_c, att_w = self.att(
|
|
hs,
|
|
ilens,
|
|
z_list[0],
|
|
prev_att_ws[-1],
|
|
last_attended_idx=last_attended_idx,
|
|
backward_window=backward_window,
|
|
forward_window=forward_window, )
|
|
|
|
att_ws.append(att_w)
|
|
prenet_out = self.prenet(
|
|
prev_out) if self.prenet is not None else prev_out
|
|
xs = paddle.concat([att_c, prenet_out], axis=1)
|
|
# we only use the second output of LSTMCell in paddle
|
|
_, next_hidden = self.lstm[0](xs, (z_list[0], c_list[0]))
|
|
|
|
z_list[0], c_list[0] = next_hidden
|
|
for i in range(1, len(self.lstm)):
|
|
# we only use the second output of LSTMCell in paddle
|
|
_, next_hidden = self.lstm[i](z_list[i - 1],
|
|
(z_list[i], c_list[i]))
|
|
z_list[i], c_list[i] = next_hidden
|
|
zcs = (paddle.concat([z_list[-1], att_c], axis=1)
|
|
if self.use_concate else z_list[-1])
|
|
# [(1, odim, r), ...]
|
|
outs.append(self.feat_out(zcs).reshape([1, self.odim, -1]))
|
|
|
|
prob = F.sigmoid(self.prob_out(zcs))[0]
|
|
probs.append(prob)
|
|
|
|
if self.output_activation_fn is not None:
|
|
prev_out = self.output_activation_fn(
|
|
outs[-1][:, :, -1]) # (1, odim)
|
|
else:
|
|
prev_out = outs[-1][:, :, -1] # (1, odim)
|
|
if self.cumulate_att_w and paddle.sum(prev_att_w) != 0:
|
|
prev_att_w = prev_att_w + att_w # Note: error when use +=
|
|
else:
|
|
prev_att_w = att_w
|
|
prev_att_ws.append(prev_att_w)
|
|
if use_att_constraint:
|
|
last_attended_idx = int(att_w.argmax())
|
|
|
|
# tacotron2 ljspeech 动转静的问题应该是这里没有正确判断 prob >= threshold 导致的
|
|
if prob >= threshold or idx >= maxlen:
|
|
# check mininum length
|
|
if idx < minlen:
|
|
continue
|
|
break
|
|
"""
|
|
仅解开 665~667 行的代码块，动转静时会卡死，但是动态图时可以正确生成音频，证明模型没问题
|
|
同时解开 665~667 行 和 668 ~ 670 行的代码块，动转静时不会卡死，但是生成的音频末尾有多余的噪声
|
|
证明动转静没有进入 prob >= threshold 的判断，但是静态图可以进入 prob >= threshold 并退出循环
|
|
动转静时是通过 idx >= maxlen 退出循环（所以没有这个逻辑的时候会一直循环，也就是卡死），
|
|
没有在模型判断该结束的时候结束，而是在超出最大长度时结束，所以合成的音频末尾有很长的额外预测的噪声
|
|
动转静用 prob <= threshold 的条件可以退出循环（虽然结果不正确），证明条件参数的类型本身没问题，可能是 prob 有问题
|
|
"""
|
|
# if prob >= threshold:
|
|
# print("prob >= threshold")
|
|
# break
|
|
# elif idx >= maxlen:
|
|
# print("idx >= maxlen")
|
|
# break
|
|
|
|
# (1, odim, L)
|
|
outs = paddle.concat(outs, axis=2)
|
|
if self.postnet is not None:
|
|
# (1, odim, L)
|
|
outs = outs + self.postnet(outs)
|
|
# (L, odim)
|
|
outs = outs.transpose([0, 2, 1]).squeeze(0)
|
|
probs = paddle.concat(probs, axis=0)
|
|
att_ws = paddle.concat(att_ws, axis=0)
|
|
|
|
if self.output_activation_fn is not None:
|
|
outs = self.output_activation_fn(outs)
|
|
|
|
return outs, probs, att_ws
|
|
|
|
def calculate_all_attentions(self, hs, hlens, ys):
|
|
"""Calculate all of the attention weights.
|
|
|
|
Args:
|
|
hs (Tensor):
|
|
Batch of the sequences of padded hidden states (B, Tmax, idim).
|
|
hlens (Tensor(int64)):
|
|
Batch of lengths of each input batch (B,).
|
|
ys (Tensor):
|
|
Batch of the sequences of padded target features (B, Lmax, odim).
|
|
|
|
Returns:
|
|
numpy.ndarray:
|
|
Batch of attention weights (B, Lmax, Tmax).
|
|
|
|
Note:
|
|
This computation is performed in teacher-forcing manner.
|
|
"""
|
|
# thin out frames (B, Lmax, odim) -> (B, Lmax/r, odim)
|
|
if self.reduction_factor > 1:
|
|
ys = ys[:, self.reduction_factor - 1::self.reduction_factor]
|
|
|
|
# length list should be list of int
|
|
hlens = list(map(int, hlens))
|
|
|
|
# initialize hidden states of decoder
|
|
c_list = [self._zero_state(hs)]
|
|
z_list = [self._zero_state(hs)]
|
|
for _ in range(1, len(self.lstm)):
|
|
c_list.append(self._zero_state(hs))
|
|
z_list.append(self._zero_state(hs))
|
|
prev_out = paddle.zeros([paddle.shape(hs)[0], self.odim])
|
|
|
|
# initialize attention
|
|
prev_att_w = None
|
|
self.att.reset()
|
|
|
|
# loop for an output sequence
|
|
att_ws = []
|
|
for y in ys.transpose([1, 0, 2]):
|
|
if self.use_att_extra_inputs:
|
|
att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_w,
|
|
prev_out)
|
|
else:
|
|
att_c, att_w = self.att(hs, hlens, z_list[0], prev_att_w)
|
|
att_ws.append(att_w)
|
|
prenet_out = self.prenet(
|
|
prev_out) if self.prenet is not None else prev_out
|
|
xs = paddle.concat([att_c, prenet_out], axis=1)
|
|
# we only use the second output of LSTMCell in paddle
|
|
_, next_hidden = self.lstm[0](xs, (z_list[0], c_list[0]))
|
|
z_list[0], c_list[0] = next_hidden
|
|
for i in range(1, len(self.lstm)):
|
|
z_list[i], c_list[i] = self.lstm[i](z_list[i - 1],
|
|
(z_list[i], c_list[i]))
|
|
# teacher forcing
|
|
prev_out = y
|
|
if self.cumulate_att_w and prev_att_w is not None:
|
|
# Note: error when use +=
|
|
prev_att_w = prev_att_w + att_w
|
|
else:
|
|
prev_att_w = att_w
|
|
# (B, Lmax, Tmax)
|
|
att_ws = paddle.stack(att_ws, axis=1)
|
|
|
|
return att_ws
|