liangym
3 years ago
parent
2b9d7c8908
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593f247460
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# Copyright (c) 2022 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 paddle
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from paddle.autograd import PyLayer
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import paddle.nn as nn
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class GradientReversalFunction(PyLayer):
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"""Gradient Reversal Layer from:
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Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
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Forward pass is the identity function. In the backward pass,
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the upstream gradients are multiplied by -lambda (i.e. gradient is reversed)
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"""
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@staticmethod
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def forward(ctx, x, lambda_=1):
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"""Forward in networks
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"""
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ctx.save_for_backward(lambda_)
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return x.clone()
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@staticmethod
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def backward(ctx, grads):
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"""Backward in networks
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"""
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lambda_, = ctx.saved_tensor()
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dx = -lambda_ * grads
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#return dx
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return paddle.clip(dx, min=-0.5, max=0.5)
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class GradientReversalLayer(nn.Layer):
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"""Gradient Reversal Layer from:
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Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
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Forward pass is the identity function. In the backward pass,
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the upstream gradients are multiplied by -lambda (i.e. gradient is reversed)
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"""
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def __init__(self, lambda_=1):
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super(GradientReversalLayer, self).__init__()
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self.lambda_ = lambda_
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def forward(self, x):
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"""Forward in networks
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"""
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return GradientReversalFunction.apply(x, self.lambda_)
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# 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 Cross-Lingual-Voice-Cloning(https://github.com/deterministic-algorithms-lab/Cross-Lingual-Voice-Cloning)
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from paddle import nn
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import paddle
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from typeguard import check_argument_types
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class SpeakerClassifier(nn.Layer):
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def __init__(self, idim: int, hidden_sc_dim: int, spk_num: int, ):
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assert check_argument_types()
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super().__init__()
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# store hyperparameters
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self.idim = idim
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self.hidden_sc_dim = hidden_sc_dim
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self.spk_num = spk_num
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self.model = nn.Sequential(nn.Linear(self.idim, self.hidden_sc_dim),
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nn.Linear(self.hidden_sc_dim, self.spk_num))
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def parse_outputs(self, out, text_lengths):
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mask = paddle.arange(out.shape[1]).expand([out.shape[0], out.shape[1]]) < text_lengths.unsqueeze(1)
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out = paddle.transpose(out, perm=[2, 0, 1])
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out = out * mask
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out = paddle.transpose(out, perm=[1, 2, 0])
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return out
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def forward(self, encoder_outputs, text_lengths):
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"""
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encoder_outputs = [batch_size, seq_len, encoder_embedding_size]
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text_lengths = [batch_size]
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log probabilities of speaker classification = [batch_size, seq_len, spk_num]
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"""
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out = self.model(encoder_outputs)
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out = self.parse_outputs(out, text_lengths)
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return out
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