138 lines
5.5 KiB
Python
138 lines
5.5 KiB
Python
# Copyright (c) 2020 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 math
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import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from parakeet.modules.customized import Conv1D
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class PostConvNet(dg.Layer):
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def __init__(self,
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n_mels=80,
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num_hidden=512,
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filter_size=5,
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padding=0,
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num_conv=5,
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outputs_per_step=1,
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use_cudnn=True,
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dropout=0.1,
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batchnorm_last=False):
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"""Decocder post conv net of TransformerTTS.
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Args:
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n_mels (int, optional): the number of mel bands when calculating mel spectrograms. Defaults to 80.
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num_hidden (int, optional): the size of hidden layer in network. Defaults to 512.
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filter_size (int, optional): the filter size of Conv. Defaults to 5.
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padding (int, optional): the padding size of Conv. Defaults to 0.
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num_conv (int, optional): the num of Conv layers in network. Defaults to 5.
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outputs_per_step (int, optional): the num of output frames per step . Defaults to 1.
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use_cudnn (bool, optional): use cudnn in Conv or not. Defaults to True.
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dropout (float, optional): dropout probability. Defaults to 0.1.
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batchnorm_last (bool, optional): if batchnorm at last layer or not. Defaults to False.
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"""
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super(PostConvNet, self).__init__()
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self.dropout = dropout
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self.num_conv = num_conv
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self.batchnorm_last = batchnorm_last
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self.conv_list = []
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k = math.sqrt(1.0 / (n_mels * outputs_per_step))
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self.conv_list.append(
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Conv1D(
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num_channels=n_mels * outputs_per_step,
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num_filters=num_hidden,
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filter_size=filter_size,
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padding=padding,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-k, high=k)),
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use_cudnn=use_cudnn))
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k = math.sqrt(1.0 / num_hidden)
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for _ in range(1, num_conv - 1):
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self.conv_list.append(
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Conv1D(
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num_channels=num_hidden,
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num_filters=num_hidden,
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filter_size=filter_size,
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padding=padding,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-k, high=k)),
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use_cudnn=use_cudnn))
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self.conv_list.append(
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Conv1D(
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num_channels=num_hidden,
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num_filters=n_mels * outputs_per_step,
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filter_size=filter_size,
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padding=padding,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Uniform(
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low=-k, high=k)),
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use_cudnn=use_cudnn))
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for i, layer in enumerate(self.conv_list):
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self.add_sublayer("conv_list_{}".format(i), layer)
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self.batch_norm_list = [
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dg.BatchNorm(
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num_hidden, data_layout='NCHW') for _ in range(num_conv - 1)
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]
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if self.batchnorm_last:
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self.batch_norm_list.append(
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dg.BatchNorm(
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n_mels * outputs_per_step, data_layout='NCHW'))
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for i, layer in enumerate(self.batch_norm_list):
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self.add_sublayer("batch_norm_list_{}".format(i), layer)
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def forward(self, input):
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"""
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Compute the mel spectrum.
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Args:
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input (Variable): shape(B, T, C), dtype float32, the result of mel linear projection.
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Returns:
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output (Variable): shape(B, T, C), the result after postconvnet.
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"""
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input = layers.transpose(input, [0, 2, 1])
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len = input.shape[-1]
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for i in range(self.num_conv - 1):
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batch_norm = self.batch_norm_list[i]
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conv = self.conv_list[i]
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input = layers.dropout(
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layers.tanh(batch_norm(conv(input)[:, :, :len])),
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self.dropout,
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dropout_implementation='upscale_in_train')
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conv = self.conv_list[self.num_conv - 1]
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input = conv(input)[:, :, :len]
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if self.batchnorm_last:
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batch_norm = self.batch_norm_list[self.num_conv - 1]
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input = layers.dropout(
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batch_norm(input),
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self.dropout,
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dropout_implementation='upscale_in_train')
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output = layers.transpose(input, [0, 2, 1])
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return output
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