112 lines
4.2 KiB
Python
112 lines
4.2 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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from parakeet.g2p.text.symbols import symbols
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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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import numpy as np
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class EncoderPrenet(dg.Layer):
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def __init__(self, embedding_size, num_hidden, use_cudnn=True):
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""" Encoder prenet layer of TransformerTTS.
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Args:
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embedding_size (int): the size of embedding.
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num_hidden (int): the size of hidden layer in network.
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use_cudnn (bool, optional): use cudnn or not. Defaults to True.
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"""
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super(EncoderPrenet, self).__init__()
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self.embedding_size = embedding_size
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self.num_hidden = num_hidden
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self.use_cudnn = use_cudnn
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self.embedding = dg.Embedding(
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size=[len(symbols), embedding_size],
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padding_idx=0,
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param_attr=fluid.initializer.Normal(
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loc=0.0, scale=1.0))
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self.conv_list = []
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k = math.sqrt(1.0 / embedding_size)
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self.conv_list.append(
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Conv1D(
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num_channels=embedding_size,
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num_filters=num_hidden,
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filter_size=5,
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padding=int(np.floor(5 / 2)),
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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(2):
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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=5,
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padding=int(np.floor(5 / 2)),
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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(3)
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]
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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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k = math.sqrt(1.0 / num_hidden)
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self.projection = dg.Linear(
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num_hidden,
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num_hidden,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k)))
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def forward(self, x):
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"""
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Prepare encoder input.
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Args:
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x (Variable): shape(B, T_text), dtype float32, the input character, where T_text means the timesteps of input text.
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Returns:
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(Variable): shape(B, T_text, C), the encoder prenet output.
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"""
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x = self.embedding(x)
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x = layers.transpose(x, [0, 2, 1])
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for batch_norm, conv in zip(self.batch_norm_list, self.conv_list):
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x = layers.dropout(
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layers.relu(batch_norm(conv(x))),
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0.2,
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dropout_implementation='upscale_in_train')
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x = layers.transpose(x, [0, 2, 1]) #(N,T,C)
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x = self.projection(x)
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return x
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