ParakeetEricRoss/parakeet/models/transformer_tts/encoderprenet.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import math
from parakeet.g2p.text.symbols import symbols
import paddle.fluid.dygraph as dg
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from parakeet.modules.customized import Conv1D
import numpy as np


class EncoderPrenet(dg.Layer):
    def __init__(self, embedding_size, num_hidden, use_cudnn=True):
        """ Encoder prenet layer of TransformerTTS.

        Args:
            embedding_size (int): the size of embedding.
            num_hidden (int): the size of hidden layer in network.
            use_cudnn (bool, optional): use cudnn or not. Defaults to True.
        """
        super(EncoderPrenet, self).__init__()
        self.embedding_size = embedding_size
        self.num_hidden = num_hidden
        self.use_cudnn = use_cudnn
        self.embedding = dg.Embedding(
            size=[len(symbols), embedding_size],
            padding_idx=0,
            param_attr=fluid.initializer.Normal(
                loc=0.0, scale=1.0))
        self.conv_list = []
        k = math.sqrt(1.0 / embedding_size)
        self.conv_list.append(
            Conv1D(
                num_channels=embedding_size,
                num_filters=num_hidden,
                filter_size=5,
                padding=int(np.floor(5 / 2)),
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.XavierInitializer()),
                bias_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Uniform(
                        low=-k, high=k)),
                use_cudnn=use_cudnn))
        k = math.sqrt(1.0 / num_hidden)
        for _ in range(2):
            self.conv_list.append(
                Conv1D(
                    num_channels=num_hidden,
                    num_filters=num_hidden,
                    filter_size=5,
                    padding=int(np.floor(5 / 2)),
                    param_attr=fluid.ParamAttr(
                        initializer=fluid.initializer.XavierInitializer()),
                    bias_attr=fluid.ParamAttr(
                        initializer=fluid.initializer.Uniform(
                            low=-k, high=k)),
                    use_cudnn=use_cudnn))

        for i, layer in enumerate(self.conv_list):
            self.add_sublayer("conv_list_{}".format(i), layer)

        self.batch_norm_list = [
            dg.BatchNorm(
                num_hidden, data_layout='NCHW') for _ in range(3)
        ]

        for i, layer in enumerate(self.batch_norm_list):
            self.add_sublayer("batch_norm_list_{}".format(i), layer)

        k = math.sqrt(1.0 / num_hidden)
        self.projection = dg.Linear(
            num_hidden,
            num_hidden,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.XavierInitializer()),
            bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                low=-k, high=k)))

    def forward(self, x):
        """
        Prepare encoder input.
        
        Args:
            x (Variable): shape(B, T_text), dtype float32, the input character, where T_text means the timesteps of input text.
                
        Returns:
            (Variable): shape(B, T_text, C), the encoder prenet output.
        """

        x = self.embedding(x)
        x = layers.transpose(x, [0, 2, 1])
        for batch_norm, conv in zip(self.batch_norm_list, self.conv_list):
            x = layers.dropout(
                layers.relu(batch_norm(conv(x))),
                0.2,
                dropout_implementation='upscale_in_train')
        x = layers.transpose(x, [0, 2, 1])  #(N,T,C)
        x = self.projection(x)

        return x