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Tacotron2模型详解

August 15, 2020 • Read: 181 • Deep Learning阅读设置

1 概述

Tacotron2是由Google Brain在2017年提出来的一个End-to-End语音合成框架。模型从下到上可以看作由两部分组成:

  1. 声谱预测网络:一个Encoder-Attention-Decoder网络,用于将输入的字符序列预测为梅尔频谱的帧序列
  2. 声码器(vocoder):一个WaveNet的修订版,用于将预测的梅尔频谱帧序列产生时域波形

2 编码器

Encoder的输入是多个句子,每个句子的基本单位是character,例如

  • 英文"hello world"就会被拆成"h e l l o w o r l d"作为输入
  • 中文"你好世界"则会先把拼音标识出来得到"ni hao shi jie",然后进一步按照声韵母的方式来分割成"n i h ao sh i j ie",或者直接按照类似英文的方式分割成"n i h a o s h i j i e"

Encoder的具体流程为:

  1. 输入的数据维度为[batch_size, char_seq_length]
  2. 使用512维的Character Embedding,把每个character映射为512维的向量,输出维度为[batch_size, char_seq_length, 512]
  3. 3个一维卷积,每个卷积包括512个kernel,每个kernel的大小是5*1(即每次看5个characters)。每做完一次卷积,进行一次BatchNorm、ReLU以及Dropout。输出维度为[batch_size, char_seq_length, 512](为了保证每次卷积的维度不变,因此使用了pad)
  4. 上面得到的输出,扔给一个单层BiLSTM,隐藏层维度是256,由于这是双向的LSTM,因此最终输出维度是[batch_size, char_seq_length, 512]
class Encoder(nn.Module):
    def __init__(self, hparams):
        super(Encoder, self).__init__()

        convolutions = []
        for _ in range(hparams.encoder_n_convolutions):
            conv_layer = nn.Sequential(
                ConvNorm(hparams.encoder_embedding_dim,
                         hparams.encoder_embedding_dim,x
                         kernel_size=hparams.encoder_kernel_size, stride=1,
                         padding=int((hparams.encoder_kernel_size - 1) / 2),
                         dilation=1, w_init_gain='relu'),
                nn.BatchNorm1d(hparams.encoder_embedding_dim))
            convolutions.append(conv_layer)
        self.convolutions = nn.ModuleList(convolutions)

        self.lstm = nn.LSTM(hparams.encoder_embedding_dim,
                            int(hparams.encoder_embedding_dim / 2), 1,
                            batch_first=True, bidirectional=True)
class ConvNorm(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super(ConvNorm, self).__init__()
        if padding is None:
            assert(kernel_size % 2 == 1)
            padding = int(dilation * (kernel_size - 1) / 2)

        self.conv = torch.nn.Conv1d(in_channels, out_channels,
                                    kernel_size=kernel_size, stride=stride,
                                    padding=padding, dilation=dilation,
                                    bias=bias)
                                    
        torch.nn.init.xavier_uniform_(
            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))

3 注意力机制

上图描述了第一次做attention时的输入和输出。其中,$y_0$是PreNet初始输入<S>的编码表示,$c_0$是当前的"注意力上下文"。初始第一步时,$y_0$和$c_0$都被初始化为全0向量,然后将$y_0$和$c_0$拼接起来,得到一个768维的向量$y_{0,c}$,将该向量与attention_hidden和attention_cell一起作为LSTMcell的输入(attention_hidden其实就是LSTMcell的hidden_state,attention_cell其实就是LSTMcell的cell_state)。得到的结果是$h_1$和attention_cell,这里没有给attention_cell单独起名字,主要考虑其是"打酱油"的,因为除了attention_rnn之外,其它地方没有用到attention_cell

Attention_Layer一共接受五个输入:

  1. $h_1$是和mel谱相关的变量
  2. $m$来自source character sequence编码的"记忆"
  3. $m'$是$m$通过一个Linear后得到的
  4. attention_weights_cat是将历史(上一时刻)的attention_weights和attention_weights_cum拼接得到的
  5. mask全false,基本没用

计算细节如下:

其中最核心的部分即get_alignment_energies,这个函数内部引入了位置特征,因此是混合注意力机制

混合注意力机制实际上是内容注意力机制(常规的Attention)与位置注意力机制的结合:

$$ e_{ij}=score(s_{i-1},\alpha_{i-1},h_j) $$

其中,$s_{i-1}$为之前解码器的隐状态,$\alpha_{i-1}$是之前的注意力权重,$h_j$是第$j$个编码器隐状态。为其添加偏置$b$,最终的score函数计算如下:

$$ e_{ij}=v_a^T\mathop{tanh}(Ws_{i-1}+Vh_j+Uf_{i,j}+b) $$

其中,$v_a$、$W$、$V$、$U$和$b$均为待训练参数,$f_{i,j}$是之前的注意力权重$\alpha_{i,j}$经卷积而得的位置特征(location feature),$f_i=F*\alpha_{i-1}$

Tancotron2的注意力机制基本上和混合注意力机制差不多,但稍有不同

$$ e_{i,j}=score(s_i,c\alpha_{i-1},h_j)=v_a^T\mathop{tanh}(Ws_i+Vh_j+Uf_{i,j}+b) $$

其中,$s_i$为当前解码器隐状态而非上一步,偏置$b$初始化为0,位置特征$f_i$是用累加注意力权重$c\alpha_i$卷积而来:

$$ \begin{align*} f_i&=F*c\alpha_{i-1}\\ c\alpha_i&=\sum_{j=1}^{i-1}\alpha_j \end{align*} $$

get_alignment_energies函数图示如下:

图中Location_Layer的代码如下:

class LocationLayer(nn.Module):
    def __init__(self, attention_n_filters, attention_kernel_size, # 32, 31
                 attention_dim): # 128
        super(LocationLayer, self).__init__()
        padding = int((attention_kernel_size - 1) / 2) # padding=15
        self.location_conv = ConvNorm(2, attention_n_filters,
                                      kernel_size=attention_kernel_size,
                                      padding=padding, bias=False, stride=1,
                                      dilation=1)
        self.location_dense = LinearNorm(attention_n_filters, attention_dim,
                                         bias=False, w_init_gain='tanh')

    def forward(self, attention_weights_cat): # [1, 2, 151]
        processed_attention = self.location_conv(attention_weights_cat) # [1, 32, 151]
        processed_attention = processed_attention.transpose(1, 2) # [1, 151, 32]
        processed_attention = self.location_dense(processed_attention) # [1, 151, 128]
        return processed_attention

Attention的代码如下:

class Attention(nn.Module):
    def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
                 attention_location_n_filters, attention_location_kernel_size):
        super(Attention, self).__init__()
        self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
                                      bias=False, w_init_gain='tanh')
        self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
                                       w_init_gain='tanh')
        self.v = LinearNorm(attention_dim, 1, bias=False)
        self.location_layer = LocationLayer(attention_location_n_filters,
                                            attention_location_kernel_size,
                                            attention_dim)
        self.score_mask_value = -float("inf")

    def get_alignment_energies(self, query, processed_memory,
                               attention_weights_cat):
        """
        PARAMS
        ------
        query: decoder output (batch, n_mel_channels * n_frames_per_step)
        processed_memory: processed encoder outputs (B, T_in, attention_dim)
        attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)

        RETURNS
        -------
        alignment (batch, max_time)
        """

        processed_query = self.query_layer(query.unsqueeze(1))
        processed_attention_weights = self.location_layer(attention_weights_cat)
        energies = self.v(torch.tanh(
            processed_query + processed_attention_weights + processed_memory))

        energies = energies.squeeze(-1)
        return energies

    def forward(self, attention_hidden_state, memory, processed_memory,
                attention_weights_cat, mask):
        """
        PARAMS
        ------
        attention_hidden_state: attention rnn last output
        memory: encoder outputs
        processed_memory: processed encoder outputs
        attention_weights_cat: previous and cummulative attention weights
        mask: binary mask for padded data
        """
        alignment = self.get_alignment_energies(
            attention_hidden_state, processed_memory, attention_weights_cat)

        if mask is not None:
            alignment.data.masked_fill_(mask, self.score_mask_value)

        attention_weights = F.softmax(alignment, dim=1)
        attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
        attention_context = attention_context.squeeze(1)

        return attention_context, attention_weights

4 解码器

解码器是一个自回归结构,它从编码的输入序列预测出声谱图,一次预测一帧

  1. 上一步预测出的频谱首先传入一个PreNet,它包含两层神经网络,PreNet作为一个信息瓶颈层(bottleneck),对于学习注意力是必要的
  2. PreNet的输出和Attention Context向量拼接在一起,传给一个含有1024个单元的两层LSTM。LSTM的输出再次和Attention Context向量拼接在一起,然后经过一个线性投影来预测目标频谱
  3. 最后,目标频谱帧经过一个5层卷积的PostNet(后处理网络),再将该输出和Linear Projection的输出相加(残差连接)作为最终的输出
  4. 另一边,LSTM的输出和Attention Context向量拼接在一起,投影成标量后传给sigmoid激活函数,来预测输出序列是否已完成预测

PreNet层的图示及代码如下所示:

class LinearNorm(torch.nn.Module):
    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
        super(LinearNorm, self).__init__()
        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)

        torch.nn.init.xavier_uniform_(
            self.linear_layer.weight,
            gain=torch.nn.init.calculate_gain(w_init_gain))

    def forward(self, x):
        return self.linear_layer(x)

class Prenet(nn.Module):
    def __init__(self, in_dim, sizes):
        super(Prenet, self).__init__()
        in_sizes = [in_dim] + sizes[:-1]
        self.layers = nn.ModuleList(
            [LinearNorm(in_size, out_size, bias=False)
             for (in_size, out_size) in zip(in_sizes, sizes)])

    def forward(self, x):
        for linear in self.layers:
            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
        return x

PostNet层的图示及代码如下所示:

class Postnet(nn.Module):
    """Postnet
        - Five 1-d convolution with 512 channels and kernel size 5
    """

    def __init__(self, hparams):
        super(Postnet, self).__init__()
        self.convolutions = nn.ModuleList()

        self.convolutions.append(
            nn.Sequential(
                ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim,
                         kernel_size=hparams.postnet_kernel_size, stride=1,
                         padding=int((hparams.postnet_kernel_size - 1) / 2),
                         dilation=1, w_init_gain='tanh'),
                nn.BatchNorm1d(hparams.postnet_embedding_dim))
        )

        for i in range(1, hparams.postnet_n_convolutions - 1):
            self.convolutions.append(
                nn.Sequential(
                    ConvNorm(hparams.postnet_embedding_dim,
                             hparams.postnet_embedding_dim,
                             kernel_size=hparams.postnet_kernel_size, stride=1,
                             padding=int((hparams.postnet_kernel_size - 1) / 2),
                             dilation=1, w_init_gain='tanh'),
                    nn.BatchNorm1d(hparams.postnet_embedding_dim))
            )

        self.convolutions.append(
            nn.Sequential(
                ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels,
                         kernel_size=hparams.postnet_kernel_size, stride=1,
                         padding=int((hparams.postnet_kernel_size - 1) / 2),
                         dilation=1, w_init_gain='linear'),
                nn.BatchNorm1d(hparams.n_mel_channels))
            )

    def forward(self, x):
        for i in range(len(self.convolutions) - 1):
            x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
        x = F.dropout(self.convolutions[-1](x), 0.5, self.training)

        return x

从下面Decoder初始化部分可以看出Decoder由prenet,attention_rnn,attention_layer,decoder_rnn,linear_projection,gate_layer组成

class Decoder(nn.Module):
    def __init__(self, hparams):
        super(Decoder, self).__init__()
        self.n_mel_channels = hparams.n_mel_channels
        self.n_frames_per_step = hparams.n_frames_per_step
        self.encoder_embedding_dim = hparams.encoder_embedding_dim
        self.attention_rnn_dim = hparams.attention_rnn_dim
        self.decoder_rnn_dim = hparams.decoder_rnn_dim
        self.prenet_dim = hparams.prenet_dim
        self.max_decoder_steps = hparams.max_decoder_steps
        self.gate_threshold = hparams.gate_threshold
        self.p_attention_dropout = hparams.p_attention_dropout
        self.p_decoder_dropout = hparams.p_decoder_dropout

        self.prenet = Prenet(
            hparams.n_mel_channels * hparams.n_frames_per_step,
            [hparams.prenet_dim, hparams.prenet_dim])

        self.attention_rnn = nn.LSTMCell(
            hparams.prenet_dim + hparams.encoder_embedding_dim,
            hparams.attention_rnn_dim)

        self.attention_layer = Attention(
            hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
            hparams.attention_dim, hparams.attention_location_n_filters,
            hparams.attention_location_kernel_size)

        self.decoder_rnn = nn.LSTMCell(
            hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
            hparams.decoder_rnn_dim, 1)

        self.linear_projection = LinearNorm(
            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
            hparams.n_mel_channels * hparams.n_frames_per_step)

        self.gate_layer = LinearNorm(
            hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
            bias=True, w_init_gain='sigmoid')

5 总结

Tacotron2模型的完整网络结构:

Tacotron2(
  (embedding): Embedding(148, 512)
  (encoder): Encoder(
    (convolutions): ModuleList(
      (0): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (lstm): LSTM(512, 256, batch_first=True, bidirectional=True)
  )
  (decoder): Decoder(
    (prenet): Prenet(
      (layers): ModuleList(
        (0): LinearNorm(
          (linear_layer): Linear(in_features=80, out_features=256, bias=False)
        )
        (1): LinearNorm(
          (linear_layer): Linear(in_features=256, out_features=256, bias=False)
        )
      )
    )
    (attention_rnn): LSTMCell(768, 1024)
    (attention_layer): Attention(
      (query_layer): LinearNorm(
        (linear_layer): Linear(in_features=1024, out_features=128, bias=False)
      )
      (memory_layer): LinearNorm(
        (linear_layer): Linear(in_features=512, out_features=128, bias=False)
      )
      (v): LinearNorm(
        (linear_layer): Linear(in_features=128, out_features=1, bias=False)
      )
      (location_layer): LocationLayer(
        (location_conv): ConvNorm(
          (conv): Conv1d(2, 32, kernel_size=(31,), stride=(1,), padding=(15,), bias=False)
        )
        (location_dense): LinearNorm(
          (linear_layer): Linear(in_features=32, out_features=128, bias=False)
        )
      )
    )
    (decoder_rnn): LSTMCell(1536, 1024, bias=1)
    (linear_projection): LinearNorm(
      (linear_layer): Linear(in_features=1536, out_features=80, bias=True)
    )
    (gate_layer): LinearNorm(
      (linear_layer): Linear(in_features=1536, out_features=1, bias=True)
    )
  )
  (postnet): Postnet(
    (convolutions): ModuleList(
      (0): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(80, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (3): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 512, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (4): Sequential(
        (0): ConvNorm(
          (conv): Conv1d(512, 80, kernel_size=(5,), stride=(1,), padding=(2,))
        )
        (1): BatchNorm1d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
)

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