repo_description: VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
repo_topics: deep-learning pytorch speech-synthesis text-to-speech tts
code: import copy
  import math
  import numpy as np
  import torch
  from torch import nn
  from torch.nn import functional as F
  
  import commons
  import modules
  from modules import LayerNorm
     
  
  class Encoder(nn.Module):
    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
      super().__init__()
      self.hidden_channels = hidden_channels
      self.filter_channels = filter_channels
      self.n_heads = n_heads
      self.n_layers = n_layers
      self.kernel_size = kernel_size
      self.p_dropout = p_dropout
      self.window_size = window_size
  
      self.drop = nn.Dropout(p_dropout)
      self.attn_layers = nn.ModuleList()
      self.norm_layers_1 = nn.ModuleList()
      self.ffn_layers = nn.ModuleList()
      self.norm_layers_2 = nn.ModuleList()
      for i in range(self.n_layers):
        self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
        self.norm_layers_1.append(LayerNorm(hidden_channels))
        self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
        self.norm_layers_2.append(LayerNorm(hidden_channels))
  
    def forward(self, x, x_mask):
      attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
      x = x * x_mask
      for i in range(self.n_layers):
        y = self.attn_layers[i](x, x, attn_mask)
        y = self.drop(y)
        x = self.norm_layers_1[i](x + y)
  
        y = self.ffn_layers[i](x, x_mask)
        y = self.drop(y)
        x = self.norm_layers_2[i](x + y)
      x = x * x_mask
      return x
  
  
  class Decoder(nn.Module):
    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
      super().__init__()
      self.hidden_channels = hidden_channels
      self.filter_channels = filter_channels
      self.n_heads = n_heads
      self.n_layers = n_layers
      self.kernel_size = kernel_size
      self.p_dropout = p_dropout
      self.proximal_bias = proximal_bias
      self.proximal_init = proximal_init
  
      self.drop = nn.Dropout(p_dropout)
      self.self_attn_layers = nn.ModuleList()
      self.norm_layers_0 = nn.ModuleList()
      self.encdec_attn_layers = nn.ModuleList()
      self.norm_layers_1 = nn.ModuleList()
      self.ffn_layers = nn.ModuleList()
      self.norm_layers_2 = nn.ModuleList()
      for i in range(self.n_layers):
        self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
        self.norm_layers_0.append(LayerNorm(hidden_channels))
        self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
        self.norm_layers_1.append(LayerNorm(hidden_channels))
        self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
        self.norm_layers_2.append(LayerNorm(hidden_channels))
  
    def forward(self, x, x_mask, h, h_mask):
      """
      x: decoder input
      h: encoder output
      """
      self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
      encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
      x = x * x_mask
      for i in range(self.n_layers):
        y = self.self_attn_layers[i](x, x, self_attn_mask)
        y = self.drop(y)
        x = self.norm_layers_0[i](x + y)
  
        y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
        y = self.drop(y)
        x = self.norm_layers_1[i](x + y)
        
        y = self.ffn_layers[i](x, x_mask)
        y = self.drop(y)
        x = self.norm_layers_2[i](x + y)
      x = x * x_mask
      return x
  
  
  class MultiHeadAttention(nn.Module):
    def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
      super().__init__()
      assert channels % n_heads == 0
  
      self.channels = channels
      self.out_channels = out_channels
      self.n_heads = n_heads
      self.p_dropout = p_dropout
      self.window_size = window_size
      self.heads_share = heads_share
      self.block_length = block_length
      self.proximal_bias = proximal_bias
      self.proximal_init = proximal_init
      self.attn = None
  
      self.k_channels = channels // n_heads
      self.conv_q = nn.Conv1d(channels, channels, 1)
      self.conv_k = nn.Conv1d(channels, channels, 1)
      self.conv_v = nn.Conv1d(channels, channels, 1)
      self.conv_o = nn.Conv1d(channels, out_channels, 1)
      self.drop = nn.Dropout(p_dropout)
  
      if window_size is not None:
        n_heads_rel = 1 if heads_share else n_heads
        rel_stddev = self.k_channels**-0.5
        self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
        self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
  
      nn.init.xavier_uniform_(self.conv_q.weight)
      nn.init.xavier_uniform_(self.conv_k.weight)
      nn.init.xavier_uniform_(self.conv_v.weight)
      if proximal_init:
        with torch.no_grad():
          self.conv_k.weight.copy_(self.conv_q.weight)
          self.conv_k.bias.copy_(self.conv_q.bias)
        
    def forward(self, x, c, attn_mask=None):
      q = self.conv_q(x)
      k = self.conv_k(c)
      v = self.conv_v(c)
      
      x, self.attn = self.attention(q, k, v, mask=attn_mask)
  
      x = self.conv_o(x)
      return x
  
    def attention(self, query, key, value, mask=None):
      # reshape [b, d, t] -> [b, n_h, t, d_k]
      b, d, t_s, t_t = (*key.size(), query.size(2))
      query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
      key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
      value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
  
      scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
      if self.window_size is not None:
        assert t_s == t_t, "Relative attention is only available for self-attention."
        key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
        rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
        scores_local = self._relative_position_to_absolute_position(rel_logits)
        scores = scores + scores_local
      if self.proximal_bias:
        assert t_s == t_t, "Proximal bias is only available for self-attention."
        scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
      if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e4)
        if self.block_length is not None:
          assert t_s == t_t, "Local attention is only available for self-attention."
          block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
          scores = scores.masked_fill(block_mask == 0, -1e4)
      p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
      p_attn = self.drop(p_attn)
      output = torch.matmul(p_attn, value)
      if self.window_size is not None:
        relative_weights = self._absolute_position_to_relative_position(p_attn)
        value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
        output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
      output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
      return output, p_attn
  
    def _matmul_with_relative_values(self, x, y):
      """
      x: [b, h, l, m]
      y: [h or 1, m, d]
      ret: [b, h, l, d]
      """
      ret = torch.matmul(x, y.unsqueeze(0))
      return ret
  
    def _matmul_with_relative_keys(self, x, y):
      """
      x: [b, h, l, d]
      y: [h or 1, m, d]
      ret: [b, h, l, m]
      """
      ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
      return ret
  
    def _get_relative_embeddings(self, relative_embeddings, length):
      max_relative_position = 2 * self.window_size + 1
      # Pad first before slice to avoid using cond ops.
      pad_length = max(length - (self.window_size + 1), 0)
      slice_start_position = max((self.window_size + 1) - length, 0)
      slice_end_position = slice_start_position + 2 * length - 1
      if pad_length > 0:
        padded_relative_embeddings = F.pad(
            relative_embeddings,
            commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
      else:
        padded_relative_embeddings = relative_embeddings
      used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
      return used_relative_embeddings
  
    def _relative_position_to_absolute_position(self, x):
      """
      x: [b, h, l, 2*l-1]
      ret: [b, h, l, l]
      """
      batch, heads, length, _ = x.size()
      # Concat columns of pad to shift from relative to absolute indexing.
      x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
  
      # Concat extra elements so to add up to shape (len+1, 2*len-1).
      x_flat = x.view([batch, heads, length * 2 * length])
      x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
  
      # Reshape and slice out the padded elements.
      x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
      return x_final
  
    def _absolute_position_to_relative_position(self, x):
      """
      x: [b, h, l, l]
      ret: [b, h, l, 2*l-1]
      """
      batch, heads, length, _ = x.size()
      # padd along column
      x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
      x_flat = x.view([batch, heads, length**2 + length*(length -1)])
      # add 0's in the beginning that will skew the elements after reshape
      x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
      x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
      return x_final
  
    def _attention_bias_proximal(self, length):
      """Bias for self-attention to encourage attention to close positions.
      Args:
        length: an integer scalar.
      Returns:
        a Tensor with shape [1, 1, length, length]
      """
      r = torch.arange(length, dtype=torch.float32)
      diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
      return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
  
  
  class FFN(nn.Module):
    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
      super().__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.filter_channels = filter_channels
      self.kernel_size = kernel_size
      self.p_dropout = p_dropout
      self.activation = activation
      self.causal = causal
  
      if causal:
        self.padding = self._causal_padding
      else:
        self.padding = self._same_padding
  
      self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
      self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
      self.drop = nn.Dropout(p_dropout)
  
    def forward(self, x, x_mask):
      x = self.conv_1(self.padding(x * x_mask))
      if self.activation == "gelu":
        x = x * torch.sigmoid(1.702 * x)
      else:
        x = torch.relu(x)
      x = self.drop(x)
      x = self.conv_2(self.padding(x * x_mask))
      return x * x_mask
    
    def _causal_padding(self, x):
      if self.kernel_size == 1:
        return x
      pad_l = self.kernel_size - 1
      pad_r = 0
      padding = [[0, 0], [0, 0], [pad_l, pad_r]]
      x = F.pad(x, commons.convert_pad_shape(padding))
      return x
  
    def _same_padding(self, x):
      if self.kernel_size == 1:
        return x
      pad_l = (self.kernel_size - 1) // 2
      pad_r = self.kernel_size // 2
      padding = [[0, 0], [0, 0], [pad_l, pad_r]]
      x = F.pad(x, commons.convert_pad_shape(padding))
      return x
  