espnet2.gan_svs.vits.pitch_predictor.Decoder

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class espnet2.gan_svs.vits.pitch_predictor.Decoder(out_channels: int = 192, attention_dim: int = 192, attention_heads: int = 2, linear_units: int = 768, blocks: int = 6, pw_layer_type: str = 'conv1d', pw_conv_kernel_size: int = 3, pos_enc_layer_type: str = 'rel_pos', self_attention_layer_type: str = 'rel_selfattn', activation_type: str = 'swish', normalize_before: bool = True, use_macaron_style: bool = False, use_conformer_conv: bool = False, conformer_kernel_size: int = 7, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.0, attention_dropout_rate: float = 0.0, global_channels: int = -1)

Bases: Module

Pitch or Mel decoder module in VISinger 2.

Initialize Decoder in VISinger 2.

• Parameters:
  • out_channels (int) – The output dimension of the module.
  • attention_dim (int) – The dimension of the attention mechanism.
  • attention_heads (int) – The number of attention heads.
  • linear_units (int) – The number of units in the linear layer.
  • blocks (int) – The number of encoder blocks.
  • pw_layer_type (str) – The type of position-wise layer to use.
  • pw_conv_kernel_size (int) – The kernel size of the position-wise convolutional layer.
  • pos_enc_layer_type (str) – The type of positional encoding layer to use.
  • self_attention_layer_type (str) – The type of self-attention layer to use.
  • activation_type (str) – The type of activation function to use.
  • normalize_before (bool) – Whether to normalize the data before the position-wise layer or after.
  • use_macaron_style (bool) – Whether to use the macaron style or not.
  • use_conformer_conv (bool) – Whether to use Conformer style conv or not.
  • conformer_kernel_size (int) – The kernel size of the conformer convolutional layer.
  • dropout_rate (float) – The dropout rate to use.
  • positional_dropout_rate (float) – The positional dropout rate to use.
  • attention_dropout_rate (float) – The attention dropout rate to use.
  • global_channels (int) – The number of channels to use for global conditioning.

forward(x, x_lengths, g=None)

Forward pass of the Decoder.

• Parameters:
  • x (Tensor) – Input tensor (B, 2 + attention_dim, T).
  • x_lengths (Tensor) – Length tensor (B,).
  • g (Tensor , optional) – Global conditioning tensor (B, global_channels, 1).
• Returns: Output tensor (B, 1, T). Tensor: Output mask (B, 1, T).
• Return type: Tensor