espnet2.gan_tts.wavenet.residual_block.ResidualBlock

source

class espnet2.gan_tts.wavenet.residual_block.ResidualBlock(kernel_size: int = 3, residual_channels: int = 64, gate_channels: int = 128, skip_channels: int = 64, aux_channels: int = 80, global_channels: int = -1, dropout_rate: float = 0.0, dilation: int = 1, bias: bool = True, scale_residual: bool = False)

Bases: Module

Residual block module in WaveNet.

Initialize ResidualBlock module.

• Parameters:
  • kernel_size (int) – Kernel size of dilation convolution layer.
  • residual_channels (int) – Number of channels for residual connection.
  • skip_channels (int) – Number of channels for skip connection.
  • aux_channels (int) – Number of local conditioning channels.
  • dropout (float) – Dropout probability.
  • dilation (int) – Dilation factor.
  • bias (bool) – Whether to add bias parameter in convolution layers.
  • scale_residual (bool) – Whether to scale the residual outputs.

forward(x: Tensor, x_mask: Tensor | None = None, c: Tensor | None = None, g: Tensor | None = None) → Tuple[Tensor, Tensor]

Calculate forward propagation.

• Parameters:
  • x (Tensor) – Input tensor (B, residual_channels, T).
  • Optional**[torch.Tensor]** (x_mask) – Mask tensor (B, 1, T).
  • c (Optional *[*Tensor ]) – Local conditioning tensor (B, aux_channels, T).
  • g (Optional *[*Tensor ]) – Global conditioning tensor (B, global_channels, 1).
• Returns: Output tensor for residual connection (B, residual_channels, T). Tensor: Output tensor for skip connection (B, skip_channels, T).
• Return type: Tensor