espnet2.gan_tts.vits.residual_coupling.ResidualAffineCouplingLayer

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class espnet2.gan_tts.vits.residual_coupling.ResidualAffineCouplingLayer(in_channels: int = 192, hidden_channels: int = 192, kernel_size: int = 5, base_dilation: int = 1, layers: int = 5, stacks: int = 1, global_channels: int = -1, dropout_rate: float = 0.0, use_weight_norm: bool = True, bias: bool = True, use_only_mean: bool = True)

Bases: Module

Residual affine coupling layer.

Initialzie ResidualAffineCouplingLayer module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • hidden_channels (int) – Number of hidden channels.
  • kernel_size (int) – Kernel size for WaveNet.
  • base_dilation (int) – Base dilation factor for WaveNet.
  • layers (int) – Number of layers of WaveNet.
  • stacks (int) – Number of stacks of WaveNet.
  • global_channels (int) – Number of global channels.
  • dropout_rate (float) – Dropout rate.
  • use_weight_norm (bool) – Whether to use weight normalization in WaveNet.
  • bias (bool) – Whether to use bias paramters in WaveNet.
  • use_only_mean (bool) – Whether to estimate only mean.

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

Calculate forward propagation.

• Parameters:
  • x (Tensor) – Input tensor (B, in_channels, T).
  • x_lengths (Tensor) – Length tensor (B,).
  • g (Optional *[*Tensor ]) – Global conditioning tensor (B, global_channels, 1).
  • inverse (bool) – Whether to inverse the flow.
• Returns: Output tensor (B, in_channels, T). Tensor: Log-determinant tensor for NLL (B,) if not inverse.
• Return type: Tensor