espnet2.gan_tts.vits.flow.ConvFlow

source

class espnet2.gan_tts.vits.flow.ConvFlow(in_channels: int, hidden_channels: int, kernel_size: int, layers: int, bins: int = 10, tail_bound: float = 5.0)

Bases: Module

Convolutional flow module.

Initialize ConvFlow module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • hidden_channels (int) – Number of hidden channels.
  • kernel_size (int) – Kernel size.
  • layers (int) – Number of layers.
  • bins (int) – Number of bins.
  • tail_bound (float) – Tail bound value.

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, channels, T).
  • x_mask (Tensor) – Mask tensor (B,).
  • g (Optional *[*Tensor ]) – Global conditioning tensor (B, channels, 1).
  • inverse (bool) – Whether to inverse the flow.
• Returns: Output tensor (B, channels, T). Tensor: Log-determinant tensor for NLL (B,) if not inverse.
• Return type: Tensor