espnet2.gan_tts.vits.posterior_encoder.PosteriorEncoder

source

class espnet2.gan_tts.vits.posterior_encoder.PosteriorEncoder(in_channels: int = 513, out_channels: int = 192, hidden_channels: int = 192, kernel_size: int = 5, layers: int = 16, stacks: int = 1, base_dilation: int = 1, global_channels: int = -1, dropout_rate: float = 0.0, bias: bool = True, use_weight_norm: bool = True)

Bases: Module

Posterior encoder module in VITS.

This is a module of posterior encoder described in Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech.

Initilialize PosteriorEncoder module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • out_channels (int) – Number of output channels.
  • hidden_channels (int) – Number of hidden channels.
  • kernel_size (int) – Kernel size in WaveNet.
  • layers (int) – Number of layers of WaveNet.
  • stacks (int) – Number of repeat stacking of WaveNet.
  • base_dilation (int) – Base dilation factor.
  • global_channels (int) – Number of global conditioning channels.
  • dropout_rate (float) – Dropout rate.
  • bias (bool) – Whether to use bias parameters in conv.
  • use_weight_norm (bool) – Whether to apply weight norm.

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

Calculate forward propagation.

• Parameters:
  • x (Tensor) – Input tensor (B, in_channels, T_feats).
  • x_lengths (Tensor) – Length tensor (B,).
  • g (Optional *[*Tensor ]) – Global conditioning tensor (B, global_channels, 1).
• Returns: Encoded hidden representation tensor (B, out_channels, T_feats). Tensor: Projected mean tensor (B, out_channels, T_feats). Tensor: Projected scale tensor (B, out_channels, T_feats). Tensor: Mask tensor for input tensor (B, 1, T_feats).
• Return type: Tensor