espnet2.gan_tts.parallel_wavegan.parallel_wavegan.ParallelWaveGANDiscriminator

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class espnet2.gan_tts.parallel_wavegan.parallel_wavegan.ParallelWaveGANDiscriminator(in_channels: int = 1, out_channels: int = 1, kernel_size: int = 3, layers: int = 10, conv_channels: int = 64, dilation_factor: int = 1, nonlinear_activation: str = 'LeakyReLU', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.2}, bias: bool = True, use_weight_norm: bool = True)

Bases: Module

Parallel WaveGAN Discriminator module.

Initialize ParallelWaveGANDiscriminator module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • out_channels (int) – Number of output channels.
  • kernel_size (int) – Number of output channels.
  • layers (int) – Number of conv layers.
  • conv_channels (int) – Number of chnn layers.
  • dilation_factor (int) – Dilation factor. For example, if dilation_factor = 2, the dilation will be 2, 4, 8, …, and so on.
  • nonlinear_activation (str) – Nonlinear function after each conv.
  • nonlinear_activation_params (Dict *[*str , Any ]) – Nonlinear function parameters
  • bias (bool) – Whether to use bias parameter in conv.
  • use_weight_norm (bool) – If set to true, it will be applied to all of the conv layers.

apply_weight_norm()

Apply weight normalization module from all of the layers.

forward(x: Tensor) → Tensor

Calculate forward propagation.

• Parameters:x (Tensor) – Input noise signal (B, 1, T).
• Returns: Output tensor (B, 1, T).
• Return type: Tensor

remove_weight_norm()

Remove weight normalization module from all of the layers.