espnet2.gan_tts.hifigan.hifigan.HiFiGANPeriodDiscriminator

source

class espnet2.gan_tts.hifigan.hifigan.HiFiGANPeriodDiscriminator(in_channels: int = 1, out_channels: int = 1, period: int = 3, kernel_sizes: List[int] = [5, 3], channels: int = 32, downsample_scales: List[int] = [3, 3, 3, 3, 1], max_downsample_channels: int = 1024, bias: bool = True, nonlinear_activation: str = 'LeakyReLU', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.1}, use_weight_norm: bool = True, use_spectral_norm: bool = False)

Bases: Module

HiFiGAN period discriminator module.

Initialize HiFiGANPeriodDiscriminator module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • out_channels (int) – Number of output channels.
  • period (int) – Period.
  • kernel_sizes (list) – Kernel sizes of initial conv layers and the final conv layer.
  • channels (int) – Number of initial channels.
  • downsample_scales (List *[*int ]) – List of downsampling scales.
  • max_downsample_channels (int) – Number of maximum downsampling channels.
  • use_additional_convs (bool) – Whether to use additional conv layers in residual blocks.
  • bias (bool) – Whether to add bias parameter in convolution layers.
  • nonlinear_activation (str) – Activation function module name.
  • nonlinear_activation_params (Dict *[*str , Any ]) – Hyperparameters for activation function.
  • use_weight_norm (bool) – Whether to use weight norm. If set to true, it will be applied to all of the conv layers.
  • use_spectral_norm (bool) – Whether to use spectral norm. If set to true, it will be applied to all of the conv layers.

apply_spectral_norm()

Apply spectral normalization module from all of the layers.

apply_weight_norm()

Apply weight normalization module from all of the layers.

forward(x: Tensor) → Tensor

Calculate forward propagation.

• Parameters:c (Tensor) – Input tensor (B, in_channels, T).
• Returns: List of each layer’s tensors.
• Return type: list