espnet2.gan_tts.hifigan.hifigan.HiFiGANMultiScaleDiscriminator

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class espnet2.gan_tts.hifigan.hifigan.HiFiGANMultiScaleDiscriminator(scales: int = 3, downsample_pooling: str = 'AvgPool1d', downsample_pooling_params: Dict[str, Any] = {'kernel_size': 4, 'padding': 2, 'stride': 2}, discriminator_params: Dict[str, Any] = {'bias': True, 'channels': 128, 'downsample_scales': [2, 2, 4, 4, 1], 'in_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'max_downsample_channels': 1024, 'max_groups': 16, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'out_channels': 1}, follow_official_norm: bool = False)

Bases: Module

HiFi-GAN multi-scale discriminator module.

Initilize HiFiGAN multi-scale discriminator module.

• Parameters:
  • scales (int) – Number of multi-scales.
  • downsample_pooling (str) – Pooling module name for downsampling of the inputs.
  • downsample_pooling_params (Dict *[*str , Any ]) – Parameters for the above pooling module.
  • discriminator_params (Dict *[*str , Any ]) – Parameters for hifi-gan scale discriminator module.
  • follow_official_norm (bool) – Whether to follow the norm setting of the official implementaion. The first discriminator uses spectral norm and the other discriminators use weight norm.

forward(x: Tensor) → List[List[Tensor]]

Calculate forward propagation.

• Parameters:x (Tensor) – Input noise signal (B, 1, T).
• Returns: List of list of each discriminator outputs, : which consists of eachlayer output tensors.
• Return type: List[List[torch.Tensor]]