espnet2.gan_tts.style_melgan.style_melgan.StyleMelGANDiscriminator

source

class espnet2.gan_tts.style_melgan.style_melgan.StyleMelGANDiscriminator(repeats: int = 2, window_sizes: List[int] = [512, 1024, 2048, 4096], pqmf_params: List[List[int]] = [[1, None, None, None], [2, 62, 0.267, 9.0], [4, 62, 0.142, 9.0], [8, 62, 0.07949, 9.0]], discriminator_params: Dict[str, Any] = {'bias': True, 'channels': 16, 'downsample_scales': [4, 4, 4, 1], 'kernel_sizes': [5, 3], 'max_downsample_channels': 512, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.2}, 'out_channels': 1, 'pad': 'ReflectionPad1d', 'pad_params': {}}, use_weight_norm: bool = True)

Bases: Module

Style MelGAN disciminator module.

Initilize StyleMelGANDiscriminator module.

• Parameters:
  • repeats (int) – Number of repititons to apply RWD.
  • window_sizes (List *[*int ]) – List of random window sizes.
  • pqmf_params (List *[*List *[*int ] ]) – List of list of Parameters for PQMF modules
  • discriminator_params (Dict *[*str , Any ]) – Parameters for base discriminator module.
  • use_weight_nom (bool) – Whether to apply weight normalization.

apply_weight_norm()

Apply weight normalization module from all of the layers.

forward(x: Tensor) → List[Tensor]

Calculate forward propagation.

• Parameters:x (Tensor) – Input tensor (B, 1, T).
• Returns: List of discriminator outputs, #items in the list will be : equal to repeats * #discriminators.
• Return type: List

reset_parameters()

Reset parameters.