espnet2.gan_tts.melgan.melgan.MelGANGenerator

source

class espnet2.gan_tts.melgan.melgan.MelGANGenerator(in_channels: int = 80, out_channels: int = 1, kernel_size: int = 7, channels: int = 512, bias: bool = True, upsample_scales: List[int] = [8, 8, 2, 2], stack_kernel_size: int = 3, stacks: int = 3, nonlinear_activation: str = 'LeakyReLU', nonlinear_activation_params: Dict[str, Any] = {'negative_slope': 0.2}, pad: str = 'ReflectionPad1d', pad_params: Dict[str, Any] = {}, use_final_nonlinear_activation: bool = True, use_weight_norm: bool = True)

Bases: Module

MelGAN generator module.

Initialize MelGANGenerator module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • out_channels (int) – Number of output channels.
  • kernel_size (int) – Kernel size of initial and final conv layer.
  • channels (int) – Initial number of channels for conv layer.
  • bias (bool) – Whether to add bias parameter in convolution layers.
  • upsample_scales (List *[*int ]) – List of upsampling scales.
  • stack_kernel_size (int) – Kernel size of dilated conv layers in residual stack.
  • stacks (int) – Number of stacks in a single residual stack.
  • nonlinear_activation (str) – Activation function module name.
  • nonlinear_activation_params (Dict *[*str , Any ]) – Hyperparameters for activation function.
  • pad (str) – Padding function module name before dilated convolution layer.
  • pad_params (Dict *[*str , Any ]) – Hyperparameters for padding function.
  • use_final_nonlinear_activation (torch.nn.Module) – Activation function for the final layer.
  • use_weight_norm (bool) – Whether to use weight norm. 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(c: Tensor) → Tensor

Calculate forward propagation.

• Parameters:c (Tensor) – Input tensor (B, channels, T).
• Returns: Output tensor (B, 1, T ** prod(upsample_scales)).
• Return type: Tensor

inference(c: Tensor) → Tensor

Perform inference.

• Parameters:c (Tensor) – Input tensor (T, in_channels).
• Returns: Output tensor (T ** prod(upsample_scales), out_channels).
• Return type: Tensor

remove_weight_norm()

Remove weight normalization module from all of the layers.

reset_parameters()

Reset parameters.

This initialization follows official implementation manner. https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py