espnet2.gan_svs.uhifigan.uhifigan.UHiFiGANGenerator

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class espnet2.gan_svs.uhifigan.uhifigan.UHiFiGANGenerator(in_channels=80, out_channels=1, channels=512, global_channels: int = -1, kernel_size=7, downsample_scales=(2, 2, 8, 8), downsample_kernel_sizes=(4, 4, 16, 16), upsample_scales=(8, 8, 2, 2), upsample_kernel_sizes=(16, 16, 4, 4), resblock_kernel_sizes=(3, 7, 11), resblock_dilations=[(1, 3, 5), (1, 3, 5), (1, 3, 5)], projection_filters: List[int] = [0, 1, 1, 1], projection_kernels: List[int] = [0, 5, 7, 11], dropout=0.3, use_additional_convs=True, bias=True, nonlinear_activation='LeakyReLU', nonlinear_activation_params={'negative_slope': 0.1}, use_causal_conv=False, use_weight_norm=True, use_avocodo=False)

Bases: Module

UHiFiGAN generator module.

Initialize Unet-based HiFiGANGenerator module.

• Parameters:
  • in_channels (int) – Number of input channels.
  • out_channels (int) – Number of output channels.
  • channels (int) – Number of hidden representation channels.
  • global_channels (int) – Number of global conditioning channels.
  • kernel_size (int) – Kernel size of initial and final conv layer.
  • upsample_scales (list) – List of upsampling scales.
  • upsample_kernel_sizes (list) – List of kernel sizes for upsampling layers.
  • resblock_kernel_sizes (list) – List of kernel sizes for residual blocks.
  • resblock_dilations (list) – List of dilation list for residual blocks.
  • 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) – Hyperparameters for activation function.
  • use_causal_conv (bool) – Whether to use causal structure.
  • 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=None, f0=None, excitation=None, g: Tensor | None = None)

Calculate forward propagation.

• Parameters:
  • c (Tensor) – Input tensor (B, in_channels, T).
  • f0 (Tensor) – Input tensor (B, 1, T).
  • excitation (Tensor) – Input tensor (B, frame_len, T).
• Returns: Output tensor (B, out_channels, T).
• Return type: Tensor

inference(excitation=None, f0=None, c=None, normalize_before=False)

Perform inference.

• Parameters:
  • c (Union *[*Tensor , ndarray ]) – Input tensor (T, in_channels).
  • normalize_before (bool) – Whether to perform normalization.
• Returns: Output tensor (T ** prod(upsample_scales), out_channels).
• Return type: Tensor

register_stats(stats)

Register stats for de-normalization as buffer.

• Parameters:stats (str) – Path of statistics file (“.npy” or “.h5”).

remove_weight_norm()

Remove weight normalization module from all of the layers.

reset_parameters()

Reset parameters.

This initialization follows the official implementation manner. https://github.com/jik876/hifi-gan/blob/master/models.py