espnet2.gan_svs.pits.ying_decoder.YingDecoder

source

class espnet2.gan_svs.pits.ying_decoder.YingDecoder(hidden_channels, kernel_size, dilation_rate, n_layers, yin_start, yin_scope, yin_shift_range, gin_channels=0)

Bases: Module

Ying decoder module.

Initialize the YingDecoder module.

• Parameters:
  • hidden_channels (int) – Number of hidden channels.
  • kernel_size (int) – Size of the convolutional kernel.
  • dilation_rate (int) – Dilation rate of the convolutional layers.
  • n_layers (int) – Number of convolutional layers.
  • yin_start (int) – Start point of the yin target signal.
  • yin_scope (int) – Scope of the yin target signal.
  • yin_shift_range (int) – Maximum number of frames to shift the yin target signal.
  • gin_channels (int , optional) – Number of global conditioning channels. Defaults to 0.

crop_scope(x, yin_start, scope_shift)

Crop the input tensor.

• Parameters:
  • x (torch.Tensor) – Input tensor of shape [B, C, T].
  • yin_start (int) – Starting point of the yin target signal.
  • scope_shift (torch.Tensor) – Shift tensor of shape [B].
• Returns: Cropped tensor of shape [B, C, yin_scope].
• Return type: torch.Tensor

forward(z_yin, yin_gt, z_mask, g=None)

Forward pass of the decoder.

• Parameters:

  • z_yin (torch.Tensor) – The input yin note sequence of shape (B, C, T_yin).
  • yin_gt (torch.Tensor) – The ground truth yin note sequence of shape (B, C, T_yin).
  • z_mask (torch.Tensor) – The mask tensor of shape (B, 1, T_yin).
  • g (torch.Tensor) – The global conditioning tensor.

• Returns: The predicted yin note sequence of shape (B, C, T_yin). torch.Tensor: The shifted ground truth yin note sequence of shape

  (B, C, T_yin).

  torch.Tensor: The cropped ground truth yin note sequence of shape : (B, C, T_yin).

  torch.Tensor: The cropped input yin note sequence of shape (B, C, T_yin). torch.Tensor: The scope shift tensor of shape (B,).

• Return type: torch.Tensor

infer(z_yin, z_mask, g=None)

Generate yin prediction.

• Parameters:
  • z_yin (torch.Tensor) – Input yin target tensor of shape [B, yin_scope, C].
  • z_mask (torch.Tensor) – Input mask tensor of shape [B, yin_scope, 1].
  • g (torch.Tensor , optional) – Global conditioning tensor of shape [B, gin_channels, 1]. Defaults to None.
• Returns: Predicted yin tensor of shape [B, yin_scope, C].
• Return type: torch.Tensor