espnet2.diar.separator.tcn_separator_nomask.TCNSeparatorNomask

source

class espnet2.diar.separator.tcn_separator_nomask.TCNSeparatorNomask(input_dim: int, layer: int = 8, stack: int = 3, bottleneck_dim: int = 128, hidden_dim: int = 512, kernel: int = 3, causal: bool = False, norm_type: str = 'gLN')

Bases: AbsSeparator

Temporal Convolution Separator

Note that this separator is equivalent to TCNSeparator except for not having the mask estimation part. This separator outputs the intermediate bottleneck feats (which is used as the input to diarization branch in enh_diar task). This separator is followed by MultiMask module, which estimates the masks.

• Parameters:
  • input_dim – input feature dimension
  • layer – int, number of layers in each stack.
  • stack – int, number of stacks
  • bottleneck_dim – bottleneck dimension
  • hidden_dim – number of convolution channel
  • kernel – int, kernel size.
  • causal – bool, defalut False.
  • norm_type – str, choose from ‘BN’, ‘gLN’, ‘cLN’

forward(input: Tensor | ComplexTensor, ilens: Tensor) → Tuple[Tensor, Tensor]

Forward.

• Parameters:
  • input (torch.Tensor or ComplexTensor) – Encoded feature [B, T, N]
  • ilens (torch.Tensor) – input lengths [Batch]
• Returns: [B, T, bottleneck_dim] ilens (torch.Tensor): (B,)
• Return type: feats (torch.Tensor)

property num_spk

property output_dim : int