espnet2.enh.layers.dpmulcat.DPMulCat

source

class espnet2.enh.layers.dpmulcat.DPMulCat(input_size: int, hidden_size: int, output_size: int, num_spk: int, dropout: float = 0.0, num_layers: int = 4, bidirectional: bool = True, input_normalize: bool = False)

Bases: Module

Dual-path RNN module with MulCat blocks.

• Parameters:
  • input_size – int, dimension of the input feature. The input should have shape (batch, seq_len, input_size).
  • hidden_size – int, dimension of the hidden state.
  • output_size – int, dimension of the output size.
  • num_spk – int, the number of speakers in the output.
  • dropout – float, the dropout rate in the LSTM layer. (Default: 0.0)
  • bidirectional – bool, whether the RNN layers are bidirectional. (Default: True)
  • num_layers – int, number of stacked MulCat blocks. (Default: 4)
  • input_normalize – bool, whether to apply GroupNorm on the input Tensor. (Default: False)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Compute output after DPMulCat module.

• Parameters:input (torch.Tensor) – The input feature. Tensor of shape (batch, N, dim1, dim2) Apply RNN on dim1 first and then dim2
• Returns: (list(torch.Tensor) or list(list(torch.Tensor)) : In training mode, the module returns output of each DPMulCat block. In eval mode, the module only returns output in the last block.