espnet2.enh.separator.dprnn_separator.DPRNNSeparator

source

class espnet2.enh.separator.dprnn_separator.DPRNNSeparator(input_dim: int, rnn_type: str = 'lstm', bidirectional: bool = True, num_spk: int = 2, predict_noise: bool = False, nonlinear: str = 'relu', layer: int = 3, unit: int = 512, segment_size: int = 20, dropout: float = 0.0)

Bases: AbsSeparator

Dual-Path RNN (DPRNN) Separator

• Parameters:
  • input_dim – input feature dimension
  • rnn_type – string, select from ‘RNN’, ‘LSTM’ and ‘GRU’.
  • bidirectional – bool, whether the inter-chunk RNN layers are bidirectional.
  • num_spk – number of speakers
  • predict_noise – whether to output the estimated noise signal
  • nonlinear – the nonlinear function for mask estimation, select from ‘relu’, ‘tanh’, ‘sigmoid’
  • layer – int, number of stacked RNN layers. Default is 3.
  • unit – int, dimension of the hidden state.
  • segment_size – dual-path segment size
  • dropout – float, dropout ratio. Default is 0.

forward(input: Tensor | ComplexTensor, ilens: Tensor, additional: Dict | None = None) → Tuple[List[Tensor | ComplexTensor], Tensor, OrderedDict]

Forward.

• Parameters:

  • input (torch.Tensor or ComplexTensor) – Encoded feature [B, T, N]
  • ilens (torch.Tensor) – input lengths [Batch]
  • additional (Dict or None) – other data included in model NOTE: not used in this model

• Returns: [(B, T, N), …] ilens (torch.Tensor): (B,) others predicted data, e.g. masks: OrderedDict[

  ’mask_spk1’: torch.Tensor(Batch, Frames, Freq), ‘mask_spk2’: torch.Tensor(Batch, Frames, Freq), … ‘mask_spkn’: torch.Tensor(Batch, Frames, Freq),

  ]

• Return type: masked (List[Union(torch.Tensor, ComplexTensor)])

property num_spk