espnet2.enh.separator.dan_separator.DANSeparator

source

class espnet2.enh.separator.dan_separator.DANSeparator(input_dim: int, rnn_type: str = 'blstm', num_spk: int = 2, nonlinear: str = 'tanh', layer: int = 2, unit: int = 512, emb_D: int = 40, dropout: float = 0.0)

Bases: AbsSeparator

Deep Attractor Network Separator

Reference: : DEEP ATTRACTOR NETWORK FOR SINGLE-MICROPHONE SPEAKER SEPARATION; Zhuo Chen. et al., 2017; https://pubmed.ncbi.nlm.nih.gov/29430212/

• Parameters:
  • input_dim – input feature dimension
  • rnn_type – string, select from ‘blstm’, ‘lstm’ etc.
  • bidirectional – bool, whether the inter-chunk RNN layers are bidirectional.
  • num_spk – number of speakers
  • 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.
  • emb_D – int, dimension of the attribute vector for one tf-bin.
  • 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, F]
  • ilens (torch.Tensor) – input lengths [Batch]
  • additional (Dict or None) – other data included in model e.g. “feature_ref”: list of reference spectra List[(B, T, F)]

• 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