espnet2.enh.separator.dccrn_separator.DCCRNSeparator

source

class espnet2.enh.separator.dccrn_separator.DCCRNSeparator(input_dim: int, num_spk: int = 1, rnn_layer: int = 2, rnn_units: int = 256, masking_mode: str = 'E', use_clstm: bool = True, bidirectional: bool = False, use_cbn: bool = False, kernel_size: int = 5, kernel_num: List[int] = [32, 64, 128, 256, 256, 256], use_builtin_complex: bool = True, use_noise_mask: bool = False)

Bases: AbsSeparator

DCCRN separator.

• Parameters:
  • input_dim (int) – input dimension。
  • num_spk (int , optional) – number of speakers. Defaults to 1.
  • rnn_layer (int , optional) – number of lstm layers in the crn. Defaults to 2.
  • rnn_units (int , optional) – rnn units. Defaults to 128.
  • masking_mode (str , optional) – usage of the estimated mask. Defaults to “E”.
  • use_clstm (bool , optional) – whether use complex LSTM. Defaults to False.
  • bidirectional (bool , optional) – whether use BLSTM. Defaults to False.
  • use_cbn (bool , optional) – whether use complex BN. Defaults to False.
  • kernel_size (int , optional) – convolution kernel size. Defaults to 5.
  • kernel_num (list , optional) – output dimension of each layer of the encoder.
  • use_builtin_complex (bool , optional) – torch.complex if True, else ComplexTensor.
  • use_noise_mask (bool , optional) – whether to estimate the mask of noise.

apply_masks(masks: List[Tensor | ComplexTensor], real: Tensor, imag: Tensor)

apply masks

• Parameters:
  • masks – est_masks, [(B, T, F), …]
  • real (torch.Tensor) – real part of the noisy spectrum, (B, F, T)
  • imag (torch.Tensor) – imag part of the noisy spectrum, (B, F, T)
• Returns: [(B, T, F), …]
• Return type: masked (List[Union(torch.Tensor, ComplexTensor)])

create_masks(mask_tensor: Tensor)

create estimated mask for each speaker

• Parameters:mask_tensor (torch.Tensor) – output of decoder, shape(B, 2*num_spk, F-1, T)

flatten_parameters()

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 NOTE: not used in this model

• Returns: [(B, T, F), …] 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