espnet2.enh.separator.asteroid_models.AsteroidModel_Converter

source

class espnet2.enh.separator.asteroid_models.AsteroidModel_Converter(encoder_output_dim: int, model_name: str, num_spk: int, pretrained_path: str = '', loss_type: str = 'si_snr', **model_related_kwargs)

Bases: AbsSeparator

The class to convert the models from asteroid to AbsSeprator.

• Parameters:
  • encoder_output_dim – input feature dimension, default=1 after the NullEncoder
  • num_spk – number of speakers
  • loss_type – loss type of enhancement
  • model_name – Asteroid model names, e.g. ConvTasNet, DPTNet. Refers to https://github.com/asteroid-team/asteroid/ blob/master/asteroid/models/_init_.py
  • pretrained_path – the name of pretrained model from Asteroid in HF hub. Refers to: https://github.com/asteroid-team/asteroid/ blob/master/docs/source/readmes/pretrained_models.md and https://huggingface.co/models?filter=asteroid
  • model_related_kwargs – more args towards each specific asteroid model.

forward(input: Tensor, ilens: Tensor | None = None, additional: Dict | None = None)

Whole forward of asteroid models.

• Parameters:

  • input (torch.Tensor) – Raw Waveforms [B, T]
  • ilens (torch.Tensor) – input lengths [B]
  • additional (Dict or None) – other data included in model

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

  ’mask_spk1’: torch.Tensor(Batch, T), ‘mask_spk2’: torch.Tensor(Batch, T), … ‘mask_spkn’: torch.Tensor(Batch, T),

  ]

• Return type: estimated Waveforms(List[Union(torch.Tensor])

forward_rawwav(input: Tensor, ilens: Tensor | None = None) → Tuple[Tensor, Tensor]

Output with waveforms.

property num_spk