class_docstr: A "ready-to-use" speech separation model.
  
  Uses Sepformer architecture.
  
  Example
  -------
  >>> tmpdir = getfixture("tmpdir")
  >>> model = SepformerSeparation.from_hparams(
  ...     source="speechbrain/sepformer-wsj02mix",
  ...     savedir=tmpdir)
  >>> mix = torch.randn(1, 400)
  >>> est_sources = model.separate_batch(mix)
  >>> print(est_sources.shape)
  torch.Size([1, 400, 2])
class_docstr_tok: ["A","``","ready-to-use","''","speech","separation","model",".","Uses","Sepformer","architecture",".","Example","--","--","--","-",">",">",">","tmpdir","=","getfixture","(","``","tmpdir","''",")",">",">",">","model","=","SepformerSeparation.from_hparams","(","...","source=","''","speechbrain\/sepformer-wsj02mix","''",",","...","savedir=tmpdir",")",">",">",">","mix","=","torch.randn","(","1",",","400",")",">",">",">","est_sources","=","model.separate_batch","(","mix",")",">",">",">","print","(","est_sources.shape",")","torch.Size","(","[","1",",","400",",","2","]",")"]
docstr: Runs separation on the input mix
docstr_tok: ["Runs","separation","on","the","input","mix"]
code: def forward(self, mix):
      """Runs separation on the input mix"""
      return self.separate_batch(mix)
  
file_path: speechbrain/speechbrain/inference/separation.py
imports: import torch
  import torchaudio
  import torch.nn.functional
  from speechbrain.utils.fetching import fetch
  from speechbrain.utils.data_utils import split_path
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 3 for calling the SepformerSeparation obj's underlying member method code with example usage: obj.forward(mix) and returns: self