class_docstr: Joint model Enhancement and Speech to Text.
class_docstr_tok: ["Joint","model","Enhancement","and","Speech","to","Text","."]
code: def asr_pit_loss(self, speech, speech_lengths, text, text_lengths):
      if self.s2t_model.ctc is None:
          raise ValueError(
              "CTC must be used to determine the permutation"
          )
      with torch.no_grad():
          # (B, n_ref, n_hyp)
          loss0 = torch.stack(
              [
                  torch.stack(
                      [
                          self.s2t_model._calc_batch_ctc_loss(
                              speech[h],
                              speech_lengths,
                              text[r],
                              text_lengths[r],
                          )
                          for r in range(self.enh_model.num_spk)
                      ],
                      dim=1,
                  )
                  for h in range(self.enh_model.num_spk)
              ],
              dim=2,
          )
          perm_detail, min_loss = self.permutation_invariant_training(
              loss0
          )
  
      speech = ESPnetEnhancementModel.sort_by_perm(speech, perm_detail)
      loss, stats, weight = self.s2t_model(
          torch.cat(speech, dim=0),
          speech_lengths.repeat(len(speech)),
          torch.cat(text, dim=0),
          torch.cat(text_lengths, dim=0),
      )
      return loss, stats, weight
  
imports: import logging
  import random
  from contextlib import contextmanager
  from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from packaging.version import parse
  from scipy.optimize import linear_sum_assignment
  from typeguard import check_argument_types
  from espnet2.asr.espnet_model import ESPnetASRModel
  from espnet2.diar.espnet_model import ESPnetDiarizationModel
  from espnet2.enh.espnet_model import ESPnetEnhancementModel
  from espnet2.st.espnet_model import ESPnetSTModel
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
label_desc: Use image node_id 7 for calling the ESPnetEnhS2TModel obj's underlying member method code with example usage: obj.asr_pit_loss(speech, speech_lengths, text, text_lengths) and returns: loss,  stats,  weight