docstr: Returns actual metric score, either pesq or stoi
  
  Arguments
  ---------
  batch_id : list of str
      A list of the utterance ids for the batch
  deg_wav : torch.Tensor
      The degraded waveform to score
  ref_wav : torch.Tensor
      The reference waveform to use for scoring
  length : torch.Tensor
      The relative lengths of the utterances
docstr_tok: ["Returns","actual","metric","score",",","either","pesq","or","stoi","Arguments","--","--","--","--","-","batch_id",":","list","of","str","A","list","of","the","utterance","ids","for","the","batch","deg_wav",":","torch.Tensor","The","degraded","waveform","to","score","ref_wav",":","torch.Tensor","The","reference","waveform","to","use","for","scoring","length",":","torch.Tensor","The","relative","lengths","of","the","utterances"]
code: def score(self, batch_id, deg_wav, ref_wav, lens):
      """Returns actual metric score, either pesq or stoi
  
      Arguments
      ---------
      batch_id : list of str
          A list of the utterance ids for the batch
      deg_wav : torch.Tensor
          The degraded waveform to score
      ref_wav : torch.Tensor
          The reference waveform to use for scoring
      length : torch.Tensor
          The relative lengths of the utterances
      """
      new_ids = [
          i
          for i, d in enumerate(batch_id)
          if d not in self.historical_set and d not in self.noisy_scores
      ]
  
      if len(new_ids) == 0:
          pass
      elif self.hparams.target_metric == "srmr" or "dnsmos":
          self.target_metric.append(
              ids=[batch_id[i] for i in new_ids],
              predict=deg_wav[new_ids].detach(),
              target=ref_wav[
                  new_ids
              ].detach(),  # target is not used in the function !!!
              lengths=lens[new_ids],
          )
          score = torch.tensor(
              [[s] for s in self.target_metric.scores],
              device=self.device,
          )
      else:
          raise ValueError(
              "Expected 'srmr' or 'dnsmos' for target_metric"
          )
  
      # Clear metric scores to prepare for next batch
      self.target_metric.clear()
  
      # Combine old scores and new
      final_score = []
      for i, d in enumerate(batch_id):
          if d in self.historical_set:
              final_score.append([self.historical_set[d]["score"]])
          elif d in self.noisy_scores:
              final_score.append([self.noisy_scores[d]])
          else:
              final_score.append([score[new_ids.index(i)]])
  
      return torch.tensor(final_score, device=self.device)
  
imports: import os
  import sys
  import shutil
  import torch
  import torchaudio
  import speechbrain
  import numpy
  import json
  import pickle
  import requests
  import time
  from urllib.parse import urlparse, urljoin
  from srmrpy import srmr
  from pesq import pesq
  from enum import Enum, auto
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import MetricStats
  from speechbrain.processing.features import spectral_magnitude
  from speechbrain.nnet.loss.stoi_loss import stoi_loss
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.sampler import ReproducibleWeightedRandomSampler
label_desc: Use image node_id 7 for calling the MetricGanBrain obj's underlying member method code with example usage: obj.score(batch_id, deg_wav, ref_wav, lens) and returns: torch