code: def _inference_with_wer(self, generator, sample, model):
      import editdistance
  
      def decode(toks):
          s = self.target_dictionary.string(
              toks.int().cpu(),
              self.cfg.eval_wer_post_process,
              escape_unk=True,
          )
          if self.tokenizer:
              s = self.tokenizer.decode(s)
          return s
  
      num_word_errors, num_char_errors = 0, 0
      num_chars, num_words = 0, 0
      gen_out = self.inference_step(generator, [model], sample, None)
      for i in range(len(gen_out)):
          hyp = decode(gen_out[i][0]["tokens"])
          ref = decode(
              utils.strip_pad(
                  sample["target"][i], self.target_dictionary.pad()
              ),
          )
          num_char_errors += editdistance.eval(hyp, ref)
          num_chars += len(ref)
          hyp_words = hyp.split()
          ref_words = ref.split()
          num_word_errors += editdistance.eval(hyp_words, ref_words)
          num_words += len(ref_words)
  
      return {
          "num_char_errors": num_char_errors,
          "num_chars": num_chars,
          "num_word_errors": num_word_errors,
          "num_words": num_words,
      }
  
imports: import logging
  import os
  from fairseq.data.multi_corpus_dataset import MultiCorpusDataset
  import torch
  import json
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Optional, Any, OrderedDict
  from fairseq.data import AddTargetDataset, Dictionary, encoders
  from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.dataclass.configs import GenerationConfig
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
  from .None import register_task
  from ..None import utils
  from ..logging import metrics
label_desc: Use image node_id 7 for calling the AudioFinetuningTask obj's underlying member method code with example usage: obj._inference_with_wer(generator, sample, model) and returns: dict, s