code: def match_fisher(text):
      """
      Clean transcripts in the Fisher corpus.
  
      This is similar to fisher_data_prep.sh
  
      Parameters
      ----------
      text : str
          Input text from the Fisher data.
  
      Returns
      -------
       A string containing the cleaned sentence.
      """
  
      cleaned_text = ""
  
      # Remove utterance when it's just optional nonwords
      text = text.strip().upper()
      for nw in ["UM-HUM", "UMM", "UH-HUH", "MHM", "UH-OH"]:
          text = text.replace(nw, "")
  
      if "((" not in text:
          cleaned_text = re.sub(
              r"\[laugh\]", "[laughter]", text, flags=re.IGNORECASE
          )
          cleaned_text = re.sub(
              r"\[sigh\]", "[noise]", cleaned_text, flags=re.IGNORECASE
          )
          cleaned_text = re.sub(
              r"\[cough\]", "[noise]", cleaned_text, flags=re.IGNORECASE
          )
          cleaned_text = re.sub(
              r"\[sigh\]", "[noise]", cleaned_text, flags=re.IGNORECASE
          )
          cleaned_text = re.sub(
              r"\[mn\]", "[noise]", cleaned_text, flags=re.IGNORECASE
          )
          cleaned_text = re.sub(
              r"\[breath\]",
              "[noise]",
              cleaned_text,
              flags=re.IGNORECASE,
          )
          cleaned_text = re.sub(
              r"\[lipsmack\]",
              "[noise]",
              cleaned_text,
              flags=re.IGNORECASE,
          )
      return cleaned_text
  
imports: import re
  import csv
  import logging
  import os
  from collections import defaultdict
  from speechbrain.dataio.dataio import merge_csvs
  from speechbrain.utils.data_utils import download_file, get_all_files
label_desc: Use image node_id 10 for calling a global function with example usage: match_fisher(text) and returns: cleaned_text