code: def match_eval2000(text):
      """
      Clean transcripts in the 2000 Hub5 english evaluation test (LDC2002S09  LDC2002T43)
      See:
      http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?catalogId=LDC2002S09
      http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2002T43
  
      This is similar to eval2000_data_prep.sh
  
      Parameters
      ----------
      text : str
          Input text from the eval2000 test 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 "IGNORE_TIME_SEGMENT_" not in text:
          # Remove <B_ASIDE> and <E_ASIDE>.
          cleaned_text = re.sub(r"<.*?>", "", text)
          # Remove everything that is declared optional e.g. (%HESITATION) or (WE-)
          cleaned_text = re.sub(r"[\(\[].*?[\)\]]", "", cleaned_text)
      else:
          logger.debug(f"Ignoring eval2000 segment: {text}")
  
      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 9 for calling a global function with example usage: match_eval2000(text) and returns: cleaned_text