code: def parse_glm_file(glm_dir, save_folder):
      """
      Parse the file called en20000405_hub5.glm.
      This file contains the transcript filtering rules for the
      Hub4-E and Hub5-E Evaluations.
  
      These filtering rules are needed during inference to find valid word alternatives.
  
      Parameters
      ----------
      glm_dir : str
          Location of the en20000405_hub5.glm file in the eval2000 test set
      save_folder : str
          Directory to store the parsed GLM file
      """
      results = defaultdict(list)
      with open(os.path.join(glm_dir, "en20000405_hub5.glm")) as file:
          is_contraction = False
          for line in file:
              # Skip comments
              if "CONTRACTIONIZER" in line:
                  is_contraction = True
              if line.startswith(";;") or line.startswith("*"):
                  continue
              line_split = line.split("=>")
              if len(line_split) > 1:
                  wrd = (
                      line_split[0]
                      .replace("[", "")
                      .replace("]", "")
                      .strip()
                  )
                  # Split alternative at comment
                  if not is_contraction:
                      alternative = line_split[1]
                      alternative = alternative.split(";;")[0].strip()
                      # Split alternative again add additional information
                      alternative = (
                          alternative.split("/")[0]
                          .replace("[", "")
                          .replace("]", "")
                          .strip()
                      )
                      results[wrd] += [alternative]
                  else:
                      # Now we parse contraction rules (last 1000 rows or so)
                      alternative = (
                          line_split[1]
                          .replace("/ [ ] __ [ ]", "")
                          .replace("[{", "")
                          .replace("}]", "")
                      )
                      alternatives = alternative.split("/")
                      alternatives = [
                          i.replace("[", "").replace("]", "").strip()
                          for i in alternatives
                      ]
                      results[wrd] += alternatives
  
      csv_file = os.path.join(save_folder, "glm.csv")
      logger.info("Writing GLM csv file")
  
      with open(csv_file, mode="w") as csv_f:
          csv_writer = csv.writer(
              csv_f,
              delimiter=",",
              quotechar='"',
              quoting=csv.QUOTE_MINIMAL,
          )
  
          for wrd, alternatives in results.items():
              line = [wrd, "|".join(alternatives)]
              csv_writer.writerow(line)
  
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 18 for calling a global function with example usage: parse_glm_file(glm_dir, save_folder) without return types