code: def _parse_transcriptions(trans_file):
      segments = []
      with codecs.open(trans_file, "r", "utf-8") as fin:
          for line in fin:
              if line.startswith("#") or len(line) <= 1:
                  continue
  
              tokens = line.split()
              start_time = float(tokens[1])
              stop_time = float(tokens[2])
              transcript = validate_label(" ".join(tokens[3:]))
  
              if transcript == None:
                  continue
  
              # We need to do the encode-decode dance here because encode
              # returns a bytes() object on Python 3, and text_to_char_array
              # expects a string.
              transcript = (
                  unicodedata.normalize("NFKD", transcript)
                  .encode("ascii", "ignore")
                  .decode("ascii", "ignore")
              )
  
              segments.append(
                  {
                      "start_time": start_time,
                      "stop_time": stop_time,
                      "transcript": transcript,
                  }
              )
      return segments
  
imports: import codecs
  import fnmatch
  import os
  import random
  import subprocess
  import sys
  import tarfile
  import unicodedata
  import wave
  import librosa
  import pandas
  import requests
  import soundfile
  from deepspeech_training.util.importers import validate_label_eng
label_desc: Use image node_id 6 for calling a global function with example usage: _parse_transcriptions(trans_file) and returns: segments