code: def transcribe_many(src_paths, dst_paths):
      pbar = create_progressbar(
          prefix="Transcribing files | ", max_value=len(src_paths)
      ).start()
      for i in range(len(src_paths)):
          p = Process(
              target=transcribe_file, args=(src_paths[i], dst_paths[i])
          )
          p.start()
          p.join()
          log_progress(
              'Transcribed file {} of {} from "{}" to "{}"'.format(
                  i + 1, len(src_paths), src_paths[i], dst_paths[i]
              )
          )
          pbar.update(i)
      pbar.finish()
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import sys
  import json
  import tensorflow
  import tensorflow.compat.v1.logging
  import logging
  import glob
  from deepspeech_training.util.audio import AudioFile
  from deepspeech_training.util.config import Config, initialize_globals
  from deepspeech_training.util.feeding import split_audio_file
  from deepspeech_training.util.flags import create_flags, FLAGS
  from deepspeech_training.util.logging import log_error, log_info, log_progress, create_progressbar
  from ds_ctcdecoder import ctc_beam_search_decoder_batch, Scorer
  from multiprocessing import Process, cpu_count
label_desc: Use image node_id 3 for calling a global function with example usage: transcribe_many(src_paths, dst_paths) without return types