code: def transcribe_file(audio_path, tlog_path):
      from deepspeech_training.train import (
          create_model,
      )  # pylint: disable=cyclic-import,import-outside-toplevel
      from deepspeech_training.util.checkpoints import (
          load_graph_for_evaluation,
      )
  
      initialize_globals()
      scorer = Scorer(
          FLAGS.lm_alpha,
          FLAGS.lm_beta,
          FLAGS.scorer_path,
          Config.alphabet,
      )
      try:
          num_processes = cpu_count()
      except NotImplementedError:
          num_processes = 1
      with AudioFile(audio_path, as_path=True) as wav_path:
          data_set = split_audio_file(
              wav_path,
              batch_size=FLAGS.batch_size,
              aggressiveness=FLAGS.vad_aggressiveness,
              outlier_duration_ms=FLAGS.outlier_duration_ms,
              outlier_batch_size=FLAGS.outlier_batch_size,
          )
          iterator = tf.data.Iterator.from_structure(
              data_set.output_types,
              data_set.output_shapes,
              output_classes=data_set.output_classes,
          )
          (
              batch_time_start,
              batch_time_end,
              batch_x,
              batch_x_len,
          ) = iterator.get_next()
          no_dropout = [None] * 6
          logits, _ = create_model(
              batch_x=batch_x,
              seq_length=batch_x_len,
              dropout=no_dropout,
          )
          transposed = tf.nn.softmax(tf.transpose(logits, [1, 0, 2]))
          tf.train.get_or_create_global_step()
          with tf.Session(config=Config.session_config) as session:
              load_graph_for_evaluation(session)
              session.run(iterator.make_initializer(data_set))
              transcripts = []
              while True:
                  try:
                      (
                          starts,
                          ends,
                          batch_logits,
                          batch_lengths,
                      ) = session.run(
                          [
                              batch_time_start,
                              batch_time_end,
                              transposed,
                              batch_x_len,
                          ]
                      )
                  except tf.errors.OutOfRangeError:
                      break
                  decoded = ctc_beam_search_decoder_batch(
                      batch_logits,
                      batch_lengths,
                      Config.alphabet,
                      FLAGS.beam_width,
                      num_processes=num_processes,
                      scorer=scorer,
                  )
                  decoded = list(d[0][1] for d in decoded)
                  transcripts.extend(zip(starts, ends, decoded))
              transcripts.sort(key=lambda t: t[0])
              transcripts = [
                  {
                      "start": int(start),
                      "end": int(end),
                      "transcript": transcript,
                  }
                  for start, end, transcript in transcripts
              ]
              with open(tlog_path, "w") as tlog_file:
                  json.dump(transcripts, tlog_file, default=float)
  
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 2 for calling a global function with example usage: transcribe_file(audio_path, tlog_path) without return types