code: def tflite_worker(model, scorer, queue_in, queue_out, gpu_mask):
      os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_mask)
      ds = Model(model)
      ds.enableExternalScorer(scorer)
  
      while True:
          try:
              msg = queue_in.get()
  
              filename = msg["filename"]
              fin = wave.open(filename, "rb")
              audio = np.frombuffer(
                  fin.readframes(fin.getnframes()), np.int16
              )
              fin.close()
  
              decoded = ds.stt(audio)
  
              queue_out.put(
                  {
                      "wav": filename,
                      "prediction": decoded,
                      "ground_truth": msg["transcript"],
                  }
              )
          except FileNotFoundError as ex:
              print("FileNotFoundError: ", ex)
  
          print(
              queue_out.qsize(), end="\r"
          )  # Update the current progress
          queue_in.task_done()
  
file_path: DeepSpeech/evaluate_tflite.py
imports: from __future__ import absolute_import, division, print_function
  import absl.app
  import argparse
  import numpy
  import wave
  import csv
  import os
  import sys
  from deepspeech import Model
  from deepspeech_training.util.evaluate_tools import calculate_and_print_report
  from deepspeech_training.util.flags import create_flags
  from functools import partial
  from multiprocessing import JoinableQueue, Process, cpu_count, Manager
  from six.moves import zip, range
label_desc: Use image node_id 1 for calling a global function with example usage: tflite_worker(model, scorer, queue_in, queue_out, gpu_mask) without return types