code: def main(args, _):
      manager = Manager()
      work_todo = (
          JoinableQueue()
      )  # this is where we are going to store input data
      work_done = (
          manager.Queue()
      )  # this where we are gonna push them out
  
      processes = []
      for i in range(args.proc):
          worker_process = Process(
              target=tflite_worker,
              args=(args.model, args.scorer, work_todo, work_done, i),
              daemon=True,
              name="tflite_process_{}".format(i),
          )
          worker_process.start()  # Launch reader() as a separate python process
          processes.append(worker_process)
  
      print([x.name for x in processes])
  
      wavlist = []
      ground_truths = []
      predictions = []
      losses = []
      wav_filenames = []
  
      with open(args.csv, "r") as csvfile:
          csvreader = csv.DictReader(csvfile)
          count = 0
          for row in csvreader:
              count += 1
              # Relative paths are relative to the folder the CSV file is in
              if not os.path.isabs(row["wav_filename"]):
                  row["wav_filename"] = os.path.join(
                      os.path.dirname(args.csv), row["wav_filename"]
                  )
              work_todo.put(
                  {
                      "filename": row["wav_filename"],
                      "transcript": row["transcript"],
                  }
              )
              wav_filenames.extend(row["wav_filename"])
  
      print("Totally %d wav entries found in csv\n" % count)
      work_todo.join()
      print("\nTotally %d wav file transcripted" % work_done.qsize())
  
      while not work_done.empty():
          msg = work_done.get()
          losses.append(0.0)
          ground_truths.append(msg["ground_truth"])
          predictions.append(msg["prediction"])
          wavlist.append(msg["wav"])
  
      # Print test summary
      _ = calculate_and_print_report(
          wav_filenames, ground_truths, predictions, losses, args.csv
      )
  
      if args.dump:
          with open(args.dump + ".txt", "w") as ftxt, open(
              args.dump + ".out", "w"
          ) as fout:
              for wav, txt, out in zip(
                  wavlist, ground_truths, predictions
              ):
                  ftxt.write("%s %s\n" % (wav, txt))
                  fout.write("%s %s\n" % (wav, out))
              print("Reference texts dumped to %s.txt" % args.dump)
              print("Transcription   dumped to %s.out" % args.dump)
  
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 2 for calling a global function with example usage: main(args, _) without return types