code: def main():
      parser = argparse.ArgumentParser(
          description="Running DeepSpeech inference."
      )
      parser.add_argument(
          "--model",
          required=True,
          help="Path to the model (protocol buffer binary file)",
      )
      parser.add_argument(
          "--scorer", nargs="?", help="Path to the external scorer file"
      )
      parser.add_argument(
          "--audio1",
          required=True,
          help="First audio file to use in interleaved streams",
      )
      parser.add_argument(
          "--audio2",
          required=True,
          help="Second audio file to use in interleaved streams",
      )
      args = parser.parse_args()
  
      ds = Model(args.model)
  
      if args.scorer:
          ds.enableExternalScorer(args.scorer)
  
      fin = wave.open(args.audio1, "rb")
      fs1 = fin.getframerate()
      audio1 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
      fin.close()
  
      fin = wave.open(args.audio2, "rb")
      fs2 = fin.getframerate()
      audio2 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
      fin.close()
  
      stream1 = ds.createStream()
      stream2 = ds.createStream()
  
      splits1 = np.array_split(audio1, 10)
      splits2 = np.array_split(audio2, 10)
  
      for part1, part2 in zip(splits1, splits2):
          stream1.feedAudioContent(part1)
          stream2.feedAudioContent(part2)
  
      print(stream1.finishStream())
      print(stream2.finishStream())
  
imports: from __future__ import absolute_import, division, print_function
  import argparse
  import numpy
  import wave
  from deepspeech import Model
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types