code: def test_Speech2Text_streaming(
      asr_config_file_streaming, lm_config_file
  ):
      file = open(asr_config_file_streaming, "r", encoding="utf-8")
      asr_train_config = file.read()
      asr_train_config = yaml.full_load(asr_train_config)
      asr_train_config["frontend"] = "default"
      asr_train_config["encoder_conf"] = {
          "look_ahead": 16,
          "hop_size": 16,
          "block_size": 40,
      }
      # Change the configuration file
      with open(
          asr_config_file_streaming, "w", encoding="utf-8"
      ) as files:
          yaml.dump(asr_train_config, files)
      speech2text = Speech2TextStreaming(
          asr_train_config=asr_config_file_streaming,
          lm_train_config=lm_config_file,
          beam_size=1,
      )
      speech = np.random.randn(10000)
      for sim_chunk_length in [1, 32, 128, 512, 1024, 2048]:
          if (len(speech) // sim_chunk_length) > 1:
              for i in range(len(speech) // sim_chunk_length):
                  speech2text(
                      speech=speech[
                          i
                          * sim_chunk_length : (i + 1)
                          * sim_chunk_length
                      ],
                      is_final=False,
                  )
              results = speech2text(
                  speech[(i + 1) * sim_chunk_length : len(speech)],
                  is_final=True,
              )
          else:
              results = speech2text(speech)
          for text, token, token_int, hyp in results:
              assert isinstance(text, str)
              assert isinstance(token[0], str)
              assert isinstance(token_int[0], int)
              assert isinstance(hyp, Hypothesis)
  
      # Test edge case: https://github.com/espnet/espnet/pull/4216
      file = open(asr_config_file_streaming, "r", encoding="utf-8")
      asr_train_config = file.read()
      asr_train_config = yaml.full_load(asr_train_config)
      asr_train_config["frontend"] = "default"
      asr_train_config["frontend_conf"] = {
          "n_fft": 256,
          "win_length": 256,
          "hop_length": 128,
      }
      # Change the configuration file
      with open(
          asr_config_file_streaming, "w", encoding="utf-8"
      ) as files:
          yaml.dump(asr_train_config, files)
      speech2text = Speech2TextStreaming(
          asr_train_config=asr_config_file_streaming,
          lm_train_config=lm_config_file,
          beam_size=1,
      )
      # edge case: speech is exactly multiple of sim_chunk_length, e.g., 10240 = 5 x 2048
      speech = np.random.randn(10240)
      for sim_chunk_length in [1, 32, 64, 128, 512, 1024, 2048]:
          if (len(speech) // sim_chunk_length) > 1:
              for i in range(len(speech) // sim_chunk_length):
                  speech2text(
                      speech=speech[
                          i
                          * sim_chunk_length : (i + 1)
                          * sim_chunk_length
                      ],
                      is_final=False,
                  )
              results = speech2text(
                  speech[(i + 1) * sim_chunk_length : len(speech)],
                  is_final=True,
              )
          else:
              results = speech2text(speech)
          for text, token, token_int, hyp in results:
              assert isinstance(text, str)
              assert isinstance(token[0], str)
              assert isinstance(token_int[0], int)
              assert isinstance(hyp, Hypothesis)
  
imports: import string
  from argparse import ArgumentParser
  from pathlib import Path
  import numpy
  import pytest
  import yaml
  from espnet2.bin.asr_inference import Speech2Text, get_parser, main
  from espnet2.bin.asr_inference_streaming import Speech2TextStreaming
  from espnet2.bin.whisper_export_vocabulary import export_vocabulary
  from espnet2.tasks.asr import ASRTask
  from espnet2.tasks.enh_s2t import EnhS2TTask
  from espnet2.tasks.lm import LMTask
  from espnet.nets.beam_search import Hypothesis
label_desc: Use image node_id 9 for calling a global function with example usage: test_Speech2Text_streaming(asr_config_file_streaming, lm_config_file) without return types