code: def test_SeparateSpeech(
      config_file,
      batch_size,
      input_size,
  ):
      separate_speech = SeparateSpeechStreaming(
          train_config=config_file,
      )
      wav = torch.rand(batch_size, input_size)
      ilens = torch.LongTensor([input_size])
  
      speech_sim_chunks = separate_speech.frame(wav)
      output_chunks = [[] for ii in range(separate_speech.num_spk)]
  
      for chunk in speech_sim_chunks:
          output = separate_speech(chunk)
          for channel in range(separate_speech.num_spk):
              output_chunks[channel].append(output[channel])
  
      separate_speech.reset()
      waves = [
          separate_speech.merge(chunks, ilens)
          for chunks in output_chunks
      ]
  
file_path: espnet/test/espnet2/bin/test_enh_inference_streaming.py
imports: from argparse import ArgumentParser
  from pathlib import Path
  import pytest
  import torch
  import yaml
  from espnet2.bin.enh_inference_streaming import SeparateSpeechStreaming, get_parser, main
  from espnet2.tasks.enh import EnhancementTask
  from espnet2.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
label_desc: Use image node_id 4 for calling a global function with example usage: test_SeparateSpeech(config_file, batch_size, input_size) without return types