code: def listen(chain):
      (
          voiceassitant_chain,
          processor,
          recogn_model,
          forced_decoder_ids,
      ) = chain
  
      # engine = pyttsx3.init()
      r = sr.Recognizer()
      with sr.Microphone(device_index=1, sample_rate=16000) as source:
          print("Calibrating...")
          r.adjust_for_ambient_noise(source, duration=5)
          # optional parameters to adjust microphone sensitivity
          # r.energy_threshold = 200
          # r.pause_threshold=0.5
  
          print("Okay, go!")
          while 1:
              text = ""
              print("listening now...")
              try:
                  audio = r.listen(
                      source, timeout=5, phrase_time_limit=30
                  )
                  # refer to https://github.com/openai/whisper/blob/main/whisper/audio.py#L63
                  frame_data = (
                      np.frombuffer(audio.frame_data, np.int16)
                      .flatten()
                      .astype(np.float32)
                      / 32768.0
                  )
                  print("Recognizing...")
                  st = time.time()
                  input_features = processor(
                      frame_data,
                      sampling_rate=audio.sample_rate,
                      return_tensors="pt",
                  ).input_features
                  predicted_ids = recogn_model.generate(
                      input_features,
                      forced_decoder_ids=forced_decoder_ids,
                  )
                  text = processor.batch_decode(
                      predicted_ids, skip_special_tokens=True
                  )[0]
                  time_0 = time.time() - st
  
              except Exception as e:
                  unrecognized_speech_text = f"Sorry, I didn't catch that. Exception was: \n {e}"
                  text = unrecognized_speech_text
              st = time.time()
              response_text = voiceassitant_chain.predict(
                  human_input=text, stop="\n\n"
              )
              print(response_text)
              print(
                  f"Recognized in {time_0}s, Predicted in {time.time() - st}s"
              )
  
file_path: BigDL/python/llm/example/CPU/LangChain/transformers_int4/voiceassistant.py
imports: from langchain import LLMChain, PromptTemplate
  from bigdl.llm.langchain.llms import TransformersLLM
  from langchain.memory import ConversationBufferWindowMemory
  from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
  from transformers import WhisperProcessor
  import speech_recognition
  import numpy
  import pyttsx3
  import argparse
  import time
label_desc: Use image node_id 2 for calling a global function with example usage: listen(chain) without return types