code: def test_transformers_auto_model_for_speech_seq2seq_int4(self):
      from transformers import (
          WhisperProcessor,
          WhisperForConditionalGeneration,
      )
      from datasets import load_from_disk
  
      model_path = os.environ.get("ORIGINAL_WHISPER_TINY_PATH")
      dataset_path = os.environ.get("SPEECH_DATASET_PATH")
      processor = WhisperProcessor.from_pretrained(model_path)
      ds = load_from_disk(dataset_path)
      sample = ds[0]["audio"]
      input_features = processor(
          sample["array"],
          sampling_rate=sample["sampling_rate"],
          return_tensors="pt",
      ).input_features
      model = AutoModelForSpeechSeq2Seq.from_pretrained(
          model_path, trust_remote_code=True, load_in_4bit=True
      )
      with torch.inference_mode():
          st = time.time()
          predicted_ids = model.generate(input_features)
          # decode token ids to text
          transcription = processor.batch_decode(
              predicted_ids, skip_special_tokens=False
          )
          end = time.time()
      print("Output:", transcription)
      print(f"Inference time: {end-st} s")
      res = (
          "Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel."
          in transcription[0]
      )
      self.assertTrue(res)
  
file_path: BigDL/python/llm/test/inference/test_transformers_api.py
imports: import unittest
  import os
  import pytest
  import time
  import torch
  from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq
  from transformers import AutoTokenizer
label_desc: Use image node_id 4 for calling the TestTransformersAPI obj's underlying member method code with example usage: obj.test_transformers_auto_model_for_speech_seq2seq_int4() without return types