code: def test_chunking_with_lm(self):
      speech_recognizer = pipeline(
          task="automatic-speech-recognition",
          model="patrickvonplaten/wav2vec2-base-100h-with-lm",
          chunk_length_s=10.0,
      )
      ds = load_dataset(
          "hf-internal-testing/librispeech_asr_dummy",
          "clean",
          split="validation",
      ).sort("id")
      audio = ds[40]["audio"]["array"]
  
      n_repeats = 10
      audio = np.tile(audio, n_repeats)
      output = speech_recognizer([audio], batch_size=2)
      expected_text = (
          "A MAN SAID TO THE UNIVERSE SIR I EXIST " * n_repeats
      )
      expected = [{"text": expected_text.strip()}]
      self.assertEqual(output, expected)
  
imports: import unittest
  import numpy
  import pytest
  from datasets import load_dataset
  from huggingface_hub import snapshot_download
  from transformers import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, AutoFeatureExtractor, AutoTokenizer, Speech2TextForConditionalGeneration, Wav2Vec2ForCTC, WhisperForConditionalGeneration, WhisperProcessor
  from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
  from transformers.pipelines.audio_utils import chunk_bytes_iter
  from transformers.pipelines.automatic_speech_recognition import chunk_iter
  from transformers.testing_utils import is_torch_available, nested_simplify, require_pyctcdecode, require_tf, require_torch, require_torchaudio, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 24 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_chunking_with_lm() without return types