code: def test_with_local_lm_fast(self):
      local_dir = snapshot_download(
          "hf-internal-testing/processor_with_lm"
      )
      speech_recognizer = pipeline(
          task="automatic-speech-recognition",
          model=local_dir,
      )
      self.assertEqual(speech_recognizer.type, "ctc_with_lm")
  
      ds = load_dataset(
          "hf-internal-testing/librispeech_asr_dummy",
          "clean",
          split="validation",
      ).sort("id")
      audio = ds[40]["audio"]["array"]
  
      n_repeats = 2
      audio_tiled = np.tile(audio, n_repeats)
  
      output = speech_recognizer([audio_tiled], batch_size=2)
  
      self.assertEqual(output, [{"text": ANY(str)}])
      self.assertEqual(output[0]["text"][:6], "<s> <s")
  
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 22 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_with_local_lm_fast() without return types