code: def run_pipeline_test(self, speech_recognizer, examples):
      audio = np.zeros((34000,))
      outputs = speech_recognizer(audio)
      self.assertEqual(outputs, {"text": ANY(str)})
  
      # Striding
      audio = {
          "raw": audio,
          "stride": (0, 4000),
          "sampling_rate": speech_recognizer.feature_extractor.sampling_rate,
      }
      if speech_recognizer.type == "ctc":
          outputs = speech_recognizer(audio)
          self.assertEqual(outputs, {"text": ANY(str)})
      elif "Whisper" in speech_recognizer.model.__class__.__name__:
          outputs = speech_recognizer(audio)
          self.assertEqual(outputs, {"text": ANY(str)})
      else:
          # Non CTC models cannot use striding.
          with self.assertRaises(ValueError):
              outputs = speech_recognizer(audio)
  
      # Timestamps
      audio = np.zeros((34000,))
      if speech_recognizer.type == "ctc":
          outputs = speech_recognizer(audio, return_timestamps="char")
          self.assertIsInstance(outputs["chunks"], list)
          n = len(outputs["chunks"])
          self.assertEqual(
              outputs,
              {
                  "text": ANY(str),
                  "chunks": [
                      {
                          "text": ANY(str),
                          "timestamp": (ANY(float), ANY(float)),
                      }
                      for i in range(n)
                  ],
              },
          )
  
          outputs = speech_recognizer(audio, return_timestamps="word")
          self.assertIsInstance(outputs["chunks"], list)
          n = len(outputs["chunks"])
          self.assertEqual(
              outputs,
              {
                  "text": ANY(str),
                  "chunks": [
                      {
                          "text": ANY(str),
                          "timestamp": (ANY(float), ANY(float)),
                      }
                      for i in range(n)
                  ],
              },
          )
      elif "Whisper" in speech_recognizer.model.__class__.__name__:
          outputs = speech_recognizer(audio, return_timestamps=True)
          self.assertIsInstance(outputs["chunks"], list)
          nb_chunks = len(outputs["chunks"])
          self.assertGreater(nb_chunks, 0)
          self.assertEqual(
              outputs,
              {
                  "text": ANY(str),
                  "chunks": [
                      {
                          "text": ANY(str),
                          "timestamp": (ANY(float), ANY(float)),
                      }
                      for i in range(nb_chunks)
                  ],
              },
          )
      else:
          # Non CTC models cannot use return_timestamps
          with self.assertRaisesRegex(
              ValueError,
              "^We cannot return_timestamps yet on non-CTC models apart from Whisper!$",
          ):
              outputs = speech_recognizer(
                  audio, return_timestamps="char"
              )
  
imports: import unittest
  import numpy
  import pytest
  from datasets import Audio, load_dataset
  from huggingface_hub import hf_hub_download, snapshot_download
  from transformers import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, AutoFeatureExtractor, AutoProcessor, AutoTokenizer, Speech2TextForConditionalGeneration, Wav2Vec2ForCTC, WhisperForConditionalGeneration
  from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
  from transformers.pipelines.audio_utils import chunk_bytes_iter
  from transformers.pipelines.automatic_speech_recognition import _find_timestamp_sequence, chunk_iter
  from transformers.testing_utils import is_pipeline_test, is_torch_available, nested_simplify, require_pyctcdecode, require_tf, require_torch, require_torch_accelerator, require_torchaudio, slow, torch_device
  from .test_pipelines_common import ANY
label_desc: Use image node_id 2 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.run_pipeline_test(speech_recognizer, examples) without return types