code: def test_chunking_and_timestamps(self):
      model = Wav2Vec2ForCTC.from_pretrained(
          "facebook/wav2vec2-base-960h"
      )
      tokenizer = AutoTokenizer.from_pretrained(
          "facebook/wav2vec2-base-960h"
      )
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          "facebook/wav2vec2-base-960h"
      )
      speech_recognizer = pipeline(
          task="automatic-speech-recognition",
          model=model,
          tokenizer=tokenizer,
          feature_extractor=feature_extractor,
          framework="pt",
          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_tiled = np.tile(audio, n_repeats)
      output = speech_recognizer([audio_tiled], batch_size=2)
      self.assertEqual(
          output,
          [
              {
                  "text": (
                      "A MAN SAID TO THE UNIVERSE SIR I EXIST "
                      * n_repeats
                  ).strip()
              }
          ],
      )
  
      output = speech_recognizer(audio, return_timestamps="char")
      self.assertEqual(audio.shape, (74_400,))
      self.assertEqual(
          speech_recognizer.feature_extractor.sampling_rate, 16_000
      )
      # The audio is 74_400 / 16_000 = 4.65s long.
      self.assertEqual(
          output,
          {
              "text": "A MAN SAID TO THE UNIVERSE SIR I EXIST",
              "chunks": [
                  {"text": "A", "timestamp": (0.6, 0.62)},
                  {"text": " ", "timestamp": (0.62, 0.66)},
                  {"text": "M", "timestamp": (0.68, 0.7)},
                  {"text": "A", "timestamp": (0.78, 0.8)},
                  {"text": "N", "timestamp": (0.84, 0.86)},
                  {"text": " ", "timestamp": (0.92, 0.98)},
                  {"text": "S", "timestamp": (1.06, 1.08)},
                  {"text": "A", "timestamp": (1.14, 1.16)},
                  {"text": "I", "timestamp": (1.16, 1.18)},
                  {"text": "D", "timestamp": (1.2, 1.24)},
                  {"text": " ", "timestamp": (1.24, 1.28)},
                  {"text": "T", "timestamp": (1.28, 1.32)},
                  {"text": "O", "timestamp": (1.34, 1.36)},
                  {"text": " ", "timestamp": (1.38, 1.42)},
                  {"text": "T", "timestamp": (1.42, 1.44)},
                  {"text": "H", "timestamp": (1.44, 1.46)},
                  {"text": "E", "timestamp": (1.46, 1.5)},
                  {"text": " ", "timestamp": (1.5, 1.56)},
                  {"text": "U", "timestamp": (1.58, 1.62)},
                  {"text": "N", "timestamp": (1.64, 1.68)},
                  {"text": "I", "timestamp": (1.7, 1.72)},
                  {"text": "V", "timestamp": (1.76, 1.78)},
                  {"text": "E", "timestamp": (1.84, 1.86)},
                  {"text": "R", "timestamp": (1.86, 1.9)},
                  {"text": "S", "timestamp": (1.96, 1.98)},
                  {"text": "E", "timestamp": (1.98, 2.02)},
                  {"text": " ", "timestamp": (2.02, 2.06)},
                  {"text": "S", "timestamp": (2.82, 2.86)},
                  {"text": "I", "timestamp": (2.94, 2.96)},
                  {"text": "R", "timestamp": (2.98, 3.02)},
                  {"text": " ", "timestamp": (3.06, 3.12)},
                  {"text": "I", "timestamp": (3.5, 3.52)},
                  {"text": " ", "timestamp": (3.58, 3.6)},
                  {"text": "E", "timestamp": (3.66, 3.68)},
                  {"text": "X", "timestamp": (3.68, 3.7)},
                  {"text": "I", "timestamp": (3.9, 3.92)},
                  {"text": "S", "timestamp": (3.94, 3.96)},
                  {"text": "T", "timestamp": (4.0, 4.02)},
                  {"text": " ", "timestamp": (4.06, 4.1)},
              ],
          },
      )
      output = speech_recognizer(audio, return_timestamps="word")
      self.assertEqual(
          output,
          {
              "text": "A MAN SAID TO THE UNIVERSE SIR I EXIST",
              "chunks": [
                  {"text": "A", "timestamp": (0.6, 0.62)},
                  {"text": "MAN", "timestamp": (0.68, 0.86)},
                  {"text": "SAID", "timestamp": (1.06, 1.24)},
                  {"text": "TO", "timestamp": (1.28, 1.36)},
                  {"text": "THE", "timestamp": (1.42, 1.5)},
                  {"text": "UNIVERSE", "timestamp": (1.58, 2.02)},
                  {"text": "SIR", "timestamp": (2.82, 3.02)},
                  {"text": "I", "timestamp": (3.5, 3.52)},
                  {"text": "EXIST", "timestamp": (3.66, 4.02)},
              ],
          },
      )
      output = speech_recognizer(
          audio, return_timestamps="word", chunk_length_s=2.0
      )
      self.assertEqual(
          output,
          {
              "text": "A MAN SAID TO THE UNIVERSE SIR I EXIST",
              "chunks": [
                  {"text": "A", "timestamp": (0.6, 0.62)},
                  {"text": "MAN", "timestamp": (0.68, 0.86)},
                  {"text": "SAID", "timestamp": (1.06, 1.24)},
                  {"text": "TO", "timestamp": (1.3, 1.36)},
                  {"text": "THE", "timestamp": (1.42, 1.48)},
                  {"text": "UNIVERSE", "timestamp": (1.58, 2.02)},
                  # Tiny change linked to chunking.
                  {"text": "SIR", "timestamp": (2.84, 3.02)},
                  {"text": "I", "timestamp": (3.5, 3.52)},
                  {"text": "EXIST", "timestamp": (3.66, 4.02)},
              ],
          },
      )
      # CTC models must specify return_timestamps type - cannot set `return_timestamps=True` blindly
      with self.assertRaisesRegex(
          ValueError,
          "^CTC can either predict character level timestamps, or word level timestamps. "
          "Set `return_timestamps='char'` or `return_timestamps='word'` as required.$",
      ):
          _ = speech_recognizer(audio, return_timestamps=True)
  
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 35 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_chunking_and_timestamps() without return types