code: def test_small_model_pt(self):
      speech_recognizer = pipeline(
          task="automatic-speech-recognition",
          model="facebook/s2t-small-mustc-en-fr-st",
          tokenizer="facebook/s2t-small-mustc-en-fr-st",
          framework="pt",
      )
      waveform = np.tile(np.arange(1000, dtype=np.float32), 34)
      output = speech_recognizer(waveform)
      self.assertEqual(output, {"text": "(Applaudissements)"})
      output = speech_recognizer(waveform, chunk_length_s=10)
      self.assertEqual(output, {"text": "(Applaudissements)"})
  
      # Non CTC models cannot use return_timestamps
      with self.assertRaisesRegex(
          ValueError,
          "^We cannot return_timestamps yet on non-CTC models apart from Whisper!$",
      ):
          _ = speech_recognizer(waveform, 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 4 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_small_model_pt() without return types