code: def test_large_model_pt(self):
      import datasets
  
      model = "superb/wav2vec2-base-superb-ks"
  
      audio_classifier = pipeline("audio-classification", model=model)
      dataset = datasets.load_dataset(
          "anton-l/superb_dummy", "ks", split="test"
      )
  
      audio = np.array(dataset[3]["speech"], dtype=np.float32)
      output = audio_classifier(audio, top_k=4)
      self.assertEqual(
          nested_simplify(output, decimals=3),
          [
              {"score": 0.981, "label": "go"},
              {"score": 0.007, "label": "up"},
              {"score": 0.006, "label": "_unknown_"},
              {"score": 0.001, "label": "down"},
          ],
      )
  
file_path: transformers/tests/pipelines/test_pipelines_audio_classification.py
imports: import unittest
  import numpy
  from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  from transformers.pipelines import AudioClassificationPipeline, pipeline
  from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, require_torchaudio, slow
  from .test_pipelines_common import ANY
label_desc: Use image node_id 5 for calling the AudioClassificationPipelineTests obj's underlying member method code with example usage: obj.test_large_model_pt() without return types