class_docstr: Audio classification pipeline using any `AutoModelForAudioClassification`. This pipeline predicts the class of a
  raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
  formats.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks")
  >>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
  [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}]
  ```
  
  Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
  
  
  This pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"audio-classification"`.
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=audio-classification).
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code: def _sanitize_parameters(self, top_k=None, **kwargs):
      # No parameters on this pipeline right now
      postprocess_params = {}
      if top_k is not None:
          if top_k > self.model.config.num_labels:
              top_k = self.model.config.num_labels
          postprocess_params["top_k"] = top_k
      return {}, {}, postprocess_params
  
file_path: transformers/src/transformers/pipelines/audio_classification.py
imports: import subprocess
  from typing import Union
  import numpy
  import requests
  from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 3 for calling the AudioClassificationPipeline obj's underlying member method code with example usage: obj._sanitize_parameters(top_k) and returns: dict,  dict,  postprocess_params