class_docstr: Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
  provide an audio and a set of `candidate_labels`.
  
  Example:
  ```python
  >>> from transformers import pipeline
  >>> from datasets import load_dataset
  
  >>> dataset = load_dataset("ashraq/esc50")
  >>> audio = next(iter(dataset["train"]["audio"]))["array"]
  >>> classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused")
  >>> classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
  [{'score': 0.9996, 'label': 'Sound of a dog'}, {'score': 0.0004, 'label': 'Sound of vaccum cleaner'}]
  ```
  
  
  Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This audio
  classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"zero-shot-audio-classification"`. See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-audio-classification).
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code: def _sanitize_parameters(self, **kwargs):
      preprocess_params = {}
      if "candidate_labels" in kwargs:
          preprocess_params["candidate_labels"] = kwargs[
              "candidate_labels"
          ]
      if "hypothesis_template" in kwargs:
          preprocess_params["hypothesis_template"] = kwargs[
              "hypothesis_template"
          ]
  
      return preprocess_params, {}, {}
  
file_path: transformers/src/transformers/pipelines/zero_shot_audio_classification.py
imports: from collections import UserDict
  from typing import Union
  import numpy
  import requests
  from ..utils import add_end_docstrings, logging
  from .audio_classification import ffmpeg_read
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 3 for calling the ZeroShotAudioClassificationPipeline obj's underlying member method code with example usage: obj._sanitize_parameters() and returns: preprocess_params,  dict,  dict