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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docstr: Assign labels to the audio(s) passed as inputs.
  
  Args:
      audios (`str`, `List[str]`, `np.array` or `List[np.array]`):
          The pipeline handles three types of inputs:
          - A string containing a http link pointing to an audio
          - A string containing a local path to an audio
          - An audio loaded in numpy
      candidate_labels (`List[str]`):
          The candidate labels for this audio
      hypothesis_template (`str`, *optional*, defaults to `"This is a sound of {}"`):
          The sentence used in cunjunction with *candidate_labels* to attempt the audio classification by
          replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
          logits_per_audio
  Return:
      A list of dictionaries containing result, one dictionary per proposed label. The dictionaries contain the
      following keys:
      - **label** (`str`) -- The label identified by the model. It is one of the suggested `candidate_label`.
      - **score** (`float`) -- The score attributed by the model for that label (between 0 and 1).
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code: def __call__(self, audios: Union[np.ndarray, bytes, str], **kwargs):
      """
      Assign labels to the audio(s) passed as inputs.
  
      Args:
          audios (`str`, `List[str]`, `np.array` or `List[np.array]`):
              The pipeline handles three types of inputs:
              - A string containing a http link pointing to an audio
              - A string containing a local path to an audio
              - An audio loaded in numpy
          candidate_labels (`List[str]`):
              The candidate labels for this audio
          hypothesis_template (`str`, *optional*, defaults to `"This is a sound of {}"`):
              The sentence used in cunjunction with *candidate_labels* to attempt the audio classification by
              replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
              logits_per_audio
      Return:
          A list of dictionaries containing result, one dictionary per proposed label. The dictionaries contain the
          following keys:
          - **label** (`str`) -- The label identified by the model. It is one of the suggested `candidate_label`.
          - **score** (`float`) -- The score attributed by the model for that label (between 0 and 1).
      """
      return super().__call__(audios, **kwargs)
  
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 2 for calling the ZeroShotAudioClassificationPipeline obj's underlying member method code with example usage: obj.__call__(audios) and returns: super