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.
  
  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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docstr: Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more
  information.
  
  Args:
      inputs (`np.ndarray` or `bytes` or `str`):
          The inputs is either a raw waveform (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
          at the correct sampling rate (no further check will be done) or a `str` that is the filename of the
          audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This
          requires *ffmpeg* to be installed on the system. If *inputs* is `bytes` it is supposed to be the
          content of an audio file and is interpreted by *ffmpeg* in the same way.
      top_k (`int`, *optional*, defaults to None):
          The number of top labels that will be returned by the pipeline. If the provided number is `None` or
          higher than the number of labels available in the model configuration, it will default to the number of
          labels.
  
  Return:
      A list of `dict` with the following keys:
  
      - **label** (`str`) -- The label predicted.
      - **score** (`float`) -- The corresponding probability.
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code: def __call__(
      self,
      inputs: Union[np.ndarray, bytes, str],
      **kwargs,
  ):
      """
      Classify the sequence(s) given as inputs. See the [`AutomaticSpeechRecognitionPipeline`] documentation for more
      information.
  
      Args:
          inputs (`np.ndarray` or `bytes` or `str`):
              The inputs is either a raw waveform (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
              at the correct sampling rate (no further check will be done) or a `str` that is the filename of the
              audio file, the file will be read at the correct sampling rate to get the waveform using *ffmpeg*. This
              requires *ffmpeg* to be installed on the system. If *inputs* is `bytes` it is supposed to be the
              content of an audio file and is interpreted by *ffmpeg* in the same way.
          top_k (`int`, *optional*, defaults to None):
              The number of top labels that will be returned by the pipeline. If the provided number is `None` or
              higher than the number of labels available in the model configuration, it will default to the number of
              labels.
  
      Return:
          A list of `dict` with the following keys:
  
          - **label** (`str`) -- The label predicted.
          - **score** (`float`) -- The corresponding probability.
      """
      return super().__call__(inputs, **kwargs)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/audio_classification.py
imports: import subprocess
  from typing import Union
  import numpy
  from ..utils import add_end_docstrings, is_torch_available, logging
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 2 for calling the AudioClassificationPipeline obj's underlying member method code with example usage: obj.__call__(inputs) and returns: super