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).
class_docstr_tok: ["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",")","."]
code: def _forward(self, model_inputs):
      model_outputs = self.model(**model_inputs)
      return model_outputs
  
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 5 for calling the AudioClassificationPipeline obj's underlying member method code with example usage: obj._forward(model_inputs) and returns: model_outputs