class_docstr: A ready-to-use class for utterance-level classification (e.g, speaker-id,
  language-id, emotion recognition, keyword spotting, etc).
  
  The class assumes that an encoder called "embedding_model" and a model
  called "classifier" are defined in the yaml file. If you want to
  convert the predicted index into a corresponding text label, please
  provide the path of the label_encoder in a variable called 'lab_encoder_file'
  within the yaml.
  
  The class can be used either to run only the encoder (encode_batch()) to
  extract embeddings or to run a classification step (classify_batch()).
  ```
  
  Example
  -------
  >>> import torchaudio
  >>> from speechbrain.inference.classifiers import AudioClassifier
  >>> tmpdir = getfixture("tmpdir")
  >>> classifier = AudioClassifier.from_hparams(
  ...     source="speechbrain/cnn14-esc50",
  ...     savedir=tmpdir,
  ... )
  >>> signal = torch.randn(1, 16000)
  >>> prediction, _, _, text_lab = classifier.classify_batch(signal)
  >>> print(prediction.shape)
  torch.Size([1, 1, 50])
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docstr: Runs the classification
docstr_tok: ["Runs","the","classification"]
code: def forward(self, wavs, wav_lens=None):
      """Runs the classification"""
      return self.classify_batch(wavs, wav_lens)
  
file_path: speechbrain/speechbrain/inference/classifiers.py
imports: import torch
  import torchaudio
  import speechbrain
  from speechbrain.utils.fetching import fetch
  from speechbrain.utils.data_utils import split_path
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 4 for calling the AudioClassifier obj's underlying member method code with example usage: obj.forward(wavs, wav_lens) and returns: self