class_docstr: A ready-to-use class for Voice Activity Detection (VAD) using a
  pre-trained model.
  
  Example
  -------
  >>> import torchaudio
  >>> from speechbrain.inference.VAD import VAD
  >>> # Model is downloaded from the speechbrain HuggingFace repo
  >>> tmpdir = getfixture("tmpdir")
  >>> VAD = VAD.from_hparams(
  ...     source="speechbrain/vad-crdnn-libriparty",
  ...     savedir=tmpdir,
  ... )
  
  >>> # Perform VAD
  >>> boundaries = VAD.get_speech_segments("tests/samples/single-mic/example1.wav")
docstr: Returns the sample rate and the length of the input audio file
docstr_tok: ["Returns","the","sample","rate","and","the","length","of","the","input","audio","file"]
code: def _get_audio_info(self, audio_file):
      """Returns the sample rate and the length of the input audio file"""
  
      # Getting the total size of the input file
      metadata = torchaudio.info(str(audio_file))
      sample_rate = metadata.sample_rate
      audio_len = metadata.num_frames
      return sample_rate, audio_len
  
imports: import torch
  import torchaudio
  from speechbrain.utils.data_utils import split_path
  from speechbrain.utils.fetching import fetch
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 12 for calling the VAD obj's underlying member method code with example usage: obj._get_audio_info(audio_file) and returns: sample_rate,  audio_len