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: Outputs the frame-level posterior probability for the input audio chunks
  Outputs close to zero refers to time steps with a low probability of speech
  activity, while outputs closer to one likely contain speech.
  
  Arguments
  ---------
  wavs : torch.Tensor
      Batch of waveforms [batch, time, channels] or [batch, time]
      depending on the model. Make sure the sample rate is fs=16000 Hz.
  wav_lens : torch.Tensor
      Lengths of the waveforms relative to the longest one in the
      batch, tensor of shape [batch]. The longest one should have
      relative length 1.0 and others len(waveform) / max_length.
      Used for ignoring padding.
  
  Returns
  -------
  torch.Tensor
      The encoded batch
docstr_tok: ["Outputs","the","frame-level","posterior","probability","for","the","input","audio","chunks","Outputs","close","to","zero","refers","to","time","steps","with","a","low","probability","of","speech","activity",",","while","outputs","closer","to","one","likely","contain","speech",".","Arguments","--","--","--","--","-","wavs",":","torch.Tensor","Batch","of","waveforms","[","batch",",","time",",","channels","]","or","[","batch",",","time","]","depending","on","the","model",".","Make","sure","the","sample","rate","is","fs=16000","Hz",".","wav_lens",":","torch.Tensor","Lengths","of","the","waveforms","relative","to","the","longest","one","in","the","batch",",","tensor","of","shape","[","batch","]",".","The","longest","one","should","have","relative","length","1.0","and","others","len","(","waveform",")","\/","max_length",".","Used","for","ignoring","padding",".","Returns","--","--","--","-","torch.Tensor","The","encoded","batch"]
code: def get_speech_prob_chunk(self, wavs, wav_lens=None):
      """Outputs the frame-level posterior probability for the input audio chunks
      Outputs close to zero refers to time steps with a low probability of speech
      activity, while outputs closer to one likely contain speech.
  
      Arguments
      ---------
      wavs : torch.Tensor
          Batch of waveforms [batch, time, channels] or [batch, time]
          depending on the model. Make sure the sample rate is fs=16000 Hz.
      wav_lens : torch.Tensor
          Lengths of the waveforms relative to the longest one in the
          batch, tensor of shape [batch]. The longest one should have
          relative length 1.0 and others len(waveform) / max_length.
          Used for ignoring padding.
  
      Returns
      -------
      torch.Tensor
          The encoded batch
      """
      # Manage single waveforms in input
      if len(wavs.shape) == 1:
          wavs = wavs.unsqueeze(0)
  
      # Assign full length if wav_lens is not assigned
      if wav_lens is None:
          wav_lens = torch.ones(wavs.shape[0], device=self.device)
  
      # Storing waveform in the specified device
      wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
      wavs = wavs.float()
  
      # Computing features and embeddings
      feats = self.mods.compute_features(wavs)
      feats = self.mods.mean_var_norm(feats, wav_lens)
      outputs = self.mods.cnn(feats)
  
      outputs = outputs.reshape(
          outputs.shape[0],
          outputs.shape[1],
          outputs.shape[2] * outputs.shape[3],
      )
  
      outputs, h = self.mods.rnn(outputs)
      outputs = self.mods.dnn(outputs)
      output_prob = torch.sigmoid(outputs)
  
      return output_prob
  
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 4 for calling the VAD obj's underlying member method code with example usage: obj.get_speech_prob_chunk(wavs, wav_lens) and returns: output_prob