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: Merges segments that are shorter than the given threshold.
  
  Arguments
  ---------
  boundaries : str
      Tensor containing the speech boundaries. It can be derived using the
      get_boundaries method.
  close_th: float
      If the distance between boundaries is smaller than close_th, the
      segments will be merged.
  
  Returns
  -------
  new_boundaries
      The new boundaries with the merged segments.
docstr_tok: ["Merges","segments","that","are","shorter","than","the","given","threshold",".","Arguments","--","--","--","--","-","boundaries",":","str","Tensor","containing","the","speech","boundaries",".","It","can","be","derived","using","the","get_boundaries","method",".","close_th",":","float","If","the","distance","between","boundaries","is","smaller","than","close_th",",","the","segments","will","be","merged",".","Returns","--","--","--","-","new_boundaries","The","new","boundaries","with","the","merged","segments","."]
code: def merge_close_segments(self, boundaries, close_th=0.250):
      """Merges segments that are shorter than the given threshold.
  
      Arguments
      ---------
      boundaries : str
          Tensor containing the speech boundaries. It can be derived using the
          get_boundaries method.
      close_th: float
          If the distance between boundaries is smaller than close_th, the
          segments will be merged.
  
      Returns
      -------
      new_boundaries
          The new boundaries with the merged segments.
      """
  
      new_boundaries = []
  
      # Single segment case
      if boundaries.shape[0] == 0:
          return boundaries
  
      # Getting beg and end of previous segment
      prev_beg_seg = boundaries[0, 0].float()
      prev_end_seg = boundaries[0, 1].float()
  
      # Process all the segments
      for i in range(1, boundaries.shape[0]):
          beg_seg = boundaries[i, 0]
          segment_distance = beg_seg - prev_end_seg
  
          # Merging close segments
          if segment_distance <= close_th:
              prev_end_seg = boundaries[i, 1]
  
          else:
              # Appending new segments
              new_boundaries.append([prev_beg_seg, prev_end_seg])
              prev_beg_seg = beg_seg
              prev_end_seg = boundaries[i, 1]
  
      new_boundaries.append([prev_beg_seg, prev_end_seg])
      new_boundaries = torch.FloatTensor(new_boundaries).to(
          boundaries.device
      )
      return new_boundaries
  
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 7 for calling the VAD obj's underlying member method code with example usage: obj.merge_close_segments(boundaries, close_th) and returns: new_boundaries, boundaries