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: Splits the input into smaller chunks of size chunk_size with
  an overlap chunk_stride. The chunks are concatenated over
  the batch axis.
  
  Arguments
  ---------
  x: torch.Tensor
      Signal to split into chunks.
  chunk_size : str
      The size of each chunk.
  chunk_stride:
      The stride (hop) of each chunk.
  
  
  Returns
  -------
  x: torch.Tensor
      A new tensors with the chunks derived from the input signal.
docstr_tok: ["Splits","the","input","into","smaller","chunks","of","size","chunk_size","with","an","overlap","chunk_stride",".","The","chunks","are","concatenated","over","the","batch","axis",".","Arguments","--","--","--","--","-","x",":","torch.Tensor","Signal","to","split","into","chunks",".","chunk_size",":","str","The","size","of","each","chunk",".","chunk_stride",":","The","stride","(","hop",")","of","each","chunk",".","Returns","--","--","--","-","x",":","torch.Tensor","A","new","tensors","with","the","chunks","derived","from","the","input","signal","."]
code: def create_chunks(self, x, chunk_size=16384, chunk_stride=16384):
      """Splits the input into smaller chunks of size chunk_size with
      an overlap chunk_stride. The chunks are concatenated over
      the batch axis.
  
      Arguments
      ---------
      x: torch.Tensor
          Signal to split into chunks.
      chunk_size : str
          The size of each chunk.
      chunk_stride:
          The stride (hop) of each chunk.
  
  
      Returns
      -------
      x: torch.Tensor
          A new tensors with the chunks derived from the input signal.
  
      """
      x = x.unfold(1, chunk_size, chunk_stride)
      x = x.reshape(x.shape[0] * x.shape[1], -1)
      return x
  
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 11 for calling the VAD obj's underlying member method code with example usage: obj.create_chunks(x, chunk_size, chunk_stride) and returns: x