class_docstr: An wrapper for the HuggingFace Vocos model
  
  Arguments
  ---------
  source : str
      A HuggingFace repository identifier or a path
  save_path : str
      The location where the pretrained model will be saved
  revision : str
      The model revision
  bandwidth : float
      The bandwidth value
      Supported:
      1.5, 3.0, 6.0, 12.0
  freeze : bool
      Whether or not parameters should be
      frozen
  
  Example
  -------
  >>> model_hub = "charactr/vocos-encodec-24khz"
  >>> save_path = "savedir"
  >>> model = Vocos(model_hub, save_path)
  >>> tokens = torch.randint(1024, (4, 10, 2))
  >>> length = torch.tensor([1.0, 0.5, 0.75, 1.0])
  >>> audio, out_length = model(tokens, length)
  >>> audio.shape
  torch.Size([4, 3200])
  >>> out_length
  tensor([1.0000, 0.5000, 0.7500, 1.0000])
docstr: Converts EnCodec tokens to audio
  
  Arguments
  ---------
  inputs : torch.Tensor
      A tensor of EnCodec tokens
  length : torch.Tensor
      A 1-D tensor of relative lengths
  
  Returns
  -------
  wavs : torch.Tensor
      A (Batch x Length) tensor of raw waveforms
  length : torch.Tensor
      Relative lengths
code: def forward(self, inputs, length):
      """Converts EnCodec tokens to audio
  
      Arguments
      ---------
      inputs : torch.Tensor
          A tensor of EnCodec tokens
      length : torch.Tensor
          A 1-D tensor of relative lengths
  
      Returns
      -------
      wavs : torch.Tensor
          A (Batch x Length) tensor of raw waveforms
      length : torch.Tensor
          Relative lengths
      """
      with torch.set_grad_enabled(not self.freeze):
          features = self.model.codes_to_features(
              inputs.permute(2, 0, 1)
          )
          wavs = self.model.decode(
              features,
              bandwidth_id=torch.tensor(
                  [self.bandwidth_id], device=inputs.device
              ),
          )
          mask = length_to_mask(
              length * wavs.size(1),
              max_len=wavs.size(1),
              device=wavs.device,
          )
          return wavs * mask, length
  
file_path: speechbrain/speechbrain/lobes/models/huggingface_transformers/vocos.py
imports: import torch
  import logging
  from torch import nn
  from speechbrain.dataio.dataio import length_to_mask
  from huggingface_hub import hf_hub_download
label_desc: Use image node_id 3 for calling the Vocos obj's underlying member method code with example usage: obj.forward(inputs, length) and returns: unknown,  length