class_docstr: This lobe enables the integration of HuggingFace and SpeechBrain
  pretrained Discrete HuBERT models.
  
  Source paper HuBERT: https://arxiv.org/abs/2106.07447
  Transformer from HuggingFace needs to be installed:
  https://huggingface.co/transformers/installation.html
  
  The model can be used as a fixed Discrete feature extractor or can be finetuned. It
  will download automatically the model from HuggingFace or use a local path.
  
  For now, HuggingFace's HuBERT and WavLM model can be loaded using the exact code for Wav2Vec2 model.
  For this reason, HuBERT and WavLM can be fine inheriting the Wav2Vec2 class.
  
  Arguments
  ---------
  source : str
      HuggingFace hub name: e.g "facebook/hubert-base-ls960"
  save_path : str
      Path (dir) of the downloaded model.
  kmeans_repo_id : str
      Huggingface repository if that contains the pretrained kmean model
  kmeans_filename : str
      Name of the file in HF repo that need to be downloaded.
  kmeans_cache_dir: str
      Path (dir) of the downloaded kmeans model.
  output_norm : bool (default: True)
      If True, a layer_norm (affine) will be applied to the output obtained
      from the HuBERT model.
  freeze : bool (default: True)
      If True, the model is frozen. If False, the model will be trained
      alongside with the rest of the pipeline.
  freeze_feature_extractor :  bool (default: False)
      When freeze = False and freeze_feature_extractor True, the featue_extractor module of the model is Frozen. If False
      all the HuBERT model will be trained including featue_extractor module.
  apply_spec_augment : bool (default: False)
      If True, the model will apply spec augment on the output of feature extractor
      (inside huggingface Hubert Model() class).
      If False, the model will not apply spec augment. We set this to false to prevent from doing it twice.
  output_all_hiddens : bool (default: True)
      If True, the forward function outputs the hidden states from all transformer layers.
      For example facebook/hubert-base-ls960 has 12 transformer layers and the output is of shape (13, B, T, C),
      where a projection of the CNN output is added to the beginning.
      If False, the forward function outputs the hidden states only from the last transformer layer.
  ssl_layer_num : (int) (default: -1)
      determine the output of which layer of the SSL model should be used for clustering.
  
  
  Example
  -------
  >>> import torch
  >>> inputs = torch.rand([10, 600])
  >>> model_hub = "facebook/hubert-base-ls960"
  >>> save_path = "savedir"
  >>> ssl_layer_num = -1
  >>> kmeans_repo_id = "speechbrain/SSL_Quantization"
  >>> kmeans_filename = "LibriSpeech_hubert_k128_L7.pt"
  >>> kmeans_cache_dir="savedir"
  >>> model = DiscreteHuBERT(model_hub, save_path,freeze = True,ssl_layer_num=ssl_layer_num,kmeans_repo_id=kmeans_repo_id, kmeans_filename=kmeans_filename, kmeans_cache_dir=kmeans_cache_dir)
  >>> embs, tokens = model(inputs)
  >>> embs.shape
  torch.Size([10, 1, 768])
  >>> tokens.shape
  torch.Size([10, 1])
code: def __init__(
      self,
      source,
      save_path,
      kmeans_filename,
      kmeans_cache_dir,
      kmeans_repo_id="speechbrain/SSL_Quantization",
      output_norm=False,
      freeze=False,
      freeze_feature_extractor=False,
      apply_spec_augment=False,
      output_all_hiddens=True,
      ssl_layer_num=-1,
  ):
      super().__init__(
          source=source,
          save_path=save_path,
          output_norm=output_norm,
          freeze=freeze,
          freeze_feature_extractor=freeze_feature_extractor,
          apply_spec_augment=apply_spec_augment,
          output_all_hiddens=output_all_hiddens,
      )
  
      self.kmeans = self.load_kmeans(
          kmeans_repo_id, kmeans_filename, kmeans_cache_dir
      )
      self.vocabulary = self.kmeans.cluster_centers_
      self.ssl_layer_num = ssl_layer_num
  
file_path: speechbrain/speechbrain/lobes/models/huggingface_transformers/discrete_hubert.py
imports: import logging
  import torch
  from huggingface_hub import hf_hub_download
  import joblib
  from speechbrain.lobes.models.huggingface_transformers.hubert import HuBERT
label_desc: Use image node_id 1 to create a new DiscreteHuBERT object from inherited base classes: HuBERT with example: obj = DiscreteHuBERT(source, save_path, kmeans_filename, kmeans_cache_dir, kmeans_repo_id, output_norm, freeze, freeze_feature_extractor, apply_spec_augment, output_all_hiddens, ssl_layer_num)