class_docstr: SSLDisentangler is a Conformer based model for extracting disentangled content and speaker embeddings
  from an audio waveform. This model uses a pre-trained Conformer SSL model. To extract the linguistic content 
  and speaker representations using a pre-trained Conformer, two randomly initialized downstream 
  heads are added and the entire setup is finetuned in multi-task manner for speech recognition and speaker verification. 
  These representations can be used by FastPitchModel_SSL for voice conversion by swapping the speaker embedding 
  of a given source utterance, with the speaker embedding of a target speaker.
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docstr: This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
  
  Returns:
      List of available pre-trained models.
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code: def list_available_models(cls) -> Optional[PretrainedModelInfo]:
      """
      This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
  
      Returns:
          List of available pre-trained models.
      """
      results = []
  
      model = PretrainedModelInfo(
          pretrained_model_name="ssl_en_conformer_large",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:ssl_en_conformer_large",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ssl_en_conformer_large/versions/1.10.1/files/ssl_en_conformer_large.nemo",
      )
      results.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="ssl_en_conformer_xlarge",
          description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:ssl_en_conformer_xlarge",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ssl_en_conformer_xlarge/versions/1.10.0/files/ssl_en_conformer_xlarge.nemo",
      )
      results.append(model)
  
      return results
  
imports: import itertools
  from typing import Iterable, Optional
  import editdistance
  import librosa
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import TensorBoardLogger
  from pytorch_lightning.utilities.combined_loader import CombinedLoader
  from nemo.collections.asr.losses.angularloss import AngularSoftmaxLoss
  from nemo.collections.tts.data.dataset import TTSDataset
  from nemo.collections.tts.modules.ssl_tts import GreedyCTCDecoder
  from nemo.collections.tts.torch.tts_tokenizers import BaseTokenizer, EnglishCharsTokenizer
  from nemo.core.classes import ModelPT
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.core.optim.lr_scheduler import WarmupPolicy
  from nemo.utils import logging
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 2 for calling the SSLDisentangler obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: results