  [Wav2Vec2Processor] for the feature extraction.
WavLM model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
  using [Wav2Vec2CTCTokenizer].
WavLM performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
Relevant checkpoints can be found under https://huggingface.co/models?other=wavlm.
This model was contributed by patrickvonplaten. The Authors' code can be
found here.
WavLMConfig
[[autodoc]] WavLMConfig
WavLM specific outputs
[[autodoc]] models.wavlm.modeling_wavlm.WavLMBaseModelOutput
WavLMModel
[[autodoc]] WavLMModel
    - forward
WavLMForCTC
[[autodoc]] WavLMForCTC
    - forward
WavLMForSequenceClassification
[[autodoc]] WavLMForSequenceClassification
    - forward
WavLMForAudioFrameClassification
