 Model Description  These are model weights originally provided by the authors of the paper [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://arxiv.org/pdf/2201.02184.pdf).  <figure>   <img src="https://huggingface.co/vumichien/AV-HuBERT/resolve/main/HuBert.png" alt="Audio-visual HuBERT">   <figcaption>Audio-visual HuBERT   </figcaption> </figure>  Video recordings of speech contain correlated audio and visual information, providing a strong signal for speech representation learning from the speaker’s lip movements and the produced sound.   Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech, which masks multi-stream video input and predicts automatically discovered and iteratively refined multimodal hidden units. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition.  The official code of this paper in [here](https://github.com/facebookresearch/av_hubert)  