AV-HuBERT

Model Description

These are model weights originally provided by the authors of the paper Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction.

<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

Example

<figure> <img src="https://huggingface.co/vumichien/AV-HuBERT/resolve/main/lipreading.gif" alt="Audio-Visual Speech Recognition"> <figcaption> Speech Recognition from visual lip movement </figcaption> </figure>

Datasets

The authors trained the model on LRS3 with 433 hours of transcribed English videos and English portion of VoxCeleb2, which amounts to 1,326 hours