license: apache-2.0
language:
- en
pipeline_tag: automatic-speech-recognition
datasets:
- LRS3
tags:
- Audio Visual to Text
- Automatic Speech Recognition
Model Description
These are model weights originally provided by the authors of the paper Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction.
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.
Datasets
The authors trained the model on lip-reading benchmark LRS3 datasets (433 hours).