  Audio-Visual Speech Recognition (AVSR) uses lip-based video to improve
performance in noise. Since videos are harder to obtain than audio, the video
training data of AVSR models is usually limited to a few thousand hours. In
contrast, speech models such as Whisper are trained with hundreds of thousands
of hours of data, and thus learn a better speech-to-text decoder. The huge
training data difference motivates us to adapt Whisper to handle video inputs.
Inspired by Flamingo which injects visual features into language models, we
propose Whisper-Flamingo which integrates visual features into the Whisper
speech recognition and translation model with gated cross attention. Our
audio-visual Whisper-Flamingo outperforms audio-only Whisper on English speech
recognition and En-X translation for 6 languages in noisy conditions. Moreover,
Whisper-Flamingo is a versatile model and conducts all of these tasks using one
set of parameters, while prior methods are trained separately on each language.
