  The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild videos. Our key contributions are: (1) we compare two models
for lip reading, one using a CTC loss, and the other using a
sequence-to-sequence loss. Both models are built on top of the transformer
self-attention architecture; (2) we investigate to what extent lip reading is
complementary to audio speech recognition, especially when the audio signal is
noisy; (3) we introduce and publicly release a new dataset for audio-visual
speech recognition, LRS2-BBC, consisting of thousands of natural sentences from
British television. The models that we train surpass the performance of all
previous work on a lip reading benchmark dataset by a significant margin.
