  This paper describes the FlySpeech speaker diarization system submitted to
the second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech
\textbf{P}rocessing~(\textbf{MISP}) Challenge held in ICASSP 2022. We develop
an end-to-end audio-visual speaker diarization~(AVSD) system, which consists of
a lip encoder, a speaker encoder, and an audio-visual decoder. Specifically, to
mitigate the degradation of diarization performance caused by separate
training, we jointly train the speaker encoder and the audio-visual decoder. In
addition, we leverage the large-data pretrained speaker extractor to initialize
the speaker encoder.
