  In this paper, we describe our submission to the Simultaneous Speech
Translation at IWSLT 2022. We explore strategies to utilize an offline model in
a simultaneous setting without the need to modify the original model. In our
experiments, we show that our onlinization algorithm is almost on par with the
offline setting while being $3\times$ faster than offline in terms of latency
on the test set. We also show that the onlinized offline model outperforms the
best IWSLT2021 simultaneous system in medium and high latency regimes and is
almost on par in the low latency regime. We make our system publicly available.
