  This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline
speech translation and simultaneous speech translation. ON-TRAC Consortium is
composed of researchers from three French academic laboratories: LIA (Avignon
Universit\'e), LIG (Universit\'e Grenoble Alpes), and LIUM (Le Mans
Universit\'e). Attention-based encoder-decoder models, trained end-to-end, were
used for our submissions to the offline speech translation track. Our
contributions focused on data augmentation and ensembling of multiple models.
In the simultaneous speech translation track, we build on Transformer-based
wait-k models for the text-to-text subtask. For speech-to-text simultaneous
translation, we attach a wait-k MT system to a hybrid ASR system. We propose an
algorithm to control the latency of the ASR+MT cascade and achieve a good
latency-quality trade-off on both subtasks.
