  Automatic dubbing (AD) is the task of translating the original speech in a
video into target language speech. The new target language speech should
satisfy isochrony; that is, the new speech should be time aligned with the
original video, including mouth movements, pauses, hand gestures, etc. In this
paper, we propose training a model that directly optimizes both the translation
as well as the speech duration of the generated translations. We show that this
system generates speech that better matches the timing of the original speech,
compared to prior work, while simplifying the system architecture.
