  Automatic subtitling is the task of automatically translating the speech of
audiovisual content into short pieces of timed text, i.e. subtitles and their
corresponding timestamps. The generated subtitles need to conform to space and
time requirements, while being synchronised with the speech and segmented in a
way that facilitates comprehension. Given its considerable complexity, the task
has so far been addressed through a pipeline of components that separately deal
with transcribing, translating, and segmenting text into subtitles, as well as
predicting timestamps. In this paper, we propose the first direct ST model for
automatic subtitling that generates subtitles in the target language along with
their timestamps with a single model. Our experiments on 7 language pairs show
that our approach outperforms a cascade system in the same data condition, also
being competitive with production tools on both in-domain and newly-released
out-domain benchmarks covering new scenarios.
