  Speech translation models are unable to directly process long audios, like
TED talks, which have to be split into shorter segments. Speech translation
datasets provide manual segmentations of the audios, which are not available in
real-world scenarios, and existing segmentation methods usually significantly
reduce translation quality at inference time. To bridge the gap between the
manual segmentation of training and the automatic one at inference, we propose
Supervised Hybrid Audio Segmentation (SHAS), a method that can effectively
learn the optimal segmentation from any manually segmented speech corpus.
First, we train a classifier to identify the included frames in a segmentation,
using speech representations from a pre-trained wav2vec 2.0. The optimal
splitting points are then found by a probabilistic Divide-and-Conquer algorithm
that progressively splits at the frame of lowest probability until all segments
are below a pre-specified length. Experiments on MuST-C and mTEDx show that the
translation of the segments produced by our method approaches the quality of
the manual segmentation on 5 language pairs. Namely, SHAS retains 95-98% of the
manual segmentation's BLEU score, compared to the 87-93% of the best existing
methods. Our method is additionally generalizable to different domains and
achieves high zero-shot performance in unseen languages.
