  We present the first self-supervised multilingual speech model trained
exclusively on African speech. The model learned from nearly 60 000 hours of
unlabeled speech segments in 21 languages and dialects spoken in sub-Saharan
Africa. On the SSA subset of the FLEURS-102 dataset, our approach based on a
HuBERT$_{base}$ (0.09B) architecture shows competitive results, for ASR
downstream task, compared to the w2v-bert-51 (0.6B) pre-trained model proposed
in the FLEURS benchmark, while being more efficient by using 7x less data and
6x less parameters. Furthermore, in the context of a LID downstream task, our
approach outperforms FLEURS baselines accuracy by over 22\%.
