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\%.

我们提出了第一个自我监督的多语种语音模型，该模型 exclusively 在非洲语音上进行训练。该模型利用了将近 60,000 小时的无标签语音片段，覆盖了撒哈拉以南非洲的 21 种语言和方言。在 FLEURS-102 数据集的 SSA 子集上，我们基于 HuBERT$_{base}$ (0.09B) 架构的方法显示出与 FLEURS 基准提出的 w2v-bert-51 (0.6B) 预训练模型相当的竞争力，同时使用 7 倍少的数据和 6 倍少的参数来提升 ASR 下游任务的性能。此外，在 LID 下游任务中，我们的方法比 FLEURS 基线的准确性提高了超过 22%。