The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora. To address this issue, we present AfroDigits, a minimalist, community-driven dataset of spoken digits for African languages, currently covering 38 African languages. As a demonstration of the practical applications of AfroDigits, we conduct audio digit classification experiments on six African languages [Igbo (ibo), Yoruba (yor), Rundi (run), Oshiwambo (kua), Shona (sna), and Oromo (gax)] using the Wav2Vec2.0-Large and XLS-R models. Our experiments reveal a useful insight on the effect of mixing African speech corpora during finetuning. AfroDigits is the first published audio digit dataset for African languages and we believe it will, among other things, pave the way for Afro-centric speech applications such as the recognition of telephone numbers, and street numbers. We release the dataset and platform publicly at https://huggingface.co/datasets/chrisjay/crowd-speech-africa and https://huggingface.co/spaces/chrisjay/afro-speech respectively.
翻译：语音技术的发展已经取得了显着进展，然而，由于非洲语言语料库的稀缺性，它与非洲语言的融合仍然有限。为了解决这个问题，我们提出了 AfroDigits，一种极简的、由社区驱动的非洲语言口语数字数据集，目前涵盖了38种非洲语言。作为对AfroDigits实际应用的演示，我们使用Wav2Vec2.0-Large和XLS-R模型在六种非洲语言（Igbo（ibo），Yoruba（yor），Rundi（run），Oshiwambo（kua），Shona（sna）和Oromo（gax）上进行了音频数字分类实验。我们的实验揭示了混合非洲语言语料库对微调的影响。AfroDigits是首个发表的非洲语言音频数字数据集，我们相信它将为Afro-centric语音应用铺平道路，例如电话号码和街道号码的识别。我们在https://huggingface.co/datasets/chrisjay/crowd-speech-africa和https://huggingface.co/spaces/chrisjay/afro-speech上公开发布数据集和平台。
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