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:语音技术的发展令人瞩目,但由于非洲语音语料库的匮乏,它与非洲语言的整合仍然有限。为解决这个问题,我们提出了AfroDigits,这是一个极简的社群驱动的非洲语言口语数字数据集,目前覆盖38种非洲语言。作为AfroDigits实际应用的演示,我们使用Wav2Vec2.0-Large和XLS-R模型对六种非洲语言[Igbo (ibo), Yoruba (yor), Rundi (run), Oshiwambo (kua), Shona (sna)和Oromo (gax)]进行了音频数字分类实验。我们的实验揭示了在微调期间混合非洲语音语料库的影响。AfroDigits是非洲语言的首个已发布的音频数字数据集,我们相信它将为非洲中心的语音应用开辟道路,例如识别电话号码和街道号码。我们在https://huggingface.co/datasets/chrisjay/crowd-speech-africa和https://huggingface.co/spaces/chrisjay/afro-speech公开发布了数据集和平台。