AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages

TL;DR

AfroDigits is the first publicly available spoken digit dataset for 38 African languages, enabling speech technology development for African languages and demonstrating its utility through classification experiments with modern models.

Contribution

This paper introduces AfroDigits, a novel community-driven speech dataset for African languages, and demonstrates its application in digit classification tasks using advanced speech models.

Findings

Mixing African speech corpora during finetuning improves model performance.

AfroDigits enables development of Afro-centric speech applications.

The dataset covers 38 African languages and is publicly available.

Abstract

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.