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

Chris Chinenye Emezue, Sanchit Gandhi, Lewis Tunstall, Abubakar Abid, Josh Meyer +8 more
3/22/2023
cs.CL

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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.

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Cite this paper

@article{emezue2023afrodigits,
  title  = {AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages},
  author = {Chris Chinenye Emezue and Sanchit Gandhi and Lewis Tunstall and Abubakar Abid and Josh Meyer and Quentin Lhoest and Pete Allen and Patrick Von Platen and Douwe Kiela and Yacine Jernite and Julien Chaumond and Merve Noyan and Omar Sanseviero},
  year   = {2023},
  eprint = {2303.12582},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.12582v2}
}

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