SALAN: A Massive ASR Dataset for the Languages of Niger

Abstract

We introduce SALAN, a large-scale speech dataset covering eight of the major indigenous languages of Niger: Zarma, Hausa, Buduma, Gourmantchema, Tubu, Tamasheq, Fulfulde, and Kanuri. The final dataset exceeds 2,000 hours of audio, largely sourced from radio broadcasts and community recordings. We transcribed portions of the audio using the MMS model and conducted manual verification for 110 hours across Zarma and Hausa. We then used active learning to expand annotation to an additional 5 hours of high-uncertainty Zarma segments. To evaluate SALAN’s utility for ASR, We fine-tuned both Wav2vec2 XLS-R and Whisper on Zarma subsets and carried out additional pre-training with multilingual unlabeled data. Our best model achieved a word error rate of 25.3% and a character error rate of 6.2%. SALAN and the trained models will be made publicly available for use by researchers and speakers, with the potential to impact over 20 million individuals in Niger and neighboring countries.

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