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The Thiomi Dataset: A Large-Scale Multimodal Corpus for Low-Resource African Languages

arXiv 2603.29244 ↗First posted 31 Mar 2026cs.CL cs.LGcited by 0

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Abstract (arXiv)

We present the Thiomi Dataset, a large-scale multimodal corpus spanning ten African languages across four language families: Swahili, Kikuyu, Kamba, Kimeru, Luo, Maasai, Kipsigis, Somali (East Africa); Wolof (West Africa); and Fulani (West/Central Africa). The dataset contains over 601,000 approved sentence-level text annotations and over 385,000 audio recordings, collected through a dedicated community data collection platform involving over 100 contributors. To validate the dataset's utility, we train and evaluate ASR, MT, and TTS models, establishing baselines across all languages. Our best ASR system achieves 3.24% WER on Swahili (Common Voice), reducing prior academic SOTA from 8.3% to 3.24% (5.1 percentage point absolute, 61% relative reduction), and 4.3% WER on Somali. The dataset will be published on HuggingFace. We describe the collection platform, quality assurance workflows, and baseline experiments, and discuss implications for African language technology infrastructure.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 31 Mar 2026.

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References · 9

  1. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  2. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  3. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  4. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
  5. 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 458
  6. 2023Scaling Speech Technology to 1,000+ Languagescited by 401
  7. 2020Masakhane -- Machine Translation For Africacited by 48
  8. 2022MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognitioncited by 38
  9. 2026Continued Pretraining for Low-Resource Swahili ASR: Achieving State-of-the-Art Performance with Minimal Labeled Datacited by 2

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