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BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus

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arxiv 2207.03546 v1 pith:2WG55OCK submitted 2022-07-07 eess.AS cs.CLcs.SD

Josh Meyer , David Ifeoluwa Adelani , Edresson Casanova , Alp Öktem , Daniel Whitenack Julian Weber , Salomon Kabongo , Elizabeth Salesky , Iroro Orife
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Colin Leong Perez Ogayo Chris Emezue Jonathan Mukiibi Salomey Osei Apelete Agbolo Victor Akinode Bernard Opoku Samuel Olanrewaju Jesujoba Alabi Shamsuddeen Muhammad
This is my paper · ORCID
classification eess.AScs.CLcs.SD
keywords corpusrecordingsalignedbiblebiblettshigh-qualitylanguagelanguages
verification ladder T0 review T1 audit T2 compute T3 formal
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BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single speaker recordings per language, enabling the development of high-quality text-to-speech models. The ten languages represented are: Akuapem Twi, Asante Twi, Chichewa, Ewe, Hausa, Kikuyu, Lingala, Luganda, Luo, and Yoruba. This corpus is a derivative work of Bible recordings made and released by the Open.Bible project from Biblica. We have aligned, cleaned, and filtered the original recordings, and additionally hand-checked a subset of the alignments for each language. We present results for text-to-speech models with Coqui TTS. The data is released under a commercial-friendly CC-BY-SA license.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Voice of a Continent: Mapping Africa's Speech Technology Frontier

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A new benchmark and fine-tuned Simba models improve speech recognition, synthesis, and language identification across 61 African languages, but the claimed state of the art lacks comparisons to prior task-specific systems.

  2. Towards Digital Preservation of Efik: TTS for a Low-Resource African Language

    cs.CL 2026-07 conditional novelty 5.5 of 10

    First end-to-end Efik TTS baseline: a 3-hour single-speaker corpus and four fine-tuned models, with MMS-TTS best at MOS 3.80±0.63 but residual tonal errors.

  3. Benchmarking Akan ASR Models Across Domain-Specific Datasets: A Comparative Evaluation of Performance, Scalability, and Adaptability

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Seven Akan ASR models were evaluated across four domains, showing strong in-domain performance but weak generalization, with Whisper and Wav2Vec2 producing distinct error types.

  4. Natural language processing for African languages

    cs.CL 2025-06 conditional novelty 3.0 of 10

    A doctoral dissertation that consolidates previously published African-language NLP contributions, including the AfroXLMR model and MasakhaNER datasets for 21 languages.

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