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

arXiv: https://arxiv.org/abs/2207.03546 · HTML: https://mlsanity.com/paper/2207.03546/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis](https://mlsanity.com/paper/2005.05957/) (2020)
- [TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese](https://mlsanity.com/paper/2005.05144/) (2020)

## Built on by (most cited first, 30 of 32)

- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [mHuBERT-147: A Compact Multilingual HuBERT Model](https://mlsanity.com/paper/2406.06371/) (2024)
- [MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages](https://mlsanity.com/paper/2305.13989/) (2023)
- [AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement](https://mlsanity.com/paper/2501.15417/) (2025)
- [AfriHuBERT: A self-supervised speech representation model for African languages](https://mlsanity.com/paper/2409.20201/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [WAXAL: A Large-Scale Multilingual African Language Speech Corpus](https://mlsanity.com/paper/2602.02734/) (2026)
- [1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis](https://mlsanity.com/paper/2406.11727/) (2024)
- [YFACC: A Yor`ub'a speech-image dataset for cross-lingual keyword localisation through visual grounding](https://mlsanity.com/paper/2210.04600/) (2022)
- [`Ir`oy`inSpeech: A multi-purpose Yor`ub'a Speech Corpus](https://mlsanity.com/paper/2307.16071/) (2023)
- [The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages](https://mlsanity.com/paper/2505.20564/) (2025)
- [A Systematic Review of NLP for Ghanaian Languages: Datasets, Models, and a Research Roadmap](https://mlsanity.com/paper/2405.06818/) (2024)
- [AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages](https://mlsanity.com/paper/2303.12582/) (2023)
- [uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes](https://mlsanity.com/paper/2407.01257/) (2024)
- [The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages](https://mlsanity.com/paper/2502.15916/) (2025)
- [Voice of a Continent: Mapping Africa's Speech Technology Frontier](https://mlsanity.com/paper/2505.18436/) (2025)
- [VoiceBridge: General Speech Restoration with One-step Latent Bridge Models](https://mlsanity.com/paper/2509.25275/) (2025)
- [A multilingual training strategy for low resource Text to Speech](https://mlsanity.com/paper/2409.01217/) (2024)
- [Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?](https://mlsanity.com/paper/2410.19935/) (2024)
- [Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects](https://mlsanity.com/paper/2502.17364/) (2025)
- [Benchmarking Akan ASR Models Across Domain-Specific Datasets: A Comparative Evaluation of Performance, Scalability, and Adaptability](https://mlsanity.com/paper/2507.02407/) (2025)
- [Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review](https://mlsanity.com/paper/2510.01145/) (2025)
- [WorldSpeech: A Multilingual Speech Corpus from Around the World](https://mlsanity.com/paper/2605.09167/) (2026)
- [OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages](https://mlsanity.com/paper/2606.09553/) (2026)
- [Towards Digital Preservation of Efik: TTS for a Low-Resource African Language](https://mlsanity.com/paper/2607.04515/) (2026)
- [Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset](https://mlsanity.com/paper/2308.14951/) (2023)
- [Improved Visually Prompted Keyword Localisation in Real Low-Resource Settings](https://mlsanity.com/paper/2409.06013/) (2024)
- [Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation](https://mlsanity.com/paper/2605.17710/) (2026)
- [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026)
- [Tone on a Budget: A Reference-Free Metric for Lexical Tone in Massively Multilingual Text-to-Speech](https://mlsanity.com/paper/2609.14817/) (2026)

## Abstract (arXiv)

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.