BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus
Josh Meyer, David Ifeoluwa Adelani, Edresson Casanova, Alp \"Oktem,, Daniel Whitenack Julian Weber, Salomon Kabongo, Elizabeth Salesky, Iroro, Orife, Colin Leong, Perez Ogayo, Chris Emezue, Jonathan Mukiibi, Salomey, Osei, Apelete Agbolo, Victor Akinode, Bernard Opoku

TL;DR
BibleTTS provides a large, high-quality, multilingual speech dataset for ten African languages, enabling the development of advanced text-to-speech models with studio-quality recordings.
Contribution
This work introduces a new, open, high-fidelity speech corpus for ten African languages, with aligned recordings and baseline TTS results, filling a gap in multilingual speech resources.
Findings
High-quality, aligned speech data for ten African languages.
Baseline TTS models demonstrate the dataset's utility.
Open license facilitates commercial and research use.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpeech Recognition and Synthesis · Natural Language Processing Techniques
