WAXAL: A Large-Scale Multilingual African Language Speech Corpus

TL;DR

WAXAL is a comprehensive, openly accessible speech dataset for 24 African languages, aiming to bridge the digital divide by providing large-scale ASR and TTS data to support inclusive speech technology development.

Contribution

This paper introduces WAXAL, the first large-scale multilingual African speech corpus with detailed collection methodology and open access, fostering inclusive speech technology research.

Findings

Contains 1,250 hours of transcribed speech for ASR

Includes 235 hours of high-quality TTS recordings

Released under CC-BY-4.0 license for public use

Abstract

The advancement of speech technology has predominantly favored high-resource languages, creating a significant digital divide for speakers of most Sub-Saharan African languages. To address this gap, we introduce WAXAL, a large-scale, openly accessible speech dataset for 24 languages representing over 100 million speakers. The collection consists of two main components: an Automated Speech Recognition (ASR) dataset containing approximately 1,250 hours of transcribed, natural speech from a diverse range of speakers, and a Text-to-Speech (TTS) dataset with around 235 hours of high-quality, single-speaker recordings reading phonetically balanced scripts. This paper details our methodology for data collection, annotation, and quality control, which involved partnerships with four African academic and community organizations. We provide a detailed statistical overview of the dataset and discuss its potential limitations and ethical considerations. The WAXAL datasets are released at https://huggingface.co/datasets/google/WaxalNLP under the permissive CC-BY-4.0 license to catalyze research, enable the development of inclusive technologies, and serve as a vital resource for the digital preservation of these languages.