WaxalNLP

The WaxalNLP dataset is a comprehensive collection of audio data for both Automated Speech Recognition (ASR) and Text-to-Speech (TTS) tasks in 14 African languages. It aims to enhance the accuracy and fluency of speech and language technologies for underserved African languages and serves as a resource for digital preservation.

The WaxalNLP dataset is a comprehensive collection of audio data for both Automated Speech Recognition (ASR) and Text-to-Speech (TTS) tasks in 14 African languages. It aims to enhance the accuracy and fluency of speech and language technologies for underserved African languages and serves as a resource for digital preservation.

• 01Contains approximately 1,250 hours of transcribed natural speech for ASR
• 02Includes about 240 hours of scripted natural speech for TTS
• 03Covers 14 African languages spoken by over 100 million people across 40 Sub-Saharan countries
• 04Acquired through partnerships with Makerere University, The University of Ghana, Digital Umuganda, and Media Trust
• 05Funded by Google and the Gates Foundation to be openly accessible