We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4 test sets which cover in total 113 unique code-switched language pairs across 52 languages: 1) a 14 X-English language pair set with real voices reading synthetically generated code-switched sentences, 2) a 16 X-English language pair set with generative text-to-speech 3) a 60 {Arabic, Mandarin, Hindi, Spanish}-X language pair set with the generative text-to-speech, and 4) a 45 X-English lower-resourced language pair test set with concatenative text-to-speech. Besides the four test sets, CS-FLEURS also provides a training set with 128 hours of generative text-to-speech data across 16 X-English language pairs. Our hope is that CS-FLEURS helps to broaden the scope of future code-switched speech research. Dataset link: https://huggingface.co/datasets/byan/cs-fleurs.
@article{arxiv.2509.14161,
title = {CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset},
author = {Brian Yan and Injy Hamed and Shuichiro Shimizu and Vasista Lodagala and William Chen and Olga Iakovenko and Bashar Talafha and Amir Hussein and Alexander Polok and Kalvin Chang and Dominik Klement and Sara Althubaiti and Puyuan Peng and Matthew Wiesner and Thamar Solorio and Ahmed Ali and Sanjeev Khudanpur and Shinji Watanabe and Chih-Chen Chen and Zhen Wu and Karim Benharrak and Anuj Diwan and Samuele Cornell and Eunjung Yeo and Kwanghee Choi and Carlos Carvalho and Karen Rosero},
journal= {arXiv preprint arXiv:2509.14161},
year = {2025}
}