The scarcity of large-scale, open-source data for dialects severely hinders progress in speech technology, a challenge particularly acute for the widely spoken Sichuanese dialects of Chinese. To address this critical gap, we introduce WenetSpeech-Chuan, a 10,000-hour, richly annotated corpus constructed using our novel Chuan-Pipeline, a complete data processing framework for dialectal speech. To facilitate rigorous evaluation and demonstrate the corpus's effectiveness, we also release high-quality ASR and TTS benchmarks, WenetSpeech-Chuan-Eval, with manually verified transcriptions. Experiments show that models trained on WenetSpeech-Chuan achieve state-of-the-art performance among open-source systems and demonstrate results comparable to commercial services. As the largest open-source corpus for Sichuanese dialects, WenetSpeech-Chuan not only lowers the barrier to research in dialectal speech processing but also plays a crucial role in promoting AI equity and mitigating bias in speech technologies. The corpus, benchmarks, models, and receipts are publicly available on our project page.
@article{arxiv.2509.18004,
title = {WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing},
author = {Yuhang Dai and Ziyu Zhang and Shuai Wang and Longhao Li and Zhao Guo and Tianlun Zuo and Shuiyuan Wang and Hongfei Xue and Chengyou Wang and Qing Wang and Xin Xu and Hui Bu and Jie Li and Jian Kang and Binbin Zhang and Lei Xie},
journal= {arXiv preprint arXiv:2509.18004},
year = {2025}
}