构建1万小时四川话语音语料库，助力方言语音技术研究
WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
- 基于自研管道框架构建大规模四川话语音数据集
- 训练模型性能达开源系统顶尖水平，接近商用服务
- 适合方言语音、AI公平性研究者使用
方言大规模开源数据匮乏严重制约语音技术发展，尤其在汉语四川话这类广泛使用的方言上。为解决这一关键问题，我们提出 WenetSpeech-Chuan，一个10,000小时、标注丰富的四川话语音语料库，基于自研的 Chuan-Pipeline 框架构建。为支持严谨评估，我们还发布了高质量的语音识别（ASR）与语音合成（TTS）基准测试集 WenetSpeech-Chuan-Eval，包含人工校验的转写文本。实验表明，基于该语料库训练的模型在开源系统中达到领先性能，且表现可媲美商业服务。作为目前最大的开源四川话语音语料库，WenetSpeech-Chuan 不仅降低了方言语音研究门槛，也对推动语音技术公平性、减少偏见具有重要意义。语料库、基准、模型及凭证均已公开发布于项目主页。
原文摘要 · Abstract (English)
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.
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