---
license: apache-2.0
---
WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus With Rich Annotation For Dialectal Speech Processing
Yuhang Dai1,*, Ziyu Zhang1,*, Shuai Wang4,5,
Longhao Li1, Zhao Guo1, Tianlun Zuo1,
Shuiyuan Wang1, Hongfei Xue1, Chengyou Wang1,
Qing Wang3, Xin Xu2, Hui Bu2, Jie Li3,
Jian Kang3, Binbin Zhang5, Lei Xie1,╀
1 Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University
2 Beijing AISHELL Technology Co., Ltd.
3 Institute of Artificial Intelligence (TeleAI), China Telecom
4 School of Intelligence Science and Technology, Nanjing University
5 WeNet Open Source Community
📑 Paper |
🐙 GitHub |
🤗 HuggingFace
🎤 Demo Page |
💬 Contact Us
## Dataset
### WenetSpeech-Chuan Overview
* Contains 10,000 hours of large-scale Chuan-Yu dialect speech corpus with rich annotations, the largest open-source resource for Chuan-Yu dialect speech research.
* Stores metadata in a single JSON file, including audio path, duration, text confidence, speaker identity, SNR, DNSMOS, age, gender, and character-level timestamps. Additional metadata tags may be added in the future.
* Covers ten domains: Short videos, Entertainment, Live streams, Documentary, Audiobook, Drama, Interview, News and others.
### Metadata Format
We store all audio metadata in a standardized JSON format, where the core fields include `utt_id` (unique identifier for each audio segment), `rover_result` (ROVER result of three ASR transcriptions), `confidence` (confidence score of text transcription), `jyutping_confidence` (confidence score of Cantonese pinyin transcriptions), and `duration` (audio duration); speaker attributes include `speaker_id`, `gender`, and `age`; audio quality assessment metrics include `sample_rate`, `DNSMOS`, and `SNR`; timestamp information includes `timestamp` (precisely recording segment boundaries with `start` and `end`); and extended metadata under the `meta_info` field includes `program` (program name), `region` (geographical information), `link` (original content link), and `domain` (domain classification).
#### 📂 Content Tree
```
WenetSpeech-Chuan
├── metadata.jsonl
├── .gitattributes
└── README.md
```
#### Data sample：
###### metadata.jsonl
{
"utt": 音频id,
"filename"：音频文件名(type: str),
"text": 转录抄本(type: str),
"domain": 参考领域信息(type: list[str]),
"gender": 说话人性别(type: str),
"age": 说话人年龄标签 (type: int范围, eg: 中年（36~59）),
"wvmos": 音频质量分数(type: float),
"confidence": 转录文本置信度（0-1）(type: str),
"emotion": 说话人情感标签 (type: str，eg: 愤怒),
}
**example:**
{
"utt": "013165495633_09mNC_9_5820",
"filename": "013165495633_09mNC_9_5820.wav",
"text": "还是选二手装好了的别墅诚心入如意的直接入住的好好",
"domain": [
"短视频"
],
"gender": "Male",
"age": "YOUTH",
"wvmos": 2.124380588531494,
"confidence": 0.8333,
"emotion": angry,
}
### WenetSpeech Usage
You can obtain the original video source through the `link` field in the metadata file (`metadata.json`). Segment the audio according to the `timestamps` field to extract the corresponding record. For pre-processed audio data, please contact us using the information provided below.
## Contact
If you have any questions or would like to collaborate, feel free to reach out to our research team via email: yhdai@mail.nwpu.edu.cn or ziyu_zhang@mail.nwpu.edu.cn.
You’re also welcome to join our WeChat group for technical discussions, updates, and — as mentioned above — access to pre-processed audio data.
Scan to join our WeChat discussion group
