---
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
│
├── audio_labels/
│ ├── wav_utt_id.jsonl
│ ├── wav_utt_id.jsonl
│ ├── ...
│ └── wav_utt_id.jsonl
│
├── .gitattributes
└── README.md
```
#### Data sample（CN）：
###### metadata.jsonl
{
"utt_id": 原始长音频id,
"wav_utt_id": 转化为wav后的长音频id,
"source_audio_path": 原始长音频路径,
"audio_labels": 转化后的长音频切分出的短音频标签文件路径,
"url": 原始长音频下载链接
}
###### audio_labels/wav_utt_id.jsonl：
{
"wav_utt_id_timestamp": 以 转化为wav后的长音频id_时间戳信息 作为切分后的短音频id (type: str),
"wav_utt_id_timestamp_path": 短音频数据路径 (type: str),
"audio_clip_id": 该段短音频在长音频中的切分顺序编号,
"timestamp": 时间戳信息，
"wvmos_score": wvmos分数，衡量音频片段质量 (type: float),
"text": 对应时间戳的音频片段的抄本 (type: str),
"text_punc": 带标点的抄本 (type: str),
"spk_num": 音频片段说话人个数，single/multi (type: str)
"confidence": 抄本置信度 (type: float),
"emotion": 说话人情感标签 (type: str，eg: 愤怒),
"age": 说话人年龄标签 (type: int范围, eg: 中年（36~59）),
"gender": 说话人性别标签 (type: str，eg: 男/女),
}
#### Data sample（EN）：
###### metadata.jsonl
{
"utt_id": Original long audio ID,
"wav_utt_id": Converted long audio ID after transforming to WAV format,
"source_audio_path": Path to the original long audio file,
"audio_labels": Path to the label file of short audio segments cut from the converted long audio,
"url": Download link for the original long audio
}
###### audio_labels/wav_utt_id.jsonl：
{
"wav_utt_id_timestamp": Short audio segment ID, composed of the converted long audio ID + timestamp information (type: str),
"wav_utt_id_timestamp_path": Path to the short audio data (type: str),
"audio_clip_id": Sequence number of this short segment within the long audio,
"timestamp": Timestamp information,
"wvmos_score": WVMOS score, measuring the quality of the audio segment (type: float),
"text": Transcript of the audio segment corresponding to the timestamp (type: str),
"text_punc": Transcript with punctuation (type: str),
"spk_num": Number of speakers in the audio segment, single/multi (type: str),
"confidence": Confidence score of the transcript (type: float),
"emotion": Speaker’s emotion label (type: str, e.g., anger),
"age": Speaker’s age label (type: int range, e.g., middle-aged (36–59)),
"gender": Speaker’s gender label (type: str, e.g., male/female)
}
### 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
