WSChuan-Train

<h1 align="center">
WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus With Rich Annotation For Dialectal Speech Processing
</h1>

<p align="center">
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^,╀
</p>
<p align="center">
¹ Audio, Speech and Language Processing Group (ASLP@NPU), Northwestern Polytechnical University <br>
² Beijing AISHELL Technology Co., Ltd. <br>
³ Institute of Artificial Intelligence (TeleAI), China Telecom <br>
⁴ School of Intelligence Science and Technology, Nanjing University <br>
⁵ WeNet Open Source Community <br>
</p>

<p align="center">
📑 <a href="https://arxiv.org/abs/2509.18004">Paper</a> &nbsp&nbsp | &nbsp&nbsp
🐙 <a href="https://github.com/ASLP-lab/WenetSpeech-Chuan">GitHub</a> &nbsp&nbsp | &nbsp&nbsp
🤗 <a href="https://huggingface.co/collections/ASLP-lab/wenetspeech-chuan-68bade9d02bcb1faece65bda">HuggingFace</a>
<br>
🎤 <a href="https://aslp-lab.github.io/WenetSpeech-Chuan/">Demo Page</a> &nbsp&nbsp | &nbsp&nbsp
💬 <a href="https://github.com/ASLP-lab/WenetSpeech-Chuan?tab=readme-ov-file#contact">Contact Us</a>
</p>

<div align="center">
<img width="800px" src="https://github.com/ASLP-lab/WenetSpeech-Chuan/blob/main/src/logo/WenetSpeech-Chuan-Logo.png?raw=true" />
</div>

## 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.</li>
* 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.</li>
* Covers ten domains: Short videos, Entertainment, Live streams, Documentary, Audiobook, Drama, Interview, News and others.</li>

<div align="center">
<img src="https://github.com/ASLP-lab/WenetSpeech-Chuan/blob/main/src/figs/domain.png?raw=true" width="300" style="display:inline-block; margin-right:10px;" />
<img src="https://github.com/ASLP-lab/WenetSpeech-Chuan/blob/main/src/figs/quality_distribution.jpg?raw=true" width="300" style="display:inline-block;" />
</div>

### 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
```
<!-- WenetSpeech-Chuan
├── metadata.jsonl
│
├── audio_labels/
│ ├── wav_utt_id.jsonl
│ ├── wav_utt_id.jsonl
│ ├── ...
│ └── wav_utt_id.jsonl
│
├── .gitattributes
└── README.md -->

#### Data sample：

###### metadata.jsonl

{<br>
"utt": 音频id, <br>
"filename"：音频文件名(type: str), <br>
"text": 转录抄本(type: str), <br>
"domain": 参考领域信息(type: list[str]), <br>
"gender": 说话人性别(type: str), <br>
"age": 说话人年龄标签 (type: int范围, eg: 中年（36~59）), <br>
"wvmos": 音频质量分数(type: float), <br>
"confidence": 转录文本置信度（0-1）(type: str), <br>
"emotion": 说话人情感标签 (type: str，eg: 愤怒), <br>
} <br>

**example:**

{ <br>
"utt": "013165495633_09mNC_9_5820", <br>
"filename": "013165495633_09mNC_9_5820.wav", <br>
"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", <br>
"domain": [ <br>
"短视频" <br>
], <br>
"gender": "Male", <br>
"age": "YOUTH", <br>
"wvmos": 2.124380588531494, <br>
"confidence": 0.8333, <br>
"emotion": angry, <br>
} <br>

<!-- ###### audio_labels/wav_utt_id.jsonl：
{ <br>
"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), <br>
"wav_utt_id_timestamp_path": 短音频数据路径 (type: str), <br>
"audio_clip_id": 该段短音频在长音频中的切分顺序编号, <br>
"timestamp": 时间戳信息， <br>
"wvmos_score": wvmos分数，衡量音频片段质量 (type: float), <br>
"text": 对应时间戳的音频片段的抄本 (type: str), <br>
"text_punc": 带标点的抄本 (type: str), <br>
"spk_num": 音频片段说话人个数，single/multi (type: str) <br>
"confidence": 抄本置信度 (type: float), <br>
"emotion": 说话人情感标签 (type: str，eg: 愤怒), <br>
"age": 说话人年龄标签 (type: int范围, eg: 中年（36~59）), <br>
"gender": 说话人性别标签 (type: str，eg: 男/女), <br>
} <br>
-->
<!-- #### Data sample（EN）：

###### metadata.jsonl
{ <br>
"utt_id": Original long audio ID, <br>
"wav_utt_id": Converted long audio ID after transforming to WAV format, <br>
"source_audio_path": Path to the original long audio file, <br>
"audio_labels": Path to the label file of short audio segments cut from the converted long audio, <br>
"url": Download link for the original long audio <br>
} <br>

###### audio_labels/wav_utt_id.jsonl：

{ <br>
"wav_utt_id_timestamp": Short audio segment ID, composed of the converted long audio ID + timestamp information (type: str), <br>
"wav_utt_id_timestamp_path": Path to the short audio data (type: str), <br>
"audio_clip_id": Sequence number of this short segment within the long audio, <br>
"timestamp": Timestamp information, <br>
"wvmos_score": WVMOS score, measuring the quality of the audio segment (type: float), <br>
"text": Transcript of the audio segment corresponding to the timestamp (type: str), <br>
"text_punc": Transcript with punctuation (type: str), <br>
"spk_num": Number of speakers in the audio segment, single/multi (type: str), <br>
"confidence": Confidence score of the transcript (type: float), <br>
"emotion": Speaker’s emotion label (type: str, e.g., anger), <br>
"age": Speaker’s age label (type: int range, e.g., middle-aged (36–59)), <br>
"gender": Speaker’s gender label (type: str, e.g., male/female) <br>
} <br>
-->
### 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: [e-mail] or [e-mail].

You’re also welcome to join our WeChat group for technical discussions, updates, and — as mentioned above — access to pre-processed audio data.

<p align="center">
<img src="https://github.com/ASLP-lab/WenetSpeech-Chuan/raw/main/src/figs/wechat.jpg" width="300" alt="WeChat Group QR Code"/>
<em>Scan to join our WeChat discussion group</em>
</p>

<p align="center">
<img src="https://github.com/ASLP-lab/WenetSpeech-Yue/raw/main/figs/[e-mail]" width="300" alt="Official Account QR Code"/>
</p>

<h1 align="center">
WenetSpeech-Chuan：面向方言语音处理的大规模带丰富标注川渝方言语料库
</h1>

<p align="center">
戴宇航¹^,*、张子瑜¹^,*、王帅⁴^,5、
李龙浩¹、郭昭¹、左天伦¹、
王水原¹、薛鸿飞¹、王成友¹、
王清³、徐鑫²、卜辉²、李杰³、
康健³、张彬彬⁵、谢磊¹^,╀
</p>
<p align="center">
¹ 西北工业大学音频、语音与语言处理组（ASLP@NPU）<br>
² 北京AISHELL科技有限公司<br>
³ 中国电信人工智能研究院（TeleAI）<br>
⁴ 南京大学智能科学与技术学院<br>
⁵ WeNet开源社区<br>
</p>

<p align="center">
📑 <a href="https://arxiv.org/abs/2509.18004">论文</a> &nbsp&nbsp | &nbsp&nbsp
🐙 <a href="https://github.com/ASLP-lab/WenetSpeech-Chuan">GitHub仓库</a> &nbsp&nbsp | &nbsp&nbsp
🤗 <a href="https://huggingface.co/collections/ASLP-lab/wenetspeech-chuan-68bade9d02bcb1faece65bda">HuggingFace集合页</a>
<br>
🎤 <a href="https://aslp-lab.github.io/WenetSpeech-Chuan/">演示页面</a> &nbsp&nbsp | &nbsp&nbsp
💬 <a href="https://github.com/ASLP-lab/WenetSpeech-Chuan?tab=readme-ov-file#contact">联系我们</a>
</p>

<div align="center">
<img width="800px" src="https://github.com/ASLP-lab/WenetSpeech-Chuan/blob/main/src/logo/WenetSpeech-Chuan-Logo.png?raw=true" />
</div>

## 数据集
### WenetSpeech-Chuan 总览

* 包含10000小时的大规模川渝方言语音语料库，附带丰富标注，是目前开源的规模最大的川渝方言语音研究资源。
* 元数据存储于单个JSON文件中，涵盖音频路径、时长、文本置信度、说话人身份、信噪比（SNR）、DNSMOS、年龄、性别以及字符级时间戳，未来或新增更多元数据标签。
* 涵盖十大领域：短视频、娱乐、直播、纪录片、有声书、戏剧、访谈、新闻及其他领域。

<div align="center">
<img src="https://github.com/ASLP-lab/WenetSpeech-Chuan/blob/main/src/figs/domain.png?raw=true" width="300" style="display:inline-block; margin-right:10px;" />
<img src="https://github.com/ASLP-lab/WenetSpeech-Chuan/blob/main/src/figs/quality_distribution.jpg?raw=true" width="300" style="display:inline-block;" />
</div>

### 元数据格式
我们将所有音频元数据以标准化JSON格式存储，核心字段包括`utt_id`（每条音频片段的唯一标识符）、`rover_result`（三种自动语音识别（ASR）转录结果的ROVER融合结果）、`confidence`（文本转录置信度评分）、`jyutping_confidence`（粤拼转录置信度评分）、`duration`（音频时长）；说话人属性包含`speaker_id`、`gender`（性别）与`age`（年龄）；音频质量评估指标包括`sample_rate`（采样率）、`DNSMOS`与`SNR`（信噪比）；时间戳信息包含`timestamp`（精准记录片段起止边界，包含`start`与`end`字段）；`meta_info`字段下的扩展元数据包括`program`（节目名称）、`region`（地域信息）、`link`（原始内容链接）以及`domain`（领域分类）。

#### 📂 目录结构

WenetSpeech-Chuan
├── metadata.jsonl
├── .gitattributes
└── README.md

#### 数据示例

###### metadata.jsonl 格式示例
json
{
"utt": "音频唯一标识符",
"filename": "音频文件名（字符串类型）",
"text": "语音转录文本（字符串类型）",
"domain": "参考领域信息（字符串列表类型）",
"gender": "说话人性别（字符串类型）",
"age": "说话人年龄标签（整数范围类型，例如：中年（36~59岁））",
"wvmos": "音频质量评分（浮点数类型）",
"confidence": "转录文本置信度（0~1区间，浮点数类型）",
"emotion": "说话人情感标签（字符串类型，例如：愤怒）"
}

**具体示例：**
json
{
"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": "男性",
"age": "青年",
"wvmos": 2.124380588531494,
"confidence": 0.8333,
"emotion": "愤怒"
}

### WenetSpeech-Chuan 使用方式
你可通过元数据文件（`metadata.jsonl`）中的`link`字段获取原始视频源，根据`timestamps`字段对音频进行分段以提取对应语音片段。如需获取预处理后的音频数据，请通过下方联系方式与我们联系。

## 联系我们
如有任何疑问或合作意向，可通过以下邮箱联系我们的研究团队：`[e-mail]` 或 `[e-mail]`。

欢迎加入我们的微信技术交流群，获取最新动态与技术讨论——如前文所述，该群也可用于申请获取预处理音频数据。

<p align="center">
<img src="https://github.com/ASLP-lab/WenetSpeech-Chuan/raw/main/src/figs/wechat.jpg" width="300" alt="微信讨论群二维码"/>
<em>扫码加入微信讨论群</em>
</p>

<p align="center">
<img src="https://github.com/ASLP-lab/WenetSpeech-Yue/raw/main/figs/[e-mail]" width="300" alt="官方公众号二维码"/>
</p>