WenetSpeech

## WenetSpeech

[**Official website**](https://wenet-e2e.github.io/WenetSpeech/)
| [**Paper**](https://arxiv.org/pdf/2110.03370.pdf)

A 10000+ Hours Multi-domain Chinese Corpus for Speech Recognition


## News

* 2026.01.09 - We find there are minor label errors in `TEST_NET` test set, and you can mannually fix it, see https://github.com/wenet-e2e/WenetSpeech/issues/63 for details.

## Download

Please visit the [official website](https://wenet-e2e.github.io/WenetSpeech/),
read the license, and follow the instruction to apply for the `PASSWORD` to download the data.

``` bash
echo 'PASSWORD' > SAFEBOX/password
```

### From Tecent Meeting (default)

Download WenetSpeech:

``` bash
bash utils/download_wenetspeech.sh DOWNLOAD_DIR UNTAR_DIR
```

### From ModelScope

Install `modelscope` (depends on `torch`) before downloading:

``` bash
conda create -n modelscope python=3.7
conda activate modelscope
pip install torch
pip install modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
```

Download [WenetSpeech](https://modelscope.cn/datasets/wenet/WenetSpeech) from modelscope:

``` bash
sed -i 's/modelscope=false/modelscope=true/g' utils/download_wenetspeech.sh
bash utils/download_wenetspeech.sh DOWNLOAD_DIR UNTAR_DIR
```

## Discussion & Communication

Please scan the QR code on the left to follow our offical account of WeNet.
We created a WeChat group for better discussion and quicker response.
Please scan the personal QR code on the right, and the guy is responsible for inviting you to the chat group.

| <img src="https://github.com/robin1001/qr/blob/master/wenet.jpeg" width="250px"> | <img src="https://github.com/wenet-e2e/wenet-contributors/blob/main/wenetspeech/lvhang.jpg" width="250px"> |
| ---- | ---- |

## Benchmark

| Toolkit | Dev | Test\_Net | Test\_Meeting | AIShell-1 |
|---------|------|:---------:|:-------------:|:---------:|
| Kaldi | 9.07 | 12.83 | 24.72 | 5.41 |
| ESPNet | 9.70 | 8.90 | 15.90 | 3.90 |
| WeNet | 8.88 | 9.70 | 15.59 | 4.61 |

## Description

### Creation

All the data are collected from YouTube and Podcast. Optical character recognition (OCR) and automatic speech recognition (ASR) techniques are adopted to label each YouTube and Podcast recording, respectively. To improve the quality of the corpus, we use a novel end-to-end label error detection method to further validate and filter the data.

### Categories

In summary, WenetSpeech groups all data into 3 categories, as the following table shows:

| Set | Hours | Confidence | Usage |
|------------|-------|-------------|---------------------------------------|
| High Label | 10005 | >=0.95 | Supervised Training |
| Weak Label | 2478 | [0.6, 0.95] | Semi-supervised or noise training |
| Unlabel | 9952 | / | Unsupervised training or Pre-training |
| In Total | 22435 | / | All above |

### High Label Data

We classify the high label into 10 groups according to its domain, speaking style, and scenarios.

| Domain | Youtube | Podcast | Total |
|-------------|---------|---------|--------|
| audiobook | 0 | 250.9 | 250.9 |
| commentary | 112.6 | 135.7 | 248.3 |
| documentary | 386.7 | 90.5 | 477.2 |
| drama | 4338.2 | 0 | 4338.2 |
| interview | 324.2 | 614 | 938.2 |
| news | 0 | 868 | 868 |
| reading | 0 | 1110.2 | 1110.2 |
| talk | 204 | 90.7 | 294.7 |
| variety | 603.3 | 224.5 | 827.8 |
| others | 144 | 507.5 | 651.5 |
| Total | 6113 | 3892 | 10005 |

As shown in the following table, we provide 3 training subsets, namely `S`, `M` and `L` for building ASR systems on different data scales.

| Training Subsets | Confidence | Hours |
|------------------|-------------|-------|
| L | [0.95, 1.0] | 10005 |
| M | 1.0 | 1000 |
| S | 1.0 | 100 |

### Evaluation Sets

| Evaluation Sets | Hours | Source | Description |
|-----------------|-------|--------------|-----------------------------------------------------------------------------------------|
| DEV | 20 | Internet | Specially designed for some speech tools which require cross-validation set in training |
| TEST\_NET | 23 | Internet | Match test |
| TEST\_MEETING | 15 | Real meeting | Mismatch test which is a far-field, conversational, spontaneous, and meeting dataset |

## Contributors

| <a href="http://lxie.npu-aslp.org" target="_blank"><img src="https://raw.githubusercontent.com/wenet-e2e/wenet-contributors/main/colleges/nwpu.png" width="250px"></a> | <a href="https://www.chumenwenwen.com" target="_blank"><img src="https://raw.githubusercontent.com/wenet-e2e/wenet-contributors/main/companies/chumenwenwen.png" width="250px"></a> | <a href="http://www.aishelltech.com" target="_blank"><img src="https://raw.githubusercontent.com/wenet-e2e/wenet-contributors/main/companies/aishelltech.png" width="250px"></a> |
| ---- | ---- | ---- |

|<a href="" target="_blank"><img src="https://raw.githubusercontent.com/wenet-e2e/WenetSpeech/gh-pages/assets/img/tencent.png" width="250px"></a> | <a href="" target="_blank"><img src="https://raw.githubusercontent.com/wenet-e2e/WenetSpeech/gh-pages/assets/img/MindSpore.png" width="250px"></a> | <a href="" target="_blank"><img src="https://raw.githubusercontent.com/wenet-e2e/WenetSpeech/gh-pages/assets/img/xian.png" width="250px"></a> |
| ---- | ---- | ---- |

## ACKNOWLEDGEMENTS

* WenetSpeech refers a lot of work of [GigaSpeech](https://github.com/SpeechColab/GigaSpeech), and we thank Jiayu Du and Guoguo Chen for their suggestions on this work.
* We thank Tencent Ethereal Audio Lab and Xi'an Future AI Innovation Center for providing hosting service for WenetSpeech. We also thank [MindSpore](https://www.mindspore.cn/) for the support of this work, which is a new deep learning computing framework.
* Our gratitude goes to Lianhui Zhang and Yu Mao for collecting some of the YouTube data.

本数据集的文件元数据与数据文件可通过访问「数据集文件」页面获取。当前本数据集卡片采用默认模板，数据集贡献者未提供更详细的介绍，您可通过下述GIT Clone命令或ModelScope SDK下载该数据集。

### 子集
| 集合类型 | 时长（小时） | 置信度区间 | 用途 |
|----------------|--------------|--------------|----------------------------------------|
| 弱标注（Weak Label，W） | 2478 | [0.6, 0.95] | 半监督学习或含噪声训练 |

### 训练子集
| 训练子集 | 置信度区间 | 时长（小时） |
|----------|--------------|--------------|
| L | [0.95, 1.0] | 10005 |
| M | 1.0 | 1000 |
| S | 1.0 | 100 |

### 评估集
| 评估集名称 | 时长（小时） | 来源 | 描述 |
|----------------|--------------|--------------------|------------------------------------------------------------------------------------------|
| DEV | 20 | 互联网 | 专为训练中需要交叉验证集的语音工具设计 |
| TEST_NET | 23 | 互联网 | 匹配域测试集 |
| TEST_MEETING | 15 | 真实会议（Real meeting） | 非匹配域测试集，为远场、会话式、自发语音会议数据集 |

#### 下载方式
:modelscope-code[]{type="sdk"}
:modelscope-code[]{type="git"}