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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A 10000+ hours dataset for Chinese speech recognition
| Date | Stars |
|---|---|
| 2026-07-31 | 624 |
| 2026-08-02 | 624 |
| 2026-08-06 | 624 |
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## 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
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Binbin Zhang · WeNet Community
37
LvHang
5
4
Xingchen Song(宋星辰) · Tsinghua University (2019-2022), WeNet Community (2021-now) · China
2
彭震东 · WeNet · China
2
Dinghao Zhou
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:60df9aebd397f564, desc:speech recognition