makinglong/wenetspeech

A 10000+ hours dataset for Chinese speech recognition

WenetSpeech

Official website | Paper

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

Download

Please visit the official website, read the license, and follow the instruction to download the data.

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

ACKNOWLEDGEMENTS

• WenetSpeech refers a lot of work of 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 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.