A 10000+ hours dataset for Chinese speech recognition

Related tags

Text Data & NLPWenetSpeech
Overview

WenetSpeech

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

WenetSpeech

Download

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

Benchmark

Toolkit Model test_net test_meeting
Kaldi Chain Model
ESPnet Joint CTC/Conformer
WeNet Joint CTC/Conformer

Description

Creation

First, we collect all the data from YouTube and Podcast; Then, OCR is used to label YouTube data, auto trancrition is used to label Podcast data; Finally, a novel end-to-end label error detection method is used 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

All of the data is from Youtube and Podcast, and we tag all the data with its source and domain. We classify the data into 10 groups according to its domain,speaking style, or 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

We provide 3 training subsets, namely S, M and L. Subsets S, M are sampled from all the high label data which has the oracle confidence 1.0

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 far-field, conversational, and spontaneous meeting speech

Contributors

ACKNOWLEDGEMENTS

  1. WenetSpeech referred a lot of work of GigaSpeech, including metadata design, license design, data encryption, downloading pipeline, and so on. The authors would like to thank Jiayu Du and Guoguo Chen for their suggestions on this work.
  2. The authors would like to thank my college Lianhui Zhang, Yu Mao for collecting some of the YouTube data.
Owner
Production First and Production Ready End-to-End Speech Toolkit
GitHub Repository
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