A 10000+ hours dataset for Chinese speech recognition

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Deep LearningWenetSpeech
Overview

WenetSpeech

Official website | Paper

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 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 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.
Owner
Production First and Production Ready End-to-End Speech Toolkit
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