A 10000+ hours dataset for Chinese speech recognition

Related tags

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
GitHub Repository
Efficient Sparse Attacks on Videos using Reinforcement Learning

EARL This repository provides a simple implementation of the work "Efficient Sparse Attacks on Videos using Reinforcement Learning" Example: Demo: Her

12 Dec 05, 2021
Official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION.

IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSUMPTION This is the official repository of IMPROVING DEEP IMAGE MATTING VIA LOCAL SMOOTHNESS ASSU

电线杆 14 Dec 15, 2022
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation Where we are ? 12.27 目前和原论文仍有1%左右得差距，但已经力压很多SOTA了 ckpt__448_epoch_25.pth mIoU

zichengsaber 60 Dec 11, 2022
Implementation of Basic Machine Learning Algorithms on small datasets using Scikit Learn.

Basic Machine Learning Algorithms All the basic Machine Learning Algorithms are implemented in Python using libraries Acknowledgements Machine Learnin

Piyal Banik 47 Oct 16, 2022
Minimalist Error collection Service compatible with Rollbar clients. Sentry or Rollbar alternative.

Minimalist Error collection Service Features Compatible with any Rollbar client(see https://docs.rollbar.com/docs). Just change the endpoint URL to yo

Haukur Rósinkranz 381 Nov 11, 2022
Best Practices on Recommendation Systems

Recommenders What's New (February 4, 2021) We have a new relase Recommenders 2021.2! It comes with lots of bug fixes, optimizations and 3 new algorith

Microsoft 14.8k Jan 03, 2023
CoMoGAN: continuous model-guided image-to-image translation. CVPR 2021 oral.

CoMoGAN: Continuous Model-guided Image-to-Image Translation Official repository. Paper CoMoGAN: continuous model-guided image-to-image translation [ar

166 Dec 31, 2022
To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features

Ashish Kumar 4 Jun 11, 2022
Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

Adaptive Methods for Aggregated Domain Generalization (AdaClust) Official Pytorch Implementation of Adaptive Methods for Aggregated Domain Generalizat

Xavier Thomas 15 Sep 20, 2022
Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

NL-CSNet-Pytorch Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021. Note: this repo only shows the strategy of

WenxueCui 7 Nov 07, 2022
ncnn is a high-performance neural network inference framework optimized for the mobile platform

ncnn ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployme

Tencent 16.2k Jan 05, 2023
This repository contains the code for the ICCV 2019 paper "Occupancy Flow - 4D Reconstruction by Learning Particle Dynamics"

Occupancy Flow This repository contains the code for the project Occupancy Flow - 4D Reconstruction by Learning Particle Dynamics. You can find detail

189 Dec 29, 2022
Official PyTorch implementation of the paper "Deep Constrained Least Squares for Blind Image Super-Resolution", CVPR 2022.

Deep Constrained Least Squares for Blind Image Super-Resolution [Paper] This is the official implementation of 'Deep Constrained Least Squares for Bli

MEGVII Research 141 Dec 30, 2022
ParaGen is a PyTorch deep learning framework for parallel sequence generation

ParaGen is a PyTorch deep learning framework for parallel sequence generation. Apart from sequence generation, ParaGen also enhances various NLP tasks, including sequence-level classification, extrac

Bytedance Inc. 169 Dec 22, 2022
This is the repository for The Machine Learning Workshops, published by AI DOJO

This is the repository for The Machine Learning Workshops, published by AI DOJO. It contains all the workshop's code with supporting project files necessary to work through the code.

AI Dojo 12 May 06, 2022
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

Hong-Jia Chen 91 Dec 02, 2022
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

Benedek Rozemberczki 251 Nov 30, 2022
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms This repo contains the source code to reproduce the results in the paper A Close

Costa Huang 73 Dec 24, 2022
Data for "Driving the Herd: Search Engines as Content Influencers" paper

herding_data Data for "Driving the Herd: Search Engines as Content Influencers" paper Dataset description The collection contains 2250 documents, 30 i

0 Aug 17, 2021
Code for the paper: Sketch Your Own GAN

Sketch Your Own GAN Project | Paper | Youtube Our method takes in one or a few hand-drawn sketches and customizes an off-the-shelf GAN to match the in

677 Dec 28, 2022