├── LICENSE.txt ├── README.md └── docs ├── .nojekyll ├── _coverpage.md ├── _media ├── _coverpage.md ├── bg.png ├── favicon.ico ├── icon.png └── icon.svg ├── _navbar.md ├── _sidebar.md ├── docsify.js ├── en ├── error.md └── img │ └── p45.png ├── index.html └── zh-cn ├── 01_LiHongyi_ASR.md ├── 02_GMM-HMM.md ├── 03_Tandem.md ├── 04_DNN-HMM-Hybrid.md ├── 05_LAS.md ├── 06_CTC.md ├── 07_RNA.md ├── 08_RNN-T.md ├── 09_NN-T.md ├── 10_MoCHA.md ├── 11_Conformer.md ├── 12_Transfomer-Transducer.md ├── 13_DeepSpeech.md ├── 14_FSMN.md ├── 15_TDNN.md ├── 16_Quartznet.md ├── 17_Jasper.md ├── 18_Citrinet.md ├── 19_ContextNet.md ├── 20_Conformer-CTC.md ├── 20_WeNet_paper.md ├── 21_WFST.md ├── 22_Hybrid_CTC_Attention.md ├── 23_datasets.md ├── 24_kaldi_1.md ├── 25_kaldi_2.md ├── 26_wenet.md ├── 27_espnet.md ├── 28_nemo.md ├── 29_Speech-Transformer.md ├── 30_sqeezeformer.md ├── 31_paraformer.md ├── 32_whisper.md ├── about.md ├── img ├── ch1 │ ├── class1 │ │ ├── p1.png │ │ ├── p10.gif │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p14.png │ │ ├── p15.png │ │ ├── p16.png │ │ ├── p17.png │ │ ├── p18.png │ │ ├── p19.png │ │ ├── p2.png │ │ ├── p20.png │ │ ├── p21.png │ │ ├── p22.png │ │ ├── p23.png │ │ ├── p24.png │ │ ├── p25.png │ │ ├── p26.png │ │ ├── p27.png │ │ ├── p28.png │ │ ├── p29.png │ │ ├── p3.png │ │ ├── p30.png │ │ ├── p31.png │ │ ├── p32.png │ │ ├── p33.png │ │ ├── p34.png │ │ ├── p35.png │ │ ├── p36.png │ │ ├── p37.png │ │ ├── p38.png │ │ ├── p4-1.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png │ ├── class2 │ │ ├── p1.png │ │ ├── p10.png │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p14.png │ │ ├── p15.png │ │ ├── p16.png │ │ ├── p17.png │ │ ├── p18.png │ │ ├── p19.png │ │ ├── p2.png │ │ ├── p20.png │ │ ├── p3.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png │ ├── class3 │ │ ├── p1.png │ │ ├── p10.png │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p14.png │ │ ├── p15.png │ │ ├── p16.png │ │ ├── p17.png │ │ ├── p18.png │ │ ├── p19.png │ │ ├── p2.png │ │ ├── p20.png │ │ ├── p21.png │ │ ├── p22.png │ │ ├── p3.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png │ ├── class4 │ │ ├── p1.png │ │ ├── p10.png │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p2.png │ │ ├── p3.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png │ ├── class5 │ │ ├── p1.png │ │ ├── p10.png │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p14.png │ │ ├── p15.png │ │ ├── p16.png │ │ ├── p17.png │ │ ├── p18.png │ │ ├── p2.png │ │ ├── p3.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png │ ├── class6 │ │ ├── p1.png │ │ ├── p10.png │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p14.png │ │ ├── p15.png │ │ ├── p16.png │ │ ├── p17.png │ │ ├── p18.png │ │ ├── p19.png │ │ ├── p2.png │ │ ├── p20.png │ │ ├── p21.png │ │ ├── p22.png │ │ ├── p23.png │ │ ├── p24.png │ │ ├── p25.png │ │ ├── p26.png │ │ ├── p27.png │ │ ├── p28.png │ │ ├── p3.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png │ └── class7 │ │ ├── p1.png │ │ ├── p10.png │ │ ├── p11.png │ │ ├── p12.png │ │ ├── p13.png │ │ ├── p14.png │ │ ├── p15.png │ │ ├── p16.png │ │ ├── p17.png │ │ ├── p2.png │ │ ├── p3.png │ │ ├── p4.png │ │ ├── p5.png │ │ ├── p6.png │ │ ├── p7.png │ │ ├── p8.png │ │ └── p9.png ├── ch10 │ ├── p1.png │ ├── p2.gif │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ └── p7.png ├── ch11 │ ├── p1.jpg │ ├── p10.jpg │ ├── p11.jpg │ ├── p12.jpg │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.jpg │ ├── p3.jpg │ ├── p4.jpg │ ├── p5.jpg │ ├── p6.jpg │ ├── p7.jpg │ ├── p8.jpg │ └── p9.jpg ├── ch12 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch13 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch14 │ ├── p1.jpg │ ├── p10.jpg │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.jpg │ ├── p20.png │ ├── p21.png │ ├── p3.gif │ ├── p3.jpg │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch15 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch16 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p2.jpg │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch17 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.jpg │ ├── p20.png │ ├── p21.png │ ├── p22.png │ ├── p23.png │ ├── p24.png │ ├── p25.png │ ├── p26.png │ ├── p27.png │ ├── p28.png │ ├── p29.png │ ├── p3.png │ ├── p30.png │ ├── p31.png │ ├── p32.png │ ├── p33.png │ ├── p34.png │ ├── p35.png │ ├── p36.png │ ├── p37.png │ ├── p38.png │ ├── p39.png │ ├── p4.png │ ├── p40.png │ ├── p41.png │ ├── p42.png │ ├── p43.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch18 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.png │ ├── p20.png │ ├── p21.png │ ├── p22.png │ ├── p23.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch19 │ ├── p1.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.jpg │ ├── p6.jpg │ ├── p7.jpg │ └── p8.jpg ├── ch2 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.png │ ├── p20.png │ ├── p21.png │ ├── p22.png │ ├── p23.png │ ├── p24.png │ ├── p25.png │ ├── p26.png │ ├── p27.png │ ├── p28.png │ ├── p29.png │ ├── p3.png │ ├── p30.png │ ├── p31.png │ ├── p32.png │ ├── p33.png │ ├── p34.png │ ├── p35.png │ ├── p36.png │ ├── p37.png │ ├── p38.png │ ├── p39.png │ ├── p4.png │ ├── p40.png │ ├── p41.png │ ├── p42.png │ ├── p43.png │ ├── p44.png │ ├── p45.png │ ├── p46.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ ├── p9.png │ └── 隐马尔可夫模型(1).pdf ├── ch20 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch21 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch22 │ ├── p1.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch23 │ ├── P11.png │ ├── p1.png │ ├── p10.png │ ├── p12.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch24 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.png │ ├── p20.png │ ├── p21.png │ ├── p22.png │ ├── p23.png │ ├── p24.png │ ├── p25.png │ ├── p26.png │ ├── p27.png │ ├── p28.png │ ├── p29.png │ ├── p3.png │ ├── p30.png │ ├── p31.png │ ├── p32.png │ ├── p33.png │ ├── p34.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch25 │ ├── NeMo语音识别模型训练.pdf │ └── p1.png ├── ch26 │ ├── p1.png │ ├── p10.jpg │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.gif │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.mp4 ├── ch27 │ └── EspNet实践.pdf ├── ch28 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.png │ ├── p20.png │ ├── p21.png │ ├── p22.png │ ├── p23.jpg │ ├── p24.jpg │ ├── p25.jpg │ ├── p26.jpg │ ├── p27.jpg │ ├── p28.jpg │ ├── p29.jpg │ ├── p3.png │ ├── p30.jpg │ ├── p31.jpg │ ├── p32.jpg │ ├── p33.png │ ├── p34.png │ ├── p35.png │ ├── p36.png │ ├── p37.png │ ├── p38.png │ ├── p39.png │ ├── p4.jpg │ ├── p40.png │ ├── p41.png │ ├── p42.png │ ├── p43.png │ ├── p44.png │ ├── p45.png │ ├── p46.png │ ├── p47.png │ ├── p48.jpg │ ├── p49.png │ ├── p5.png │ ├── p50.png │ ├── p51.png │ ├── p52.jpg │ ├── p53.jpg │ ├── p54.png │ ├── p55.png │ ├── p56.png │ ├── p57.png │ ├── p58.png │ ├── p59.png │ ├── p6.png │ ├── p60.png │ ├── p61.png │ ├── p62.png │ ├── p63.png │ ├── p64.png │ ├── p65.png │ ├── p66.png │ ├── p67.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch3 │ ├── p1.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ └── p5.png ├── ch31 │ ├── funasr_gradio1.png │ ├── funasr_gradio2.png │ ├── offline_structure.jpg │ ├── online_structure.png │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p16.png │ ├── p17.png │ ├── p18.png │ ├── p19.png │ ├── p2.png │ ├── p20.png │ ├── p21.png │ ├── p22.png │ ├── p23.png │ ├── p24.png │ ├── p25.png │ ├── p26.png │ ├── p27.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch32 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p14.png │ ├── p15.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch4 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p2.jpg │ ├── p2_0.jpg │ ├── p3.jpg │ ├── p3_0.jpg │ ├── p4.jpg │ ├── p4_0.jpg │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch5 │ ├── p1.png │ ├── p10.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch6 │ ├── p1.gif │ ├── p10.png │ ├── p11.png │ ├── p12.jpg │ ├── p13.jpg │ ├── p14.jpg │ ├── p15.jpg │ ├── p16.jpg │ ├── p17.png │ ├── p18.jpg │ ├── p19.png │ ├── p2.png │ ├── p20.png │ ├── p21.jpg │ ├── p22.jpg │ ├── p23.png │ ├── p24.jpg │ ├── p25.png │ ├── p26.jpg │ ├── p27.jpg │ ├── p28.jpg │ ├── p29.png │ ├── p3.png │ ├── p30.jpg │ ├── p31.png │ ├── p32.png │ ├── p33.png │ ├── p34.png │ ├── p35.png │ ├── p36.png │ ├── p37.png │ ├── p38.png │ ├── p39.png │ ├── p4.png │ ├── p40.png │ ├── p41.png │ ├── p42.png │ ├── p43.png │ ├── p44.png │ ├── p45.png │ ├── p46.png │ ├── p47.png │ ├── p48.png │ ├── p49.png │ ├── p5.png │ ├── p50.png │ ├── p51.png │ ├── p52.png │ ├── p53.png │ ├── p54.png │ ├── p55.png │ ├── p56.png │ ├── p57.png │ ├── p58.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png ├── ch7 │ ├── p1.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ └── p7.png ├── ch8 │ ├── p1.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ └── p6.png └── ch9 │ ├── p1.png │ ├── p10.png │ ├── p11.png │ ├── p12.png │ ├── p13.png │ ├── p2.png │ ├── p3.png │ ├── p4.png │ ├── p5.png │ ├── p6.png │ ├── p7.png │ ├── p8.png │ └── p9.png └── index.md /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Freeware License, some rights reserved 2 | 3 | F**K License 4 | 5 | Xu Jing 2022/10/11 6 | 7 | **************************************************************************************************************************** 8 | * * 9 | * 禁止用于商业培训，作为公司内部培训材料，请声明参考来源于：https://github.com/DataXujing/ASR-paper * 10 | * * 11 | **************************************************************************************************************************** 12 | 13 | + 可以随意转载，学习，但是不能用于商业培训赚钱 14 | + 引用请注明来源：https://github.com/DataXujing/ASR-paper 15 | + 除了商业培训，该文档没有任何使用限制 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | logo 2 | 3 | # Welcome to ASR learning 4 | 5 | ## GitHub最全ASR教程 6 | 7 | **徐静** 8 | 9 | 2017年开始关注语音识别(ASR)，中间放弃了一段时间，这两年由于Transformer的流行，其端到端的ASR和基于工业应用的开源框架 10 | 层出不穷，让我们开始学习吧！ 11 | 12 | 15 | 16 | 17 | ### 目录 18 | 19 | - [x] 1.李宏毅ASR课程笔记 20 | - [x] 2.GMM-HMM 21 | - [x] 3.Tandem 22 | - [x] 4.DNN-HMM Hybrid 23 | - [x] 5.LAS 24 | - [x] 6.CTC 25 | - [x] 7.RNA 26 | - [x] 8.RNN-Transducer(RNN-T) 27 | - [x] 9.Neural Transducer 28 | - [x] 10.MoChA] 29 | - [x] 11.Conformer 30 | - [x] 12.Transfomer-Transducer 31 | - [x] 13.DeepSpeech V1,V2,V3 32 | - [x] 14.FSMN 33 | - [x] 15.TDNN 34 | - [x] 16.Quartznet 35 | - [x] 17.Jasper 36 | - [x] 18.Citrinet 37 | - [x] 19.ContextNet 38 | - [x] 20.WeNet_paper 39 | - [x] 21.WFST 40 | - [x] 22.Hybrid CTC/Attention 41 | - [x] 29.Speech-Transformer 42 | - [x] 30.squeezeformer 43 | - [x] 23.开源语音识别数据集介绍 44 | - [x] 24.Kaldi学习 45 | - ~~25.K2学习~~ 46 | - [x] 26.WeNet学习 47 | - [x] 27.ESPNet学习 48 | - [x] 28.NeMo学习 49 | - Triton inference server部署ASR服务(暂时不开源,可提供有偿服务) 50 | - TensorRT ASR模型部署(暂时不开源，可提供有偿服务) 51 | 52 | - [x] 31.Paraformer and FunASR 53 | - [x] 32.Whisper 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | -------------------------------------------------------------------------------- /docs/.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataXujing/ASR-paper/7fcdcb4bd3ea7e0b3836abe799b9905b14fdaadc/docs/.nojekyll -------------------------------------------------------------------------------- /docs/_coverpage.md: -------------------------------------------------------------------------------- 1 | ![logo](_media/icon.svg) 2 | 3 | # 语音识别(ASR) 4 | 5 | 6 | > 语音识别教程 7 | 8 | 23 | 24 | 25 | [GitHub](https://github.com/DataXujing/ASR-paper) 26 | [Get Started](/zh-cn/index) -------------------------------------------------------------------------------- /docs/_media/_coverpage.md: -------------------------------------------------------------------------------- 1 | # docsify 2 | 3 | [GitHub](https://github.com/docsifyjs/docsify/) 4 | [Get Started](#quick-start) 5 | 6 | 7 | 8 | ![](_media/bg.png) 9 | 10 | 11 | 12 | ![color](#f0f0f0) -------------------------------------------------------------------------------- /docs/_media/bg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataXujing/ASR-paper/7fcdcb4bd3ea7e0b3836abe799b9905b14fdaadc/docs/_media/bg.png -------------------------------------------------------------------------------- /docs/_media/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataXujing/ASR-paper/7fcdcb4bd3ea7e0b3836abe799b9905b14fdaadc/docs/_media/favicon.ico 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[5.LAS](zh-cn/05_LAS.md) 8 | * [6.CTC](zh-cn/06_CTC.md) 9 | * [7.RNA](zh-cn/07_RNA.md) 10 | * [8.RNN-Transducer(RNN-T)](zh-cn/08_RNN-T.md) 11 | * [9.Neural Transducer](zh-cn/09_NN-T.md) 12 | * [10.MoChA](zh-cn/10_MoCHA.md) 13 | * [11.Conformer](zh-cn/11_Conformer.md) 14 | * [12.Transfomer-Transducer](zh-cn/12_Transfomer-Transducer.md) 15 | * [13.DeepSpeech V1,V2,V3](zh-cn/13_DeepSpeech.md) 16 | * [14.FSMN](zh-cn/14_FSMN.md) 17 | * [15.TDNN](zh-cn/15_TDNN.md) 18 | * [16.Quartznet](zh-cn/16_Quartznet.md) 19 | * [17.Jasper](zh-cn/17_Jasper.md) 20 | * [18.Citrinet](zh-cn/18_Citrinet.md) 21 | * [19.ContextNet](zh-cn/19_ContextNet.md) 22 | * [20.WeNet Paper](zh-cn/20_WeNet_paper.md) 23 | * [21.WFST](zh-cn/21_WFST.md) 24 | 25 | * [22.Hybrid CTC/Attention](zh-cn/22_Hybrid_CTC_Attention.md) 26 | * [23.Speech-Transformer](zh-cn/29_Speech-Transformer.md) 27 | * [30.Squeezeformer](zh-cn/30_sqeezeformer.md) 28 | * [24.开源语音识别数据集介绍](zh-cn/23_datasets.md) 29 | * [25.Kaldi学习](zh-cn/24_kaldi_1.md) 30 | * [26.K2学习](zh-cn/25_kaldi_2.md) 31 | * [27.WeNet学习](zh-cn/26_wenet.md) 32 | * [28.ESPNet学习](zh-cn/27_espnet.md) 33 | * [29.NeMo学习](zh-cn/28_nemo.md) 34 | * [30.Paraformer](zh-cn/31_paraformer.md) 35 | * [31.Whisper](zh-cn/32_whisper.md) 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /docs/en/error.md: -------------------------------------------------------------------------------- 1 | # 暂时不支持英文版 2 | 3 |
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-------------------------------------------------------------------------------- /docs/en/img/p45.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataXujing/ASR-paper/7fcdcb4bd3ea7e0b3836abe799b9905b14fdaadc/docs/en/img/p45.png -------------------------------------------------------------------------------- /docs/zh-cn/05_LAS.md: -------------------------------------------------------------------------------- 1 | 2 | ## LAS: Listen, Attend and Spell 3 | 4 | 5 | 6 | 7 | 8 | 9 | ### 1.简介 10 | 11 | Listen,Attend and Spell(LAS)是一种学习将语音转换成字符的神经网络。与传统的DNN-HMM模型不同的是，LAS模型联合学习（jointly）语音识别器的所有组件。LAS系统有两个组件:一个监听器(Listener)和一个拼写器(Speller)。Listener是一个接受滤波器组谱作为输入的金字塔递归网络（pBiLSTM）编码器;speller是一种基于注意力的周期性网络译码器，它将字符作为输出来发射,该网络生成字符序列时不做任何字符间的独立性假设。这是LAS较以往端到端CTC模型的关键改进。在谷歌语音搜索任务的一个子集上，LAS在没有字典或语言模型的情况下实现了14.1%的单词错误率(WER)，而在有语言模型重评分的情况下，实现了10.3%的单词错误率（WER）。 12 | 13 | ### 2.网络结构 14 | 15 |
16 | 17 |
18 | 19 | LAS在标签序列中不做独立假设，也不依赖于HMMs。LAS是基于注意力的Seq2Seq学习框架,它由编码器循环神经网络(RNN)和解码器循环神经网络(RNN)组成。Listener是一个金字塔形的RNN，它将低级的语音信号转换为高级的特征信号。speller是一个RNN，它通过注意机制指定字符序列上的概率分布，将这些高级特征转换成输出话语，Listener和Speller联合训练。 20 | 21 | 22 | ### 3.网络输入 23 | 24 | fBank滤波器组光谱特征(filter bank spectra features)的输入序列：$x = (x_1,……,x_T )$ 25 | 26 | 27 | ### 4.网络输出 28 | 29 | 字符序列：$y = (y_1,……,y_s,)$ 30 | 31 | ### 5.目标函数 32 | 33 |
34 | 35 |
36 | 37 | ### 6.模型结构 38 | 39 | **Listen** 40 | 41 | 采用3层512个nodes的金字塔形BiLSTM(pBiLSTM)网络结构： 42 | 43 |
44 | 45 |
46 | 47 | Listen结构的好处： 48 | 49 | 1）时间分辨率（time resolution）减少8倍 50 | 51 | 2）更方便注意力机制提取信息 52 | 53 | 3）深层网络提供更好的非线性特征 54 | 55 | 56 | **Attend & Spell** 57 | 58 | 采用基于注意力（attention-based）的LSTM Transducer。在每个输出步骤中，Transducer根据前面看到的所有字符生成下一个字符的概率分布。$y_i$的分布是解码器状态$s_i$和上下文$c_i$的函数。解码器状态$s_i$是先前的状态$s_{i-1}$，先前发出的字符$y{i-1}$和上下文$c_{i-1}$的函数。语境向量$c_i$是由注意机制产生的: 59 | 60 |
61 | 62 |
63 | 64 | 其中CharacterDistribution是一个softmax的MLP，RNN是一个两层的LSTM，$c_i$封装了用以生成下一个字符的声学信号信息，AttentionContext生成上下文向量$c$。 65 | 66 | 67 | ### 7.Training 68 | 69 | 无需pre-train,seq2seq模型基于前一个字符来预测下一个字符，并最大化log概率： 70 | 71 |
72 | 73 |
74 | 75 | 76 | ### 8.Decoding & Rescoring 77 | 78 | 在推理过程中，通过最大化log概率找出输入声学中最有可能的字符序列： 79 | 80 |
81 | 82 |
83 | 84 | 结合语言模型LM来消除短对话的偏差（bias）,其中语言模型的权重设置为$0.008$: 85 | 86 |
87 | 88 |
89 | 90 | 91 | ### 9.性能对比 92 | 93 |
94 | 95 |
96 | 97 | 那时的性能还不如CD-DNN-HMM! 98 | 99 | **注意力可视化** 100 | 101 |
102 | 103 |
104 | 105 | **Beam Search Width的选择** 106 | 107 |
108 | 109 |
110 | 111 | 112 | ### 基于普通话的模型训练和测试 113 | 114 | 我们基于开源项目实现了LAS的训练，验证和离线推断，请参考： 115 | -------------------------------------------------------------------------------- /docs/zh-cn/07_RNA.md: -------------------------------------------------------------------------------- 1 | 2 | ## RNA（Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model forSequence to Sequence Mapping） 3 | 4 | 5 | ### 1.RNA解决的问题 6 | 7 | CTC的其中的一个缺点是条件独立性即该模型假设每个输出在给定输入的情况下都与其他输出有条件独立，对于许多序列到序列问题，这是一个糟糕的假设。RNA解决了这个问题，RNA网络就是将CTC中Encoder后的多个分类器（Decoder）换成了一个RNN网络，使网络能够参考序列上下文信息。 8 | 9 | 10 | ### 2.RNA的模型结构 11 | 12 |
13 | 14 |
15 | 16 | + CTC的Decoder是输入一个vector输出一个token 17 | + RNA添加了依赖，通过RNN使得当前output依赖于之前的output和output的隐层单元 18 | 19 | 20 | ### 3.RNA的损失函数 21 | 22 | RNA的Alignment和CTC是相同的，如下图所示： 23 | 24 |
25 | 26 |
27 | 28 | + 上图展示了所有可能的Alignments 29 | + 横轴表示input的特征 30 | + 纵轴表示RNA解码的label 31 | + 每一个点(t，n)表示RNA解码的状态 32 | + 对角线的箭头表示预测为下一个label的概率，横向箭头表示预测为blank的概率 33 | + RNA走过上所有的合法Alignments,input是声学特征，output是一个one-hot编码的label 34 | 35 | 其余CTC本质的不同如下： 36 | 37 |
38 | 39 |
40 | 41 | 这里的$z=(z_1,z_2,...,z_T)$表示一个长度为$T$的合法路径，去掉blank和重复后变为$y$.$P(y|x)$表示所有的和标注$y$相关的alignments $z$在给定声学特征$x$下的条件概率和。这个也是给定声学特征$x$预测label为$y$的条件分布。这是典型的CTC的做法。但是RNA的表示如下： 42 | 43 |
44 | 45 |
46 | 47 | 对比CTC的表示： 48 | 49 |
50 | 51 |
52 | 53 | 你会发现其中的不同！ 54 | 55 | 56 | 最后RNA的损失是由两部分构成的一部分是`log-likelihood loss`,一部分是`Expected loss` 57 | 58 | $$L1 = \sum_{(x,y)} - log(P(y|x))$$ 59 | 60 | 61 | $L1$表示对数似然损失，用来优化RNA模型生成目标label的概率。然而，当我们将模型应用于任务时我们通常使用序列级损失或度量来度量模型的性能，例如用于语音识别的词错误率（WER）。理想情况下，我们希望模型为具有较小序列级损失的标记序列分配更高的概率。其它研究表明经过似然损失训练的RNA模型也存在暴露偏差。在训练时间中通过对所有可能的Alignments求和来计算目标标签序列的概率，然而，RNA解码器网络总是以来自目标序列的真实标记为条件。因此，当用于Beam Search的推断时，模型只暴露于正确的标签，并且可能无法学会对其标签预测中的错误具有鲁棒性。另一个缺点是本事对数似然中的概率计算我们也是使用近似的方法得到的而非真正的穷举了所有的Alignments。 62 | 63 | 可以使用一个序列级别的loss来解决这些问题。 64 | 65 |
66 | 67 |
68 | 69 | 这里的$P(z|x)$表示一个aligenment $z$在给定声学特征条件下的RNA的预测概率，$loss(x,z,y)$表示序列级别的loss的估计，这里的序列级别的loss可以使用目标序列$y$与去掉blank的alignment $z$的编辑距离(Edit Distance)。 70 | 71 | ### 4.RNA的性能分析 72 | 73 | 74 |
75 | 76 |
77 | 78 | 个人感觉RNA和CTC的性能对比差不多，没有什么太大优势！ 79 | 80 | !> 问题：目前我们可以实现多个input的特征对应1个label，有时候我们因为特征切分的原因会出现一个特征对应多个label的情况，CTC个RNA仍未解决该问题，RNN-T将解决。 -------------------------------------------------------------------------------- /docs/zh-cn/20_Conformer-CTC.md: -------------------------------------------------------------------------------- 1 | ## Conformer-CTC -------------------------------------------------------------------------------- /docs/zh-cn/25_kaldi_2.md: -------------------------------------------------------------------------------- 1 | ## Kaldi v2 2 | 3 | Kaldi之父Daniel Povey离开了翰霍普金斯大学（Jonhs Hopkins University）加入小米后组建了团队进行了下一代Kaldi的研发工作[https://zhuanlan.zhihu.com/p/87356515](https://zhuanlan.zhihu.com/p/87356515),关于K2我们不做介绍，感兴趣的朋友可以参考： 4 | [https://k2-fsa.github.io/k2/](https://k2-fsa.github.io/k2/) -------------------------------------------------------------------------------- /docs/zh-cn/27_espnet.md: -------------------------------------------------------------------------------- 1 | ## ESPNet 2 | 3 | 4 | !> https://github.com/espnet/espnet 5 | 6 | 在端到端的语音识别领域，有一套类似于Kaldi的工具包对研究的推动也是很明显的。2018年，Espnet团队开源可Espnet(End-to-end Speech Processing Toolkit)工具箱，该工具箱融合了Kaldi的数据处理和特征提取，同时借助Pytorch和Chainer，使用Python实现了CTC,Attention,RNN-T和Transformer,Conformer等端到端的模型，Espnet2支持流式模型和推断，我们参考了厦门大学洪青阳老师的《语音识别原理与应用》一书，内有关于Espnet1的环境搭建，模型训练，解码和解码可视化的介绍。Espnet2的用法基本和Espnet1保持一致。这里我们放出其提供的Espnet使用教程的slide,共大家参考。 7 | 8 | 9 | 10 | 11 |

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12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /docs/zh-cn/about.md: -------------------------------------------------------------------------------- 1 | 2 | ## 关于我们 3 | 4 | + 个人主页:https://dataxujing.github.io/ 5 | 6 | ------ 7 | 8 | **个人介绍：** 9 | 10 | !> **徐静** （[dataxujing](https://github.com/DataXujing)） AI图像算法研发工程师，硕士研究生，数据科学爱好者，喜欢R, Python, C++ 关注机器学习，深度学习等AI领域； 喜欢网络爬虫，关注前端可视化; 对ASR，NLP，CV均有涉猎； 11 | 目前从事医疗影像AI算法的研究和落地工作。个人还是个小白，希望多交流！ 12 | 13 | ------ 14 | 15 | **更新日志** 16 | 17 | | 日期 | 更新内容 | 18 | |:--------------------:|:-------------------------:| 19 | |2022-10-11 | 项目创建 | 20 | |2022-10-18 | Lee class 1-2 | 21 | |2022-10-19 | Lee class 3-4 | 22 | |2022-10-20 | Lee class 5 | 23 | |2022-10-21 | Lee class 6 | 24 | |2022-10-24 | Lee class 7 | 25 | |2022-10-25 | GMM-HMM | 26 | |2022-10-26 | Tandem | 27 | |2023-02-16 | DNN-HMM | 28 | |2023-02-17 | LAS | 29 | |2023-02-22 | CTC | 30 | |2023-03-01 | RNA | 31 | |2023-03-01 | RNN-T | 32 | |2023-03-06 | Neural Transducer | 33 | |2023-03-07 | MoCha | 34 | |2023-03-08 | Conformer | 35 | |2023-03-09 | Transformer-Transducer | 36 | |2023-03-09 | QuartzNet | 37 | |2023-03-10 | Jasper | 38 | |2023-03-11 | Citrinet | 39 | |2023-03-12 | ContextNet | 40 | |2023-03-27 | WFST | 41 | |2023-03-28 | Speech-Transformer | 42 | |2023-03-29 | Squeezeformer | 43 | |2023-03-30 | 开源语音识别数据集介绍 | 44 | |2023-03-31 | Hybrid CTC/Attention | 45 | |2023-04-04 | WeNet1.0,2.0 | 46 | |2023-04-04 | TDNN | 47 | |2023-04-04 | FSMN | 48 | |2023-04-05 | DeepSpeech v1,v2 | 49 | |2023-04-06 | DeepSpeech v3 | 50 | |2023-04-10 | NeMo | 51 | |2023-04-14 | WeNet | 52 | |2023-04-14 | Espnet | 53 | |2023-05-11 | Kaldi | 54 | |2023-05-25 | ASR更新完成，告一段落 | 55 | |2024-02-xx | Paraformer | 56 | |2024-06-26 | Whisper | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 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