View source for QSLocal-history


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!FreeNeb Release List!!                                              !!          !!          
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!   Date    ||      Version       || note    ||QR Code
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|    || README || 时实更新的README文档，使用前请先认真阅读 || [https://gitlab.com/freeneb/app-vpr-local/blob/master/doc/README] 
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|12.14||V4.2.1||更新log：在x-vector模型中，加入了nnet-vad，并把nnet模型压缩至int16。|| [[Image:VPR_V4.2.1.png|100px|VPR_V4.2.1]] 
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|12.05||V4.2.0||引擎更新到2.0.1 模型更新到2.1.1 支持x-vector || [[Image:VPR_V4.2.0.png|100px|VPR_V4.2.0]] 
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|11.20||V4.1.2||Network:  ReLU tdnn, splice (-2,-1,0,1,2 -2,2 0 -1,1 0 -2,2 0 -4,4 0 0) * 1000. Training: trained on VoxCeleb (1+2) for 3 epoches, then full-info adaptation with the same training set for 100 iters. Config:   full-info_fix_inter100_time1_momen0.9 num-gpu-initial=2 num-gpu-final=4 initial_effective_lrate=0.00015, final_effective_lrate=0.00015 LDA:      the same training set.  Add nnet-vad nnet_vad_threshold=-3|| [[Image:VPR_V4.1.2.png|100px|VPR_V4.1.2]] 
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|10.08||V4.1.1||Network:  ReLU tdnn, splice (-2,-1,0,1,2 -2,2 0 -1,1 0 -2,2 0 -4,4 0 0) * 1000. Training: trained on VoxCeleb (1+2) for 3 epoches, then full-info adaptation with the same training set for 100 iters. Config:   full-info_fix_inter100_time1_momen0.9 num-gpu-initial=2 num-gpu-final=4 initial_effective_lrate=0.00015, final_effective_lrate=0.00015 LDA:      the same training set.  Add nnet-vad nnet_vad_threshold=-3|| [[Image:VPR_V4.1.1.png|100px|VPR_V4.1.1]] 
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|9.30||V4.1.0||Network:  ReLU tdnn, splice (-2,-1,0,1,2 -2,2 0 -1,1 0 -2,2 0 -4,4 0 0) * 1000. Training: trained on VoxCeleb (1+2) for 3 epoches, then full-info adaptation with the same training set for 100 iters. Config:   full-info_fix_inter100_time1_momen0.9 num-gpu-initial=2 num-gpu-final=4 initial_effective_lrate=0.00015, final_effective_lrate=0.00015 LDA:      the same training set.  || [[Image:VPR_V4.1.0.png|100px|VPR_V4.1.0]] 
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|9.25||V3.11.0||Network:  ReLU tdnn, splice (-2,-1,0,1,2 -2,2 0 -1,1 0 -2,2 0 -4,4 0 0) * 1000. Training: trained on VoxCeleb (1+2) for 3 epoches, then full-info adaptation with the same training set for 100 iters. Config:   full-info_fix_inter100_time1_momen0.9 num-gpu-initial=2 num-gpu-final=4 initial_effective_lrate=0.00015, final_effective_lrate=0.00015 LDA:      the same training set.  || [[Image:VPR_V3.11.0.png|100px|VPR_V3.11.0]] 
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|8.5||V3.10.0||QSLocal 3.10 增加阿里训练数据、新vad模型  || [[Image:VPR_V3.10.0.png|100px|VPR_V3.10.0]] 
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|7.6||V3.8.5||使用VPR0.6版本引擎  || [[Image:VPR_V3.8.5.png|100px|VPR_V3.8.5]] 
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|6.29||V3.8.4||使用3.8模型+DNN Vad，VPR0.5版本引擎  || [[Image:VPR_V3.8.4.png|100px|VPR_V3.8.4]] 
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|6.21||V3.9.3||在3.9版模型基础上，加入了dnn-based vad  || [[Image:VPR_V3.9.3.png|100px|VPR_V3.9.3]] 
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|6.8||V3.8.3||在V3.8.2增强版 基础上，fix data buffer bug  || [[Image:VPR_V3.8.3.png|100px|VPR_V3.8.3]] 
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|6.5||V3.8.2增强版||在V3.8.2 基础上，去掉尾Null数据（梦原把这一feature移到了Android-dev），设置default abs threshold=17.0  || [[Image:VPR_V3.8.2.png|100px|VPR_V3.8.2]] 
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|6.5||V3.8.2||在V3.8.2 基础上，去掉语音前后的context padding || [[Image:VPR_V3.8.2.png|100px|VPR_V3.8.2]] 
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|6.4||V3.8.1||在V3.8 model基础上，加入SVM VAD || [[Image:VPR_V3.8.1.png|100px|VPR_V3.8.1]] 
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|6.4||V3.9.1||在v3.9基础上，去掉语音前后的context padding || [[Image:VPR_V3.9.1.png|100px|VPR_V3.9.1]] 
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|6.1||V3.9||修改模型 训练数据：基于ali-phase2数据 在7500人的full-info模型上 进一步full-info得到的模型|| [[Image:VPR_V3.9.png|100px|VPR_V3.9]] 
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|5.28||V3.8||修改模型 训练数据：speech-ocean_datatang_7500_mix_clean_reverb_volume_noise；模型：ReLU 预先训练 3 个 epoches，然后用 full-info 自适应 200 个 iterations.修改最后传输数据为空值的bug|| [[Image:VPR_V3.8.png|100px|VPR_V3.8]] 
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|5.17||V3.7||修复AudioRecord bug，将环形buffer改成线性。 || [[Image:VPR_V3.7.png|100px|VPR_V3.7]] 
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|5.7||V3.6||石颖大模型：reverbe+volume+noise+clean 总量为基本数据的3又三分之一倍 || [[Image:VPR_V3.6.png|100px|VPR_V3.6]] 
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|4.27||V3.5||小模型：仅添加noise 总量为基本数据的一半，人数不变 || [[Image:VPR_V3.5.png|100px|VPR_V3.5]] 
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|4.24 ||V3.4||修改bug || [[Image:VPR_V3.4.png|100px|VPR_V3.4]] 
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|4.16 ||V3.3||修改滚动样式 || [[Image:VPR_V3.4.png|100px|VPR_V3.3]] 
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|4.3||V3.0||依时间认证版本 || [[Image:VPR_V3.4.png|100px|VPR_V3.0]] 
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|3.31||v2.0||带反馈的认证版本 || [[Image:VPR_V2.0.png|100px|VPR_V2.0]] 
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|3.25||V1.0||盲认证版本|| [[Image:VPR_V1.0.png|100px|VPR_V1.0]] 
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