Kaldi的nnet2 Component

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Kaldi的nnet2 Component

FixedAffineComponent:类 LDA-like 的非相关转换,由标准的 weight matrix plus bias 组成(即Wx+b),通过标准的 stochastic gradient descent(非minibatch SGD?) 训练而来,使用 global learning rate AffineComponentPreconditionedOnline:为 FixedAffineComponent 的一种提炼,训练过程中不仅使用global learning ra…

kaldi的TIMIT实例三

============================================================================ MMI + SGMM2 Training & Decoding ============================================================================ steps/align_sgmm2.sh --nj 30 --cmd run.pl --mem 4G --transform-d…

Dan版本的nnet2

除了chain,nnet1, nnet2, nnet3训练时调整转移模型,chain模型使用类似与MMI的训练准则 Dan's setup does not uses pre-training. Dan's setup uses a fixed number of epochs and averages the parameters over the last few epochs of training. 首先是脚本首先可以从脚本中获得神经网络训练的顶层概述. 在egs/rm/s5,egs/…

Xvector in Kaldi nnet3

Xvector nnet Training of Xvector nnet Xvector nnet in Kaldi Statistics Extraction Layer in Kaldi Statistics Pooling Layer in Kaldi Implementation in Kaldi Construct specific ComputationRequest for Xvector kaldi::nnet3::RunNnetComputation at nnet3…

Kaldi nnet3的fastlstm与标准LSTM

标准LSTM: 与标准LSTM相比,Kaldi的fastlstm对相同或类似的矩阵运算进行了合并. # Component specific to 'projected' LSTM (LSTMP), contains both recurrent and non-recurrent projections fastlstm.W_rp, fast lstm Weight recurrent projected fastlstm.W_all, fast lst…

[转]kaldi 神经网络

转自:http://blog.csdn.net/xmdxcsj/article/details/54695506 overview type author CPU/GPU feature nnet1 Karel GPU单卡训练使用pre-training,使用early stopping nnet2 Dan 支持多GPU训练,以及多CPU多线程使用固定epoch个数,最后几个epoch参数平均 nnet3 Dan 是nnet2的扩展支持更多的网络类型(比如RNN/LSTM) Karel’s…

Kaldi中的L2正则化

steps/nnet3/train_dnn.py --l2-regularize-factor 影响模型参数的l2正则化强度的因子.要进行l2正则化,主要方法是在配置文件中使用'l2-regularize'进行配置.l2正则化因子将乘以组件中的l2正则化值,并且可用于通过模型平均化以校正与并行化带来的影响. (float,默认值= 1) src/nnet3/nnet-utils.cc:2030 void ApplyL2Regularization(const Nnet &nnet, BaseFl…