Xvector in Kaldi nnet3

本文详细介绍了Xvectornnet在Kaldi语音识别框架中的训练过程及其实现细节,包括StatisticsExtractionLayer和StatisticsPoolingLayer的具体应用,以及如何通过特定的计算请求构造来优化Xvector的计算流程。

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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 nnet3bin/nnet3-xvector-compute.cc

44 output_spec.indexes.resize(1);

Rather than

kaldi::nnet3::DecodableNnetSimple::DoNnetComputation at nnet3/nnet-am-decodable-simple.cc

244 output_spec.indexes.resize(num_subsampled_frames);

   

Compile ComputationRequest, get NnetComputation

std::shared_ptr<const NnetComputation> computation = compiler_.Compile(request);

From output to input, build dependency once a layer

BuildGraphOneIter();

For each Cindex,add dependency

AddDependencies(cindex_id);

For Statistics*Component

component->GetInputIndexe(...);

Organize Data and Computation as a group of Cindexes, called step.

Optimize Computation

For each step Run NnetComputer:

kPropagate: component->Propagate(...)

kBackprop: component->Backprop(...)

Get output from NnetComputer:

computer.GetOutputDestructive("output", &cu_output);

转载于:https://www.cnblogs.com/JarvanWang/p/10183576.html

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