nnet3-compute计算chain前向传播概率矩阵(声学模型输出)

这里是通过nnet3-compute将声学模型输出得到矩阵如125(下采样总帧数)x4064(pdf个数)对齐到每帧(这里输出的是矩阵如125帧数x4064pdf个数)

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假设已经训练好了chain模型，想计算每一帧的pdf-id的前向概率，nnet3-compute可以帮助我们做到

nnet3-compute在steps/nnet3/compute_output.sh脚本中有提到，还有nnet3-compute-batch呢

通过本文分析，nnet3-compute输出的就是$P(stat{e}_t|x_t)$,表示在第t帧音频$x_t$状态为$stat{e}_t$时的后验概率

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1 准备数据和特征

chain模型所需要的的特征是mfcc的hires特征(40维)，这部分见chain模型音素对齐

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2 输出chain前向概率(声学模型输出)

2.1 构建输出计算命令

feats="ark,s,cs:apply-cmvn --norm-means=false --norm-vars=false \
--utt2spk=ark:data/test_hires/split30/1/utt2spk scp:data/test_hires/split30/1/cmvn.scp \
scp:data/test_hires/split30/1/feats.scp ark:- |"
scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
#ivector_opts="--online-ivectors=scp:exp/nnet3/ivectors_test/ivector_online.scp \
#--online-ivector-period=10"
ivector_opts=""
# "--use-gpu=wait"
gpu_opt="--use-gpu=no" 
output_wspecifier="ark:| copy-feats --compress=false ark:- \
ark,scp:exp/chain/nnet3_ali/output.nnet3.1.ark,exp/chain/nnet3_ali/output.nnet3.1.scp"
model="exp/chain/tdnn_1a_sp/final.mdl"

nnet3-compute $gpu_opt $ivector_opts $gpu_opt \
--frame-subsampling-factor=3 --frames-per-chunk=50 \
--extra-left-context=0 --extra-right-context=0 \
--extra-left-context-initial=-1 --extra-right-context-final=-1 \
"$model" "$feats" "$output_wspecifier"

【注意】可以先将$output_wspecifier临时更改值为ark,t:-|head -n 5来查看部分结果是什么样的

output_wspecifier的这种【scp和ark组合方式】可以直接输出对应id的特征位置，如果嫌麻烦可以直接ark,t:demo.txt写到txt里

LOG (nnet3-compute[5.5]:SelectGpuId():cu-device.cc:153) Manually selected to compute on CPU.
copy-feats --compress=false ark:- ark,scp:exp/chain/nnet3_ali/output.nnet3.1.ark,exp/chain/nnet3_ali/output.nnet3.1.scp 
apply-cmvn --norm-means=false --norm-vars=false --utt2spk=ark:data/test_hires/split30/1/utt2spk scp:data/test_hires/split30/1/cmvn.scp scp:data/test_hires/split30/1/feats.scp ark:- 
LOG (nnet3-compute[5.5]:CheckAndFixConfigs():nnet-am-decodable-simple.cc:294) Increasing --frames-per-chunk from 50 to 51 to make it a multiple of --frame-subsampling-factor=3
LOG (apply-cmvn[5.5]:main():apply-cmvn.cc:81) Copied 41 utterances.
LOG (nnet3-compute[5.5]:main():nnet3-compute.cc:189) Time taken 2.01176s: real-time factor assuming 100 frames/sec is 0.0112182
LOG (nnet3-compute[5.5]:main():nnet3-compute.cc:192) Done 41 utterances, failed for 0
LOG (copy-feats[5.5]:main():copy-feats.cc:143) Copied 41 feature matrices.
LOG (nnet3-compute[5.5]:~CachingOptimizingCompiler():nnet-optimize.cc:710) 0.0513 seconds taken in nnet3 compilation total (breakdown: 0.0254 compilation, 0.0202 optimization, 0 shortcut expansion, 0.00393 checking, 5.13e-05 computing indexes, 0.00178 misc.) + 0 I/O.

output.nnet3.1.scp的内容

HAO0007501-000000 exp/chain/nnet3_ali/output.nnet3.1.ark:24
HAO0007501-000001 exp/chain/nnet3_ali/output.nnet3.1.ark:2032063
HAO0007501-000002 exp/chain/nnet3_ali/output.nnet3.1.ark:4275430

2.2 查看output输出内容

copy-matrix --binary=false exp/chain/nnet3_ali/output.nnet3.1.ark:24 demo.txt

其输出的维度是125(下采样总帧数)x4064(pdf个数)

之前在 chain模型音素对齐 提到
chain这个音频是125个输出值，373帧，因为chain的frame-shift=0.03,做了跳帧下采样处理，所以约等于3的倍数。
这里每一行是4064个浮点数，刚好是之前配置网络过程中的output节点的num_targets的个数，即pdf节点个数的概率
这里输出的没有归一化

num_targets=$(tree-info exp/chain/tri6_7d_tree_sp/tree |grep num-pdfs|awk '{print $2}')
output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5

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