从说话人识别demo开始学习kaldi--（6）

完整步骤在这里：
https://github.com/kaldi-asr/kaldi/blob/master/egs/aishell/v1/run.sh
下面是从训练对角矩阵开始的

sid/train_diag_ubm.sh --cmd "$train_cmd" --num-threads 16 data/dev 1024 exp/diag_ubm_1024

现在我们使用dev的数据来训练一个对角ubm
除了必要的参数设置，data/dev文件夹下面一定要有的是feats.scp，vad.scp
最后生成的是一个exp/diag_ubm_1024/final.dubm
下面是过程中出现的：

sid/train_diag_ubm.sh --cmd run.pl --num-threads 16 data/dev 1024 exp/diag_ubm_1024
sid/train_diag_ubm.sh: initializing model from E-M in memory, 
sid/train_diag_ubm.sh: starting from 512 Gaussians, reaching 1024;
sid/train_diag_ubm.sh: for 20 iterations, using at most 500000 frames of data
Getting Gaussian-selection info
sid/train_diag_ubm.sh: will train for 4 iterations, in parallel over
sid/train_diag_ubm.sh: 4 machines, parallelized with 'run.pl'
sid/train_diag_ubm.sh: Training pass 0
sid/train_diag_ubm.sh: Training pass 1
sid/train_diag_ubm.sh: Training pass 2
sid/train_diag_ubm.sh: Training pass 3

16个线程，JOB数是4，只需要迭代4次，最多使用50万帧等等，都是提前设置好的参数，可以改的。但是train_diag_ubm.sh 里面的逻辑还是没有看明白，还需要以后再看。

可以用下面命令查看生成的dubm文件
/data/kaldi/src/gmmbin/gmm-global-copy --binary=false final.dubm final_dubm.txt
（当前目录是final.dubm所在的文件夹哦）
vi 打开后可以看到gconst，weights，means_invvars都是什么，
GMM模型中这些参数都是什么，可以参考：
http://notes.funcwj.cn/2017/05/28/kaldi-gmm/

sid/train_full_ubm.sh --cmd "$train_cmd" data/dev exp/diag_ubm_1024 exp/full_ubm_1024

现在我们使用dev的数据来训练一个full-ubm，前面已经训练好了对角的ubm
除了必要的参数设置，data/dev文件夹下面一定要有的是feats.scp，vad.scp。
exp/diag_ubm_1024文件夹中一定要有final.ubm or final.dubm
最后生成的结果是exp/full_ubm_1024/final.ubm

可以用下面命令查看生成的ubm文件
/data/kaldi/src/fgmmbin/fgmm-global-copy --binary=false final.ubm final_ubm.txt
（当前目录是final.ubm所在的文件夹哦）
vi 打开后可以看到gconst，weights，means_invvars都是什么，
GMM模型中这些参数都是什么，可以参考：
http://notes.funcwj.cn/2017/05/28/kaldi-gmm/

sid/train_ivector_extractor.sh  --num-iters 5 exp/full_ubm_1024/final.ubm data/dev  exp/extractor_1024

这一步需要之前生成的ubm文件和feats.scp文件，生成的文件是exp/extractor_1024/final.ie，有的文章说这个final.ie就是T矩阵，不知道对不对

exp/extractor_1024文件夹里还有个5.ie，我感觉这个final.ie就是5.ie的软链接，它俩大小是一模一样的，过程中的显示如下：
在这里插入图片描述
4.

sid/extract_ivectors.sh exp/extractor_1024 data/dev exp/ivector_train_1024

输入主要是这三个文件：final.ie ，final.ubm ，feats.scp
生成的文件主要是：ivector.scp，num_utts.ark，spk_ivector.scp
还有log文件

ivector.scp是concat得来的，因为设置了nj=30，所以它是由30个小文件合并的
最后的ivector.scp是经过ivector-normalize-length生成的
num_utts.ark是经过ivector-mean生成的
spk_ivector.scp是经过ivector-normalize-length生成的

vi ivector.scp可以看到前几行是这样的，共有14329行

BAC009S0724W0121 exp/ivector_train_1024/ivector.1.ark:17
BAC009S0724W0122 exp/ivector_train_1024/ivector.1.ark:4092
BAC009S0724W0123 exp/ivector_train_1024/ivector.1.ark:8170
BAC009S0724W0124 exp/ivector_train_1024/ivector.1.ark:12227
BAC009S0724W0125 exp/ivector_train_1024/ivector.1.ark:16311

vi spk_ivector.scp可以看到前几行是这样的，共有40行，我们的dev数据中确实只有40个说话人

S0724 exp/ivector_train_1024/spk_ivector.ark:6
S0725 exp/ivector_train_1024/spk_ivector.ark:1622
S0726 exp/ivector_train_1024/spk_ivector.ark:3238
S0727 exp/ivector_train_1024/spk_ivector.ark:4854
S0728 exp/ivector_train_1024/spk_ivector.ark:6470
S0729 exp/ivector_train_1024/spk_ivector.ark:8086

我想把num_utts.ark和spk_ivector.ark都转换成txt文件看一看，但是下面的命令都出错了，
/data/kaldi/src/featbin/copy-feats ark:num_utts.ark ark,t:num_utts.txt

Failed to read matrix from stream.  : Expected "[", got "354" File position at start is 6, currently 9

/data/kaldi/src/featbin/copy-feats ark:spk_ivector.ark ark,t:spk_ivector.txt

 Failed to read matrix from stream.  : Expected token FM, got FV File position at start is 8, currently 11

看上去好像错误都一样，但是不知道怎么改。
经人指点：
mfcc是矩阵，用copy-feats没问题，spk_vector这些是向量，你要用copy-vector

原来还是用的工具不对。
用下面命令转换spk_ivector.ark成功
/data/kaldi/src/bin/copy-vector ark:spk_ivector.ark ark,t:spk_ivector.txt

vi spk_ivector.txt可以看到有40行，每行是一个说话人的i-vector向量

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但是每个说话人的向量不是一样长的，第一个spk是4249列，第二个是4244列，第三个是4260列。

下面命令转换num_utts.ark成功：
/data/kaldi/src/bin/copy-int-vector ark:num_utts.ark ark,t:num_utts.txt
打开txt文件查看，也有40行，前几行是这样的:

其实num_utts.ark是由dev/spk2utt得到的，估计就是统计了一下每个spk总计说了多少句话。经验证dev/S0724文件夹下面确实有354个wav文件。

#train plda
$train_cmd exp/ivector_train_1024/log/plda.log \
  ivector-compute-plda ark:data/dev/spk2utt \
  'ark:ivector-normalize-length scp:exp/ivector_train_1024/ivector.scp  ark:- |' \
  exp/ivector_train_1024/plda

单引号中的ivector.scp在上一步中是经过了ivector-normalize-length的呀，为什么要再来一遍呢？

这一步是训练plda model，主要输入是：dev/spk2utt，ivector.scp，生成的文件是exp/ivector_train_1024/plda，应该是就是plda model了。

这一步具体的逻辑下面博客好像说的很清楚，但我依然完全不懂，只知道PLDA的基本公式，也不知道哪个对应哪个：
https://blog.csdn.net/liusongxiang666/article/details/83024845
https://blog.csdn.net/zjm750617105/article/details/52832295

#split the test to enroll and eval
mkdir -p data/test/enroll data/test/eval
cp data/test/{spk2utt,feats.scp,vad.scp} data/test/enroll
cp data/test/{spk2utt,feats.scp,vad.scp} data/test/eval
local/split_data_enroll_eval.py data/test/utt2spk  data/test/enroll/utt2spk  data/test/eval/utt2spk
trials=data/test/aishell_speaker_ver.lst
local/produce_trials.py data/test/eval/utt2spk $trials
utils/fix_data_dir.sh data/test/enroll
utils/fix_data_dir.sh data/test/eval

对于split_data_enroll_eval.py，内部注释如下，也就是说没个spk只有3句话被用于注册，其它的都被用于eval

# This script splits the test set utt2spk into enroll set and eval set
# For each speaker, 3 utterances are randomly selected as enroll samples,
# and the others are used as eval samples for evaluation
# input: test utt2spk
# output: enroll utt2spk, eval utt2spk

对于produce_trials.py，内部注释如下，

# This script generate trials file.
# Trial file is formatted as:
# uttid spkid target|nontarget

# If uttid belong to spkid, it is marked 'target',
# otherwise is 'nontarget'.
# input: eval set uttspk file
# output: trial file

打开aishell_speaker_ver.lst可以看到，前几行如下，一共有14232行

BAC009S0764W0166 S0764 target
BAC009S0764W0166 S0765 nontarget
BAC009S0764W0166 S0766 nontarget
BAC009S0764W0166 S0767 nontarget

最后两个检查，结果如下:
在这里插入图片描述
7.

#extract enroll ivector
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 10 \
  exp/extractor_1024 data/test/enroll  exp/ivector_enroll_1024
#extract eval ivector
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 10 \
  exp/extractor_1024 data/test/eval  exp/ivector_eval_1024

这里跟第4步是一样的，输入和输出，可直接参考上面第4步。

对于i-vector的提取公式，M=m+T*w
w是我们要求的i-vector
有文章说，final.ie就是T矩阵，是由EM算法得到的
m在final.ubm中，是ubm的均值超矢量
M是由feats.scp和m得到的，

针对每一帧语音，用最大后验概率MAP去自适应当前句子的GMM模型，只更新均值，
然后形成M个分量的GMM，以每个GMM分量的均值矢量串接，就形成了该帧语音的高斯均值超矢量M

不知道对不对。

应该是先算帧级别（frame level）的i-vector，再聚合成句子级别（utterence level）的i-vector，再聚合成说话人级别（spk level）的i-vector。至于怎么聚合，可能是取平均？

中间显示如下：
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8.

#compute plda score
$train_cmd exp/ivector_eval_1024/log/plda_score.log \
  ivector-plda-scoring --num-utts=ark:exp/ivector_enroll_1024/num_utts.ark \
  exp/ivector_train_1024/plda \
  ark:exp/ivector_enroll_1024/spk_ivector.ark \
  "ark:ivector-normalize-length scp:exp/ivector_eval_1024/ivector.scp ark:- |" \
  "cat '$trials' | awk '{print \\\$2, \\\$1}' |" exp/trials_out

把exp/ivector_enroll_1024/num_utts.ark转成TXT，打开是这样的：
只有20行，test数据集也只有20个说话人，每人随机挑3句作为注册语音

打开trials_out，共有14232行，前几行是这个样子:

S0764 BAC009S0764W0166 13.97638
S0765 BAC009S0764W0166 -32.16938
S0766 BAC009S0764W0166 -39.67115
S0767 BAC009S0764W0166 -51.88986
S0768 BAC009S0764W0166 -82.01186

#compute eer
awk '{print $3}' exp/trials_out | paste - $trials | awk '{print $1, $4}' | compute-eer -

# Result
# Scoring against data/test/aishell_speaker_ver.lst
# Equal error rate is 0.140528%, at threshold -12.018

我的结果如下，1.447，它用的是train数据集，我用的是dev数据集
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至于eer是怎么计算的，可以参考下面：
https://blog.csdn.net/zjm750617105/article/details/52558779
https://blog.csdn.net/zjm750617105/article/details/60503253