Kaldi：从零搭建语音识别系统

参考博客

DNN-HMM 语音识别系统搭建

基于Kaldi平台搭建DNN-HMM语音识别系统，这里针对汉语普通话建立语音识别系统，并在后期对识别率进行了分析。搭建一个完整的DNN-HMM系统首先需要准备训练数据，包括音频数据、声学数据和语言数据，这些数据需要按照一定的格式准备，完成之后使用训练脚本训练出一个基于三音素的GMM-HMM模型，然后进行强制对齐，之后使用DNN训练脚本进行DNN模型的训练。

1.创建一个工程目录

liuyuanyuan@liuyuanyuan-Lenovo-C4005:~/kaldi/egs$ mkdir -p speech/s5
liuyuanyuan@liuyuanyuan-Lenovo-C4005:~/kaldi/egs$ cd speech/s5

2.从官方wsj示例链接工具脚本集utils和训练脚本集steps

ln -s ../../wsj/s5/utils/ utils

ln -s ../../wsj/s5/steps/ steps

3.复制环境变量

cp ../../wsj/s5/path.sh ./

4.数据准备

创建data/train, data/test, (data/dev可选)，每个文件里面必须包含text, wav.scp, utt2spk, spk2utt
utils/utt2spk_to_spk2utt.pl data/test/utt2spk >data/test/spk2utt
utils/utt2spk_to_spk2utt.pl data/train/utt2spk >data/train/spk2utt
数据准备完毕

sh Create_ngram_LM.sh
sh DNN_train.sh
结果

liuyuanyuan@liuyuanyuan-Lenovo-C4005:~/kaldi/egs/asr1$ sh DNN_train.sh

============================================================================
         MFCC Feature Extration & CMVN for Training               
============================================================================
fix_data_dir.sh: kept all 558 utterances.
fix_data_dir.sh: old files are kept in data/train/.backup
steps/make_mfcc.sh --cmd run.pl --nj 10 data/train exp_FG/make_mfcc/train mfcc
steps/make_mfcc.sh: moving data/train/feats.scp to data/train/.backup
utils/validate_data_dir.sh: Successfully validated data-directory data/train
steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
Succeeded creating MFCC features for train
steps/compute_cmvn_stats.sh data/train exp_FG/make_mfcc/train mfcc
Succeeded creating CMVN stats for train
utils/validate_data_dir.sh: Successfully validated data-directory data/train
fix_data_dir.sh: kept all 162 utterances.
fix_data_dir.sh: old files are kept in data/test/.backup
steps/make_mfcc.sh --cmd run.pl --nj 10 data/test exp_FG/make_mfcc/test mfcc
steps/make_mfcc.sh: moving data/test/feats.scp to data/test/.backup
utils/validate_data_dir.sh: Successfully validated data-directory data/test
steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
Succeeded creating MFCC features for test
steps/compute_cmvn_stats.sh data/test exp_FG/make_mfcc/test mfcc
Succeeded creating CMVN stats for test
utils/validate_data_dir.sh: Successfully validated data-directory data/test
============================================================================
                   MonoPhone Training                	        
============================================================================
steps/train_mono.sh --nj 18 --cmd run.pl data/train data/lang_bigram exp_FG/mono
steps/train_mono.sh: Initializing monophone system.
steps/train_mono.sh: Compiling training graphs
steps/train_mono.sh: Aligning data equally (pass 0)
steps/train_mono.sh: Pass 1
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 2
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 3
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 4
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 5
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 6
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 7
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 8
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 9
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 10
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 11
steps/train_mono.sh: Pass 12
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 13
steps/train_mono.sh: Pass 14
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 15
steps/train_mono.sh: Pass 16
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 17
steps/train_mono.sh: Pass 18
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 19
steps/train_mono.sh: Pass 20
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 21
steps/train_mono.sh: Pass 22
steps/train_mono.sh: Pass 23
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 24
steps/train_mono.sh: Pass 25
steps/train_mono.sh: Pass 26
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 27
steps/train_mono.sh: Pass 28
steps/train_mono.sh: Pass 29
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 30
steps/train_mono.sh: Pass 31
steps/train_mono.sh: Pass 32
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 33
steps/train_mono.sh: Pass 34
steps/train_mono.sh: Pass 35
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 36
steps/train_mono.sh: Pass 37
steps/train_mono.sh: Pass 38
steps/train_mono.sh: Aligning data
steps/train_mono.sh: Pass 39
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang_bigram exp_FG/mono
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 15.0537634409% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.8279569892% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_alignments.sh: see stats in exp_FG/mono/log/analyze_alignments.log
2 warnings in exp_FG/mono/log/analyze_alignments.log
467 warnings in exp_FG/mono/log/align.*.*.log
exp_FG/mono: nj=18 align prob=-87.17 over 0.43h [retry=0.9%, fail=0.0%] states=159 gauss=1001
steps/train_mono.sh: Done training monophone system in exp_FG/mono
============================================================================
                   MonoPhone Testing             	        
============================================================================
WARNING: the --mono, --left-biphone and --quinphone options are now deprecated and ignored.
tree-info exp_FG/mono/tree 
tree-info exp_FG/mono/tree 
make-h-transducer --disambig-syms-out=exp_FG/mono/graph/disambig_tid.int --transition-scale=1.0 data/lang_bigram/tmp/ilabels_1_0 exp_FG/mono/tree exp_FG/mono/final.mdl 
fstrmsymbols exp_FG/mono/graph/disambig_tid.int 
fstrmepslocal 
fstdeterminizestar --use-log=true 
fstminimizeencoded 
fsttablecompose exp_FG/mono/graph/Ha.fst data/lang_bigram/tmp/CLG_1_0.fst 
fstisstochastic exp_FG/mono/graph/HCLGa.fst 
0.000440476 -0.000462013
add-self-loops --self-loop-scale=0.1 --reorder=true exp_FG/mono/final.mdl 
steps/decode.sh --nj 18 --cmd run.pl exp_FG/mono/graph data/test exp_FG/mono/decode
decode.sh: feature type is delta
steps/diagnostic/analyze_lats.sh --cmd run.pl exp_FG/mono/graph exp_FG/mono/decode
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.1111111111% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 8.64197530864% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_lats.sh: see stats in exp_FG/mono/decode/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,1,3) and mean=1.6
steps/diagnostic/analyze_lats.sh: see stats in exp_FG/mono/decode/log/analyze_lattice_depth_stats.log
============================================================================
                      Tri-phone Training                    
============================================================================
steps/align_si.sh --boost-silence 1.25 --nj 18 --cmd run.pl data/train data/lang_bigram exp_FG/mono exp_FG/mono_ali
steps/align_si.sh: feature type is delta
steps/align_si.sh: aligning data in data/train using model from exp_FG/mono, putting alignments in exp_FG/mono_ali
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang_bigram exp_FG/mono_ali
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 15.0537634409% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.8279569892% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_alignments.sh: see stats in exp_FG/mono_ali/log/analyze_alignments.log
steps/align_si.sh: done aligning data.
=========================
 Sen = 2000  Gauss = 16000
=========================
steps/train_deltas.sh --cmd run.pl 2000 16000 data/train data/lang_bigram exp_FG/mono_ali exp_FG/tri_8_2000
steps/train_deltas.sh: accumulating tree stats
steps/train_deltas.sh: getting questions for tree-building, via clustering
steps/train_deltas.sh: building the tree
steps/train_deltas.sh: converting alignments from exp_FG/mono_ali to use current tree
steps/train_deltas.sh: compiling graphs of transcripts
steps/train_deltas.sh: training pass 1
steps/train_deltas.sh: training pass 2
steps/train_deltas.sh: training pass 3
steps/train_deltas.sh: training pass 4
steps/train_deltas.sh: training pass 5
steps/train_deltas.sh: training pass 6
steps/train_deltas.sh: training pass 7
steps/train_deltas.sh: training pass 8
steps/train_deltas.sh: training pass 9
steps/train_deltas.sh: training pass 10
steps/train_deltas.sh: aligning data
steps/train_deltas.sh: training pass 11
steps/train_deltas.sh: training pass 12
steps/train_deltas.sh: training pass 13
steps/train_deltas.sh: training pass 14
steps/train_deltas.sh: training pass 15
steps/train_deltas.sh: training pass 16
steps/train_deltas.sh: training pass 17
steps/train_deltas.sh: training pass 18
steps/train_deltas.sh: training pass 19
steps/train_deltas.sh: training pass 20
steps/train_deltas.sh: aligning data
steps/train_deltas.sh: training pass 21
steps/train_deltas.sh: training pass 22
steps/train_deltas.sh: training pass 23
steps/train_deltas.sh: training pass 24
steps/train_deltas.sh: training pass 25
steps/train_deltas.sh: training pass 26
steps/train_deltas.sh: training pass 27
steps/train_deltas.sh: training pass 28
steps/train_deltas.sh: training pass 29
steps/train_deltas.sh: training pass 30
steps/train_deltas.sh: aligning data
steps/train_deltas.sh: training pass 31
steps/train_deltas.sh: training pass 32
steps/train_deltas.sh: training pass 33
steps/train_deltas.sh: training pass 34
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang_bigram exp_FG/tri_8_2000
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 15.0537634409% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.8279569892% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_alignments.sh: see stats in exp_FG/tri_8_2000/log/analyze_alignments.log
2 warnings in exp_FG/tri_8_2000/log/analyze_alignments.log
54 warnings in exp_FG/tri_8_2000/log/align.*.*.log
109 warnings in exp_FG/tri_8_2000/log/init_model.log
1110 warnings in exp_FG/tri_8_2000/log/update.*.log
1 warnings in exp_FG/tri_8_2000/log/build_tree.log
exp_FG/tri_8_2000: nj=18 align prob=-64.72 over 0.43h [retry=0.0%, fail=0.0%] states=376 gauss=7619 tree-impr=7.78
steps/train_deltas.sh: Done training system with delta+delta-delta features in exp_FG/tri_8_2000
============================================================================
                  Tri-phone  Decoding                     
============================================================================
=========================
 Sen = 2000 
=========================
tree-info exp_FG/tri_8_2000/tree 
tree-info exp_FG/tri_8_2000/tree 
make-h-transducer --disambig-syms-out=exp_FG/tri_8_2000/graph/disambig_tid.int --transition-scale=1.0 data/lang_bigram/tmp/ilabels_3_1 exp_FG/tri_8_2000/tree exp_FG/tri_8_2000/final.mdl 
fsttablecompose exp_FG/tri_8_2000/graph/Ha.fst data/lang_bigram/tmp/CLG_3_1.fst 
fstdeterminizestar --use-log=true 
fstrmepslocal 
fstminimizeencoded 
fstrmsymbols exp_FG/tri_8_2000/graph/disambig_tid.int 
fstisstochastic exp_FG/tri_8_2000/graph/HCLGa.fst 
0.000588827 -0.000462013
add-self-loops --self-loop-scale=0.1 --reorder=true exp_FG/tri_8_2000/final.mdl 
steps/decode.sh --nj 18 --cmd run.pl exp_FG/tri_8_2000/graph data/test exp_FG/tri_8_2000/decode
decode.sh: feature type is delta
steps/diagnostic/analyze_lats.sh --cmd run.pl exp_FG/tri_8_2000/graph exp_FG/tri_8_2000/decode
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 18.5185185185% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 16.049382716% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_lats.sh: see stats in exp_FG/tri_8_2000/decode/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,1,2) and mean=1.3
steps/diagnostic/analyze_lats.sh: see stats in exp_FG/tri_8_2000/decode/log/analyze_lattice_depth_stats.log
============================================================================
                    DNN Hybrid Training                   
============================================================================
steps/align_si.sh --nj 18 --cmd run.pl data/train data/lang_bigram exp_FG/tri_8_2000 exp_FG/tri_8_2000_ali
steps/align_si.sh: feature type is delta
steps/align_si.sh: aligning data in data/train using model from exp_FG/tri_8_2000, putting alignments in exp_FG/tri_8_2000_ali
steps/diagnostic/analyze_alignments.sh --cmd run.pl data/lang_bigram exp_FG/tri_8_2000_ali
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 15.0537634409% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.8279569892% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_alignments.sh: see stats in exp_FG/tri_8_2000_ali/log/analyze_alignments.log
steps/align_si.sh: done aligning data.
steps/nnet2/train_tanh.sh --mix-up 5000 --initial-learning-rate 0.015 --final-learning-rate 0.002 --num-hidden-layers 3 --minibatch-size 128 --hidden-layer-dim 256 --num-jobs-nnet 18 --cmd run.pl --num-epochs 15 data/train data/lang_bigram exp_FG/tri_8_2000_ali exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256
steps/nnet2/train_tanh.sh: calling get_lda.sh
steps/nnet2/get_lda.sh --transform-dir exp_FG/tri_8_2000_ali --splice-width 4 --cmd run.pl data/train data/lang_bigram exp_FG/tri_8_2000_ali exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256
steps/nnet2/get_lda.sh: feature type is raw
feat-to-dim 'ark,s,cs:utils/subset_scp.pl --quiet 555 data/train/split18/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split18/1/utt2spk scp:data/train/split18/1/cmvn.scp scp:- ark:- |' - 
apply-cmvn --utt2spk=ark:data/train/split18/1/utt2spk scp:data/train/split18/1/cmvn.scp scp:- ark:- 
WARNING (feat-to-dim[5.5.773~1-99a8]:Close():kaldi-io.cc:515) Pipe utils/subset_scp.pl --quiet 555 data/train/split18/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split18/1/utt2spk scp:data/train/split18/1/cmvn.scp scp:- ark:- | had nonzero return status 36096
feat-to-dim 'ark,s,cs:utils/subset_scp.pl --quiet 555 data/train/split18/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split18/1/utt2spk scp:data/train/split18/1/cmvn.scp scp:- ark:- | splice-feats --left-context=4 --right-context=4 ark:- ark:- |' - 
apply-cmvn --utt2spk=ark:data/train/split18/1/utt2spk scp:data/train/split18/1/cmvn.scp scp:- ark:- 
splice-feats --left-context=4 --right-context=4 ark:- ark:- 
WARNING (feat-to-dim[5.5.773~1-99a8]:Close():kaldi-io.cc:515) Pipe utils/subset_scp.pl --quiet 555 data/train/split18/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split18/1/utt2spk scp:data/train/split18/1/cmvn.scp scp:- ark:- | splice-feats --left-context=4 --right-context=4 ark:- ark:- | had nonzero return status 36096
steps/nnet2/get_lda.sh: Accumulating LDA statistics.
steps/nnet2/get_lda.sh: Finished estimating LDA
steps/nnet2/train_tanh.sh: calling get_egs.sh
steps/nnet2/get_egs.sh --transform-dir exp_FG/tri_8_2000_ali --splice-width 4 --samples-per-iter 200000 --num-jobs-nnet 18 --stage 0 --cmd run.pl --io-opts --max-jobs-run 5 data/train data/lang_bigram exp_FG/tri_8_2000_ali exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256
steps/nnet2/get_egs.sh: feature type is raw
steps/nnet2/get_egs.sh: working out number of frames of training data
feat-to-len 'scp:head -n 10 data/train/feats.scp|' ark,t:- 
steps/nnet2/get_egs.sh: Every epoch, splitting the data up into 1 iterations,
steps/nnet2/get_egs.sh: giving samples-per-iteration of 8702 (you requested 200000).
Getting validation and training subset examples.
steps/nnet2/get_egs.sh: extracting validation and training-subset alignments.
copy-int-vector ark:- ark,t:- 
LOG (copy-int-vector[5.5.773~1-99a8]:main():copy-int-vector.cc:83) Copied 558 vectors of int32.
Getting subsets of validation examples for diagnostics and combination.
Creating training examples
Generating training examples on disk
steps/nnet2/get_egs.sh: rearranging examples into parts for different parallel jobs
steps/nnet2/get_egs.sh: Since iters-per-epoch == 1, just concatenating the data.
Shuffling the order of training examples
(in order to avoid stressing the disk, these won't all run at once).
steps/nnet2/get_egs.sh: Finished preparing training examples
steps/nnet2/train_tanh.sh: initializing neural net
Training transition probabilities and setting priors
steps/nnet2/train_tanh.sh: Will train for 15 + 5 epochs, equalling 
steps/nnet2/train_tanh.sh: 15 + 5 = 20 iterations, 
steps/nnet2/train_tanh.sh: (while reducing learning rate) + (with constant learning rate).
Training neural net (pass 0)
Training neural net (pass 1)
Training neural net (pass 2)
Training neural net (pass 3)
Training neural net (pass 4)
Training neural net (pass 5)
Training neural net (pass 6)
Training neural net (pass 7)
Training neural net (pass 8)
Training neural net (pass 9)
Training neural net (pass 10)
Training neural net (pass 11)
Training neural net (pass 12)
Training neural net (pass 13)
Mixing up from 376 to 5000 components
Training neural net (pass 14)
Training neural net (pass 15)
Training neural net (pass 16)
Training neural net (pass 17)
Training neural net (pass 18)
Training neural net (pass 19)
Setting num_iters_final=5
Getting average posterior for purposes of adjusting the priors.
Re-adjusting priors based on computed posteriors
Done
Cleaning up data
steps/nnet2/remove_egs.sh: Finished deleting examples in exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256/egs
Removing most of the models
============================================================================
                    DNN Hybrid Testing                    
============================================================================
steps/nnet2/decode.sh --cmd run.pl --nj 18 exp_FG/tri_8_2000/graph data/test exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256/decode
steps/nnet2/decode.sh: feature type is raw
steps/diagnostic/analyze_lats.sh --cmd run.pl --iter final exp_FG/tri_8_2000/graph exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256/decode
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.7283950617% of the time at utterance begin.  This may not be optimal.
analyze_phone_length_stats.py: WARNING: optional-silence sil is seen only 11.1111111111% of the time at utterance end.  This may not be optimal.
steps/diagnostic/analyze_lats.sh: see stats in exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256/decode/log/analyze_alignments.log
Overall, lattice depth (10,50,90-percentile)=(1,1,5) and mean=2.2
steps/diagnostic/analyze_lats.sh: see stats in exp_FG/DNN_tri_8_2000_aligned_layer3_nodes256/decode/log/analyze_lattice_depth_stats.log
score best paths
score confidence and timing with sclite
Decoding done.