Changkun's Blog

Science and art, life in between.


  • Home

  • Ideas

  • Archives

  • Tags

  • Bio

记一次完整的 Kaldi-TIMIT 示例运行

Published at: 2016-06-05   |   Reading: 7779 words ~16min   |   PV/UV: /

整个例子从 Sat Jun 4 22:45:55 CST 2016 开始,于 Sun Jun 5 11:16:53 CST 2016 结束,共经历约 12 个小时。

在 TIMIT 的代码中,一共分为了以下几个示例:

  1. 数据预处理;
  2. MFCC 特征提取 & 训练集和测试集的 CMVN,这里只提取了 MFCC,Kaldi 里支持 MFCC,PLP,PITCH;
  3. 单音树训练和解码,是语音识别最基础的部分
  4. 三音素的训练和解码(Deltas + Delta-Deltas)
  5. 三音素模型基础上做了LDA + MLLT变换的训练和解码
  6. 三音素模型基础上做了LDA + MLLT +SAT变换的训练和解码
  7. 三音素模型基础上做了SGMM2的训练和解码,SGMM2是povey 提出的
  8. 三音素模型基础上做了 MMI + SGMM2 的训练和解码
  9. DNN 混合训练和解码(povey 版本模型,看网上说不建议使用?)
  10. 系统融合(DNN+SGMM)
  11. Karel DNN 通用深度学习模型的训练和解码
  12. 获取结果

总的来说,计算机的资源没有完全被利用起来,整个过程中第十步是耗时是最长的,共花费约七个小时左右,主要时间消耗在下面代码的第三行中:

1
2
3
utils/subset_data_dir_tr_cv.sh data-fmllr-tri3/train data-fmllr-tri3/train_tr90 data-fmllr-tri3/train_cv10
/Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/utils/subset_data_dir.sh: reducing #utt from     3696 to     3320
/Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/utils/subset_data_dir.sh: reducing #utt from     3696 to      376

这部分主要使用显卡进行计算,显卡是目前这台MacBook Pro的最重要的瓶颈。

整个过程中 CPU 的使用情况记录如下图所示:

JOB

GPU 的使用情况如下图所示:

JOB

GPU 的显存消耗情况如下图所示:

JOB

处理器平均负载如下图所示:

JOB

下面是整个过程的输出日志:

   1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
  58
  59
  60
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
Last login: Sat Jun  4 22:37:50 on ttys001
➜  kaldi/egs/timit/s5 master ✗ ./run.sh
============================================================================
TIMIT Start at  Sat Jun 4 22:45:55 CST 2016
============================================================================
============================================================================
                Data & Lexicon & Language Preparation                     
============================================================================
wav-to-duration scp:train_wav.scp ark,t:train_dur.ark 
WARNING (wav-to-duration:Close():kaldi-io.cc:500) Pipe /Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/../../../tools/sph2pipe_v2.5/sph2pipe -f wav /Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/data/TIMIT-old/TRAIN/DR2/FAEM0/si1392.wav | had nonzero return status 13
…………[中间都是警告 故省略]
WARNING (wav-to-duration:Close():kaldi-io.cc:500) Pipe /Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/../../../tools/sph2pipe_v2.5/sph2pipe -f wav /Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/data/TIMIT-old/TEST/DR3/MTHC0/sx295.wav | had nonzero return status 13
LOG (wav-to-duration:main():wav-to-duration.cc:90) Printed duration for 192 audio files.
LOG (wav-to-duration:main():wav-to-duration.cc:92) Mean duration was 3.03646, min and max durations were 1.30562, 6.21444
Data preparation succeeded
Temporary directory stat_26716 does not exist
creating stat_26716
Extracting dictionary from training corpus
Splitting dictionary into 3 lists
Extracting n-gram statistics for each word list
Important: dictionary must be ordered according to order of appearance of words in data
used to generate n-gram blocks,  so that sub language model blocks results ordered too
dict.000
dict.001
dict.002
$bin/ngt -i="$inpfile" -n=$order -gooout=y -o="$gzip -c > $tmpdir/ngram.${sdict}.gz" -fd="$tmpdir/$sdict" $dictionary -iknstat="$tmpdir/ikn.stat.$sdict" >> $logfile 2>&1
Estimating language models for each word list
dict.000
dict.001
dict.002
$scr/build-sublm.pl $verbose $prune $smoothing --size $order --ngrams "$gunzip -c $tmpdir/ngram.${sdict}.gz" -sublm $tmpdir/lm.$sdict >> $logfile 2>&1
Merging language models into data/local/lm_tmp/lm_phone_bg.ilm.gz
Cleaning temporary directory stat_26716
Removing temporary directory stat_26716
inpfile: data/local/lm_tmp/lm_phone_bg.ilm.gz
outfile: /dev/stdout
loading up to the LM level 1000 (if any)
dub: 10000000
Language Model Type of data/local/lm_tmp/lm_phone_bg.ilm.gz is 1
Language Model Type is 1
iARPA
loadtxt_ram()
1-grams: reading 51 entries
done level 1
2-grams: reading 1694 entries
done level 2
done
OOV code is 50
OOV code is 50
OOV code is 50
Saving in txt format to /dev/stdout
savetxt: /dev/stdout
save: 51 1-grams
save: 1694 2-grams
done
Dictionary & language model preparation succeeded
Checking data/local/dict/silence_phones.txt ...
--> reading data/local/dict/silence_phones.txt
--> data/local/dict/silence_phones.txt is OK

Checking data/local/dict/optional_silence.txt ...
--> reading data/local/dict/optional_silence.txt
--> data/local/dict/optional_silence.txt is OK

Checking data/local/dict/nonsilence_phones.txt ...
--> reading data/local/dict/nonsilence_phones.txt
--> data/local/dict/nonsilence_phones.txt is OK

Checking disjoint: silence_phones.txt, nonsilence_phones.txt
--> disjoint property is OK.

Checking data/local/dict/lexicon.txt
--> reading data/local/dict/lexicon.txt
--> data/local/dict/lexicon.txt is OK

Checking data/local/dict/lexiconp.txt
--> reading data/local/dict/lexiconp.txt
--> data/local/dict/lexiconp.txt is OK

Checking lexicon pair data/local/dict/lexicon.txt and data/local/dict/lexiconp.txt
--> lexicon pair data/local/dict/lexicon.txt and data/local/dict/lexiconp.txt match

Checking data/local/dict/extra_questions.txt ...
--> reading data/local/dict/extra_questions.txt
--> data/local/dict/extra_questions.txt is OK
--> SUCCESS [validating dictionary directory data/local/dict]

fstaddselfloops data/lang/phones/wdisambig_phones.int data/lang/phones/wdisambig_words.int 
prepare_lang.sh: validating output directory
utils/validate_lang.pl data/lang
Checking data/lang/phones.txt ...
--> data/lang/phones.txt is OK

Checking words.txt: #0 ...
--> data/lang/words.txt is OK

Checking disjoint: silence.txt, nonsilence.txt, disambig.txt ...
--> silence.txt and nonsilence.txt are disjoint
--> silence.txt and disambig.txt are disjoint
--> disambig.txt and nonsilence.txt are disjoint
--> disjoint property is OK

Checking sumation: silence.txt, nonsilence.txt, disambig.txt ...
--> summation property is OK

Checking data/lang/phones/context_indep.{txt, int, csl} ...
--> 1 entry/entries in data/lang/phones/context_indep.txt
--> data/lang/phones/context_indep.int corresponds to data/lang/phones/context_indep.txt
--> data/lang/phones/context_indep.csl corresponds to data/lang/phones/context_indep.txt
--> data/lang/phones/context_indep.{txt, int, csl} are OK

Checking data/lang/phones/nonsilence.{txt, int, csl} ...
--> 47 entry/entries in data/lang/phones/nonsilence.txt
--> data/lang/phones/nonsilence.int corresponds to data/lang/phones/nonsilence.txt
--> data/lang/phones/nonsilence.csl corresponds to data/lang/phones/nonsilence.txt
--> data/lang/phones/nonsilence.{txt, int, csl} are OK

Checking data/lang/phones/silence.{txt, int, csl} ...
--> 1 entry/entries in data/lang/phones/silence.txt
--> data/lang/phones/silence.int corresponds to data/lang/phones/silence.txt
--> data/lang/phones/silence.csl corresponds to data/lang/phones/silence.txt
--> data/lang/phones/silence.{txt, int, csl} are OK

Checking data/lang/phones/optional_silence.{txt, int, csl} ...
--> 1 entry/entries in data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.int corresponds to data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.csl corresponds to data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.{txt, int, csl} are OK

Checking data/lang/phones/disambig.{txt, int, csl} ...
--> 2 entry/entries in data/lang/phones/disambig.txt
--> data/lang/phones/disambig.int corresponds to data/lang/phones/disambig.txt
--> data/lang/phones/disambig.csl corresponds to data/lang/phones/disambig.txt
--> data/lang/phones/disambig.{txt, int, csl} are OK

Checking data/lang/phones/roots.{txt, int} ...
--> 48 entry/entries in data/lang/phones/roots.txt
--> data/lang/phones/roots.int corresponds to data/lang/phones/roots.txt
--> data/lang/phones/roots.{txt, int} are OK

Checking data/lang/phones/sets.{txt, int} ...
--> 48 entry/entries in data/lang/phones/sets.txt
--> data/lang/phones/sets.int corresponds to data/lang/phones/sets.txt
--> data/lang/phones/sets.{txt, int} are OK

Checking data/lang/phones/extra_questions.{txt, int} ...
--> 2 entry/entries in data/lang/phones/extra_questions.txt
--> data/lang/phones/extra_questions.int corresponds to data/lang/phones/extra_questions.txt
--> data/lang/phones/extra_questions.{txt, int} are OK

Checking optional_silence.txt ...
--> reading data/lang/phones/optional_silence.txt
--> data/lang/phones/optional_silence.txt is OK

Checking disambiguation symbols: #0 and #1
--> data/lang/phones/disambig.txt has "#0" and "#1"
--> data/lang/phones/disambig.txt is OK

Checking topo ...

Checking word-level disambiguation symbols...
--> data/lang/phones/wdisambig.txt exists (newer prepare_lang.sh)
Checking data/lang/oov.{txt, int} ...
--> 1 entry/entries in data/lang/oov.txt
--> data/lang/oov.int corresponds to data/lang/oov.txt
--> data/lang/oov.{txt, int} are OK

--> data/lang/L.fst is olabel sorted
--> data/lang/L_disambig.fst is olabel sorted
--> SUCCESS [validating lang directory data/lang]
Preparing train, dev and test data
utils/validate_data_dir.sh: Successfully validated data-directory data/train
utils/validate_data_dir.sh: Successfully validated data-directory data/dev
utils/validate_data_dir.sh: Successfully validated data-directory data/test
Preparing language models for test
arpa2fst --disambig-symbol=#0 --read-symbol-table=data/lang_test_bg/words.txt - data/lang_test_bg/G.fst 
LOG (arpa2fst:Read():arpa-file-parser.cc:90) Reading \data\ section.
LOG (arpa2fst:Read():arpa-file-parser.cc:145) Reading \1-grams: section.
LOG (arpa2fst:Read():arpa-file-parser.cc:145) Reading \2-grams: section.
fstisstochastic data/lang_test_bg/G.fst 
0.000367058 -0.0763018
utils/validate_lang.pl data/lang_test_bg
Checking data/lang_test_bg/phones.txt ...
--> data/lang_test_bg/phones.txt is OK

Checking words.txt: #0 ...
--> data/lang_test_bg/words.txt is OK

Checking disjoint: silence.txt, nonsilence.txt, disambig.txt ...
--> silence.txt and nonsilence.txt are disjoint
--> silence.txt and disambig.txt are disjoint
--> disambig.txt and nonsilence.txt are disjoint
--> disjoint property is OK

Checking sumation: silence.txt, nonsilence.txt, disambig.txt ...
--> summation property is OK

Checking data/lang_test_bg/phones/context_indep.{txt, int, csl} ...
--> 1 entry/entries in data/lang_test_bg/phones/context_indep.txt
--> data/lang_test_bg/phones/context_indep.int corresponds to data/lang_test_bg/phones/context_indep.txt
--> data/lang_test_bg/phones/context_indep.csl corresponds to data/lang_test_bg/phones/context_indep.txt
--> data/lang_test_bg/phones/context_indep.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/nonsilence.{txt, int, csl} ...
--> 47 entry/entries in data/lang_test_bg/phones/nonsilence.txt
--> data/lang_test_bg/phones/nonsilence.int corresponds to data/lang_test_bg/phones/nonsilence.txt
--> data/lang_test_bg/phones/nonsilence.csl corresponds to data/lang_test_bg/phones/nonsilence.txt
--> data/lang_test_bg/phones/nonsilence.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/silence.{txt, int, csl} ...
--> 1 entry/entries in data/lang_test_bg/phones/silence.txt
--> data/lang_test_bg/phones/silence.int corresponds to data/lang_test_bg/phones/silence.txt
--> data/lang_test_bg/phones/silence.csl corresponds to data/lang_test_bg/phones/silence.txt
--> data/lang_test_bg/phones/silence.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/optional_silence.{txt, int, csl} ...
--> 1 entry/entries in data/lang_test_bg/phones/optional_silence.txt
--> data/lang_test_bg/phones/optional_silence.int corresponds to data/lang_test_bg/phones/optional_silence.txt
--> data/lang_test_bg/phones/optional_silence.csl corresponds to data/lang_test_bg/phones/optional_silence.txt
--> data/lang_test_bg/phones/optional_silence.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/disambig.{txt, int, csl} ...
--> 2 entry/entries in data/lang_test_bg/phones/disambig.txt
--> data/lang_test_bg/phones/disambig.int corresponds to data/lang_test_bg/phones/disambig.txt
--> data/lang_test_bg/phones/disambig.csl corresponds to data/lang_test_bg/phones/disambig.txt
--> data/lang_test_bg/phones/disambig.{txt, int, csl} are OK

Checking data/lang_test_bg/phones/roots.{txt, int} ...
--> 48 entry/entries in data/lang_test_bg/phones/roots.txt
--> data/lang_test_bg/phones/roots.int corresponds to data/lang_test_bg/phones/roots.txt
--> data/lang_test_bg/phones/roots.{txt, int} are OK

Checking data/lang_test_bg/phones/sets.{txt, int} ...
--> 48 entry/entries in data/lang_test_bg/phones/sets.txt
--> data/lang_test_bg/phones/sets.int corresponds to data/lang_test_bg/phones/sets.txt
--> data/lang_test_bg/phones/sets.{txt, int} are OK

Checking data/lang_test_bg/phones/extra_questions.{txt, int} ...
--> 2 entry/entries in data/lang_test_bg/phones/extra_questions.txt
--> data/lang_test_bg/phones/extra_questions.int corresponds to data/lang_test_bg/phones/extra_questions.txt
--> data/lang_test_bg/phones/extra_questions.{txt, int} are OK

Checking optional_silence.txt ...
--> reading data/lang_test_bg/phones/optional_silence.txt
--> data/lang_test_bg/phones/optional_silence.txt is OK

Checking disambiguation symbols: #0 and #1
--> data/lang_test_bg/phones/disambig.txt has "#0" and "#1"
--> data/lang_test_bg/phones/disambig.txt is OK

Checking topo ...

Checking word-level disambiguation symbols...
--> data/lang_test_bg/phones/wdisambig.txt exists (newer prepare_lang.sh)
Checking data/lang_test_bg/oov.{txt, int} ...
--> 1 entry/entries in data/lang_test_bg/oov.txt
--> data/lang_test_bg/oov.int corresponds to data/lang_test_bg/oov.txt
--> data/lang_test_bg/oov.{txt, int} are OK

--> data/lang_test_bg/L.fst is olabel sorted
--> data/lang_test_bg/L_disambig.fst is olabel sorted
--> data/lang_test_bg/G.fst is ilabel sorted
--> data/lang_test_bg/G.fst has 50 states
fstdeterminizestar data/lang_test_bg/G.fst /dev/null 
--> data/lang_test_bg/G.fst is determinizable
--> utils/lang/check_g_properties.pl successfully validated data/lang_test_bg/G.fst
--> utils/lang/check_g_properties.pl succeeded.
--> Testing determinizability of L_disambig . G
fstdeterminizestar 
fsttablecompose data/lang_test_bg/L_disambig.fst data/lang_test_bg/G.fst 
--> L_disambig . G is determinizable
--> SUCCESS [validating lang directory data/lang_test_bg]
Succeeded in formatting data.
============================================================================
         MFCC Feature Extration & CMVN for Training and Test set          
============================================================================
steps/make_mfcc.sh --cmd run.pl --nj 10 data/train exp/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/make_mfcc/train mfcc
Succeeded creating CMVN stats for train
steps/make_mfcc.sh --cmd run.pl --nj 10 data/dev exp/make_mfcc/dev mfcc
steps/make_mfcc.sh: moving data/dev/feats.scp to data/dev/.backup
utils/validate_data_dir.sh: Successfully validated data-directory data/dev
steps/make_mfcc.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
Succeeded creating MFCC features for dev
steps/compute_cmvn_stats.sh data/dev exp/make_mfcc/dev mfcc
Succeeded creating CMVN stats for dev
steps/make_mfcc.sh --cmd run.pl --nj 10 data/test exp/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/make_mfcc/test mfcc
Succeeded creating CMVN stats for test
============================================================================
                     MonoPhone Training & Decoding                        
============================================================================
steps/train_mono.sh --nj 30 --cmd run.pl data/train data/lang exp/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
2 warnings in exp/mono/log/align.*.*.log
Done
tree-info exp/mono/tree 
tree-info exp/mono/tree 
fstdeterminizestar --use-log=true 
fsttablecompose data/lang_test_bg/L_disambig.fst data/lang_test_bg/G.fst 
fstpushspecial 
fstminimizeencoded 
fstisstochastic data/lang_test_bg/tmp/LG.fst 
-0.0084541 -0.00929638
fstcomposecontext --context-size=1 --central-position=0 --read-disambig-syms=data/lang_test_bg/phones/disambig.int --write-disambig-syms=data/lang_test_bg/tmp/disambig_ilabels_1_0.int data/lang_test_bg/tmp/ilabels_1_0 
fstisstochastic data/lang_test_bg/tmp/CLG_1_0.fst 
-0.0084541 -0.00929645
make-h-transducer --disambig-syms-out=exp/mono/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_1_0 exp/mono/tree exp/mono/final.mdl 
fstdeterminizestar --use-log=true 
fsttablecompose exp/mono/graph/Ha.fst data/lang_test_bg/tmp/CLG_1_0.fst 
fstminimizeencoded 
fstrmsymbols exp/mono/graph/disambig_tid.int 
fstrmepslocal 
fstisstochastic exp/mono/graph/HCLGa.fst 
0.000381767 -0.00951818
add-self-loops --self-loop-scale=0.1 --reorder=true exp/mono/final.mdl 
steps/decode.sh --nj 5 --cmd run.pl exp/mono/graph data/dev exp/mono/decode_dev
decode.sh: feature type is delta
steps/decode.sh --nj 5 --cmd run.pl exp/mono/graph data/test exp/mono/decode_test
decode.sh: feature type is delta
============================================================================
           tri1 : Deltas + Delta-Deltas Training & Decoding               
============================================================================
steps/align_si.sh --boost-silence 1.25 --nj 30 --cmd run.pl data/train data/lang exp/mono exp/mono_ali
steps/align_si.sh: feature type is delta
steps/align_si.sh: aligning data in data/train using model from exp/mono, putting alignments in exp/mono_ali
steps/align_si.sh: done aligning data.
steps/train_deltas.sh --cmd run.pl 2500 15000 data/train data/lang exp/mono_ali exp/tri1
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/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
85 warnings in exp/tri1/log/update.*.log
81 warnings in exp/tri1/log/init_model.log
1 warnings in exp/tri1/log/compile_questions.log
steps/train_deltas.sh: Done training system with delta+delta-delta features in exp/tri1
tree-info exp/tri1/tree 
tree-info exp/tri1/tree 
fstcomposecontext --context-size=3 --central-position=1 --read-disambig-syms=data/lang_test_bg/phones/disambig.int --write-disambig-syms=data/lang_test_bg/tmp/disambig_ilabels_3_1.int data/lang_test_bg/tmp/ilabels_3_1 
fstisstochastic data/lang_test_bg/tmp/CLG_3_1.fst 
0 -0.00929618
make-h-transducer --disambig-syms-out=exp/tri1/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/tri1/tree exp/tri1/final.mdl 
fsttablecompose exp/tri1/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst 
fstrmsymbols exp/tri1/graph/disambig_tid.int 
fstdeterminizestar --use-log=true 
fstrmepslocal 
fstminimizeencoded 
fstisstochastic exp/tri1/graph/HCLGa.fst 
0.000443735 -0.0171465
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri1/final.mdl 
steps/decode.sh --nj 5 --cmd run.pl exp/tri1/graph data/dev exp/tri1/decode_dev
decode.sh: feature type is delta
steps/decode.sh --nj 5 --cmd run.pl exp/tri1/graph data/test exp/tri1/decode_test
decode.sh: feature type is delta
============================================================================
                 tri2 : LDA + MLLT Training & Decoding                    
============================================================================
steps/align_si.sh --nj 30 --cmd run.pl data/train data/lang exp/tri1 exp/tri1_ali
steps/align_si.sh: feature type is delta
steps/align_si.sh: aligning data in data/train using model from exp/tri1, putting alignments in exp/tri1_ali
steps/align_si.sh: done aligning data.
steps/train_lda_mllt.sh --cmd run.pl --splice-opts --left-context=3 --right-context=3 2500 15000 data/train data/lang exp/tri1_ali exp/tri2
Accumulating LDA statistics.
Accumulating tree stats
Getting questions for tree clustering.
Building the tree
steps/train_lda_mllt.sh: Initializing the model
Converting alignments from exp/tri1_ali to use current tree
Compiling graphs of transcripts
Training pass 1
Training pass 2
Estimating MLLT
Training pass 3
Training pass 4
Estimating MLLT
Training pass 5
Training pass 6
Estimating MLLT
Training pass 7
Training pass 8
Training pass 9
Training pass 10
Aligning data
Training pass 11
Training pass 12
Estimating MLLT
Training pass 13
Training pass 14
Training pass 15
Training pass 16
Training pass 17
Training pass 18
Training pass 19
Training pass 20
Aligning data
Training pass 21
Training pass 22
Training pass 23
Training pass 24
Training pass 25
Training pass 26
Training pass 27
Training pass 28
Training pass 29
Training pass 30
Aligning data
Training pass 31
Training pass 32
Training pass 33
Training pass 34
250 warnings in exp/tri2/log/update.*.log
105 warnings in exp/tri2/log/init_model.log
1 warnings in exp/tri2/log/compile_questions.log
Done training system with LDA+MLLT features in exp/tri2
tree-info exp/tri2/tree 
tree-info exp/tri2/tree 
make-h-transducer --disambig-syms-out=exp/tri2/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/tri2/tree exp/tri2/final.mdl 
fstminimizeencoded 
fstrmepslocal 
fstdeterminizestar --use-log=true 
fsttablecompose exp/tri2/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst 
fstrmsymbols exp/tri2/graph/disambig_tid.int 
fstisstochastic exp/tri2/graph/HCLGa.fst 
0.00046259 -0.0171465
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri2/final.mdl 
steps/decode.sh --nj 5 --cmd run.pl exp/tri2/graph data/dev exp/tri2/decode_dev
decode.sh: feature type is lda
steps/decode.sh --nj 5 --cmd run.pl exp/tri2/graph data/test exp/tri2/decode_test
decode.sh: feature type is lda
============================================================================
              tri3 : LDA + MLLT + SAT Training & Decoding                 
============================================================================
steps/align_si.sh --nj 30 --cmd run.pl --use-graphs true data/train data/lang exp/tri2 exp/tri2_ali
steps/align_si.sh: feature type is lda
steps/align_si.sh: aligning data in data/train using model from exp/tri2, putting alignments in exp/tri2_ali
steps/align_si.sh: done aligning data.
steps/train_sat.sh --cmd run.pl 2500 15000 data/train data/lang exp/tri2_ali exp/tri3
steps/train_sat.sh: feature type is lda
steps/train_sat.sh: obtaining initial fMLLR transforms since not present in exp/tri2_ali
steps/train_sat.sh: Accumulating tree stats
steps/train_sat.sh: Getting questions for tree clustering.
steps/train_sat.sh: Building the tree
steps/train_sat.sh: Initializing the model
steps/train_sat.sh: Converting alignments from exp/tri2_ali to use current tree
steps/train_sat.sh: Compiling graphs of transcripts
Pass 1
Pass 2
Estimating fMLLR transforms
Pass 3
Pass 4
Estimating fMLLR transforms
Pass 5
Pass 6
Estimating fMLLR transforms
Pass 7
Pass 8
Pass 9
Pass 10
Aligning data
Pass 11
Pass 12
Estimating fMLLR transforms
Pass 13
Pass 14
Pass 15
Pass 16
Pass 17
Pass 18
Pass 19
Pass 20
Aligning data
Pass 21
Pass 22
Pass 23
Pass 24
Pass 25
Pass 26
Pass 27
Pass 28
Pass 29
Pass 30
Aligning data
Pass 31
Pass 32
Pass 33
Pass 34
54 warnings in exp/tri3/log/init_model.log
1 warnings in exp/tri3/log/compile_questions.log
4 warnings in exp/tri3/log/update.*.log
steps/train_sat.sh: Likelihood evolution:
-50.1111 -49.2407 -49.0362 -48.828 -48.1678 -47.4899 -47.0224 -46.7228 -46.4847 -45.9498 -45.693 -45.3736 -45.1913 -45.0527 -44.9283 -44.8162 -44.7086 -44.6033 -44.5019 -44.3417 -44.2037 -44.1136 -44.0283 -43.9478 -43.8703 -43.7937 -43.7204 -43.6485 -43.5755 -43.4783 -43.4019 -43.3752 -43.3591 -43.3474 
Done
tree-info exp/tri3/tree 
tree-info exp/tri3/tree 
make-h-transducer --disambig-syms-out=exp/tri3/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/tri3/tree exp/tri3/final.mdl 
fsttablecompose exp/tri3/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst 
fstdeterminizestar --use-log=true 
fstminimizeencoded 
fstrmsymbols exp/tri3/graph/disambig_tid.int 
fstrmepslocal 
fstisstochastic exp/tri3/graph/HCLGa.fst 
0.00045076 -0.0171463
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/tri3/final.mdl 
steps/decode_fmllr.sh --nj 5 --cmd run.pl exp/tri3/graph data/dev exp/tri3/decode_dev
steps/decode.sh --scoring-opts  --num-threads 1 --skip-scoring false --acwt 0.083333 --nj 5 --cmd run.pl --beam 10.0 --model exp/tri3/final.alimdl --max-active 2000 exp/tri3/graph data/dev exp/tri3/decode_dev.si
decode.sh: feature type is lda
steps/decode_fmllr.sh: feature type is lda
steps/decode_fmllr.sh: getting first-pass fMLLR transforms.
steps/decode_fmllr.sh: doing main lattice generation phase
steps/decode_fmllr.sh: estimating fMLLR transforms a second time.
steps/decode_fmllr.sh: doing a final pass of acoustic rescoring.
steps/decode_fmllr.sh --nj 5 --cmd run.pl exp/tri3/graph data/test exp/tri3/decode_test
steps/decode.sh --scoring-opts  --num-threads 1 --skip-scoring false --acwt 0.083333 --nj 5 --cmd run.pl --beam 10.0 --model exp/tri3/final.alimdl --max-active 2000 exp/tri3/graph data/test exp/tri3/decode_test.si
decode.sh: feature type is lda
steps/decode_fmllr.sh: feature type is lda
steps/decode_fmllr.sh: getting first-pass fMLLR transforms.
steps/decode_fmllr.sh: doing main lattice generation phase
steps/decode_fmllr.sh: estimating fMLLR transforms a second time.
steps/decode_fmllr.sh: doing a final pass of acoustic rescoring.
============================================================================
                        SGMM2 Training & Decoding                         
============================================================================
steps/align_fmllr.sh --nj 30 --cmd run.pl data/train data/lang exp/tri3 exp/tri3_ali
steps/align_fmllr.sh: feature type is lda
steps/align_fmllr.sh: compiling training graphs
steps/align_fmllr.sh: aligning data in data/train using exp/tri3/final.alimdl and speaker-independent features.
steps/align_fmllr.sh: computing fMLLR transforms
steps/align_fmllr.sh: doing final alignment.
steps/align_fmllr.sh: done aligning data.
steps/train_ubm.sh --cmd run.pl 400 data/train data/lang exp/tri3_ali exp/ubm4
steps/train_ubm.sh: feature type is lda
steps/train_ubm.sh: using transforms from exp/tri3_ali
steps/train_ubm.sh: clustering model exp/tri3_ali/final.mdl to get initial UBM
steps/train_ubm.sh: doing Gaussian selection
Pass 0
Pass 1
Pass 2
steps/train_sgmm2.sh --cmd run.pl 7000 9000 data/train data/lang exp/tri3_ali exp/ubm4/final.ubm exp/sgmm2_4
steps/train_sgmm2.sh: feature type is lda
steps/train_sgmm2.sh: using transforms from exp/tri3_ali
steps/train_sgmm2.sh: accumulating tree stats
steps/train_sgmm2.sh: Getting questions for tree clustering.
steps/train_sgmm2.sh: Building the tree
steps/train_sgmm2.sh: Initializing the model
steps/train_sgmm2.sh: doing Gaussian selection
steps/train_sgmm2.sh: compiling training graphs
steps/train_sgmm2.sh: converting alignments
steps/train_sgmm2.sh: training pass 0 ... 
steps/train_sgmm2.sh: training pass 1 ... 
steps/train_sgmm2.sh: training pass 2 ... 
steps/train_sgmm2.sh: training pass 3 ... 
steps/train_sgmm2.sh: training pass 4 ... 
steps/train_sgmm2.sh: training pass 5 ... 
steps/train_sgmm2.sh: re-aligning data
steps/train_sgmm2.sh: training pass 6 ... 
steps/train_sgmm2.sh: training pass 7 ... 
steps/train_sgmm2.sh: training pass 8 ... 
steps/train_sgmm2.sh: training pass 9 ... 
steps/train_sgmm2.sh: training pass 10 ... 
steps/train_sgmm2.sh: re-aligning data
steps/train_sgmm2.sh: training pass 11 ... 
steps/train_sgmm2.sh: training pass 12 ... 
steps/train_sgmm2.sh: training pass 13 ... 
steps/train_sgmm2.sh: training pass 14 ... 
steps/train_sgmm2.sh: training pass 15 ... 
steps/train_sgmm2.sh: re-aligning data
steps/train_sgmm2.sh: training pass 16 ... 
steps/train_sgmm2.sh: training pass 17 ... 
steps/train_sgmm2.sh: training pass 18 ... 
steps/train_sgmm2.sh: training pass 19 ... 
steps/train_sgmm2.sh: training pass 20 ... 
steps/train_sgmm2.sh: training pass 21 ... 
steps/train_sgmm2.sh: training pass 22 ... 
steps/train_sgmm2.sh: training pass 23 ... 
steps/train_sgmm2.sh: training pass 24 ... 
steps/train_sgmm2.sh: building alignment model (pass 25)
steps/train_sgmm2.sh: building alignment model (pass 26)
steps/train_sgmm2.sh: building alignment model (pass 27)
1880 warnings in exp/sgmm2_4/log/update.*.log
217 warnings in exp/sgmm2_4/log/update_ali.*.log
1 warnings in exp/sgmm2_4/log/compile_questions.log
Done
tree-info exp/sgmm2_4/tree 
tree-info exp/sgmm2_4/tree 
make-h-transducer --disambig-syms-out=exp/sgmm2_4/graph/disambig_tid.int --transition-scale=1.0 data/lang_test_bg/tmp/ilabels_3_1 exp/sgmm2_4/tree exp/sgmm2_4/final.mdl 
fstdeterminizestar --use-log=true 
fsttablecompose exp/sgmm2_4/graph/Ha.fst data/lang_test_bg/tmp/CLG_3_1.fst 
fstrmepslocal 
fstminimizeencoded 
fstrmsymbols exp/sgmm2_4/graph/disambig_tid.int 
fstisstochastic exp/sgmm2_4/graph/HCLGa.fst 
0.000485192 -0.0171458
HCLGa is not stochastic
add-self-loops --self-loop-scale=0.1 --reorder=true exp/sgmm2_4/final.mdl 
steps/decode_sgmm2.sh --nj 5 --cmd run.pl --transform-dir exp/tri3/decode_dev exp/sgmm2_4/graph data/dev exp/sgmm2_4/decode_dev
steps/decode_sgmm2.sh: feature type is lda
steps/decode_sgmm2.sh: using transforms from exp/tri3/decode_dev
steps/decode_sgmm2.sh --nj 5 --cmd run.pl --transform-dir exp/tri3/decode_test exp/sgmm2_4/graph data/test exp/sgmm2_4/decode_test
steps/decode_sgmm2.sh: feature type is lda
steps/decode_sgmm2.sh: using transforms from exp/tri3/decode_test
============================================================================
                    MMI + SGMM2 Training & Decoding                       
============================================================================
steps/align_sgmm2.sh --nj 30 --cmd run.pl --transform-dir exp/tri3_ali --use-graphs true --use-gselect true data/train data/lang exp/sgmm2_4 exp/sgmm2_4_ali
steps/align_sgmm2.sh: feature type is lda
steps/align_sgmm2.sh: using transforms from exp/tri3_ali
steps/align_sgmm2.sh: aligning data in data/train using model exp/sgmm2_4/final.alimdl
steps/align_sgmm2.sh: computing speaker vectors (1st pass)
steps/align_sgmm2.sh: computing speaker vectors (2nd pass)
steps/align_sgmm2.sh: doing final alignment.
steps/align_sgmm2.sh: done aligning data.
steps/make_denlats_sgmm2.sh --nj 30 --sub-split 30 --acwt 0.2 --lattice-beam 10.0 --beam 18.0 --cmd run.pl --transform-dir exp/tri3_ali data/train data/lang exp/sgmm2_4_ali exp/sgmm2_4_denlats
steps/make_denlats_sgmm2.sh: Making unigram grammar FST in exp/sgmm2_4_denlats/lang
steps/make_denlats_sgmm2.sh: Compiling decoding graph in exp/sgmm2_4_denlats/dengraph
tree-info exp/sgmm2_4_ali/tree 
tree-info exp/sgmm2_4_ali/tree 
fstpushspecial 
fstdeterminizestar --use-log=true 
fstminimizeencoded 
fsttablecompose exp/sgmm2_4_denlats/lang/L_disambig.fst exp/sgmm2_4_denlats/lang/G.fst 
fstisstochastic exp/sgmm2_4_denlats/lang/tmp/LG.fst 
1.2886e-05 1.2886e-05
fstcomposecontext --context-size=3 --central-position=1 --read-disambig-syms=exp/sgmm2_4_denlats/lang/phones/disambig.int --write-disambig-syms=exp/sgmm2_4_denlats/lang/tmp/disambig_ilabels_3_1.int exp/sgmm2_4_denlats/lang/tmp/ilabels_3_1 
fstisstochastic exp/sgmm2_4_denlats/lang/tmp/CLG_3_1.fst 
1.26958e-05 0
make-h-transducer --disambig-syms-out=exp/sgmm2_4_denlats/dengraph/disambig_tid.int --transition-scale=1.0 exp/sgmm2_4_denlats/lang/tmp/ilabels_3_1 exp/sgmm2_4_ali/tree exp/sgmm2_4_ali/final.mdl 
fstdeterminizestar --use-log=true 
fstrmepslocal 
fsttablecompose exp/sgmm2_4_denlats/dengraph/Ha.fst exp/sgmm2_4_denlats/lang/tmp/CLG_3_1.fst 
fstrmsymbols exp/sgmm2_4_denlats/dengraph/disambig_tid.int 
fstminimizeencoded 
fstisstochastic exp/sgmm2_4_denlats/dengraph/HCLGa.fst 
0.000481188 -0.000485808
add-self-loops --self-loop-scale=0.1 --reorder=true exp/sgmm2_4_ali/final.mdl 
steps/make_denlats_sgmm2.sh: feature type is lda
steps/make_denlats_sgmm2.sh: using fMLLR transforms from exp/tri3_ali
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 1
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 2
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 3
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 4
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 5
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 6
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 7
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 8
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 9
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 10
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 11
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 12
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 13
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 14
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 15
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 16
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 17
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 18
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 19
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 20
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 21
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 22
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 23
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 24
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 25
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 26
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 27
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 28
filter_scps.pl: warning: some input lines were output to multiple files
filter_scps.pl: warning: some input lines were output to multiple files
steps/make_denlats_sgmm2.sh: Merging archives for data subset 29
steps/make_denlats_sgmm2.sh: Merging archives for data subset 30
steps/make_denlats_sgmm2.sh: done generating denominator lattices with SGMMs.
steps/train_mmi_sgmm2.sh --acwt 0.2 --cmd run.pl --transform-dir exp/tri3_ali --boost 0.1 --drop-frames true data/train data/lang exp/sgmm2_4_ali exp/sgmm2_4_denlats exp/sgmm2_4_mmi_b0.1
steps/train_mmi_sgmm2.sh: feature type is lda
steps/train_mmi_sgmm2.sh: using transforms from exp/tri3_ali
steps/train_mmi_sgmm2.sh: using speaker vectors from exp/sgmm2_4_ali
steps/train_mmi_sgmm2.sh: using Gaussian-selection info from exp/sgmm2_4_ali
Iteration 0 of MMI training
Iteration 0: objf was 0.500726156431174, MMI auxf change was 0.0161713620720771
Iteration 1 of MMI training
Iteration 1: objf was 0.515630891203772, MMI auxf change was 0.00245126566579809
Iteration 2 of MMI training
Iteration 2: objf was 0.518355502911444, MMI auxf change was 0.000653581941336548
Iteration 3 of MMI training
Iteration 3: objf was 0.519283249728163, MMI auxf change was 0.000388201521483825
MMI training finished
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 1 --transform-dir exp/tri3/decode_dev data/lang_test_bg data/dev exp/sgmm2_4/decode_dev exp/sgmm2_4_mmi_b0.1/decode_dev_it1
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_dev
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_dev
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/1.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 1 --transform-dir exp/tri3/decode_test data/lang_test_bg data/test exp/sgmm2_4/decode_test exp/sgmm2_4_mmi_b0.1/decode_test_it1
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_test
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_test
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/1.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 2 --transform-dir exp/tri3/decode_dev data/lang_test_bg data/dev exp/sgmm2_4/decode_dev exp/sgmm2_4_mmi_b0.1/decode_dev_it2
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_dev
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_dev
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/2.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 2 --transform-dir exp/tri3/decode_test data/lang_test_bg data/test exp/sgmm2_4/decode_test exp/sgmm2_4_mmi_b0.1/decode_test_it2
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_test
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_test
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/2.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 3 --transform-dir exp/tri3/decode_dev data/lang_test_bg data/dev exp/sgmm2_4/decode_dev exp/sgmm2_4_mmi_b0.1/decode_dev_it3
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_dev
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_dev
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/3.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 3 --transform-dir exp/tri3/decode_test data/lang_test_bg data/test exp/sgmm2_4/decode_test exp/sgmm2_4_mmi_b0.1/decode_test_it3
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_test
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_test
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/3.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 4 --transform-dir exp/tri3/decode_dev data/lang_test_bg data/dev exp/sgmm2_4/decode_dev exp/sgmm2_4_mmi_b0.1/decode_dev_it4
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_dev
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_dev
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/4.mdl
steps/decode_sgmm2_rescore.sh --cmd run.pl --iter 4 --transform-dir exp/tri3/decode_test data/lang_test_bg data/test exp/sgmm2_4/decode_test exp/sgmm2_4_mmi_b0.1/decode_test_it4
steps/decode_sgmm2_rescore.sh: using speaker vectors from exp/sgmm2_4/decode_test
steps/decode_sgmm2_rescore.sh: feature type is lda
steps/decode_sgmm2_rescore.sh: using transforms from exp/tri3/decode_test
steps/decode_sgmm2_rescore.sh: rescoring lattices with SGMM model in exp/sgmm2_4_mmi_b0.1/4.mdl
============================================================================
                    DNN Hybrid Training & Decoding                        
============================================================================
steps/nnet2/train_tanh.sh --mix-up 5000 --initial-learning-rate 0.015 --final-learning-rate 0.002 --num-hidden-layers 2 --num-jobs-nnet 30 --cmd run.pl data/train data/lang exp/tri3_ali exp/tri4_nnet
steps/nnet2/train_tanh.sh: calling get_lda.sh
steps/nnet2/get_lda.sh --transform-dir exp/tri3_ali --splice-width 4 --cmd run.pl data/train data/lang exp/tri3_ali exp/tri4_nnet
steps/nnet2/get_lda.sh: feature type is lda
steps/nnet2/get_lda.sh: using transforms from exp/tri3_ali
feat-to-dim 'ark,s,cs:utils/subset_scp.pl --quiet 333 data/train/split30/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split30/1/utt2spk scp:data/train/split30/1/cmvn.scp scp:- ark:- | splice-feats --left-context=3 --right-context=3 ark:- ark:- | transform-feats exp/tri4_nnet/final.mat ark:- ark:- | transform-feats --utt2spk=ark:data/train/split30/1/utt2spk ark:exp/tri3_ali/trans.1 ark:- ark:- |' - 
transform-feats --utt2spk=ark:data/train/split30/1/utt2spk ark:exp/tri3_ali/trans.1 ark:- ark:- 
apply-cmvn --utt2spk=ark:data/train/split30/1/utt2spk scp:data/train/split30/1/cmvn.scp scp:- ark:- 
transform-feats exp/tri4_nnet/final.mat ark:- ark:- 
splice-feats --left-context=3 --right-context=3 ark:- ark:- 
WARNING (feat-to-dim:Close():kaldi-io.cc:500) Pipe utils/subset_scp.pl --quiet 333 data/train/split30/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split30/1/utt2spk scp:data/train/split30/1/cmvn.scp scp:- ark:- | splice-feats --left-context=3 --right-context=3 ark:- ark:- | transform-feats exp/tri4_nnet/final.mat ark:- ark:- | transform-feats --utt2spk=ark:data/train/split30/1/utt2spk ark:exp/tri3_ali/trans.1 ark:- ark:- | had nonzero return status 36096
feat-to-dim 'ark,s,cs:utils/subset_scp.pl --quiet 333 data/train/split30/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split30/1/utt2spk scp:data/train/split30/1/cmvn.scp scp:- ark:- | splice-feats --left-context=3 --right-context=3 ark:- ark:- | transform-feats exp/tri4_nnet/final.mat ark:- ark:- | transform-feats --utt2spk=ark:data/train/split30/1/utt2spk ark:exp/tri3_ali/trans.1 ark:- ark:- | splice-feats --left-context=4 --right-context=4 ark:- ark:- |' - 
transform-feats exp/tri4_nnet/final.mat ark:- ark:- 
apply-cmvn --utt2spk=ark:data/train/split30/1/utt2spk scp:data/train/split30/1/cmvn.scp scp:- ark:- 
splice-feats --left-context=3 --right-context=3 ark:- ark:- 
transform-feats --utt2spk=ark:data/train/split30/1/utt2spk ark:exp/tri3_ali/trans.1 ark:- ark:- 
splice-feats --left-context=4 --right-context=4 ark:- ark:- 
WARNING (feat-to-dim:Close():kaldi-io.cc:500) Pipe utils/subset_scp.pl --quiet 333 data/train/split30/1/feats.scp | apply-cmvn  --utt2spk=ark:data/train/split30/1/utt2spk scp:data/train/split30/1/cmvn.scp scp:- ark:- | splice-feats --left-context=3 --right-context=3 ark:- ark:- | transform-feats exp/tri4_nnet/final.mat ark:- ark:- | transform-feats --utt2spk=ark:data/train/split30/1/utt2spk ark:exp/tri3_ali/trans.1 ark:- 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/tri3_ali --splice-width 4 --samples-per-iter 200000 --num-jobs-nnet 30 --stage 0 --cmd run.pl --io-opts -tc 5 data/train data/lang exp/tri3_ali exp/tri4_nnet
steps/nnet2/get_egs.sh: feature type is lda
steps/nnet2/get_egs.sh: using transforms from exp/tri3_ali
steps/nnet2/get_egs.sh: working out number of frames of training data
utils/data/get_utt2dur.sh: segments file does not exist so getting durations from wave files
utils/data/get_utt2dur.sh: successfully obtained utterance lengths from sphere-file headers
utils/data/get_utt2dur.sh: computed data/train/utt2dur
feat-to-len scp: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 37740 (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:main():copy-int-vector.cc:83) Copied 3696 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)
Mixing up from 1956 to 5000 components
Training neural net (pass 13)
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/tri4_nnet/egs
Removing most of the models
steps/nnet2/decode.sh --cmd run.pl --nj 5 --num-threads 6 --transform-dir exp/tri3/decode_dev exp/tri3/graph data/dev exp/tri4_nnet/decode_dev
steps/nnet2/decode.sh: feature type is lda
steps/nnet2/decode.sh: using transforms from exp/tri3/decode_dev
score best paths
score confidence and timing with sclite
Decoding done.
steps/nnet2/decode.sh --cmd run.pl --nj 5 --num-threads 6 --transform-dir exp/tri3/decode_test exp/tri3/graph data/test exp/tri4_nnet/decode_test
steps/nnet2/decode.sh: feature type is lda
steps/nnet2/decode.sh: using transforms from exp/tri3/decode_test
score best paths
score confidence and timing with sclite
Decoding done.
============================================================================
                    System Combination (DNN+SGMM)                         
============================================================================
============================================================================
               DNN Hybrid Training & Decoding (Karel's recipe)            
============================================================================
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd run.pl --transform-dir exp/tri3/decode_test data-fmllr-tri3/test data/test exp/tri3 data-fmllr-tri3/test/log data-fmllr-tri3/test/data
steps/nnet/make_fmllr_feats.sh: feature type is lda_fmllr
utils/copy_data_dir.sh: copied data from data/test to data-fmllr-tri3/test
utils/validate_data_dir.sh: Successfully validated data-directory data-fmllr-tri3/test
steps/nnet/make_fmllr_feats.sh: Done!, type lda_fmllr, data/test --> data-fmllr-tri3/test, using : raw-trans None, gmm exp/tri3, trans exp/tri3/decode_test
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd run.pl --transform-dir exp/tri3/decode_dev data-fmllr-tri3/dev data/dev exp/tri3 data-fmllr-tri3/dev/log data-fmllr-tri3/dev/data
steps/nnet/make_fmllr_feats.sh: feature type is lda_fmllr
utils/copy_data_dir.sh: copied data from data/dev to data-fmllr-tri3/dev
utils/validate_data_dir.sh: Successfully validated data-directory data-fmllr-tri3/dev
steps/nnet/make_fmllr_feats.sh: Done!, type lda_fmllr, data/dev --> data-fmllr-tri3/dev, using : raw-trans None, gmm exp/tri3, trans exp/tri3/decode_dev
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd run.pl --transform-dir exp/tri3_ali data-fmllr-tri3/train data/train exp/tri3 data-fmllr-tri3/train/log data-fmllr-tri3/train/data
steps/nnet/make_fmllr_feats.sh: feature type is lda_fmllr
utils/copy_data_dir.sh: copied data from data/train to data-fmllr-tri3/train
utils/validate_data_dir.sh: Successfully validated data-directory data-fmllr-tri3/train
steps/nnet/make_fmllr_feats.sh: Done!, type lda_fmllr, data/train --> data-fmllr-tri3/train, using : raw-trans None, gmm exp/tri3, trans exp/tri3_ali
utils/subset_data_dir_tr_cv.sh data-fmllr-tri3/train data-fmllr-tri3/train_tr90 data-fmllr-tri3/train_cv10
/Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/utils/subset_data_dir.sh: reducing #utt from     3696 to     3320
/Users/ouchangkun/Work/Git/kaldi/egs/timit/s5/utils/subset_data_dir.sh: reducing #utt from     3696 to      376
steps/nnet/decode.sh --nj 20 --cmd run.pl --acwt 0.2 exp/tri3/graph data-fmllr-tri3/test exp/dnn4_pretrain-dbn_dnn/decode_test
steps/nnet/decode.sh --nj 20 --cmd run.pl --acwt 0.2 exp/tri3/graph data-fmllr-tri3/dev exp/dnn4_pretrain-dbn_dnn/decode_dev
steps/nnet/align.sh --nj 20 --cmd run.pl data-fmllr-tri3/train data/lang exp/dnn4_pretrain-dbn_dnn exp/dnn4_pretrain-dbn_dnn_ali
steps/nnet/align.sh: aligning data 'data-fmllr-tri3/train' using nnet/model 'exp/dnn4_pretrain-dbn_dnn', putting alignments in 'exp/dnn4_pretrain-dbn_dnn_ali'
steps/nnet/align.sh: done aligning data.
steps/nnet/make_denlats.sh --nj 20 --cmd run.pl --acwt 0.2 --lattice-beam 10.0 --beam 18.0 data-fmllr-tri3/train data/lang exp/dnn4_pretrain-dbn_dnn exp/dnn4_pretrain-dbn_dnn_denlats
Making unigram grammar FST in exp/dnn4_pretrain-dbn_dnn_denlats/lang
Compiling decoding graph in exp/dnn4_pretrain-dbn_dnn_denlats/dengraph
tree-info exp/dnn4_pretrain-dbn_dnn/tree 
tree-info exp/dnn4_pretrain-dbn_dnn/tree 
fstpushspecial 
fsttablecompose exp/dnn4_pretrain-dbn_dnn_denlats/lang/L_disambig.fst exp/dnn4_pretrain-dbn_dnn_denlats/lang/G.fst 
fstminimizeencoded 
fstdeterminizestar --use-log=true 
fstisstochastic exp/dnn4_pretrain-dbn_dnn_denlats/lang/tmp/LG.fst 
1.2886e-05 1.2886e-05
fstcomposecontext --context-size=3 --central-position=1 --read-disambig-syms=exp/dnn4_pretrain-dbn_dnn_denlats/lang/phones/disambig.int --write-disambig-syms=exp/dnn4_pretrain-dbn_dnn_denlats/lang/tmp/disambig_ilabels_3_1.int exp/dnn4_pretrain-dbn_dnn_denlats/lang/tmp/ilabels_3_1 
fstisstochastic exp/dnn4_pretrain-dbn_dnn_denlats/lang/tmp/CLG_3_1.fst 
1.26958e-05 0
make-h-transducer --disambig-syms-out=exp/dnn4_pretrain-dbn_dnn_denlats/dengraph/disambig_tid.int --transition-scale=1.0 exp/dnn4_pretrain-dbn_dnn_denlats/lang/tmp/ilabels_3_1 exp/dnn4_pretrain-dbn_dnn/tree exp/dnn4_pretrain-dbn_dnn/final.mdl 
fstdeterminizestar --use-log=true 
fstminimizeencoded 
fsttablecompose exp/dnn4_pretrain-dbn_dnn_denlats/dengraph/Ha.fst exp/dnn4_pretrain-dbn_dnn_denlats/lang/tmp/CLG_3_1.fst 
fstrmepslocal 
fstrmsymbols exp/dnn4_pretrain-dbn_dnn_denlats/dengraph/disambig_tid.int 
fstisstochastic exp/dnn4_pretrain-dbn_dnn_denlats/dengraph/HCLGa.fst 
0.000473648 -0.000485808
add-self-loops --self-loop-scale=0.1 --reorder=true exp/dnn4_pretrain-dbn_dnn/final.mdl 
steps/nnet/make_denlats.sh: generating denlats from data 'data-fmllr-tri3/train', putting lattices in 'exp/dnn4_pretrain-dbn_dnn_denlats'
steps/nnet/make_denlats.sh: done generating denominator lattices.
steps/nnet/train_mpe.sh --cmd run.pl --num-iters 6 --acwt 0.2 --do-smbr true data-fmllr-tri3/train data/lang exp/dnn4_pretrain-dbn_dnn exp/dnn4_pretrain-dbn_dnn_ali exp/dnn4_pretrain-dbn_dnn_denlats exp/dnn4_pretrain-dbn_dnn_smbr
Pass 1 (learnrate 0.00001)
 TRAINING FINISHED; Time taken = 8.11796 min; processed 2309.33 frames per second.
 Done 3696 files, 0 with no reference alignments, 0 with no lattices, 0 with other errors.
 Overall average frame-accuracy is 0.870485 over 1124823 frames.
Pass 2 (learnrate 1e-05)
 TRAINING FINISHED; Time taken = 8.23882 min; processed 2275.45 frames per second.
 Done 3696 files, 0 with no reference alignments, 0 with no lattices, 0 with other errors.
 Overall average frame-accuracy is 0.877623 over 1124823 frames.
Pass 3 (learnrate 1e-05)
 TRAINING FINISHED; Time taken = 8.03047 min; processed 2334.49 frames per second.
 Done 3696 files, 0 with no reference alignments, 0 with no lattices, 0 with other errors.
 Overall average frame-accuracy is 0.881648 over 1124823 frames.
Pass 4 (learnrate 1e-05)
 TRAINING FINISHED; Time taken = 6.56847 min; processed 2854.1 frames per second.
 Done 3696 files, 0 with no reference alignments, 0 with no lattices, 0 with other errors.
 Overall average frame-accuracy is 0.884561 over 1124823 frames.
Pass 5 (learnrate 1e-05)
 TRAINING FINISHED; Time taken = 6.45276 min; processed 2905.28 frames per second.
 Done 3696 files, 0 with no reference alignments, 0 with no lattices, 0 with other errors.
 Overall average frame-accuracy is 0.886877 over 1124823 frames.
Pass 6 (learnrate 1e-05)
 TRAINING FINISHED; Time taken = 6.51641 min; processed 2876.9 frames per second.
 Done 3696 files, 0 with no reference alignments, 0 with no lattices, 0 with other errors.
 Overall average frame-accuracy is 0.8888 over 1124823 frames.
MPE/sMBR training finished
Re-estimating priors by forwarding 10k utterances from training set.
steps/nnet/make_priors.sh --cmd run.pl --nj 20 data-fmllr-tri3/train exp/dnn4_pretrain-dbn_dnn_smbr
Accumulating prior stats by forwarding 'data-fmllr-tri3/train' with 'exp/dnn4_pretrain-dbn_dnn_smbr'
Succeeded creating prior counts 'exp/dnn4_pretrain-dbn_dnn_smbr/prior_counts' from 'data-fmllr-tri3/train'
steps/nnet/train_mpe.sh: Done. 'exp/dnn4_pretrain-dbn_dnn_smbr'
steps/nnet/decode.sh --nj 20 --cmd run.pl --nnet exp/dnn4_pretrain-dbn_dnn_smbr/1.nnet --acwt 0.2 exp/tri3/graph data-fmllr-tri3/test exp/dnn4_pretrain-dbn_dnn_smbr/decode_test_it1
steps/nnet/decode.sh --nj 20 --cmd run.pl --nnet exp/dnn4_pretrain-dbn_dnn_smbr/1.nnet --acwt 0.2 exp/tri3/graph data-fmllr-tri3/dev exp/dnn4_pretrain-dbn_dnn_smbr/decode_dev_it1
steps/nnet/decode.sh --nj 20 --cmd run.pl --nnet exp/dnn4_pretrain-dbn_dnn_smbr/6.nnet --acwt 0.2 exp/tri3/graph data-fmllr-tri3/test exp/dnn4_pretrain-dbn_dnn_smbr/decode_test_it6
steps/nnet/decode.sh --nj 20 --cmd run.pl --nnet exp/dnn4_pretrain-dbn_dnn_smbr/6.nnet --acwt 0.2 exp/tri3/graph data-fmllr-tri3/dev exp/dnn4_pretrain-dbn_dnn_smbr/decode_dev_it6
Success
============================================================================
                    Getting Results [see RESULTS file]                    
============================================================================
%WER 32.0 | 400 15057 | 71.6 19.4 9.0 3.6 32.0 100.0 | -0.459 | exp/mono/decode_dev/score_5/ctm_39phn.filt.sys
%WER 24.8 | 400 15057 | 79.0 15.9 5.1 3.8 24.8 100.0 | -0.162 | exp/tri1/decode_dev/score_10/ctm_39phn.filt.sys
%WER 22.8 | 400 15057 | 80.9 14.3 4.9 3.6 22.8 99.5 | -0.288 | exp/tri2/decode_dev/score_10/ctm_39phn.filt.sys
%WER 20.3 | 400 15057 | 82.9 12.6 4.5 3.1 20.3 99.3 | -0.591 | exp/tri3/decode_dev/score_10/ctm_39phn.filt.sys
%WER 23.1 | 400 15057 | 80.2 14.7 5.0 3.4 23.1 99.5 | -0.210 | exp/tri3/decode_dev.si/score_10/ctm_39phn.filt.sys
%WER 21.2 | 400 15057 | 82.2 12.4 5.4 3.5 21.2 99.5 | -0.856 | exp/tri4_nnet/decode_dev/score_4/ctm_39phn.filt.sys
%WER 17.8 | 400 15057 | 85.0 11.1 4.0 2.8 17.8 99.0 | -0.306 | exp/sgmm2_4/decode_dev/score_8/ctm_39phn.filt.sys
%WER 18.2 | 400 15057 | 84.9 11.3 3.8 3.1 18.2 99.3 | -0.286 | exp/sgmm2_4_mmi_b0.1/decode_dev_it1/score_8/ctm_39phn.filt.sys
%WER 18.4 | 400 15057 | 84.6 11.4 4.1 3.0 18.4 99.8 | -0.226 | exp/sgmm2_4_mmi_b0.1/decode_dev_it2/score_9/ctm_39phn.filt.sys
%WER 18.5 | 400 15057 | 84.6 11.4 4.0 3.0 18.5 99.8 | -0.244 | exp/sgmm2_4_mmi_b0.1/decode_dev_it3/score_9/ctm_39phn.filt.sys
%WER 18.4 | 400 15057 | 84.6 11.4 4.0 3.0 18.4 99.8 | -0.252 | exp/sgmm2_4_mmi_b0.1/decode_dev_it4/score_9/ctm_39phn.filt.sys
%WER 17.6 | 400 15057 | 85.0 10.6 4.4 2.6 17.6 99.3 | -1.126 | exp/dnn4_pretrain-dbn_dnn/decode_dev/score_4/ctm_39phn.filt.sys
%WER 17.6 | 400 15057 | 85.0 10.6 4.4 2.6 17.6 99.3 | -0.771 | exp/dnn4_pretrain-dbn_dnn_smbr/decode_dev_it1/score_5/ctm_39phn.filt.sys
%WER 17.5 | 400 15057 | 85.6 10.7 3.7 3.2 17.5 99.3 | -0.749 | exp/dnn4_pretrain-dbn_dnn_smbr/decode_dev_it6/score_5/ctm_39phn.filt.sys
%WER 16.9 | 400 15057 | 85.6 11.0 3.3 2.5 16.9 99.3 | -0.024 | exp/combine_2/decode_dev_it1/score_7/ctm_39phn.filt.sys
%WER 17.0 | 400 15057 | 85.9 11.0 3.1 2.9 17.0 99.5 | -0.105 | exp/combine_2/decode_dev_it2/score_6/ctm_39phn.filt.sys
%WER 17.0 | 400 15057 | 85.6 11.0 3.3 2.7 17.0 99.3 | -0.024 | exp/combine_2/decode_dev_it3/score_7/ctm_39phn.filt.sys
%WER 17.0 | 400 15057 | 85.7 11.0 3.3 2.7 17.0 99.3 | -0.028 | exp/combine_2/decode_dev_it4/score_7/ctm_39phn.filt.sys
%WER 32.3 | 192 7215 | 70.4 19.4 10.2 2.7 32.3 100.0 | -0.292 | exp/mono/decode_test/score_6/ctm_39phn.filt.sys
%WER 25.9 | 192 7215 | 78.0 16.4 5.6 3.9 25.9 100.0 | -0.103 | exp/tri1/decode_test/score_10/ctm_39phn.filt.sys
%WER 23.8 | 192 7215 | 79.7 14.8 5.5 3.4 23.8 99.5 | -0.272 | exp/tri2/decode_test/score_10/ctm_39phn.filt.sys
%WER 21.2 | 192 7215 | 81.7 13.4 4.9 3.0 21.2 99.0 | -0.582 | exp/tri3/decode_test/score_10/ctm_39phn.filt.sys
%WER 23.8 | 192 7215 | 79.6 15.1 5.3 3.4 23.8 99.5 | -0.289 | exp/tri3/decode_test.si/score_9/ctm_39phn.filt.sys
%WER 22.5 | 192 7215 | 81.0 13.2 5.8 3.5 22.5 100.0 | -0.896 | exp/tri4_nnet/decode_test/score_4/ctm_39phn.filt.sys
%WER 19.2 | 192 7215 | 83.1 12.1 4.8 2.4 19.2 99.0 | -0.139 | exp/sgmm2_4/decode_test/score_10/ctm_39phn.filt.sys
%WER 19.6 | 192 7215 | 83.4 12.2 4.4 3.0 19.6 99.0 | -0.230 | exp/sgmm2_4_mmi_b0.1/decode_test_it1/score_9/ctm_39phn.filt.sys
%WER 19.9 | 192 7215 | 83.7 12.3 4.0 3.6 19.9 99.0 | -0.420 | exp/sgmm2_4_mmi_b0.1/decode_test_it2/score_7/ctm_39phn.filt.sys
%WER 20.0 | 192 7215 | 84.0 12.4 3.6 4.0 20.0 99.0 | -0.632 | exp/sgmm2_4_mmi_b0.1/decode_test_it3/score_6/ctm_39phn.filt.sys
%WER 19.9 | 192 7215 | 83.9 12.2 3.9 3.7 19.9 99.0 | -0.459 | exp/sgmm2_4_mmi_b0.1/decode_test_it4/score_7/ctm_39phn.filt.sys
%WER 18.3 | 192 7215 | 84.6 10.8 4.6 2.8 18.3 100.0 | -1.264 | exp/dnn4_pretrain-dbn_dnn/decode_test/score_4/ctm_39phn.filt.sys
%WER 18.2 | 192 7215 | 84.5 10.8 4.7 2.7 18.2 100.0 | -0.852 | exp/dnn4_pretrain-dbn_dnn_smbr/decode_test_it1/score_5/ctm_39phn.filt.sys
%WER 18.3 | 192 7215 | 85.0 11.2 3.9 3.3 18.3 100.0 | -0.830 | exp/dnn4_pretrain-dbn_dnn_smbr/decode_test_it6/score_5/ctm_39phn.filt.sys
%WER 18.3 | 192 7215 | 84.6 11.9 3.5 2.9 18.3 99.5 | -0.058 | exp/combine_2/decode_test_it1/score_6/ctm_39phn.filt.sys
%WER 18.3 | 192 7215 | 84.6 11.9 3.5 2.9 18.3 99.0 | -0.067 | exp/combine_2/decode_test_it2/score_6/ctm_39phn.filt.sys
%WER 18.3 | 192 7215 | 84.6 11.9 3.5 2.9 18.3 99.0 | -0.069 | exp/combine_2/decode_test_it3/score_6/ctm_39phn.filt.sys
%WER 18.4 | 192 7215 | 84.6 12.0 3.5 3.0 18.4 99.0 | -0.058 | exp/combine_2/decode_test_it4/score_6/ctm_39phn.filt.sys
============================================================================
Finished successfully on Sun Jun 5 11:16:53 CST 2016
============================================================================
➜  kaldi/egs/timit/s5 master ✗ 
#语音识别# #Kaldi#
  • Author: Changkun Ou
  • Link: https://changkun.de/blog/posts/kaldi-timit-example2/
  • License: All articles in this blog are licensed under CC BY-NC-ND 4.0 unless stating additionally.
Hash 碰撞的一种思路
Kaldi 上的 TIMIT 例子
  • TOC
  • Overview
Changkun Ou

Changkun Ou

Stop Talking. Just Coding.

276 Blogs
165 Tags
Homepage GitHub Email YouTube Twitter Zhihu
Friends
    Frimin ZZZero march1993 qcrao maiyang Xargin Muniao
© 2008 - 2024 Changkun Ou. All rights reserved. | PV/UV: /
0%