Yannick Estève / ONTRAC-Kaldi

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egs/sre08/v1/local/run_more_data.sh 12.5 KB
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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  # Copyright 2013   Daniel Povey
  # Apache 2.0.
  #
  # See README.txt for more info on data required.
  # Results (EERs) are inline in comments below.
  
  # This example script is still a bit of a mess, and needs to be
  # cleaned up, but it shows you all the basic ingredients.
  
  . ./cmd.sh
  . ./path.sh
  set -e
  mfccdir=`pwd`/mfcc
  vaddir=`pwd`/mfcc
  
  local/make_fisher.sh /export/corpora3/LDC/{LDC2004S13,LDC2004T19} data/fisher1
  #Processed 4948 utterances; 902 had missing wav data. (note: we should figure
  #out why so much data goes missing.)
  local/make_fisher.sh /export/corpora3/LDC/{LDC2005S13,LDC2005T19} data/fisher2
  #Processed 5848 utterances; 1 had missing wav data.
  
  local/make_sre_2008_train.pl /export/corpora5/LDC/LDC2011S05 data
  local/make_sre_2008_test.sh  /export/corpora5/LDC/LDC2011S08 data
  local/make_sre_2006_train.pl /export/corpora5/LDC/LDC2011S09 data
  local/make_sre_2005_train.pl /export/corpora5/LDC/LDC2011S01 data
  local/make_swbd_cellular1.pl /export/corpora5/LDC/LDC2001S13 data/swbd_cellular1_train
  local/make_swbd_cellular2.pl /export/corpora5/LDC/LDC2004S07 data/swbd_cellular2_train
  
  utils/combine_data.sh data/train data/fisher1 data/fisher2 data/swbd_cellular1_train data/swbd_cellular2_train data/sre05_train_3conv4w_female data/sre05_train_8conv4w_female data/sre06_train_3conv4w_female data/sre06_train_8conv4w_female data/sre05_train_3conv4w_male data/sre05_train_8conv4w_male data/sre06_train_3conv4w_male data/sre06_train_8conv4w_male
  
  
  mfccdir=`pwd`/mfcc
  vaddir=`pwd`/mfcc
  
  set -e
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" data/train exp/make_mfcc $mfccdir
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" data/sre08_train_short2_female exp/make_mfcc $mfccdir
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" data/sre08_train_short2_male exp/make_mfcc $mfccdir
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" data/sre08_test_short3_female exp/make_mfcc $mfccdir
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" data/sre08_test_short3_male exp/make_mfcc $mfccdir
  
  
  sid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/train exp/make_vad $vaddir
  sid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/sre08_train_short2_female exp/make_vad $vaddir
  sid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/sre08_train_short2_male exp/make_vad $vaddir
  sid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/sre08_test_short3_female exp/make_vad $vaddir
  sid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/sre08_test_short3_male exp/make_vad $vaddir
  
  
  # Note: to see the proportion of voiced frames you can do,
  # grep Prop exp/make_vad/vad_*.1.log
  
  # Get male and female subsets of training data.
  grep -w m data/train/spk2gender | awk '{print $1}' > foo;
  utils/subset_data_dir.sh --spk-list foo data/train data/train_male
  grep -w f data/train/spk2gender | awk '{print $1}' > foo;
  utils/subset_data_dir.sh --spk-list foo data/train data/train_female
  rm foo
  
  # Get smaller subsets of training data for faster training.
  utils/subset_data_dir.sh data/train 2000 data/train_2k
  utils/subset_data_dir.sh data/train 4000 data/train_4k
  utils/subset_data_dir.sh data/train_male 4000 data/train_male_4k
  utils/subset_data_dir.sh data/train_female 4000 data/train_female_4k
  
  
  sid/train_diag_ubm.sh --nj 30 --cmd "$train_cmd" data/train_2k 2048 exp/diag_ubm_2048
  
  sid/train_full_ubm.sh --nj 30 --cmd "$train_cmd" data/train_4k exp/diag_ubm_2048 exp/full_ubm_2048
  
  # Get male and female versions of the UBM in one pass; make sure not to remove
  # any Gaussians due to low counts (so they stay matched).  This will be more convenient
  # for gender-id.
  sid/train_full_ubm.sh --nj 30 --remove-low-count-gaussians false --num-iters 1 --cmd "$train_cmd" \
     data/train_male_4k exp/full_ubm_2048 exp/full_ubm_2048_male &
  sid/train_full_ubm.sh --nj 30 --remove-low-count-gaussians false --num-iters 1 --cmd "$train_cmd" \
     data/train_female_4k exp/full_ubm_2048 exp/full_ubm_2048_female &
  wait
  
  # note, the --mem is counted per thread... in this setup each
  # job has 4 processes running each with 4 threads; each job takes about 5G
  # of memory so we need about 20G, plus add memory for sum-accs to make it 25G.
  # but we'll submit using --num-threads 16, and this multiplies the memory requirement
  # by 16, so submitting with 2G as the requirement, to make the total requirement
  # 32, is reasonable.
  
  # Train the iVector extractor for male speakers.
  sid/train_ivector_extractor.sh --cmd "$train_cmd --mem 2G" \
    --num-iters 5 exp/full_ubm_2048_male/final.ubm data/train_male \
    exp/extractor_2048_male
  
  # The same for female speakers.
  sid/train_ivector_extractor.sh --cmd "$train_cmd --mem 2G" \
    --num-iters 5 exp/full_ubm_2048_female/final.ubm data/train_female \
    exp/extractor_2048_female
  
  # The script below demonstrates the gender-id script.  We don't really use
  # it for anything here, because the SRE 2008 data is already split up by
  # gender and gender identification is not required for the eval.
  # It prints out the error rate based on the info in the spk2gender file;
  # see exp/gender_id_fisher/error_rate where it is also printed.
  sid/gender_id.sh --cmd "$train_cmd" --nj 150 exp/full_ubm_2048{,_male,_female} \
    data/train exp/gender_id_train
  # Gender-id error rate is 2.58%
  
  # Extract the iVectors for the Fisher data.
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048_male data/train_male exp/ivectors_train_male
  
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048_female data/train_female exp/ivectors_train_female
  
  # .. and for the SRE08 training and test data. (We focus on the main
  # evaluation condition, the only required one in that eval, which is
  # the short2-short3 eval.)
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048_female data/sre08_train_short2_female exp/ivectors_sre08_train_short2_female
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048_male data/sre08_train_short2_male exp/ivectors_sre08_train_short2_male
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048_female data/sre08_test_short3_female exp/ivectors_sre08_test_short3_female
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048_male data/sre08_test_short3_male exp/ivectors_sre08_test_short3_male
  
  
  ### Demonstrate simple cosine-distance scoring:
  
  trials=data/sre08_trials/short2-short3-female.trials
  cat $trials | awk '{print $1, $2}' | \
   ivector-compute-dot-products - \
    scp:exp/ivectors_sre08_train_short2_female/spk_ivector.scp \
    scp:exp/ivectors_sre08_test_short3_female/spk_ivector.scp \
     foo
  
  local/score_sre08.sh $trials foo
  
  # Results for Female:
  # Scoring against data/sre08_trials/short2-short3-female.trials
  #  Condition:      1      2      3      4      5      6      7      8
  #        EER:  27.28   4.78  26.86  21.02  18.87   9.92   6.46   7.11
  trials=data/sre08_trials/short2-short3-male.trials
  cat $trials | awk '{print $1, $2}' | \
   ivector-compute-dot-products - \
    scp:exp/ivectors_sre08_train_short2_male/spk_ivector.scp \
    scp:exp/ivectors_sre08_test_short3_male/spk_ivector.scp \
     foo
  
  local/score_sre08.sh $trials foo
  
  # Results for Male:
  # Scoring against data/sre08_trials/short2-short3-male.trials
  #  Condition:      1      2      3      4      5      6      7      8
  #        EER:  25.69   3.63  25.36  17.54  17.50   7.78   6.38   5.26
  
  # The following shows a more direct way to get the scores.
  #condition=6
  #awk '{print $3}' foo | paste - $trials | awk -v c=$condition '{n=4+c; if ($n == "Y") print $1, $4}' | \
  #  compute-eer -
  # LOG (compute-eer:main():compute-eer.cc:136) Equal error rate is 11.1419%, at threshold 55.9827
  
  # Note: to see how you can plot the DET curve, look at
  # local/det_curve_example.sh
  
  
  ### Demonstrate what happens if we reduce the dimension with LDA
  
   ivector-compute-lda --dim=150  --total-covariance-factor=0.1 \
    'ark:ivector-normalize-length scp:exp/ivectors_train_female/ivector.scp  ark:- |' ark:data/train_female/utt2spk \
      exp/ivectors_train_female/transform.mat
  
   trials=data/sre08_trials/short2-short3-female.trials
   cat $trials | awk '{print $1, $2}' | \
    ivector-compute-dot-products - \
     'ark:ivector-transform exp/ivectors_train_female/transform.mat scp:exp/ivectors_sre08_train_short2_female/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     'ark:ivector-transform exp/ivectors_train_female/transform.mat scp:exp/ivectors_sre08_test_short3_female/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     foo
  
  local/score_sre08.sh $trials foo
  
  # Results for Female:
  # Scoring against data/sre08_trials/short2-short3-female.trials
  #  Condition:      1      2      3      4      5      6      7      8
  #        EER:  21.36   2.09  20.97  14.26  15.26   9.26   6.08   6.32
  
   ivector-compute-lda --dim=150 --total-covariance-factor=0.1 \
    'ark:ivector-normalize-length scp:exp/ivectors_train_male/ivector.scp  ark:- |' ark:data/train_male/utt2spk \
      exp/ivectors_train_male/transform.mat
  
   trials=data/sre08_trials/short2-short3-male.trials
   cat $trials | awk '{print $1, $2}' | \
    ivector-compute-dot-products - \
     'ark:ivector-transform exp/ivectors_train_male/transform.mat scp:exp/ivectors_sre08_train_short2_male/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     'ark:ivector-transform exp/ivectors_train_male/transform.mat scp:exp/ivectors_sre08_test_short3_male/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     foo
  
  local/score_sre08.sh $trials foo
  
  # Results for Male:
  # Scoring against data/sre08_trials/short2-short3-male.trials
  #  Condition:      1      2      3      4      5      6      7      8
  #        EER:  18.10   1.61  18.03  11.39  11.25   7.44   5.92   3.95
  
  ### Demonstrate PLDA scoring:
  
  ## Note: below, the ivector-subtract-global-mean step doesn't appear to affect
  ## the EER, although it does shift the threshold.
  
   trials=data/sre08_trials/short2-short3-female.trials
   cat $trials | awk '{print $1, $2}' | \
    ivector-compute-dot-products - \
     'ark:ivector-transform exp/ivectors_train_female/transform.mat scp:exp/ivectors_sre08_train_short2_female/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     'ark:ivector-transform exp/ivectors_train_female/transform.mat scp:exp/ivectors_sre08_test_short3_female/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     foo
  
  ivector-compute-plda ark:data/train_female/spk2utt \
    'ark:ivector-normalize-length scp:exp/ivectors_train_female/ivector.scp  ark:- |' \
      exp/ivectors_train_female/plda 2>exp/ivectors_train_female/log/plda.log
  
  
  ivector-plda-scoring --num-utts=ark:exp/ivectors_sre08_train_short2_female/num_utts.ark \
     "ivector-copy-plda --smoothing=0.0 exp/ivectors_train_female/plda - |" \
     "ark:ivector-subtract-global-mean scp:exp/ivectors_sre08_train_short2_female/spk_ivector.scp ark:- |" \
     "ark:ivector-subtract-global-mean scp:exp/ivectors_sre08_test_short3_female/ivector.scp ark:- |" \
     "cat '$trials' | awk '{print \$1, \$2}' |" foo
  
  local/score_sre08.sh $trials foo
  
  # Result for Female is below:
  # Scoring against data/sre08_trials/short2-short3-female.trials
  #  Condition:      1      2      3      4      5      6      7      8
  #        EER:  19.04   2.39  19.21  16.52  12.62   9.48   5.83   6.05
  
   trials=data/sre08_trials/short2-short3-male.trials
   cat $trials | awk '{print $1, $2}' | \
    ivector-compute-dot-products - \
     'ark:ivector-transform exp/ivectors_train_male/transform.mat scp:exp/ivectors_sre08_train_short2_male/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     'ark:ivector-transform exp/ivectors_train_male/transform.mat scp:exp/ivectors_sre08_test_short3_male/spk_ivector.scp ark:- | ivector-normalize-length ark:- ark:- |' \
     foo
  
  ivector-compute-plda ark:data/train_male/spk2utt \
    'ark:ivector-normalize-length scp:exp/ivectors_train_male/ivector.scp  ark:- |' \
      exp/ivectors_train_male/plda 2>exp/ivectors_train_male/log/plda.log
  
  ivector-plda-scoring --num-utts=ark:exp/ivectors_sre08_train_short2_male/num_utts.ark \
     "ivector-copy-plda --smoothing=0.0 exp/ivectors_train_male/plda - |" \
     "ark:ivector-subtract-global-mean scp:exp/ivectors_sre08_train_short2_male/spk_ivector.scp ark:- |" \
     "ark:ivector-subtract-global-mean scp:exp/ivectors_sre08_test_short3_male/ivector.scp ark:- |" \
     "cat '$trials' | awk '{print \$1, \$2}' |" foo
  
  local/score_sre08.sh $trials foo
  
  
  # Result for Male is below:
  # Scoring against data/sre08_trials/short2-short3-male.trials
  #  Condition:      1      2      3      4      5      6      7      8
  #        EER:  15.18   1.61  15.45  12.76  10.94   8.12   5.24   4.82