Yannick Estève / ONTRAC-Kaldi

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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  # Copyright 2013   Daniel Povey
  #      2014-2016   David Snyder
  # 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_2005_test.pl /export/corpora5/LDC/LDC2011S04 data
  local/make_sre_2004_test.pl \
    /export/corpora5/LDC/LDC2006S44/r93_5_1/sp04-05/test data/sre_2004_1
  local/make_sre_2004_test.pl \
    /export/corpora5/LDC/LDC2006S44/r93_6_1/sp04-06/test data/sre_2004_2
  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 \
    data/sre_2004_1/ data/sre_2004_2/ data/sre05_test
  
  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 4000 data/train_4k
  utils/subset_data_dir.sh data/train 8000 data/train_8k
  utils/subset_data_dir.sh data/train_male 8000 data/train_male_8k
  utils/subset_data_dir.sh data/train_female 8000 data/train_female_8k
  
  # The recipe currently uses delta-window=3 and delta-order=2. However
  # the accuracy is almost as good using delta-window=4 and delta-order=1
  # and could be faster due to lower dimensional features.  Alternative
  # delta options (e.g., --delta-window 4 --delta-order 1) can be provided to
  # sid/train_diag_ubm.sh.  The options will be propagated to the other scripts.
  sid/train_diag_ubm.sh --nj 30 --cmd "$train_cmd" data/train_4k 2048 \
    exp/diag_ubm_2048
  
  sid/train_full_ubm.sh --nj 30 --cmd "$train_cmd" data/train_8k \
    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 "$train_cmd" \
    data/train_male_8k exp/full_ubm_2048 exp/full_ubm_2048_male &
  sid/train_full_ubm.sh --nj 30 --remove-low-count-gaussians false \
    --num-iters 1 --cmd "$train_cmd" \
    data/train_female_8k exp/full_ubm_2048 exp/full_ubm_2048_female &
  wait
  
  # Train the iVector extractor for male speakers.
  sid/train_ivector_extractor.sh --cmd "$train_cmd --mem 35G" \
    --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 35G" \
    --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 3.41%
  
  # Extract the iVectors for the training data.
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 6G" --nj 50 \
    exp/extractor_2048_male data/train_male exp/ivectors_train_male
  
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 6G" --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 "$train_cmd --mem 6G" --nj 50 \
    exp/extractor_2048_female data/sre08_train_short2_female \
    exp/ivectors_sre08_train_short2_female
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 6G" --nj 50 \
    exp/extractor_2048_male data/sre08_train_short2_male \
    exp/ivectors_sre08_train_short2_male
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 6G" --nj 50 \
    exp/extractor_2048_female data/sre08_test_short3_female \
    exp/ivectors_sre08_test_short3_female
  sid/extract_ivectors.sh --cmd "$train_cmd --mem 6G" --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
  
  # Note: speaker-level i-vectors have already been length-normalized
  # by sid/extract_ivectors.sh, but the utterance-level test i-vectors
  # have not.
  cat $trials | awk '{print $1, $2}' | \
    ivector-compute-dot-products - \
    scp:exp/ivectors_sre08_train_short2_female/spk_ivector.scp \
    'ark:ivector-normalize-length scp:exp/ivectors_sre08_test_short3_female/ivector.scp ark:- |' \
    foo
  local/score_sre08.sh $trials foo
  
  # Results for Female:
  # Scoring against data/sre08_trials/short2-short3-female.trials
  #  Condition:      0      1      2      3      4      5      6      7      8
  #        EER:  12.70  20.09   4.78  19.08  16.37  15.87  10.42   7.10   7.89
  
  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 \
    'ark:ivector-normalize-length scp:exp/ivectors_sre08_test_short3_male/ivector.scp ark:- |' \
    foo
  local/score_sre08.sh $trials foo
  
  # Results for Male:
  # Scoring against data/sre08_trials/short2-short3-male.trials
  #  Condition:      0      1      2      3      4      5      6      7      8
  #        EER:  11.10  18.55   5.24  18.03  14.35  13.44   8.47   5.92   4.82
  
  # 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 11.10%,
  # 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-normalize-length scp:exp/ivectors_sre08_test_short3_female/ivector.scp ark:- | ivector-transform exp/ivectors_train_female/transform.mat ark:- 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:      0      1      2      3      4      5      6      7      8
  #        EER:   7.96   9.82   1.49   9.44  10.51  10.70   8.81   5.83   7.11
  
  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-normalize-length scp:exp/ivectors_sre08_test_short3_male/ivector.scp ark:- | ivector-transform exp/ivectors_train_male/transform.mat ark:- 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:      0      1      2      3      4      5      6      7      8
  #        EER:   6.20   8.30   1.21   8.10   8.43   7.03   7.32   5.70   3.51
  
  
  ### 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
  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 --simple-length-normalization=true --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-normalize-length scp:exp/ivectors_sre08_test_short3_female/ivector.scp ark:- | ivector-subtract-global-mean ark:- 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:      0      1      2      3      4      5      6      7      8
  #        EER:   6.44   9.76   1.49   9.76   7.66   7.21   6.87   4.06   4.74
  
  trials=data/sre08_trials/short2-short3-male.trials
  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 --simple-length-normalization=true --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-normalize-length scp:exp/ivectors_sre08_test_short3_male/ivector.scp ark:- | ivector-subtract-global-mean ark:- 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:      0      1      2      3      4      5      6      7      8
  #        EER:   4.68   7.41   1.21   7.48   5.70   4.69   5.61   3.19   2.19
  
  
  ### Demonstrate PLDA scoring after adapting the out-of-domain PLDA model with in-domain training data:
  
  # first, female.
  trials=data/sre08_trials/short2-short3-female.trials
  cat exp/ivectors_sre08_train_short2_female/spk_ivector.scp exp/ivectors_sre08_test_short3_female/ivector.scp > female.scp
  ivector-plda-scoring --simple-length-normalization=true --num-utts=ark:exp/ivectors_sre08_train_short2_female/num_utts.ark \
    "ivector-adapt-plda $adapt_opts exp/ivectors_train_female/plda scp:female.scp -|" \
    scp:exp/ivectors_sre08_train_short2_female/spk_ivector.scp \
    "ark:ivector-normalize-length scp:exp/ivectors_sre08_test_short3_female/ivector.scp ark:- |" \
    "cat '$trials' | awk '{print \$1, \$2}' |" foo; local/score_sre08.sh $trials foo
  # Results:
  #  Condition:      0      1      2      3      4      5      6      7      8
  #        EER:   5.45   6.73   1.19   6.79   7.06   6.61   6.32   4.18   4.74
  # Baseline (repeated from above):
  #  Condition:      0      1      2      3      4      5      6      7      8
  #        EER:   6.44   9.76   1.49   9.76   7.66   7.21   6.87   4.06   4.74
  
  # next, male.
  trials=data/sre08_trials/short2-short3-male.trials
  cat exp/ivectors_sre08_train_short2_male/spk_ivector.scp exp/ivectors_sre08_test_short3_male/ivector.scp > male.scp
  ivector-plda-scoring --simple-length-normalization=true --num-utts=ark:exp/ivectors_sre08_train_short2_male/num_utts.ark \
    "ivector-adapt-plda $adapt_opts exp/ivectors_train_male/plda scp:male.scp -|" \
    scp:exp/ivectors_sre08_train_short2_male/spk_ivector.scp \
    "ark:ivector-normalize-length scp:exp/ivectors_sre08_test_short3_male/ivector.scp ark:- |" \
    "cat '$trials' | awk '{print \$1, \$2}' |" foo; local/score_sre08.sh $trials foo
  
  # Results:
  #  Condition:      0      1      2      3      4      5      6      7      8
  #        EER:   4.03   4.71   0.81   4.73   5.01   4.84   5.61   3.87   2.63
  # Baseline is as follows, repeated from above.  Focus on condition 0 (= all).
  #  Condition:      0      1      2      3      4      5      6      7      8
  #        EER:   4.68   7.41   1.21   7.48   5.70   4.69   5.61   3.19   2.19