#!/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_free,ram_free 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 -pe smp 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 -l mem_free=2G,ram_free=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 -l mem_free=2G,ram_free=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 -l mem_free=3G,ram_free=3G" --nj 50 \
   exp/extractor_2048_male data/train_male exp/ivectors_train_male

sid/extract_ivectors.sh --cmd "$train_cmd -l mem_free=3G,ram_free=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 "$train_cmd -l mem_free=3G,ram_free=3G" --nj 50 \
   exp/extractor_2048_female data/sre08_train_short2_female exp/ivectors_sre08_train_short2_female
sid/extract_ivectors.sh --cmd "$train_cmd -l mem_free=3G,ram_free=3G" --nj 50 \
   exp/extractor_2048_male data/sre08_train_short2_male exp/ivectors_sre08_train_short2_male
sid/extract_ivectors.sh --cmd "$train_cmd -l mem_free=3G,ram_free=3G" --nj 50 \
   exp/extractor_2048_female data/sre08_test_short3_female exp/ivectors_sre08_test_short3_female
sid/extract_ivectors.sh --cmd "$train_cmd -l mem_free=3G,ram_free=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