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  #!/bin/bash
  # Copyright  2014-2015  David Snyder
  #                       Daniel Povey
  # Apache 2.0.
  #
  # This script runs the NIST 2007 General Language Recognition Closed-Set
  # evaluation.
  
  . ./cmd.sh
  . ./path.sh
  set -e
  
  mfccdir=`pwd`/mfcc
  vaddir=`pwd`/mfcc
  languages=local/general_lr_closed_set_langs.txt
  data_root=/export/corpora/LDC
  
  # Training data sources
  local/make_sre_2008_train.pl $data_root/LDC2011S05 data
  local/make_callfriend.pl $data_root/LDC96S60 vietnamese data
  local/make_callfriend.pl $data_root/LDC96S59 tamil data
  local/make_callfriend.pl $data_root/LDC96S53 japanese data
  local/make_callfriend.pl $data_root/LDC96S52 hindi data
  local/make_callfriend.pl $data_root/LDC96S51 german data
  local/make_callfriend.pl $data_root/LDC96S50 farsi data
  local/make_callfriend.pl $data_root/LDC96S48 french data
  local/make_callfriend.pl $data_root/LDC96S49 arabic.standard data
  local/make_callfriend.pl $data_root/LDC96S54 korean data
  local/make_callfriend.pl $data_root/LDC96S55 chinese.mandarin.mainland data
  local/make_callfriend.pl $data_root/LDC96S56 chinese.mandarin.taiwan data
  local/make_callfriend.pl $data_root/LDC96S57 spanish.caribbean data
  local/make_callfriend.pl $data_root/LDC96S58 spanish.noncaribbean data
  local/make_lre03.pl $data_root/LDC/LDC2006S31 data
  local/make_lre05.pl $data_root/LDC/LDC2008S05 data
  local/make_lre07_train.pl $data_root/LDC2009S05 data
  local/make_lre09.pl /export/corpora5/NIST/LRE/LRE2009/eval data
  
  # Make the evaluation data set. We're concentrating on the General Language
  # Recognition Closed-Set evaluation, so we remove the dialects and filter
  # out the unknown languages used in the open-set evaluation.
  local/make_lre07.pl $data_root/LDC2009S04 data/lre07_all
  
  cp -r data/lre07_all data/lre07
  utils/filter_scp.pl -f 2 $languages <(lid/remove_dialect.pl data/lre07_all/utt2lang) \
    > data/lre07/utt2lang
  utils/fix_data_dir.sh data/lre07
  
  src_list="data/sre08_train_10sec_female \
      data/sre08_train_10sec_male data/sre08_train_3conv_female \
      data/sre08_train_3conv_male data/sre08_train_8conv_female \
      data/sre08_train_8conv_male data/sre08_train_short2_male \
      data/sre08_train_short2_female data/ldc96* data/lid05d1 \
      data/lid05e1 data/lid96d1 data/lid96e1 data/lre03 \
      data/ldc2009* data/lre09"
  
  # Remove any spk2gender files that we have: since not all data
  # sources have this info, it will cause problems with combine_data.sh
  for d in $src_list; do rm -f $d/spk2gender 2>/dev/null; done
  
  utils/combine_data.sh data/train_unsplit $src_list
  
  # original utt2lang will remain in data/train_unsplit/.backup/utt2lang.
  utils/apply_map.pl -f 2 --permissive local/lang_map.txt \
    < data/train_unsplit/utt2lang 2>/dev/null > foo
  cp foo data/train_unsplit/utt2lang
  rm foo
  
  local/split_long_utts.sh --max-utt-len 120 data/train_unsplit data/train
  
  echo "**Language count in i-Vector extractor training (after splitting long utterances):**"
  awk '{print $2}' data/train/utt2lang | sort | uniq -c | sort -nr
  
  use_vtln=true
  if $use_vtln; then
    for t in train lre07; do
      cp -r data/${t} data/${t}_novtln
      rm -r data/${t}_novtln/{split,.backup,spk2warp} 2>/dev/null || true
      steps/make_mfcc.sh --mfcc-config conf/mfcc_vtln.conf --nj 100 --cmd "$train_cmd" \
         data/${t}_novtln exp/make_mfcc $mfccdir
      lid/compute_vad_decision.sh data/${t}_novtln exp/make_mfcc $mfccdir
    done
  
    # Vtln-related things:
    # We'll use a subset of utterances to train the GMM we'll use for VTLN
    # warping.
    utils/subset_data_dir.sh data/train_novtln 5000 data/train_5k_novtln
  
    # Note, we're using the speaker-id version of the train_diag_ubm.sh script, which
    # uses double-delta instead of SDC features to train a 256-Gaussian UBM.
    sid/train_diag_ubm.sh --nj 30 --cmd "$train_cmd" data/train_5k_novtln 256 \
      exp/diag_ubm_vtln
    lid/train_lvtln_model.sh --mfcc-config conf/mfcc_vtln.conf --nj 30 --cmd "$train_cmd" \
       data/train_5k_novtln exp/diag_ubm_vtln exp/vtln
  
    for t in lre07 train; do
      lid/get_vtln_warps.sh --nj 50 --cmd "$train_cmd" \
         data/${t}_novtln exp/vtln exp/${t}_warps
      cp exp/${t}_warps/utt2warp data/$t/
    done
  fi
  
  
  utils/fix_data_dir.sh data/train
  utils/filter_scp.pl data/train/utt2warp data/train/utt2spk > data/train/utt2spk_tmp
  cp data/train/utt2spk_tmp data/train/utt2spk
  utils/fix_data_dir.sh data/train
  
  
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 100 --cmd "$train_cmd" \
    data/train exp/make_mfcc $mfccdir
  steps/make_mfcc.sh --mfcc-config conf/mfcc.conf --nj 40 --cmd "$train_cmd" \
    data/lre07 exp/make_mfcc $mfccdir
  
  lid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/train \
    exp/make_vad $vaddir
  lid/compute_vad_decision.sh --nj 4 --cmd "$train_cmd" data/lre07 \
    exp/make_vad $vaddir
  
  
  utils/subset_data_dir.sh data/train 5000 data/train_5k
  utils/subset_data_dir.sh data/train 10000 data/train_10k
  
  
  lid/train_diag_ubm.sh --nj 30 --cmd "$train_cmd --mem 20G" \
    data/train_5k 2048 exp/diag_ubm_2048
  lid/train_full_ubm.sh --nj 30 --cmd "$train_cmd --mem 20G" \
    data/train_10k exp/diag_ubm_2048 exp/full_ubm_2048_10k
  
  lid/train_full_ubm.sh --nj 30 --cmd "$train_cmd --mem 35G" \
    data/train exp/full_ubm_2048_10k exp/full_ubm_2048
  
  # Alternatively, a diagonal UBM can replace the full UBM used above.
  # The preceding calls to train_diag_ubm.sh and train_full_ubm.sh
  # can be commented out and replaced with the following lines.
  #
  # This results in a slight degradation but could improve error rate when
  # there is less training data than used in this example.
  #
  #lid/train_diag_ubm.sh --nj 30 --cmd "$train_cmd" data/train 2048 \
  #  exp/diag_ubm_2048
  #
  #gmm-global-to-fgmm exp/diag_ubm_2048/final.dubm \
  #  exp/full_ubm_2048/final.ubm
  
  lid/train_ivector_extractor.sh --cmd "$train_cmd --mem 35G" \
    --use-weights true \
    --num-iters 5 exp/full_ubm_2048/final.ubm data/train \
    exp/extractor_2048
  
  # Filter out the languages we don't need for the closed-set eval
  cp -r data/train data/train_lr
  utils/filter_scp.pl -f 2 $languages <(lid/remove_dialect.pl data/train/utt2lang) \
    > data/train_lr/utt2lang
  utils/fix_data_dir.sh data/train_lr
  
  echo "**Language count for logistic regression training (after splitting long utterances):**"
  awk '{print $2}' data/train_lr/utt2lang | sort | uniq -c | sort -nr
  
  lid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048 data/train_lr exp/ivectors_train
  
  lid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048 data/lre07 exp/ivectors_lre07
  
  lid/run_logistic_regression.sh --prior-scale 0.70 \
    --conf conf/logistic-regression.conf
  # Training error-rate
  # ER (%): 3.95
  
  # General LR 2007 closed-set eval
  local/lre07_eval/lre07_eval.sh exp/ivectors_lre07 \
    local/general_lr_closed_set_langs.txt
  # Duration (sec):    avg      3     10     30
  #         ER (%):  23.11  42.84  19.33   7.18
  #      C_avg (%):  14.17  26.04  11.93   4.52