Yannick Estève / ONTRAC-Kaldi

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8dcb6dfcb   Yannick Estève   first commit 
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  #!/bin/bash
  # Copyright  2014   David Snyder
  #            2014   Daniel Povey
  # Apache 2.0.
  #
  # An incomplete run.sh for this example.
  
  . ./cmd.sh
  . ./path.sh
  set -e
  
  mfccdir=`pwd`/mfcc
  vaddir=`pwd`/mfcc
  
  
  local/make_sre_2008_train.pl /export/corpora5/LDC/LDC2011S05 data
  
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S48 french data
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S49 arabic.standard data
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S54 korean data
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S55 chinese.mandarin.mainland data
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S56 chinese.mandarin.taiwan data
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S57 spanish.caribbean data
  local/make_callfriend.pl /export/corpora5/LDC/LDC96S58 spanish.noncaribbean data
  
  local/make_lre03.pl /export/corpora4/LDC/LDC2006S31 data
  local/make_lre05.pl /export/corpora5/LDC/LDC2008S05 data
  local/make_lre07.pl /export/corpora5/LDC/LDC2009S04 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/ldc96s* data/lid05d1 \
      data/lid05e1 data/lid96d1 data/lid96e1 data/lre03"
  
  # 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
  echo "**Language count in training:**"
  awk '{print $2}' foo | sort | uniq -c | sort -nr
  rm foo
  
  local/split_long_utts.sh --max-utt-len 120 data/train_unsplit data/train
  
  use_vtln=true
  if $use_vtln; then
    for t in train lre07; do
      cp -rt data/${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_novtln_5k
  
    # for the features we use to estimate VTLN warp factors, we use more cepstra
    # (13 instead of just 7); this needs to be tuned.
    steps/make_mfcc.sh --mfcc-config conf/mfcc_vtln.conf --nj 50 --cmd "$train_cmd" \
      data/train_5k_novtln exp/make_mfcc $mfccdir
  
    # note, we're using the speaker-id version of the train_diag_ubm.sh script, which
    # uses double-delta instead of SDC features.  We train a 256-Gaussian UBM; this
    # has to be tuned.
    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 100 --cmd "$train_cmd" \
         data/${t}_novtln exp/vtln exp/${t}_warps
      cp exp/${t}_warps/utt2warp $data/$t/
    done
  fi
  
  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 "$train_cmd" data/train_5k 2048 \
    exp/diag_ubm_2048
  lid/train_full_ubm.sh --nj 30 --cmd "$train_cmd" data/train_10k \
    exp/diag_ubm_2048 exp/full_ubm_2048_10k
  
  lid/train_full_ubm.sh --nj 30 --cmd "$train_cmd" 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 2G" \
    --num-iters 5 exp/full_ubm_2048/final.ubm data/train \
    exp/extractor_2048
  
  lid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048 data/train exp/ivectors_train
  
  lid/extract_ivectors.sh --cmd "$train_cmd --mem 3G" --nj 50 \
     exp/extractor_2048 data/lre07 exp/ivectors_lre07