#!/bin/bash
  
  . ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
             ## This relates to the queue.
  
  # This is a shell script, but it's recommended that you run the commands one by
  # one by copying and pasting into the shell.
  
  case 0 in    #goto here
      1)
  ;;           #here:
  esac
  
  #exit 1;
  #need wsj0 for the clean version and LMs
  #wsj0=/mnt/spdb/wall_street_journal
  wsj0=/export/corpora5/LDC/LDC93S6B
  local/clean_wsj0_data_prep.sh $wsj0
  
  #reverb=/mnt/spdb/CHiME/chime2-wsj0/reverberated 
  reverb=/export/corpora5/ChiME/chime2-wsj0/reverberated
  local/reverb_wsj0_data_prep.sh $reverb 
  
  #noisy=/mnt/spdb/CHiME/chime2-wsj0/isolated
  noisy=/export/corpora5/ChiME/chime2-wsj0/isolated
  local/noisy_wsj0_data_prep.sh $noisy 
  
  local/wsj_prepare_dict.sh || exit 1;
  
  utils/prepare_lang.sh data/local/dict "<SPOKEN_NOISE>" data/local/lang_tmp data/lang || exit 1;
  
  local/chime_format_data.sh || exit 1;
  
  # Now make MFCC features.
  # mfccdir should be some place with a largish disk where you
  # want to store MFCC features.
  
  mfccdir=mfcc
  for x in test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean train_si84_clean; do 
   steps/make_mfcc.sh --nj 10 --cmd "$train_cmd" \
     data/$x exp/make_mfcc/$x $mfccdir || exit 1;
   steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  done
  
  # Note: the --boost-silence option should probably be omitted by default
  # for normal setups.  It doesn't always help. [it's to discourage non-silence
  # models from modeling silence.]
  mfccdir=mfcc
  for x in test_eval92_5k_noisy dev_dt_05_noisy train_si84_noisy; do 
   steps/make_mfcc.sh --nj 10 --cmd "$train_cmd" \
     data/$x exp/make_mfcc/$x $mfccdir || exit 1;
   steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  done
  
  mfccdir=mfcc
  for x in dev_dt_05_reverb train_si84_reverb; do 
   steps/make_mfcc.sh --nj 10 --cmd "$train_cmd" \
     data/$x exp/make_mfcc/$x $mfccdir || exit 1;
   steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  done
  
  # make fbank features
  mkdir -p data-fbank
  fbankdir=fbank
  for x in test_eval92_clean test_eval92_5k_clean dev_dt_05_clean dev_dt_20_clean train_si84_clean; do 
   cp -r data/$x data-fbank/$x
   steps/make_fbank.sh --nj 10 --cmd "$train_cmd" \
     data-fbank/$x exp/make_fbank/$x $fbankdir || exit 1;
  done
  
  fbankdir=fbank
  for x in test_eval92_5k_noisy dev_dt_05_noisy train_si84_noisy; do 
   cp -r data/$x data-fbank/$x
   steps/make_fbank.sh --nj 10 --cmd "$train_cmd" \
     data-fbank/$x exp/make_fbank/$x $fbankdir || exit 1;
  done
  
  fbankdir=fbank
  for x in dev_dt_05_reverb train_si84_reverb; do 
   cp -r data/$x data-fbank/$x
   steps/make_fbank.sh --nj 10 --cmd "$train_cmd" \
     data-fbank/$x exp/make_fbank/$x $fbankdir || exit 1;
  done
  
  #begin train gmm systems using multi condition data
  #train_si84 = clean+reverb+noisy, 
  for s in train_si84 ; do 
    mkdir -p data/$s
    cp data/${s}_clean/spk2gender data/$s/ 
    for x in text wav.scp; do
      cat data/${s}_clean/$x data/${s}_reverb/$x data/${s}_noisy/$x | sort -k1 > data/$s/$x 
    done
    cat data/$s/wav.scp | awk '{print $1}' | perl -ane 'chop; m:^...:; print "$_ $&
  ";' > data/$s/utt2spk 
    cat data/$s/utt2spk | utils/utt2spk_to_spk2utt.pl > data/$s/spk2utt 
  done
  
  mfccdir=mfcc
  for x in train_si84; do 
   steps/make_mfcc.sh --nj 10 --cmd "$train_cmd" \
     data/$x exp/make_mfcc/$x $mfccdir || exit 1;
   steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  done
  
  fbankdir=fbank
  for x in train_si84; do 
   cp -r data/$x data-fbank/$x
   steps/make_fbank.sh --nj 10 --cmd "$train_cmd" \
     data-fbank/$x exp/make_fbank/$x $fbankdir || exit 1;
  done
  
  
  steps/train_mono.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \
    data/train_si84 data/lang exp/mono0a || exit 1;
  
  
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/mono0a exp/mono0a/graph_tgpr_5k
  #steps/decode.sh --nj 8  \
  #  exp/mono0a/graph_tgpr_5k data/test_eval92_5k_clean exp/mono0a/decode_tgpr_eval92_5k_clean
  steps/decode.sh --nj 8  --cmd "$train_cmd" \
    exp/mono0a/graph_tgpr_5k data/test_eval92_5k_noisy exp/mono0a/decode_tgpr_eval92_5k_noisy
   
  
  steps/align_si.sh --boost-silence 1.25 --nj 10 --cmd "$train_cmd" \
     data/train_si84 data/lang exp/mono0a exp/mono0a_ali || exit 1;
  
  steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
      2000 10000 data/train_si84 data/lang exp/mono0a_ali exp/tri1 || exit 1;
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri1 exp/tri1/graph_tgpr_5k || exit 1;
  
  #steps/decode.sh --nj 8 \
  #  exp/tri1/graph_tgpr data/test_eval92_5k_clean exp/tri1/decode_tgpr_eval92_5k_clean || exit 1;
  steps/decode.sh --nj 8 --cmd "$train_cmd" \
    exp/tri1/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri1/decode_tgpr_eval92_5k_noisy || exit 1;
  
  
  # test various modes of LM rescoring (4 is the default one).
  # This is just confirming they're equivalent.
  #for mode in 1 2 3 4; do
  #steps/lmrescore.sh --mode $mode --cmd "$decode_cmd" data/lang_test_{tgpr,tg} \
  #  data/test_dev93 exp/tri1/decode_tgpr_dev93 exp/tri1/decode_tgpr_dev93_tg$mode  || exit 1;
  #done
  
  # demonstrate how to get lattices that are "word-aligned" (arcs coincide with
  # words, with boundaries in the right place).
  #sil_label=`grep '!SIL' data/lang_test_tgpr/words.txt | awk '{print $2}'`
  #steps/word_align_lattices.sh --cmd "$train_cmd" --silence-label $sil_label \
  #  data/lang_test_tgpr exp/tri1/decode_tgpr_dev93 exp/tri1/decode_tgpr_dev93_aligned || exit 1;
  
  steps/align_si.sh --nj 10 --cmd "$train_cmd" \
    data/train_si84 data/lang exp/tri1 exp/tri1_ali_si84 || exit 1;
  
  # Train tri2a, which is deltas + delta-deltas, on si84 data.
  steps/train_deltas.sh --cmd "$train_cmd" \
    2500 15000 data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2a || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri2a exp/tri2a/graph_tgpr_5k || exit 1;
  
  #steps/decode.sh --nj 8  \
  #  exp/tri2a/graph_tgpr_5k data/test_eval92_5k_clean exp/tri2a/decode_tgpr_eval92_5k_clean || exit 1;
  steps/decode.sh --nj 8 --cmd "$train_cmd" \
    exp/tri2a/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri2a/decode_tgpr_eval92_5k_noisy|| exit 1;
  
  #utils/mkgraph.sh data/lang_test_bg_5k exp/tri2a exp/tri2a/graph_bg5k
  #steps/decode.sh --nj 8 \
  #  exp/tri2a/graph_bg5k data/test_eval92_5k_clean exp/tri2a/decode_bg_eval92_5k_clean || exit 1;
  
  steps/train_lda_mllt.sh --cmd "$train_cmd" \
     --splice-opts "--left-context=3 --right-context=3" \
     2500 15000 data/train_si84 data/lang exp/tri1_ali_si84 exp/tri2b || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri2b exp/tri2b/graph_tgpr_5k || exit 1;
  steps/decode.sh --nj 8 --cmd "$train_cmd" \
    exp/tri2b/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri2b/decode_tgpr_eval92_5k_noisy || exit 1;
  #steps/decode.sh --nj 8 \
  #  exp/tri2b/graph_tgpr data/test_eval92_clean exp/tri2b/decode_tgpr_eval92_clean || exit 1;
  
  
  # Align tri2b system with si84 data.
  steps/align_si.sh  --nj 10 --cmd "$train_cmd" \
    --use-graphs true data/train_si84 data/lang exp/tri2b exp/tri2b_ali_si84  || exit 1;
  
  
  # From 2b system, train 3b which is LDA + MLLT + SAT.
  steps/train_sat.sh --cmd "$train_cmd" \
    2500 15000 data/train_si84 data/lang exp/tri2b_ali_si84 exp/tri3b || exit 1;
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri3b exp/tri3b/graph_tgpr_5k || exit 1;
  steps/decode_fmllr.sh --nj 8 --cmd "$train_cmd" \
    exp/tri3b/graph_tgpr_5k data/test_eval92_5k_noisy exp/tri3b/decode_tgpr_eval92_5k_noisy || exit 1;
  
  
  # From 3b multi-condition system, align noisy si84 data.
  steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
    data/train_si84_noisy data/lang exp/tri3b exp/tri3b_ali_si84_noisy || exit 1;
  
  steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
    data/dev_dt_05_noisy data/lang exp/tri3b exp/tri3b_ali_dev_dt_05 || exit 1;
  
  #begin training DNN-HMM system
  #only on noisy si84 
  
  . ./path.sh
  #RBM pretraining
  dir=exp/tri4a_dnn_pretrain
  $cuda_cmd $dir/_pretrain_dbn.log \
    steps/nnet/pretrain_dbn.sh --nn-depth 7 --rbm-iter 3 data-fbank/train_si84_noisy $dir
  #BP 
  dir=exp/tri4a_dnn
  ali=exp/tri3b_ali_si84_noisy
  ali_dev=exp/tri3b_ali_dev_dt_05
  feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
  dbn=exp/tri4a_dnn_pretrain/7.dbn
  $cuda_cmd $dir/_train_nnet.log \
    steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
    data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri4a_dnn exp/tri4a_dnn/graph_tgpr_5k || exit 1;
  steps/nnet/decode.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
    exp/tri4a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
  
  #Retrain system using new ali,
  #this is essential 
  #repeat this process for 3 times 
  srcdir=exp/tri4a_dnn
  steps/nnet/align.sh --nj 10 \
    data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali_si84_noisy || exit 1;
  steps/nnet/align.sh --nj 10 \
    data-fbank/dev_dt_05_noisy data/lang $srcdir ${srcdir}_ali_dt_05_noisy || exit 1;
  
  #no need to do pretraining again
  dir=exp/tri5a_dnn
  ali=exp/tri4a_dnn_ali_si84_noisy
  ali_dev=exp/tri4a_dnn_ali_dt_05_noisy
  feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
  dbn=exp/tri4a_dnn_pretrain/7.dbn
  $cuda_cmd $dir/_train_nnet.log \
    steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
    data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri5a_dnn exp/tri5a_dnn/graph_tgpr_5k || exit 1;
  steps/nnet/decode.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
    exp/tri5a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
  
  
  srcdir=exp/tri5a_dnn
  steps/nnet/align.sh --nj 10 \
    data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali_si84_noisy || exit 1;
  steps/nnet/align.sh --nj 10 \
    data-fbank/dev_dt_05_noisy data/lang $srcdir ${srcdir}_ali_dt_05_noisy || exit 1;
  
  . ./path.sh
  dir=exp/tri6a_dnn
  ali=exp/tri5a_dnn_ali_si84_noisy
  ali_dev=exp/tri5a_dnn_ali_dt_05_noisy
  feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
  dbn=exp/tri4a_dnn_pretrain/7.dbn
  $cuda_cmd $dir/_train_nnet.log \
    steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
    data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri6a_dnn exp/tri6a_dnn/graph_tgpr_5k || exit 1;
  steps/nnet/decode.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
    exp/tri6a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
  
  srcdir=exp/tri6a_dnn
  steps/nnet/align.sh --nj 10 \
    data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali_si84_noisy || exit 1;
  steps/nnet/align.sh --nj 10 \
    data-fbank/dev_dt_05_noisy data/lang $srcdir ${srcdir}_ali_dt_05_noisy || exit 1;
  
  . ./path.sh
  dir=exp/tri7a_dnn
  ali=exp/tri6a_dnn_ali_si84_noisy
  ali_dev=exp/tri6a_dnn_ali_dt_05_noisy
  feature_transform=exp/tri4a_dnn_pretrain/final.feature_transform
  dbn=exp/tri4a_dnn_pretrain/7.dbn
  $cuda_cmd $dir/_train_nnet.log \
    steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
    data-fbank/train_si84_noisy data-fbank/dev_dt_05_noisy data/lang $ali $ali_dev $dir || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri7a_dnn exp/tri7a_dnn/graph_tgpr_5k || exit 1;
  steps/nnet/decode.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
    exp/tri7a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy || exit 1;
  
  # Sequence training using sMBR criterion, we do Stochastic-GD 
  # with per-utterance updates. We use usually good acwt 0.1
  # Lattices are re-generated after 1st epoch, to get faster convergence.
  dir=exp/tri7a_dnn_smbr
  srcdir=exp/tri7a_dnn
  acwt=0.1
  
  # First we generate lattices and alignments:
  # awk -v FS="/" '{ NF_nosuffix=$NF; gsub(".gz","",NF_nosuffix); print NF_nosuffix gunzip -c "$0" |"; }' in 
  # steps/nnet/make_denlats.sh
  steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
      data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali || exit 1;
  steps/nnet/make_denlats.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
      data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_denlats || exit 1;
  
  # Re-train the DNN by 1 iteration of sMBR 
  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 1 --acwt $acwt --do-smbr true \
      data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
  # Decode (reuse HCLG graph)
  for ITER in 1; do
      steps/nnet/decode.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
      --nnet $dir/${ITER}.nnet --acwt $acwt \
      exp/tri7a_dnn/graph_tgpr_5k data-fbank/dev_dt_05_noisy $dir/decode_tgpr_5k_dt_05_noisy_it${ITER} || exit 1;
      steps/nnet/decode.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
      --nnet $dir/${ITER}.nnet --acwt $acwt \
      exp/tri7a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy_it${ITER} || exit 1;
  done 
  
  # Re-generate lattices, run 4 more sMBR iterations
  dir=exp/tri7a_dnn_smbr_i1lats
  srcdir=exp/tri7a_dnn_smbr
  acwt=0.1
  
  # Generate lattices and alignments:
  steps/nnet/align.sh --nj 10 --cmd "$train_cmd" \
      data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali || exit 1;
  steps/nnet/make_denlats.sh --nj 10 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
      data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_denlats || exit 1;
  
  # Re-train the DNN by 1 iteration of sMBR 
  steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 4 --acwt $acwt --do-smbr true \
      data-fbank/train_si84_noisy data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
  
      # Decode (reuse HCLG graph)
  for ITER in 1 2 3 4; do
      steps/nnet/decode.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
      --nnet $dir/${ITER}.nnet --acwt $acwt \
      exp/tri7a_dnn/graph_tgpr_5k data-fbank/dev_dt_05_noisy $dir/decode_tgpr_5k_dt_05_noisy_it${ITER} || exit 1;
      steps/nnet/decode.sh --nj 8 --cmd "$decode_cmd" --config conf/decode_dnn.config \
      --nnet $dir/${ITER}.nnet --acwt $acwt \
      exp/tri7a_dnn/graph_tgpr_5k data-fbank/test_eval92_5k_noisy $dir/decode_tgpr_5k_eval92_5k_noisy_it${ITER} || exit 1;
  done