#!/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.
  
  #clean LDC wsj0 corpus available in CLSP server: /export/corpora5/LDC/LDC93S6B
  #aurora4 directory in CLSP server: /export/corpora5/AURORA
  
  #aurora4=/mnt/spdb/aurora4
  aurora4=/export/corpora5/AURORA
  #we need lm, trans, from WSJ0 CORPUS
  #wsj0=/mnt/spdb/wall_street_journal
  wsj0=/export/corpora5/LDC/LDC93S6B
  
  local/aurora4_data_prep.sh $aurora4 $wsj0
  
  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/aurora4_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 train_si84_clean train_si84_multi test_eval92 test_0166 dev_0330 dev_1206; do 
   steps/make_mfcc.sh  --nj 10 \
     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
  fbankdir=fbank
  mkdir -p data-fbank
  for x in train_si84_clean train_si84_multi dev_0330 dev_1206 test_eval92 test_0166; do
    cp -r data/$x data-fbank/$x
    steps/make_fbank.sh --nj 10 \
      data-fbank/$x exp/make_fbank/$x $fbankdir || 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.]
  #steps/train_mono.sh --boost-silence 1.25 --nj 10  \
  #  data/train_si84_clean data/lang exp/mono0a || exit 1;
  
  steps/train_mono.sh --boost-silence 1.25 --nj 10  \
    data/train_si84_multi data/lang exp/mono0a_multi || exit 1;
  #(
  # utils/mkgraph.sh data/lang_test_tgpr exp/mono0a exp/mono0a/graph_tgpr && \
  # steps/decode.sh --nj 8  \
  #   exp/mono0a/graph_tgpr data/test_eval92 exp/mono0a/decode_tgpr_eval92 
  #) &
  
  #steps/align_si.sh --boost-silence 1.25 --nj 10  \
  #   data/train_si84_clean data/lang exp/mono0a exp/mono0a_ali || exit 1;
  steps/align_si.sh --boost-silence 1.25 --nj 10  \
     data/train_si84_multi data/lang exp/mono0a_multi exp/mono0a_multi_ali || exit 1;
  
  #steps/train_deltas.sh --boost-silence 1.25 \
  #    2000 10000 data/train_si84_clean data/lang exp/mono0a_ali exp/tri1 || exit 1;
  
  steps/train_deltas.sh --boost-silence 1.25 \
      2000 10000 data/train_si84_multi data/lang exp/mono0a_multi_ali exp/tri1_multi || exit 1;
  
  
  steps/align_si.sh --nj 10 \
    data/train_si84_multi data/lang exp/tri1_multi exp/tri1_multi_ali_si84 || exit 1;
  
  steps/train_deltas.sh  \
    2500 15000 data/train_si84_multi data/lang exp/tri1_multi_ali_si84 exp/tri2a_multi || exit 1;
  
  steps/train_lda_mllt.sh \
     --splice-opts "--left-context=3 --right-context=3" \
     2500 15000 data/train_si84_multi data/lang exp/tri1_multi_ali_si84 exp/tri2b_multi || exit 1;
  
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri2b_multi exp/tri2b_multi/graph_tgpr_5k || exit 1;
  steps/decode.sh --nj 8 \
    exp/tri2b_multi/graph_tgpr_5k data/test_eval92 exp/tri2b_multi/decode_tgpr_5k_eval92 || exit 1;
  
  # Align tri2b system with si84 multi-condition data.
  steps/align_si.sh  --nj 10 \
    --use-graphs true data/train_si84_multi data/lang exp/tri2b_multi exp/tri2b_multi_ali_si84  || exit 1;
  
  steps/align_si.sh  --nj 10 \
    data/dev_0330 data/lang exp/tri2b_multi exp/tri2b_multi_ali_dev_0330 || exit 1;
  
  steps/align_si.sh  --nj 10 \
    data/dev_1206 data/lang exp/tri2b_multi exp/tri2b_multi_ali_dev_1206 || exit 1;
  
  #Now begin train DNN systems on multi data
  . ./path.sh
  #RBM pretrain
  dir=exp/tri3a_dnn_pretrain
  $cuda_cmd $dir/_pretrain_dbn.log \
    steps/nnet/pretrain_dbn.sh --nn-depth 7 --rbm-iter 3 data-fbank/train_si84_multi $dir
  
  dir=exp/tri3a_dnn
  ali=exp/tri2b_multi_ali_si84
  ali_dev=exp/tri2b_multi_ali_dev_0330
  feature_transform=exp/tri3a_dnn_pretrain/final.feature_transform
  dbn=exp/tri3a_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_multi data-fbank/dev_0330 data/lang $ali $ali_dev $dir || exit 1;
  
  utils/mkgraph.sh data/lang_test_tgpr_5k exp/tri3a_dnn exp/tri3a_dnn/graph_tgpr_5k || exit 1;
  dir=exp/tri3a_dnn
  steps/nnet/decode.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
    exp/tri3a_dnn/graph_tgpr_5k data-fbank/test_eval92 $dir/decode_tgpr_5k_eval92 || exit 1;
  
  
  #realignments
  srcdir=exp/tri3a_dnn
  steps/nnet/align.sh --nj 10 \
    data-fbank/train_si84_multi data/lang $srcdir ${srcdir}_ali_si84_multi || exit 1;
  steps/nnet/align.sh --nj 10 \
    data-fbank/dev_0330 data/lang $srcdir ${srcdir}_ali_dev_0330 || exit 1;
  
  #train system again 
  
  dir=exp/tri4a_dnn
  ali=exp/tri3a_dnn_ali_si84_multi
  ali_dev=exp/tri3a_dnn_ali_dev_0330
  feature_transform=exp/tri3a_dnn_pretrain/final.feature_transform
  dbn=exp/tri3a_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_multi data-fbank/dev_0330 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;
  dir=exp/tri4a_dnn
  steps/nnet/decode.sh --nj 8 --acwt 0.10 --config conf/decode_dnn.config \
    exp/tri4a_dnn/graph_tgpr_5k data-fbank/test_eval92 $dir/decode_tgpr_5k_eval92 || exit 1;
  
  
  # DNN Sequential DT training
  #......