#!/bin/bash
  
  . ./cmd.sh
  set -e # exit on error
  
  
  # call the next line with the directory where the RM data is
  # (the argument below is just an example).  This should contain
  # subdirectories named as follows:
  #    rm1_audio1  rm1_audio2    rm2_audio
  
  #local/rm_data_prep.sh /mnt/matylda2/data/RM
  
  local/rm_data_prep.sh /export/corpora5/LDC/LDC93S3A/rm_comp
  
  #local/rm_data_prep.sh /home/dpovey/data/LDC93S3A/rm_comp
  
  utils/prepare_lang.sh data/local/dict '!SIL' data/local/lang data/lang
  
  local/rm_prepare_grammar.sh      # Traditional RM grammar (bigram word-pair)
  local/rm_prepare_grammar_ug.sh   # Unigram grammar (gives worse results, but
                                   # changes in WER will be more significant.)
  
  # mfccdir should be some place with a largish disk where you
  # want to store MFCC features.   You can make a soft link if you want.
  featdir=mfcc
  
  for x in test_mar87 test_oct87 test_feb89 test_oct89 test_feb91 test_sep92 train; do
    steps/make_mfcc.sh --nj 8 --cmd "$train_cmd" data/$x exp/make_feat/$x $featdir
    #steps/make_plp.sh --nj 8 --cmd "$train_cmd" data/$x exp/make_feat/$x $featdir
    steps/compute_cmvn_stats.sh data/$x exp/make_feat/$x $featdir
  done
  
  # Make a combined data dir where the data from all the test sets goes-- we do
  # all our testing on this averaged set.  This is just less hassle.  We
  # regenerate the CMVN stats as one of the speakers appears in two of the
  # test sets; otherwise tools complain as the archive has 2 entries.
  utils/combine_data.sh data/test data/test_{mar87,oct87,feb89,oct89,feb91,sep92}
  steps/compute_cmvn_stats.sh data/test exp/make_feat/test $featdir
  
  utils/subset_data_dir.sh data/train 1000 data/train.1k
  
  
  steps/train_mono.sh --nj 4 --cmd "$train_cmd" data/train.1k data/lang exp/mono
  
  #show-transitions data/lang/phones.txt exp/tri2a/final.mdl  exp/tri2a/final.occs | perl -e 'while(<>) { if (m/ sil /) { $l = <>; $l =~ m/pdf = (\d+)/|| die "bad line $l";  $tot += $1; }} print "Total silence count $tot
  ";'
  
  
  
  utils/mkgraph.sh data/lang exp/mono exp/mono/graph
  
  
  steps/decode.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
    exp/mono/graph data/test exp/mono/decode
  
  
  # Get alignments from monophone system.
  steps/align_si.sh --nj 8 --cmd "$train_cmd" \
    data/train data/lang exp/mono exp/mono_ali
  
  # train tri1 [first triphone pass]
  steps/train_deltas.sh --cmd "$train_cmd" \
   1800 9000 data/train data/lang exp/mono_ali exp/tri1
  
  # decode tri1
  utils/mkgraph.sh data/lang exp/tri1 exp/tri1/graph
  steps/decode.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
    exp/tri1/graph data/test exp/tri1/decode
  
  local/test_decoders.sh # This is a test program that we run only in the
                         # RM setup, it does some comparison tests on decoders
                         # to help validate the code.
  #draw-tree data/lang/phones.txt exp/tri1/tree | dot -Tps -Gsize=8,10.5 | ps2pdf - tree.pdf
  
  # align tri1
  steps/align_si.sh --nj 8 --cmd "$train_cmd" \
    --use-graphs true data/train data/lang exp/tri1 exp/tri1_ali
  
  # the tri2a experiments are not needed downstream, so commenting them out.
  # # train tri2a [delta+delta-deltas]
  # steps/train_deltas.sh --cmd "$train_cmd" 1800 9000 \
  #  data/train data/lang exp/tri1_ali exp/tri2a
  
  # # decode tri2a
  # utils/mkgraph.sh data/lang exp/tri2a exp/tri2a/graph
  # steps/decode.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
  #   exp/tri2a/graph data/test exp/tri2a/decode
  
  # train and decode tri2b [LDA+MLLT]
  steps/train_lda_mllt.sh --cmd "$train_cmd" \
    --splice-opts "--left-context=3 --right-context=3" \
   1800 9000 data/train data/lang exp/tri1_ali exp/tri2b
  utils/mkgraph.sh data/lang exp/tri2b exp/tri2b/graph
  
  steps/decode.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b/decode
  
  # you could run these scripts at this point, that use VTLN.
  # local/run_vtln.sh
  # local/run_vtln2.sh
  
  # Align all data with LDA+MLLT system (tri2b)
  steps/align_si.sh --nj 8 --cmd "$train_cmd" --use-graphs true \
     data/train data/lang exp/tri2b exp/tri2b_ali
  
  #  Do MMI on top of LDA+MLLT.
  steps/make_denlats.sh --nj 8 --cmd "$train_cmd" \
    data/train data/lang exp/tri2b exp/tri2b_denlats
  steps/train_mmi.sh data/train data/lang exp/tri2b_ali exp/tri2b_denlats exp/tri2b_mmi
  steps/decode.sh --config conf/decode.config --iter 4 --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b_mmi/decode_it4
  steps/decode.sh --config conf/decode.config --iter 3 --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b_mmi/decode_it3
  
  # Do the same with boosting.
  steps/train_mmi.sh --boost 0.05 data/train data/lang \
     exp/tri2b_ali exp/tri2b_denlats exp/tri2b_mmi_b0.05
  steps/decode.sh --config conf/decode.config --iter 4 --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b_mmi_b0.05/decode_it4
  steps/decode.sh --config conf/decode.config --iter 3 --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b_mmi_b0.05/decode_it3
  
  # Do MPE.
  steps/train_mpe.sh data/train data/lang exp/tri2b_ali exp/tri2b_denlats exp/tri2b_mpe
  steps/decode.sh --config conf/decode.config --iter 4 --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b_mpe/decode_it4
  steps/decode.sh --config conf/decode.config --iter 3 --nj 20 --cmd "$decode_cmd" \
     exp/tri2b/graph data/test exp/tri2b_mpe/decode_it3
  
  
  ## Do LDA+MLLT+SAT, and decode.
  steps/train_sat.sh 1800 9000 data/train data/lang exp/tri2b_ali exp/tri3b
  utils/mkgraph.sh data/lang exp/tri3b exp/tri3b/graph
  steps/decode_fmllr.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
    exp/tri3b/graph data/test exp/tri3b/decode
  
  (
   utils/mkgraph.sh data/lang_ug exp/tri3b exp/tri3b/graph_ug
   steps/decode_fmllr.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
     exp/tri3b/graph_ug data/test exp/tri3b/decode_ug
  )
  
  
  # Align all data with LDA+MLLT+SAT system (tri3b)
  steps/align_fmllr.sh --nj 8 --cmd "$train_cmd" --use-graphs true \
    data/train data/lang exp/tri3b exp/tri3b_ali
  
  
  # # We have now added a script that will help you find portions of your data that
  # # has bad transcripts, so you can filter it out.  Below we demonstrate how to
  # # run this script.
  # steps/cleanup/find_bad_utts.sh --nj 20 --cmd "$train_cmd" data/train data/lang \
  #   exp/tri3b_ali exp/tri3b_cleanup
  # # The following command will show you some of the hardest-to-align utterances in the data.
  # head  exp/tri3b_cleanup/all_info.sorted.txt
  
  ## MMI on top of tri3b (i.e. LDA+MLLT+SAT+MMI)
  steps/make_denlats.sh --config conf/decode.config \
     --nj 8 --cmd "$train_cmd" --transform-dir exp/tri3b_ali \
    data/train data/lang exp/tri3b exp/tri3b_denlats
  steps/train_mmi.sh data/train data/lang exp/tri3b_ali exp/tri3b_denlats exp/tri3b_mmi
  
  steps/decode_fmllr.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
    --alignment-model exp/tri3b/final.alimdl --adapt-model exp/tri3b/final.mdl \
     exp/tri3b/graph data/test exp/tri3b_mmi/decode
  
  # Do a decoding that uses the exp/tri3b/decode directory to get transforms from.
  steps/decode.sh --config conf/decode.config --nj 20 --cmd "$decode_cmd" \
    --transform-dir exp/tri3b/decode  exp/tri3b/graph data/test exp/tri3b_mmi/decode2
  
  # demonstration scripts for online decoding.
  # local/online/run_gmm.sh
  # local/online/run_nnet2.sh
  # local/online/run_baseline.sh
  # Note: for online decoding with pitch, look at local/run_pitch.sh,
  # which calls local/online/run_gmm_pitch.sh
  
  #
  # local/online/run_nnet2_multisplice.sh
  # local/online/run_nnet2_multisplice_disc.sh
  
  # ##some older scripts:
  # # local/run_nnet2.sh
  # # local/online/run_nnet2_baseline.sh
  
  # ## if you have a WSJ setup, you can use the following script to do joint
  # ## RM/WSJ training; this doesn't require that the phone set be the same, it's
  # ## a demonstration of a multilingual script.
  # local/online/run_nnet2_wsj_joint.sh
  # ## and the discriminative-training continuation of the above.
  # local/online/run_nnet2_wsj_joint_disc.sh
  
  # ## The following is an older way to do multilingual training, from an
  # ## already-trained system.
  # #local/online/run_nnet2_wsj.sh
  
  
  
  #first, train UBM for fMMI experiments.
  steps/train_diag_ubm.sh --silence-weight 0.5 --nj 8 --cmd "$train_cmd" \
    250 data/train data/lang exp/tri3b_ali exp/dubm3b
  
  # Next, various fMMI+MMI configurations.
  steps/train_mmi_fmmi.sh --learning-rate 0.0025 \
    --boost 0.1 --cmd "$train_cmd" data/train data/lang exp/tri3b_ali exp/dubm3b exp/tri3b_denlats \
    exp/tri3b_fmmi_b
  
  for iter in 3 4 5 6 7 8; do
   steps/decode_fmmi.sh --nj 20 --config conf/decode.config --cmd "$decode_cmd" --iter $iter \
     --transform-dir exp/tri3b/decode  exp/tri3b/graph data/test exp/tri3b_fmmi_b/decode_it$iter &
  done
  
  steps/train_mmi_fmmi.sh --learning-rate 0.001 \
    --boost 0.1 --cmd "$train_cmd" data/train data/lang exp/tri3b_ali exp/dubm3b exp/tri3b_denlats \
    exp/tri3b_fmmi_c
  
  for iter in 3 4 5 6 7 8; do
   steps/decode_fmmi.sh --nj 20 --config conf/decode.config --cmd "$decode_cmd" --iter $iter \
     --transform-dir exp/tri3b/decode  exp/tri3b/graph data/test exp/tri3b_fmmi_c/decode_it$iter &
  done
  
  # for indirect one, use twice the learning rate.
  steps/train_mmi_fmmi_indirect.sh --learning-rate 0.01 --schedule "fmmi fmmi fmmi fmmi mmi mmi mmi mmi" \
    --boost 0.1 --cmd "$train_cmd" data/train data/lang exp/tri3b_ali exp/dubm3b exp/tri3b_denlats \
    exp/tri3b_fmmi_d
  
  for iter in 3 4 5 6 7 8; do
   steps/decode_fmmi.sh --nj 20 --config conf/decode.config --cmd "$decode_cmd" --iter $iter \
     --transform-dir exp/tri3b/decode  exp/tri3b/graph data/test exp/tri3b_fmmi_d/decode_it$iter &
  done
  
  # Demo of "raw fMLLR"
  # local/run_raw_fmllr.sh
  
  
  # You don't have to run all 2 of the below, e.g. you can just run the run_sgmm2.sh
  local/run_sgmm2.sh
  #local/run_sgmm2x.sh
  
  # The following script depends on local/run_raw_fmllr.sh having been run.
  #
  # local/run_nnet2.sh
  
  # Karel's neural net recipe.
  # local/nnet/run_dnn.sh
  
  # Karel's CNN recipe.
  # local/nnet/run_cnn.sh
  
  # Karel's 2D-CNN recipe (from Harish).
  # local/nnet/run_cnn2d.sh
  
  # chain recipe
  # local/chain/run_tdnn.sh