#!/bin/bash
  
  set -e 
  
  # Copyright 2014 QCRI (author: Ahmed Ali)
  # Apache 2.0
  
  . ./path.sh
  . ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
             ## This relates to the queue.
  nJobs=120
  nDecodeJobs=40
  
  #NB: You can add whatever number of copora you like. The supported extensions 
  #NB: (formats) are wav and flac. Flac will be converted using sox and in contrast
  #NB: with the old approach, the conversion will be on-the-fly and one-time-only
  #NB: during the parametrization.
  
  #NB: Text corpora scpecification. We support either tgz files, which are unpacked
  #NB: or just plain (already unpacked) directories. The list of transcript is then
  #NB: obtained using find command
  
  #This is CLSP configuration. We add the 2014 GALE data. We got around 2 % 
  #improvement just by including it. The gain might be large if someone would tweak
  # the number of leaves and states and so on.
  
  #Make sure you edit this section to reflect whers you keep the LDC data on your cluster
  audio=(
    /data/sls/scratch/amali/data/GALE/LDC2013S02
    /data/sls/scratch/amali/data/GALE/LDC2013S07 
    /data/sls/scratch/amali/data/GALE/LDC2014S07 
  )
  text=(
    /data/sls/scratch/amali/data/GALE/LDC2013T17.tgz 
    /data/sls/scratch/amali/data/GALE/LDC2013T04.tgz 
    /data/sls/scratch/amali/data/GALE/LDC2014T17.tgz
  )
  
  galeData=GALE
  #prepare the data
  #split train dev test 
  #prepare lexicon and LM 
  
  # You can run the script from here automatically, but it is recommended to run the data preparation,
  # and features extraction manually and and only once.
  # By copying and pasting into your shell.
  
  #copy the audio files to local folder wav and convet flac files to wav
  local/gale_data_prep_audio.sh  "${audio[@]}" $galeData || exit 1;
  
  #get the transcription and remove empty prompts and all noise markers  
  local/gale_data_prep_txt.sh  "${text[@]}" $galeData || exit 1;
  
  # split the data to reports and conversational and for each class will have rain/dev and test
  local/gale_data_prep_split.sh $galeData  || exit 1;
  
  # get QCRI dictionary and add silence and UN
  local/gale_prep_dict.sh || exit 1;
  
  
  #prepare the langauge resources
  utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang   || exit 1;
  
  # LM training
  local/gale_train_lms.sh || exit 1;
  
  local/gale_format_data.sh  || exit 1;
  # G compilation, check LG composition
  
  # 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 test ; do
    steps/make_mfcc.sh --cmd "$train_cmd" --nj $nJobs \
      data/$x exp/make_mfcc/$x $mfccdir
    utils/fix_data_dir.sh data/$x # some files fail to get mfcc for many reasons
    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir
  done
  
  
  # Here we start the AM
  
  # Let's create a subset with 10k segments to make quick flat-start training:
  utils/subset_data_dir.sh data/train 10000 data/train.10K || exit 1;
  
  # Train monophone models on a subset of the data, 10K segment
  # Note: the --boost-silence option should probably be omitted by default
  steps/train_mono.sh --nj 40 --cmd "$train_cmd" \
    data/train.10K data/lang exp/mono || exit 1;
  
  
  # Get alignments from monophone system.
  steps/align_si.sh --nj $nJobs --cmd "$train_cmd" \
    data/train data/lang exp/mono exp/mono_ali || exit 1;
  
  # train tri1 [first triphone pass]
  steps/train_deltas.sh --cmd "$train_cmd" \
    2500 30000 data/train data/lang exp/mono_ali exp/tri1 || exit 1;
  
  # First triphone decoding
  utils/mkgraph.sh data/lang_test exp/tri1 exp/tri1/graph
  steps/decode.sh  --nj $nDecodeJobs --cmd "$decode_cmd" \
    exp/tri1/graph data/test exp/tri1/decode
    
  steps/align_si.sh --nj $nJobs --cmd "$train_cmd" \
    data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
  
  # Train tri2a, which is deltas+delta+deltas
  steps/train_deltas.sh --cmd "$train_cmd" \
    3000 40000 data/train data/lang exp/tri1_ali exp/tri2a || exit 1;
  
  # tri2a decoding
  utils/mkgraph.sh data/lang_test exp/tri2a exp/tri2a/graph
  steps/decode.sh --nj $nDecodeJobs --cmd "$decode_cmd" \
    exp/tri2a/graph data/test exp/tri2a/decode
  
  # train and decode tri2b [LDA+MLLT]
  steps/train_lda_mllt.sh --cmd "$train_cmd" 4000 50000 \
    data/train data/lang exp/tri1_ali exp/tri2b || exit 1;
  
  utils/mkgraph.sh data/lang_test exp/tri2b exp/tri2b/graph
  steps/decode.sh --nj $nDecodeJobs --cmd "$decode_cmd" \
    exp/tri2b/graph data/test exp/tri2b/decode
  
  # Align all data with LDA+MLLT system (tri2b)
  steps/align_si.sh --nj $nJobs --cmd "$train_cmd" \
    --use-graphs true data/train data/lang exp/tri2b exp/tri2b_ali  || exit 1;
  
  
  # From 2b system, train 3b which is LDA + MLLT + SAT.
  steps/train_sat.sh --cmd "$train_cmd" \
    5000 100000 data/train data/lang exp/tri2b_ali exp/tri3b || exit 1;
  
  utils/mkgraph.sh data/lang_test exp/tri3b exp/tri3b/graph
  steps/decode_fmllr.sh --nj $nDecodeJobs --cmd \
    "$decode_cmd" exp/tri3b/graph data/test exp/tri3b/decode
  
  # From 3b system, align all data.
  steps/align_fmllr.sh --nj $nJobs --cmd "$train_cmd" \
    data/train data/lang exp/tri3b exp/tri3b_ali || exit 1;
    
  
  # train mmi mpe 
  local/run_mmi_mpe.sh &  # we keep it for completion but not getting the best results
  
  # train sgmm 
  local/run_sgmm.sh &    # we keep it for completion but not getting the best results
  
  wait 
  local/nnet/run_dnn.sh
  
  time=$(date +"%Y-%m-%d-%H-%M-%S")
  #get WER
  for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; \
  done | sort -n -r -k2 > RESULTS.$USER.$time # to make sure you keep the results timed and owned
  
  #get detailed WER; reports, conversational and combined
  local/split_wer.sh $galeData > RESULTS.details.$USER.$time
   
  
  echo training succedded
  exit 0