#!/bin/bash
  
  . ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
             ## This relates to the queue.
  . ./path.sh # so python3 is on the path if not on the system (we made a link to utils/).a
  
  # This is a shell script, but it's recommended that you run the commands one by
  # one by copying and pasting into the shell.
  
  
  # Download the corpus and prepare parallel lists of sound files and text files
  # Divide the corpus into train, dev and test sets
  local/sprak_data_prep.sh  || exit 1;
  utils/fix_data_dir.sh data/train || exit 1;
  
  # Perform text normalisation, prepare dict folder and LM data transcriptions
  local/copy_dict || exit 1;
  
  utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang_tmp data/lang || exit 1;
  
  # Now make MFCC features.
  # mfccdir should be some place with a largish disk where you
  # want to store MFCC features.
  mfccdir=mfcctest
  
  
  # Extract mfccs
  # p was added to the rspecifier (scp,p:$logdir/wav.JOB.scp) in make_mfcc.sh because some
  # wave files are corrupt
  # Will return a warning message because of the corrupt audio files, but compute them anyway
  # If this step fails and prints a partial diff, rerun from sprak_data_prep.sh
  
  steps/make_mfcc.sh --nj 10 --cmd $train_cmd data/test exp/make_mfcc/test test mfcc || exit 1;
  steps/make_mfcc.sh --nj 10 --cmd $train_cmd data/train exp/make_mfcc/train mfcc || exit 1;
  
  
  # Compute cepstral mean and variance normalisation
  steps/compute_cmvn_stats.sh data/test exp/make_mfcc/test mfcc || exit 1;
  steps/compute_cmvn_stats.sh data/train exp/make_mfcc/train mfcc || exit 1;
  
  
  
  # Repair data set (remove corrupt data points with corrupt audio)
  
  utils/fix_data_dir.sh data/test || exit 1;
  utils/fix_data_dir.sh data/train || exit 1;
  
  
  # Train LM with irstlm
  #creates 3g or 4g dictionary and importantly G.fst
  #local/train_irstlm.sh data/local/transcript_lm/transcripts.uniq 3 "3g" data/lang data/local/train3_lm &> data/local/3g.log &
  local/train_irstlm.sh data/local/transcript_lm/transcripts.uniq 4 "4g" data/lang data/local/train4_lm &> data/local/4g.log || exit 1;
  
  #speed test only 120 utterances per speaker
  utils/subset_data_dir.sh --per-spk data/test 120 data/test120_p_spk || exit 1;
  
  
  # Train monophone model on short utterances  AFTER THIS ONE CAN SEE THE ALIGNMNT BETWEEN FRAMES AND PHONES USING COMMAND SHOW_ALIGNMENTS
  steps/train_mono.sh --nj 10 --cmd "$train_cmd" data/train data/lang exp/mono || exit 1;
  
  # Ensure that LMs are created
  
  utils/mkgraph.sh data/lang_test_4g exp/mono exp/mono/graph_4g || exit 1;
  
  # Ensure that all graphs are constructed
  
  
  steps/decode.sh --config conf/decode.config --nj 10 --cmd "$decode_cmd" \
    exp/mono/graph_4g data/test120_p_spk exp/mono/decode || exit 1;
  
  # Get alignments from monophone system.
  steps/align_si.sh --nj 10 --cmd "$train_cmd" \
    data/train data/lang exp/mono exp/mono_ali || exit 1;
  
  # train tri1 [first triphone pass]
  # steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
  steps/train_deltas.sh --cmd "$train_cmd" \
    5800 96000 data/train data/lang exp/mono_ali exp/tri1|| exit 1;
  
  
  
  #make graph
  utils/mkgraph.sh data/lang_test_4g exp/tri1 exp/tri1/graph_4g || exit 1;
  
  steps/decode.sh --config conf/decode.config --nj 10 --cmd "$decode_cmd" \
    exp/tri1/graph_4g data/test120_p_spk exp/tri1/decode_test120_p_spk || exit 1;
  
  
  
  
  steps/align_si.sh --nj 10 --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" \
    7500 125000 data/train data/lang exp/tri1_ali exp/tri2a || exit 1;
  
  utils/mkgraph.sh data/lang_test_4g exp/tri2a exp/tri2a/graph_4g || exit 1;
  
  steps/decode.sh --nj 10 --cmd "$decode_cmd" \
    exp/tri2a/graph_4g data/test120_p_spk exp/tri2a/decode_test120_p_spk|| exit 1;
  
  
  steps/train_lda_mllt.sh --cmd "$train_cmd" \
     --splice-opts "--left-context=5 --right-context=5" \
     7500 125000 data/train data/lang exp/tri1_ali exp/tri2b || exit 1;
  
  utils/mkgraph.sh data/lang_test_4g exp/tri2b exp/tri2b/graph_4g || exit 1;
  steps/decode.sh --nj 10 --cmd "$decode_cmd" \
    exp/tri2b/graph_4g data/test120_p_spk exp/tri2b/decode_test120_p_spk || exit 1;
  
  
  steps/align_si.sh  --nj 10 --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" \
    7500 125000 data/train data/lang exp/tri2b_ali exp/tri3b || exit 1;
  
  # Trying 4-gram language model
  utils/mkgraph.sh data/lang_test_4g exp/tri3b exp/tri3b/graph_4g || exit 1;
  
  steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 \
    exp/tri3b/graph_4g data/test120_p_spk exp/tri3b/decode_test120_p_spk || exit 1;
  
  # This is commented out for now as it's not important for the main recipe.
  ## Train RNN for reranking
  #local/sprak_train_rnnlms.sh data/local/dict data/dev/transcripts.uniq data/local/rnnlms/g_c380_d1k_h100_v130k
  ## Consumes a lot of memory! Do not run in parallel
  #local/sprak_run_rnnlms_tri3b.sh data/lang_test_3g data/local/rnnlms/g_c380_d1k_h100_v130k data/test1k exp/tri3b/decode_3g_test1k
  
  
  # From 3b system
  steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
    data/train data/lang exp/tri3b exp/tri3b_ali || exit 1;
  
  # From 3b system, train another SAT system (tri4a) with all the si284 data.
  
  steps/train_sat.sh  --cmd "$train_cmd" \
    13000 300000 data/train data/lang exp/tri3b_ali exp/tri4a || exit 1;
  
  utils/mkgraph.sh data/lang_test_4g exp/tri4a exp/tri4a/graph_4g || exit 1;
  steps/decode_fmllr.sh --nj 10 --cmd "$decode_cmd" \
     exp/tri4a/graph_4g data/test120_p_spk exp/tri4a/decode_test120_p_spk || exit 1;
  
  
  
  # alignment used to train nnets
  steps/align_fmllr.sh --nj 10 --cmd "$train_cmd" \
    data/train data/lang exp/tri4a exp/tri4a_ali || exit 1;
  
  ## Works
  local/sprak_run_nnet_cpu.sh 4g test120_p_spk || exit 1;
  
  
  
  # Getting results [see RESULTS file]
  for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done