#!/bin/bash
  #
  # Copyright 2018, Yuan-Fu Liao, National Taipei University of Technology, yfliao@mail.ntut.edu.tw
  #
  # Before you run this recipe, please apply, download and put or make a link of the corpus under this folder (folder name: "NER-Trs-Vol1").
  # For more detail, please check:
  # 1. Formosa Speech in the Wild (FSW) project (https://sites.google.com/speech.ntut.edu.tw/fsw/home/corpus)
  # 2. Formosa Speech Recognition Challenge (FSW) 2018 (https://sites.google.com/speech.ntut.edu.tw/fsw/home/challenge)
  stage=-2
  num_jobs=20
  
  train_dir=NER-Trs-Vol1/Train
  eval_dir=NER-Trs-Vol1-Eval
  eval_key_dir=NER-Trs-Vol1-Eval-Key
  
  # shell options
  set -eo pipefail
  
  . ./cmd.sh
  . ./utils/parse_options.sh
  
  # configure number of jobs running in parallel, you should adjust these numbers according to your machines
  # data preparation
  if [ $stage -le -2 ]; then
    # Lexicon Preparation,
    echo "$0: Lexicon Preparation"
    local/prepare_dict.sh || exit 1;
  
    # Data Preparation
    echo "$0: Data Preparation"
    local/prepare_data.sh --train-dir $train_dir --eval-dir $eval_dir --eval-key-dir $eval_key_dir || exit 1;
  
    # Phone Sets, questions, L compilation
    echo "$0: Phone Sets, questions, L compilation Preparation"
    rm -rf data/lang
    utils/prepare_lang.sh --position-dependent-phones false data/local/dict \
        "<SIL>" data/local/lang data/lang || exit 1;
  
    # LM training
    echo "$0: LM training"
    rm -rf data/local/lm/3gram-mincount
    local/train_lms.sh || exit 1;
  
    # G compilation, check LG composition
    echo "$0: G compilation, check LG composition"
    utils/format_lm.sh data/lang data/local/lm/3gram-mincount/lm_unpruned.gz \
        data/local/dict/lexicon.txt data/lang_test || exit 1;
  
  fi
  
  # Now make MFCC plus pitch features.
  # mfccdir should be some place with a largish disk where you
  # want to store MFCC features.
  mfccdir=mfcc
  
  # mfcc
  if [ $stage -le -1 ]; then
    echo "$0: making mfccs"
    for x in train test eval; do
      steps/make_mfcc_pitch.sh --cmd "$train_cmd" --nj $num_jobs data/$x exp/make_mfcc/$x $mfccdir || exit 1;
      steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
      utils/fix_data_dir.sh data/$x || exit 1;
    done
  fi
  
  # mono
  if [ $stage -le 0 ]; then
    echo "$0: train mono model"
    # Make some small data subsets for early system-build stages.
    echo "$0: make training subsets"
    utils/subset_data_dir.sh --shortest data/train 3000 data/train_mono
  
    # train mono
    steps/train_mono.sh --boost-silence 1.25 --cmd "$train_cmd" --nj $num_jobs \
      data/train_mono data/lang exp/mono || exit 1;
  
    # Get alignments from monophone system.
    steps/align_si.sh --boost-silence 1.25 --cmd "$train_cmd" --nj $num_jobs \
      data/train data/lang exp/mono exp/mono_ali || exit 1;
  
    # Monophone decoding
    (
    utils/mkgraph.sh data/lang_test exp/mono exp/mono/graph || exit 1;
    steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj $num_jobs \
      exp/mono/graph data/test exp/mono/decode_test
    )&
  fi
  
  # tri1
  if [ $stage -le 1 ]; then
    echo "$0: train tri1 model"
    # train tri1 [first triphone pass]
    steps/train_deltas.sh --boost-silence 1.25 --cmd "$train_cmd" \
     2500 20000 data/train data/lang exp/mono_ali exp/tri1 || exit 1;
  
    # align tri1
    steps/align_si.sh --cmd "$train_cmd" --nj $num_jobs \
      data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
  
    # decode tri1
    (
    utils/mkgraph.sh data/lang_test exp/tri1 exp/tri1/graph || exit 1;
    steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj $num_jobs \
      exp/tri1/graph data/test exp/tri1/decode_test
    )&
  fi
  
  # tri2
  if [ $stage -le 2 ]; then
    echo "$0: train tri2 model"
    # train tri2 [delta+delta-deltas]
    steps/train_deltas.sh --cmd "$train_cmd" \
     2500 20000 data/train data/lang exp/tri1_ali exp/tri2 || exit 1;
  
    # align tri2b
    steps/align_si.sh --cmd "$train_cmd" --nj $num_jobs \
      data/train data/lang exp/tri2 exp/tri2_ali || exit 1;
  
    # decode tri2
    (
    utils/mkgraph.sh data/lang_test exp/tri2 exp/tri2/graph
    steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj $num_jobs \
      exp/tri2/graph data/test exp/tri2/decode_test
    )&
  fi
  
  # tri3a
  if [ $stage -le 3 ]; then
    echo "$-: train tri3 model"
    # Train tri3a, which is LDA+MLLT,
    steps/train_lda_mllt.sh --cmd "$train_cmd" \
     2500 20000 data/train data/lang exp/tri2_ali exp/tri3a || exit 1;
  
    # decode tri3a
    (
    utils/mkgraph.sh data/lang_test exp/tri3a exp/tri3a/graph || exit 1;
    steps/decode.sh --cmd "$decode_cmd" --nj $num_jobs --config conf/decode.config \
      exp/tri3a/graph data/test exp/tri3a/decode_test
    )&
  fi
  
  # tri4
  if [ $stage -le 4 ]; then
    echo "$0: train tri4 model"
    # From now, we start building a more serious system (with SAT), and we'll
    # do the alignment with fMLLR.
    steps/align_fmllr.sh --cmd "$train_cmd" --nj $num_jobs \
      data/train data/lang exp/tri3a exp/tri3a_ali || exit 1;
  
    steps/train_sat.sh --cmd "$train_cmd" \
      2500 20000 data/train data/lang exp/tri3a_ali exp/tri4a || exit 1;
  
    # align tri4a
    steps/align_fmllr.sh  --cmd "$train_cmd" --nj $num_jobs \
      data/train data/lang exp/tri4a exp/tri4a_ali
  
    # decode tri4a
    (
    utils/mkgraph.sh data/lang_test exp/tri4a exp/tri4a/graph
    steps/decode_fmllr.sh --cmd "$decode_cmd" --nj $num_jobs --config conf/decode.config \
      exp/tri4a/graph data/test exp/tri4a/decode_test
    )&
  fi
  
  # tri5
  if [ $stage -le 5 ]; then
    echo "$0: train tri5 model"
    # Building a larger SAT system.
    steps/train_sat.sh --cmd "$train_cmd" \
      3500 100000 data/train data/lang exp/tri4a_ali exp/tri5a || exit 1;
  
    # align tri5a
    steps/align_fmllr.sh --cmd "$train_cmd" --nj $num_jobs \
      data/train data/lang exp/tri5a exp/tri5a_ali || exit 1;
  
    # decode tri5
    (
    utils/mkgraph.sh data/lang_test exp/tri5a exp/tri5a/graph || exit 1;
    steps/decode_fmllr.sh --cmd "$decode_cmd" --nj $num_jobs --config conf/decode.config \
       exp/tri5a/graph data/test exp/tri5a/decode_test || exit 1;
    )&
  fi
  
  # nnet3 tdnn models
  # commented out by default, since the chain model is usually faster and better
  #if [ $stage -le 6 ]; then
    # echo "$0: train nnet3 model"
    # local/nnet3/run_tdnn.sh
  #fi
  
  # chain model
  if [ $stage -le 7 ]; then
    # The iVector-extraction and feature-dumping parts coulb be skipped by setting "--train_stage 7"
    echo "$0: train chain model"
    local/chain/run_tdnn.sh
  fi
  
  # getting results (see RESULTS file)
  if [ $stage -le 8 ]; then
    echo "$0: extract the results"
    for test_set in test eval; do
    echo "WER: $test_set"
    for x in exp/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done 2>/dev/null
    for x in exp/*/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done 2>/dev/null
    echo
  
    echo "CER: $test_set"
    for x in exp/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null
    for x in exp/*/*/decode_${test_set}*; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null
    echo
    done
  fi
  
  # finish
  echo "$0: all done"
  
  exit 0;