#!/bin/bash
  
  # Copyright 2012 Chao Weng
  #           2016 Alibaba Robotics Corp. (Author: Xingyu Na)
  # Apache 2.0
  
  # This is a shell script, but it's recommended that you run the commands one by
  # one by copying and pasting into the shell.
  # Caution: some of the graph creation steps use quite a bit of memory, so you
  # should run this on a machine that has sufficient memory.
  
  . ./cmd.sh
  
  # Data Preparation,
  local/hkust_data_prep.sh /export/corpora/LDC/LDC2005S15/  /export/corpora/LDC/LDC2005T32/
  
  # Lexicon Preparation,
  local/hkust_prepare_dict.sh || exit 1;
  
  # Phone Sets, questions, L compilation
  utils/prepare_lang.sh data/local/dict "<UNK>" data/local/lang data/lang
  
  # LM training
  local/hkust_train_lms.sh
  
  # G compilation, check LG composition
  local/hkust_format_data.sh
  
  # Now make MFCC plus pitch features.
  # mfccdir should be some place with a largish disk where you
  # want to store MFCC features.
  mfccdir=mfcc
  for x in train dev; do
    steps/make_mfcc_pitch_online.sh --cmd "$train_cmd" --nj 10 data/$x exp/make_mfcc/$x $mfccdir || exit 1;
    steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  done
  # after this, the next command will remove the small number of utterances
  # that couldn't be extracted for some reason (e.g. too short; no such file).
  utils/fix_data_dir.sh data/train || exit 1;
  utils/subset_data_dir.sh --first data/train 100000 data/train_100k || exit 1;
  
  steps/train_mono.sh --cmd "$train_cmd" --nj 10 \
    data/train data/lang exp/mono0a || exit 1;
  
  # Monophone decoding
  utils/mkgraph.sh data/lang_test exp/mono0a exp/mono0a/graph || exit 1
  steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
    exp/mono0a/graph data/dev exp/mono0a/decode
  
  # Get alignments from monophone system.
  steps/align_si.sh --cmd "$train_cmd" --nj 10 \
    data/train data/lang exp/mono0a exp/mono_ali || exit 1;
  
  # train tri1 [first triphone pass]
  steps/train_deltas.sh --cmd "$train_cmd" \
   2500 20000 data/train data/lang exp/mono_ali exp/tri1 || 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 10 \
    exp/tri1/graph data/dev exp/tri1/decode
  
  # align tri1
  steps/align_si.sh --cmd "$train_cmd" --nj 10 \
    data/train data/lang exp/tri1 exp/tri1_ali || exit 1;
  
  # 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;
  
  # decode tri2
  utils/mkgraph.sh data/lang_test exp/tri2 exp/tri2/graph
  steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
    exp/tri2/graph data/dev exp/tri2/decode
  
  # train and decode tri2b [LDA+MLLT]
  
  steps/align_si.sh --cmd "$train_cmd" --nj 10 \
    data/train data/lang exp/tri2 exp/tri2_ali || exit 1;
  
  steps/align_si.sh --cmd "$train_cmd" --nj 10 \
    data/train_100k data/lang exp/tri2 exp/tri2_ali_100k || exit 1;
  
  # 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;
  
  utils/mkgraph.sh data/lang_test exp/tri3a exp/tri3a/graph || exit 1;
  steps/decode.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
    exp/tri3a/graph data/dev exp/tri3a/decode
  # 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 10 \
    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;
  
  utils/mkgraph.sh data/lang_test exp/tri4a exp/tri4a/graph
  steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
    exp/tri4a/graph data/dev exp/tri4a/decode
  
  steps/align_fmllr.sh  --cmd "$train_cmd" --nj 10 \
    data/train data/lang exp/tri4a exp/tri4a_ali
  
  # 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;
  
  utils/mkgraph.sh data/lang_test exp/tri5a exp/tri5a/graph || exit 1;
  steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
     exp/tri5a/graph data/dev exp/tri5a/decode || exit 1;
  
  steps/align_fmllr.sh --cmd "$train_cmd" --nj 10 \
    data/train data/lang exp/tri5a exp/tri5a_ali || exit 1;
  
  # discriminative training
  # local/run_discriminative.sh
  
  # SGMM system [sgmm5a]
  # local/run_sgmm.sh
  
  # nnet1 dnn
  # local/nnet/run_dnn.sh
  
  # online nnet2
  local/online/run_nnet2_ms.sh
  
  # online nnet3
  local/nnet3/run_tdnn.sh
  
  # online chain
  local/chain/run_tdnn.sh
  
  # getting results (see RESULTS file)
  for x in exp/*/decode; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null
  
  exit 0;