#!/bin/bash
  
  # Copyright 2012-2014  Brno University of Technology (Author: Karel Vesely)
  # Apache 2.0
  
  # This example script trains a DNN on top of FBANK features.
  # The training is done in 3 stages,
  #
  # 1) RBM pre-training:
  #    in this unsupervised stage we train stack of RBMs,
  #    a good starting point for frame cross-entropy trainig.
  # 2) frame cross-entropy training:
  #    the objective is to classify frames to correct pdfs.
  # 3) sequence-training optimizing sMBR:
  #    the objective is to emphasize state-sequences with better
  #    frame accuracy w.r.t. reference alignment.
  
  # Note: With DNNs in RM, the optimal LMWT is 2-6. Don't be tempted to try acwt's like 0.2,
  # the value 0.1 is better both for decoding and sMBR.
  
  . ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
             ## This relates to the queue.
  
  . ./path.sh ## Source the tools/utils (import the queue.pl)
  
  dev=data-fbank/test
  train=data-fbank/train
  
  dev_original=data/test
  train_original=data/train
  
  gmm=exp/tri3b
  
  stage=0
  . utils/parse_options.sh || exit 1;
  
  set -eu
  
  # Make the FBANK features
  [ ! -e $dev ] && if [ $stage -le 0 ]; then
    # Dev set
    utils/copy_data_dir.sh $dev_original $dev || exit 1; rm $dev/{cmvn,feats}.scp
    steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd" \
       $dev $dev/log $dev/data || exit 1;
    steps/compute_cmvn_stats.sh $dev $dev/log $dev/data || exit 1;
    # Training set
    utils/copy_data_dir.sh $train_original $train || exit 1; rm $train/{cmvn,feats}.scp
    steps/make_fbank_pitch.sh --nj 10 --cmd "$train_cmd --max-jobs-run 10" \
       $train $train/log $train/data || exit 1;
    steps/compute_cmvn_stats.sh $train $train/log $train/data || exit 1;
    # Split the training set
    utils/subset_data_dir_tr_cv.sh --cv-spk-percent 10 $train ${train}_tr90 ${train}_cv10
  fi
  
  if [ $stage -le 1 ]; then
    # Pre-train DBN, i.e. a stack of RBMs (small database, smaller DNN)
    dir=exp/dnn4d-fbank_pretrain-dbn
    $cuda_cmd $dir/log/pretrain_dbn.log \
      steps/nnet/pretrain_dbn.sh \
        --cmvn-opts "--norm-means=true --norm-vars=true" \
        --delta-opts "--delta-order=2" --splice 5 \
        --hid-dim 1024 --rbm-iter 20 $train $dir || exit 1;
  fi
  
  if [ $stage -le 2 ]; then
    # Train the DNN optimizing per-frame cross-entropy.
    dir=exp/dnn4d-fbank_pretrain-dbn_dnn
    ali=${gmm}_ali
    feature_transform=exp/dnn4d-fbank_pretrain-dbn/final.feature_transform
    dbn=exp/dnn4d-fbank_pretrain-dbn/6.dbn
    # Train
    $cuda_cmd $dir/log/train_nnet.log \
      steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
      ${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
    # Decode (reuse HCLG graph)
    steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt 0.1 \
      $gmm/graph $dev $dir/decode || exit 1;
  fi
  
  
  # Sequence training using sMBR criterion, we do Stochastic-GD with per-utterance updates.
  # Note: With DNNs in RM, the optimal LMWT is 2-6. Don't be tempted to try acwt's like 0.2,
  # the value 0.1 is better both for decoding and sMBR.
  dir=exp/dnn4d-fbank_pretrain-dbn_dnn_smbr
  srcdir=exp/dnn4d-fbank_pretrain-dbn_dnn
  acwt=0.1
  
  if [ $stage -le 3 ]; then
    # First we generate lattices and alignments:
    steps/nnet/align.sh --nj 20 --cmd "$train_cmd" \
      $train data/lang $srcdir ${srcdir}_ali || exit 1;
    steps/nnet/make_denlats.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt $acwt \
      $train data/lang $srcdir ${srcdir}_denlats || exit 1;
  fi
  
  if [ $stage -le 4 ]; then
    # Re-train the DNN by 6 iterations of sMBR
    steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 6 --acwt $acwt --do-smbr true \
      $train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
    # Decode
    for ITER in 6 3 1; do
      steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config \
        --nnet $dir/${ITER}.nnet --acwt $acwt \
        $gmm/graph $dev $dir/decode_it${ITER} || exit 1
    done
  fi
  
  echo Success
  exit 0
  
  # Getting results [see RESULTS file]
  # for x in exp/*/decode*; do [ -d $x ] && grep WER $x/wer_* | utils/best_wer.sh; done