#!/bin/bash
  
  # Copyright 2015 QCRI (author: Ahmed Ali)
  # Apache 2.0
  # This example script trains a LSTM network on FBANK features.
  # The LSTM code originally comes from tedlium 
  
  . ./cmd.sh
  . ./path.sh
  
  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;
  
  # 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" \
       $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 [ $stage -le 1 ]; then
    # Train the DNN optimizing per-frame cross-entropy.
    dir=exp/lstm4f
    ali=${gmm}_ali
  
    # Train
    $cuda_cmd $dir/log/train_nnet.log \
      steps/nnet/train.sh --network-type lstm --learn-rate 0.00001 \
        --cmvn-opts "--norm-means=true --norm-vars=true" --feat-type plain --splice 0 \
        --proto-opts "--clip-gradient 5.0" \
        --train-tool-opts "--momentum 0.9 --halving-factor 0.65" \
        --train-tool "nnet-train-lstm-streams --num-stream=4 --targets-delay=5" \
      ${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_test || exit 1;
  fi
  
  # TODO : sequence training,
  
  echo LSTM FBANK success
  exit 0