trunk,nnet1 : adding lstm example for tedlium (needs tuning)

git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@4876 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8

egs/tedlium/s5/conf/fbank.conf

+--window-type=hamming # disable Dans window, use the standard
+--use-energy=false    # only fbank outputs
+--dither=1
+--num-mel-bins=40     # 8 filters/octave, 40 filters/16Khz as used by IBM
+--htk-compat=true     # try to make it compatible with HTK

egs/tedlium/s5/conf/pitch.conf

+--nccf-ballast-online=true  # helps for online operation.
+

egs/tedlium/s5/local/nnet/run_lstm.sh

+#!/bin/bash
+
+# Copyright 2015  Brno University of Technology (Author: Karel Vesely)
+# Apache 2.0
+
+# This example script trains a LSTM network on FBANK features.
+# The LSTM code comes from Yiayu DU, and Wei Li, thanks!
+
+. ./cmd.sh
+. ./path.sh
+
+dev=data-fbank/test
+train=data-fbank/train
+
+dev_original=data/test
+train_original=data/train
+
+gmm=exp/tri3
+
+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 -tc 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 [ $stage -le 1 ]; then
+  # Train the DNN optimizing per-frame cross-entropy.
+  dir=exp/lstm4f
+  ali=${gmm}_ali
+
+  # Train
+  $cuda_cmd $dir/log/train_nnet.log \
+    steps/nnet/train.sh --network-type lstm --learn-rate 0.0001 \
+      --cmvn-opts "--norm-means=true --norm-vars=true" --feat-type plain --splice 0 \
+      --train-opts "--momentum 0.9 --halving-factor 0.8" \
+      --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 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