run_dnn.sh
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#!/bin/bash
# Copyright 2012-2014 Brno University of Technology (Author: Karel Vesely)
# Apache 2.0
# This example script trains a DNN on top of fMLLR 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.
. ./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)
# Config:
gmmdir=exp/tri3
data_fmllr=data-fmllr-tri3
stage=0 # resume training with --stage=N
# End of config.
. utils/parse_options.sh || exit 1;
#
if [ $stage -le 0 ]; then
# Store fMLLR features, so we can train on them easily,
# test
dir=$data_fmllr/test
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
--transform-dir $gmmdir/decode_test \
$dir data/test $gmmdir $dir/log $dir/data || exit 1
# dev
dir=$data_fmllr/dev
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
--transform-dir $gmmdir/decode_dev \
$dir data/dev $gmmdir $dir/log $dir/data || exit 1
# train
dir=$data_fmllr/train
steps/nnet/make_fmllr_feats.sh --nj 10 --cmd "$train_cmd" \
--transform-dir ${gmmdir}_ali \
$dir data/train $gmmdir $dir/log $dir/data || exit 1
# split the data : 90% train 10% cross-validation (held-out)
utils/subset_data_dir_tr_cv.sh $dir ${dir}_tr90 ${dir}_cv10 || exit 1
fi
if [ $stage -le 1 ]; then
# Pre-train DBN, i.e. a stack of RBMs (small database, smaller DNN)
dir=exp/dnn4_pretrain-dbn
(tail --pid=$$ -F $dir/log/pretrain_dbn.log 2>/dev/null)& # forward log
"$train_cmd" --gpu 1 $dir/log/pretrain_dbn.log \
steps/nnet/pretrain_dbn.sh --hid-dim 1024 --rbm-iter 20 $data_fmllr/train $dir || exit 1;
fi
if [ $stage -le 2 ]; then
# Train the DNN optimizing per-frame cross-entropy.
dir=exp/dnn4_pretrain-dbn_dnn
ali=${gmmdir}_ali
feature_transform=exp/dnn4_pretrain-dbn/final.feature_transform
dbn=exp/dnn4_pretrain-dbn/6.dbn
(tail --pid=$$ -F $dir/log/train_nnet.log 2>/dev/null)& # forward log
# Train
"$train_cmd" --gpu 1 $dir/log/train_nnet.log \
steps/nnet/train.sh --feature-transform $feature_transform --dbn $dbn --hid-layers 0 --learn-rate 0.008 \
$data_fmllr/train_tr90 $data_fmllr/train_cv10 data/lang $ali $ali $dir || exit 1;
# Decode (reuse HCLG graph)
steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --acwt 0.2 \
$gmmdir/graph $data_fmllr/test $dir/decode_test || exit 1;
steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --acwt 0.2 \
$gmmdir/graph $data_fmllr/dev $dir/decode_dev || exit 1;
fi
# Sequence training using sMBR criterion, we do Stochastic-GD
# with per-utterance updates. We use usually good acwt 0.1
dir=exp/dnn4_pretrain-dbn_dnn_smbr
srcdir=exp/dnn4_pretrain-dbn_dnn
acwt=0.2
if [ $stage -le 3 ]; then
# First we generate lattices and alignments:
steps/nnet/align.sh --nj 20 --cmd "$train_cmd" \
$data_fmllr/train data/lang $srcdir ${srcdir}_ali || exit 1;
steps/nnet/make_denlats.sh --nj 20 --cmd "$decode_cmd" --acwt $acwt \
--lattice-beam 10.0 --beam 18.0 \
$data_fmllr/train data/lang $srcdir ${srcdir}_denlats || exit 1;
fi
if [ $stage -le 4 ]; then
# Re-train the DNN by 6 iterations of sMBR
steps/nnet/train_mpe.sh --cmd ""$train_cmd" --gpu 1" --num-iters 6 --acwt $acwt --do-smbr true \
$data_fmllr/train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
# Decode
for ITER in 1 6; do
steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" \
--nnet $dir/${ITER}.nnet --acwt $acwt \
$gmmdir/graph $data_fmllr/test $dir/decode_test_it${ITER} || exit 1
steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" \
--nnet $dir/${ITER}.nnet --acwt $acwt \
$gmmdir/graph $data_fmllr/dev $dir/decode_dev_it${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