run_cnn.sh
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#!/bin/bash
# Copyright 2012-2015 Brno University of Technology (Author: Karel Vesely)
# Apache 2.0
# This example shows how to build CNN with convolution along frequency axis.
# First we train CNN, then build RBMs on top, then do train per-frame training
# and sequence-discriminative training.
# 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
set -euxo pipefail
# 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
# Run the CNN pre-training,
hid_layers=2
if [ $stage -le 1 ]; then
dir=exp/cnn4c
ali=${gmm}_ali
# Train
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh \
--cmvn-opts "--norm-means=true --norm-vars=true" \
--delta-opts "--delta-order=2" --splice 5 \
--network-type cnn1d --cnn-proto-opts "--patch-dim1 8 --pitch-dim 3" \
--hid-layers $hid_layers --learn-rate 0.008 \
${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
# Decode,
steps/nnet/decode.sh --nj 20 --cmd "$decode_cmd" --config conf/decode_dnn.config --acwt 0.1 \
$gmm/graph $dev $dir/decode || exit 1;
fi
if [ $stage -le 2 ]; then
# Concat 'feature_transform' with convolutional layers,
dir=exp/cnn4c
nnet-concat $dir/final.feature_transform \
"nnet-copy --remove-last-components=$(((hid_layers+1)*2)) $dir/final.nnet - |" \
$dir/final.feature_transform_cnn
fi
# Pre-train stack of RBMs on top of the convolutional layers (4 layers, 1024 units),
if [ $stage -le 3 ]; then
dir=exp/cnn4c_pretrain-dbn
transf_cnn=exp/cnn4c/final.feature_transform_cnn # transform with convolutional layers
# Train
$cuda_cmd $dir/log/pretrain_dbn.log \
steps/nnet/pretrain_dbn.sh --nn-depth 4 --hid-dim 1024 --rbm-iter 20 \
--feature-transform $transf_cnn --input-vis-type bern \
--param-stddev-first 0.05 --param-stddev 0.05 \
$train $dir || exit 1
fi
# Re-align using CNN,
if [ $stage -le 4 ]; then
dir=exp/cnn4c
steps/nnet/align.sh --nj 20 --cmd "$train_cmd" \
$train data/lang $dir ${dir}_ali || exit 1
fi
# Train the DNN optimizing cross-entropy,
if [ $stage -le 5 ]; then
dir=exp/cnn4c_pretrain-dbn_dnn; [ ! -d $dir ] && mkdir -p $dir/log;
ali=exp/cnn4c_ali
feature_transform=exp/cnn4c/final.feature_transform
feature_transform_dbn=exp/cnn4c_pretrain-dbn/final.feature_transform
dbn=exp/cnn4c_pretrain-dbn/4.dbn
cnn_dbn=$dir/cnn_dbn.nnet
{ # Concatenate CNN layers and DBN,
num_components=$(nnet-info $feature_transform | grep -m1 num-components | awk '{print $2;}')
cnn="nnet-copy --remove-first-components=$num_components $feature_transform_dbn - |"
nnet-concat "$cnn" $dbn $cnn_dbn 2>$dir/log/concat_cnn_dbn.log || exit 1
}
# Train
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh --feature-transform $feature_transform --dbn $cnn_dbn --hid-layers 0 \
${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/cnn4c_pretrain-dbn_dnn_smbr
srcdir=exp/cnn4c_pretrain-dbn_dnn
acwt=0.1
# First we generate lattices and alignments,
if [ $stage -le 6 ]; then
steps/nnet/align.sh --nj 20 --cmd "$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
# Re-train the DNN by 6 iterations of sMBR,
if [ $stage -le 7 ]; then
steps/nnet/train_mpe.sh --cmd "$cuda_cmd" --num-iters 6 --acwt $acwt --do-smbr true \
$train data/lang $srcdir ${srcdir}_ali ${srcdir}_denlats $dir || exit 1
# Decode
for ITER in 1 3 6; do
steps/nnet/decode.sh --nj 20 --cmd "$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