run_tdnn_5n.sh
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#!/bin/bash
# this script is a modified version of run_tdnn_5g.sh. It uses
# the new transition model and the python version of training scripts.
set -e
# configs for 'chain'
stage=0
train_stage=-10
get_egs_stage=-10
dir=exp/chain/tdnn_5n
# training options
num_epochs=12
initial_effective_lrate=0.005
final_effective_lrate=0.0005
max_param_change=2.0
final_layer_normalize_target=0.5
num_jobs_initial=2
num_jobs_final=4
minibatch_size=128
frames_per_eg=150
remove_egs=false
#common_egs_dir=exp/chain/tdnn_5g/egs/
common_egs_dir=
# End configuration section.
echo "$0 $@" # Print the command line for logging
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed.
EOF
fi
# The iVector-extraction and feature-dumping parts are the same as the standard
# nnet2 setup, and you can skip them by setting "--stage 4" if you have already
# run those things.
ali_dir=exp/tri3b_ali
treedir=exp/chain/tri4_5n_tree
lang=data/lang_chain_5n
local/online/run_nnet2_common.sh --stage $stage || exit 1;
if [ $stage -le 4 ]; then
# Get the alignments as lattices (gives the chain training more freedom).
# use the same num-jobs as the alignments
nj=$(cat exp/tri3b_ali/num_jobs) || exit 1;
steps/align_fmllr_lats.sh --nj $nj --cmd "$train_cmd" data/train \
data/lang exp/tri3b exp/tri3b_lats
rm exp/tri3b_lats/fsts.*.gz # save space
fi
if [ $stage -le 5 ]; then
# Create a version of the lang/ directory that has one state per phone in the
# topo file. [note, it really has two states.. the first one is only repeated
# once, the second one has zero or more repeats.]
rm -rf $lang
cp -r data/lang $lang
silphonelist=$(cat $lang/phones/silence.csl) || exit 1;
nonsilphonelist=$(cat $lang/phones/nonsilence.csl) || exit 1;
# Use our special topology... note that later on may have to tune this
# topology.
steps/nnet3/chain/gen_topo.py $nonsilphonelist $silphonelist >$lang/topo
fi
if [ $stage -le 6 ]; then
# Build a tree using our new topology.
steps/nnet3/chain/build_tree.sh --frame-subsampling-factor 3 \
--cmd "$train_cmd" 1200 data/train $lang $ali_dir $treedir
fi
if [ $stage -le 7 ]; then
mkdir -p $dir
echo "$0: creating neural net configs";
steps/nnet3/tdnn/make_configs.py \
--self-repair-scale-nonlinearity 0.00001 \
--feat-dir data/train \
--ivector-dir exp/nnet2_online/ivectors \
--tree-dir $treedir \
--relu-dim 450 \
--splice-indexes "-1,0,1 -2,-1,0,1 -3,0,3 -6,-3,0 0" \
--use-presoftmax-prior-scale false \
--xent-regularize 0.1 \
--xent-separate-forward-affine true \
--include-log-softmax false \
--final-layer-normalize-target 1.0 \
$dir/configs || exit 1;
fi
if [ $stage -le 8 ]; then
steps/nnet3/chain/train.py --stage $train_stage \
--cmd "$decode_cmd" \
--feat.online-ivector-dir exp/nnet2_online/ivectors \
--feat.cmvn-opts "--norm-means=false --norm-vars=false" \
--chain.xent-regularize 0.1 \
--chain.leaky-hmm-coefficient 0.1 \
--chain.l2-regularize 0.00005 \
--chain.apply-deriv-weights false \
--chain.lm-opts="--num-extra-lm-states=200" \
--egs.dir "$common_egs_dir" \
--egs.opts "--frames-overlap-per-eg 0" \
--egs.chunk-width $frames_per_eg \
--trainer.num-chunk-per-minibatch $minibatch_size \
--trainer.frames-per-iter 1000000 \
--trainer.num-epochs $num_epochs \
--trainer.optimization.num-jobs-initial $num_jobs_initial \
--trainer.optimization.num-jobs-final $num_jobs_final \
--trainer.optimization.initial-effective-lrate $initial_effective_lrate \
--trainer.optimization.final-effective-lrate $final_effective_lrate \
--trainer.max-param-change $max_param_change \
--cleanup.remove-egs $remove_egs \
--feat-dir data/train \
--tree-dir $treedir \
--lat-dir exp/tri3b_lats \
--dir $dir
fi
if [ $stage -le 9 ]; then
steps/online/nnet2/extract_ivectors_online.sh --cmd "$train_cmd" --nj 4 \
data/test exp/nnet2_online/extractor exp/nnet2_online/ivectors_test || exit 1;
fi
if [ $stage -le 10 ]; then
# Note: it might appear that this $lang directory is mismatched, and it is as
# far as the 'topo' is concerned, but this script doesn't read the 'topo' from
# the lang directory.
utils/mkgraph.sh --self-loop-scale 1.0 data/lang $dir $dir/graph
steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
--scoring-opts "--min-lmwt 1" \
--nj 20 --cmd "$decode_cmd" \
--online-ivector-dir exp/nnet2_online/ivectors_test \
$dir/graph data/test $dir/decode || exit 1;
fi
if [ $stage -le 11 ]; then
utils/mkgraph.sh --self-loop-scale 1.0 data/lang_ug $dir $dir/graph_ug
steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 \
--nj 20 --cmd "$decode_cmd" \
--online-ivector-dir exp/nnet2_online/ivectors_test \
$dir/graph_ug data/test $dir/decode_ug || exit 1;
fi
wait;
exit 0;