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#!/usr/bin/env bash
# Adapted from egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1e.sh
# local/chain/compare_wer.sh exp/chain/tdnn1a_sp exp/chain/tdnn1a_sp_online
# System tdnn1a_sp tdnn1a_sp_online
#WER valid_dev 4.82 4.88
#WER valid_test 4.44 4.27
# Final train prob -0.0579
# Final valid prob -0.0718
# Final train prob (xent) -1.1069
# Final valid prob (xent) -1.1325
set -euo pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=0
decode_nj=10
train_set=valid_train
test_sets="valid_dev valid_test"
gmm=tri4b
nnet3_affix=
# The rest are configs specific to this script. Most of the parameters
# are just hardcoded at this level, in the commands below.
affix=1a # affix for the TDNN directory name
tree_affix=
train_stage=-10
get_egs_stage=-10
decode_iter=
# training options
# training chunk-options
chunk_width=140,100,160
# we don't need extra left/right context for TDNN systems.
chunk_left_context=0
chunk_right_context=0
common_egs_dir=
xent_regularize=0.1
# training options
srand=123
remove_egs=false
reporting_email=
#decode options
test_online_decoding=true # if true, it will run the last decoding stage.
# 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
# nnet3 setup, and you can skip them by setting "--stage 11" if you have already
# run those things.
local/nnet3/run_ivector_common.sh --stage $stage \
--train-set $train_set \
--gmm $gmm \
--nnet3-affix "$nnet3_affix" || exit 1;
# Problem: We have removed the "train_" prefix of our training set in
# the alignment directory names! Bad!
gmm_dir=exp/$gmm
ali_dir=exp/${gmm}_ali_${train_set}_sp
tree_dir=exp/chain${nnet3_affix}/tree_sp${tree_affix:+_$tree_affix}
lang=data/lang_chain
lat_dir=exp/chain${nnet3_affix}/${gmm}_${train_set}_sp_lats
dir=exp/chain${nnet3_affix}/tdnn${affix}_sp
train_data_dir=data/${train_set}_sp_hires
lores_train_data_dir=data/${train_set}_sp
train_ivector_dir=exp/nnet3${nnet3_affix}/ivectors_${train_set}_sp_hires
for f in $gmm_dir/final.mdl $train_data_dir/feats.scp $train_ivector_dir/ivector_online.scp \
$lores_train_data_dir/feats.scp $ali_dir/ali.1.gz; do
[ ! -f $f ] && echo "$0: expected file $f to exist" && exit 1
done
if [ $stage -le 10 ]; then
echo "$0: creating lang directory $lang with chain-type topology"
# 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.]
if [ -d $lang ]; then
if [ $lang/L.fst -nt data/lang/L.fst ]; then
echo "$0: $lang already exists, not overwriting it; continuing"
else
echo "$0: $lang already exists and seems to be older than data/lang..."
echo " ... not sure what to do. Exiting."
exit 1;
fi
else
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
fi
if [ $stage -le 11 ]; then
# Get the alignments as lattices (gives the chain training more freedom).
# use the same num-jobs as the alignments
steps/align_fmllr_lats.sh --nj 75 --cmd "$train_cmd" ${lores_train_data_dir} \
data/lang $gmm_dir $lat_dir
rm $lat_dir/fsts.*.gz # save space
fi
if [ $stage -le 12 ]; then
# Build a tree using our new topology. We know we have alignments for the
# speed-perturbed data (local/nnet3/run_ivector_common.sh made them), so use
# those. The num-leaves is always somewhat less than the num-leaves from
# the GMM baseline.
if [ -f $tree_dir/final.mdl ]; then
echo "$0: $tree_dir/final.mdl already exists, refusing to overwrite it."
exit 1;
fi
steps/nnet3/chain/build_tree.sh \
--frame-subsampling-factor 3 \
--context-opts "--context-width=2 --central-position=1" \
--cmd "$train_cmd" 4200 ${lores_train_data_dir} \
$lang $ali_dir $tree_dir
fi
if [ $stage -le 13 ]; then
mkdir -p $dir
echo "$0: creating neural net configs using the xconfig parser";
num_targets=$(tree-info $tree_dir/tree |grep num-pdfs|awk '{print $2}')
learning_rate_factor=$(echo "print (0.5/$xent_regularize)" | python)
mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=100 name=ivector
input dim=40 name=input
# please note that it is important to have input layer with the name=input
# as the layer immediately preceding the fixed-affine-layer to enable
# the use of short notation for the descriptor
fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=$dir/configs/lda.mat
# the first splicing is moved before the lda layer, so no splicing here
relu-batchnorm-layer name=tdnn1 dim=768
relu-batchnorm-layer name=tdnn2 dim=768 input=Append(-1,0,1)
relu-batchnorm-layer name=tdnn3 dim=768
relu-batchnorm-layer name=tdnn4 dim=768 input=Append(-1,0,1)
relu-batchnorm-layer name=tdnn5 dim=768
relu-batchnorm-layer name=tdnn6 dim=768 input=Append(-3,0,3)
relu-batchnorm-layer name=tdnn7 dim=768 input=Append(-3,0,3)
relu-batchnorm-layer name=tdnn8 dim=768 input=Append(-6,-3,0)
## adding the layers for chain branch
relu-batchnorm-layer name=prefinal-chain dim=768 target-rms=0.5
output-layer name=output include-log-softmax=false dim=$num_targets max-change=1.5
# adding the layers for xent branch
# This block prints the configs for a separate output that will be
# trained with a cross-entropy objective in the 'chain' models... this
# has the effect of regularizing the hidden parts of the model. we use
# 0.5 / args.xent_regularize as the learning rate factor- the factor of
# 0.5 / args.xent_regularize is suitable as it means the xent
# final-layer learns at a rate independent of the regularization
# constant; and the 0.5 was tuned so as to make the relative progress
# similar in the xent and regular final layers.
relu-batchnorm-layer name=prefinal-xent input=tdnn8 dim=768 target-rms=0.5
output-layer name=output-xent dim=$num_targets learning-rate-factor=$learning_rate_factor max-change=1.5
EOF
steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi
if [ $stage -le 14 ]; then
if [[ $(hostname -f) == *.clsp.jhu.edu ]] && [ ! -d $dir/egs/storage ]; then
utils/create_split_dir.pl \
/export/b0{3,4,5,6}/$USER/kaldi-data/egs/commonvoice-$(date +'%m_%d_%H_%M')/s5/$dir/egs/storage $dir/egs/storage
fi
steps/nnet3/chain/train.py --stage=$train_stage \
--cmd="$decode_cmd" \
--feat.online-ivector-dir=$train_ivector_dir \
--feat.cmvn-opts="--norm-means=false --norm-vars=false" \
--chain.xent-regularize $xent_regularize \
--chain.leaky-hmm-coefficient=0.1 \
--chain.l2-regularize=0.00005 \
--chain.apply-deriv-weights=false \
--chain.lm-opts="--num-extra-lm-states=2000" \
--trainer.srand=$srand \
--trainer.max-param-change=2.0 \
--trainer.num-epochs=4 \
--trainer.frames-per-iter=1500000 \
--trainer.optimization.num-jobs-initial=3 \
--trainer.optimization.num-jobs-final=12 \
--trainer.optimization.initial-effective-lrate=0.001 \
--trainer.optimization.final-effective-lrate=0.0001 \
--trainer.optimization.shrink-value=1.0 \
--trainer.num-chunk-per-minibatch=128,64 \
--trainer.optimization.momentum=0.0 \
--egs.chunk-width=$chunk_width \
--egs.chunk-left-context=$chunk_left_context \
--egs.chunk-right-context=$chunk_right_context \
--egs.chunk-left-context-initial=0 \
--egs.chunk-right-context-final=0 \
--egs.dir="$common_egs_dir" \
--egs.opts="--frames-overlap-per-eg 0" \
--cleanup.remove-egs=$remove_egs \
--use-gpu=true \
--reporting.email="$reporting_email" \
--feat-dir=$train_data_dir \
--tree-dir=$tree_dir \
--lat-dir=$lat_dir \
--dir=$dir || exit 1;
fi
if [ $stage -le 15 ]; then
# Note: it's not important to give mkgraph.sh the lang directory with the
# matched topology (since it gets the topology file from the model).
utils/mkgraph.sh \
--self-loop-scale 1.0 data/lang_test \
$tree_dir $tree_dir/graph || exit 1;
fi
if [ $stage -le 16 ]; then
frames_per_chunk=$(echo $chunk_width | cut -d, -f1)
rm $dir/.error 2>/dev/null || true
for data in $test_sets; do
(
nspk=$(wc -l <data/${data}_hires/spk2utt)
steps/nnet3/decode.sh \
--acwt 1.0 --post-decode-acwt 10.0 \
--extra-left-context $chunk_left_context \
--extra-right-context $chunk_right_context \
--extra-left-context-initial 0 \
--extra-right-context-final 0 \
--frames-per-chunk $frames_per_chunk \
--nj $nspk --cmd "$decode_cmd" --num-threads 4 \
--online-ivector-dir exp/nnet3${nnet3_affix}/ivectors_${data}_hires \
$tree_dir/graph data/${data}_hires ${dir}/decode_${data} || exit 1
) || touch $dir/.error &
done
wait
[ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
fi
# Not testing the 'looped' decoding separately, because for
# TDNN systems it would give exactly the same results as the
# normal decoding.
if $test_online_decoding && [ $stage -le 17 ]; then
# note: if the features change (e.g. you add pitch features), you will have to
# change the options of the following command line.
steps/online/nnet3/prepare_online_decoding.sh \
--mfcc-config conf/mfcc_hires.conf \
$lang exp/nnet3${nnet3_affix}/extractor ${dir} ${dir}_online
rm $dir/.error 2>/dev/null || true
for data in $test_sets; do
(
nspk=$(wc -l <data/${data}_hires/spk2utt)
# note: we just give it "data/${data}" as it only uses the wav.scp, the
# feature type does not matter.
steps/online/nnet3/decode.sh \
--acwt 1.0 --post-decode-acwt 10.0 \
--nj $nspk --cmd "$decode_cmd" \
$tree_dir/graph data/${data} ${dir}_online/decode_${data} || exit 1
) || touch $dir/.error &
done
wait
[ -f $dir/.error ] && echo "$0: there was a problem while decoding" && exit 1
fi
exit 0;