#!/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;