train_ivector_extractor_dnn.sh
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#!/bin/bash
# Copyright 2013 Daniel Povey
# 2014-2017 David Snyder
# 2015 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2015 Johns Hopkins University (Author: Daniel Povey)
# Apache 2.0.
# This script trains the i-vector extractor using a DNN-based UBM. It also requires
# an fGMM, usually created by the script sid/init_full_gmm_from_dnn.sh.
# Note: there are 3 separate levels of parallelization: num_threads, num_processes,
# and num_jobs. This may seem a bit excessive. It has to do with minimizing
# memory usage and disk I/O, subject to various constraints. The "num_threads"
# is how many threads a program uses; the "num_processes" is the number of separate
# processes a single job spawns, and then sums the accumulators in memory.
# Our recommendation:
# - Set num_threads to the minimum of (4, or how many virtual cores your machine has).
# (because of needing to lock various global quantities, the program can't
# use many more than 4 threads with good CPU utilization).
# - Set num_processes to the number of virtual cores on each machine you have, divided by
# num_threads. E.g. 4, if you have 16 virtual cores. If you're on a shared queue
# that's busy with other people's jobs, it may be wise to set it to rather less
# than this maximum though, or your jobs won't get scheduled. And if memory is
# tight you need to be careful; in our normal setup, each process uses about 5G.
# - Set num_jobs to as many of the jobs (each using $num_threads * $num_processes CPUs)
# your queue will let you run at one time, but don't go much more than 10 or 20, or
# summing the accumulators will possibly get slow. If you have a lot of data, you
# may want more jobs, though.
# Begin configuration section.
nj=5 # this is the number of separate queue jobs we run, but each one
# contains num_processes sub-jobs.. the real number of threads we
# run is nj * num_processes * num_threads, and the number of
# separate pieces of data is nj * num_processes.
num_threads=4
num_processes=2 # each job runs this many processes, each with --num-threads threads
cmd="run.pl"
stage=-4
num_gselect=20 # Gaussian-selection using diagonal model: number of Gaussians to select
ivector_dim=400 # dimension of the extracted i-vector
use_weights=false # set to true to turn on the regression of log-weights on the ivector.
num_iters=10
min_post=0.025 # Minimum posterior to use (posteriors below this are pruned out)
num_samples_for_weights=3 # smaller than the default for speed (relates to a sampling method)
cleanup=true
posterior_scale=1.0 # This scale helps to control for successve features being highly
# correlated. E.g. try 0.1 or 0.3
sum_accs_opt=
use_gpu=true
chunk_size=256
nnet_job_opt=
# End configuration section.
echo "$0 $@" # Print the command line for logging
if [ -f path.sh ]; then . ./path.sh; fi
. parse_options.sh || exit 1;
if [ $# != 5 ]; then
echo "Usage: $0 <fgmm-model> <dnn-model> <data-speaker-id> <data-dnn> <extractor-dir>"
echo " e.g.: $0 exp/sup_ubm/final.ubm exp/dnn/final.mdl data/train data/train_dnn exp/extractor_male"
echo "main options (for others, see top of script file)"
echo " --config <config-file> # config containing options"
echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
echo " --num-iters <#iters|10> # Number of iterations of E-M"
echo " --nj <n|10> # Number of jobs (also see num-processes and num-threads)"
echo " --num-processes <n|4> # Number of processes for each queue job (relates"
echo " # to summing accs in memory)"
echo " --num-threads <n|4> # Number of threads for each process (can't be usefully"
echo " # increased much above 4)"
echo " --stage <stage|-4> # To control partial reruns"
echo " --num-gselect <n|20> # Number of Gaussians to select using"
echo " # diagonal model."
echo " --sum-accs-opt <option|''> # Option e.g. '-l hostname=a15' to localize"
echo " # sum-accs process to nfs server."
echo " --nnet-job-opt <option|''> # Options for the DNN jobs which add to or"
echo " # replace those specified by --cmd"
echo " --chunk-size <n|256> # Number of frames processed at a time by the DNN"
exit 1;
fi
fgmm_model=$1
nnet=$2
data=$3
data_dnn=$4
dir=$5
gpu_opt=""
if $use_gpu; then
nnet_job_opt="$nnet_job_opt --gpu 1"
gpu_opt="--use-gpu=yes"
if ! cuda-compiled; then
echo "$0: WARNING: you are trying to use the GPU but you have not compiled"
echo " for CUDA. If you have GPUs and have nvcc installed, go to src/"
echo " and do ./configure; make"
exit 1
fi
else
echo "$0: without using a GPU this will be slow."
gpu_opt="--use-gpu=no"
fi
srcdir=$(dirname $fgmm_model)
for f in $fgmm_model $data/feats.scp ; do
[ ! -f $f ] && echo "No such file $f" && exit 1;
done
# Set various variables.
mkdir -p $dir/log
nj_full=$[$nj*$num_processes]
sdata=$data/split$nj_full;
utils/split_data.sh $data $nj_full || exit 1;
sdata_dnn=$data_dnn/split$nj_full;
utils/split_data.sh $data_dnn $nj_full || exit 1;
delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
if [ -f $srcdir/delta_opts ]; then
cp $srcdir/delta_opts $dir/ 2>/dev/null
fi
parallel_opts="--num-threads $[$num_threads*$num_processes]"
## Set up features.
feats="ark,s,cs:add-deltas $delta_opts scp:$sdata/JOB/feats.scp ark:- | apply-cmvn-sliding --norm-vars=false --center=true --cmn-window=300 ark:- ark:- | select-voiced-frames ark:- scp,s,cs:$sdata/JOB/vad.scp ark:- |"
nnet_feats="ark,s,cs:apply-cmvn-sliding --center=true scp:$sdata_dnn/JOB/feats.scp ark:- |"
# Initialize the i-vector extractor using the FGMM input
if [ $stage -le -2 ]; then
cp $fgmm_model $dir/final.ubm || exit 1;
$cmd $dir/log/convert.log \
fgmm-global-to-gmm $dir/final.ubm $dir/final.dubm || exit 1;
$cmd $dir/log/init.log \
ivector-extractor-init --ivector-dim=$ivector_dim --use-weights=$use_weights \
$dir/final.ubm $dir/0.ie || exit 1;
fi
# Do Gaussian selection and posterior extraction
if [ $stage -le -1 ]; then
echo $nj_full > $dir/num_jobs
echo "$0: doing DNN posterior computation"
for g in $(seq $nj_full); do
$cmd $nnet_job_opt $dir/log/post.$g.log \
nnet-am-compute $gpu_opt \
--chunk-size=${chunk_size} --apply-log=true $nnet \
"`echo $nnet_feats | sed s/JOB/$g/g`" \
ark:- \
\| select-voiced-frames ark:- scp,s,cs:$sdata/$g/vad.scp ark:- \
\| logprob-to-post ark:- ark:- \
\| scale-post ark:- $posterior_scale "ark:|gzip -c >$dir/post.$g.gz" || exit 1 &
done
wait
else
if ! [ $nj_full -eq $(cat $dir/num_jobs) ]; then
echo "Num-jobs mismatch $nj_full versus $(cat $dir/num_jobs)"
exit 1
fi
fi
x=0
while [ $x -lt $num_iters ]; do
if [ $stage -le $x ]; then
rm $dir/.error 2>/dev/null
Args=() # bash array of training commands for 1:nj, that put accs to stdout.
for j in $(seq $nj_full); do
Args[$j]=`echo "ivector-extractor-acc-stats --num-threads=$num_threads --num-samples-for-weights=$num_samples_for_weights $dir/$x.ie '$feats' 'ark,s,cs:gunzip -c $dir/post.JOB.gz|' -|" | sed s/JOB/$j/g`
done
echo "Accumulating stats (pass $x)"
for g in $(seq $nj); do
start=$[$num_processes*($g-1)+1]
$cmd $parallel_opts $dir/log/acc.$x.$g.log \
ivector-extractor-sum-accs --parallel=true "${Args[@]:$start:$num_processes}" \
$dir/acc.$x.$g || touch $dir/.error &
done
wait
[ -f $dir/.error ] && echo "Error accumulating stats on iteration $x" && exit 1;
accs=""
for j in $(seq $nj); do
accs+="$dir/acc.$x.$j "
done
echo "Summing accs (pass $x)"
$cmd $sum_accs_opt $dir/log/sum_acc.$x.log \
ivector-extractor-sum-accs $accs $dir/acc.$x || exit 1;
echo "Updating model (pass $x)"
nt=$[$num_threads*$num_processes] # use the same number of threads that
# each accumulation process uses, since we
# can be sure the queue will support this many.
$cmd $parallel_opts $dir/log/update.$x.log \
ivector-extractor-est --num-threads=$nt $dir/$x.ie $dir/acc.$x $dir/$[$x+1].ie || exit 1;
rm $dir/acc.$x.*
$cleanup && rm $dir/acc.$x $dir/$x.ie
fi
x=$[$x+1]
done
$cleanup && rm -f $dir/post.*.gz
rm -f $dir/final.ie
ln -s $x.ie $dir/final.ie