extract_ivectors_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 extracts iVectors for a set of utterances, given
# features and a trained DNN-based iVector extractor.
# Begin configuration section.
nj=30
cmd="run.pl"
stage=0
min_post=0.025 # Minimum posterior to use (posteriors below this are pruned out)
posterior_scale=1.0 # This scale helps to control for successive features being highly
# correlated. E.g. try 0.1 or 0.3.
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 <extractor-dir> <data> <ivector-dir>"
echo " e.g.: $0 exp/extractor_2048_male data/train_male exp/ivectors_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 " --nj <n|10> # Number of jobs"
echo " --stage <stage|0> # To control partial reruns"
echo " --num-gselect <n|20> # Number of Gaussians to select using"
echo " # diagonal model."
echo " --min-post <min-post|0.025> # Pruning threshold for posteriors"
echo " --nnet-job-opt <option|''> # Options for the DNN jobs which add to or"
echo " # replace those specified by --cmd"
exit 1;
fi
srcdir=$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
for f in $srcdir/final.ie $srcdir/final.ubm $data/feats.scp ; do
[ ! -f $f ] && echo "No such file $f" && exit 1;
done
# Set various variables.
mkdir -p $dir/log
sdata=$data/split$nj;
utils/split_data.sh $data $nj || exit 1;
sdata_dnn=$data_dnn/split$nj;
utils/split_data.sh $data_dnn $nj || exit 1;
delta_opts=`cat $srcdir/delta_opts 2>/dev/null`
## 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:- |"
if [ $stage -le 0 ]; then
echo "$0: extracting iVectors"
for g in $(seq $nj); do
$cmd $nnet_job_opt $dir/log/extract_ivectors.$g.log \
nnet-am-compute $gpu_opt --apply-log=true --chunk-size=${chunk_size} \
$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 --min-post=$min_post ark:- ark:- \| \
scale-post ark:- $posterior_scale ark:- \| \
ivector-extract --verbose=2 $srcdir/final.ie \
"`echo $feats | sed s/JOB/$g/g`" ark,s,cs:- \
ark,scp,t:$dir/ivector.$g.ark,$dir/ivector.$g.scp || exit 1 &
done
wait
fi
if [ $stage -le 1 ]; then
echo "$0: combining iVectors across jobs"
for j in $(seq $nj); do cat $dir/ivector.$j.scp; done >$dir/ivector.scp || exit 1;
fi
if [ $stage -le 2 ]; then
# Be careful here: the speaker-level iVectors are now length-normalized,
# even if they are otherwise the same as the utterance-level ones.
echo "$0: computing mean of iVectors for each speaker and length-normalizing"
$cmd $dir/log/speaker_mean.log \
ivector-normalize-length scp:$dir/ivector.scp ark:- \| \
ivector-mean ark:$data/spk2utt ark:- ark:- ark,t:$dir/num_utts.ark \| \
ivector-normalize-length ark:- ark,scp:$dir/spk_ivector.ark,$dir/spk_ivector.scp || exit 1;
fi