run.sh
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#!/bin/bash
# Copyright 2015 David Snyder
# 2015 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2015 Johns Hopkins University (Author: Daniel Povey)
# 2017 Radboud University (Author Emre Yilmaz)
# Apache 2.0.
#
# See README.txt for more info on data required.
# Results (EERs) are inline in comments below.
. ./cmd.sh
. ./path.sh
set -e
mfccdir=`pwd`/mfcc
vaddir=`pwd`/mfcc
famecorpus=./corpus
num_components=2048
# Data preparation
if [ -d $famecorpus ] ; then
echo "Fame corpus present. OK."
elif [ -f ./fame.tar.gz ] ; then
echo "Unpacking..."
tar xzf fame.tar.gz
elif [ ! -d $famecorpus ] && [ ! -f ./fame.tar.gz ] ; then
echo "The Fame! corpus is not present. Please register here: http://www.ru.nl/clst/datasets/ "
echo " and download the corpus and put it at $famecorpus" && exit 1
fi
echo "Preparing data/train.."
local/prepare_train.sh $famecorpus/SC
for task in complete ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
echo "Preparing data/fame_${task}_${subtask}_${sets}.."
trials_female=data/fame_${task}_${subtask}_${sets}_female/trials
trials_male=data/fame_${task}_${subtask}_${sets}_male/trials
trials=data/fame_${task}_${subtask}_${sets}/trials
local/make_fame_test.pl $famecorpus/SV data $task $subtask $sets
local/make_fame_train.pl $famecorpus/SV data $task $subtask $sets
done
done
done
for task in ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
for year in _1t3 _4t10 _mt10; do
echo "Preparing data/fame_${task}_${subtask}_${sets}${year}.."
trials_female=data/fame_${task}_${subtask}_${sets}${year}_female/trials
trials_male=data/fame_${task}_${subtask}_${sets}${year}_male/trials
trials=data/fame_${task}_${subtask}_${sets}${year}/trials
local/make_fame_test_year.pl $famecorpus/SV data $task $subtask $sets $year
local/make_fame_train_year.pl $famecorpus/SV data $task $subtask $sets $year
done
done
done
done
# MFCC extraction
echo "Extracting MFCC features for data/train.."
steps/make_mfcc.sh --mfcc-config conf/mfcc_16k.conf --nj 100 --cmd "$train_cmd" \
data/train exp/make_mfcc $mfccdir
utils/fix_data_dir.sh data/train
for task in complete ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
echo "Extracting MFCC features for data/fame_${task}_${subtask}_${sets}.."
steps/make_mfcc.sh --mfcc-config conf/mfcc_16k.conf --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}_enroll exp/make_mfcc $mfccdir
utils/fix_data_dir.sh data/fame_${task}_${subtask}_${sets}_enroll
steps/make_mfcc.sh --mfcc-config conf/mfcc_16k.conf --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}_test exp/make_mfcc $mfccdir
utils/fix_data_dir.sh data/fame_${task}_${subtask}_${sets}_test
done
done
done
for task in ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
for year in _1t3 _4t10 _mt10; do
echo "Extracting MFCC features for data/fame_${task}_${subtask}_${sets}${year}.."
steps/make_mfcc.sh --mfcc-config conf/mfcc_16k.conf --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}${year}_enroll exp/make_mfcc $mfccdir
utils/fix_data_dir.sh data/fame_${task}_${subtask}_${sets}${year}_enroll
steps/make_mfcc.sh --mfcc-config conf/mfcc_16k.conf --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}${year}_test exp/make_mfcc $mfccdir
utils/fix_data_dir.sh data/fame_${task}_${subtask}_${sets}${year}_test
done
done
done
done
# VAD computation
echo "Computing VAD for data/train.."
sid/compute_vad_decision.sh --nj 100 --cmd "$train_cmd" \
data/train exp/make_vad $vaddir
for task in complete ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
echo "Computing VAD for data/fame_${task}_${subtask}_${sets}.."
sid/compute_vad_decision.sh --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}_enroll exp/make_vad $vaddir
sid/compute_vad_decision.sh --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}_test exp/make_vad $vaddir
done
done
done
for task in ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
for year in _1t3 _4t10 _mt10; do
echo "Computing VAD for data/fame_${task}_${subtask}_${sets}${year}.."
sid/compute_vad_decision.sh --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}${year}_enroll exp/make_vad $vaddir
sid/compute_vad_decision.sh --nj 100 --cmd "$train_cmd" \
data/fame_${task}_${subtask}_${sets}${year}_test exp/make_vad $vaddir
done
done
done
done
# Train UBM and i-vector extractor
echo "Training UBM and the i-vector extractor.."
sid/train_diag_ubm.sh --nj 40 --cmd "$train_cmd" \
data/train $num_components \
exp/diag_ubm_$num_components
sid/train_full_ubm.sh --nj 40 --remove-low-count-gaussians false \
--cmd "$train_cmd" data/train \
exp/diag_ubm_$num_components exp/full_ubm_$num_components
sid/train_ivector_extractor.sh --cmd "$train_cmd" --nj 20 --num-threads 4 --num-processes 2 \
--ivector-dim 600 \
--num-iters 5 exp/full_ubm_$num_components/final.ubm data/train \
exp/extractor
# Extract i-vectors
echo "Extracting i-vectors for data/train.."
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 100 \
exp/extractor data/train \
exp/ivectors_train
for task in complete ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
echo "Extracting i-vectors for data/fame_${task}_${subtask}_${sets}"
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 100 \
exp/extractor data/fame_${task}_${subtask}_${sets}_enroll \
exp/ivectors_fame_${task}_${subtask}_${sets}_enroll
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 100 \
exp/extractor data/fame_${task}_${subtask}_${sets}_test \
exp/ivectors_fame_${task}_${subtask}_${sets}_test
done
done
done
for task in ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
for year in _1t3 _4t10 _mt10; do
echo "Extracting i-vectors for data/fame_${task}_${subtask}_${sets}${year}"
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 100 \
exp/extractor data/fame_${task}_${subtask}_${sets}${year}_enroll \
exp/ivectors_fame_${task}_${subtask}_${sets}${year}_enroll
sid/extract_ivectors.sh --cmd "$train_cmd" --nj 100 \
exp/extractor data/fame_${task}_${subtask}_${sets}${year}_test \
exp/ivectors_fame_${task}_${subtask}_${sets}${year}_test
done
done
done
done
# Calculate i-vector means used by the scoring scripts
echo "Calculating i-vectors means.."
for task in complete ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
local/scoring_common.sh data/train data/fame_${task}_${subtask}_${sets}_enroll data/fame_${task}_${subtask}_${sets}_test \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}_enroll exp/ivectors_fame_${task}_${subtask}_${sets}_test
trials_female=data/fame_${task}_${subtask}_${sets}_test_female/trials
trials_male=data/fame_${task}_${subtask}_${sets}_test_male/trials
trials=data/fame_${task}_${subtask}_${sets}_test/trials
local/plda_scoring.sh data/train data/fame_${task}_${subtask}_${sets}_enroll data/fame_${task}_${subtask}_${sets}_test \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}_enroll exp/ivectors_fame_${task}_${subtask}_${sets}_test $trials local/scores_gmm_2048_ind_pooled_${task}_${subtask}_${sets}
local/plda_scoring.sh --use-existing-models true data/train data/fame_${task}_${subtask}_${sets}_enroll_female data/fame_${task}_${subtask}_${sets}_test_female \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}_enroll_female exp/ivectors_fame_${task}_${subtask}_${sets}_test_female $trials_female local/scores_gmm_2048_ind_female_${task}_${subtask}_${sets}
local/plda_scoring.sh --use-existing-models true data/train data/fame_${task}_${subtask}_${sets}_enroll_male data/fame_${task}_${subtask}_${sets}_test_male \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}_enroll_male exp/ivectors_fame_${task}_${subtask}_${sets}_test_male $trials_male local/scores_gmm_2048_ind_male_${task}_${subtask}_${sets}
done
done
done
for task in ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
for year in _1t3 _4t10 _mt10; do
local/scoring_common.sh data/train data/fame_${task}_${subtask}_${sets}${year}_enroll data/fame_${task}_${subtask}_${sets}${year}_test \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}${year}_enroll exp/ivectors_fame_${task}_${subtask}_${sets}${year}_test
trials_female=data/fame_${task}_${subtask}_${sets}${year}_test_female/trials
trials_male=data/fame_${task}_${subtask}_${sets}${year}_test_male/trials
trials=data/fame_${task}_${subtask}_${sets}${year}_test/trials
local/plda_scoring.sh data/train data/fame_${task}_${subtask}_${sets}${year}_enroll data/fame_${task}_${subtask}_${sets}${year}_test \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}${year}_enroll exp/ivectors_fame_${task}_${subtask}_${sets}${year}_test $trials local/scores_gmm_2048_ind_pooled_${task}_${subtask}_${sets}${year}
local/plda_scoring.sh --use-existing-models true data/train data/fame_${task}_${subtask}_${sets}${year}_enroll_female data/fame_${task}_${subtask}_${sets}${year}_test_female \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}${year}_enroll_female exp/ivectors_fame_${task}_${subtask}_${sets}${year}_test_female $trials_female local/scores_gmm_2048_ind_female_${task}_${subtask}_${sets}${year}
local/plda_scoring.sh --use-existing-models true data/train data/fame_${task}_${subtask}_${sets}${year}_enroll_male data/fame_${task}_${subtask}_${sets}${year}_test_male \
exp/ivectors_train exp/ivectors_fame_${task}_${subtask}_${sets}${year}_enroll_male exp/ivectors_fame_${task}_${subtask}_${sets}${year}_test_male $trials_male local/scores_gmm_2048_ind_male_${task}_${subtask}_${sets}${year}
done
done
done
done
# Calculating EER
echo "Calculating EER.."
for task in complete ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
trials=data/fame_${task}_${subtask}_${sets}_test/trials
echo "GMM-$num_components EER for fame_${task}_${subtask}_${sets}"
for x in ind; do
for y in female male pooled; do
echo "python local/prepare_for_eer.py $trials local/scores_gmm_${num_components}_${x}_${y}_${task}_${subtask}_${sets}/plda_scores"
eer=`compute-eer <(python local/prepare_for_eer.py $trials local/scores_gmm_${num_components}_${x}_${y}_${task}_${subtask}_${sets}/plda_scores) 2> /dev/null`
echo "${x} ${y}: $eer"
done
done
done
done
done
for task in ageing; do
for subtask in 3sec 10sec 30sec; do
for sets in eval; do
for year in _1t3 _4t10 _mt10; do
trials=data/fame_${task}_${subtask}_${sets}${year}_test/trials
echo "GMM-$num_components EER for fame_${task}_${subtask}_${sets}${year}"
for x in ind; do
for y in female male pooled; do
echo "python local/prepare_for_eer.py $trials local/scores_gmm_${num_components}_${x}_${y}_${task}_${subtask}_${sets}${year}/plda_scores"
eer=`compute-eer <(python local/prepare_for_eer.py $trials local/scores_gmm_${num_components}_${x}_${y}_${task}_${subtask}_${sets}${year}/plda_scores) 2> /dev/null`
echo "${x} ${y}: $eer"
done
done
done
done
done
done