#!/bin/bash
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# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita, Shota Horiguchi)
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# Licensed under the MIT license.
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#
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# This script prepares kaldi-style data sets shared with different experiments
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# - data/xxxx
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# callhome, sre, swb2, and swb_cellular datasets
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# - data/simu_${simu_outputs}
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# simulation mixtures generated with various options
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stage=0
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# Modify corpus directories
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# - callhome_dir
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# CALLHOME (LDC2001S97)
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# - swb2_phase1_train
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# Switchboard-2 Phase 1 (LDC98S75)
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# - data_root
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# LDC99S79, LDC2002S06, LDC2001S13, LDC2004S07,
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# LDC2006S44, LDC2011S01, LDC2011S04, LDC2011S09,
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# LDC2011S10, LDC2012S01, LDC2011S05, LDC2011S08
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# - musan_root
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# MUSAN corpus (https://www.openslr.org/17/)
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callhome_dir=
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swb2_phase1_train=
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data_root=
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musan_root=
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# Modify simulated data storage area.
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# This script distributes simulated data under these directories
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simu_actual_dirs=(
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./s05/$USER/diarization-data
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./s08/$USER/diarization-data
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./s09/$USER/diarization-data
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)
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# data preparation options
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max_jobs_run=4
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sad_num_jobs=30
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sad_opts="--extra-left-context 79 --extra-right-context 21 --frames-per-chunk 150 --extra-left-context-initial 0 --extra-right-context-final 0 --acwt 0.3"
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sad_graph_opts="--min-silence-duration=0.03 --min-speech-duration=0.3 --max-speech-duration=10.0"
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sad_priors_opts="--sil-scale=0.1"
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# simulation options
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simu_opts_overlap=yes
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simu_opts_num_speaker_array=(1 2 3 4)
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simu_opts_sil_scale_array=(2 2 5 9)
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simu_opts_rvb_prob=0.5
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simu_opts_num_train=100000
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simu_opts_min_utts=10
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simu_opts_max_utts=20
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simu_cmd="run.pl"
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train_cmd="run.pl"
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random_mixture_cmd="run.pl"
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make_mixture_cmd="run.pl"
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. parse_options.sh || exit
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if [ $stage -le 0 ]; then
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echo "prepare kaldi-style datasets"
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# Prepare CALLHOME dataset. This will be used to evaluation.
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if ! validate_data_dir.sh --no-text --no-feats data/callhome1_spkall \
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|| ! validate_data_dir.sh --no-text --no-feats data/callhome2_spkall; then
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# imported from https://github.com/kaldi-asr/kaldi/blob/master/egs/callhome_diarization/v1
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local/make_callhome.sh $callhome_dir data
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# Generate two-speaker subsets
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for dset in callhome1 callhome2; do
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# Extract two-speaker recordings in wav.scp
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copy_data_dir.sh data/${dset} data/${dset}_spkall
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# Regenerate segments file from fullref.rttm
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# $2: recid, $4: start_time, $5: duration, $8: speakerid
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awk '{printf "%s_%s_%07d_%07d %s %.2f %.2f\n", \
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$2, $8, $4*100, ($4+$5)*100, $2, $4, $4+$5}' \
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data/callhome/fullref.rttm | sort > data/${dset}_spkall/segments
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utils/fix_data_dir.sh data/${dset}_spkall
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# Speaker ID is '[recid]_[speakerid]
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awk '{split($1,A,"_"); printf "%s %s_%s\n", $1, A[1], A[2]}' \
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data/${dset}_spkall/segments > data/${dset}_spkall/utt2spk
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utils/fix_data_dir.sh data/${dset}_spkall
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# Generate rttm files for scoring
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steps/segmentation/convert_utt2spk_and_segments_to_rttm.py \
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data/${dset}_spkall/utt2spk data/${dset}_spkall/segments \
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data/${dset}_spkall/rttm
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utils/data/get_reco2dur.sh data/${dset}_spkall
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done
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fi
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# Prepare a collection of NIST SRE and SWB data. This will be used to train,
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if ! validate_data_dir.sh --no-text --no-feats data/swb_sre_comb; then
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local/make_sre.sh $data_root data
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# Prepare SWB for x-vector DNN training.
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local/make_swbd2_phase1.pl $swb2_phase1_train \
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data/swbd2_phase1_train
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local/make_swbd2_phase2.pl $data_root/LDC99S79 \
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data/swbd2_phase2_train
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local/make_swbd2_phase3.pl $data_root/LDC2002S06 \
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data/swbd2_phase3_train
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local/make_swbd_cellular1.pl $data_root/LDC2001S13 \
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data/swbd_cellular1_train
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local/make_swbd_cellular2.pl $data_root/LDC2004S07 \
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data/swbd_cellular2_train
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# Combine swb and sre data
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utils/combine_data.sh data/swb_sre_comb \
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data/swbd_cellular1_train data/swbd_cellular2_train \
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data/swbd2_phase1_train \
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data/swbd2_phase2_train data/swbd2_phase3_train data/sre
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fi
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# musan data. "back-ground
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if ! validate_data_dir.sh --no-text --no-feats data/musan_noise_bg; then
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local/make_musan.sh $musan_root data
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utils/copy_data_dir.sh data/musan_noise data/musan_noise_bg
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awk '{if(NR>1) print $1,$1}' $musan_root/noise/free-sound/ANNOTATIONS > data/musan_noise_bg/utt2spk
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utils/fix_data_dir.sh data/musan_noise_bg
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fi
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# simu rirs 8k
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if ! validate_data_dir.sh --no-text --no-feats data/simu_rirs_8k; then
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mkdir -p data/simu_rirs_8k
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# if [ ! -e sim_rir_8k.zip ]; then
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# wget --no-check-certificate http://www.openslr.org/resources/26/sim_rir_8k.zip
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# fi
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unzip sim_rir_8k.zip -d data/sim_rir_8k
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find $PWD/data/sim_rir_8k -iname "*.wav" \
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| awk '{n=split($1,A,/[\/\.]/); print A[n-3]"_"A[n-1], $1}' \
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| sort > data/simu_rirs_8k/wav.scp
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awk '{print $1, $1}' data/simu_rirs_8k/wav.scp > data/simu_rirs_8k/utt2spk
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utils/fix_data_dir.sh data/simu_rirs_8k
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fi
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# Automatic segmentation using pretrained SAD model
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# it will take one day using 30 CPU jobs:
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# make_mfcc: 1 hour, compute_output: 18 hours, decode: 0.5 hours
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sad_nnet_dir=exp/segmentation_1a/tdnn_stats_asr_sad_1a
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sad_work_dir=exp/segmentation_1a/tdnn_stats_asr_sad_1a
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if ! validate_data_dir.sh --no-text $sad_work_dir/swb_sre_comb_seg; then
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if [ ! -d exp/segmentation_1a ]; then
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# wget http://kaldi-asr.org/models/4/0004_tdnn_stats_asr_sad_1a.tar.gz
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tar zxf 0004_tdnn_stats_asr_sad_1a.tar.gz
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fi
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steps/segmentation/detect_speech_activity.sh \
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--nj $sad_num_jobs \
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--graph-opts "$sad_graph_opts" \
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--transform-probs-opts "$sad_priors_opts" $sad_opts \
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data/swb_sre_comb $sad_nnet_dir mfcc_hires $sad_work_dir \
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$sad_work_dir/swb_sre_comb || exit 1
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fi
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# Extract >1.5 sec segments and split into train/valid sets
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if ! validate_data_dir.sh --no-text --no-feats data/swb_sre_cv; then
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copy_data_dir.sh data/swb_sre_comb data/swb_sre_comb_seg
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awk '$4-$3>1.5{print;}' $sad_work_dir/swb_sre_comb_seg/segments > data/swb_sre_comb_seg/segments
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cp $sad_work_dir/swb_sre_comb_seg/{utt2spk,spk2utt} data/swb_sre_comb_seg
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fix_data_dir.sh data/swb_sre_comb_seg
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utils/subset_data_dir_tr_cv.sh data/swb_sre_comb_seg data/swb_sre_tr data/swb_sre_cv
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fi
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fi
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simudir=data/simu
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if [ $stage -le 1 ]; then
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echo "simulation of mixture"
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mkdir -p $simudir/.work
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random_mixture_cmd=local/random_mixture.py
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make_mixture_cmd=local/make_mixture.py
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for ((i=0; i<${#simu_opts_sil_scale_array[@]}; ++i)); do
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simu_opts_num_speaker=${simu_opts_num_speaker_array[i]}
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simu_opts_sil_scale=${simu_opts_sil_scale_array[i]}
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for dset in swb_sre_tr swb_sre_cv; do
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if [ "$dset" == "swb_sre_tr" ]; then
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n_mixtures=${simu_opts_num_train}
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else
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n_mixtures=500
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fi
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simuid=${dset}_ns${simu_opts_num_speaker}_beta${simu_opts_sil_scale}_${n_mixtures}
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# check if you have the simulation
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if ! validate_data_dir.sh --no-text --no-feats $simudir/data/$simuid; then
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# random mixture generation
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$train_cmd $simudir/.work/random_mixture_$simuid.log \
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$random_mixture_cmd --n_speakers $simu_opts_num_speaker --n_mixtures $n_mixtures \
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--speech_rvb_probability $simu_opts_rvb_prob \
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--sil_scale $simu_opts_sil_scale \
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data/$dset data/musan_noise_bg data/simu_rirs_8k \
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\> $simudir/.work/mixture_$simuid.scp
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nj=64
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mkdir -p $simudir/wav/$simuid
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# distribute simulated data to $simu_actual_dir
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split_scps=
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for n in $(seq $nj); do
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split_scps="$split_scps $simudir/.work/mixture_$simuid.$n.scp"
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mkdir -p $simudir/.work/data_$simuid.$n
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actual=${simu_actual_dirs[($n-1)%${#simu_actual_dirs[@]}]}/$simudir/wav/$simuid/$n
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mkdir -p $actual
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ln -nfs $actual $simudir/wav/$simuid/$n
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done
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utils/split_scp.pl $simudir/.work/mixture_$simuid.scp $split_scps || exit 1
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$simu_cmd --max-jobs-run 64 JOB=1:$nj $simudir/.work/make_mixture_$simuid.JOB.log \
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$make_mixture_cmd --rate=8000 \
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$simudir/.work/mixture_$simuid.JOB.scp \
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$simudir/.work/data_$simuid.JOB $simudir/wav/$simuid/JOB
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utils/combine_data.sh $simudir/data/$simuid $simudir/.work/data_$simuid.*
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steps/segmentation/convert_utt2spk_and_segments_to_rttm.py \
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$simudir/data/$simuid/utt2spk $simudir/data/$simuid/segments \
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$simudir/data/$simuid/rttm
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utils/data/get_reco2dur.sh $simudir/data/$simuid
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fi
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simuid_concat=${dset}_ns"$(IFS="n"; echo "${simu_opts_num_speaker_array[*]}")"_beta"$(IFS="n"; echo "${simu_opts_sil_scale_array[*]}")"_${n_mixtures}
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mkdir -p $simudir/data/$simuid_concat
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for f in `ls -F $simudir/data/$simuid | grep -v "/"`; do
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cat $simudir/data/$simuid/$f >> $simudir/data/$simuid_concat/$f
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done
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done
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done
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fi
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if [ $stage -le 3 ]; then
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# compose eval/callhome2_spkall
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eval_set=data/eval/callhome2_spkall
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if ! validate_data_dir.sh --no-text --no-feats $eval_set; then
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utils/copy_data_dir.sh data/callhome2_spkall $eval_set
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cp data/callhome2_spkall/rttm $eval_set/rttm
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awk -v dstdir=wav/eval/callhome2_spkall '{print $1, dstdir"/"$1".wav"}' data/callhome2_spkall/wav.scp > $eval_set/wav.scp
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mkdir -p wav/eval/callhome2_spkall
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wav-copy scp:data/callhome2_spkall/wav.scp scp:$eval_set/wav.scp
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utils/data/get_reco2dur.sh $eval_set
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fi
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# compose eval/callhome1_spkall
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adapt_set=data/eval/callhome1_spkall
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if ! validate_data_dir.sh --no-text --no-feats $adapt_set; then
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utils/copy_data_dir.sh data/callhome1_spkall $adapt_set
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cp data/callhome1_spkall/rttm $adapt_set/rttm
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awk -v dstdir=wav/eval/callhome1_spkall '{print $1, dstdir"/"$1".wav"}' data/callhome1_spkall/wav.scp > $adapt_set/wav.scp
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mkdir -p wav/eval/callhome1_spkall
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wav-copy scp:data/callhome1_spkall/wav.scp scp:$adapt_set/wav.scp
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utils/data/get_reco2dur.sh $adapt_set
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fi
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fi
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