python/FunASR-XL.git

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			#!/bin/bash

			# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita, Shota Horiguchi)
			# Licensed under the MIT license.
			#
			# This script prepares kaldi-style data sets shared with different experiments
			# - data/xxxx
			# callhome, sre, swb2, and swb_cellular datasets
			# - data/simu_${simu_outputs}
			# simulation mixtures generated with various options

			stage=0

			# Modify corpus directories
			# - callhome_dir
			# CALLHOME (LDC2001S97)
			# - swb2_phase1_train
			# Switchboard-2 Phase 1 (LDC98S75)
			# - data_root
			# LDC99S79, LDC2002S06, LDC2001S13, LDC2004S07,
			# LDC2006S44, LDC2011S01, LDC2011S04, LDC2011S09,
			# LDC2011S10, LDC2012S01, LDC2011S05, LDC2011S08
			# - musan_root
			# MUSAN corpus (https://www.openslr.org/17/)
			callhome_dir=
			swb2_phase1_train=
			data_root=
			musan_root=
			# Modify simulated data storage area.
			# This script distributes simulated data under these directories
			simu_actual_dirs=(
			./s05/$USER/diarization-data
			./s08/$USER/diarization-data
			./s09/$USER/diarization-data
			)

			# data preparation options
			max_jobs_run=4
			sad_num_jobs=30
			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"
			sad_graph_opts="--min-silence-duration=0.03 --min-speech-duration=0.3 --max-speech-duration=10.0"
			sad_priors_opts="--sil-scale=0.1"

			# simulation options
			simu_opts_overlap=yes
			simu_opts_num_speaker_array=(1 2 3 4)
			simu_opts_sil_scale_array=(2 2 5 9)
			simu_opts_rvb_prob=0.5
			simu_opts_num_train=100000
			simu_opts_min_utts=10
			simu_opts_max_utts=20

			simu_cmd="run.pl"
			train_cmd="run.pl"
			random_mixture_cmd="run.pl"
			make_mixture_cmd="run.pl"

			. parse_options.sh \|\| exit

			if [ $stage -le 0 ]; then
			echo "prepare kaldi-style datasets"
			# Prepare CALLHOME dataset. This will be used to evaluation.
			if ! validate_data_dir.sh --no-text --no-feats data/callhome1_spkall \
			\|\| ! validate_data_dir.sh --no-text --no-feats data/callhome2_spkall; then
			# imported from https://github.com/kaldi-asr/kaldi/blob/master/egs/callhome_diarization/v1
			local/make_callhome.sh $callhome_dir data
			# Generate two-speaker subsets
			for dset in callhome1 callhome2; do
			# Extract two-speaker recordings in wav.scp
			copy_data_dir.sh data/${dset} data/${dset}_spkall
			# Regenerate segments file from fullref.rttm
			# $2: recid, $4: start_time, $5: duration, $8: speakerid
			awk '{printf "%s_%s_%07d_%07d %s %.2f %.2f\n", \
			$2, $8, $4100, ($4+$5)100, $2, $4, $4+$5}' \
			data/callhome/fullref.rttm \| sort > data/${dset}_spkall/segments
			utils/fix_data_dir.sh data/${dset}_spkall
			# Speaker ID is '[recid]_[speakerid]
			awk '{split($1,A,"_"); printf "%s %s_%s\n", $1, A[1], A[2]}' \
			data/${dset}_spkall/segments > data/${dset}_spkall/utt2spk
			utils/fix_data_dir.sh data/${dset}_spkall
			# Generate rttm files for scoring
			steps/segmentation/convert_utt2spk_and_segments_to_rttm.py \
			data/${dset}_spkall/utt2spk data/${dset}_spkall/segments \
			data/${dset}_spkall/rttm
			utils/data/get_reco2dur.sh data/${dset}_spkall
			done
			fi
			# Prepare a collection of NIST SRE and SWB data. This will be used to train,
			if ! validate_data_dir.sh --no-text --no-feats data/swb_sre_comb; then
			local/make_sre.sh $data_root data
			# Prepare SWB for x-vector DNN training.
			local/make_swbd2_phase1.pl $swb2_phase1_train \
			data/swbd2_phase1_train
			local/make_swbd2_phase2.pl $data_root/LDC99S79 \
			data/swbd2_phase2_train
			local/make_swbd2_phase3.pl $data_root/LDC2002S06 \
			data/swbd2_phase3_train
			local/make_swbd_cellular1.pl $data_root/LDC2001S13 \
			data/swbd_cellular1_train
			local/make_swbd_cellular2.pl $data_root/LDC2004S07 \
			data/swbd_cellular2_train
			# Combine swb and sre data
			utils/combine_data.sh data/swb_sre_comb \
			data/swbd_cellular1_train data/swbd_cellular2_train \
			data/swbd2_phase1_train \
			data/swbd2_phase2_train data/swbd2_phase3_train data/sre
			fi
			# musan data. "back-ground
			if ! validate_data_dir.sh --no-text --no-feats data/musan_noise_bg; then
			local/make_musan.sh $musan_root data
			utils/copy_data_dir.sh data/musan_noise data/musan_noise_bg
			awk '{if(NR>1) print $1,$1}' $musan_root/noise/free-sound/ANNOTATIONS > data/musan_noise_bg/utt2spk
			utils/fix_data_dir.sh data/musan_noise_bg
			fi
			# simu rirs 8k
			if ! validate_data_dir.sh --no-text --no-feats data/simu_rirs_8k; then
			mkdir -p data/simu_rirs_8k
			# if [ ! -e sim_rir_8k.zip ]; then
			# wget --no-check-certificate http://www.openslr.org/resources/26/sim_rir_8k.zip
			# fi
			unzip sim_rir_8k.zip -d data/sim_rir_8k
			find $PWD/data/sim_rir_8k -iname "*.wav" \
			\| awk '{n=split($1,A,/[\/\.]/); print A[n-3]"_"A[n-1], $1}' \
			\| sort > data/simu_rirs_8k/wav.scp
			awk '{print $1, $1}' data/simu_rirs_8k/wav.scp > data/simu_rirs_8k/utt2spk
			utils/fix_data_dir.sh data/simu_rirs_8k
			fi
			# Automatic segmentation using pretrained SAD model
			# it will take one day using 30 CPU jobs:
			# make_mfcc: 1 hour, compute_output: 18 hours, decode: 0.5 hours
			sad_nnet_dir=exp/segmentation_1a/tdnn_stats_asr_sad_1a
			sad_work_dir=exp/segmentation_1a/tdnn_stats_asr_sad_1a
			if ! validate_data_dir.sh --no-text $sad_work_dir/swb_sre_comb_seg; then
			if [ ! -d exp/segmentation_1a ]; then
			# wget http://kaldi-asr.org/models/4/0004_tdnn_stats_asr_sad_1a.tar.gz
			tar zxf 0004_tdnn_stats_asr_sad_1a.tar.gz
			fi
			steps/segmentation/detect_speech_activity.sh \
			--nj $sad_num_jobs \
			--graph-opts "$sad_graph_opts" \
			--transform-probs-opts "$sad_priors_opts" $sad_opts \
			data/swb_sre_comb $sad_nnet_dir mfcc_hires $sad_work_dir \
			$sad_work_dir/swb_sre_comb \|\| exit 1
			fi
			# Extract >1.5 sec segments and split into train/valid sets
			if ! validate_data_dir.sh --no-text --no-feats data/swb_sre_cv; then
			copy_data_dir.sh data/swb_sre_comb data/swb_sre_comb_seg
			awk '$4-$3>1.5{print;}' $sad_work_dir/swb_sre_comb_seg/segments > data/swb_sre_comb_seg/segments
			cp $sad_work_dir/swb_sre_comb_seg/{utt2spk,spk2utt} data/swb_sre_comb_seg
			fix_data_dir.sh data/swb_sre_comb_seg
			utils/subset_data_dir_tr_cv.sh data/swb_sre_comb_seg data/swb_sre_tr data/swb_sre_cv
			fi
			fi

			simudir=data/simu
			if [ $stage -le 1 ]; then
			echo "simulation of mixture"
			mkdir -p $simudir/.work
			random_mixture_cmd=local/random_mixture.py
			make_mixture_cmd=local/make_mixture.py

			for ((i=0; i<${#simu_opts_sil_scale_array[@]}; ++i)); do
			simu_opts_num_speaker=${simu_opts_num_speaker_array[i]}
			simu_opts_sil_scale=${simu_opts_sil_scale_array[i]}
			for dset in swb_sre_tr swb_sre_cv; do
			if [ "$dset" == "swb_sre_tr" ]; then
			n_mixtures=${simu_opts_num_train}
			else
			n_mixtures=500
			fi
			simuid=${dset}_ns${simu_opts_num_speaker}_beta${simu_opts_sil_scale}_${n_mixtures}
			# check if you have the simulation
			if ! validate_data_dir.sh --no-text --no-feats $simudir/data/$simuid; then
			# random mixture generation
			$train_cmd $simudir/.work/random_mixture_$simuid.log \
			$random_mixture_cmd --n_speakers $simu_opts_num_speaker --n_mixtures $n_mixtures \
			--speech_rvb_probability $simu_opts_rvb_prob \
			--sil_scale $simu_opts_sil_scale \
			data/$dset data/musan_noise_bg data/simu_rirs_8k \
			\> $simudir/.work/mixture_$simuid.scp
			nj=64
			mkdir -p $simudir/wav/$simuid
			# distribute simulated data to $simu_actual_dir
			split_scps=
			for n in $(seq $nj); do
			split_scps="$split_scps $simudir/.work/mixture_$simuid.$n.scp"
			mkdir -p $simudir/.work/data_$simuid.$n
			actual=${simu_actual_dirs[($n-1)%${#simu_actual_dirs[@]}]}/$simudir/wav/$simuid/$n
			mkdir -p $actual
			ln -nfs $actual $simudir/wav/$simuid/$n
			done
			utils/split_scp.pl $simudir/.work/mixture_$simuid.scp $split_scps \|\| exit 1

			$simu_cmd --max-jobs-run 64 JOB=1:$nj $simudir/.work/make_mixture_$simuid.JOB.log \
			$make_mixture_cmd --rate=8000 \
			$simudir/.work/mixture_$simuid.JOB.scp \
			$simudir/.work/data_$simuid.JOB $simudir/wav/$simuid/JOB
			utils/combine_data.sh $simudir/data/$simuid $simudir/.work/data_$simuid.*
			steps/segmentation/convert_utt2spk_and_segments_to_rttm.py \
			$simudir/data/$simuid/utt2spk $simudir/data/$simuid/segments \
			$simudir/data/$simuid/rttm
			utils/data/get_reco2dur.sh $simudir/data/$simuid
			fi
			simuid_concat=${dset}_ns"$(IFS="n"; echo "${simu_opts_num_speaker_array[]}")"_beta"$(IFS="n"; echo "${simu_opts_sil_scale_array[]}")"_${n_mixtures}
			mkdir -p $simudir/data/$simuid_concat
			for f in `ls -F $simudir/data/$simuid \| grep -v "/"`; do
			cat $simudir/data/$simuid/$f >> $simudir/data/$simuid_concat/$f
			done
			done
			done
			fi

			if [ $stage -le 3 ]; then
			# compose eval/callhome2_spkall
			eval_set=data/eval/callhome2_spkall
			if ! validate_data_dir.sh --no-text --no-feats $eval_set; then
			utils/copy_data_dir.sh data/callhome2_spkall $eval_set
			cp data/callhome2_spkall/rttm $eval_set/rttm
			awk -v dstdir=wav/eval/callhome2_spkall '{print $1, dstdir"/"$1".wav"}' data/callhome2_spkall/wav.scp > $eval_set/wav.scp
			mkdir -p wav/eval/callhome2_spkall
			wav-copy scp:data/callhome2_spkall/wav.scp scp:$eval_set/wav.scp
			utils/data/get_reco2dur.sh $eval_set
			fi

			# compose eval/callhome1_spkall
			adapt_set=data/eval/callhome1_spkall
			if ! validate_data_dir.sh --no-text --no-feats $adapt_set; then
			utils/copy_data_dir.sh data/callhome1_spkall $adapt_set
			cp data/callhome1_spkall/rttm $adapt_set/rttm
			awk -v dstdir=wav/eval/callhome1_spkall '{print $1, dstdir"/"$1".wav"}' data/callhome1_spkall/wav.scp > $adapt_set/wav.scp
			mkdir -p wav/eval/callhome1_spkall
			wav-copy scp:data/callhome1_spkall/wav.scp scp:$adapt_set/wav.scp
			utils/data/get_reco2dur.sh $adapt_set
			fi
			fi