-
Notifications
You must be signed in to change notification settings - Fork 636
/
run.sh
991 lines (879 loc) · 43.5 KB
/
run.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
#!/usr/bin/env bash
. ./path.sh || exit 1;
# This recipe aims at reimplement the results of SOND on Callhome corpus which is represented in
# [1] TOLD: A Novel Two-stage Overlap-aware Framework for Speaker Diarization, ICASSP 2023
# You can also use it on other dataset such AliMeeting to reproduce the results in
# [2] Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis, EMNLP 2022
# We recommend you run this script stage by stage.
# This recipe includes:
# 1. simulating data with switchboard and NIST.
# 2. training the model from scratch for 3 stages:
# 2-1. pre-train on simu_swbd_sre
# 2-2. train on simu_swbd_sre
# 2-3. finetune on callhome1
# 3. evaluating model with the results from the first stage EEND-OLA,
# Finally, you will get a similar DER result claimed in the paper.
# environment configuration
# path/to/kaldi
kaldi_root=
if [ -z "${kaldi_root}" ]; then
echo "We need kaldi to prepare dataset, extract fbank features, please install kaldi first and set kaldi_root."
echo "Kaldi installation guide can be found at https://kaldi-asr.org/"
exit;
fi
if [ ! -e local ]; then
ln -s ${kaldi_root}/egs/callhome_diarization/v2/local ./local
fi
if [ ! -e utils ]; then
ln -s ${kaldi_root}/egs/callhome_diarization/v2/utils ./utils
fi
# path to Switchboard and NIST including:
# LDC98S75, LDC99S79, LDC2002S06, LDC2001S13, LDC2004S07
data_root=
if [ -z "${data_root}" ]; then
echo "We need Switchboard and NIST to simulate data for pretraining."
echo "If you can't get them, please use 'finetune.sh' to finetune a pretrained model."
exit;
fi
# path/to/NIST/LDC2001S97
callhome_root=
if [ -z "${callhome_root}" ]; then
echo "We need callhome corpus for training."
echo "If you want inference only, please refer https://www.modelscope.cn/models/damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch/summary"
exit;
fi
# machines configuration
gpu_devices="4,5,6,7" # for V100-16G, use 4 GPUs
gpu_num=4
count=1
# general configuration
stage=0
stop_stage=19
# number of jobs for data process
nj=16
sr=8000
# experiment configuration
lang=en
feats_type=fbank
datadir=data
dumpdir=dump
expdir=exp
train_cmd=utils/run.pl
# training related
tag=""
train_set=simu_swbd_sre
valid_set=callhome1
train_config=conf/EAND_ResNet34_SAN_L4N512_None_FFN_FSMN_L6N512_bce_dia_loss_01.yaml
token_list=${datadir}/token_list/powerset_label_n16k4.txt
init_param=
freeze_param=
# inference related
inference_model=valid.der.ave_5best.pb
inference_config=conf/basic_inference.yaml
inference_tag=""
test_sets="callhome2"
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
# number of jobs for inference
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
njob=4
infer_cmd=utils/run.pl
told_max_iter=4
. utils/parse_options.sh || exit 1;
model_dir="$(basename "${train_config}" .yaml)_${feats_type}_${lang}${tag}"
# you can set gpu num for decoding here
gpuid_list=$gpu_devices # set gpus for decoding, the same as training stage by default
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
if ${gpu_inference}; then
inference_nj=$[${ngpu}*${njob}]
_ngpu=1
else
inference_nj=$njob
_ngpu=0
fi
# Prepare datasets
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# 1. Prepare a collection of NIST SRE data.
echp "Stage 0: Prepare a collection of NIST SRE data."
local/make_sre.sh $data_root ${datadir}
# 2.a Prepare SWB.
local/make_swbd2_phase1.pl ${data_root}/LDC98S75 \
${datadir}/swbd2_phase1_train
local/make_swbd2_phase2.pl $data_root/LDC99S79 \
${datadir}/swbd2_phase2_train
local/make_swbd2_phase3.pl $data_root/LDC2002S06 \
${datadir}/swbd2_phase3_train
local/make_swbd_cellular1.pl $data_root/LDC2001S13 \
${datadir}/swbd_cellular1_train
local/make_swbd_cellular2.pl $data_root/LDC2004S07 \
${datadir}/swbd_cellular2_train
# 2.b combine all swbd data.
utils/combine_data.sh ${datadir}/swbd \
${datadir}/swbd2_phase1_train ${datadir}/swbd2_phase2_train ${datadir}/swbd2_phase3_train \
${datadir}/swbd_cellular1_train ${datadir}/swbd_cellular2_train
utils/validate_data_dir.sh --no-text --no-feats ${datadir}/swbd
utils/fix_data_dir.sh ${datadir}/swbd
utils/combine_data.sh ${datadir}/swbd_sre ${datadir}/swbd ${datadir}/sre
utils/validate_data_dir.sh --no-text --no-feats ${datadir}/swbd_sre
utils/fix_data_dir.sh ${datadir}/swbd_sre
# 3. Prepare the Callhome portion of NIST SRE 2000.
local/make_callhome.sh ${callhome_root} ${datadir}/
# 4. split ref.rttm
for dset in callhome1 callhome2; do
rm -rf ${datadir}/${dset}/ref.rttm
for name in `awk '{print $1}' ${datadir}/${dset}/wav.scp`; do
grep ${name} ${datadir}/callhome/fullref.rttm >> ${datadir}/${dset}/ref.rttm;
done
# filter out records which don't have rttm labels.
awk '{print $2}' ${datadir}/${dset}/ref.rttm | sort | uniq > ${datadir}/${dset}/uttid
mv ${datadir}/${dset}/wav.scp ${datadir}/${dset}/wav.scp.bak
awk '{if (NR==FNR){a[$1]=1}else{if (a[$1]==1){print $0}}}' ${datadir}/${dset}/uttid ${datadir}/${dset}/wav.scp.bak > ${datadir}/${dset}/wav.scp
mkdir ${datadir}/${dset}/raw
mv ${datadir}/${dset}/{reco2num_spk,segments,spk2utt,utt2spk,uttid,wav.scp.bak} ${datadir}/${dset}/raw/
awk '{print $1,$1}' ${datadir}/${dset}/wav.scp > ${datadir}/${dset}/utt2spk
done
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Stage 1: Dump sph file to wav"
export PATH=${kaldi_root}/tools/sph2pipe/:${PATH}
if [ ! -f ${kaldi_root}/tools/sph2pipe/sph2pipe ]; then
echo "Can not find sph2pipe in ${kaldi_root}/tools/sph2pipe/,"
echo "please install sph2pipe and put it in the right place."
exit;
fi
for dset in callhome1 callhome2 swbd_sre; do
echo "Stage 1: start to dump ${dset}."
mv ${datadir}/${dset}/wav.scp ${datadir}/${dset}/sph.scp
mkdir -p ${dumpdir}/${dset}/wavs
python -Wignore script/dump_pipe_wav.py ${datadir}/${dset}/sph.scp ${dumpdir}/${dset}/wavs \
--sr ${sr} --nj ${nj} --no_pbar
find `pwd`/${dumpdir}/${dset}/wavs -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/${dset}/wav.scp
done
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "Stage 2: Extract non-overlap segments from callhome dataset"
for dset in callhome1 callhome2; do
echo "Stage 2: Extracting non-overlap segments for "${dset}
mkdir -p ${dumpdir}/${dset}/nonoverlap_0s
python -Wignore script/extract_nonoverlap_segments.py \
${datadir}/${dset}/wav.scp ${datadir}/${dset}/ref.rttm ${dumpdir}/${dset}/nonoverlap_0s \
--min_dur 0.1 --max_spk_num 8 --sr ${sr} --no_pbar --nj ${nj}
mkdir -p ${datadir}/${dset}/nonoverlap_0s
find ${dumpdir}/${dset}/nonoverlap_0s/ -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/${dset}/nonoverlap_0s/wav.scp
awk -F'[/.]' '{print $(NF-1),$(NF-2)}' ${datadir}/${dset}/nonoverlap_0s/wav.scp > ${datadir}/${dset}/nonoverlap_0s/utt2spk
echo "Done."
done
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "Stage 3: Generate concatenated waveforms for each speaker in switchboard, sre and callhome1"
mkdir swb_sre_resources
wget --no-check-certificate -P swb_sre_resources/ https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/Speaker_Diar/swb_sre_resources/noise.scp
wget --no-check-certificate -P swb_sre_resources/ https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/Speaker_Diar/swb_sre_resources/swbd_sre_tdnn_vad_segments
mkdir ${datadir}/swbd_sre/none_silence
ln -s swb_sre_resources/swbd_sre_tdnn_vad_segments ${datadir}/swbd_sre/none_silence/segments
cp ${datadir}/swbd_sre/wav.scp ${datadir}/swbd_sre/none_silence/reco.scp
mkdir -p ${dumpdir}/swbd_sre/none_silence
python -Wignore script/remove_silence_from_wav.py \
${datadir}/swbd_sre/none_silence ${dumpdir}/swbd_sre/none_silence --nj ${nj} --sr 8000
# The utterance number in wav.scp may be different from reco.scp,
# since some recordings don't appear in the segments file, may due to the VAD
echo "find wavs_nosil"
find `pwd`/${dumpdir}/swbd_sre/none_silence -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/swbd_sre/none_silence/wav.scp
echo "concat spk segments"
ln -s ${datadir}/swbd_sre/utt2spk ${datadir}/swbd_sre/none_silence/utt2spk
echo "Stage 3: Start to concatnate waveforms for speakers in switchboard and sre"
python -Wignore egs/callhome/concat_spk_segs.py \
${datadir}/swbd_sre/none_silence ${dumpdir}/swbd_sre/spk_wavs --nj ${nj} --sr 8000
echo "Stage 3: Start to concatnate waveforms for speakers in callhome1"
# only use callhome1 as training set to simulate data
python -Wignore egs/callhome/concat_spk_segs.py \
${datadir}/callhome1/nonoverlap_0s ${dumpdir}/callhome1/spk_wavs --nj ${nj} --sr 8000
fi
# simulate data with the pattern of callhome1
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "Stage 4: Start to simulate recordings."
if [ ! -e ${dumpdir}/musan ]; then
echo "Stage 4-1: Start to download MUSAN noises from openslr"
wget --no-check-certificate -P ${dumpdir}/musan https://www.openslr.org/resources/17/musan.tar.gz
tar -C ${dumpdir}/musan -xvf ${dumpdir}/musan/musan.tar.gz
fi
if [ ! -e ${dumpdir}/rirs ]; then
echo "Stage 4-2: Start to download RIRs from openslr"
wget --no-check-certificate -P ${dumpdir}/rirs https://www.openslr.org/resources/28/rirs_noises.zip
unzip ${dumpdir}/rirs/rirs_noises.zip -d ${dumpdir}/rirs
fi
mkdir -p ${datadir}/simu_swbd_sre
# only use background noises instead of all noises in MUSAN.
sed "s:/path/to/musan/:`pwd`/${dumpdir}/musan/:g" swb_sre_resources/noise.scp > ${datadir}/simu_swbd_sre/noise.scp
# use simulated RIRs.
find `pwd`/${dumpdir}/rirs/RIRS_NOISES/simulated_rirs/ -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-3)"-"$(NF-1), $0}' > ${datadir}/simu_swbd_sre/rirs.scp
cp ${datadir}/callhome1/{ref.rttm,reco2num_spk} ${datadir}/simu_swbd_sre
find `pwd`/${dumpdir}/swbd_sre/spk_wavs -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/simu_swbd_sre/spk2wav.scp
echo "Stage 4-3: Start to simulate recordings with variable speakers as Callhome1 patterns."
# average duration of callhome is 125s, about 20 chunk with silence
# simulating 22500 (45 jobs x 500 reco) recordings, without random_assign and random_shift_interval
for i in $(seq 0 44); do
cmd="python -Wignore egs/callhome/simu_whole_recordings.py \
${datadir}/simu_swbd_sre \
${dumpdir}/simu_swbd_sre/wavs \
--corpus_name simu_swbd_sre --task_id $i --total_mix 500 --sr 8000 --no_bar &"
echo $cmd
eval $cmd
done
wait;
echo "Stage 4-4: Start to simulate recordings with fixed speakers as Callhome1 patterns."
# simulating 30000 (30 jobs x 1000 reco) recordings for different speaker number 2, 3, 4
for n_spk in $(seq 2 4); do
mkdir -p /home/neo.dzh/corpus/simu_swbd_sre/${n_spk}spk_wavs
for i in $(seq 0 29); do
cmd="python -Wignore egs/callhome/simu_whole_recordings.py \
${datadir}/simu_swbd_sre \
${dumpdir}/simu_swbd_sre/${n_spk}spk_wavs \
--random_assign_spk --random_interval --spk_num ${n_spk} \
--corpus_name simu_swbd_sre --task_id $i --total_mix 1000 --sr 8000 --no_bar &"
echo $cmd
eval $cmd
done
wait;
done
find `pwd`/${dumpdir}/simu_swbd_sre -iname "*.wav" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/simu_swbd_sre/wav.scp
awk '{print $1,$1}' ${datadir}/simu_swbd_sre/wav.scp > ${datadir}/simu_swbd_sre/utt2spk
find `pwd`/${dumpdir}/simu_swbd_sre -iname "*.rttm" | sort | awk -F'[/.]' '{print $(NF-1),$0}' > ${datadir}/simu_swbd_sre/rttm.scp
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
echo "Stage 5: Generate fbank features"
home_path=`pwd`
cd ${kaldi_root}/egs/callhome_diarization/v2 || exit
. ./cmd.sh
. ./path.sh
for dset in simu_swbd_sre callhome1 callhome2; do
steps/make_fbank.sh --write-utt2num-frames true --fbank-config conf/fbank.conf --nj ${nj} --cmd "$train_cmd" \
${datadir}/${dset} ${expdir}/make_fbank/${dset} ${dumpdir}/${dset}/fbank
utils/fix_data_dir.sh ${datadir}/${dset}
done
for dset in swbd_sre/none_silence callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
steps/make_fbank.sh --write-utt2num-frames true --fbank-config conf/fbank.conf --nj ${nj} --cmd "$train_cmd" \
${datadir}/${dset} ${expdir}/make_fbank/${dset} ${dumpdir}/${dset}/fbank
utils/fix_data_dir.sh ${datadir}/${dset}
done
cd ${home_path} || exit
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "Stage 6: Extract speaker embeddings."
sv_exp_dir=exp/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
if [ ! -e ${sv_exp_dir} ]; then
echo "start to download sv models"
git lfs install
git clone https://www.modelscope.cn/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch.git
mv speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ${expdir}/
echo "Done."
fi
sed "s/input_size: null/input_size: 80/g" ${sv_exp_dir}/sv.yaml > ${sv_exp_dir}/sv_fbank.yaml
for dset in swbd_sre/none_silence callhome1/nonoverlap_0s callhome2/nonoverlap_0s; do
key_file=${datadir}/${dset}/feats.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
_logdir=${dumpdir}/${dset}/xvecs
mkdir -p ${_logdir}
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/sv_inference.JOB.log \
python -m funasr.bin.sv_inference_launch \
--batch_size 1 \
--njob ${njob} \
--ngpu "${_ngpu}" \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${key_file},speech,kaldi_ark" \
--key_file "${_logdir}"/keys.JOB.scp \
--sv_train_config ${sv_exp_dir}/sv_fbank.yaml \
--sv_model_file ${sv_exp_dir}/sv.pth \
--output_dir "${_logdir}"/output.JOB
cat ${_logdir}/output.*/xvector.scp | sort > ${datadir}/${dset}/utt2xvec
done
fi
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
echo "Stage 7: Generate label files."
for dset in simu_swbd_sre callhome1 callhome2; do
echo "Stage 7: Generate labels for ${dset}."
python -Wignore script/calc_real_meeting_frame_labels.py \
${datadir}/${dset} ${dumpdir}/${dset}/labels \
--n_spk 8 --frame_shift 0.01 --nj 16 --sr 8000
find `pwd`/${dumpdir}/${dset}/labels/ -iname "*.lbl.mat" | awk -F'[/.]' '{print $(NF-2),$0}' | sort > ${datadir}/${dset}/labels.scp
done
fi
if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
echo "Stage 8: Make training and evaluation files."
# dump simulated data in training mode (randomly shuffle the speaker order).
data_dir=${datadir}/simu_swbd_sre/files_for_dump
mkdir ${data_dir}
cp ${datadir}/simu_swbd_sre/{feats.scp,labels.scp} ${data_dir}/
cp ${datadir}/swbd_sre/none_silence/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
# dump data with the window length of 1600 frames and hop length of 400 frames.
echo "Stage 8: start to dump for simu_swbd_sre."
for i in $(seq 0 49); do
cmd="python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
--out ${dumpdir}/simu_swbd_sre/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode train \
--chunk_size 1600 --chunk_shift 400 \
--task_id ${i} --task_size 2250 &"
echo $cmd
eval $cmd
done
wait;
mkdir -p ${datadir}/simu_swbd_sre/dumped_files
cat ${dumpdir}/simu_swbd_sre/dumped_files/data_parts*_feat.scp | sort > ${datadir}/simu_swbd_sre/dumped_files/feats.scp
cat ${dumpdir}/simu_swbd_sre/dumped_files/data_parts*_xvec.scp | sort > ${datadir}/simu_swbd_sre/dumped_files/profile.scp
cat ${dumpdir}/simu_swbd_sre/dumped_files/data_parts*_label.scp | sort > ${datadir}/simu_swbd_sre/dumped_files/label.scp
mkdir -p ${expdir}/simu_swbd_sre_states
awk '{print $1,"1600"}' ${datadir}/simu_swbd_sre/dumped_files/feats.scp | shuf > ${expdir}/simu_swbd_sre_states/speech_shape
# dump callhome1 data in training mode.
data_dir=${datadir}/callhome1/files_for_dump
mkdir ${data_dir}
# filter out zero duration segments
LC_ALL=C awk '{if ($5 > 0){print $0}}' ${datadir}/callhome1/ref.rttm > ${data_dir}/ref.rttm
cp ${datadir}/callhome1/{feats.scp,labels.scp} ${data_dir}/
cp ${datadir}/callhome1/nonoverlap_0s/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
echo "Stage 8: start to dump for callhome1."
python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
--out ${dumpdir}/callhome1/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode train \
--chunk_size 1600 --chunk_shift 400 --add_mid_to_speaker true
mkdir -p ${datadir}/callhome1/dumped_files
cat ${dumpdir}/callhome1/dumped_files/data_parts*_feat.scp | sort > ${datadir}/callhome1/dumped_files/feats.scp
cat ${dumpdir}/callhome1/dumped_files/data_parts*_xvec.scp | sort > ${datadir}/callhome1/dumped_files/profile.scp
cat ${dumpdir}/callhome1/dumped_files/data_parts*_label.scp | sort > ${datadir}/callhome1/dumped_files/label.scp
mkdir -p ${expdir}/callhome1_states
awk '{print $1,"1600"}' ${datadir}/callhome1/dumped_files/feats.scp | shuf > ${expdir}/callhome1_states/speech_shape
python -Wignore script/convert_rttm_to_seg_file.py --rttm_scp ${data_dir}/ref.rttm --seg_file ${data_dir}/org_vad.txt
# dump callhome2 data in test mode.
data_dir=${datadir}/callhome2/files_for_dump
mkdir ${data_dir}
# filter out zero duration segments
LC_ALL=C awk '{if ($5 > 0){print $0}}' ${datadir}/callhome2/ref.rttm > ${data_dir}/ref.rttm
cp ${datadir}/callhome2/{feats.scp,labels.scp} ${data_dir}/
cp ${datadir}/callhome2/nonoverlap_0s/{utt2spk,utt2xvec,utt2num_frames} ${data_dir}/
echo "Stage 8: start to dump for callhome2."
python -Wignore script/dump_meeting_chunks.py --dir ${data_dir} \
--out ${dumpdir}/callhome2/dumped_files/data --n_spk 16 --no_pbar --sr 8000 --mode test \
--chunk_size 1600 --chunk_shift 400 --add_mid_to_speaker true
mkdir -p ${datadir}/callhome2/dumped_files
cat ${dumpdir}/callhome2/dumped_files/data_parts*_feat.scp | sort > ${datadir}/callhome2/dumped_files/feats.scp
cat ${dumpdir}/callhome2/dumped_files/data_parts*_xvec.scp | sort > ${datadir}/callhome2/dumped_files/profile.scp
cat ${dumpdir}/callhome2/dumped_files/data_parts*_label.scp | sort > ${datadir}/callhome2/dumped_files/label.scp
mkdir -p ${expdir}/callhome2_states
awk '{print $1,"1600"}' ${datadir}/callhome2/dumped_files/feats.scp | shuf > ${expdir}/callhome2_states/speech_shape
python -Wignore script/convert_rttm_to_seg_file.py --rttm_scp ${data_dir}/ref.rttm --seg_file ${data_dir}/org_vad.txt
fi
# Training Stage, phase 1, pretraining on simulated data with frozen encoder parameters.
# This training may cost about 1.8 days.
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
echo "stage 10: training phase 1, pretraining on simulated data"
world_size=$gpu_num # run on one machine
mkdir -p ${expdir}/${model_dir}
mkdir -p ${expdir}/${model_dir}/log
mkdir -p /tmp/${model_dir}
INIT_FILE=/tmp/${model_dir}/ddp_init
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_opt=""
if [ ! -z "${init_param}" ]; then
init_opt="--init_param ${init_param}"
echo ${init_opt}
fi
freeze_opt=""
if [ ! -z "${freeze_param}" ]; then
freeze_opt="--freeze_param ${freeze_param}"
echo ${freeze_opt}
fi
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $gpu_num; ++i)); do
{
rank=$i
local_rank=$i
gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1])
python -m funasr.bin.diar_train \
--gpu_id $gpu_id \
--use_preprocessor false \
--token_type char \
--token_list $token_list \
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/feats.scp,speech,kaldi_ark \
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/profile.scp,profile,kaldi_ark \
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
--train_shape_file ${expdir}/${train_set}_states/speech_shape \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
--valid_shape_file ${expdir}/${valid_set}_states/speech_shape \
--init_param ${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/sv.pth:encoder:encoder \
--unused_parameters true \
--freeze_param encoder \
${init_opt} \
${freeze_opt} \
--ignore_init_mismatch true \
--resume true \
--output_dir ${expdir}/${model_dir} \
--config $train_config \
--ngpu $gpu_num \
--num_worker_count $count \
--multiprocessing_distributed true \
--dist_init_method $init_method \
--dist_world_size $world_size \
--dist_rank $rank \
--local_rank $local_rank 1> ${expdir}/${model_dir}/log/train.log.$i 2>&1
} &
done
echo "Training log can be found at ${expdir}/${model_dir}/log/train.log.*"
wait
fi
# evaluate for pretrained model
if [ ${stage} -le 11 ] && [ ${stop_stage} -ge 11 ]; then
echo "stage 11: evaluation for phase-1 model."
for dset in ${test_sets}; do
echo "Processing for $dset"
exp_model_dir=${expdir}/${model_dir}
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${exp_model_dir}/${_inference_tag}/${inference_model}/${dset}"
_logdir="${_dir}/logdir"
if [ -d ${_dir} ]; then
echo "WARNING: ${_dir} is already exists."
fi
mkdir -p "${_logdir}"
_data="${datadir}/${dset}/dumped_files"
key_file=${_data}/feats.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
_opt=
if [ ! -z "${inference_config}" ]; then
_opt="--config ${inference_config}"
fi
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
echo "Inference log can be found at ${_logdir}/inference.*.log"
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
python -m funasr.bin.diar_inference_launch \
--batch_size 1 \
--ngpu "${_ngpu}" \
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
--data_path_and_name_and_type "${_data}/profile.scp,profile,kaldi_ark" \
--key_file "${_logdir}"/keys.JOB.scp \
--diar_train_config "${exp_model_dir}"/config.yaml \
--diar_model_file "${exp_model_dir}"/"${inference_model}" \
--output_dir "${_logdir}"/output.JOB \
--mode sond ${_opt}
done
fi
# Scoring for pretrained model, you may get a DER like 13.29 16.54
# 13.29: with oracle VAD, 16.54: with only SOND outputs, aka, system VAD.
if [ ${stage} -le 12 ] && [ ${stop_stage} -ge 12 ]; then
echo "stage 12: Scoring phase-1 models"
if [ ! -e dscore ]; then
git clone https://github.com/nryant/dscore.git
# add intervaltree to setup.py
fi
for dset in ${test_sets}; do
echo "stage 12: Scoring for ${dset}"
diar_exp=${expdir}/${model_dir}
_data="${datadir}/${dset}"
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
_logdir="${_dir}/logdir"
cat ${_logdir}/*/labels.txt | sort > ${_dir}/labels.txt
cmd="python -Wignore script/convert_label_to_rttm.py ${_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${_dir}/sys.rttm \
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
# echo ${cmd}
eval ${cmd}
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${_dir}/sys.rttm.ref_vad
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${_dir}/sys.rttm.sys_vad
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
echo -e "${inference_model} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${_dir}/results.txt
done
fi
# Training Stage, phase 2, training on simulated data without frozen parameters.
# For V100-16G, please set batch_size to 8 in the config, and use 4 GPU to train the model with options like --gpu_devices 4,5,6,7 --gpu_num 4.
# For V100-32G, please set batch_size to 16 in the config, and use 2 GPU to train the model with options like --gpu_devices 4,5,6,7 --gpu_num 2.
# This training may cost about 3.5 days.
if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
echo "stage 13: training phase 2, training on simulated data"
world_size=$gpu_num # run on one machine
mkdir -p ${expdir}/${model_dir}_phase2
mkdir -p ${expdir}/${model_dir}_phase2/log
mkdir -p /tmp/${model_dir}_phase2
INIT_FILE=/tmp/${model_dir}_phase2/ddp_init
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_opt=""
if [ ! -z "${init_param}" ]; then
init_opt="--init_param ${init_param}"
echo ${init_opt}
fi
freeze_opt=""
if [ ! -z "${freeze_param}" ]; then
freeze_opt="--freeze_param ${freeze_param}"
echo ${freeze_opt}
fi
phase2_config="$(dirname "${train_config}")/$(basename "${train_config}" .yaml)_phase2.yaml"
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $gpu_num; ++i)); do
{
rank=$i
local_rank=$i
gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1])
python -m funasr.bin.diar_train \
--gpu_id $gpu_id \
--use_preprocessor false \
--token_type char \
--token_list $token_list \
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/feats.scp,speech,kaldi_ark \
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/profile.scp,profile,kaldi_ark \
--train_data_path_and_name_and_type ${datadir}/${train_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
--train_shape_file ${expdir}/${train_set}_states/speech_shape \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
--valid_shape_file ${expdir}/${valid_set}_states/speech_shape \
--init_param exp/${model_dir}/valid.der.ave_5best.pb \
--unused_parameters true \
${init_opt} \
${freeze_opt} \
--ignore_init_mismatch true \
--resume true \
--output_dir ${expdir}/${model_dir}_phase2 \
--config ${phase2_config} \
--ngpu $gpu_num \
--num_worker_count $count \
--multiprocessing_distributed true \
--dist_init_method $init_method \
--dist_world_size $world_size \
--dist_rank $rank \
--local_rank $local_rank 1> ${expdir}/${model_dir}_phase2/log/train.log.$i 2>&1
} &
done
echo "Training log can be found at ${expdir}/${model_dir}_phase2/log/train.log.*"
wait
fi
# evaluate for phase-2 model
if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
echo "stage 14: evaluation for phase-2 model ${inference_model}."
for dset in ${test_sets}; do
echo "Processing for $dset"
exp_model_dir=${expdir}/${model_dir}_phase2
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${exp_model_dir}/${_inference_tag}/${inference_model}/${dset}"
_logdir="${_dir}/logdir"
if [ -d ${_dir} ]; then
echo "WARNING: ${_dir} is already exists."
fi
mkdir -p "${_logdir}"
_data="${datadir}/${dset}/dumped_files"
key_file=${_data}/feats.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
_opt=
if [ ! -z "${inference_config}" ]; then
_opt="--config ${inference_config}"
fi
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
echo "Inference log can be found at ${_logdir}/inference.*.log"
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
python -m funasr.bin.diar_inference_launch \
--batch_size 1 \
--ngpu "${_ngpu}" \
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
--data_path_and_name_and_type "${_data}/profile.scp,profile,kaldi_ark" \
--key_file "${_logdir}"/keys.JOB.scp \
--diar_train_config "${exp_model_dir}"/config.yaml \
--diar_model_file "${exp_model_dir}"/${inference_model} \
--output_dir "${_logdir}"/output.JOB \
--mode sond ${_opt}
done
fi
# Scoring for pretrained model, you may get a DER like 11.54 15.41
# 11.54: with oracle VAD, 15.41: with only SOND outputs, aka, system VAD.
if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
echo "stage 15: Scoring phase-2 models"
if [ ! -e dscore ]; then
git clone https://github.com/nryant/dscore.git
# add intervaltree to setup.py
fi
for dset in ${test_sets}; do
echo "stage 15: Scoring for ${dset}"
diar_exp=${expdir}/${model_dir}_phase2
_data="${datadir}/${dset}"
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
_logdir="${_dir}/logdir"
cat ${_logdir}/*/labels.txt | sort > ${_dir}/labels.txt
cmd="python -Wignore script/convert_label_to_rttm.py ${_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${_dir}/sys.rttm \
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
# echo ${cmd}
eval ${cmd}
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${_dir}/sys.rttm.ref_vad
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${_dir}/sys.rttm.sys_vad
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
echo -e "${inference_model} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${_dir}/results.txt
done
fi
# Finetune Stage, phase 3, training on callhom1 training set
if [ ${stage} -le 16 ] && [ ${stop_stage} -ge 16 ]; then
echo "stage 16: training phase 3, finetuing on callhome1 real data"
world_size=$gpu_num # run on one machine
mkdir -p ${expdir}/${model_dir}_phase3
mkdir -p ${expdir}/${model_dir}_phase3/log
mkdir -p /tmp/${model_dir}_phase3
INIT_FILE=/tmp/${model_dir}_phase3/ddp_init
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_opt=""
if [ ! -z "${init_param}" ]; then
init_opt="--init_param ${init_param}"
echo ${init_opt}
fi
freeze_opt=""
if [ ! -z "${freeze_param}" ]; then
freeze_opt="--freeze_param ${freeze_param}"
echo ${freeze_opt}
fi
phase3_config="$(dirname "${train_config}")/$(basename "${train_config}" .yaml)_phase3.yaml"
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $gpu_num; ++i)); do
{
rank=$i
local_rank=$i
gpu_id=$(echo $gpu_devices | cut -d',' -f$[$i+1])
python -m funasr.bin.diar_train \
--gpu_id $gpu_id \
--use_preprocessor false \
--token_type char \
--token_list $token_list \
--train_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
--train_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
--train_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
--train_shape_file ${expdir}/${valid_set}_states/speech_shape \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/feats.scp,speech,kaldi_ark \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/profile.scp,profile,kaldi_ark \
--valid_data_path_and_name_and_type ${datadir}/${valid_set}/dumped_files/label.scp,binary_labels,kaldi_ark \
--valid_shape_file ${expdir}/${valid_set}_states/speech_shape \
--init_param exp/${model_dir}_phase2/valid.forward_steps.ave_5best.pb \
--unused_parameters true \
${init_opt} \
${freeze_opt} \
--ignore_init_mismatch true \
--resume true \
--output_dir ${expdir}/${model_dir}_phase3 \
--config ${phase3_config} \
--ngpu $gpu_num \
--num_worker_count $count \
--multiprocessing_distributed true \
--dist_init_method $init_method \
--dist_world_size $world_size \
--dist_rank $rank \
--local_rank $local_rank 1> ${expdir}/${model_dir}_phase3/log/train.log.$i 2>&1
} &
done
echo "Training log can be found at ${expdir}/${model_dir}_phase3/log/train.log.*"
wait
fi
# evaluate for finetuned model
if [ ${stage} -le 17 ] && [ ${stop_stage} -ge 17 ]; then
echo "stage 17: evaluation for finetuned model ${inference_model}."
for dset in ${test_sets}; do
echo "Processing for $dset"
exp_model_dir=${expdir}/${model_dir}_phase3
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${exp_model_dir}/${_inference_tag}/${inference_model}/${dset}"
_logdir="${_dir}/logdir"
if [ -d ${_dir} ]; then
echo "WARNING: ${_dir} is already exists."
fi
mkdir -p "${_logdir}"
_data="${datadir}/${dset}/dumped_files"
key_file=${_data}/feats.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
_opt=
if [ ! -z "${inference_config}" ]; then
_opt="--config ${inference_config}"
fi
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
echo "Inference log can be found at ${_logdir}/inference.*.log"
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
python -m funasr.bin.diar_inference_launch \
--batch_size 1 \
--ngpu "${_ngpu}" \
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
--data_path_and_name_and_type "${_data}/profile.scp,profile,kaldi_ark" \
--key_file "${_logdir}"/keys.JOB.scp \
--diar_train_config "${exp_model_dir}"/config.yaml \
--diar_model_file "${exp_model_dir}"/${inference_model} \
--output_dir "${_logdir}"/output.JOB \
--mode sond ${_opt}
done
fi
# average 3 4 5 6 7 epoch
# Scoring for pretrained model, you may get a DER like
# 7.21 8.05 on callhome1
# 8.31 9.32 on callhome2
if [ ${stage} -le 18 ] && [ ${stop_stage} -ge 18 ]; then
echo "stage 18: Scoring finetuned models"
if [ ! -e dscore ]; then
git clone https://github.com/nryant/dscore.git
# add intervaltree to setup.py
fi
for dset in ${test_sets}; do
echo "stage 18: Scoring for ${dset}"
diar_exp=${expdir}/${model_dir}_phase3
_data="${datadir}/${dset}"
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
_logdir="${_dir}/logdir"
cat ${_logdir}/*/labels.txt | sort > ${_dir}/labels.txt
cmd="python -Wignore script/convert_label_to_rttm.py ${_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${_dir}/sys.rttm \
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
echo ${cmd}
eval ${cmd}
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${_dir}/sys.rttm.ref_vad
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${_dir}/sys.rttm.sys_vad
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
echo -e "${inference_model} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${_dir}/results.txt
done
fi
if [ ${stage} -le 19 ] && [ ${stop_stage} -ge 19 ]; then
for dset in ${test_sets}; do
echo "stage 19: Evaluating phase-3 system on ${dset} set with medfilter_size=83 clustering=EEND-OLA"
sv_exp_dir=${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
diar_exp=${expdir}/${model_dir}_phase3
_data="${datadir}/${dset}/dumped_files"
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
for iter in `seq 0 ${told_max_iter}`; do
eval_dir=${_dir}/iter_${iter}
if [ $iter -eq 0 ]; then
prev_rttm=${expdir}/EEND-OLA/sys.rttm
else
prev_rttm=${_dir}/iter_$((${iter}-1))/sys.rttm.sys_vad
fi
echo "Use ${prev_rttm} as system outputs."
echo "Iteration ${iter}, step 1: extracting non-overlap segments"
cmd="python -Wignore script/extract_nonoverlap_segments.py ${datadir}/${dset}/wav.scp \
$prev_rttm ${eval_dir}/nonoverlap_segs/ --min_dur 0.1 --max_spk_num 16 --no_pbar --sr 8000"
# echo ${cmd}
eval ${cmd}
echo "Iteration ${iter}, step 2: make data directory"
mkdir -p ${eval_dir}/data
find `pwd`/${eval_dir}/nonoverlap_segs/ -iname "*.wav" | sort > ${eval_dir}/data/wav.flist
awk -F'[/.]' '{print $(NF-1),$0}' ${eval_dir}/data/wav.flist > ${eval_dir}/data/wav.scp
awk -F'[/.]' '{print $(NF-1),$(NF-2)}' ${eval_dir}/data/wav.flist > ${eval_dir}/data/utt2spk
cp $prev_rttm ${eval_dir}/data/sys.rttm
home_path=`pwd`
echo "Iteration ${iter}, step 3: calc x-vector for each utt"
key_file=${eval_dir}/data/wav.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
_logdir=${eval_dir}/data/xvecs
mkdir -p ${_logdir}
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/sv_inference.JOB.log \
python -m funasr.bin.sv_inference_launch \
--njob ${njob} \
--batch_size 1 \
--ngpu "${_ngpu}" \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${key_file},speech,sound" \
--key_file "${_logdir}"/keys.JOB.scp \
--sv_train_config ${sv_exp_dir}/sv.yaml \
--sv_model_file ${sv_exp_dir}/sv.pth \
--output_dir "${_logdir}"/output.JOB
cat ${_logdir}/output.*/xvector.scp | sort > ${eval_dir}/data/utt2xvec
echo "Iteration ${iter}, step 4: dump x-vector record"
awk '{print $1}' ${_data}/feats.scp > ${eval_dir}/data/idx
python script/dump_speaker_profiles.py --dir ${eval_dir}/data \
--out ${eval_dir}/global_n16 --n_spk 16 --no_pbar --emb_type global
spk_profile=${eval_dir}/global_n16_parts00_xvec.scp
echo "Iteration ${iter}, step 5: perform NN diarization"
_logdir=${eval_dir}/diar
mkdir -p ${_logdir}
key_file=${_data}/feats.scp
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
split_scps=
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/keys.${n}.scp"
done
_opt=
if [ ! -z "${inference_config}" ]; then
_opt="--config ${inference_config}"
fi
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
echo "Inference log can be found at ${_logdir}/inference.*.log"
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/inference.JOB.log \
python -m funasr.bin.diar_inference_launch \
--batch_size 1 \
--ngpu "${_ngpu}" \
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/feats.scp,speech,kaldi_ark" \
--data_path_and_name_and_type "${spk_profile},profile,kaldi_ark" \
--key_file "${_logdir}"/keys.JOB.scp \
--diar_train_config ${diar_exp}/config.yaml \
--diar_model_file ${diar_exp}/${inference_model} \
--output_dir "${_logdir}"/output.JOB \
--mode sond ${_opt}
echo "Iteration ${iter}, step 6: calc diarization results"
cat ${_logdir}/output.*/labels.txt | sort > ${eval_dir}/labels.txt
cmd="python -Wignore script/convert_label_to_rttm.py ${eval_dir}/labels.txt ${datadir}/${dset}/files_for_dump/org_vad.txt ${eval_dir}/sys.rttm \
--ignore_len 10 --no_pbar --smooth_size 83 --vote_prob 0.5 --n_spk 16"
# echo ${cmd}
eval ${cmd}
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${eval_dir}/sys.rttm.ref_vad
OVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
ref=${datadir}/${dset}/files_for_dump/ref.rttm
sys=${eval_dir}/sys.rttm.sys_vad
SysVAD_DER=$(python -Wignore dscore/score.py -r $ref -s $sys --collar 0.25 2>&1 | grep OVERALL | awk '{print $4}')
echo -e "${inference_model}/iter_${iter} ${OVAD_DER} ${SysVAD_DER}" | tee -a ${eval_dir}/results.txt
done
echo "Done."
done
fi