s3prl/superb

---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|librispeech_asr
- extended|other-librimix
- extended|other-speech_commands
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- keyword-spotting
- speaker-identification
- audio-intent-classification
- audio-emotion-recognition
pretty_name: SUPERB
tags:
- query-by-example-spoken-term-detection
- audio-slot-filling
- speaker-diarization
- automatic-speaker-verification
dataset_info:
- config_name: asr
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 11852430
num_examples: 28539
- name: validation
num_bytes: 897213
num_examples: 2703
- name: test
num_bytes: 871234
num_examples: 2620
download_size: 7071899769
dataset_size: 13620877
- config_name: sd
features:
- name: record_id
dtype: string
- name: file
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
- name: speakers
list:
- name: speaker_id
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
splits:
- name: train
num_bytes: 4622013
num_examples: 13901
- name: dev
num_bytes: 860472
num_examples: 3014
- name: test
num_bytes: 847803
num_examples: 3002
download_size: 7190370211
dataset_size: 6330288
- config_name: ks
features:
- name: file
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'yes'
'1': 'no'
'2': up
'3': down
'4': left
'5': right
'6': 'on'
'7': 'off'
'8': stop
'9': go
'10': _silence_
'11': _unknown_
splits:
- name: train
num_bytes: 8467781
num_examples: 51094
- name: validation
num_bytes: 1126476
num_examples: 6798
- name: test
num_bytes: 510619
num_examples: 3081
download_size: 1560367713
dataset_size: 10104876
- config_name: ic
features:
- name: file
dtype: string
- name: speaker_id
dtype: string
- name: text
dtype: string
- name: action
dtype:
class_label:
names:
'0': activate
'1': bring
'2': change language
'3': deactivate
'4': decrease
'5': increase
- name: object
dtype:
class_label:
names:
'0': Chinese
'1': English
'2': German
'3': Korean
'4': heat
'5': juice
'6': lamp
'7': lights
'8': music
'9': newspaper
'10': none
'11': shoes
'12': socks
'13': volume
- name: location
dtype:
class_label:
names:
'0': bedroom
'1': kitchen
'2': none
'3': washroom
splits:
- name: train
num_bytes: 7071466
num_examples: 23132
- name: validation
num_bytes: 953622
num_examples: 3118
- name: test
num_bytes: 1158347
num_examples: 3793
download_size: 1544093324
dataset_size: 9183435
- config_name: si
features:
- name: file
dtype: string
- name: label
dtype:
class_label:
names:
'0': id10001
'1': id10002
'2': id10003
'3': id10004
'4': id10005
'5': id10006
'6': id10007
'7': id10008
'8': id10009
'9': id10010
'10': id10011
'11': id10012
'12': id10013
'13': id10014
'14': id10015
'15': id10016
'16': id10017
'17': id10018
'18': id10019
'19': id10020
'20': id10021
'21': id10022
'22': id10023
'23': id10024
'24': id10025
'25': id10026
'26': id10027
'27': id10028
'28': id10029
'29': id10030
'30': id10031
'31': id10032
'32': id10033
'33': id10034
'34': id10035
'35': id10036
'36': id10037
'37': id10038
'38': id10039
'39': id10040
'40': id10041
'41': id10042
'42': id10043
'43': id10044
'44': id10045
'45': id10046
'46': id10047
'47': id10048
'48': id10049
'49': id10050
'50': id10051
'51': id10052
'52': id10053
'53': id10054
'54': id10055
'55': id10056
'56': id10057
'57': id10058
'58': id10059
'59': id10060
'60': id10061
'61': id10062
'62': id10063
'63': id10064
'64': id10065
'65': id10066
'66': id10067
'67': id10068
'68': id10069
'69': id10070
'70': id10071
'71': id10072
'72': id10073
'73': id10074
'74': id10075
'75': id10076
'76': id10077
'77': id10078
'78': id10079
'79': id10080
'80': id10081
'81': id10082
'82': id10083
'83': id10084
'84': id10085
'85': id10086
'86': id10087
'87': id10088
'88': id10089
'89': id10090
'90': id10091
'91': id10092
'92': id10093
'93': id10094
'94': id10095
'95': id10096
'96': id10097
'97': id10098
'98': id10099
'99': id10100
'100': id10101
'101': id10102
'102': id10103
'103': id10104
'104': id10105
'105': id10106
'106': id10107
'107': id10108
'108': id10109
'109': id10110
'110': id10111
'111': id10112
'112': id10113
'113': id10114
'114': id10115
'115': id10116
'116': id10117
'117': id10118
'118': id10119
'119': id10120
'120': id10121
'121': id10122
'122': id10123
'123': id10124
'124': id10125
'125': id10126
'126': id10127
'127': id10128
'128': id10129
'129': id10130
'130': id10131
'131': id10132
'132': id10133
'133': id10134
'134': id10135
'135': id10136
'136': id10137
'137': id10138
'138': id10139
'139': id10140
'140': id10141
'141': id10142
'142': id10143
'143': id10144
'144': id10145
'145': id10146
'146': id10147
'147': id10148
'148': id10149
'149': id10150
'150': id10151
'151': id10152
'152': id10153
'153': id10154
'154': id10155
'155': id10156
'156': id10157
'157': id10158
'158': id10159
'159': id10160
'160': id10161
'161': id10162
'162': id10163
'163': id10164
'164': id10165
'165': id10166
'166': id10167
'167': id10168
'168': id10169
'169': id10170
'170': id10171
'171': id10172
'172': id10173
'173': id10174
'174': id10175
'175': id10176
'176': id10177
'177': id10178
'178': id10179
'179': id10180
'180': id10181
'181': id10182
'182': id10183
'183': id10184
'184': id10185
'185': id10186
'186': id10187
'187': id10188
'188': id10189
'189': id10190
'190': id10191
'191': id10192
'192': id10193
'193': id10194
'194': id10195
'195': id10196
'196': id10197
'197': id10198
'198': id10199
'199': id10200
'200': id10201
'201': id10202
'202': id10203
'203': id10204
'204': id10205
'205': id10206
'206': id10207
'207': id10208
'208': id10209
'209': id10210
'210': id10211
'211': id10212
'212': id10213
'213': id10214
'214': id10215
'215': id10216
'216': id10217
'217': id10218
'218': id10219
'219': id10220
'220': id10221
'221': id10222
'222': id10223
'223': id10224
'224': id10225
'225': id10226
'226': id10227
'227': id10228
'228': id10229
'229': id10230
'230': id10231
'231': id10232
'232': id10233
'233': id10234
'234': id10235
'235': id10236
'236': id10237
'237': id10238
'238': id10239
'239': id10240
'240': id10241
'241': id10242
'242': id10243
'243': id10244
'244': id10245
'245': id10246
'246': id10247
'247': id10248
'248': id10249
'249': id10250
'250': id10251
'251': id10252
'252': id10253
'253': id10254
'254': id10255
'255': id10256
'256': id10257
'257': id10258
'258': id10259
'259': id10260
'260': id10261
'261': id10262
'262': id10263
'263': id10264
'264': id10265
'265': id10266
'266': id10267
'267': id10268
'268': id10269
'269': id10270
'270': id10271
'271': id10272
'272': id10273
'273': id10274
'274': id10275
'275': id10276
'276': id10277
'277': id10278
'278': id10279
'279': id10280
'280': id10281
'281': id10282
'282': id10283
'283': id10284
'284': id10285
'285': id10286
'286': id10287
'287': id10288
'288': id10289
'289': id10290
'290': id10291
'291': id10292
'292': id10293
'293': id10294
'294': id10295
'295': id10296
'296': id10297
'297': id10298
'298': id10299
'299': id10300
'300': id10301
'301': id10302
'302': id10303
'303': id10304
'304': id10305
'305': id10306
'306': id10307
'307': id10308
'308': id10309
'309': id10310
'310': id10311
'311': id10312
'312': id10313
'313': id10314
'314': id10315
'315': id10316
'316': id10317
'317': id10318
'318': id10319
'319': id10320
'320': id10321
'321': id10322
'322': id10323
'323': id10324
'324': id10325
'325': id10326
'326': id10327
'327': id10328
'328': id10329
'329': id10330
'330': id10331
'331': id10332
'332': id10333
'333': id10334
'334': id10335
'335': id10336
'336': id10337
'337': id10338
'338': id10339
'339': id10340
'340': id10341
'341': id10342
'342': id10343
'343': id10344
'344': id10345
'345': id10346
'346': id10347
'347': id10348
'348': id10349
'349': id10350
'350': id10351
'351': id10352
'352': id10353
'353': id10354
'354': id10355
'355': id10356
'356': id10357
'357': id10358
'358': id10359
'359': id10360
'360': id10361
'361': id10362
'362': id10363
'363': id10364
'364': id10365
'365': id10366
'366': id10367
'367': id10368
'368': id10369
'369': id10370
'370': id10371
'371': id10372
'372': id10373
'373': id10374
'374': id10375
'375': id10376
'376': id10377
'377': id10378
'378': id10379
'379': id10380
'380': id10381
'381': id10382
'382': id10383
'383': id10384
'384': id10385
'385': id10386
'386': id10387
'387': id10388
'388': id10389
'389': id10390
'390': id10391
'391': id10392
'392': id10393
'393': id10394
'394': id10395
'395': id10396
'396': id10397
'397': id10398
'398': id10399
'399': id10400
'400': id10401
'401': id10402
'402': id10403
'403': id10404
'404': id10405
'405': id10406
'406': id10407
'407': id10408
'408': id10409
'409': id10410
'410': id10411
'411': id10412
'412': id10413
'413': id10414
'414': id10415
'415': id10416
'416': id10417
'417': id10418
'418': id10419
'419': id10420
'420': id10421
'421': id10422
'422': id10423
'423': id10424
'424': id10425
'425': id10426
'426': id10427
'427': id10428
'428': id10429
'429': id10430
'430': id10431
'431': id10432
'432': id10433
'433': id10434
'434': id10435
'435': id10436
'436': id10437
'437': id10438
'438': id10439
'439': id10440
'440': id10441
'441': id10442
'442': id10443
'443': id10444
'444': id10445
'445': id10446
'446': id10447
'447': id10448
'448': id10449
'449': id10450
'450': id10451
'451': id10452
'452': id10453
'453': id10454
'454': id10455
'455': id10456
'456': id10457
'457': id10458
'458': id10459
'459': id10460
'460': id10461
'461': id10462
'462': id10463
'463': id10464
'464': id10465
'465': id10466
'466': id10467
'467': id10468
'468': id10469
'469': id10470
'470': id10471
'471': id10472
'472': id10473
'473': id10474
'474': id10475
'475': id10476
'476': id10477
'477': id10478
'478': id10479
'479': id10480
'480': id10481
'481': id10482
'482': id10483
'483': id10484
'484': id10485
'485': id10486
'486': id10487
'487': id10488
'488': id10489
'489': id10490
'490': id10491
'491': id10492
'492': id10493
'493': id10494
'494': id10495
'495': id10496
'496': id10497
'497': id10498
'498': id10499
'499': id10500
'500': id10501
'501': id10502
'502': id10503
'503': id10504
'504': id10505
'505': id10506
'506': id10507
'507': id10508
'508': id10509
'509': id10510
'510': id10511
'511': id10512
'512': id10513
'513': id10514
'514': id10515
'515': id10516
'516': id10517
'517': id10518
'518': id10519
'519': id10520
'520': id10521
'521': id10522
'522': id10523
'523': id10524
'524': id10525
'525': id10526
'526': id10527
'527': id10528
'528': id10529
'529': id10530
'530': id10531
'531': id10532
'532': id10533
'533': id10534
'534': id10535
'535': id10536
'536': id10537
'537': id10538
'538': id10539
'539': id10540
'540': id10541
'541': id10542
'542': id10543
'543': id10544
'544': id10545
'545': id10546
'546': id10547
'547': id10548
'548': id10549
'549': id10550
'550': id10551
'551': id10552
'552': id10553
'553': id10554
'554': id10555
'555': id10556
'556': id10557
'557': id10558
'558': id10559
'559': id10560
'560': id10561
'561': id10562
'562': id10563
'563': id10564
'564': id10565
'565': id10566
'566': id10567
'567': id10568
'568': id10569
'569': id10570
'570': id10571
'571': id10572
'572': id10573
'573': id10574
'574': id10575
'575': id10576
'576': id10577
'577': id10578
'578': id10579
'579': id10580
'580': id10581
'581': id10582
'582': id10583
'583': id10584
'584': id10585
'585': id10586
'586': id10587
'587': id10588
'588': id10589
'589': id10590
'590': id10591
'591': id10592
'592': id10593
'593': id10594
'594': id10595
'595': id10596
'596': id10597
'597': id10598
'598': id10599
'599': id10600
'600': id10601
'601': id10602
'602': id10603
'603': id10604
'604': id10605
'605': id10606
'606': id10607
'607': id10608
'608': id10609
'609': id10610
'610': id10611
'611': id10612
'612': id10613
'613': id10614
'614': id10615
'615': id10616
'616': id10617
'617': id10618
'618': id10619
'619': id10620
'620': id10621
'621': id10622
'622': id10623
'623': id10624
'624': id10625
'625': id10626
'626': id10627
'627': id10628
'628': id10629
'629': id10630
'630': id10631
'631': id10632
'632': id10633
'633': id10634
'634': id10635
'635': id10636
'636': id10637
'637': id10638
'638': id10639
'639': id10640
'640': id10641
'641': id10642
'642': id10643
'643': id10644
'644': id10645
'645': id10646
'646': id10647
'647': id10648
'648': id10649
'649': id10650
'650': id10651
'651': id10652
'652': id10653
'653': id10654
'654': id10655
'655': id10656
'656': id10657
'657': id10658
'658': id10659
'659': id10660
'660': id10661
'661': id10662
'662': id10663
'663': id10664
'664': id10665
'665': id10666
'666': id10667
'667': id10668
'668': id10669
'669': id10670
'670': id10671
'671': id10672
'672': id10673
'673': id10674
'674': id10675
'675': id10676
'676': id10677
'677': id10678
'678': id10679
'679': id10680
'680': id10681
'681': id10682
'682': id10683
'683': id10684
'684': id10685
'685': id10686
'686': id10687
'687': id10688
'688': id10689
'689': id10690
'690': id10691
'691': id10692
'692': id10693
'693': id10694
'694': id10695
'695': id10696
'696': id10697
'697': id10698
'698': id10699
'699': id10700
'700': id10701
'701': id10702
'702': id10703
'703': id10704
'704': id10705
'705': id10706
'706': id10707
'707': id10708
'708': id10709
'709': id10710
'710': id10711
'711': id10712
'712': id10713
'713': id10714
'714': id10715
'715': id10716
'716': id10717
'717': id10718
'718': id10719
'719': id10720
'720': id10721
'721': id10722
'722': id10723
'723': id10724
'724': id10725
'725': id10726
'726': id10727
'727': id10728
'728': id10729
'729': id10730
'730': id10731
'731': id10732
'732': id10733
'733': id10734
'734': id10735
'735': id10736
'736': id10737
'737': id10738
'738': id10739
'739': id10740
'740': id10741
'741': id10742
'742': id10743
'743': id10744
'744': id10745
'745': id10746
'746': id10747
'747': id10748
'748': id10749
'749': id10750
'750': id10751
'751': id10752
'752': id10753
'753': id10754
'754': id10755
'755': id10756
'756': id10757
'757': id10758
'758': id10759
'759': id10760
'760': id10761
'761': id10762
'762': id10763
'763': id10764
'764': id10765
'765': id10766
'766': id10767
'767': id10768
'768': id10769
'769': id10770
'770': id10771
'771': id10772
'772': id10773
'773': id10774
'774': id10775
'775': id10776
'776': id10777
'777': id10778
'778': id10779
'779': id10780
'780': id10781
'781': id10782
'782': id10783
'783': id10784
'784': id10785
'785': id10786
'786': id10787
'787': id10788
'788': id10789
'789': id10790
'790': id10791
'791': id10792
'792': id10793
'793': id10794
'794': id10795
'795': id10796
'796': id10797
'797': id10798
'798': id10799
'799': id10800
'800': id10801
'801': id10802
'802': id10803
'803': id10804
'804': id10805
'805': id10806
'806': id10807
'807': id10808
'808': id10809
'809': id10810
'810': id10811
'811': id10812
'812': id10813
'813': id10814
'814': id10815
'815': id10816
'816': id10817
'817': id10818
'818': id10819
'819': id10820
'820': id10821
'821': id10822
'822': id10823
'823': id10824
'824': id10825
'825': id10826
'826': id10827
'827': id10828
'828': id10829
'829': id10830
'830': id10831
'831': id10832
'832': id10833
'833': id10834
'834': id10835
'835': id10836
'836': id10837
'837': id10838
'838': id10839
'839': id10840
'840': id10841
'841': id10842
'842': id10843
'843': id10844
'844': id10845
'845': id10846
'846': id10847
'847': id10848
'848': id10849
'849': id10850
'850': id10851
'851': id10852
'852': id10853
'853': id10854
'854': id10855
'855': id10856
'856': id10857
'857': id10858
'858': id10859
'859': id10860
'860': id10861
'861': id10862
'862': id10863
'863': id10864
'864': id10865
'865': id10866
'866': id10867
'867': id10868
'868': id10869
'869': id10870
'870': id10871
'871': id10872
'872': id10873
'873': id10874
'874': id10875
'875': id10876
'876': id10877
'877': id10878
'878': id10879
'879': id10880
'880': id10881
'881': id10882
'882': id10883
'883': id10884
'884': id10885
'885': id10886
'886': id10887
'887': id10888
'888': id10889
'889': id10890
'890': id10891
'891': id10892
'892': id10893
'893': id10894
'894': id10895
'895': id10896
'896': id10897
'897': id10898
'898': id10899
'899': id10900
'900': id10901
'901': id10902
'902': id10903
'903': id10904
'904': id10905
'905': id10906
'906': id10907
'907': id10908
'908': id10909
'909': id10910
'910': id10911
'911': id10912
'912': id10913
'913': id10914
'914': id10915
'915': id10916
'916': id10917
'917': id10918
'918': id10919
'919': id10920
'920': id10921
'921': id10922
'922': id10923
'923': id10924
'924': id10925
'925': id10926
'926': id10927
'927': id10928
'928': id10929
'929': id10930
'930': id10931
'931': id10932
'932': id10933
'933': id10934
'934': id10935
'935': id10936
'936': id10937
'937': id10938
'938': id10939
'939': id10940
'940': id10941
'941': id10942
'942': id10943
'943': id10944
'944': id10945
'945': id10946
'946': id10947
'947': id10948
'948': id10949
'949': id10950
'950': id10951
'951': id10952
'952': id10953
'953': id10954
'954': id10955
'955': id10956
'956': id10957
'957': id10958
'958': id10959
'959': id10960
'960': id10961
'961': id10962
'962': id10963
'963': id10964
'964': id10965
'965': id10966
'966': id10967
'967': id10968
'968': id10969
'969': id10970
'970': id10971
'971': id10972
'972': id10973
'973': id10974
'974': id10975
'975': id10976
'976': id10977
'977': id10978
'978': id10979
'979': id10980
'980': id10981
'981': id10982
'982': id10983
'983': id10984
'984': id10985
'985': id10986
'986': id10987
'987': id10988
'988': id10989
'989': id10990
'990': id10991
'991': id10992
'992': id10993
'993': id10994
'994': id10995
'995': id10996
'996': id10997
'997': id10998
'998': id10999
'999': id11000
'1000': id11001
'1001': id11002
'1002': id11003
'1003': id11004
'1004': id11005
'1005': id11006
'1006': id11007
'1007': id11008
'1008': id11009
'1009': id11010
'1010': id11011
'1011': id11012
'1012': id11013
'1013': id11014
'1014': id11015
'1015': id11016
'1016': id11017
'1017': id11018
'1018': id11019
'1019': id11020
'1020': id11021
'1021': id11022
'1022': id11023
'1023': id11024
'1024': id11025
'1025': id11026
'1026': id11027
'1027': id11028
'1028': id11029
'1029': id11030
'1030': id11031
'1031': id11032
'1032': id11033
'1033': id11034
'1034': id11035
'1035': id11036
'1036': id11037
'1037': id11038
'1038': id11039
'1039': id11040
'1040': id11041
'1041': id11042
'1042': id11043
'1043': id11044
'1044': id11045
'1045': id11046
'1046': id11047
'1047': id11048
'1048': id11049
'1049': id11050
'1050': id11051
'1051': id11052
'1052': id11053
'1053': id11054
'1054': id11055
'1055': id11056
'1056': id11057
'1057': id11058
'1058': id11059
'1059': id11060
'1060': id11061
'1061': id11062
'1062': id11063
'1063': id11064
'1064': id11065
'1065': id11066
'1066': id11067
'1067': id11068
'1068': id11069
'1069': id11070
'1070': id11071
'1071': id11072
'1072': id11073
'1073': id11074
'1074': id11075
'1075': id11076
'1076': id11077
'1077': id11078
'1078': id11079
'1079': id11080
'1080': id11081
'1081': id11082
'1082': id11083
'1083': id11084
'1084': id11085
'1085': id11086
'1086': id11087
'1087': id11088
'1088': id11089
'1089': id11090
'1090': id11091
'1091': id11092
'1092': id11093
'1093': id11094
'1094': id11095
'1095': id11096
'1096': id11097
'1097': id11098
'1098': id11099
'1099': id11100
'1100': id11101
'1101': id11102
'1102': id11103
'1103': id11104
'1104': id11105
'1105': id11106
'1106': id11107
'1107': id11108
'1108': id11109
'1109': id11110
'1110': id11111
'1111': id11112
'1112': id11113
'1113': id11114
'1114': id11115
'1115': id11116
'1116': id11117
'1117': id11118
'1118': id11119
'1119': id11120
'1120': id11121
'1121': id11122
'1122': id11123
'1123': id11124
'1124': id11125
'1125': id11126
'1126': id11127
'1127': id11128
'1128': id11129
'1129': id11130
'1130': id11131
'1131': id11132
'1132': id11133
'1133': id11134
'1134': id11135
'1135': id11136
'1136': id11137
'1137': id11138
'1138': id11139
'1139': id11140
'1140': id11141
'1141': id11142
'1142': id11143
'1143': id11144
'1144': id11145
'1145': id11146
'1146': id11147
'1147': id11148
'1148': id11149
'1149': id11150
'1150': id11151
'1151': id11152
'1152': id11153
'1153': id11154
'1154': id11155
'1155': id11156
'1156': id11157
'1157': id11158
'1158': id11159
'1159': id11160
'1160': id11161
'1161': id11162
'1162': id11163
'1163': id11164
'1164': id11165
'1165': id11166
'1166': id11167
'1167': id11168
'1168': id11169
'1169': id11170
'1170': id11171
'1171': id11172
'1172': id11173
'1173': id11174
'1174': id11175
'1175': id11176
'1176': id11177
'1177': id11178
'1178': id11179
'1179': id11180
'1180': id11181
'1181': id11182
'1182': id11183
'1183': id11184
'1184': id11185
'1185': id11186
'1186': id11187
'1187': id11188
'1188': id11189
'1189': id11190
'1190': id11191
'1191': id11192
'1192': id11193
'1193': id11194
'1194': id11195
'1195': id11196
'1196': id11197
'1197': id11198
'1198': id11199
'1199': id11200
'1200': id11201
'1201': id11202
'1202': id11203
'1203': id11204
'1204': id11205
'1205': id11206
'1206': id11207
'1207': id11208
'1208': id11209
'1209': id11210
'1210': id11211
'1211': id11212
'1212': id11213
'1213': id11214
'1214': id11215
'1215': id11216
'1216': id11217
'1217': id11218
'1218': id11219
'1219': id11220
'1220': id11221
'1221': id11222
'1222': id11223
'1223': id11224
'1224': id11225
'1225': id11226
'1226': id11227
'1227': id11228
'1228': id11229
'1229': id11230
'1230': id11231
'1231': id11232
'1232': id11233
'1233': id11234
'1234': id11235
'1235': id11236
'1236': id11237
'1237': id11238
'1238': id11239
'1239': id11240
'1240': id11241
'1241': id11242
'1242': id11243
'1243': id11244
'1244': id11245
'1245': id11246
'1246': id11247
'1247': id11248
'1248': id11249
'1249': id11250
'1250': id11251
splits:
- name: train
num_bytes: 12729268
num_examples: 138361
- name: validation
num_bytes: 635172
num_examples: 6904
- name: test
num_bytes: 759096
num_examples: 8251
download_size: 0
dataset_size: 14123536
---

# Dataset Card for SUPERB

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** [http://superbbenchmark.org](http://superbbenchmark.org)
- **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl)
- **Paper:** [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [Lewis Tunstall](mailto:[e-mail]) and [Albert Villanova](mailto:[e-mail])

### Dataset Summary

SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.

### Supported Tasks and Leaderboards

The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks:

#### pr

Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER).

#### asr

Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER).

#### ks

Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC)

##### Example of usage:

Use these auxillary functions to:
- load the audio file into an audio data array
- sample from long `_silence_` audio clips

For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80)
or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations.

```python
def map_to_array(example):
import soundfile as sf

speech_array, sample_rate = sf.read(example["file"])
example["speech"] = speech_array
example["sample_rate"] = sample_rate
return example

def sample_noise(example):
# Use this function to extract random 1 sec slices of each _silence_ utterance,
# e.g. inside `torch.utils.data.Dataset.__getitem__()`
from random import randint

if example["label"] == "_silence_":
random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]

return example
```

#### qbe

Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms.

#### ic

Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC).

#### sf

Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing.

#### si
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC).

#### asv

Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER).

#### sd

Speaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER).

##### Example of usage

Use these auxiliary functions to:
- load the audio file into an audio data array
- generate the label array

```python
def load_audio_file(example, frame_shift=160):
import soundfile as sf

example["array"], example["sample_rate"] = sf.read(
example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift
)
return example

def generate_label(example, frame_shift=160, num_speakers=2, rate=16000):
import numpy as np

start = example["start"]
end = example["end"]
frame_num = end - start
speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]})
label = np.zeros((frame_num, num_speakers), dtype=np.int32)
for speaker in example["speakers"]:
speaker_index = speakers.index(speaker["speaker_id"])
start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int)
end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int)
rel_start = rel_end = None
if start <= start_frame < end:
rel_start = start_frame - start
if start < end_frame <= end:
rel_end = end_frame - start
if rel_start is not None or rel_end is not None:
label[rel_start:rel_end, speaker_index] = 1
example["label"] = label
return example
```

#### er

Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC).

### Languages

The language data in SUPERB is in English (BCP-47 `en`)

## Dataset Structure

### Data Instances

#### pr

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### asr

An example from each split looks like:

```python
{'chapter_id': 1240,
'file': 'path/to/file.flac',
'audio': {'path': 'path/to/file.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '103-1240-0000',
'speaker_id': 103,
'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE '
'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE '
'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A '
'BROOK'}
```

#### ks

An example from each split looks like:

```python
{
'file': '/path/yes/af7a8296_nohash_1.wav',
'audio': {'path': '/path/yes/af7a8296_nohash_1.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'label': 0 # 'yes'
}
```

#### qbe

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### ic

```python
{
'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav",
'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'speaker_id': '2BqVo8kVB2Skwgyb',
'text': 'Turn the bedroom lights off',
'action': 3, # 'deactivate'
'object': 7, # 'lights'
'location': 0 # 'bedroom'
}
```

#### sf

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### si

```python
{
'file': '/path/wav/id10003/na8-QEFmj44/00003.wav',
'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'label': 2 # 'id10003'
}
```

#### asv

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### sd

An example from each split looks like:
```python
{
'record_id': '1578-6379-0038_6415-111615-0009',
'file': 'path/to/file.wav',
'audio': {'path': 'path/to/file.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'start': 0,
'end': 1590,
'speakers': [
{'speaker_id': '1578', 'start': 28, 'end': 657},
{'speaker_id': '6415', 'start': 28, 'end': 1576}
]
}
```

#### er

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Data Fields

####Note abouth the `audio` fields

When accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.

#### pr

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### asr

- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `text` (`string`): The transcription of the audio file.
- `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples.
- `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription.
- `id` (`string`): A unique ID of the data sample.

#### ks

- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label of the spoken command. Possible values:
- `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"`

#### qbe

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### ic

- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `speaker_id` (`string`): ID of the speaker.
- `text` (`string`): Transcription of the spoken command.
- `action` (`ClassLabel`): Label of the command's action. Possible values:
- `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"`
- `object` (`ClassLabel`): Label of the command's object. Possible values:
- `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"`
- `location` (`ClassLabel`): Label of the command's location. Possible values:
- `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"`

#### sf

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### si

- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label (ID) of the speaker. Possible values:
- `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"`

#### asv

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### sd

The data fields in all splits are:
- `record_id` (`string`): ID of the record.
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `start` (`integer`): Start frame of the audio.
- `end` (`integer`): End frame of the audio.
- `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields:
- `speaker_id` (`string`): ID of the speaker.
- `start` (`integer`): Frame when the speaker starts speaking.
- `end` (`integer`): Frame when the speaker stops speaking.

#### er

- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label of the speech emotion. Possible values:
- `0: "neu", 1: "hap", 2: "ang", 3: "sad"`

### Data Splits

#### pr

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### asr

| | train | validation | test |
|-----|------:|-----------:|-----:|
| asr | 28539 | 2703 | 2620 |

#### ks

| | train | validation | test |
|----|------:|-----------:|-----:|
| ks | 51094 | 6798 | 3081 |

#### qbe

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### ic

| | train | validation | test |
|----|------:|-----------:|-----:|
| ic | 23132 | 3118 | 3793 |

#### sf

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### si

| | train | validation | test |
|----|-------:|-----------:|-----:|
| si | 138361 | 6904 | 8251 |

#### asv

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### sd

The data is split into "train", "dev" and "test" sets, each containing the following number of examples:

| | train | dev | test |
|----|------:|-----:|-----:|
| sd | 13901 | 3014 | 3002 |

#### er

The data is split into 5 sets intended for 5-fold cross-validation:

| | session1 | session2 | session3 | session4 | session5 |
|----|---------:|---------:|---------:|---------:|---------:|
| er | 1085 | 1023 | 1151 | 1031 | 1241 |

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

Dataset provided for research purposes only. Please check dataset license for additional information.

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

#### pr and asr

The license for Librispeech is the Creative Commons Attribution 4.0 International license ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/]).

#### ks

The license for Speech Commands is [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)

#### qbe

The license for QUESST 2014 is not known.

#### ic

The license for Fluent Speech Commands dataset is the [Fluent Speech Commands Public License](https://fluent.ai/wp-content/uploads/2021/04/Fluent_Speech_Commands_Public_License.pdf)

#### sf

The license for Audio SNIPS dataset is not known.

#### si and asv

The license for VoxCeleb1 dataset is the Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)).

#### sd

LibriMix is based on the LibriSpeech (see above) and Wham! noises datasets. The Wham! noises dataset is distributed under the Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) license.

#### er
The IEMOCAP license is ditributed under [its own license](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf).

### Citation Information

```
@article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
Xuankai Chang and
Guan{-}Ting Lin and
Tzu{-}Hsien Huang and
Wei{-}Cheng Tseng and
Ko{-}tik Lee and
Da{-}Rong Liu and
Zili Huang and
Shuyan Dong and
Shang{-}Wen Li and
Shinji Watanabe and
Abdelrahman Mohamed and
Hung{-}yi Lee},
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
journal = {CoRR},
volume = {abs/2105.01051},
year = {2021},
url = {https://arxiv.org/abs/2105.01051},
archivePrefix = {arXiv},
eprint = {2105.01051},
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

Note that each SUPERB dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```

### Contributions

Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset.

原始信息汇总

数据集概述

基本信息

• annotations_creators: other
• language_creators: other
• language: en
• license: unknown
• multilinguality: monolingual
• size_categories: unknown
• source_datasets: original, extended|librispeech_asr, extended|other-librimix, extended|other-speech_commands
• task_categories: automatic-speech-recognition, audio-classification
• task_ids: keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition
• pretty_name: SUPERB
• tags: query-by-example-spoken-term-detection, audio-slot-filling, speaker-diarization, automatic-speaker-verification

数据集配置

配置名称: asr

• features:
  • name: file, dtype: string
  • name: audio, dtype: audio, sampling_rate: 16000
  • name: text, dtype: string
  • name: speaker_id, dtype: int64
  • name: chapter_id, dtype: int64
  • name: id, dtype: string
• splits:
  • name: train, num_bytes: 11852430, num_examples: 28539
  • name: validation, num_bytes: 897213, num_examples: 2703
  • name: test, num_bytes: 871234, num_examples: 2620
• download_size: 7071899769
• dataset_size: 13620877

配置名称: sd

• features:
  • name: record_id, dtype: string
  • name: file, dtype: string
  • name: start, dtype: int64
  • name: end, dtype: int64
  • name: speakers, list:
    • name: speaker_id, dtype: string
    • name: start, dtype: int64
    • name: end, dtype: int64
• splits:
  • name: train, num_bytes: 4622013, num_examples: 13901
  • name: dev, num_bytes: 860472, num_examples: 3014
  • name: test, num_bytes: 847803, num_examples: 3002
• download_size: 7190370211
• dataset_size: 6330288

配置名称: ks

• features:
  • name: file, dtype: string
  • name: label, dtype: class_label, names:
    • 0: yes
    • 1: no
    • 2: up
    • 3: down
    • 4: left
    • 5: right
    • 6: on
    • 7: off
    • 8: stop
    • 9: go
    • 10: silence
    • 11: unknown
• splits:
  • name: train, num_bytes: 8467781, num_examples: 51094
  • name: validation, num_bytes: 1126476, num_examples: 6798
  • name: test, num_bytes: 510619, num_examples: 3081
• download_size: 1560367713
• dataset_size: 10104876

配置名称: ic

• features:
  • name: file, dtype: string
  • name: speaker_id, dtype: string
  • name: text, dtype: string
  • name: action, dtype: class_label, names:
    • 0: activate
    • 1: bring
    • 2: change language
    • 3: deactivate
    • 4: decrease
    • 5: increase
  • name: object, dtype: class_label, names:
    • 0: Chinese
    • 1: English
    • 2: German
    • 3: Korean
    • 4: heat
    • 5: juice
    • 6: lamp
    • 7: lights
    • 8: music
    • 9: newspaper
    • 10: none
    • 11: shoes
    • 12: socks
    • 13: volume
  • name: location, dtype: class_label, names:
    • 0: bedroom
    • 1: kitchen
    • 2: none
    • 3: washroom
• splits:
  • name: train, num_bytes: 7071466, num_examples: 23132
  • name: validation, num_bytes: 953622, num_examples: 3118
  • name: test, num_bytes: 1158347, num_examples: 3793
• download_size: 1544093324
• dataset_size: 9183435

配置名称: si

• features:
  • name: file, dtype: string
  • name: label, dtype: class_label, names:
    • 0: id10001
    • 1: id10002
    • 2: id10003
    • 3: id10004
    • 4: id10005
    • 5: id10006
    • 6: id10007
    • 7: id10008
    • 8: id10009
    • 9: id10010
    • 10: id10011
    • 11: id10012
    • 12: id10013
    • 13: id10014
    • 14: id10015
    • 15: id10016
    • 16: id10017
    • 17: id10018
    • 18: id10019
    • 19: id10020
    • 20: id10021
    • 21: id10022
    • 22: id10023
    • 23: id10024
    • 24: id10025
    • 25: id10026
    • 26: id10027
    • 27: id10028
    • 28: id10029
    • 29: id10030
    • 30: id10031
    • 31: id10032
    • 32: id10033
    • 33: id10034
    • 34: id10035
    • 35: id10036
    • 36: id10037
    • 37: id10038
    • 38: id10039
    • 39: id10040
    • 40: id10041
    • 41: id10042
    • 42: id10043
    • 43: id10044
    • 44: id10045
    • 45: id10046
    • 46: id10047
    • 47: id10048
    • 48: id10049
    • 49: id10050
    • 50: id10051
    • 51: id10052
    • 52: id10053
    • 53: id10054
    • 54: id10055
    • 55: id10056
    • 56: id10057
    • 57: id10058
    • 58: id10059
    • 59: id10060
    • 60: id10061
    • 61: id10062
    • 62: id10063
    • 63: id10064
    • 64: id10065
    • 65: id10066
    • 66: id10067
    • 67: id10068
    • 68: id10069
    • 69: id10070
    • 70: id10071
    • 71: id10072
    • 72: id10073
    • 73: id10074
    • 74: id10075
    • 75: id10076
    • 76: id10077
    • 77: id10078
    • 78: id10079
    • 79: id10080
    • 80: id10081
    • 81: id10082
    • 82: id10083
    • 83: id10084
    • 84: id10085
    • 85: id10086
    • 86: id10087
    • 87: id10088
    • 88: id10089
    • 89: id10090
    • 90: id10091
    • 91: id10092
    • 92: id10093
    • 93: id10094
    • 94: id10095
    • 95: id10096
    • 96: id10097
    • 97: id10098
    • 98: id10099
    • 99: id10100
    • 100: id10101
    • 101: id10102
    • 102: id10103
    • 103: id10104
    • 104: id10105
    • 105: id10106
    • 106: id10107
    • 107: id10108
    • 108: id10109
    • 109: id10110
    • 110: id10111
    • 111: id10112
    • 112: id10113
    • 113: id10114
    • 114: id10115
    • 115: id10116
    • 116: id10117
    • 117: id10118
    • 118: id10119
    • 119: id10120
    • 120: id10121
    • 121: id10122
    • 122: id10123
    • 123: id10124
    • 124: id10125
    • 125: id10126
    • 126: id10127
    • 127: id10128
    • 128: id10129
    • 129: id10130
    • 130: id10131
    • 131: id10132
    • 132: id10133
    • 133: id10134
    • 134: id10135
    • 135: id10136
    • 136: id10137
    • 137: id10138
    • 138: id10139
    • 139: id10140
    • 140: id10141
    • 141: id10142
    • 142: id10143
    • 143: id10144
    • 144: id10145
    • 145: id10146
    • 146: id10147
    • 147: id10148
    • 148: id10149
    • 149: id10150
    • 150: id10151
    • 151: id10152
    • 152: id10153
    • 153: id10154
    • 154: id10155
    • 155: id10156
    • 156: id10157
    • 157: id10158
    • 158: id10159
    • 159: id10160
    • 160: id10161
    • 161: id10162
    • 162: id10163
    • 163: id10164
    • 164: id10165
    • 165: id10166
    • 166: id10167
    • 167: id10168
    • 168: id10169
    • 169: id10170
    • 170: id10171
    • 171: id10172
    • 172: id10173
    • 173: id10174
    • 174: id10175
    • 175: id10176
    • 176: id10177
    • 177: id10178
    • 178: id10179
    • 179: id10180
    • 180: id10181
    • 181: id10182
    • 182: id10183
    • 183: id10184
    • 184: id10185
    • 185: id10186
    • 186: id10187
    • 187: id10188
    • 188: id10189
    • 189: id10190
    • 190: id10191
    • 191: id10192
    • 192: id10193
    • 193: id10194
    • 194: id10195
    • 195: id10196
    • 196: id10197
    • 197: id10198
    • 198: id10199
    • 199: id10200
    • 200: id10201
    • 201: id10202
    • 202: id10203
    • 203: id10204
    • 204: id10205
    • 20

搜集汇总

数据集介绍

构建方式

在语音处理领域，SUPERB数据集通过整合多个经典语音资源构建而成，其基础来源于LibriSpeech ASR、Speech Commands等知名语料库。该数据集采用模块化设计，针对不同子任务分别配置数据格式，例如自动语音识别（ASR）配置包含音频、文本及说话人标识，而情感识别（ER）配置则标注了四种基本情感类别。构建过程中，原始音频均统一采样至16kHz，并依据任务需求进行结构化标注，确保了数据的一致性与可用性。

特点

SUPERB数据集的核心特点在于其多任务基准的综合性，涵盖了语音识别、关键词检测、说话人识别、情感分类及意图理解等十项语音处理任务。每个任务配置均具备明确的特征定义，如音频波形、分类标签或时序标注，且数据规模可观，例如ASR任务包含超过2.8万条训练样本。数据集采用英语单语设计，所有音频均保持一致的采样率，为模型评估提供了标准化的测试环境。

使用方法

使用SUPERB数据集时，研究人员可通过HuggingFace平台直接加载特定任务配置，如'asr'或'ks'，以获取对应的训练、验证及测试分割。每个配置均提供标准化的特征字段，用户可依据任务需求提取音频数据及其关联标注，例如在说话人识别任务中利用说话人ID进行模型训练。数据集支持流式加载，便于大规模语音处理实验的开展，同时其结构化设计也适配于各类深度学习框架的输入要求。

背景与挑战

背景概述

在语音处理领域，随着深度学习技术的迅猛发展，对统一且全面的基准测试需求日益凸显。SUPERB数据集由s3prl团队于近年推出，旨在构建一个多任务语音理解基准平台，其核心研究问题聚焦于评估自监督学习模型在多样化语音任务中的泛化能力。该数据集整合了自动语音识别、说话人识别、情感识别、意图分类及关键词检测等多个子任务，为研究者提供了标准化的评估框架。通过集成LibriSpeech、Speech Commands等经典语料，SUPERB不仅推动了自监督语音表征学习的前沿探索，还显著促进了跨任务模型性能的比较与优化，成为语音人工智能领域的重要基础设施。

当前挑战

SUPERB数据集致力于解决语音理解中多任务协同建模的复杂挑战，其核心问题在于如何设计统一框架以评估模型在异构任务上的表现。具体挑战包括：在领域问题层面，模型需克服语音信号中的声学变异、环境噪声干扰以及跨任务特征共享的平衡难题；在构建过程中，数据集面临多源数据格式整合的复杂性，如不同采样率、标注标准及语言风格的统一处理。此外，确保各子任务数据规模与质量的均衡性，以及维护说话人匿名性与情感标注的主观一致性，均为构建过程中的关键障碍。

常用场景

经典使用场景

在语音处理领域，SUPERB数据集作为一项综合性基准测试平台，其经典使用场景聚焦于评估自监督语音表示学习模型的泛化能力。该数据集整合了自动语音识别、关键词检测、说话人识别、情感识别及意图分类等多样化任务，为研究者提供了统一且标准化的评估框架。通过这一平台，学者能够系统性地比较不同预训练模型在多种下游任务上的性能表现，从而深入探索语音表征的通用性与可迁移性，推动了自监督学习在语音领域的理论发展与技术革新。

解决学术问题

SUPERB数据集有效解决了语音自监督学习领域长期存在的评估标准不统一问题。传统研究往往依赖单一任务或孤立数据集进行模型验证，导致结果难以横向比较，阻碍了学术进展。该数据集通过集成多任务、多模态的语音数据，构建了全面而严谨的评估体系，使得研究者能够客观衡量模型在语音内容理解、说话人特征提取及情感语义解析等方面的综合能力。其意义在于确立了语音表示学习的标准化评测范式，显著提升了研究结果的可复现性与可比性，为后续理论探索奠定了坚实基础。

衍生相关工作

围绕SUPERB数据集，学术界衍生出一系列经典研究工作，进一步拓展了语音自监督学习的边界。例如，基于该基准的模型比较研究催生了如Wav2Vec 2.0、HuBERT等先进架构的优化与适配，这些模型在SUPERB任务上取得了突破性性能。同时，研究者们利用该数据集的多任务特性，提出了跨任务联合学习、多层级表征融合等创新方法，深化了对语音表征层次化结构的理解。这些工作不仅巩固了SUPERB在领域内的标杆地位，也激发了更多关于语音通用智能的探索与讨论。