File size: 37,439 Bytes
47a82b3
 
bc3e42c
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
575de92
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
f89a158
8dcaa93
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47a82b3
 
 
 
3f27873
47a82b3
 
 
 
 
 
 
 
 
e6830b1
47a82b3
 
 
 
 
 
 
 
 
 
7938c10
47a82b3
 
 
 
 
 
 
 
 
 
7938c10
47a82b3
7938c10
bcf6d8e
79fbf31
 
bcf6d8e
 
 
 
 
 
 
 
 
 
 
 
47a82b3
 
 
 
 
 
 
 
 
 
 
7938c10
 
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bb0964b
47a82b3
bb0964b
47a82b3
bb0964b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47a82b3
 
 
 
7938c10
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7d6cb96
 
 
 
 
 
47a82b3
7d6cb96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47a82b3
 
 
 
 
 
 
 
13d505c
47a82b3
 
 
 
 
 
 
 
f4e96f3
575de92
 
 
 
47a82b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7938c10
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
---
license: cc-by-4.0
track_downloads: true
language:
- en
- es
- fr
- de
- bg
- hr
- cs
- da
- nl
- et
- fi
- el
- hu
- it
- lv
- lt
- mt
- pl
- pt
- ro
- sk
- sl
- sv
- ru
- uk

pipeline_tag: automatic-speech-recognition
library_name: nemo
datasets:
- nvidia/Granary
- nemo/asr-set-3.0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- Transducer
- TDT
- FastConformer
- Conformer
- pytorch
- NeMo
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: parakeet-tdt-0.6b-v3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: AMI (Meetings test)
      type: edinburghcstr/ami
      config: ihm
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 11.31
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Earnings-22
      type: revdotcom/earnings22
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 11.42
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: GigaSpeech
      type: speechcolab/gigaspeech
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 9.59
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech (clean)
      type: librispeech_asr
      config: other
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 1.93
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech (other)
      type: librispeech_asr
      config: other
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 3.59
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: SPGI Speech
      type: kensho/spgispeech
      config: test
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 3.97
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: tedlium-v3
      type: LIUM/tedlium
      config: release1
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 2.75
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Vox Populi
      type: facebook/voxpopuli
      config: en
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 6.14
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: bg_bg
      split: test
      args:
        language: bg
    metrics:
    - name: Test WER (Bg)
      type: wer
      value: 12.64
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: cs_cz
      split: test
      args:
        language: cs
    metrics:
    - name: Test WER (Cs)
      type: wer
      value: 11.01
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: da_dk
      split: test
      args:
        language: da
    metrics:
    - name: Test WER (Da)
      type: wer
      value: 18.41
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: de_de
      split: test
      args:
        language: de
    metrics:
    - name: Test WER (De)
      type: wer
      value: 5.04
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: el_gr
      split: test
      args:
        language: el
    metrics:
    - name: Test WER (El)
      type: wer
      value: 20.70
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: en_us
      split: test
      args:
        language: en
    metrics:
    - name: Test WER (En)
      type: wer
      value: 4.85
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: es_419
      split: test
      args:
        language: es
    metrics:
    - name: Test WER (Es)
      type: wer
      value: 3.45
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: et_ee
      split: test
      args:
        language: et
    metrics:
    - name: Test WER (Et)
      type: wer
      value: 17.73
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: fi_fi
      split: test
      args:
        language: fi
    metrics:
    - name: Test WER (Fi)
      type: wer
      value: 13.21
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: fr_fr
      split: test
      args:
        language: fr
    metrics:
    - name: Test WER (Fr)
      type: wer
      value: 5.15
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: hr_hr
      split: test
      args:
        language: hr
    metrics:
    - name: Test WER (Hr)
      type: wer
      value: 12.46
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: hu_hu
      split: test
      args:
        language: hu
    metrics:
    - name: Test WER (Hu)
      type: wer
      value: 15.72
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: it_it
      split: test
      args:
        language: it
    metrics:
    - name: Test WER (It)
      type: wer
      value: 3.00
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: lt_lt
      split: test
      args:
        language: lt
    metrics:
    - name: Test WER (Lt)
      type: wer
      value: 20.35
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: lv_lv
      split: test
      args:
        language: lv
    metrics:
    - name: Test WER (Lv)
      type: wer
      value: 22.84
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: mt_mt
      split: test
      args:
        language: mt
    metrics:
    - name: Test WER (Mt)
      type: wer
      value: 20.46
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: nl_nl
      split: test
      args:
        language: nl
    metrics:
    - name: Test WER (Nl)
      type: wer
      value: 7.48
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: pl_pl
      split: test
      args:
        language: pl
    metrics:
    - name: Test WER (Pl)
      type: wer
      value: 7.31
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: pt_br
      split: test
      args:
        language: pt
    metrics:
    - name: Test WER (Pt)
      type: wer
      value: 4.76
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: ro_ro
      split: test
      args:
        language: ro
    metrics:
    - name: Test WER (Ro)
      type: wer
      value: 12.44
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: ru_ru
      split: test
      args:
        language: ru
    metrics:
    - name: Test WER (Ru)
      type: wer
      value: 5.51
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: sk_sk
      split: test
      args:
        language: sk
    metrics:
    - name: Test WER (Sk)
      type: wer
      value: 8.82
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: sl_si
      split: test
      args:
        language: sl
    metrics:
    - name: Test WER (Sl)
      type: wer
      value: 24.03
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: sv_se
      split: test
      args:
        language: sv
    metrics:
    - name: Test WER (Sv)
      type: wer
      value: 15.08
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      config: uk_ua
      split: test
      args:
        language: uk
    metrics:
    - name: Test WER (Uk)
      type: wer
      value: 6.79
  # Multilingual LibriSpeech ASR Results
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: spanish
      split: test
      args:
        language: es
    metrics:
    - name: Test WER (Es)
      type: wer
      value: 4.39
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: french
      split: test
      args:
        language: fr
    metrics:
    - name: Test WER (Fr)
      type: wer
      value: 4.97
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: italian
      split: test
      args:
        language: it
    metrics:
    - name: Test WER (It)
      type: wer
      value: 10.08
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: dutch
      split: test
      args:
        language: nl
    metrics:
    - name: Test WER (Nl)
      type: wer
      value: 12.78
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: polish
      split: test
      args:
        language: pl
    metrics:
    - name: Test WER (Pl)
      type: wer
      value: 7.28
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Multilingual LibriSpeech
      type: facebook/multilingual_librispeech
      config: portuguese
      split: test
      args:
        language: pt
    metrics:
    - name: Test WER (Pt)
      type: wer
      value: 7.50
  # CoVoST2 ASR Results
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: de
      split: test
      args:
        language: de
    metrics:
    - name: Test WER (De)
      type: wer
      value: 4.84
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: en
      split: test
      args:
        language: en
    metrics:
    - name: Test WER (En)
      type: wer
      value: 6.80
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: es
      split: test
      args:
        language: es
    metrics:
    - name: Test WER (Es)
      type: wer
      value: 3.41
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: et
      split: test
      args:
        language: et
    metrics:
    - name: Test WER (Et)
      type: wer
      value: 22.04
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: fr
      split: test
      args:
        language: fr
    metrics:
    - name: Test WER (Fr)
      type: wer
      value: 6.05
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: it
      split: test
      args:
        language: it
    metrics:
    - name: Test WER (It)
      type: wer
      value: 3.69
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: lv
      split: test
      args:
        language: lv
    metrics:
    - name: Test WER (Lv)
      type: wer
      value: 38.36
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: nl
      split: test
      args:
        language: nl
    metrics:
    - name: Test WER (Nl)
      type: wer
      value: 6.50
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: pt
      split: test
      args:
        language: pt
    metrics:
    - name: Test WER (Pt)
      type: wer
      value: 3.96
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: ru
      split: test
      args:
        language: ru
    metrics:
    - name: Test WER (Ru)
      type: wer
      value: 3.00
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: sl
      split: test
      args:
        language: sl
    metrics:
    - name: Test WER (Sl)
      type: wer
      value: 31.80
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: sv
      split: test
      args:
        language: sv
    metrics:
    - name: Test WER (Sv)
      type: wer
      value: 20.16
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: CoVoST2
      type: covost2
      config: uk
      split: test
      args:
        language: uk
    metrics:
    - name: Test WER (Uk)
      type: wer
      value: 5.10
metrics:
- wer
---

# **<span style="color:#76b900;">🦜 parakeet-tdt-0.6b-v3: Multilingual Speech-to-Text Model</span>**

<style>
img {
 display: inline;
}
</style>

[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--TDT-blue#model-badge)](#model-architecture)
| [![Model size](https://img.shields.io/badge/Params-0.6B-green#model-badge)](#model-architecture)
| [![Language](https://img.shields.io/badge/Language-EU_Languages-blue#model-badge)](#datasets)

## <span style="color:#466f00;">Description:</span>

`parakeet-tdt-0.6b-v3` is a 600-million-parameter multilingual automatic speech recognition (ASR) model designed for high-throughput speech-to-text transcription. It extends the [parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) model by expanding language support from English to 25 European languages. The model automatically detects the language of the audio and transcribes it without requiring additional prompting. It is part of a series of models that leverage the [Granary](https://huggingface.co/datasets/nvidia/Granary) [1, 2] multilingual corpus as their primary training dataset.

🗣️ Try Demo here: https://huggingface.co/spaces/nvidia/parakeet-tdt-0.6b-v3 

**Supported Languages:**  
Bulgarian (**bg**), Croatian (**hr**), Czech (**cs**), Danish (**da**), Dutch (**nl**), English (**en**), Estonian (**et**), Finnish (**fi**), French (**fr**), German (**de**), Greek (**el**), Hungarian (**hu**), Italian (**it**), Latvian (**lv**), Lithuanian (**lt**), Maltese (**mt**), Polish (**pl**), Portuguese (**pt**), Romanian (**ro**), Slovak (**sk**), Slovenian (**sl**), Spanish (**es**), Swedish (**sv**), Russian (**ru**), Ukrainian (**uk**)

This model is ready for commercial/non-commercial use.

## <span style="color:#466f00;">Key Features:</span>

`parakeet-tdt-0.6b-v3`'s key features are built on the foundation of its predecessor, [parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2), and include:

* Automatic **punctuation** and **capitalization**
* Accurate **word-level** and **segment-level** timestamps
* **Long audio** transcription, supporting audio **up to 24 minutes** long with full attention (on A100 80GB) or up to 3 hours with local attention.
* Released under a **permissive CC BY 4.0 license**

## <span style="color:#466f00;">License/Terms of Use:</span>

GOVERNING TERMS: Use of this model is governed by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en) license.

## Automatic Speech Recognition (ASR) Performance

![ASR WER Comparison](plots/asr.png)

*Figure 1: ASR WER comparison across different models. This does not include Punctuation and Capitalisation errors.*

---

### Evaluation Notes

**Note 1:** The above evaluations are conducted  for 24 supported languages, excluding Latvian since `seamless-m4t-v2-large` and `seamless-m4t-medium` do not support it.

**Note 2:** Performance differences may be partly attributed to Portuguese variant differences - our training data uses European Portuguese while most benchmarks use Brazilian Portuguese.

### <span style="color:#466f00;">Deployment Geography:</span>
Global


### <span style="color:#466f00;">Use Case:</span>

This model serves developers, researchers, academics, and industries building applications that require speech-to-text capabilities, including but not limited to: conversational AI, voice assistants, transcription services, subtitle generation, and voice analytics platforms.


### <span style="color:#466f00;">Release Date:</span>

Huggingface [08/14/2025](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3)


### <span style="color:#466f00;">Model Architecture:</span>

**Architecture Type**: 

FastConformer-TDT

**Network Architecture**:

* This model was developed based on [FastConformer encoder](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) architecture[3] and TDT decoder[4]
* This model has 600 million model parameters.

### <span style="color:#466f00;">Input:</span>
**Input Type(s):** 16kHz Audio
**Input Format(s):** `.wav` and `.flac` audio formats
**Input Parameters:** 1D (audio signal)
**Other Properties Related to Input:**  Monochannel audio

### <span style="color:#466f00;">Output:</span>
**Output Type(s):**  Text
**Output Format:**  String
**Output Parameters:**  1D (text)
**Other Properties Related to Output:** Punctuations and Capitalizations included.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. 

For more information, refer to the [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).

## <span style="color:#466f00;">How to Use this Model:</span>

To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
```bash
pip install -U nemo_toolkit['asr']
``` 
The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

#### Automatically instantiate the model

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="nvidia/parakeet-tdt-0.6b-v3")
```

#### Transcribing using Python
First, let's get a sample
```bash
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```python
output = asr_model.transcribe(['2086-149220-0033.wav'])
print(output[0].text)
```

#### Transcribing with timestamps

To transcribe with timestamps:
```python
output = asr_model.transcribe(['2086-149220-0033.wav'], timestamps=True)
# by default, timestamps are enabled for char, word and segment level
word_timestamps = output[0].timestamp['word'] # word level timestamps for first sample
segment_timestamps = output[0].timestamp['segment'] # segment level timestamps
char_timestamps = output[0].timestamp['char'] # char level timestamps

for stamp in segment_timestamps:
    print(f"{stamp['start']}s - {stamp['end']}s : {stamp['segment']}")
```

#### Transcribing long-form audio

```python
#updating self-attention model of fast-conformer encoder
#setting attention left and right context sizes to 256
asr_model.change_attention_model(self_attention_model="rel_pos_local_attn", att_context_size=[256, 256])

output = asr_model.transcribe(['2086-149220-0033.wav'])

print(output[0].text)
```

#### Streaming with Parakeet models

To use parakeet models in streaming mode use this [script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py) as shown below:

```bash
python NeMo/main/examples/asr/asr_chunked_inference/rnnt/speech_to_text_streaming_infer_rnnt.py \
    pretrained_name="nvidia/parakeet-tdt-0.6b-v3" \
    model_path=null \
    audio_dir="<optional path to folder of audio files>" \
    dataset_manifest="<optional path to manifest>" \
    output_filename="<optional output filename>" \
    right_context_secs=2.0 \
    chunk_secs=2 \
    left_context_secs=10.0 \
    batch_size=32 \
    clean_groundtruth_text=False
```

NVIDIA NIM for v2 parakeet model is available at [https://build.nvidia.com/nvidia/parakeet-tdt-0_6b-v2](https://build.nvidia.com/nvidia/parakeet-tdt-0_6b-v2).

## <span style="color:#466f00;">Software Integration:</span>

**Runtime Engine(s):**
* NeMo 2.4  


**Supported Hardware Microarchitecture Compatibility:** 
* NVIDIA Ampere
* NVIDIA Blackwell  
* NVIDIA Hopper
* NVIDIA Volta

**[Preferred/Supported] Operating System(s):**

- Linux

**Hardware Specific Requirements:**

Atleast 2GB RAM for model to load. The bigger the RAM, the larger audio input it supports.

#### Model Version

Current version: `parakeet-tdt-0.6b-v3`. Previous versions can be [accessed](https://huggingface.co/collections/nvidia/parakeet-659711f49d1469e51546e021) here. 

## <span style="color:#466f00;">Training and Evaluation Datasets:</span>

### <span style="color:#466f00;">Training</span>

This model was trained using the NeMo toolkit [5], following the strategies below:

- Initialized from a CTC multilingual checkpoint pretrained on the Granary dataset \[1] \[2].  
- Trained for 150,000 steps on 128 A100 GPUs. 
- Dataset corpora and languages were balanced using a temperature sampling value of 0.5.  
- Stage 2 fine-tuning was performed for 5,000 steps on 4 A100 GPUs using approximately 7,500 hours of high-quality, human-transcribed data of NeMo ASR Set 3.0.

Training was conducted using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and [TDT configuration](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_tdt_ctc_bpe.yaml).

During the training, a unified SentencePiece Tokenizer \[6] with a vocabulary of **8,192 tokens** was used. The unified tokenizer was constructed from the training set transcripts using this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py) and was optimized across all 25 supported languages.

### <span style="color:#466f00;">Training Dataset</span>
The model was trained on the combination of [Granary dataset's ASR subset](https://huggingface.co/datasets/nvidia/Granary) and in-house dataset NeMo ASR Set 3.0:

- 10,000 hours from human-transcribed NeMo ASR Set 3.0, including:
  - LibriSpeech (960 hours)
  - Fisher Corpus
  - National Speech Corpus Part 1 
  - VCTK
  - Europarl-ASR
  - Multilingual LibriSpeech
  - Mozilla Common Voice (v7.0)
  - AMI

- 660,000 hours of pseudo-labeled data from Granary \[1] \[2], including:
  - [YTC](https://huggingface.co/datasets/FBK-MT/mosel) \[7]
  - [MOSEL](https://huggingface.co/datasets/FBK-MT/mosel) \[8]
  - [YODAS](https://huggingface.co/datasets/espnet/yodas-granary) \[9]

All transcriptions preserve punctuation and capitalization. The Granary dataset will be made publicly available after presentation at Interspeech 2025.

**Data Collection Method by dataset**

* Hybrid: Automated, Human

**Labeling Method by dataset**

* Hybrid: Synthetic, Human 

**Properties:**

* Noise robust data from various sources
* Single channel, 16kHz sampled data

#### Evaluation Datasets

For multilingual ASR performance evaluation:
  - Fleurs [10]
  - MLS [11]  
  - CoVoST [12]

For English ASR performance evaluation:
  - Hugging Face Open ASR Leaderboard [13] datasets

**Data Collection Method by dataset**
* Human

**Labeling Method by dataset**
* Human

**Properties:**

* All are commonly used for benchmarking English ASR systems.
* Audio data is typically processed into a 16kHz mono channel format for ASR evaluation, consistent with benchmarks like the [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard).

## <span style="color:#466f00;">Performance</span>

#### Multilingual ASR

The tables below summarizes the WER (%) using a Transducer decoder with greedy decoding (without an external language model):


| Language | Fleurs | MLS | CoVoST |
|----------|--------|-----|--------|
| **Average WER ↓** | *11.97%* | *7.83%* | *11.98%* |
| **bg** | 12.64% | - | - |
| **cs** | 11.01% | - | - |
| **da** | 18.41% | - | - |
| **de** | 5.04% | - | 4.84% |
| **el** | 20.70% | - | - |
| **en** | 4.85% | - | 6.80% |
| **es** | 3.45% | 4.39% | 3.41% |
| **et** | 17.73% | - | 22.04% |
| **fi** | 13.21% | - | - |
| **fr** | 5.15% | 4.97% | 6.05% |
| **hr** | 12.46% | - | - |
| **hu** | 15.72% | - | - |
| **it** | 3.00% | 10.08% | 3.69% |
| **lt** | 20.35% | - | - |
| **lv** | 22.84% | - | 38.36% |
| **mt** | 20.46% | - | - |
| **nl** | 7.48% | 12.78% | 6.50% |
| **pl** | 7.31% | 7.28% | - |
| **pt** | 4.76% | 7.50% | 3.96% |
| **ro** | 12.44% | - | - |
| **ru** | 5.51% | - | 3.00% |
| **sk** | 8.82% | - | - |
| **sl** | 24.03% | - | 31.80% |
| **sv** | 15.08% | - | 20.16% |
| **uk** | 6.79% | - | 5.10% |

**Note:** WERs are calculated after removing Punctuation and Capitalization from reference and predicted text.


#### Huggingface Open-ASR-Leaderboard

| **Model** | **Avg WER** | **AMI** | **Earnings-22** | **GigaSpeech** | **LS test-clean** | **LS test-other** | **SPGI Speech** | **TEDLIUM-v3** | **VoxPopuli** |
|:-------------|:-------------:|:---------:|:------------------:|:----------------:|:-----------------:|:-----------------:|:------------------:|:----------------:|:---------------:|
| `parakeet-tdt-0.6b-v3` | 6.34% | 11.31% | 11.42% | 9.59% | 1.93% | 3.59% | 3.97% | 2.75% | 6.14% |

Additional evaluation details are available on the [Hugging Face ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard).[13]

### Noise Robustness
Performance across different Signal-to-Noise Ratios (SNR) using MUSAN music and noise samples [14]:

| **SNR Level** | **Avg WER** | **AMI** | **Earnings** | **GigaSpeech** | **LS test-clean** | **LS test-other** | **SPGI** | **Tedlium** | **VoxPopuli** | **Relative Change** |
|:---------------|:-------------:|:----------:|:------------:|:----------------:|:-----------------:|:-----------------:|:-----------:|:-------------:|:---------------:|:-----------------:|
| Clean | 6.34% | 11.31% | 11.42% | 9.59% | 1.93% | 3.59% | 3.97% | 2.75% | 6.14% | - |
| SNR 10 | 7.12% | 13.99% | 11.79% | 9.96% | 2.15% | 4.55% | 4.45% | 3.05% | 6.99% | -12.28% |
| SNR 5 | 8.23% | 17.59% | 13.01% | 10.69% | 2.62% | 6.05% | 5.23% | 3.33% | 7.31% | -29.81% |
| SNR 0 | 11.66% | 24.44% | 17.34% | 13.60% | 4.82% | 10.38% | 8.41% | 5.39% | 8.91% | -83.97% |
| SNR -5 | 19.88% | 34.91% | 26.92% | 21.41% | 12.21% | 19.98% | 16.96% | 11.36% | 15.30% | -213.64% |



## <span style="color:#466f00;">References</span>

[1] [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://arxiv.org/abs/2505.13404)

[2] [NVIDIA Granary Dataset Card](https://huggingface.co/datasets/nvidia/Granary)

[3] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)

[4] [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795)

[5] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

[6] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)

[7] [Youtube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons)

[8] [MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages](https://arxiv.org/abs/2410.01036)

[9] [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://arxiv.org/pdf/2406.00899)

[10] [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://arxiv.org/abs/2205.12446)

[11] [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)

[12] [CoVoST 2 and Massively Multilingual Speech-to-Text Translation](https://arxiv.org/abs/2007.10310)

[13] [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)

[14] [MUSAN: A Music, Speech, and Noise Corpus](https://arxiv.org/abs/1510.08484)

## <span style="color:#466f00;">Inference:</span>

**Engine**: 
* NVIDIA NeMo

**Test Hardware**:
* NVIDIA A10
* NVIDIA A100
* NVIDIA A30
* NVIDIA H100
* NVIDIA L4
* NVIDIA L40
* NVIDIA Turing T4
* NVIDIA Volta V100

## <span style="color:#466f00;">Ethical Considerations:</span>
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [here](https://developer.nvidia.com/blog/enhancing-ai-transparency-and-ethical-considerations-with-model-card/).

Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

## <span style="color:#466f00;">Bias:</span>

Field                                                                                               |  Response
---------------------------------------------------------------------------------------------------|---------------
Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing  |  None
Measures taken to mitigate against unwanted bias    | None

## <span style="color:#466f00;">Explainability:</span>

Field                                                                                                  |  Response
------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------
Intended Domain                                                                   |  Speech to Text Transcription
Model Type                                                                                            |  FastConformer
Intended Users                                                                                        |  This model is intended for developers, researchers, academics, and industries building conversational based applications. 
Output                                                                                                |  Text 
Describe how the model works                                                                          |  Speech input is encoded into embeddings and passed into conformer-based model and output a text response.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of  |  Not Applicable
Technical Limitations & Mitigation                                                                    |  Transcripts may be not 100% accurate. Accuracy varies based on language and characteristics of input audio (Domain, Use Case, Accent, Noise, Speech Type, Context of speech, etc.)
Verified to have met prescribed NVIDIA quality standards  |  Yes
Performance Metrics                                                                                   | Word Error Rate
Potential Known Risks                                                                                 |  If a word is not trained in the language model and not presented in vocabulary, the word is not likely to be recognized. Not recommended for word-for-word/incomplete sentences as accuracy varies based on the context of input text
Licensing                                                                                             |  GOVERNING TERMS: Use of this model is governed by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en) license.

## <span style="color:#466f00;">Privacy:</span>

Field                                                                                                                              |  Response
----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------
Generatable or reverse engineerable personal data?                                                     |  None
Personal data used to create this model?                                                                                       |  None
Is there provenance for all datasets used in training?                                                                                |  Yes
Does data labeling (annotation, metadata) comply with privacy laws?                                                                |  Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  No, not possible with externally-sourced data.
Applicable Privacy Policy        | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ 

## <span style="color:#466f00;">Safety:</span>

Field                                               |  Response
---------------------------------------------------|----------------------------------
Model Application(s)                               |  Speech to Text Transcription
Describe the life critical impact   |  None
Use Case Restrictions                              | Abide by [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en) License
Model and dataset restrictions            |  The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.