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)
| [](#model-architecture)
| [](#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

*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. |