modelId
stringlengths 5
139
| author
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42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 18:29:29
| downloads
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11.7k
| library_name
stringclasses 555
values | tags
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4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-11 18:25:24
| card
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|---|---|---|---|---|---|---|---|---|---|
ravirajoshi/wav2vec2-large-xls-r-300m-hindi
|
ravirajoshi
| 2022-03-24T11:56:00Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
model-index:
- name: wav2vec2-large-xls-r-300m-hindi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7049
- Wer: 0.3200
|
masapasa/xls-r-300m-sv-cv8
|
masapasa
| 2022-03-24T11:55:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sv-SE
license: apache-2.0
tags:
- robust-speech-event
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 102.43
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3347
- Wer: 1.0286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.7838 | 0.01 | 5 | 14.5035 | 1.0 |
| 13.0582 | 0.03 | 10 | 13.6658 | 1.0 |
| 7.3034 | 0.04 | 15 | 9.7898 | 1.0 |
| 6.1847 | 0.05 | 20 | 6.9148 | 1.0 |
| 5.3371 | 0.07 | 25 | 5.3661 | 1.0 |
| 4.4274 | 0.08 | 30 | 4.6945 | 1.0 |
| 4.0918 | 0.1 | 35 | 4.3172 | 1.0 |
| 4.1734 | 0.11 | 40 | 4.0759 | 1.0 |
| 3.7332 | 0.12 | 45 | 3.9039 | 1.0 |
| 3.6871 | 0.14 | 50 | 3.7777 | 1.0 |
| 3.4428 | 0.15 | 55 | 3.6718 | 1.0 |
| 3.5514 | 0.16 | 60 | 3.5947 | 1.0 |
| 3.4307 | 0.18 | 65 | 3.5144 | 1.0 |
| 3.4102 | 0.19 | 70 | 3.4432 | 1.0 |
| 3.4964 | 0.21 | 75 | 3.3890 | 1.0 |
| 3.3936 | 0.22 | 80 | 3.3467 | 1.0 |
| 3.3051 | 0.23 | 85 | 3.3102 | 1.0 |
| 3.278 | 0.25 | 90 | 3.2801 | 1.0 |
| 3.2223 | 0.26 | 95 | 3.2440 | 1.0 |
| 3.1888 | 0.27 | 100 | 3.2900 | 1.0 |
| 3.218 | 0.29 | 105 | 3.2627 | 1.0 |
| 3.1308 | 0.3 | 110 | 3.2152 | 1.0 |
| 3.109 | 0.31 | 115 | 3.1686 | 1.0 |
| 3.1188 | 0.33 | 120 | 3.1734 | 1.0 |
| 3.1132 | 0.34 | 125 | 3.1431 | 1.0 |
| 3.0667 | 0.36 | 130 | 3.1686 | 1.0 |
| 3.1167 | 0.37 | 135 | 3.1885 | 1.0 |
| 3.0592 | 0.38 | 140 | 3.1100 | 1.0 |
| 3.0531 | 0.4 | 145 | 3.1149 | 1.0 |
| 3.1224 | 0.41 | 150 | 3.1205 | 1.0 |
| 3.0651 | 0.42 | 155 | 3.1101 | 1.0 |
| 3.0077 | 0.44 | 160 | 3.0980 | 1.0 |
| 3.0027 | 0.45 | 165 | 3.1132 | 1.0 |
| 3.0423 | 0.47 | 170 | 3.0886 | 1.0 |
| 3.0462 | 0.48 | 175 | 3.0865 | 1.0 |
| 3.0701 | 0.49 | 180 | 3.0863 | 1.0 |
| 3.0871 | 0.51 | 185 | 3.0825 | 1.0 |
| 3.0585 | 0.52 | 190 | 3.0720 | 1.0 |
| 3.0274 | 0.53 | 195 | 3.0736 | 1.0 |
| 3.0983 | 0.55 | 200 | 3.0658 | 1.0 |
| 3.0538 | 0.56 | 205 | 3.1241 | 1.0 |
| 3.0862 | 0.57 | 210 | 3.0573 | 1.0 |
| 3.0041 | 0.59 | 215 | 3.0608 | 1.0 |
| 3.027 | 0.6 | 220 | 3.0614 | 1.0 |
| 2.9916 | 0.62 | 225 | 3.0527 | 1.0 |
| 3.0157 | 0.63 | 230 | 3.0514 | 1.0 |
| 3.0429 | 0.64 | 235 | 3.0391 | 1.0 |
| 2.999 | 0.66 | 240 | 3.0462 | 1.0 |
| 3.0053 | 0.67 | 245 | 3.0438 | 1.0 |
| 2.9812 | 0.68 | 250 | 3.0447 | 1.0 |
| 3.0062 | 0.7 | 255 | 3.0660 | 1.0 |
| 3.0045 | 0.71 | 260 | 3.0103 | 1.0 |
| 2.9684 | 0.73 | 265 | 3.0106 | 1.0 |
| 2.9885 | 0.74 | 270 | 3.0014 | 1.0 |
| 3.0062 | 0.75 | 275 | 2.9885 | 1.0 |
| 2.9736 | 0.77 | 280 | 3.0330 | 1.0 |
| 2.9766 | 0.78 | 285 | 2.9910 | 1.0 |
| 2.9545 | 0.79 | 290 | 2.9972 | 1.0 |
| 2.9936 | 0.81 | 295 | 2.9872 | 1.0 |
| 3.0832 | 0.82 | 300 | 2.9978 | 1.0 |
| 2.974 | 0.83 | 305 | 2.9978 | 1.0 |
| 2.9846 | 0.85 | 310 | 2.9849 | 1.0 |
| 2.9554 | 0.86 | 315 | 2.9810 | 1.0 |
| 2.9524 | 0.88 | 320 | 2.9731 | 1.0 |
| 2.9426 | 0.89 | 325 | 2.9824 | 1.0 |
| 2.9416 | 0.9 | 330 | 2.9731 | 1.0 |
| 2.9705 | 0.92 | 335 | 2.9830 | 1.0 |
| 2.9502 | 0.93 | 340 | 2.9713 | 1.0 |
| 2.9393 | 0.94 | 345 | 2.9790 | 1.0 |
| 2.9336 | 0.96 | 350 | 2.9684 | 1.0 |
| 2.9542 | 0.97 | 355 | 2.9689 | 1.0 |
| 2.9408 | 0.98 | 360 | 2.9556 | 1.0 |
| 2.9544 | 1.0 | 365 | 2.9563 | 1.0 |
| 2.9187 | 1.01 | 370 | 2.9624 | 1.0 |
| 2.9935 | 1.03 | 375 | 2.9500 | 1.0 |
| 2.9803 | 1.04 | 380 | 2.9558 | 1.0 |
| 2.9867 | 1.05 | 385 | 2.9473 | 1.0 |
| 2.8925 | 1.07 | 390 | 2.9444 | 1.0 |
| 2.9633 | 1.08 | 395 | 2.9490 | 1.0 |
| 2.9191 | 1.1 | 400 | 2.9362 | 1.0 |
| 2.9081 | 1.11 | 405 | 2.9394 | 1.0 |
| 2.9381 | 1.12 | 410 | 2.9846 | 1.0 |
| 2.9271 | 1.14 | 415 | 2.9638 | 1.0 |
| 2.959 | 1.15 | 420 | 2.9835 | 1.0 |
| 2.9486 | 1.16 | 425 | 2.9361 | 1.0 |
| 2.9246 | 1.18 | 430 | 2.9615 | 1.0 |
| 2.923 | 1.19 | 435 | 2.9313 | 1.0 |
| 2.8908 | 1.21 | 440 | 2.9362 | 1.0 |
| 2.8976 | 1.22 | 445 | 2.9224 | 1.0 |
| 2.9278 | 1.23 | 450 | 2.9276 | 1.0 |
| 2.8429 | 1.25 | 455 | 2.9299 | 1.0 |
| 2.867 | 1.26 | 460 | 2.9258 | 1.0 |
| 2.9734 | 1.27 | 465 | 2.9281 | 1.0000 |
| 2.934 | 1.29 | 470 | 2.9229 | 1.0 |
| 2.9521 | 1.3 | 475 | 2.9134 | 1.0 |
| 2.9098 | 1.31 | 480 | 2.9051 | 0.9993 |
| 2.9112 | 1.33 | 485 | 2.9028 | 0.9999 |
| 2.8799 | 1.34 | 490 | 2.9101 | 0.9986 |
| 2.857 | 1.36 | 495 | 2.9005 | 0.9992 |
| 2.8525 | 1.37 | 500 | 2.8937 | 1.0 |
| 2.8682 | 1.38 | 505 | 2.8904 | 1.0000 |
| 2.8899 | 1.4 | 510 | 2.8914 | 0.9964 |
| 2.7475 | 1.41 | 515 | 2.8842 | 0.9950 |
| 2.9263 | 1.42 | 520 | 2.8852 | 0.9972 |
| 2.8603 | 1.44 | 525 | 2.8762 | 0.9966 |
| 2.864 | 1.45 | 530 | 2.8680 | 0.9978 |
| 2.8632 | 1.47 | 535 | 2.8602 | 0.9964 |
| 2.9289 | 1.48 | 540 | 2.8584 | 0.9952 |
| 2.8689 | 1.49 | 545 | 2.8587 | 0.9956 |
| 2.8304 | 1.51 | 550 | 2.8511 | 0.9993 |
| 2.8024 | 1.52 | 555 | 2.8460 | 1.0 |
| 2.7649 | 1.53 | 560 | 2.8460 | 1.0000 |
| 2.8756 | 1.55 | 565 | 2.8348 | 0.9987 |
| 2.8808 | 1.56 | 570 | 2.8539 | 0.9993 |
| 2.9027 | 1.57 | 575 | 2.8282 | 0.9975 |
| 2.8586 | 1.59 | 580 | 2.8288 | 0.9976 |
| 2.8193 | 1.6 | 585 | 2.8101 | 1.0051 |
| 2.811 | 1.62 | 590 | 2.7965 | 1.0014 |
| 2.7332 | 1.63 | 595 | 2.7884 | 1.0026 |
| 2.7717 | 1.64 | 600 | 2.7883 | 1.0060 |
| 2.6901 | 1.66 | 605 | 2.7801 | 0.9974 |
| 2.6905 | 1.67 | 610 | 2.8113 | 0.9968 |
| 2.7442 | 1.68 | 615 | 2.8113 | 1.0007 |
| 2.8431 | 1.7 | 620 | 2.8152 | 1.0343 |
| 2.8028 | 1.71 | 625 | 2.7790 | 1.0250 |
| 2.7151 | 1.73 | 630 | 2.7653 | 1.0287 |
| 2.7405 | 1.74 | 635 | 2.7714 | 1.1303 |
| 2.7566 | 1.75 | 640 | 2.7488 | 1.0312 |
| 2.7337 | 1.77 | 645 | 2.7498 | 1.0176 |
| 2.7486 | 1.78 | 650 | 2.7496 | 1.0760 |
| 2.6918 | 1.79 | 655 | 2.7391 | 1.0353 |
| 2.7142 | 1.81 | 660 | 2.7500 | 1.0283 |
| 2.7057 | 1.82 | 665 | 2.7612 | 1.0127 |
| 2.8348 | 1.83 | 670 | 2.7441 | 1.0056 |
| 2.705 | 1.85 | 675 | 2.7473 | 1.0519 |
| 2.7547 | 1.86 | 680 | 2.7216 | 1.0218 |
| 2.7045 | 1.88 | 685 | 2.7261 | 1.1414 |
| 2.7121 | 1.89 | 690 | 2.7223 | 1.0287 |
| 2.6877 | 1.9 | 695 | 2.7283 | 1.0274 |
| 2.6879 | 1.92 | 700 | 2.7451 | 1.1322 |
| 2.6958 | 1.93 | 705 | 2.7166 | 1.0364 |
| 2.6692 | 1.94 | 710 | 2.7148 | 1.0074 |
| 2.5786 | 1.96 | 715 | 2.7101 | 1.0504 |
| 2.6919 | 1.97 | 720 | 2.6963 | 1.0454 |
| 2.7256 | 1.98 | 725 | 2.7201 | 1.0349 |
| 2.6507 | 2.0 | 730 | 2.7099 | 1.1339 |
| 2.7833 | 2.01 | 735 | 2.7111 | 1.0124 |
| 2.7521 | 2.03 | 740 | 2.7024 | 1.0275 |
| 2.6732 | 2.04 | 745 | 2.7058 | 1.0647 |
| 2.719 | 2.05 | 750 | 2.7200 | 1.0211 |
| 2.701 | 2.07 | 755 | 2.7024 | 1.0808 |
| 2.6444 | 2.08 | 760 | 2.6813 | 1.0582 |
| 2.5592 | 2.1 | 765 | 2.6783 | 1.1010 |
| 2.6444 | 2.11 | 770 | 2.6707 | 1.0946 |
| 2.6944 | 2.12 | 775 | 2.7012 | 1.1315 |
| 2.6733 | 2.14 | 780 | 2.7072 | 1.1144 |
| 2.6998 | 2.15 | 785 | 2.7132 | 1.0206 |
| 2.796 | 2.16 | 790 | 2.7076 | 1.1262 |
| 2.6881 | 2.18 | 795 | 2.6953 | 1.0841 |
| 2.7382 | 2.19 | 800 | 2.6605 | 1.1234 |
| 2.5814 | 2.21 | 805 | 2.6814 | 1.1865 |
| 2.6695 | 2.22 | 810 | 2.6531 | 1.0985 |
| 2.6415 | 2.23 | 815 | 2.6590 | 1.0804 |
| 2.646 | 2.25 | 820 | 2.6514 | 1.0853 |
| 2.6028 | 2.26 | 825 | 2.6723 | 1.1411 |
| 2.6429 | 2.27 | 830 | 2.6729 | 1.0395 |
| 2.6736 | 2.29 | 835 | 2.7039 | 1.0355 |
| 2.6959 | 2.3 | 840 | 2.6510 | 1.0414 |
| 2.6426 | 2.31 | 845 | 2.6660 | 1.1591 |
| 2.7152 | 2.33 | 850 | 2.6361 | 1.0276 |
| 2.7148 | 2.34 | 855 | 2.6723 | 1.2461 |
| 2.6336 | 2.36 | 860 | 2.6332 | 1.0310 |
| 2.665 | 2.37 | 865 | 2.6365 | 1.1312 |
| 2.5607 | 2.38 | 870 | 2.6344 | 1.1301 |
| 2.5614 | 2.4 | 875 | 2.6437 | 1.1513 |
| 2.4899 | 2.41 | 880 | 2.6418 | 1.1532 |
| 2.6794 | 2.42 | 885 | 2.6403 | 1.0272 |
| 2.6814 | 2.44 | 890 | 2.6420 | 1.1323 |
| 2.6614 | 2.45 | 895 | 2.6183 | 1.0525 |
| 2.6629 | 2.47 | 900 | 2.6414 | 1.1569 |
| 2.6166 | 2.48 | 905 | 2.6167 | 1.0265 |
| 2.6374 | 2.49 | 910 | 2.6299 | 1.1720 |
| 2.6035 | 2.51 | 915 | 2.6139 | 1.1565 |
| 2.595 | 2.52 | 920 | 2.6126 | 1.0557 |
| 2.6416 | 2.53 | 925 | 2.6190 | 1.0414 |
| 2.6785 | 2.55 | 930 | 2.6352 | 1.0289 |
| 2.6986 | 2.56 | 935 | 2.6268 | 1.0077 |
| 2.6145 | 2.57 | 940 | 2.6166 | 1.0445 |
| 2.6961 | 2.59 | 945 | 2.6142 | 1.0185 |
| 2.6852 | 2.6 | 950 | 2.6072 | 1.0122 |
| 2.5792 | 2.62 | 955 | 2.6078 | 1.1165 |
| 2.6118 | 2.63 | 960 | 2.6177 | 1.1210 |
| 2.5472 | 2.64 | 965 | 2.6126 | 1.0044 |
| 2.577 | 2.66 | 970 | 2.6051 | 1.0881 |
| 2.5602 | 2.67 | 975 | 2.5992 | 1.0178 |
| 2.695 | 2.68 | 980 | 2.6023 | 1.0248 |
| 2.7017 | 2.7 | 985 | 2.6190 | 1.0041 |
| 2.6327 | 2.71 | 990 | 2.6024 | 1.0142 |
| 2.6193 | 2.73 | 995 | 2.5897 | 1.0148 |
| 2.5939 | 2.74 | 1000 | 2.5900 | 1.0329 |
| 2.5477 | 2.75 | 1005 | 2.5971 | 1.0338 |
| 2.6089 | 2.77 | 1010 | 2.5969 | 1.0064 |
| 2.5625 | 2.78 | 1015 | 2.5899 | 1.0648 |
| 2.5745 | 2.79 | 1020 | 2.5861 | 1.0627 |
| 2.5702 | 2.81 | 1025 | 2.5923 | 1.0526 |
| 2.645 | 2.82 | 1030 | 2.6053 | 1.0199 |
| 2.6869 | 2.83 | 1035 | 2.6227 | 1.0011 |
| 2.6678 | 2.85 | 1040 | 2.6094 | 1.0179 |
| 2.6787 | 2.86 | 1045 | 2.5978 | 1.0028 |
| 2.6246 | 2.88 | 1050 | 2.5965 | 1.0093 |
| 2.5676 | 2.89 | 1055 | 2.5927 | 1.0627 |
| 2.6773 | 2.9 | 1060 | 2.5907 | 1.0817 |
| 2.6114 | 2.92 | 1065 | 2.5932 | 1.1013 |
| 2.6227 | 2.93 | 1070 | 2.5840 | 1.0402 |
| 2.594 | 2.94 | 1075 | 2.5997 | 1.1371 |
| 2.751 | 2.96 | 1080 | 2.5909 | 1.0972 |
| 2.6366 | 2.97 | 1085 | 2.6081 | 1.0598 |
| 2.577 | 2.98 | 1090 | 2.5915 | 1.0410 |
| 2.579 | 3.0 | 1095 | 2.5953 | 1.1433 |
| 2.6706 | 3.01 | 1100 | 2.5913 | 1.0456 |
| 2.6161 | 3.03 | 1105 | 2.6079 | 1.1009 |
| 2.6397 | 3.04 | 1110 | 2.5951 | 1.1771 |
| 2.6246 | 3.05 | 1115 | 2.5730 | 1.0299 |
| 2.5637 | 3.07 | 1120 | 2.5622 | 1.0848 |
| 2.5692 | 3.08 | 1125 | 2.5561 | 1.1472 |
| 2.5948 | 3.1 | 1130 | 2.5568 | 1.0802 |
| 2.5372 | 3.11 | 1135 | 2.5638 | 1.1261 |
| 2.4995 | 3.12 | 1140 | 2.5727 | 1.1395 |
| 2.6304 | 3.14 | 1145 | 2.5671 | 1.0259 |
| 2.6395 | 3.15 | 1150 | 2.5778 | 1.0212 |
| 2.6127 | 3.16 | 1155 | 2.5609 | 1.0457 |
| 2.5919 | 3.18 | 1160 | 2.5604 | 1.0902 |
| 2.6111 | 3.19 | 1165 | 2.5463 | 1.0014 |
| 2.5971 | 3.21 | 1170 | 2.5429 | 1.0022 |
| 2.5887 | 3.22 | 1175 | 2.5394 | 1.0412 |
| 2.5644 | 3.23 | 1180 | 2.5342 | 1.0469 |
| 2.4805 | 3.25 | 1185 | 2.6066 | 1.2668 |
| 2.5324 | 3.26 | 1190 | 2.5395 | 1.0234 |
| 2.5491 | 3.27 | 1195 | 2.5431 | 1.0644 |
| 2.6302 | 3.29 | 1200 | 2.5558 | 1.0680 |
| 2.6139 | 3.3 | 1205 | 2.5711 | 1.0565 |
| 2.5607 | 3.31 | 1210 | 2.5635 | 1.0415 |
| 2.6535 | 3.33 | 1215 | 2.5505 | 1.0613 |
| 2.6129 | 3.34 | 1220 | 2.5403 | 1.0724 |
| 2.5157 | 3.36 | 1225 | 2.5294 | 1.0585 |
| 2.551 | 3.37 | 1230 | 2.5242 | 1.1599 |
| 2.5527 | 3.38 | 1235 | 2.5474 | 1.2327 |
| 2.4964 | 3.4 | 1240 | 2.5244 | 1.0857 |
| 2.5781 | 3.41 | 1245 | 2.5299 | 1.0470 |
| 2.6143 | 3.42 | 1250 | 2.5313 | 1.0019 |
| 2.6566 | 3.44 | 1255 | 2.5431 | 1.0488 |
| 2.5373 | 3.45 | 1260 | 2.5281 | 1.0901 |
| 2.6597 | 3.47 | 1265 | 2.5300 | 1.0610 |
| 2.5457 | 3.48 | 1270 | 2.5130 | 1.0420 |
| 2.5632 | 3.49 | 1275 | 2.5306 | 1.1418 |
| 2.5267 | 3.51 | 1280 | 2.5021 | 1.0293 |
| 2.507 | 3.52 | 1285 | 2.5013 | 1.0196 |
| 2.5713 | 3.53 | 1290 | 2.4978 | 1.0664 |
| 2.4783 | 3.55 | 1295 | 2.4958 | 1.0530 |
| 2.5874 | 3.56 | 1300 | 2.4968 | 1.0059 |
| 2.5744 | 3.57 | 1305 | 2.5078 | 1.0287 |
| 2.5701 | 3.59 | 1310 | 2.4971 | 1.0366 |
| 2.5366 | 3.6 | 1315 | 2.4897 | 1.0191 |
| 2.5679 | 3.62 | 1320 | 2.4830 | 1.0223 |
| 2.5239 | 3.63 | 1325 | 2.4833 | 1.0784 |
| 2.5411 | 3.64 | 1330 | 2.4851 | 1.1522 |
| 2.5037 | 3.66 | 1335 | 2.4792 | 1.0928 |
| 2.5907 | 3.67 | 1340 | 2.4750 | 1.0187 |
| 2.5107 | 3.68 | 1345 | 2.4805 | 1.0873 |
| 2.5908 | 3.7 | 1350 | 2.4753 | 1.0098 |
| 2.6274 | 3.71 | 1355 | 2.4765 | 1.0045 |
| 2.5708 | 3.73 | 1360 | 2.4597 | 1.0456 |
| 2.6039 | 3.74 | 1365 | 2.4503 | 1.0485 |
| 2.5305 | 3.75 | 1370 | 2.4439 | 1.0126 |
| 2.4878 | 3.77 | 1375 | 2.4407 | 1.0162 |
| 2.5055 | 3.78 | 1380 | 2.4421 | 1.0605 |
| 2.5249 | 3.79 | 1385 | 2.4499 | 1.1163 |
| 2.5508 | 3.81 | 1390 | 2.4654 | 1.1472 |
| 2.5827 | 3.82 | 1395 | 2.4510 | 1.0561 |
| 2.6148 | 3.83 | 1400 | 2.4496 | 0.9998 |
| 2.5763 | 3.85 | 1405 | 2.4417 | 1.0067 |
| 2.6077 | 3.86 | 1410 | 2.4458 | 1.0682 |
| 2.5388 | 3.88 | 1415 | 2.4352 | 1.0820 |
| 2.5235 | 3.89 | 1420 | 2.4277 | 1.0784 |
| 2.4996 | 3.9 | 1425 | 2.4245 | 1.0671 |
| 2.5601 | 3.92 | 1430 | 2.4202 | 1.0650 |
| 2.5805 | 3.93 | 1435 | 2.4199 | 1.0530 |
| 2.5841 | 3.94 | 1440 | 2.4228 | 1.0797 |
| 2.4877 | 3.96 | 1445 | 2.4284 | 1.1159 |
| 2.5542 | 3.97 | 1450 | 2.4190 | 1.0575 |
| 2.5961 | 3.98 | 1455 | 2.4162 | 1.0676 |
| 2.495 | 4.0 | 1460 | 2.4165 | 1.0821 |
| 2.6157 | 4.01 | 1465 | 2.4119 | 1.0117 |
| 2.5415 | 4.03 | 1470 | 2.4089 | 1.0110 |
| 2.4916 | 4.04 | 1475 | 2.4032 | 1.0498 |
| 2.5445 | 4.05 | 1480 | 2.3997 | 1.0429 |
| 2.4941 | 4.07 | 1485 | 2.4008 | 1.0141 |
| 2.5113 | 4.08 | 1490 | 2.3975 | 1.0357 |
| 2.4707 | 4.1 | 1495 | 2.3938 | 1.0288 |
| 2.4952 | 4.11 | 1500 | 2.3910 | 1.0300 |
| 2.5017 | 4.12 | 1505 | 2.3861 | 1.0813 |
| 2.5566 | 4.14 | 1510 | 2.3919 | 1.1082 |
| 2.5754 | 4.15 | 1515 | 2.3947 | 1.0074 |
| 2.6138 | 4.16 | 1520 | 2.4040 | 0.9989 |
| 2.5024 | 4.18 | 1525 | 2.3949 | 1.0039 |
| 2.5136 | 4.19 | 1530 | 2.3993 | 1.0496 |
| 2.5646 | 4.21 | 1535 | 2.3981 | 1.0729 |
| 2.4556 | 4.22 | 1540 | 2.3952 | 1.0494 |
| 2.5774 | 4.23 | 1545 | 2.3924 | 1.0345 |
| 2.5126 | 4.25 | 1550 | 2.3888 | 1.0306 |
| 2.4596 | 4.26 | 1555 | 2.3960 | 1.0775 |
| 2.521 | 4.27 | 1560 | 2.3978 | 1.1025 |
| 2.6304 | 4.29 | 1565 | 2.3885 | 1.0433 |
| 2.543 | 4.3 | 1570 | 2.3849 | 1.0072 |
| 2.5601 | 4.31 | 1575 | 2.3855 | 1.0110 |
| 2.6304 | 4.33 | 1580 | 2.3878 | 1.0369 |
| 2.4121 | 4.34 | 1585 | 2.3783 | 1.0366 |
| 2.4261 | 4.36 | 1590 | 2.3746 | 1.0307 |
| 2.5038 | 4.37 | 1595 | 2.3789 | 1.0611 |
| 2.5391 | 4.38 | 1600 | 2.3849 | 1.0738 |
| 2.4341 | 4.4 | 1605 | 2.3779 | 1.0573 |
| 2.5306 | 4.41 | 1610 | 2.3751 | 1.0460 |
| 2.5818 | 4.42 | 1615 | 2.3743 | 1.0251 |
| 2.5531 | 4.44 | 1620 | 2.3723 | 1.0209 |
| 2.51 | 4.45 | 1625 | 2.3755 | 1.0316 |
| 2.5788 | 4.47 | 1630 | 2.3725 | 1.0396 |
| 2.5701 | 4.48 | 1635 | 2.3663 | 1.0292 |
| 2.4194 | 4.49 | 1640 | 2.3641 | 1.0261 |
| 2.5439 | 4.51 | 1645 | 2.3629 | 1.0376 |
| 2.4527 | 4.52 | 1650 | 2.3629 | 1.0563 |
| 2.5705 | 4.53 | 1655 | 2.3654 | 1.0766 |
| 2.4552 | 4.55 | 1660 | 2.3708 | 1.0802 |
| 2.5657 | 4.56 | 1665 | 2.3638 | 1.0248 |
| 2.5371 | 4.57 | 1670 | 2.3639 | 1.0053 |
| 2.5365 | 4.59 | 1675 | 2.3626 | 1.0072 |
| 2.5383 | 4.6 | 1680 | 2.3584 | 1.0170 |
| 2.546 | 4.62 | 1685 | 2.3574 | 1.0469 |
| 2.6006 | 4.63 | 1690 | 2.3517 | 1.0509 |
| 2.4894 | 4.64 | 1695 | 2.3489 | 1.0452 |
| 2.4732 | 4.66 | 1700 | 2.3489 | 1.0586 |
| 2.4933 | 4.67 | 1705 | 2.3501 | 1.0694 |
| 2.4784 | 4.68 | 1710 | 2.3472 | 1.0647 |
| 2.5349 | 4.7 | 1715 | 2.3419 | 1.0299 |
| 2.553 | 4.71 | 1720 | 2.3420 | 1.0115 |
| 2.5035 | 4.73 | 1725 | 2.3415 | 1.0117 |
| 2.561 | 4.74 | 1730 | 2.3418 | 1.0242 |
| 2.4773 | 4.75 | 1735 | 2.3420 | 1.0325 |
| 2.4691 | 4.77 | 1740 | 2.3422 | 1.0394 |
| 2.4959 | 4.78 | 1745 | 2.3405 | 1.0418 |
| 2.4928 | 4.79 | 1750 | 2.3394 | 1.0449 |
| 2.5058 | 4.81 | 1755 | 2.3392 | 1.0489 |
| 2.5193 | 4.82 | 1760 | 2.3390 | 1.0506 |
| 2.5369 | 4.83 | 1765 | 2.3392 | 1.0384 |
| 2.4843 | 4.85 | 1770 | 2.3398 | 1.0236 |
| 2.5074 | 4.86 | 1775 | 2.3400 | 1.0150 |
| 2.4941 | 4.88 | 1780 | 2.3386 | 1.0150 |
| 2.4352 | 4.89 | 1785 | 2.3370 | 1.0172 |
| 2.4372 | 4.9 | 1790 | 2.3362 | 1.0208 |
| 2.4855 | 4.92 | 1795 | 2.3358 | 1.0238 |
| 2.4516 | 4.93 | 1800 | 2.3355 | 1.0276 |
| 2.5281 | 4.94 | 1805 | 2.3356 | 1.0312 |
| 2.5519 | 4.96 | 1810 | 2.3352 | 1.0318 |
| 2.4641 | 4.97 | 1815 | 2.3349 | 1.0294 |
| 2.4515 | 4.98 | 1820 | 2.3348 | 1.0284 |
| 2.553 | 5.0 | 1825 | 2.3347 | 1.0286 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
kapilkd13/xls-r-hi-test
|
kapilkd13
| 2022-03-24T11:55:50Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 38.18
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7346
- Wer: 1.0479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.36 | 400 | 1.4595 | 1.0039 |
| 4.7778 | 2.71 | 800 | 0.8082 | 1.0115 |
| 0.6408 | 4.07 | 1200 | 0.7032 | 1.0079 |
| 0.3937 | 5.42 | 1600 | 0.6889 | 1.0433 |
| 0.3 | 6.78 | 2000 | 0.6820 | 1.0069 |
| 0.3 | 8.14 | 2400 | 0.6670 | 1.0196 |
| 0.226 | 9.49 | 2800 | 0.7216 | 1.0422 |
| 0.197 | 10.85 | 3200 | 0.7669 | 1.0534 |
| 0.165 | 12.2 | 3600 | 0.7517 | 1.0200 |
| 0.1486 | 13.56 | 4000 | 0.7125 | 1.0357 |
| 0.1486 | 14.92 | 4400 | 0.7447 | 1.0347 |
| 0.122 | 16.27 | 4800 | 0.6899 | 1.0440 |
| 0.1069 | 17.63 | 5200 | 0.7212 | 1.0350 |
| 0.0961 | 18.98 | 5600 | 0.7417 | 1.0408 |
| 0.086 | 20.34 | 6000 | 0.7402 | 1.0356 |
| 0.086 | 21.69 | 6400 | 0.7761 | 1.0420 |
| 0.0756 | 23.05 | 6800 | 0.7346 | 1.0369 |
| 0.0666 | 24.41 | 7200 | 0.7506 | 1.0449 |
| 0.0595 | 25.76 | 7600 | 0.7319 | 1.0476 |
| 0.054 | 27.12 | 8000 | 0.7346 | 1.0479 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
jcmc/wav2vec-cv7-1b-ir
|
jcmc
| 2022-03-24T11:55:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ga-IE",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ga-IE
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec-cv7-1b-ir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ga-IE
metrics:
- name: Test WER
type: wer
value: 39.1
- name: Test CER
type: cer
value: 16.4
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9562
- Wer: 0.4801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.3731 | 15.62 | 500 | 1.5517 | 0.9499 |
| 1.3312 | 31.25 | 1000 | 0.8717 | 0.6189 |
| 0.9135 | 46.86 | 1500 | 0.8299 | 0.5310 |
| 0.6719 | 62.49 | 2000 | 0.8842 | 0.5044 |
| 0.5583 | 78.12 | 2500 | 0.9093 | 0.4801 |
| 0.4728 | 93.74 | 3000 | 0.9488 | 0.4813 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
jcmc/wav2vec-1b-cv8-ir
|
jcmc
| 2022-03-24T11:55:44Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ga-IE",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ga-IE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ga-IE
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec-1b-cv8-ir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ga-IE
metrics:
- name: Test WER
type: wer
value: 43.7
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - GA-IE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8445
- Wer: 0.5585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7135 | 31.24 | 500 | 0.9609 | 0.6926 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-chuvash
|
infinitejoy
| 2022-03-24T11:55:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"cv",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cv
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- cv
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Chuvash
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: cv
metrics:
- name: Test WER
type: wer
value: 60.31
- name: Test CER
type: cer
value: 15.08
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-chuvash
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - CV dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7651
- Wer: 0.6166
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8032 | 8.77 | 500 | 0.8059 | 0.8352 |
| 1.2608 | 17.54 | 1000 | 0.5828 | 0.6769 |
| 1.1337 | 26.32 | 1500 | 0.6892 | 0.6908 |
| 1.0457 | 35.09 | 2000 | 0.7077 | 0.6781 |
| 0.97 | 43.86 | 2500 | 0.5993 | 0.6228 |
| 0.8767 | 52.63 | 3000 | 0.7213 | 0.6604 |
| 0.8223 | 61.4 | 3500 | 0.8161 | 0.6968 |
| 0.7441 | 70.18 | 4000 | 0.7057 | 0.6184 |
| 0.7011 | 78.95 | 4500 | 0.7027 | 0.6024 |
| 0.6542 | 87.72 | 5000 | 0.7092 | 0.5979 |
| 0.6081 | 96.49 | 5500 | 0.7917 | 0.6324 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
emre/wav2vec2-xls-r-300m-bas-CV8-v2
|
emre
| 2022-03-24T11:55:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"bas",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: bas
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- bas
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-300m-bas-CV8-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bas
metrics:
- name: Test WER
type: wer
value: 56.97
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-bas-CV8-v2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6121
- Wer: 0.5697
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 90
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5211 | 16.13 | 500 | 1.2661 | 0.9153 |
| 0.7026 | 32.25 | 1000 | 0.6245 | 0.6516 |
| 0.3752 | 48.38 | 1500 | 0.6039 | 0.6148 |
| 0.2752 | 64.51 | 2000 | 0.6080 | 0.5808 |
| 0.2155 | 80.63 | 2500 | 0.6121 | 0.5697 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
emre/wav2vec2-xls-r-300m-Br-small
|
emre
| 2022-03-24T11:55:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language: br
tags:
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-Br-small
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice br
type: common_voice
args: br
metrics:
- name: Test WER
type: wer
value: 66.75
---
# wav2vec2-xls-r-300m-Br-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0573
- Wer: 0.6675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.7464 | 2.79 | 400 | 1.7474 | 1.1018 |
| 1.1117 | 5.59 | 800 | 0.9434 | 0.8697 |
| 0.6481 | 8.39 | 1200 | 0.9251 | 0.7910 |
| 0.4754 | 11.19 | 1600 | 0.9208 | 0.7412 |
| 0.3602 | 13.98 | 2000 | 0.9284 | 0.7232 |
| 0.2873 | 16.78 | 2400 | 0.9299 | 0.6940 |
| 0.2386 | 19.58 | 2800 | 1.0182 | 0.6927 |
| 0.1971 | 22.38 | 3200 | 1.0456 | 0.6898 |
| 0.1749 | 25.17 | 3600 | 1.0208 | 0.6769 |
| 0.1487 | 27.97 | 4000 | 1.0573 | 0.6675 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
chmanoj/xls-r-2B-te
|
chmanoj
| 2022-03-24T11:55:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- te
license: apache-2.0
tags:
- automatic-speech-recognition
- openslr_SLR66
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- openslr
- SLR66
metrics:
- wer
model-index:
- name: xls-r-1B-te
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR66
metrics:
- type: wer
value: 0.51
name: Test WER
- type: cer
value: 0.097
name: Test CER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4253
- Wer: 0.5109
### Evaluation metrics
| Metric | Split | Decode with LM | Value |
|:------:|:------:|:--------------:|:---------:|
| WER | Train | No | |
| CER | Train | No | |
| WER | Test | No | |
| CER | Test | No | |
| WER | Train | Yes | |
| CER | Train | Yes | |
| WER | Test | Yes | |
| CER | Test | Yes | |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- learning_rate: 3e-6
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 150.0
- hidden_dropout: 0.15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
anuragshas/wav2vec2-large-xls-r-300m-mr
|
anuragshas
| 2022-03-24T11:55:19Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"mr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mr
license: apache-2.0
tags:
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-mr
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 8
args: mr
metrics:
- type: wer
value: 32.811
name: Test WER
- name: Test CER
type: cer
value: 7.692
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-mr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5479
- Wer: 0.5740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.7378 | 18.18 | 400 | 3.5047 | 1.0 |
| 3.1707 | 36.36 | 800 | 2.6166 | 0.9912 |
| 1.4942 | 54.55 | 1200 | 0.5778 | 0.6927 |
| 1.2058 | 72.73 | 1600 | 0.5168 | 0.6362 |
| 1.0558 | 90.91 | 2000 | 0.5105 | 0.6069 |
| 0.9488 | 109.09 | 2400 | 0.5151 | 0.6089 |
| 0.8588 | 127.27 | 2800 | 0.5157 | 0.5989 |
| 0.7991 | 145.45 | 3200 | 0.5179 | 0.5740 |
| 0.7545 | 163.64 | 3600 | 0.5348 | 0.5740 |
| 0.7144 | 181.82 | 4000 | 0.5518 | 0.5724 |
| 0.7041 | 200.0 | 4400 | 0.5479 | 0.5740 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-mr --dataset mozilla-foundation/common_voice_8_0 --config mr --split test
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-large-xls-r-300m-mr"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mr", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "या पानास लेखाचे स्वरूप यायला हावे"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 49.177 | 32.811 |
|
Saitomar/wav2vec2-large-xls-r-300m-hindi-kaggle
|
Saitomar
| 2022-03-24T11:55:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
tags:
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-kaggle
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-kaggle
This model was trained from scratch on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
RuudVelo/wav2vec2-large-xls-r-1b-nl-lm
|
RuudVelo
| 2022-03-24T11:55:12Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"nl",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- nl
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-1b-nl-lm
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: nl
metrics:
- name: Test WER
type: wer
value: 9.73
- name: Test CER
type: cer
value: 2.89
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 27.27
- name: Test CER
type: cer
value: 13.23
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 27.67
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-nl-lm
This model is a fine-tuned version of [wav2vec2-large-xls-r-1b-nl-lm](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice 8 dataset.
It achieves the following results on the test set:
- Loss: 0.1479
- Wer: 0.1156
Note that the above test results come from the original model without LM (language model) which can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl. The results with the LM model can be found on the right side of this model card.
## Model description
Model RuudVelo/wav2vec2-large-xls-r-1b-nl which has been improved with a KenLM 5-gram.
## Intended uses & limitations
More information needed
## Training and evaluation data
Common Voice 8 nl dataset has been used for the model
## Training procedure
### Training hyperparameters
Parameters can be found in the run.sh file at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
RASMUS/wav2vec2-xlsr-1b-et
|
RASMUS
| 2022-03-24T11:55:09Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"mozilla-foundation/common_voice_8_0",
"audio",
"speech",
"robust-speech-event",
"hf-asr-leaderboard",
"et",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: et
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
tags:
- generated_from_trainer
- mozilla-foundation/common_voice_8_0
- audio
- automatic-speech-recognition
- speech
- robust-speech-event
- hf-asr-leaderboard
model-index:
- name: XLS-R 1B Wav2Vec2 Estonian by Rasmus Toivanen
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: et
metrics:
- name: Test WER
type: wer
value: 20.12
- name: Test CER
type: cer
value: 3.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: et
metrics:
- name: Test WER
type: wer
value: 40.77
- name: Test CER
type: cer
value: 12.32
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: et
metrics:
- name: Test WER
type: wer
value: 41.97
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-et-lm-1B
This model was finetuned with mozilla_foundation/common_voice_8_0 et with train+other+validation splits.
It achieves the following results on the test set:
(Loss reported with last eval step at step 2000/2040 during training)
- Loss: 0.2150
- Wer: 0.2012
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Plim/xls-r-1b-cv_8-fr
|
Plim
| 2022-03-24T11:55:06Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-1B - French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: fr
metrics:
- name: Test WER (with LM)
type: wer
value: 15.4
- name: Test CER (with LM)
type: cer
value: 5.36
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER (with LM)
type: wer
value: 25.05
- name: Test CER (with LM)
type: cer
value: 12.45
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 27.1
---
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 6.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9827 | 0.29 | 1000 | inf | 0.2937 |
| 1.0203 | 0.57 | 2000 | inf | 0.2711 |
| 1.0048 | 0.86 | 3000 | inf | 0.2620 |
| 0.9858 | 1.15 | 4000 | inf | 0.2522 |
| 0.9709 | 1.43 | 5000 | inf | 0.2365 |
| 0.9347 | 1.72 | 6000 | inf | 0.2332 |
| 0.9256 | 2.01 | 7000 | inf | 0.2261 |
| 0.8936 | 2.29 | 8000 | inf | 0.2203 |
| 0.877 | 2.58 | 9000 | inf | 0.2096 |
| 0.8393 | 2.87 | 10000 | inf | 0.2017 |
| 0.8156 | 3.15 | 11000 | inf | 0.1936 |
| 0.8015 | 3.44 | 12000 | inf | 0.1880 |
| 0.774 | 3.73 | 13000 | inf | 0.1834 |
| 0.8372 | 4.01 | 14000 | inf | 0.1934 |
| 0.8075 | 4.3 | 15000 | inf | 0.1923 |
| 0.8069 | 4.59 | 16000 | inf | 0.1877 |
| 0.8064 | 4.87 | 17000 | inf | 0.1955 |
| 0.801 | 5.16 | 18000 | inf | 0.1891 |
| 0.8022 | 5.45 | 19000 | inf | 0.1895 |
| 0.792 | 5.73 | 20000 | inf | 0.1854 |
It achieves the best result on the validation set on STEP 13000:
- Wer: 0.1834
Some problem occurs when calculating the validation loss.
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8` with split `test`
```bash
python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset mozilla-foundation/common_voice_8_0 --config fr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Evaluation Results
Without LM:
| Dataset | WER | CER |
|:----------:|:-----:|:-----:|
| TEST CV | 18.33 | 5.60 |
| DEV audio | 31.33 | 13.20 |
| TEST audio | / | / |
With LM:
| Dataset | WER | CER |
|:----------:|:-----:|:-----:|
| TEST CV | 15.40 | 5.36 |
| DEV audio | 25.05 | 12.45 |
| TEST audio | / | / |
|
NbAiLab/wav2vec2-xls-r-1b-npsc-bokmaal-low-27k
|
NbAiLab
| 2022-03-24T11:55:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- NbAiLab/NPSC
- robust-speech-event
- false
- nb-NO
- hf-asr-leaderboard
datasets:
- NbAiLab/NPSC
language:
- nb-NO
model-index:
- name: wav2vec2-xls-r-1b-npsc-bokmaal-low-27k
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: NPSC
type: NbAiLab/NPSC
args: 16K_mp3_bokmaal
metrics:
- name: "Test (Bokm\xE5l) WER"
type: wer
value: 0.06332329423537675
- name: "Test (Bokm\xE5l) CER"
type: cer
value: 0.02480899861950731
---
|
DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2
|
DrishtiSharma
| 2022-03-24T11:54:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"mr",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- mr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- mr
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-mr-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: mr
metrics:
- name: Test WER
type: wer
value: 0.49378259125551544
- name: Test CER
type: cer
value: 0.12470799640610962
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: mr
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-mr-v2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8729
- Wer: 0.4942
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset mozilla-foundation/common_voice_8_0 --config mr --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev_data --config mr --split validation --chunk_length_s 10 --stride_length_s 1
Note: Marathi language not found in speech-recognition-community-v2/dev_data!
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000333
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.4934 | 9.09 | 200 | 3.7326 | 1.0 |
| 3.4234 | 18.18 | 400 | 3.3383 | 0.9996 |
| 3.2628 | 27.27 | 600 | 2.7482 | 0.9992 |
| 1.7743 | 36.36 | 800 | 0.6755 | 0.6787 |
| 1.0346 | 45.45 | 1000 | 0.6067 | 0.6193 |
| 0.8137 | 54.55 | 1200 | 0.6228 | 0.5612 |
| 0.6637 | 63.64 | 1400 | 0.5976 | 0.5495 |
| 0.5563 | 72.73 | 1600 | 0.7009 | 0.5383 |
| 0.4844 | 81.82 | 1800 | 0.6662 | 0.5287 |
| 0.4057 | 90.91 | 2000 | 0.6911 | 0.5303 |
| 0.3582 | 100.0 | 2200 | 0.7207 | 0.5327 |
| 0.3163 | 109.09 | 2400 | 0.7107 | 0.5118 |
| 0.2761 | 118.18 | 2600 | 0.7538 | 0.5118 |
| 0.2415 | 127.27 | 2800 | 0.7850 | 0.5178 |
| 0.2127 | 136.36 | 3000 | 0.8016 | 0.5034 |
| 0.1873 | 145.45 | 3200 | 0.8302 | 0.5187 |
| 0.1723 | 154.55 | 3400 | 0.9085 | 0.5223 |
| 0.1498 | 163.64 | 3600 | 0.8396 | 0.5126 |
| 0.1425 | 172.73 | 3800 | 0.8776 | 0.5094 |
| 0.1258 | 181.82 | 4000 | 0.8651 | 0.5014 |
| 0.117 | 190.91 | 4200 | 0.8772 | 0.4970 |
| 0.1093 | 200.0 | 4400 | 0.8729 | 0.4942 |
### Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8
|
DrishtiSharma
| 2022-03-24T11:54:40Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hi",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- hi
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-hi-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hi
metrics:
- name: Test WER
type: wer
value: 0.3628727037755008
- name: Test CER
type: cer
value: 0.11933724247521164
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hi
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hi-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6510
- Wer: 0.3179
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev_data --config hi --split validation --chunk_length_s 10 --stride_length_s 1
Note: Hindi language not found in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 12.5576 | 1.04 | 200 | 6.6594 | 1.0 |
| 4.4069 | 2.07 | 400 | 3.6011 | 1.0 |
| 3.4273 | 3.11 | 600 | 3.3370 | 1.0 |
| 2.1108 | 4.15 | 800 | 1.0641 | 0.6562 |
| 0.8817 | 5.18 | 1000 | 0.7178 | 0.5172 |
| 0.6508 | 6.22 | 1200 | 0.6612 | 0.4839 |
| 0.5524 | 7.25 | 1400 | 0.6458 | 0.4889 |
| 0.4992 | 8.29 | 1600 | 0.5791 | 0.4382 |
| 0.4669 | 9.33 | 1800 | 0.6039 | 0.4352 |
| 0.4441 | 10.36 | 2000 | 0.6276 | 0.4297 |
| 0.4172 | 11.4 | 2200 | 0.6183 | 0.4474 |
| 0.3872 | 12.44 | 2400 | 0.5886 | 0.4231 |
| 0.3692 | 13.47 | 2600 | 0.6448 | 0.4399 |
| 0.3385 | 14.51 | 2800 | 0.6344 | 0.4075 |
| 0.3246 | 15.54 | 3000 | 0.5896 | 0.4087 |
| 0.3026 | 16.58 | 3200 | 0.6158 | 0.4016 |
| 0.284 | 17.62 | 3400 | 0.6038 | 0.3906 |
| 0.2682 | 18.65 | 3600 | 0.6165 | 0.3900 |
| 0.2577 | 19.69 | 3800 | 0.5754 | 0.3805 |
| 0.2509 | 20.73 | 4000 | 0.6028 | 0.3925 |
| 0.2426 | 21.76 | 4200 | 0.6335 | 0.4138 |
| 0.2346 | 22.8 | 4400 | 0.6128 | 0.3870 |
| 0.2205 | 23.83 | 4600 | 0.6223 | 0.3831 |
| 0.2104 | 24.87 | 4800 | 0.6122 | 0.3781 |
| 0.1992 | 25.91 | 5000 | 0.6467 | 0.3792 |
| 0.1916 | 26.94 | 5200 | 0.6277 | 0.3636 |
| 0.1835 | 27.98 | 5400 | 0.6317 | 0.3773 |
| 0.1776 | 29.02 | 5600 | 0.6124 | 0.3614 |
| 0.1751 | 30.05 | 5800 | 0.6475 | 0.3628 |
| 0.1662 | 31.09 | 6000 | 0.6266 | 0.3504 |
| 0.1584 | 32.12 | 6200 | 0.6347 | 0.3532 |
| 0.1494 | 33.16 | 6400 | 0.6636 | 0.3491 |
| 0.1457 | 34.2 | 6600 | 0.6334 | 0.3507 |
| 0.1427 | 35.23 | 6800 | 0.6397 | 0.3442 |
| 0.1397 | 36.27 | 7000 | 0.6468 | 0.3496 |
| 0.1283 | 37.31 | 7200 | 0.6291 | 0.3416 |
| 0.1255 | 38.34 | 7400 | 0.6652 | 0.3461 |
| 0.1195 | 39.38 | 7600 | 0.6587 | 0.3342 |
| 0.1169 | 40.41 | 7800 | 0.6478 | 0.3319 |
| 0.1126 | 41.45 | 8000 | 0.6280 | 0.3291 |
| 0.1112 | 42.49 | 8200 | 0.6434 | 0.3290 |
| 0.1069 | 43.52 | 8400 | 0.6542 | 0.3268 |
| 0.1027 | 44.56 | 8600 | 0.6536 | 0.3239 |
| 0.0993 | 45.6 | 8800 | 0.6622 | 0.3257 |
| 0.0973 | 46.63 | 9000 | 0.6572 | 0.3192 |
| 0.0911 | 47.67 | 9200 | 0.6522 | 0.3175 |
| 0.0897 | 48.7 | 9400 | 0.6521 | 0.3200 |
| 0.0905 | 49.74 | 9600 | 0.6510 | 0.3179 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9
|
DrishtiSharma
| 2022-03-24T11:54:35Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"as",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- as
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- as
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-as-v9
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hsb
metrics:
- name: Test WER
type: wer
value: 0.6163737676810973
- name: Test CER
type: cer
value: 0.19496397642093005
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: as
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: as
metrics:
- name: Test WER
type: wer
value: 61.64
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-as-v9
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1679
- Wer: 0.5761
### Evaluation Command
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Assamese (as) language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000111
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.3852 | 10.51 | 200 | 3.6402 | 1.0 |
| 3.5374 | 21.05 | 400 | 3.3894 | 1.0 |
| 2.8645 | 31.56 | 600 | 1.3143 | 0.8303 |
| 1.1784 | 42.1 | 800 | 0.9417 | 0.6661 |
| 0.7805 | 52.62 | 1000 | 0.9292 | 0.6237 |
| 0.5973 | 63.15 | 1200 | 0.9489 | 0.6014 |
| 0.4784 | 73.67 | 1400 | 0.9916 | 0.5962 |
| 0.4138 | 84.21 | 1600 | 1.0272 | 0.6121 |
| 0.3491 | 94.72 | 1800 | 1.0412 | 0.5984 |
| 0.3062 | 105.26 | 2000 | 1.0769 | 0.6005 |
| 0.2707 | 115.77 | 2200 | 1.0708 | 0.5752 |
| 0.2459 | 126.31 | 2400 | 1.1285 | 0.6009 |
| 0.2234 | 136.82 | 2600 | 1.1209 | 0.5949 |
| 0.2035 | 147.36 | 2800 | 1.1348 | 0.5842 |
| 0.1876 | 157.87 | 3000 | 1.1480 | 0.5872 |
| 0.1669 | 168.41 | 3200 | 1.1496 | 0.5838 |
| 0.1595 | 178.92 | 3400 | 1.1721 | 0.5778 |
| 0.1505 | 189.46 | 3600 | 1.1654 | 0.5744 |
| 0.1486 | 199.97 | 3800 | 1.1679 | 0.5761 |
### Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-ab-CV7
|
DrishtiSharma
| 2022-03-24T11:54:32Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ab
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ab
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-large-xls-r-300m-ab-CV7
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ab
metrics:
- name: Test WER
type: wer
value: 0.5291160452450775
- name: Test CER
type: cer
value: 0.10630270750110964
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ab
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5620
- Wer: 0.5651
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-ab-CV7 --dataset mozilla-foundation/common_voice_7_0 --config ab --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
NA
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.6445 | 13.64 | 300 | 4.3963 | 1.0 |
| 3.6459 | 27.27 | 600 | 3.2267 | 1.0 |
| 3.0978 | 40.91 | 900 | 3.0927 | 1.0 |
| 2.8357 | 54.55 | 1200 | 2.1462 | 1.0029 |
| 1.2723 | 68.18 | 1500 | 0.6747 | 0.6996 |
| 0.6528 | 81.82 | 1800 | 0.5928 | 0.6422 |
| 0.4905 | 95.45 | 2100 | 0.5587 | 0.5681 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
AndrewMcDowell/wav2vec2-xls-r-1B-german
|
AndrewMcDowell
| 2022-03-24T11:54:30Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"de",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- de
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - German
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: de
metrics:
- name: Test WER
type: wer
value: 15.25
- name: Test CER
type: cer
value: 3.78
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: de
metrics:
- name: Test WER
type: wer
value: 35.29
- name: Test CER
type: cer
value: 13.83
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: de
metrics:
- name: Test WER
type: wer
value: 36.2
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1355
- Wer: 0.1532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 2.5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.0826 | 0.07 | 1000 | 0.4637 | 0.4654 |
| 1.118 | 0.15 | 2000 | 0.2595 | 0.2687 |
| 1.1268 | 0.22 | 3000 | 0.2635 | 0.2661 |
| 1.0919 | 0.29 | 4000 | 0.2417 | 0.2566 |
| 1.1013 | 0.37 | 5000 | 0.2414 | 0.2567 |
| 1.0898 | 0.44 | 6000 | 0.2546 | 0.2731 |
| 1.0808 | 0.51 | 7000 | 0.2399 | 0.2535 |
| 1.0719 | 0.59 | 8000 | 0.2353 | 0.2528 |
| 1.0446 | 0.66 | 9000 | 0.2427 | 0.2545 |
| 1.0347 | 0.73 | 10000 | 0.2266 | 0.2402 |
| 1.0457 | 0.81 | 11000 | 0.2290 | 0.2448 |
| 1.0124 | 0.88 | 12000 | 0.2295 | 0.2448 |
| 1.025 | 0.95 | 13000 | 0.2138 | 0.2345 |
| 1.0107 | 1.03 | 14000 | 0.2108 | 0.2294 |
| 0.9758 | 1.1 | 15000 | 0.2019 | 0.2204 |
| 0.9547 | 1.17 | 16000 | 0.2000 | 0.2178 |
| 0.986 | 1.25 | 17000 | 0.2018 | 0.2200 |
| 0.9588 | 1.32 | 18000 | 0.1992 | 0.2138 |
| 0.9413 | 1.39 | 19000 | 0.1898 | 0.2049 |
| 0.9339 | 1.47 | 20000 | 0.1874 | 0.2056 |
| 0.9268 | 1.54 | 21000 | 0.1797 | 0.1976 |
| 0.9194 | 1.61 | 22000 | 0.1743 | 0.1905 |
| 0.8987 | 1.69 | 23000 | 0.1738 | 0.1932 |
| 0.8884 | 1.76 | 24000 | 0.1703 | 0.1873 |
| 0.8939 | 1.83 | 25000 | 0.1633 | 0.1831 |
| 0.8629 | 1.91 | 26000 | 0.1549 | 0.1750 |
| 0.8607 | 1.98 | 27000 | 0.1550 | 0.1738 |
| 0.8316 | 2.05 | 28000 | 0.1512 | 0.1709 |
| 0.8321 | 2.13 | 29000 | 0.1481 | 0.1657 |
| 0.825 | 2.2 | 30000 | 0.1446 | 0.1627 |
| 0.8115 | 2.27 | 31000 | 0.1396 | 0.1583 |
| 0.7959 | 2.35 | 32000 | 0.1389 | 0.1569 |
| 0.7835 | 2.42 | 33000 | 0.1362 | 0.1545 |
| 0.7959 | 2.49 | 34000 | 0.1355 | 0.1531 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1B-german --dataset mozilla-foundation/common_voice_8_0 --config de --split test --log_outputs
```
2. To evaluate on test dev data
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-1B-german --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
AlexN/xls-r-300m-fr-0
|
AlexN
| 2022-03-24T11:54:27Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-300m-fr
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0 fr
type: mozilla-foundation/common_voice_8_0
args: fr
metrics:
- name: Test WER
type: wer
value: 36.81
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 35.55
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 39.94
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2388
- Wer: 0.3681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3748 | 0.07 | 500 | 3.8784 | 1.0 |
| 2.8068 | 0.14 | 1000 | 2.8289 | 0.9826 |
| 1.6698 | 0.22 | 1500 | 0.8811 | 0.7127 |
| 1.3488 | 0.29 | 2000 | 0.5166 | 0.5369 |
| 1.2239 | 0.36 | 2500 | 0.4105 | 0.4741 |
| 1.1537 | 0.43 | 3000 | 0.3585 | 0.4448 |
| 1.1184 | 0.51 | 3500 | 0.3336 | 0.4292 |
| 1.0968 | 0.58 | 4000 | 0.3195 | 0.4180 |
| 1.0737 | 0.65 | 4500 | 0.3075 | 0.4141 |
| 1.0677 | 0.72 | 5000 | 0.3015 | 0.4089 |
| 1.0462 | 0.8 | 5500 | 0.2971 | 0.4077 |
| 1.0392 | 0.87 | 6000 | 0.2870 | 0.3997 |
| 1.0178 | 0.94 | 6500 | 0.2805 | 0.3963 |
| 0.992 | 1.01 | 7000 | 0.2748 | 0.3935 |
| 1.0197 | 1.09 | 7500 | 0.2691 | 0.3884 |
| 1.0056 | 1.16 | 8000 | 0.2682 | 0.3889 |
| 0.9826 | 1.23 | 8500 | 0.2647 | 0.3868 |
| 0.9815 | 1.3 | 9000 | 0.2603 | 0.3832 |
| 0.9717 | 1.37 | 9500 | 0.2561 | 0.3807 |
| 0.9605 | 1.45 | 10000 | 0.2523 | 0.3783 |
| 0.96 | 1.52 | 10500 | 0.2494 | 0.3788 |
| 0.9442 | 1.59 | 11000 | 0.2478 | 0.3760 |
| 0.9564 | 1.66 | 11500 | 0.2454 | 0.3733 |
| 0.9436 | 1.74 | 12000 | 0.2439 | 0.3747 |
| 0.938 | 1.81 | 12500 | 0.2411 | 0.3716 |
| 0.9353 | 1.88 | 13000 | 0.2397 | 0.3698 |
| 0.9271 | 1.95 | 13500 | 0.2388 | 0.3681 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
shpotes/xls-r-eus
|
shpotes
| 2022-03-24T11:54:17Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"et",
"hf-asr-leaderboard",
"eu",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- eu
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- et
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-eus
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: eu
metrics:
- name: Test WER
type: wer
value: 0.17871523648578164
- name: Test CER
type: cer
value: 0.032624506085144
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - EU dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2278
- Wer: 0.1787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 72
- eval_batch_size: 72
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2548 | 4.24 | 500 | 0.2470 | 0.3663 |
| 0.1435 | 8.47 | 1000 | 0.2000 | 0.2791 |
| 0.1158 | 12.71 | 1500 | 0.2030 | 0.2652 |
| 0.1094 | 16.95 | 2000 | 0.2096 | 0.2605 |
| 0.1004 | 21.19 | 2500 | 0.2150 | 0.2477 |
| 0.0945 | 25.42 | 3000 | 0.2072 | 0.2369 |
| 0.0844 | 29.66 | 3500 | 0.1981 | 0.2328 |
| 0.0877 | 33.89 | 4000 | 0.2041 | 0.2425 |
| 0.0741 | 38.14 | 4500 | 0.2353 | 0.2421 |
| 0.0676 | 42.37 | 5000 | 0.2092 | 0.2213 |
| 0.0623 | 46.61 | 5500 | 0.2217 | 0.2250 |
| 0.0574 | 50.84 | 6000 | 0.2152 | 0.2179 |
| 0.0583 | 55.08 | 6500 | 0.2207 | 0.2186 |
| 0.0488 | 59.32 | 7000 | 0.2225 | 0.2159 |
| 0.0456 | 63.56 | 7500 | 0.2293 | 0.2031 |
| 0.041 | 67.79 | 8000 | 0.2277 | 0.2013 |
| 0.0379 | 72.03 | 8500 | 0.2287 | 0.1991 |
| 0.0381 | 76.27 | 9000 | 0.2233 | 0.1954 |
| 0.0308 | 80.51 | 9500 | 0.2195 | 0.1835 |
| 0.0291 | 84.74 | 10000 | 0.2266 | 0.1825 |
| 0.0266 | 88.98 | 10500 | 0.2285 | 0.1801 |
| 0.0266 | 93.22 | 11000 | 0.2292 | 0.1801 |
| 0.0262 | 97.46 | 11500 | 0.2278 | 0.1788 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
|
shpotes/xls-r-et
|
shpotes
| 2022-03-24T11:54:15Z | 29 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"robust-speech-event",
"et",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- et
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- robust-speech-event
- et
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: et
metrics:
- name: Test WER
type: wer
value: 0.34753420299077314
- name: Test CER
type: cer
value: 0.07542956089330906
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: et
metrics:
- name: Test WER
type: wer
value: 47.17
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: et
metrics:
- name: Test WER
type: wer
value: 54.72
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ET dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4835
- Wer: 0.3475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 72
- eval_batch_size: 72
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3825 | 12.5 | 500 | 0.4022 | 0.5059 |
| 0.1592 | 25.0 | 1000 | 0.4585 | 0.4456 |
| 0.1215 | 37.5 | 1500 | 0.4550 | 0.4164 |
| 0.0972 | 50.0 | 2000 | 0.4725 | 0.4088 |
| 0.0731 | 62.5 | 2500 | 0.4568 | 0.3824 |
| 0.0527 | 75.0 | 3000 | 0.4712 | 0.3653 |
| 0.0428 | 87.5 | 3500 | 0.4813 | 0.3520 |
| 0.0383 | 100.0 | 4000 | 0.4835 | 0.3475 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
|
sammy786/wav2vec2-xlsr-Basaa
|
sammy786
| 2022-03-24T11:54:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"bas",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- bas
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-basaa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bas
metrics:
- name: Test WER
type: wer
value: 41.23
- name: Test CER
type: cer
value: 13.54
---
# sammy786/wav2vec2-xlsr-basaa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - bas dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 21.39
- Wer: 30.99
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 70
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 200 | 6.734100 | 1.605006 | 0.980456 |
| 400 | 1.011200 | 0.364686 | 0.442997 |
| 600 | 0.709300 | 0.300204 | 0.377850 |
| 800 | 0.469800 | 0.315612 | 0.405537 |
| 1000 | 0.464700 | 0.352494 | 0.372964 |
| 1200 | 0.421900 | 0.342533 | 0.368078 |
| 1400 | 0.401900 | 0.351398 | 0.343648 |
| 1600 | 0.429800 | 0.350570 | 0.348534 |
| 1800 | 0.352600 | 0.356601 | 0.358306 |
| 2000 | 0.387200 | 0.355814 | 0.356678 |
| 2200 | 0.362400 | 0.345573 | 0.355049 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-basaa --dataset mozilla-foundation/common_voice_8_0 --config bas --split test
```
|
patrickvonplaten/xls-r-300m-sv-cv8
|
patrickvonplaten
| 2022-03-24T11:54:05Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sv",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sv
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Swedish - CV8 - v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sv-SE
metrics:
- name: Test WER
type: wer
value: 17.33
- name: Test CER
type: cer
value: 5.8
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 27.01
- name: Test CER
type: cer
value: 12.92
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
- Wer: 0.2525
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3224 | 1.37 | 500 | 3.3354 | 1.0 |
| 2.9318 | 2.74 | 1000 | 2.9361 | 1.0000 |
| 2.1371 | 4.11 | 1500 | 1.1157 | 0.8359 |
| 1.6883 | 5.48 | 2000 | 0.6003 | 0.6314 |
| 1.5812 | 6.85 | 2500 | 0.4746 | 0.4725 |
| 1.5145 | 8.22 | 3000 | 0.4376 | 0.4736 |
| 1.4763 | 9.59 | 3500 | 0.4006 | 0.3863 |
| 1.4215 | 10.96 | 4000 | 0.3783 | 0.3629 |
| 1.3638 | 12.33 | 4500 | 0.3555 | 0.3425 |
| 1.3561 | 13.7 | 5000 | 0.3340 | 0.3228 |
| 1.3406 | 15.07 | 5500 | 0.3373 | 0.3295 |
| 1.3055 | 16.44 | 6000 | 0.3432 | 0.3210 |
| 1.3048 | 17.81 | 6500 | 0.3282 | 0.3118 |
| 1.2863 | 19.18 | 7000 | 0.3226 | 0.3018 |
| 1.2389 | 20.55 | 7500 | 0.3050 | 0.2986 |
| 1.2361 | 21.92 | 8000 | 0.3048 | 0.2980 |
| 1.2263 | 23.29 | 8500 | 0.3011 | 0.2977 |
| 1.2225 | 24.66 | 9000 | 0.3017 | 0.2959 |
| 1.2044 | 26.03 | 9500 | 0.2977 | 0.2782 |
| 1.2017 | 27.4 | 10000 | 0.2966 | 0.2781 |
| 1.1912 | 28.77 | 10500 | 0.2999 | 0.2786 |
| 1.1658 | 30.14 | 11000 | 0.2991 | 0.2757 |
| 1.148 | 31.51 | 11500 | 0.2915 | 0.2684 |
| 1.1423 | 32.88 | 12000 | 0.2913 | 0.2643 |
| 1.123 | 34.25 | 12500 | 0.2777 | 0.2630 |
| 1.1297 | 35.62 | 13000 | 0.2873 | 0.2646 |
| 1.0987 | 36.98 | 13500 | 0.2829 | 0.2619 |
| 1.0873 | 38.36 | 14000 | 0.2864 | 0.2608 |
| 1.0848 | 39.73 | 14500 | 0.2827 | 0.2577 |
| 1.0628 | 41.1 | 15000 | 0.2896 | 0.2581 |
| 1.0815 | 42.47 | 15500 | 0.2814 | 0.2561 |
| 1.0587 | 43.83 | 16000 | 0.2738 | 0.2542 |
| 1.0709 | 45.21 | 16500 | 0.2785 | 0.2578 |
| 1.0512 | 46.57 | 17000 | 0.2793 | 0.2539 |
| 1.0396 | 47.94 | 17500 | 0.2788 | 0.2525 |
| 1.0481 | 49.31 | 18000 | 0.2777 | 0.2534 |
### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id patrickvonplaten/xls-r-300m-sv-cv8 --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id patrickvonplaten/xls-r-300m-sv-cv8 --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.10.3
|
lgris/wav2vec2-xls-r-300m-gn-cv8
|
lgris
| 2022-03-24T11:54:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gn",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- gn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gn
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-300m-gn-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pt
metrics:
- name: Test WER
type: wer
value: 69.05
- name: Test CER
type: cer
value: 14.7
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: gn
metrics:
- name: Test WER
type: wer
value: 69.05
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-gn-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9392
- Wer: 0.7033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 20.0601 | 5.54 | 100 | 5.1622 | 1.0 |
| 3.7052 | 11.11 | 200 | 3.2869 | 1.0 |
| 3.3275 | 16.65 | 300 | 3.2162 | 1.0 |
| 3.2984 | 22.22 | 400 | 3.1638 | 1.0 |
| 3.1111 | 27.76 | 500 | 2.5541 | 1.0 |
| 2.238 | 33.32 | 600 | 1.2198 | 0.9616 |
| 1.5284 | 38.86 | 700 | 0.9571 | 0.8593 |
| 1.2735 | 44.43 | 800 | 0.8719 | 0.8363 |
| 1.1269 | 49.97 | 900 | 0.8334 | 0.7954 |
| 1.0427 | 55.54 | 1000 | 0.7700 | 0.7749 |
| 1.0152 | 61.11 | 1100 | 0.7747 | 0.7877 |
| 0.943 | 66.65 | 1200 | 0.7151 | 0.7442 |
| 0.9132 | 72.22 | 1300 | 0.7224 | 0.7289 |
| 0.8397 | 77.76 | 1400 | 0.7354 | 0.7059 |
| 0.8577 | 83.32 | 1500 | 0.7285 | 0.7263 |
| 0.7931 | 88.86 | 1600 | 0.7863 | 0.7084 |
| 0.7995 | 94.43 | 1700 | 0.7562 | 0.6880 |
| 0.799 | 99.97 | 1800 | 0.7905 | 0.7059 |
| 0.7373 | 105.54 | 1900 | 0.7791 | 0.7161 |
| 0.749 | 111.11 | 2000 | 0.8125 | 0.7161 |
| 0.6925 | 116.65 | 2100 | 0.7722 | 0.6905 |
| 0.7034 | 122.22 | 2200 | 0.8989 | 0.7136 |
| 0.6745 | 127.76 | 2300 | 0.8270 | 0.6982 |
| 0.6837 | 133.32 | 2400 | 0.8569 | 0.7161 |
| 0.6689 | 138.86 | 2500 | 0.8339 | 0.6982 |
| 0.6471 | 144.43 | 2600 | 0.8441 | 0.7110 |
| 0.615 | 149.97 | 2700 | 0.9038 | 0.7212 |
| 0.6477 | 155.54 | 2800 | 0.9089 | 0.7059 |
| 0.6047 | 161.11 | 2900 | 0.9149 | 0.7059 |
| 0.5613 | 166.65 | 3000 | 0.8582 | 0.7263 |
| 0.6017 | 172.22 | 3100 | 0.8787 | 0.7084 |
| 0.5546 | 177.76 | 3200 | 0.8753 | 0.6957 |
| 0.5747 | 183.32 | 3300 | 0.9167 | 0.7212 |
| 0.5535 | 188.86 | 3400 | 0.8448 | 0.6905 |
| 0.5331 | 194.43 | 3500 | 0.8644 | 0.7161 |
| 0.5428 | 199.97 | 3600 | 0.8730 | 0.7033 |
| 0.5219 | 205.54 | 3700 | 0.9047 | 0.6982 |
| 0.5158 | 211.11 | 3800 | 0.8706 | 0.7033 |
| 0.5107 | 216.65 | 3900 | 0.9139 | 0.7084 |
| 0.4903 | 222.22 | 4000 | 0.9456 | 0.7315 |
| 0.4772 | 227.76 | 4100 | 0.9475 | 0.7161 |
| 0.4713 | 233.32 | 4200 | 0.9237 | 0.7059 |
| 0.4743 | 238.86 | 4300 | 0.9305 | 0.6957 |
| 0.4705 | 244.43 | 4400 | 0.9561 | 0.7110 |
| 0.4908 | 249.97 | 4500 | 0.9389 | 0.7084 |
| 0.4717 | 255.54 | 4600 | 0.9234 | 0.6982 |
| 0.4462 | 261.11 | 4700 | 0.9323 | 0.6957 |
| 0.4556 | 266.65 | 4800 | 0.9432 | 0.7033 |
| 0.4691 | 272.22 | 4900 | 0.9389 | 0.7059 |
| 0.4601 | 277.76 | 5000 | 0.9392 | 0.7033 |
### Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-maltese
|
infinitejoy
| 2022-03-24T11:53:52Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"mt",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mt
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- mt
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Maltese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: mt
metrics:
- name: Test WER
type: wer
value: 23.503
- name: Test CER
type: cer
value: 5.065
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-maltese
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2005
- Wer: 0.1897
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.2238 | 18.02 | 2000 | 0.3911 | 0.4310 |
| 0.7871 | 36.04 | 4000 | 0.2063 | 0.2309 |
| 0.6653 | 54.05 | 6000 | 0.1960 | 0.2091 |
| 0.5861 | 72.07 | 8000 | 0.1986 | 0.2000 |
| 0.5283 | 90.09 | 10000 | 0.1993 | 0.1909 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-greek
|
infinitejoy
| 2022-03-24T11:53:50Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"el",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- el
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- el
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Greek
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: el
metrics:
- name: Test WER
type: wer
value: 102.23963133640552
- name: Test CER
type: cer
value: 146.28
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: el
metrics:
- name: Test WER
type: wer
value: 99.92
- name: Test CER
type: cer
value: 132.38
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-greek
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - EL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6592
- Wer: 0.4564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0928 | 4.42 | 500 | 3.0804 | 1.0073 |
| 1.4505 | 8.85 | 1000 | 0.9038 | 0.7330 |
| 1.2207 | 13.27 | 1500 | 0.7375 | 0.6045 |
| 1.0695 | 17.7 | 2000 | 0.7119 | 0.5441 |
| 1.0104 | 22.12 | 2500 | 0.6069 | 0.5296 |
| 0.9299 | 26.55 | 3000 | 0.6168 | 0.5206 |
| 0.8588 | 30.97 | 3500 | 0.6382 | 0.5171 |
| 0.7942 | 35.4 | 4000 | 0.6048 | 0.4988 |
| 0.7808 | 39.82 | 4500 | 0.6730 | 0.5084 |
| 0.743 | 44.25 | 5000 | 0.6749 | 0.5012 |
| 0.6652 | 48.67 | 5500 | 0.6491 | 0.4735 |
| 0.6386 | 53.1 | 6000 | 0.6928 | 0.4954 |
| 0.5945 | 57.52 | 6500 | 0.6359 | 0.4798 |
| 0.5561 | 61.95 | 7000 | 0.6409 | 0.4799 |
| 0.5464 | 66.37 | 7500 | 0.6452 | 0.4691 |
| 0.5119 | 70.8 | 8000 | 0.6376 | 0.4657 |
| 0.474 | 75.22 | 8500 | 0.6541 | 0.4700 |
| 0.45 | 79.65 | 9000 | 0.6374 | 0.4571 |
| 0.4315 | 84.07 | 9500 | 0.6568 | 0.4625 |
| 0.3967 | 88.5 | 10000 | 0.6636 | 0.4605 |
| 0.3937 | 92.92 | 10500 | 0.6537 | 0.4597 |
| 0.3788 | 97.35 | 11000 | 0.6614 | 0.4589 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
comodoro/wav2vec2-xls-r-300m-hsb-cv8
|
comodoro
| 2022-03-24T11:53:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"hsb",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hsb
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- xlsr-fine-tuning-week
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: Upper Sorbian comodoro Wav2Vec2 XLSR 300M CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hsb
metrics:
- name: Test WER
type: wer
value: 56.3
- name: Test CER
type: cer
value: 14.3
---
# Upper Sorbian wav2vec2-xls-r-300m-hsb-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9643
- Wer: 0.5037
- Cer: 0.1278
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-hsb-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config hsb
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 4.3121 | 19.35 | 1200 | 3.2059 | 1.0 | 1.0 |
| 2.6525 | 38.71 | 2400 | 1.1324 | 0.9387 | 0.3204 |
| 1.3644 | 58.06 | 3600 | 0.8767 | 0.8099 | 0.2271 |
| 1.093 | 77.42 | 4800 | 0.8739 | 0.7603 | 0.2090 |
| 0.9546 | 96.77 | 6000 | 0.8454 | 0.6983 | 0.1882 |
| 0.8554 | 116.13 | 7200 | 0.8197 | 0.6484 | 0.1708 |
| 0.775 | 135.48 | 8400 | 0.8452 | 0.6345 | 0.1681 |
| 0.7167 | 154.84 | 9600 | 0.8551 | 0.6241 | 0.1631 |
| 0.6609 | 174.19 | 10800 | 0.8442 | 0.5821 | 0.1531 |
| 0.616 | 193.55 | 12000 | 0.8892 | 0.5864 | 0.1527 |
| 0.5815 | 212.9 | 13200 | 0.8839 | 0.5772 | 0.1503 |
| 0.55 | 232.26 | 14400 | 0.8905 | 0.5665 | 0.1436 |
| 0.5173 | 251.61 | 15600 | 0.8995 | 0.5471 | 0.1417 |
| 0.4969 | 270.97 | 16800 | 0.8633 | 0.5325 | 0.1334 |
| 0.4803 | 290.32 | 18000 | 0.9074 | 0.5253 | 0.1352 |
| 0.4596 | 309.68 | 19200 | 0.9159 | 0.5146 | 0.1294 |
| 0.4415 | 329.03 | 20400 | 0.9055 | 0.5189 | 0.1314 |
| 0.434 | 348.39 | 21600 | 0.9435 | 0.5208 | 0.1314 |
| 0.4199 | 367.74 | 22800 | 0.9199 | 0.5136 | 0.1290 |
| 0.4008 | 387.1 | 24000 | 0.9342 | 0.5174 | 0.1303 |
| 0.4051 | 406.45 | 25200 | 0.9436 | 0.5132 | 0.1292 |
| 0.3861 | 425.81 | 26400 | 0.9417 | 0.5084 | 0.1283 |
| 0.3738 | 445.16 | 27600 | 0.9573 | 0.5079 | 0.1299 |
| 0.3768 | 464.52 | 28800 | 0.9682 | 0.5062 | 0.1289 |
| 0.3647 | 483.87 | 30000 | 0.9643 | 0.5037 | 0.1278 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
chmanoj/xls-r-300m-te
|
chmanoj
| 2022-03-24T11:53:34Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"openslr_SLR66",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"te",
"dataset:openslr",
"dataset:SLR66",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- te
license: apache-2.0
tags:
- automatic-speech-recognition
- openslr_SLR66
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- openslr
- SLR66
metrics:
- wer
model-index:
- name: xls-r-300m-te
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR66
metrics:
- type: wer
value: 24.695121951219512
name: Test WER
- type: cer
value: 4.861934182322532
name: Test CER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2680
- Wer: 0.3467
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0304 | 4.81 | 500 | 1.5676 | 1.0554 |
| 1.5263 | 9.61 | 1000 | 0.4693 | 0.8023 |
| 1.5299 | 14.42 | 1500 | 0.4368 | 0.7311 |
| 1.5063 | 19.23 | 2000 | 0.4360 | 0.7302 |
| 1.455 | 24.04 | 2500 | 0.4213 | 0.6692 |
| 1.4755 | 28.84 | 3000 | 0.4329 | 0.5943 |
| 1.352 | 33.65 | 3500 | 0.4074 | 0.5765 |
| 1.3122 | 38.46 | 4000 | 0.3866 | 0.5630 |
| 1.2799 | 43.27 | 4500 | 0.3860 | 0.5480 |
| 1.212 | 48.08 | 5000 | 0.3590 | 0.5317 |
| 1.1645 | 52.88 | 5500 | 0.3283 | 0.4757 |
| 1.0854 | 57.69 | 6000 | 0.3162 | 0.4687 |
| 1.0292 | 62.5 | 6500 | 0.3126 | 0.4416 |
| 0.9607 | 67.31 | 7000 | 0.2990 | 0.4066 |
| 0.9156 | 72.12 | 7500 | 0.2870 | 0.4009 |
| 0.8329 | 76.92 | 8000 | 0.2791 | 0.3909 |
| 0.7979 | 81.73 | 8500 | 0.2770 | 0.3670 |
| 0.7144 | 86.54 | 9000 | 0.2841 | 0.3661 |
| 0.6997 | 91.35 | 9500 | 0.2721 | 0.3485 |
| 0.6568 | 96.15 | 10000 | 0.2681 | 0.3437 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
RuudVelo/wav2vec2-large-xls-r-1b-nl
|
RuudVelo
| 2022-03-24T11:53:24Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"nl",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- nl
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-1b-nl
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: nl
metrics:
- name: Test WER
type: wer
value: 11.12
- name: Test CER
type: cer
value: 3.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: 31.92
- name: Test CER
type: cer
value: 13.87
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 32.17
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. This model is also available with a language model which improves these results. This model can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl-lm. The Common Voice 8 Dutch test Wer is 9.73 of that model.
It achieves the following results on the evaluation set:
- Loss: 0.1479
- Wer: 0.1156
## Model description
Model fine-tuned using the wav2vec-als-r-1b model architecture
## Intended uses & limitations
More information needed
## Training and evaluation data
Model has been trained on Common Voice 8 Dutch
## Training procedure
### Training hyperparameters
Model parameters can be found under Files and versions in the run.sh file.
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.2223 | 0.52 | 500 | 0.3866 | 0.3425 |
| 1.0748 | 1.03 | 1000 | 0.2574 | 0.2169 |
| 1.0416 | 1.55 | 1500 | 0.2177 | 0.1946 |
| 0.9951 | 2.06 | 2000 | 0.2008 | 0.1760 |
| 0.975 | 2.58 | 2500 | 0.1961 | 0.1751 |
| 0.9461 | 3.1 | 3000 | 0.1989 | 0.1782 |
| 0.9381 | 3.61 | 3500 | 0.1928 | 0.1699 |
| 0.934 | 4.13 | 4000 | 0.1923 | 0.1633 |
| 0.9322 | 4.64 | 4500 | 0.1871 | 0.1634 |
| 0.9012 | 5.16 | 5000 | 0.1890 | 0.1702 |
| 0.9045 | 5.68 | 5500 | 0.1882 | 0.1740 |
| 0.8826 | 6.19 | 6000 | 0.1856 | 0.1575 |
| 0.8848 | 6.71 | 6500 | 0.1861 | 0.1617 |
| 0.8723 | 7.22 | 7000 | 0.1927 | 0.1646 |
| 0.8725 | 7.74 | 7500 | 0.1798 | 0.1531 |
| 0.8573 | 8.26 | 8000 | 0.1781 | 0.1587 |
| 0.8633 | 8.77 | 8500 | 0.1852 | 0.1628 |
| 0.8603 | 9.29 | 9000 | 0.1833 | 0.1601 |
| 0.8421 | 9.8 | 9500 | 0.1788 | 0.1543 |
| 0.8404 | 10.32 | 10000 | 0.1844 | 0.1556 |
| 0.8342 | 10.84 | 10500 | 0.1770 | 0.1538 |
| 0.8161 | 11.35 | 11000 | 0.1821 | 0.1567 |
| 0.8371 | 11.87 | 11500 | 0.1909 | 0.1629 |
| 0.8083 | 12.38 | 12000 | 0.1778 | 0.1498 |
| 0.806 | 12.9 | 12500 | 0.1802 | 0.1547 |
| 0.8013 | 13.42 | 13000 | 0.1859 | 0.1584 |
| 0.7913 | 13.93 | 13500 | 0.1875 | 0.1517 |
| 0.8063 | 14.45 | 14000 | 0.1799 | 0.1571 |
| 0.7991 | 14.96 | 14500 | 0.1792 | 0.1538 |
| 0.7843 | 15.48 | 15000 | 0.1753 | 0.1464 |
| 0.7905 | 16.0 | 15500 | 0.1784 | 0.1508 |
| 0.7808 | 16.51 | 16000 | 0.1771 | 0.1485 |
| 0.7743 | 17.03 | 16500 | 0.1795 | 0.1491 |
| 0.7833 | 17.54 | 17000 | 0.1722 | 0.1484 |
| 0.7763 | 18.06 | 17500 | 0.1767 | 0.1518 |
| 0.7698 | 18.58 | 18000 | 0.1720 | 0.1460 |
| 0.7571 | 19.09 | 18500 | 0.1735 | 0.1478 |
| 0.7673 | 19.61 | 19000 | 0.1817 | 0.1511 |
| 0.7415 | 20.12 | 19500 | 0.1763 | 0.1481 |
| 0.751 | 20.64 | 20000 | 0.1742 | 0.1484 |
| 0.7563 | 21.16 | 20500 | 0.1810 | 0.1611 |
| 0.7423 | 21.67 | 21000 | 0.1817 | 0.1557 |
| 0.7242 | 22.19 | 21500 | 0.1690 | 0.1446 |
| 0.7251 | 22.7 | 22000 | 0.1684 | 0.1446 |
| 0.7302 | 23.22 | 22500 | 0.1735 | 0.1430 |
| 0.733 | 23.74 | 23000 | 0.1720 | 0.1454 |
| 0.7128 | 24.25 | 23500 | 0.1668 | 0.1383 |
| 0.7184 | 24.77 | 24000 | 0.1635 | 0.1377 |
| 0.7015 | 25.28 | 24500 | 0.1646 | 0.1389 |
| 0.7198 | 25.8 | 25000 | 0.1775 | 0.1462 |
| 0.7178 | 26.32 | 25500 | 0.1705 | 0.1419 |
| 0.7199 | 26.83 | 26000 | 0.1649 | 0.1416 |
| 0.6981 | 27.35 | 26500 | 0.1724 | 0.1418 |
| 0.6886 | 27.86 | 27000 | 0.1633 | 0.1382 |
| 0.6922 | 28.38 | 27500 | 0.1698 | 0.1420 |
| 0.6833 | 28.9 | 28000 | 0.1611 | 0.1351 |
| 0.6798 | 29.41 | 28500 | 0.1639 | 0.1365 |
| 0.6711 | 29.93 | 29000 | 0.1668 | 0.1358 |
| 0.6762 | 30.44 | 29500 | 0.1682 | 0.1355 |
| 0.6594 | 30.96 | 30000 | 0.1629 | 0.1345 |
| 0.6664 | 31.48 | 30500 | 0.1625 | 0.1321 |
| 0.6838 | 31.99 | 31000 | 0.1597 | 0.1372 |
| 0.6603 | 32.51 | 31500 | 0.1583 | 0.1302 |
| 0.6468 | 33.02 | 32000 | 0.1595 | 0.1322 |
| 0.6464 | 33.54 | 32500 | 0.1609 | 0.1315 |
| 0.6623 | 34.06 | 33000 | 0.1622 | 0.1366 |
| 0.6414 | 34.57 | 33500 | 0.1587 | 0.1330 |
| 0.6242 | 35.09 | 34000 | 0.1614 | 0.1337 |
| 0.632 | 35.6 | 34500 | 0.1568 | 0.1272 |
| 0.6346 | 36.12 | 35000 | 0.1583 | 0.1274 |
| 0.6143 | 36.64 | 35500 | 0.1576 | 0.1264 |
| 0.6208 | 37.15 | 36000 | 0.1621 | 0.1263 |
| 0.6185 | 37.67 | 36500 | 0.1623 | 0.1270 |
| 0.6128 | 38.18 | 37000 | 0.1604 | 0.1268 |
| 0.6151 | 38.7 | 37500 | 0.1593 | 0.1246 |
| 0.6082 | 39.22 | 38000 | 0.1532 | 0.1238 |
| 0.6 | 39.73 | 38500 | 0.1524 | 0.1224 |
| 0.6032 | 40.25 | 39000 | 0.1521 | 0.1212 |
| 0.6016 | 40.76 | 39500 | 0.1551 | 0.1215 |
| 0.6009 | 41.28 | 40000 | 0.1523 | 0.1215 |
| 0.5875 | 41.8 | 40500 | 0.1541 | 0.1216 |
| 0.608 | 42.31 | 41000 | 0.1536 | 0.1209 |
| 0.5876 | 42.83 | 41500 | 0.1567 | 0.1211 |
| 0.5714 | 43.34 | 42000 | 0.1532 | 0.1217 |
| 0.5756 | 43.86 | 42500 | 0.1516 | 0.1196 |
| 0.5719 | 44.38 | 43000 | 0.1491 | 0.1191 |
| 0.5829 | 44.89 | 43500 | 0.1497 | 0.1193 |
| 0.5664 | 45.41 | 44000 | 0.1487 | 0.1173 |
| 0.5707 | 45.92 | 44500 | 0.1470 | 0.1164 |
| 0.5696 | 46.44 | 45000 | 0.1479 | 0.1161 |
| 0.5767 | 46.96 | 45500 | 0.1492 | 0.1175 |
| 0.5573 | 47.47 | 46000 | 0.1471 | 0.1165 |
| 0.5625 | 47.99 | 46500 | 0.1484 | 0.1168 |
| 0.5671 | 48.5 | 47000 | 0.1474 | 0.1162 |
| 0.5484 | 49.02 | 47500 | 0.1479 | 0.1158 |
| 0.555 | 49.54 | 48000 | 0.1477 | 0.1157 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
NbAiLab/XLSR-300M-bokmaal
|
NbAiLab
| 2022-03-24T11:53:12Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
- automatic-speech-recognition
- NbAiLab/NPSC
- robust-speech-event
- false
- nb-NO
- hf-asr-leaderboard
datasets:
- NbAiLab/NPSC
language:
- nb-NO
model-index:
- name: XLSR-300M-bokmaal
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: NPSC
type: NbAiLab/NPSC
args: 16K_mp3_bokmaal
metrics:
- name: "Test (Bokm\xE5l) WER"
type: wer
value: 0.07699635320946434
- name: "Test (Bokm\xE5l) CER"
type: cer
value: 0.0284288464829
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLSR-300M-bokmaal
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3_BOKMAAL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1635
- Wer: 0.1005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0307 | 0.32 | 500 | 3.0026 | 1.0 |
| 2.7865 | 0.64 | 1000 | 2.4849 | 0.9926 |
| 0.7522 | 0.95 | 1500 | 0.4567 | 0.3594 |
| 0.5703 | 1.27 | 2000 | 0.3440 | 0.2586 |
| 0.4762 | 1.59 | 2500 | 0.2925 | 0.2178 |
| 0.4585 | 1.91 | 3000 | 0.2442 | 0.1981 |
| 0.4013 | 2.23 | 3500 | 0.2495 | 0.1818 |
| 0.449 | 2.54 | 4000 | 0.2152 | 0.1808 |
| 0.355 | 2.86 | 4500 | 0.2179 | 0.1670 |
| 0.3142 | 3.18 | 5000 | 0.1953 | 0.1542 |
| 0.3242 | 3.5 | 5500 | 0.2103 | 0.1526 |
| 0.3016 | 3.82 | 6000 | 0.1911 | 0.1477 |
| 0.2713 | 4.13 | 6500 | 0.1836 | 0.1422 |
| 0.2807 | 4.45 | 7000 | 0.1924 | 0.1447 |
| 0.2929 | 4.77 | 7500 | 0.1848 | 0.1402 |
| 0.2595 | 5.09 | 8000 | 0.1783 | 0.1330 |
| 0.2289 | 5.41 | 8500 | 0.1901 | 0.1313 |
| 0.2567 | 5.72 | 9000 | 0.1784 | 0.1298 |
| 0.2401 | 6.04 | 9500 | 0.1956 | 0.1298 |
| 0.2098 | 6.36 | 10000 | 0.1748 | 0.1277 |
| 0.2246 | 6.68 | 10500 | 0.1777 | 0.1254 |
| 0.2197 | 7.0 | 11000 | 0.1703 | 0.1222 |
| 0.2122 | 7.32 | 11500 | 0.1917 | 0.1221 |
| 0.2746 | 7.63 | 12000 | 0.1769 | 0.1215 |
| 0.2148 | 7.95 | 12500 | 0.1736 | 0.1193 |
| 0.1915 | 8.27 | 13000 | 0.1814 | 0.1161 |
| 0.2462 | 8.59 | 13500 | 0.1748 | 0.1166 |
| 0.1872 | 8.91 | 14000 | 0.1769 | 0.1133 |
| 0.1886 | 9.22 | 14500 | 0.1852 | 0.1143 |
| 0.1789 | 9.54 | 15000 | 0.1696 | 0.1126 |
| 0.1692 | 9.86 | 15500 | 0.1817 | 0.1122 |
| 0.1765 | 10.18 | 16000 | 0.1769 | 0.1093 |
| 0.1699 | 10.5 | 16500 | 0.1604 | 0.1084 |
| 0.1591 | 10.81 | 17000 | 0.1777 | 0.1080 |
| 0.1499 | 11.13 | 17500 | 0.1645 | 0.1074 |
| 0.163 | 11.45 | 18000 | 0.1704 | 0.1065 |
| 0.1597 | 11.77 | 18500 | 0.1576 | 0.1064 |
| 0.1484 | 12.09 | 19000 | 0.1637 | 0.1041 |
| 0.1464 | 12.4 | 19500 | 0.1631 | 0.1047 |
| 0.156 | 12.72 | 20000 | 0.1686 | 0.1029 |
| 0.1625 | 13.04 | 20500 | 0.1648 | 0.1023 |
| 0.1395 | 13.36 | 21000 | 0.1688 | 0.1027 |
| 0.1387 | 13.68 | 21500 | 0.1670 | 0.1013 |
| 0.1434 | 13.99 | 22000 | 0.1677 | 0.1017 |
| 0.1442 | 14.31 | 22500 | 0.1688 | 0.1008 |
| 0.1439 | 14.63 | 23000 | 0.1647 | 0.1004 |
| 0.137 | 14.95 | 23500 | 0.1636 | 0.1006 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
Iskaj/xlsr300m_cv_8.0_nl
|
Iskaj
| 2022-03-24T11:53:05Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"mozilla-foundation/common_voice_7_0",
"nl",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- nl
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- mozilla-foundation/common_voice_7_0
- nl
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Dutch
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8 NL
type: mozilla-foundation/common_voice_8_0
args: nl
metrics:
- name: Test WER
type: wer
value: 46.94
- name: Test CER
type: cer
value: 21.65
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: nl
metrics:
- name: Test WER
type: wer
value: ???
- name: Test CER
type: cer
value: ???
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: nl
metrics:
- name: Test WER
type: wer
value: 42.56
---
# xlsr300m_cv_8.0_nl
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset mozilla-foundation/common_voice_8_0 --config nl --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset speech-recognition-community-v2/dev_data --config nl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "Iskaj/xlsr300m_cv_8.0_nl"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "nl", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
inputs = processor(resampled_audio, sampling_rate=16_000, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
transcription[0].lower()
#'het kontine schip lag aangemeert in de aven'
```
|
DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3
|
DrishtiSharma
| 2022-03-24T11:52:54Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"hi",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- hi
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-large-xls-r-300m-hi-d3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: vot
metrics:
- name: Test WER
type: wer
value: 0.4204111781361566
- name: Test CER
type: cer
value: 0.13869169624556316
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hi
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 42.04
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hi-d3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7988
- Wer: 0.3713
###Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Hindi language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000388
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 750
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.2826 | 1.36 | 200 | 3.5253 | 1.0 |
| 2.7019 | 2.72 | 400 | 1.1744 | 0.7360 |
| 0.7358 | 4.08 | 600 | 0.7781 | 0.5501 |
| 0.4942 | 5.44 | 800 | 0.7590 | 0.5345 |
| 0.4056 | 6.8 | 1000 | 0.6885 | 0.4776 |
| 0.3243 | 8.16 | 1200 | 0.7195 | 0.4861 |
| 0.2785 | 9.52 | 1400 | 0.7473 | 0.4930 |
| 0.2448 | 10.88 | 1600 | 0.7201 | 0.4574 |
| 0.2155 | 12.24 | 1800 | 0.7686 | 0.4648 |
| 0.2039 | 13.6 | 2000 | 0.7440 | 0.4624 |
| 0.1792 | 14.96 | 2200 | 0.7815 | 0.4658 |
| 0.1695 | 16.33 | 2400 | 0.7678 | 0.4557 |
| 0.1598 | 17.68 | 2600 | 0.7468 | 0.4393 |
| 0.1568 | 19.05 | 2800 | 0.7440 | 0.4422 |
| 0.1391 | 20.41 | 3000 | 0.7656 | 0.4317 |
| 0.1283 | 21.77 | 3200 | 0.7892 | 0.4299 |
| 0.1194 | 23.13 | 3400 | 0.7646 | 0.4192 |
| 0.1116 | 24.49 | 3600 | 0.8156 | 0.4330 |
| 0.1111 | 25.85 | 3800 | 0.7661 | 0.4322 |
| 0.1023 | 27.21 | 4000 | 0.7419 | 0.4276 |
| 0.1007 | 28.57 | 4200 | 0.8488 | 0.4245 |
| 0.0925 | 29.93 | 4400 | 0.8062 | 0.4070 |
| 0.0918 | 31.29 | 4600 | 0.8412 | 0.4218 |
| 0.0813 | 32.65 | 4800 | 0.8045 | 0.4087 |
| 0.0805 | 34.01 | 5000 | 0.8411 | 0.4113 |
| 0.0774 | 35.37 | 5200 | 0.7664 | 0.3943 |
| 0.0666 | 36.73 | 5400 | 0.8082 | 0.3939 |
| 0.0655 | 38.09 | 5600 | 0.7948 | 0.4000 |
| 0.0617 | 39.45 | 5800 | 0.8084 | 0.3932 |
| 0.0606 | 40.81 | 6000 | 0.8223 | 0.3841 |
| 0.0569 | 42.18 | 6200 | 0.7892 | 0.3832 |
| 0.0544 | 43.54 | 6400 | 0.8326 | 0.3834 |
| 0.0508 | 44.89 | 6600 | 0.7952 | 0.3774 |
| 0.0492 | 46.26 | 6800 | 0.7923 | 0.3756 |
| 0.0459 | 47.62 | 7000 | 0.7925 | 0.3701 |
| 0.0423 | 48.98 | 7200 | 0.7988 | 0.3713 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2
|
DrishtiSharma
| 2022-03-24T11:52:52Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hi-cv8-b2
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 7
args: hi
metrics:
- type: wer
value: 0.3891350503092403
name: Test WER
- name: Test CER
type: cer
value: 0.13016327327131985
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hi
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hi-cv8-b2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7322
- Wer: 0.3469
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Hindi language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.6226 | 1.04 | 200 | 3.8855 | 1.0 |
| 3.4678 | 2.07 | 400 | 3.4283 | 1.0 |
| 2.3668 | 3.11 | 600 | 1.0743 | 0.7175 |
| 0.7308 | 4.15 | 800 | 0.7663 | 0.5498 |
| 0.4985 | 5.18 | 1000 | 0.6957 | 0.5001 |
| 0.3817 | 6.22 | 1200 | 0.6932 | 0.4866 |
| 0.3281 | 7.25 | 1400 | 0.7034 | 0.4983 |
| 0.2752 | 8.29 | 1600 | 0.6588 | 0.4606 |
| 0.2475 | 9.33 | 1800 | 0.6514 | 0.4328 |
| 0.219 | 10.36 | 2000 | 0.6396 | 0.4176 |
| 0.2036 | 11.4 | 2200 | 0.6867 | 0.4162 |
| 0.1793 | 12.44 | 2400 | 0.6943 | 0.4196 |
| 0.1724 | 13.47 | 2600 | 0.6862 | 0.4260 |
| 0.1554 | 14.51 | 2800 | 0.7615 | 0.4222 |
| 0.151 | 15.54 | 3000 | 0.7058 | 0.4110 |
| 0.1335 | 16.58 | 3200 | 0.7172 | 0.3986 |
| 0.1326 | 17.62 | 3400 | 0.7182 | 0.3923 |
| 0.1225 | 18.65 | 3600 | 0.6995 | 0.3910 |
| 0.1146 | 19.69 | 3800 | 0.7075 | 0.3875 |
| 0.108 | 20.73 | 4000 | 0.7297 | 0.3858 |
| 0.1048 | 21.76 | 4200 | 0.7413 | 0.3850 |
| 0.0979 | 22.8 | 4400 | 0.7452 | 0.3793 |
| 0.0946 | 23.83 | 4600 | 0.7436 | 0.3759 |
| 0.0897 | 24.87 | 4800 | 0.7289 | 0.3754 |
| 0.0854 | 25.91 | 5000 | 0.7271 | 0.3667 |
| 0.0803 | 26.94 | 5200 | 0.7378 | 0.3656 |
| 0.0752 | 27.98 | 5400 | 0.7488 | 0.3680 |
| 0.0718 | 29.02 | 5600 | 0.7185 | 0.3619 |
| 0.0702 | 30.05 | 5800 | 0.7428 | 0.3554 |
| 0.0653 | 31.09 | 6000 | 0.7447 | 0.3559 |
| 0.0638 | 32.12 | 6200 | 0.7327 | 0.3523 |
| 0.058 | 33.16 | 6400 | 0.7339 | 0.3488 |
| 0.0594 | 34.2 | 6600 | 0.7322 | 0.3469 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
lgris/wav2vec2-large-xls-r-300m-pt-cv
|
lgris
| 2022-03-24T11:52:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"pt",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- robust-speech-event
- pt
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pt-cv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 24.29
- name: Test CER
type: cer
value: 7.51
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 55.72
- name: Test CER
type: cer
value: 21.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 47.88
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 50.78
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-pt-cv
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3418
- Wer: 0.3581
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.9035 | 0.2 | 100 | 4.2750 | 1.0 |
| 3.3275 | 0.41 | 200 | 3.0334 | 1.0 |
| 3.0016 | 0.61 | 300 | 2.9494 | 1.0 |
| 2.1874 | 0.82 | 400 | 1.4355 | 0.8721 |
| 1.09 | 1.02 | 500 | 0.9987 | 0.7165 |
| 0.8251 | 1.22 | 600 | 0.7886 | 0.6406 |
| 0.6927 | 1.43 | 700 | 0.6753 | 0.5801 |
| 0.6143 | 1.63 | 800 | 0.6300 | 0.5509 |
| 0.5451 | 1.84 | 900 | 0.5586 | 0.5156 |
| 0.5003 | 2.04 | 1000 | 0.5493 | 0.5027 |
| 0.3712 | 2.24 | 1100 | 0.5271 | 0.4872 |
| 0.3486 | 2.45 | 1200 | 0.4953 | 0.4817 |
| 0.3498 | 2.65 | 1300 | 0.4619 | 0.4538 |
| 0.3112 | 2.86 | 1400 | 0.4570 | 0.4387 |
| 0.3013 | 3.06 | 1500 | 0.4437 | 0.4147 |
| 0.2136 | 3.27 | 1600 | 0.4176 | 0.4124 |
| 0.2131 | 3.47 | 1700 | 0.4281 | 0.4194 |
| 0.2099 | 3.67 | 1800 | 0.3864 | 0.3949 |
| 0.1925 | 3.88 | 1900 | 0.3926 | 0.3913 |
| 0.1709 | 4.08 | 2000 | 0.3764 | 0.3804 |
| 0.1406 | 4.29 | 2100 | 0.3787 | 0.3742 |
| 0.1342 | 4.49 | 2200 | 0.3645 | 0.3693 |
| 0.1305 | 4.69 | 2300 | 0.3463 | 0.3625 |
| 0.1298 | 4.9 | 2400 | 0.3418 | 0.3581 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
infinitejoy/wav2vec2-large-xls-r-300m-kyrgyz
|
infinitejoy
| 2022-03-24T11:52:31Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ky",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ky
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ky
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Kyrgyz
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ky
metrics:
- name: Test WER
type: wer
value: 40.908
- name: Test CER
type: cer
value: 10.999
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-kyrgyz
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KY dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5817
- Wer: 0.4096
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5412 | 18.69 | 2000 | 0.6161 | 0.5747 |
| 1.311 | 37.38 | 4000 | 0.5707 | 0.5070 |
| 1.1367 | 56.07 | 6000 | 0.5372 | 0.4664 |
| 0.9696 | 74.77 | 8000 | 0.5443 | 0.4328 |
| 0.8163 | 93.46 | 10000 | 0.5916 | 0.4124 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-georgian
|
infinitejoy
| 2022-03-24T11:52:25Z | 18 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ka",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ka
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ka
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Georgian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ka
metrics:
- name: Test WER
type: wer
value: 42.09
- name: Test CER
type: cer
value: 8.01
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ka
metrics:
- name: Test WER
type: wer
value: 65.32
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ka
metrics:
- name: Test WER
type: wer
value: 65.03
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-georgian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3666
- Wer: 0.4211
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8805 | 5.95 | 500 | 0.7547 | 0.8438 |
| 1.2123 | 11.9 | 1000 | 0.4732 | 0.6542 |
| 1.0822 | 17.86 | 1500 | 0.4027 | 0.5778 |
| 0.9938 | 23.81 | 2000 | 0.3847 | 0.5524 |
| 0.9383 | 29.76 | 2500 | 0.3845 | 0.5204 |
| 0.8932 | 35.71 | 3000 | 0.3833 | 0.5297 |
| 0.8495 | 41.67 | 3500 | 0.3759 | 0.5036 |
| 0.8201 | 47.62 | 4000 | 0.3616 | 0.4859 |
| 0.7794 | 53.57 | 4500 | 0.3874 | 0.4938 |
| 0.735 | 59.52 | 5000 | 0.3748 | 0.4782 |
| 0.7082 | 65.48 | 5500 | 0.3615 | 0.4675 |
| 0.669 | 71.43 | 6000 | 0.3797 | 0.4601 |
| 0.6457 | 77.38 | 6500 | 0.3812 | 0.4515 |
| 0.6098 | 83.33 | 7000 | 0.3660 | 0.4343 |
| 0.5874 | 89.29 | 7500 | 0.3640 | 0.4257 |
| 0.5627 | 95.24 | 8000 | 0.3661 | 0.4239 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
glob-asr/wav2vec2-large-xls-r-300m-guarani-small
|
glob-asr
| 2022-03-24T11:52:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"gn",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- gn
license: apache-2.0
tags:
- generated_from_trainer
- robust-speech-event
- gn
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-guarani-small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-guarani-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4964
- Wer: 0.5957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 6.65 | 100 | 1.1326 | 1.0 |
| 1.6569 | 13.32 | 200 | 0.5264 | 0.6478 |
| 1.6569 | 19.97 | 300 | 0.5370 | 0.6261 |
| 0.2293 | 26.65 | 400 | 0.4964 | 0.5957 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
comodoro/wav2vec2-xls-r-300m-pl-cv8
|
comodoro
| 2022-03-24T11:52:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"xlsr-fine-tuning-week",
"hf-asr-leaderboard",
"pl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- pl
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- xlsr-fine-tuning-week
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: Polish comodoro Wav2Vec2 XLSR 300M CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pl
metrics:
- name: Test WER
type: wer
value: 17.0
- name: Test CER
type: cer
value: 3.8
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pl
metrics:
- name: Test WER
type: wer
value: 38.97
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pl
metrics:
- name: Test WER
type: wer
value: 46.05
---
# wav2vec2-xls-r-300m-pl-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset.
It achieves the following results on the evaluation set while training:
- Loss: 0.1716
- Wer: 0.1697
- Cer: 0.0385
The `eval.py` script results are:
WER: 0.16970531733661967
CER: 0.03839135416519316
## Model description
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "pl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated using the attached `eval.py` script:
```
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl
```
## Training and evaluation data
The Common Voice 8.0 `train` and `validation` datasets were used for training
## Training procedure
### Training hyperparameters
The following hyperparameters were used:
- learning_rate: 1e-4
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 150
- mixed_precision_training: Native AMP
The training was interrupted after 3250 steps.
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
RASMUS/wav2vec2-xlsr-fi-lm-1B
|
RASMUS
| 2022-03-24T11:51:54Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"fi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- fi
license: apache-2.0
tags:
- generated_from_trainer
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
model-index:
- name: wav2vec2-xlsr-fi-lm-1B
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-fi-lm-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common voice train/dev/other datasets.
It achieves the following results on the evaluation set without language model:
- Loss: 0.1853
- Wer: 0.2205
With language model:
- Wer: 0.1026
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8158 | 0.67 | 400 | 0.4835 | 0.6310 |
| 0.5679 | 1.33 | 800 | 0.4806 | 0.5538 |
| 0.6055 | 2.0 | 1200 | 0.3888 | 0.5083 |
| 0.5353 | 2.67 | 1600 | 0.3258 | 0.4365 |
| 0.4883 | 3.33 | 2000 | 0.3313 | 0.4204 |
| 0.4513 | 4.0 | 2400 | 0.2924 | 0.3904 |
| 0.3753 | 4.67 | 2800 | 0.2593 | 0.3608 |
| 0.3478 | 5.33 | 3200 | 0.2832 | 0.3551 |
| 0.3796 | 6.0 | 3600 | 0.2495 | 0.3402 |
| 0.2556 | 6.67 | 4000 | 0.2342 | 0.3106 |
| 0.229 | 7.33 | 4400 | 0.2181 | 0.2812 |
| 0.205 | 8.0 | 4800 | 0.2041 | 0.2523 |
| 0.1654 | 8.67 | 5200 | 0.2015 | 0.2416 |
| 0.152 | 9.33 | 5600 | 0.1942 | 0.2294 |
| 0.1569 | 10.0 | 6000 | 0.1853 | 0.2205 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
HarrisDePerceptron/xls-r-300m-ur
|
HarrisDePerceptron
| 2022-03-24T11:51:43Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ur",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ur
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: ur
metrics:
- name: Test WER
type: wer
value: 47.38
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [HarrisDePerceptron/xls-r-300m-ur](https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0517
- WER: 0.5151291512915129
- CER: 0.23689640940982254
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.2991 | 1.96 | 100 | 0.9769 | 0.6627 |
| 1.3415 | 3.92 | 200 | 0.9701 | 0.6594 |
| 1.2998 | 5.88 | 300 | 0.9678 | 0.6668 |
| 1.2881 | 7.84 | 400 | 0.9650 | 0.6613 |
| 1.2369 | 9.8 | 500 | 0.9392 | 0.6502 |
| 1.2293 | 11.76 | 600 | 0.9536 | 0.6480 |
| 1.1709 | 13.73 | 700 | 0.9265 | 0.6402 |
| 1.1492 | 15.69 | 800 | 0.9636 | 0.6506 |
| 1.1044 | 17.65 | 900 | 0.9305 | 0.6351 |
| 1.0704 | 19.61 | 1000 | 0.9329 | 0.6280 |
| 1.0039 | 21.57 | 1100 | 0.9413 | 0.6295 |
| 0.9756 | 23.53 | 1200 | 0.9718 | 0.6185 |
| 0.9633 | 25.49 | 1300 | 0.9731 | 0.6133 |
| 0.932 | 27.45 | 1400 | 0.9659 | 0.6199 |
| 0.9252 | 29.41 | 1500 | 0.9766 | 0.6196 |
| 0.9172 | 31.37 | 1600 | 1.0052 | 0.6199 |
| 0.8733 | 33.33 | 1700 | 0.9955 | 0.6203 |
| 0.868 | 35.29 | 1800 | 1.0069 | 0.6240 |
| 0.8547 | 37.25 | 1900 | 0.9783 | 0.6258 |
| 0.8451 | 39.22 | 2000 | 0.9845 | 0.6052 |
| 0.8374 | 41.18 | 2100 | 0.9496 | 0.6137 |
| 0.8153 | 43.14 | 2200 | 0.9756 | 0.6122 |
| 0.8134 | 45.1 | 2300 | 0.9712 | 0.6096 |
| 0.8019 | 47.06 | 2400 | 0.9565 | 0.5970 |
| 0.7746 | 49.02 | 2500 | 0.9864 | 0.6096 |
| 0.7664 | 50.98 | 2600 | 0.9988 | 0.6092 |
| 0.7708 | 52.94 | 2700 | 1.0181 | 0.6255 |
| 0.7468 | 54.9 | 2800 | 0.9918 | 0.6148 |
| 0.7241 | 56.86 | 2900 | 1.0150 | 0.6018 |
| 0.7165 | 58.82 | 3000 | 1.0439 | 0.6063 |
| 0.7104 | 60.78 | 3100 | 1.0016 | 0.6037 |
| 0.6954 | 62.75 | 3200 | 1.0117 | 0.5970 |
| 0.6753 | 64.71 | 3300 | 1.0191 | 0.6037 |
| 0.6803 | 66.67 | 3400 | 1.0190 | 0.6033 |
| 0.661 | 68.63 | 3500 | 1.0284 | 0.6007 |
| 0.6597 | 70.59 | 3600 | 1.0060 | 0.5967 |
| 0.6398 | 72.55 | 3700 | 1.0372 | 0.6048 |
| 0.6105 | 74.51 | 3800 | 1.0048 | 0.6044 |
| 0.6164 | 76.47 | 3900 | 1.0398 | 0.6148 |
| 0.6354 | 78.43 | 4000 | 1.0272 | 0.6133 |
| 0.5952 | 80.39 | 4100 | 1.0364 | 0.6081 |
| 0.5814 | 82.35 | 4200 | 1.0418 | 0.6092 |
| 0.6079 | 84.31 | 4300 | 1.0277 | 0.5967 |
| 0.5748 | 86.27 | 4400 | 1.0362 | 0.6041 |
| 0.5624 | 88.24 | 4500 | 1.0427 | 0.6007 |
| 0.5767 | 90.2 | 4600 | 1.0370 | 0.5919 |
| 0.5793 | 92.16 | 4700 | 1.0442 | 0.6011 |
| 0.547 | 94.12 | 4800 | 1.0516 | 0.5982 |
| 0.5513 | 96.08 | 4900 | 1.0461 | 0.5989 |
| 0.5429 | 98.04 | 5000 | 1.0504 | 0.5996 |
| 0.5404 | 100.0 | 5100 | 1.0517 | 0.5967 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
lsb/wav2vec2-base-it-latin
|
lsb
| 2022-03-24T11:51:21Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"hf-asr-leaderboard",
"la",
"dataset:lsb/poetaexmachina-mp3-recitations",
"license:agpl-3.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- la
license: agpl-3.0
tags:
- robust-speech-event
- hf-asr-leaderboard
datasets:
- lsb/poetaexmachina-mp3-recitations
metrics:
- wer
model-index:
- name: wav2vec2-base-it-latin
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: lsb/poetaexmachina-mp3-recitations
name: Poeta Ex Machina mp3 recitations
metrics:
- type: wer
value: 0.398
name: Test WER
---
---
# wav2vec2-base-it-latin
This model is a fine-tuned version of [wav2vec2-base-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-base-it-voxpopuli)
The dataset used is the [poetaexmachina-mp3-recitations](https://github.com/lsb/poetaexmachina-mp3-recitations),
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's [database](https://github.com/lsb/poetaexmachina/blob/master/merged-scansions.db) of words with scansions).
It achieves the following [results](https://github.com/lsb/tironiculum/blame/trunk/wav2vec2%20base%20it%20latin.ipynb#L1234) on the evaluation set:
- Loss: 0.1943
- WER: 0.398
|
infinitejoy/wav2vec2-large-xls-r-300m-bulgarian
|
infinitejoy
| 2022-03-24T11:47:30Z | 445 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"bg",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bg
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- bg
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Bulgarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: bg
metrics:
- name: Test WER
type: wer
value: 46.68
- name: Test CER
type: cer
value: 10.75
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: bg
metrics:
- name: Test WER
type: wer
value: 63.68
- name: Test CER
type: cer
value: 19.88
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: bg
metrics:
- name: Test WER
type: wer
value: 64.08
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bulgarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4487
- Wer: 0.4674
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9774 | 6.33 | 500 | 2.9769 | 1.0 |
| 1.3453 | 12.66 | 1000 | 0.6523 | 0.6980 |
| 1.1658 | 18.99 | 1500 | 0.5636 | 0.6359 |
| 1.0797 | 25.32 | 2000 | 0.5004 | 0.5759 |
| 1.044 | 31.65 | 2500 | 0.4958 | 0.5569 |
| 0.9915 | 37.97 | 3000 | 0.4971 | 0.5350 |
| 0.9429 | 44.3 | 3500 | 0.4829 | 0.5229 |
| 0.9266 | 50.63 | 4000 | 0.4515 | 0.5074 |
| 0.8965 | 56.96 | 4500 | 0.4599 | 0.5039 |
| 0.878 | 63.29 | 5000 | 0.4735 | 0.4954 |
| 0.8494 | 69.62 | 5500 | 0.4460 | 0.4878 |
| 0.8343 | 75.95 | 6000 | 0.4510 | 0.4795 |
| 0.8236 | 82.28 | 6500 | 0.4538 | 0.4789 |
| 0.8069 | 88.61 | 7000 | 0.4526 | 0.4748 |
| 0.7958 | 94.94 | 7500 | 0.4496 | 0.4700 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
|
JustAdvanceTechonology
| 2022-03-24T11:33:03Z | 5 | 4 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-24T10:20:14Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# JustAdvanceTechonology/bert-fine-tuned-medical-insurance-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0269
- Validation Loss: 0.0551
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1775 | 0.0646 | 0 |
| 0.0454 | 0.0580 | 1 |
| 0.0269 | 0.0551 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.5.0
- Datasets 1.18.3
- Tokenizers 0.11.6
|
joe5campbell/Horovod_Tweet_Sentiment_1k_5eps
|
joe5campbell
| 2022-03-24T11:01:59Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-24T11:01:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Horovod_Tweet_Sentiment_1k_5eps
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Horovod_Tweet_Sentiment_1k_5eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5216092
- Train Accuracy: 0.784375
- Validation Loss: 0.92405033
- Validation Accuracy: 0.4875
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.7129049 | 0.50937504 | 0.7314203 | 0.490625 | 0 |
| 0.73165804 | 0.47343752 | 0.6929074 | 0.484375 | 1 |
| 0.6827939 | 0.55 | 0.6864271 | 0.50625 | 2 |
| 0.66076773 | 0.5578125 | 0.60817575 | 0.69687504 | 3 |
| 0.5216092 | 0.784375 | 0.92405033 | 0.4875 | 4 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.6.0
- Tokenizers 0.11.6
|
huggingtweets/vi0linheart
|
huggingtweets
| 2022-03-24T10:11:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T10:09:41Z |
---
language: en
thumbnail: http://www.huggingtweets.com/vi0linheart/1648116634962/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500859213622300673/izXwf0KK_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">sal</div>
<div style="text-align: center; font-size: 14px;">@vi0linheart</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from sal.
| Data | sal |
| --- | --- |
| Tweets downloaded | 3114 |
| Retweets | 421 |
| Short tweets | 541 |
| Tweets kept | 2152 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21y9qo98/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vi0linheart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3t019c6m) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3t019c6m/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/vi0linheart')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/madeleine
|
huggingtweets
| 2022-03-24T09:38:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T09:37:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/madeleine/1648114714373/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1227670393453936642/6rdB_DqU_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Madeleine Albright</div>
<div style="text-align: center; font-size: 14px;">@madeleine</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Madeleine Albright.
| Data | Madeleine Albright |
| --- | --- |
| Tweets downloaded | 1111 |
| Retweets | 249 |
| Short tweets | 3 |
| Tweets kept | 859 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2a3z3e8y/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @madeleine's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q01k6dh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q01k6dh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/madeleine')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/kytalli-vi0linheart
|
huggingtweets
| 2022-03-24T09:38:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T09:25:29Z |
---
language: en
thumbnail: http://www.huggingtweets.com/kytalli-vi0linheart/1648114676311/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500859213622300673/izXwf0KK_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1376749372831002627/2B9FZTnI_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">sal & G</div>
<div style="text-align: center; font-size: 14px;">@kytalli-vi0linheart</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from sal & G.
| Data | sal | G |
| --- | --- | --- |
| Tweets downloaded | 3114 | 3249 |
| Retweets | 421 | 55 |
| Short tweets | 541 | 226 |
| Tweets kept | 2152 | 2968 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tj76wad/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kytalli-vi0linheart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a1bludi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a1bludi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/kytalli-vi0linheart')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
niksmer/PolicyBERTa-7d
|
niksmer
| 2022-03-24T09:19:57Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
model-index:
- name: PolicyBERTa-7d
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# PolicyBERTa-7d
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d).
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Accuracy: 0.7059
- F1-micro: 0.7059
- F1-macro: 0.6683
- F1-weighted: 0.7033
- Precision: 0.7059
- Recall: 0.7059
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of seven political categories: "external relations", "freedom and democracy", "political system", "economy", "welfare and quality of life", "fabric of society" and "social groups".
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/PolicyBERTa-7d")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Training and evaluation data
PolicyBERTa-7d was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The Manifesto Project mannually annotates individual sentences from political party manifestos in 7 main political domains: 'Economy', 'External Relations', 'Fabric of Society', 'Freedom and Democracy', 'Political System', 'Welfare and Quality of Life' or 'Social Groups' - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each domain.
### Tain data
Train data was higly imbalanced.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 7,640 |
| 1 | freedom and democracy | 5,880 |
| 2 | political system | 11,234 |
| 3 | economy | 29,218 |
| 4 | welfare and quality of life | 37,200 |
| 5 | fabric of society | 13,594 |
| 6 | social groups | 11,177 |
Overall count: 115,943
### Validation data
The validation was created by chance.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 1,345 |
| 1 | freedom and democracy | 1,043 |
| 2 | political system | 2,038 |
| 3 | economy | 5,140 |
| 4 | welfare and quality of life | 6,554 |
| 5 | fabric of society | 2,384 |
| 6 | social groups | 1,957 |
Overall count: 20,461
## Test data
The test dataset contains ten canadian manifestos between 2004 and 2008.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | external relations | 824 |
| 1 | freedom and democracy | 296 |
| 2 | political system | 1,041 |
| 3 | economy | 2,188 |
| 4 | welfare and quality of life | 2,654 |
| 5 | fabric of society | 940 |
| 6 | social groups | 387 |
Overall count: 8,330
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_steps=0,
weight_decay=0.1,
learning_rate=1e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
overwrite_output_dir=True,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.9154 | 1.0 | 1812 | 0.8984 | 0.6785 | 0.6785 | 0.6383 | 0.6772 | 0.6785 | 0.6785 |
| 0.8374 | 2.0 | 3624 | 0.8569 | 0.6957 | 0.6957 | 0.6529 | 0.6914 | 0.6957 | 0.6957 |
| 0.7053 | 3.0 | 5436 | 0.8582 | 0.7019 | 0.7019 | 0.6594 | 0.6967 | 0.7019 | 0.7019 |
| 0.7178 | 4.0 | 7248 | 0.8488 | 0.7030 | 0.7030 | 0.6662 | 0.7011 | 0.7030 | 0.7030 |
| 0.6688 | 5.0 | 9060 | 0.8549 | 0.7059 | 0.7059 | 0.6683 | 0.7033 | 0.7059 | 0.7059 |
### Validation evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| PolicyBERTa-7d | 0.71 | 0.67 | 0.70 |
### Test evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| PolicyBERTa-7d | 0.65 | 0.60 | 0.65 |
### Evaluation per category
| Label | Validation F1-Score | Test F1-Score |
|-----------------------------|---------------------|---------------|
| external relations | 0.76 | 0.70 |
| freedom and democracy | 0.61 | 0.55 |
| political system | 0.55 | 0.55 |
| economy | 0.74 | 0.67 |
| welfare and quality of life | 0.77 | 0.72 |
| fabric of society | 0.67 | 0.60 |
| social groups | 0.58 | 0.41 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions). But PolicyBERTa isn't fine-tuned to predict the rile-index, if you're interested in that, check [ManiBERT](https://huggingface.co/niksmer/ManiBERT) or [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE).
In the following table, the predicted and original share of the individual policy domains are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original shares is 0.965.
| Party-ID | Year | Type | Share external relations | Share freedom and democracy | Share political system | Share economy | Share welfare and quality of life | Share fabric of society | Share social groups |
|--------------|-------------|---------------|--------------------------|-----------------------------|------------------------|----------------|-----------------------------------|-------------------------|---------------------|
| 62320 | 2004 | Predicted | 7.1% | 4.8% | 13.2% | 20.3% | 35.2% | 9.6% | 9.8% |
| | | Original | 10.2% | 2.5% | 13.7% | 23.8% | 31.7% | 11.6% | 6.4% |
| 62320 | 2006 | Predicted | 2.9% | 4.7% | 16.4% | 18.9% | 38.3% | 11.9% | 6.9% |
| | | Original | 5.6% | 5.0% | 15.8% | 20.7% | 38.7% | 9.3% | 4.9% |
| 62320 | 2008 | Predicted | 6.8% | 4.7% | 6.2% | 24.7% | 38.3% | 10.3% | 9.0% |
| | | Original | 5.6% | 3.7% | 8.2% | 33.1% | 29.5% | 11.7% | 4.3% |
| 62420 | 2004 | Predicted | 9.7% | 3.5% | 14.5% | 24.7% | 34.8% | 8.5% | 4.3% |
| | | Original | 12.6% | 1.3% | 18.8% | 23.0% | 33.2% | 9.0% | 2.0% |
| 62420 | 2006 | Predicted | 9.5% | 2.2% | 7.9% | 27.8% | 34.8% | 9.2% | 8.7% |
| | | Original | 10.6% | 2.5% | 9.6% | 29.7% | 33.1% | 8.3% | 6.2% |
| 62420 | 2008 | Predicted | 0.7% | 0.5% | 3.5% | 41.7% | 46.4% | 3.7% | 3.5% |
| | | Original | 2.0% | 0.2% | 4.4% | 33.3% | 45.9% | 7.7% | 6.4% |
| 62623 | 2004 | Predicted | 7.1% | 11.4% | 24.5% | 17.6% | 21.5% | 13.6% | 4.3% |
| | | Original | 8.4% | 6.7% | 28.8% | 17.4% | 18.7% | 15.5% | 4.5% |
| 62623 | 2006 | Predicted | 5.6% | 8.5% | 23.6% | 15.6% | 14.8% | 24.3% | 7.6% |
| | | Original | 5.0% | 8.9% | 22.2% | 17.4% | 17.2% | 25.7% | 3.6% |
| 62623 | 2008 | Predicted | 5.0% | 4.4% | 12.2% | 33.1% | 21.9% | 17.5% | 5.9% |
| | | Original | 5.6% | 2.2% | 11.6% | 37.8% | 17.8% | 20.9% | 4.1% |
| 62110 | 2008 | Predicted | 10.0% | 3.1% | 6.8% | 22.7% | 41.3% | 10.1% | 6.0% |
| | | Original | 13.4% | 3.3% | 7.7% | 26.9% | 35.6% | 8.9% | 4.3% |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
niksmer/RoBERTa-RILE
|
niksmer
| 2022-03-24T09:19:40Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
metrics:
- accuracy
- precision
- recall
model-index:
- name: RoBERTa-RILE
results: []
widget:
- text: "Russia must end the war."
- text: "Democratic institutions must be supported."
- text: "The state must fight political corruption."
- text: "Our energy economy must be nationalised."
- text: "We must increase social spending."
---
# RoBERTa-RILE
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/).
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of three political categories: "neutral", "left", "right".
## Intended uses & limitations
The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
```python
from transformers import pipeline
import pandas as pd
classifier = pipeline(
task="text-classification",
model="niksmer/RoBERTa-RILE")
# Load text data you want to classify
text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list()
# Inference
output = classifier(text)
# Print output
pd.DataFrame(output).head()
```
## Training and evaluation data
## Training and evaluation data
RoBERTa-RILE was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020.
| Country | Count manifestos | Count sentences | Time span |
|----------------|------------------|-----------------|--------------------|
| Australia | 18 | 14,887 | 2010-2016 |
| Ireland | 23 | 24,966 | 2007-2016 |
| Canada | 14 | 12,344 | 2004-2008 & 2015 |
| New Zealand | 46 | 35,079 | 1993-2017 |
| South Africa | 29 | 13,334 | 1994-2019 |
| USA | 9 | 13,188 | 1992 & 2004-2020 |
| United Kingdom | 34 | 30,936 | 1997-2019 |
Canadian manifestos between 2004 and 2008 are used as test data.
The Manifesto Project mannually annotates individual sentences from political party manifestos in over 50 main categories - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each categorie. It has created a valid left-right-scale, the rile-index, to aaggregate manifesto in a standardized, onde-dimensional political space from left to right based on saliency-theory.
RoBERTa-RILE classifies texts based on the rile index.
### Tain data
Train data was slightly imbalanced.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 52,277 |
| 1 | left | 37,106 |
| 2 | right | 26,560 |
Overall count: 115,943
### Validation data
The validation was created by chance.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 9,198 |
| 1 | left | 6,637 |
| 2 | right | 4,626 |
Overall count: 20,461
### Test data
The test dataset contains ten canadian manifestos between 2004 and 2008.
| Label | Description | Count |
|------------|--------------|--------|
| 0 | neutral | 3,881 |
| 1 | left | 2,611 |
| 2 | right | 1,838 |
Overall count: 8,330
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
```
training_args = TrainingArguments(
warmup_ratio=0.05,
weight_decay=0.1,
learning_rate=1e-05,
fp16 = True,
evaluation_strategy="epoch",
num_train_epochs=5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
save_strategy="no",
logging_dir='logs',
logging_strategy= 'steps',
logging_steps=10,
push_to_hub=True,
hub_strategy="end")
```
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.7442 | 1.0 | 1812 | 0.6827 | 0.7120 | 0.7120 | 0.7007 | 0.7126 | 0.7120 | 0.7120 |
| 0.6447 | 2.0 | 3624 | 0.6618 | 0.7281 | 0.7281 | 0.7169 | 0.7281 | 0.7281 | 0.7281 |
| 0.5467 | 3.0 | 5436 | 0.6657 | 0.7309 | 0.7309 | 0.7176 | 0.7295 | 0.7309 | 0.7309 |
| 0.5179 | 4.0 | 7248 | 0.6654 | 0.7346 | 0.7346 | 0.7240 | 0.7345 | 0.7346 | 0.7346 |
| 0.4787 | 5.0 | 9060 | 0.6757 | 0.7350 | 0.7350 | 0.7241 | 0.7347 | 0.7350 | 0.7350 |
### Validation evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| RoBERTa-RILE | 0.74 | 0.72 | 0.73 |
### Test evaluation
| Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
|----------------|----------------|----------------|-------------------|
| RoBERTa-RILE | 0.69 | 0.67 | 0.69 |
### Evaluation per category
| Label | Validation F1-Score | Test F1-Score |
|-----------------------------|---------------------|---------------|
| neutral | 0.77 | 0.74 |
| left | 0.73 | 0.65 |
| right | 0.67 | 0.62 |
### Evaluation based on saliency theory
Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions).
In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [ManiBERT](https://huggingface.co/niksmer/ManiBERT).
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3
|
buvnswrn/daml-t5-pretrain
|
buvnswrn
| 2022-03-24T09:08:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T07:11:08Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- imdb
model-index:
- name: daml-t5-pretrain-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# daml-t5-pretrain-imdb
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
tartuNLP/liv4ever-hugging-mt
|
tartuNLP
| 2022-03-24T07:33:01Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"fsmt",
"text2text-generation",
"translation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T01:38:25Z |
---
license: apache-2.0
tags:
- translation
widget:
- text: "<2li> Let us generate some Livonian text!"
---
|
tiennvcs/distilbert-base-uncased-finetuned-ner
|
tiennvcs
| 2022-03-24T07:29:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-24T07:17:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9264836138175376
- name: Recall
type: recall
value: 0.9361226087929299
- name: F1
type: f1
value: 0.9312781703856213
- name: Accuracy
type: accuracy
value: 0.9836529143565221
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0616
- Precision: 0.9265
- Recall: 0.9361
- F1: 0.9313
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2437 | 1.0 | 878 | 0.0745 | 0.9144 | 0.9173 | 0.9158 | 0.9799 |
| 0.0518 | 2.0 | 1756 | 0.0621 | 0.9177 | 0.9353 | 0.9264 | 0.9826 |
| 0.03 | 3.0 | 2634 | 0.0616 | 0.9265 | 0.9361 | 0.9313 | 0.9837 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Pavithra/codeparrot-ds-sample
|
Pavithra
| 2022-03-24T06:41:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T05:12:32Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-sample
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-sample
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5219
- eval_runtime: 603.3856
- eval_samples_per_second: 154.402
- eval_steps_per_second: 4.826
- epoch: 0.15
- step: 10000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
FuriouslyAsleep/unhappyZebra100
|
FuriouslyAsleep
| 2022-03-24T04:39:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:FuriouslyAsleep/autotrain-data-techDataClassifeier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-24T04:38:22Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- FuriouslyAsleep/autotrain-data-techDataClassifeier
co2_eq_emissions: 0.6969569001670619
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 664919631
- CO2 Emissions (in grams): 0.6969569001670619
## Validation Metrics
- Loss: 0.022509008646011353
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- AUC: 1.0
- F1: 1.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/FuriouslyAsleep/autotrain-techDataClassifeier-664919631
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("FuriouslyAsleep/autotrain-techDataClassifeier-664919631", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("FuriouslyAsleep/autotrain-techDataClassifeier-664919631", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
rurupang/roberta-base-finetuned-sts
|
rurupang
| 2022-03-24T01:54:26Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-22T14:13:32Z |
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: roberta-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.956039443806831
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-sts
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1999
- Pearsonr: 0.9560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 329 | 0.2462 | 0.9478 |
| 1.2505 | 2.0 | 658 | 0.1671 | 0.9530 |
| 1.2505 | 3.0 | 987 | 0.1890 | 0.9525 |
| 0.133 | 4.0 | 1316 | 0.2360 | 0.9548 |
| 0.0886 | 5.0 | 1645 | 0.2265 | 0.9528 |
| 0.0886 | 6.0 | 1974 | 0.2097 | 0.9518 |
| 0.0687 | 7.0 | 2303 | 0.2281 | 0.9523 |
| 0.0539 | 8.0 | 2632 | 0.2212 | 0.9542 |
| 0.0539 | 9.0 | 2961 | 0.1843 | 0.9532 |
| 0.045 | 10.0 | 3290 | 0.1999 | 0.9560 |
| 0.0378 | 11.0 | 3619 | 0.2357 | 0.9533 |
| 0.0378 | 12.0 | 3948 | 0.2134 | 0.9541 |
| 0.033 | 13.0 | 4277 | 0.2273 | 0.9540 |
| 0.03 | 14.0 | 4606 | 0.2148 | 0.9533 |
| 0.03 | 15.0 | 4935 | 0.2207 | 0.9534 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/btohtoh
|
huggingtweets
| 2022-03-24T01:35:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-24T01:35:48Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506402743296020484/X79Yfcx5_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">BToh</div>
<div style="text-align: center; font-size: 14px;">@btohtoh</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from BToh.
| Data | BToh |
| --- | --- |
| Tweets downloaded | 3241 |
| Retweets | 347 |
| Short tweets | 480 |
| Tweets kept | 2414 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xnk5832/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btohtoh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gdcu3k6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gdcu3k6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/btohtoh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
microsoft/amos
|
microsoft
| 2022-03-24T01:24:38Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-24T01:16:31Z |
---
license: mit
---
# Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
This model card contains the AMOS model (**base++** version) proposed in [this paper](). The official GitHub repository can be found [here](https://github.com/microsoft/AMOS).
# Citation
If you find this model card useful for your research, please cite the following paper:
```
@inproceedings{meng2022amos,
title={Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators},
author={Meng, Yu and Xiong, Chenyan and Bajaj, Payal and Tiwary, Saurabh and Bennett, Paul and Han, Jiawei and Song, Xia},
booktitle={ICLR},
year={2022}
}
```
|
negfir/distilbert-base-uncased-finetuned-cola
|
negfir
| 2022-03-24T00:39:00Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-15T15:29:20Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: negfir/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# negfir/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [negfir/uncased_L-12_H-128_A-2](https://huggingface.co/negfir/uncased_L-12_H-128_A-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6077
- Validation Loss: 0.6185
- Train Matthews Correlation: 0.0
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.6116 | 0.6187 | 0.0 | 0 |
| 0.6070 | 0.6190 | 0.0 | 1 |
| 0.6077 | 0.6185 | 0.0 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
public-data/dlib_face_landmark_model
|
public-data
| 2022-03-23T22:54:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-23T22:52:02Z |
# dlib face landmark model
- http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
|
ydshieh/roberta-large-ner-english
|
ydshieh
| 2022-03-23T22:24:57Z | 36 | 2 |
transformers
|
[
"transformers",
"tf",
"roberta",
"token-classification",
"en",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-23T22:13:16Z |
---
language: en
datasets:
- conll2003
widget:
- text: "My name is jean-baptiste and I live in montreal"
- text: "My name is clara and I live in berkeley, california."
- text: "My name is wolfgang and I live in berlin"
---
# roberta-large-ner-english: model fine-tuned from roberta-large for NER task
## Introduction
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on emails/chat data and outperformed other models on this type of data specifically.
In particular the model seems to work better on entity that don't start with an upper case.
## Training data
Training data was classified as follow:
Abbreviation|Description
-|-
O |Outside of a named entity
MISC |Miscellaneous entity
PER |Person’s name
ORG |Organization
LOC |Location
In order to simplify, the prefix B- or I- from original conll2003 was removed.
I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size:
Train | Validation
-|-
17494 | 3250
## How to use camembert-ner with HuggingFace
##### Load camembert-ner and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
##### Process text sample (from wikipedia)
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal computer")
[{'entity_group': 'ORG',
'score': 0.99381506,
'word': ' Apple',
'start': 0,
'end': 5},
{'entity_group': 'PER',
'score': 0.99970853,
'word': ' Steve Jobs',
'start': 29,
'end': 39},
{'entity_group': 'PER',
'score': 0.99981767,
'word': ' Steve Wozniak',
'start': 41,
'end': 54},
{'entity_group': 'PER',
'score': 0.99956465,
'word': ' Ronald Wayne',
'start': 59,
'end': 71},
{'entity_group': 'PER',
'score': 0.9997918,
'word': ' Wozniak',
'start': 92,
'end': 99},
{'entity_group': 'MISC',
'score': 0.99956393,
'word': ' Apple I',
'start': 102,
'end': 109}]
```
## Model performances
Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
entity|precision|recall|f1
-|-|-|-
PER|0.9914|0.9927|0.9920
ORG|0.9627|0.9661|0.9644
LOC|0.9795|0.9862|0.9828
MISC|0.9292|0.9262|0.9277
Overall|0.9740|0.9766|0.9753
On private dataset (email, chat, informal discussion), computed on word predictions:
entity|precision|recall|f1
-|-|-|-
PER|0.8823|0.9116|0.8967
ORG|0.7694|0.7292|0.7487
LOC|0.8619|0.7768|0.8171
By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving:
entity|precision|recall|f1
-|-|-|-
PER|0.9146|0.8287|0.8695
ORG|0.7655|0.6437|0.6993
LOC|0.8727|0.6180|0.7236
|
huggingtweets/coscorrodrift
|
huggingtweets
| 2022-03-23T22:21:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T21:14:41Z |
---
language: en
thumbnail: http://www.huggingtweets.com/coscorrodrift/1648073956402/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1363260889164623877/vz-U9f3l_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">coscorrodrift</div>
<div style="text-align: center; font-size: 14px;">@coscorrodrift</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from coscorrodrift.
| Data | coscorrodrift |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 192 |
| Short tweets | 405 |
| Tweets kept | 2650 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3elna51z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @coscorrodrift's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mof7q9s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mof7q9s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/coscorrodrift')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
bigmorning/my-gpt-model-4
|
bigmorning
| 2022-03-23T20:00:04Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:52:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-gpt-model-4
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.0556
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 5.0556 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
huggingtweets/interrogami
|
huggingtweets
| 2022-03-23T19:41:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:19:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/interrogami/1648064415193/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1502292592914046984/F1N4kjHh_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">interrobang</div>
<div style="text-align: center; font-size: 14px;">@interrogami</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from interrobang.
| Data | interrobang |
| --- | --- |
| Tweets downloaded | 1453 |
| Retweets | 20 |
| Short tweets | 139 |
| Tweets kept | 1294 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1awhdfgt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @interrogami's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ibo4fum) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ibo4fum/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/interrogami')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BigSalmon/MASKGPT2
|
BigSalmon
| 2022-03-23T19:26:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-23T19:20:45Z |
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
|
DrishtiSharma/wav2vec2-xls-r-sl-a1
|
DrishtiSharma
| 2022-03-23T18:35:30Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sl
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-sl-a1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 0.20626555409164105
- name: Test CER
type: cer
value: 0.051648321634392154
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Test WER
type: wer
value: 0.5406156320830592
- name: Test CER
type: cer
value: 0.22249723590310583
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 55.24
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2756
- Wer: 0.2279
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3881 | 6.1 | 500 | 2.9710 | 1.0 |
| 2.6401 | 12.2 | 1000 | 1.7677 | 0.9734 |
| 1.5152 | 18.29 | 1500 | 0.5564 | 0.6011 |
| 1.2191 | 24.39 | 2000 | 0.4319 | 0.4390 |
| 1.0237 | 30.49 | 2500 | 0.3141 | 0.3175 |
| 0.8892 | 36.59 | 3000 | 0.2748 | 0.2689 |
| 0.8296 | 42.68 | 3500 | 0.2680 | 0.2534 |
| 0.7602 | 48.78 | 4000 | 0.2820 | 0.2506 |
| 0.7186 | 54.88 | 4500 | 0.2672 | 0.2398 |
| 0.6887 | 60.98 | 5000 | 0.2729 | 0.2402 |
| 0.6507 | 67.07 | 5500 | 0.2767 | 0.2361 |
| 0.6226 | 73.17 | 6000 | 0.2817 | 0.2332 |
| 0.6024 | 79.27 | 6500 | 0.2679 | 0.2279 |
| 0.5787 | 85.37 | 7000 | 0.2837 | 0.2316 |
| 0.5744 | 91.46 | 7500 | 0.2838 | 0.2284 |
| 0.5556 | 97.56 | 8000 | 0.2763 | 0.2281 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11
|
DrishtiSharma
| 2022-03-23T18:35:27Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- rm-sursilv
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-rm-sursilv-d11
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 8
args: rm-sursilv
metrics:
- type: wer
value: 0.24094169578811844
name: Test WER
- name: Test CER
type: cer
value: 0.049832791672554284
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: rm-sursilv
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RM-SURSILV dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2511
- Wer: 0.2415
#### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 --dataset mozilla-foundation/common_voice_8_0 --config rm-sursilv --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Romansh-Sursilv language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 125.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.3958 | 17.44 | 1500 | 0.6808 | 0.6521 |
| 0.9663 | 34.88 | 3000 | 0.3023 | 0.3718 |
| 0.7963 | 52.33 | 4500 | 0.2588 | 0.3046 |
| 0.6893 | 69.77 | 6000 | 0.2436 | 0.2718 |
| 0.6148 | 87.21 | 7500 | 0.2521 | 0.2572 |
| 0.5556 | 104.65 | 9000 | 0.2490 | 0.2442 |
| 0.5258 | 122.09 | 10500 | 0.2515 | 0.2442 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1
|
DrishtiSharma
| 2022-03-23T18:35:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sl
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-sl-with-LM-v1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 0.20626555409164105
- name: Test CER
type: cer
value: 0.051648321634392154
- name: Test WER (+LM)
type: wer
value: 0.13482652613087395
- name: Test CER (+LM)
type: cer
value: 0.038838663862562475
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Dev WER
type: wer
value: 0.5406156320830592
- name: Dev CER
type: cer
value: 0.22249723590310583
- name: Dev WER (+LM)
type: wer
value: 0.49783147459727384
- name: Dev CER (+LM)
type: cer
value: 0.1591062599627158
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 46.17
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2756
- Wer: 0.2279
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3881 | 6.1 | 500 | 2.9710 | 1.0 |
| 2.6401 | 12.2 | 1000 | 1.7677 | 0.9734 |
| 1.5152 | 18.29 | 1500 | 0.5564 | 0.6011 |
| 1.2191 | 24.39 | 2000 | 0.4319 | 0.4390 |
| 1.0237 | 30.49 | 2500 | 0.3141 | 0.3175 |
| 0.8892 | 36.59 | 3000 | 0.2748 | 0.2689 |
| 0.8296 | 42.68 | 3500 | 0.2680 | 0.2534 |
| 0.7602 | 48.78 | 4000 | 0.2820 | 0.2506 |
| 0.7186 | 54.88 | 4500 | 0.2672 | 0.2398 |
| 0.6887 | 60.98 | 5000 | 0.2729 | 0.2402 |
| 0.6507 | 67.07 | 5500 | 0.2767 | 0.2361 |
| 0.6226 | 73.17 | 6000 | 0.2817 | 0.2332 |
| 0.6024 | 79.27 | 6500 | 0.2679 | 0.2279 |
| 0.5787 | 85.37 | 7000 | 0.2837 | 0.2316 |
| 0.5744 | 91.46 | 7500 | 0.2838 | 0.2284 |
| 0.5556 | 97.56 | 8000 | 0.2763 | 0.2281 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
masapasa/xls-r-300m-it-cv8-ds13
|
masapasa
| 2022-03-23T18:35:02Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"it",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- it
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: it
metrics:
- name: Test WER
type: wer
value: 100.0
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: it
metrics:
- name: Test WER
type: wer
value: 100.0
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: it
metrics:
- name: Test WER
type: wer
value: 100.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3549
- Wer: 0.3827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4129 | 5.49 | 500 | 3.3224 | 1.0 |
| 2.9323 | 10.98 | 1000 | 2.9128 | 1.0000 |
| 1.6839 | 16.48 | 1500 | 0.7740 | 0.6854 |
| 1.485 | 21.97 | 2000 | 0.5830 | 0.5976 |
| 1.362 | 27.47 | 2500 | 0.4866 | 0.4905 |
| 1.2752 | 32.96 | 3000 | 0.4240 | 0.4967 |
| 1.1957 | 38.46 | 3500 | 0.3899 | 0.4258 |
| 1.1646 | 43.95 | 4000 | 0.3597 | 0.4014 |
| 1.1265 | 49.45 | 4500 | 0.3559 | 0.3829 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
manifoldix/xlsr-sg-lm
|
manifoldix
| 2022-03-23T18:34:59Z | 9 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"gsw",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: gsw
tags:
- hf-asr-leaderboard
- robust-speech-event
widget:
- example_title: swiss parliament sample 1
src: https://huggingface.co/manifoldix/xlsr-sg-lm/resolve/main/07e73bcaa2ab192aea9524d72db45f34f274d1b3d5672434c462d32d44d792be.mp3
- example_title: swiss parliament sample 2
src: https://huggingface.co/manifoldix/xlsr-sg-lm/resolve/main/14a2f855363920f111c7b30e8632c19e5f340ab5031e1ed2621db39baf452ae0.mp3
model-index:
- name: XLS-R-1b Wav2Vec2 Swiss German
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER on Swiss parliament
type: wer
value: 34.6%
- name: Test WER on Swiss dialect test set
type: wer
value: 40%
---
## XLSR-1b Swiss German
Fine-tuned on the Swiss parliament dataset from FHNW v1 (70h).
Tested on the Swiss parliament test set with a WER of 34.6%
Tested on the "Swiss German Dialects" with a WER of 40%
Both test sets can be accessed here: [fhnw_datasets](https://www.cs.technik.fhnw.ch/i4ds-datasets)
The Swiss German dialect private test set has been uploaded on huggingface: [huggingface_swiss_dialects](https://huggingface.co/datasets/manifoldix/swg_parliament_fhnw)
|
infinitejoy/wav2vec2-large-xls-r-300m-hungarian
|
infinitejoy
| 2022-03-23T18:34:54Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hu",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Hungarian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: hu
metrics:
- name: Test WER
type: wer
value: 31.099
- name: Test CER
type: cer
value: 6.737
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hu
metrics:
- name: Test WER
type: wer
value: 45.469
- name: Test CER
type: cer
value: 15.727
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: hu
metrics:
- name: Test WER
type: wer
value: 48.2
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hungarian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HU dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2562
- Wer: 0.3112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.3964 | 3.52 | 1000 | 1.2251 | 0.8781 |
| 1.3176 | 7.04 | 2000 | 0.3872 | 0.4462 |
| 1.1999 | 10.56 | 3000 | 0.3244 | 0.3922 |
| 1.1633 | 14.08 | 4000 | 0.3014 | 0.3704 |
| 1.1132 | 17.61 | 5000 | 0.2913 | 0.3623 |
| 1.0888 | 21.13 | 6000 | 0.2864 | 0.3498 |
| 1.0487 | 24.65 | 7000 | 0.2821 | 0.3435 |
| 1.0431 | 28.17 | 8000 | 0.2739 | 0.3308 |
| 0.9896 | 31.69 | 9000 | 0.2629 | 0.3243 |
| 0.9839 | 35.21 | 10000 | 0.2806 | 0.3308 |
| 0.9586 | 38.73 | 11000 | 0.2650 | 0.3235 |
| 0.9501 | 42.25 | 12000 | 0.2585 | 0.3173 |
| 0.938 | 45.77 | 13000 | 0.2561 | 0.3117 |
| 0.921 | 49.3 | 14000 | 0.2559 | 0.3115 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-hindi
|
infinitejoy
| 2022-03-23T18:34:51Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hi
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Hindi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 100
- name: Test CER
type: cer
value: 92.98
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5414
- Wer: 1.0194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.6095 | 3.38 | 500 | 4.5881 | 0.9999 |
| 3.3396 | 6.76 | 1000 | 3.3301 | 1.0001 |
| 2.0061 | 10.14 | 1500 | 1.2096 | 1.0063 |
| 1.523 | 13.51 | 2000 | 0.7836 | 1.0051 |
| 1.3868 | 16.89 | 2500 | 0.6837 | 1.0080 |
| 1.2807 | 20.27 | 3000 | 0.6568 | 1.0112 |
| 1.231 | 23.65 | 3500 | 0.6120 | 1.0105 |
| 1.1673 | 27.03 | 4000 | 0.5972 | 1.0089 |
| 1.1416 | 30.41 | 4500 | 0.5780 | 1.0132 |
| 1.0738 | 33.78 | 5000 | 0.5806 | 1.0123 |
| 1.0771 | 37.16 | 5500 | 0.5586 | 1.0067 |
| 1.0287 | 40.54 | 6000 | 0.5464 | 1.0058 |
| 1.0106 | 43.92 | 6500 | 0.5407 | 1.0062 |
| 0.9538 | 47.3 | 7000 | 0.5334 | 1.0089 |
| 0.9607 | 50.68 | 7500 | 0.5395 | 1.0110 |
| 0.9108 | 54.05 | 8000 | 0.5502 | 1.0137 |
| 0.9252 | 57.43 | 8500 | 0.5498 | 1.0062 |
| 0.8943 | 60.81 | 9000 | 0.5448 | 1.0158 |
| 0.8728 | 64.19 | 9500 | 0.5257 | 1.0113 |
| 0.8577 | 67.57 | 10000 | 0.5550 | 1.0178 |
| 0.8332 | 70.95 | 10500 | 0.5607 | 1.0166 |
| 0.8174 | 74.32 | 11000 | 0.5429 | 1.0145 |
| 0.8168 | 77.7 | 11500 | 0.5561 | 1.0116 |
| 0.7872 | 81.08 | 12000 | 0.5478 | 1.0164 |
| 0.7707 | 84.46 | 12500 | 0.5412 | 1.0216 |
| 0.7742 | 87.84 | 13000 | 0.5391 | 1.0207 |
| 0.7594 | 91.22 | 13500 | 0.5379 | 1.0208 |
| 0.7678 | 94.59 | 14000 | 0.5415 | 1.0198 |
| 0.7502 | 97.97 | 14500 | 0.5409 | 1.0191 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-galician
|
infinitejoy
| 2022-03-23T18:34:49Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"gl",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- gl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gl
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Galician
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: gl
metrics:
- name: Test WER
type: wer
value: 101.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: gl
metrics:
- name: Test WER
type: wer
value: 105.69
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: gl
metrics:
- name: Test WER
type: wer
value: 101.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-galician
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1525
- Wer: 0.1542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0067 | 4.35 | 500 | 2.9632 | 1.0 |
| 1.4939 | 8.7 | 1000 | 0.5005 | 0.4157 |
| 0.9982 | 13.04 | 1500 | 0.1967 | 0.1857 |
| 0.8726 | 17.39 | 2000 | 0.1587 | 0.1564 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
Harveenchadha/odia_large_wav2vec2
|
Harveenchadha
| 2022-03-23T18:34:27Z | 21 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"or",
"robust-speech-event",
"dataset:Harveenchadha/indic-voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
language:
- or
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- or
- robust-speech-event
datasets:
- Harveenchadha/indic-voice
model-index:
- name: Hindi Large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice
type: common_voice
args: or
metrics:
- name: Test WER
type: wer
value: 54.26
- name: Test CER
type: cer
value: 11.36
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice-7.0
type: mozilla-foundation/common_voice_7_0
args: or
metrics:
- name: Test WER
type: wer
value: 53.58
- name: Test CER
type: cer
value: 11.26
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice-8.0
type: mozilla-foundation/common_voice_8_0
args: or
metrics:
- name: Test WER
type: wer
value: 55.26
- name: Test CER
type: cer
value: 13.01
---
|
AndrewMcDowell/wav2vec2-xls-r-300m-japanese
|
AndrewMcDowell
| 2022-03-23T18:34:20Z | 38 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- ja
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300-m
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER
type: wer
value: 95.82
- name: Test CER
type: cer
value: 23.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: de
metrics:
- name: Test WER
type: wer
value: 100.0
- name: Test CER
type: cer
value: 30.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Test CER
type: cer
value: 30.37
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 34.42
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable.
On mozilla-foundation/common_voice_8_0 it achieved:
- cer: 23.64%
On speech-recognition-community-v2/dev_data it achieved:
- cer: 30.99%
It achieves the following results on the evaluation set:
- Loss: 0.5212
- Wer: 1.3068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0974 | 4.72 | 1000 | 4.0178 | 1.9535 |
| 2.1276 | 9.43 | 2000 | 0.9301 | 1.2128 |
| 1.7622 | 14.15 | 3000 | 0.7103 | 1.5527 |
| 1.6397 | 18.87 | 4000 | 0.6729 | 1.4269 |
| 1.5468 | 23.58 | 5000 | 0.6087 | 1.2497 |
| 1.4885 | 28.3 | 6000 | 0.5786 | 1.3222 |
| 1.451 | 33.02 | 7000 | 0.5726 | 1.3768 |
| 1.3912 | 37.74 | 8000 | 0.5518 | 1.2497 |
| 1.3617 | 42.45 | 9000 | 0.5352 | 1.2694 |
| 1.3113 | 47.17 | 10000 | 0.5228 | 1.2781 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
|
vutankiet2901/wav2vec2-xls-r-1b-ja
|
vutankiet2901
| 2022-03-23T18:34:17Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common-voice",
"hf-asr-leaderboard",
"ja",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- ja
tags:
- automatic-speech-recognition
- common-voice
- hf-asr-leaderboard
- ja
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-1b
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 11.77
- name: Test CER (with LM)
type: cer
value: 5.22
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 12.23
- name: Test CER (with LM)
type: cer
value: 5.33
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Test WER (with LM)
type: wer
value: 29.35
- name: Test CER (with LM)
type: cer
value: 16.43
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 19.48
---
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA
### Benchmark WER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 16.97 | 17.95 |
|with 4-grams LM| 11.77 | 12.23|
### Benchmark CER result:
| | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0)
|---|---|---|
|without LM| 6.82 | 7.05 |
|with 4-grams LM| 5.22 | 5.33 |
## Evaluation
Please use the eval.py file to run the evaluation:
```python
pip install mecab-python3 unidic-lite pykakasi
python eval.py --model_id vutankiet2901/wav2vec2-xls-r-1b-ja --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.484 | 9.49 | 1500 | 1.1849 | 0.7543 | 0.4099 |
| 1.3582 | 18.98 | 3000 | 0.4320 | 0.3489 | 0.1591 |
| 1.1716 | 28.48 | 4500 | 0.3835 | 0.3175 | 0.1454 |
| 1.0951 | 37.97 | 6000 | 0.3732 | 0.3033 | 0.1405 |
| 1.04 | 47.47 | 7500 | 0.3485 | 0.2898 | 0.1360 |
| 0.9768 | 56.96 | 9000 | 0.3386 | 0.2787 | 0.1309 |
| 0.9129 | 66.45 | 10500 | 0.3363 | 0.2711 | 0.1272 |
| 0.8614 | 75.94 | 12000 | 0.3386 | 0.2676 | 0.1260 |
| 0.8092 | 85.44 | 13500 | 0.3356 | 0.2610 | 0.1240 |
| 0.7658 | 94.93 | 15000 | 0.3316 | 0.2564 | 0.1218 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
shahukareem/xls-r-300m-dv
|
shahukareem
| 2022-03-23T18:34:14Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dv",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- dv
license: apache-2.0
tags:
- automatic-speech-recognition
- dv
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Dhivehi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: dv
metrics:
- name: Test WER
type: wer
value: 21.31
- name: Test CER
type: cer
value: 3.82
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-dv
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2855
- Wer: 0.2665
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.3386 | 0.66 | 400 | 1.1411 | 0.9432 |
| 0.6543 | 1.33 | 800 | 0.5099 | 0.6749 |
| 0.4646 | 1.99 | 1200 | 0.4133 | 0.5968 |
| 0.3748 | 2.65 | 1600 | 0.3534 | 0.5515 |
| 0.3323 | 3.32 | 2000 | 0.3635 | 0.5527 |
| 0.3269 | 3.98 | 2400 | 0.3587 | 0.5423 |
| 0.2984 | 4.64 | 2800 | 0.3340 | 0.5073 |
| 0.2841 | 5.31 | 3200 | 0.3279 | 0.5004 |
| 0.2664 | 5.97 | 3600 | 0.3114 | 0.4845 |
| 0.2397 | 6.63 | 4000 | 0.3174 | 0.4920 |
| 0.2332 | 7.3 | 4400 | 0.3110 | 0.4911 |
| 0.2304 | 7.96 | 4800 | 0.3123 | 0.4785 |
| 0.2134 | 8.62 | 5200 | 0.2984 | 0.4557 |
| 0.2066 | 9.29 | 5600 | 0.3013 | 0.4723 |
| 0.1951 | 9.95 | 6000 | 0.2934 | 0.4487 |
| 0.1806 | 10.61 | 6400 | 0.2802 | 0.4547 |
| 0.1727 | 11.28 | 6800 | 0.2842 | 0.4333 |
| 0.1666 | 11.94 | 7200 | 0.2873 | 0.4272 |
| 0.1562 | 12.6 | 7600 | 0.3042 | 0.4373 |
| 0.1483 | 13.27 | 8000 | 0.3122 | 0.4313 |
| 0.1465 | 13.93 | 8400 | 0.2760 | 0.4226 |
| 0.1335 | 14.59 | 8800 | 0.3112 | 0.4243 |
| 0.1293 | 15.26 | 9200 | 0.3002 | 0.4133 |
| 0.1264 | 15.92 | 9600 | 0.2985 | 0.4145 |
| 0.1179 | 16.58 | 10000 | 0.2925 | 0.4012 |
| 0.1171 | 17.25 | 10400 | 0.3127 | 0.4012 |
| 0.1141 | 17.91 | 10800 | 0.2980 | 0.3908 |
| 0.108 | 18.57 | 11200 | 0.3108 | 0.3951 |
| 0.1045 | 19.24 | 11600 | 0.3269 | 0.3908 |
| 0.1047 | 19.9 | 12000 | 0.2998 | 0.3868 |
| 0.0937 | 20.56 | 12400 | 0.2918 | 0.3875 |
| 0.0949 | 21.23 | 12800 | 0.2906 | 0.3657 |
| 0.0879 | 21.89 | 13200 | 0.2974 | 0.3731 |
| 0.0854 | 22.55 | 13600 | 0.2943 | 0.3711 |
| 0.0851 | 23.22 | 14000 | 0.2919 | 0.3580 |
| 0.0789 | 23.88 | 14400 | 0.2983 | 0.3560 |
| 0.0796 | 24.54 | 14800 | 0.3131 | 0.3544 |
| 0.0761 | 25.21 | 15200 | 0.2996 | 0.3616 |
| 0.0755 | 25.87 | 15600 | 0.2972 | 0.3506 |
| 0.0726 | 26.53 | 16000 | 0.2902 | 0.3474 |
| 0.0707 | 27.2 | 16400 | 0.3083 | 0.3480 |
| 0.0669 | 27.86 | 16800 | 0.3035 | 0.3330 |
| 0.0637 | 28.52 | 17200 | 0.2963 | 0.3370 |
| 0.0596 | 29.19 | 17600 | 0.2830 | 0.3326 |
| 0.0583 | 29.85 | 18000 | 0.2969 | 0.3287 |
| 0.0566 | 30.51 | 18400 | 0.3002 | 0.3480 |
| 0.0574 | 31.18 | 18800 | 0.2916 | 0.3296 |
| 0.0536 | 31.84 | 19200 | 0.2933 | 0.3225 |
| 0.0548 | 32.5 | 19600 | 0.2900 | 0.3179 |
| 0.0506 | 33.17 | 20000 | 0.3073 | 0.3225 |
| 0.0511 | 33.83 | 20400 | 0.2925 | 0.3275 |
| 0.0483 | 34.49 | 20800 | 0.2919 | 0.3245 |
| 0.0456 | 35.16 | 21200 | 0.2859 | 0.3105 |
| 0.0445 | 35.82 | 21600 | 0.2864 | 0.3080 |
| 0.0437 | 36.48 | 22000 | 0.2989 | 0.3084 |
| 0.04 | 37.15 | 22400 | 0.2887 | 0.3060 |
| 0.0406 | 37.81 | 22800 | 0.2870 | 0.3013 |
| 0.0397 | 38.47 | 23200 | 0.2793 | 0.3020 |
| 0.0383 | 39.14 | 23600 | 0.2955 | 0.2943 |
| 0.0345 | 39.8 | 24000 | 0.2813 | 0.2905 |
| 0.0331 | 40.46 | 24400 | 0.2845 | 0.2845 |
| 0.0338 | 41.13 | 24800 | 0.2832 | 0.2925 |
| 0.0333 | 41.79 | 25200 | 0.2889 | 0.2849 |
| 0.0325 | 42.45 | 25600 | 0.2808 | 0.2847 |
| 0.0314 | 43.12 | 26000 | 0.2867 | 0.2801 |
| 0.0288 | 43.78 | 26400 | 0.2865 | 0.2834 |
| 0.0291 | 44.44 | 26800 | 0.2863 | 0.2806 |
| 0.0269 | 45.11 | 27200 | 0.2941 | 0.2736 |
| 0.0275 | 45.77 | 27600 | 0.2897 | 0.2736 |
| 0.0271 | 46.43 | 28000 | 0.2857 | 0.2695 |
| 0.0251 | 47.1 | 28400 | 0.2881 | 0.2702 |
| 0.0243 | 47.76 | 28800 | 0.2901 | 0.2684 |
| 0.0244 | 48.42 | 29200 | 0.2849 | 0.2679 |
| 0.0232 | 49.09 | 29600 | 0.2849 | 0.2677 |
| 0.0224 | 49.75 | 30000 | 0.2855 | 0.2665 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-welsh
|
infinitejoy
| 2022-03-23T18:33:58Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"cy",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cy
license: apache-2.0
tags:
- automatic-speech-recognition
- cy
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Welsh
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: cy
metrics:
- name: Test WER
type: wer
value: 31.003
- name: Test CER
type: cer
value: 7.775
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-welsh
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - CY dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2650
- Wer: 0.2702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3454 | 8.2 | 3000 | 0.4926 | 0.5703 |
| 1.1202 | 16.39 | 6000 | 0.3529 | 0.3944 |
| 1.0058 | 24.59 | 9000 | 0.3143 | 0.3341 |
| 0.9287 | 32.79 | 12000 | 0.2896 | 0.2980 |
| 0.8849 | 40.98 | 15000 | 0.2727 | 0.2798 |
| 0.8665 | 49.18 | 18000 | 0.2662 | 0.2696 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-romanian
|
infinitejoy
| 2022-03-23T18:33:55Z | 471 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"ro",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- ro
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Romanian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 14.194
- name: Test CER
type: cer
value: 3.288
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 40.869
- name: Test CER
type: cer
value: 12.049
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 47.2
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-romanian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1167
- Wer: 0.1421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1973 | 8.89 | 2000 | 0.4481 | 0.4849 |
| 0.6005 | 17.78 | 4000 | 0.1420 | 0.1777 |
| 0.5248 | 26.67 | 6000 | 0.1303 | 0.1651 |
| 0.4871 | 35.56 | 8000 | 0.1207 | 0.1523 |
| 0.4428 | 44.44 | 10000 | 0.1143 | 0.1425 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-mongolian
|
infinitejoy
| 2022-03-23T18:33:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mn",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mn
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Mongolian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: mn
metrics:
- name: Test WER
type: wer
value: 44.709
- name: Test CER
type: cer
value: 13.532
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: mn
metrics:
- name: Test WER
type: wer
value: 76.643
- name: Test CER
type: cer
value: 36.997
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: mn
metrics:
- name: Test WER
type: wer
value: 78.45
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-mongolian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - MN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6003
- Wer: 0.4473
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3677 | 15.87 | 2000 | 0.6432 | 0.6198 |
| 1.1379 | 31.75 | 4000 | 0.6196 | 0.5592 |
| 1.0093 | 47.62 | 6000 | 0.5828 | 0.5117 |
| 0.8888 | 63.49 | 8000 | 0.5754 | 0.4822 |
| 0.7985 | 79.37 | 10000 | 0.5987 | 0.4690 |
| 0.697 | 95.24 | 12000 | 0.6014 | 0.4471 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-basaa
|
infinitejoy
| 2022-03-23T18:33:50Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"bas",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Basaa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: bas
metrics:
- name: Test WER
type: wer
value: 104.08
- name: Test CER
type: cer
value: 228.48
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-basaa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BAS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5975
- Wer: 0.4981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 200.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 2.9287 | 15.62 | 500 | 2.8774 | 1.0 |
| 1.1182 | 31.25 | 1000 | 0.6248 | 0.7131 |
| 0.8329 | 46.88 | 1500 | 0.5573 | 0.5792 |
| 0.7109 | 62.5 | 2000 | 0.5420 | 0.5683 |
| 0.6295 | 78.12 | 2500 | 0.5166 | 0.5395 |
| 0.5715 | 93.75 | 3000 | 0.5487 | 0.5629 |
| 0.5016 | 109.38 | 3500 | 0.5370 | 0.5471 |
| 0.4661 | 125.0 | 4000 | 0.5621 | 0.5395 |
| 0.423 | 140.62 | 4500 | 0.5658 | 0.5248 |
| 0.3793 | 156.25 | 5000 | 0.5921 | 0.4981 |
| 0.3651 | 171.88 | 5500 | 0.5987 | 0.4888 |
| 0.3351 | 187.5 | 6000 | 0.6017 | 0.4948 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
LegolasTheElf/Wav2Vec2_xls_r_lm_300m_hi
|
LegolasTheElf
| 2022-03-23T18:33:41Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"Openslr Multilingual",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"hi",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hi
license: apache-2.0
tags:
- Openslr Multilingual
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2_xls_r_300m_hi_final
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 34.21
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2Vec2_xls_r_300m_hi_final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the ['Openslr Multilingual and code-switching ASR challenge'](http://www.openslr.org/103/) dataset and ['mozilla-foundation/common_voice_7_0'](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3035
- Wer: 0.3137
- Cer: 0.0972
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.9821 | 0.64 | 400 | 0.5059 | 0.4783 | 0.1573 |
| 0.6861 | 1.28 | 800 | 0.4201 | 0.4247 | 0.1356 |
| 0.585 | 1.92 | 1200 | 0.3797 | 0.3811 | 0.1210 |
| 0.5193 | 2.56 | 1600 | 0.3577 | 0.3652 | 0.1152 |
| 0.4583 | 3.21 | 2000 | 0.3422 | 0.3519 | 0.1111 |
| 0.4282 | 3.85 | 2400 | 0.3261 | 0.3450 | 0.1071 |
| 0.3951 | 4.49 | 2800 | 0.3201 | 0.3325 | 0.1048 |
| 0.3619 | 5.13 | 3200 | 0.3167 | 0.3296 | 0.1030 |
| 0.345 | 5.77 | 3600 | 0.3157 | 0.3210 | 0.1013 |
| 0.338 | 6.41 | 4000 | 0.3051 | 0.3143 | 0.0982 |
| 0.3155 | 7.05 | 4400 | 0.3059 | 0.3154 | 0.0986 |
| 0.3057 | 7.69 | 4800 | 0.3035 | 0.3137 | 0.0972 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Harveenchadha/vakyansh_hindi_base_pretrained
|
Harveenchadha
| 2022-03-23T18:33:38Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"hf-asr-leaderboard",
"hi",
"model_for_talk",
"pretrained",
"robust-speech-event",
"speech",
"arxiv:2107.07402",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z |
---
language: hi
tags:
- hf-asr-leaderboard
- hi
- model_for_talk
- pretrained
- robust-speech-event
- speech
license: apache-2.0
---
Hindi Pretrained model on 4200 hours. [Link](https://arxiv.org/abs/2107.07402)
|
Akashpb13/xlsr_hungarian_new
|
Akashpb13
| 2022-03-23T18:33:33Z | 41 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hu",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_hungarian_new
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hu
metrics:
- name: Test WER
type: wer
value: 0.2851621517163838
- name: Test CER
type: cer
value: 0.06112982522287432
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hu
metrics:
- name: Test WER
type: wer
value: 0.2851621517163838
- name: Test CER
type: cer
value: 0.06112982522287432
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: hu
metrics:
- name: Test WER
type: wer
value: 47.15
---
# Akashpb13/xlsr_hungarian_new
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets):
- Loss: 0.197464
- Wer: 0.330094
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice hungarian train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000095637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 16
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|------|---------------|-----------------|----------|
| 500 | 4.785300 | 0.952295 | 0.796236 |
| 1000 | 0.535800 | 0.217474 | 0.381613 |
| 1500 | 0.258400 | 0.205524 | 0.345056 |
| 2000 | 0.202800 | 0.198680 | 0.336264 |
| 2500 | 0.182700 | 0.197464 | 0.330094 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/xlsr_hungarian_new --dataset mozilla-foundation/common_voice_8_0 --config hu --split test
```
|
shivam/wav2vec2-xls-r-hindi
|
shivam
| 2022-03-23T18:33:12Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hi
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
- cer
model-index:
- name: shivam/wav2vec2-xls-r-hindi
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice Corpus 7.0
type: mozilla-foundation/common_voice_7_0
args: hi
metrics:
- name: Test WER
type: wer
value: 52.3
- name: Test CER
type: cer
value: 26.09
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2282
- Wer: 0.6838
## Evaluation results on Common Voice 7 "test" (Running ./eval.py):
### With LM
- WER: 52.30
- CER: 26.09
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.3155 | 3.4 | 500 | 4.5582 | 1.0 |
| 3.3369 | 6.8 | 1000 | 3.4269 | 1.0 |
| 2.1785 | 10.2 | 1500 | 1.7191 | 0.8831 |
| 1.579 | 13.6 | 2000 | 1.3604 | 0.7647 |
| 1.3773 | 17.01 | 2500 | 1.2737 | 0.7519 |
| 1.3165 | 20.41 | 3000 | 1.2457 | 0.7401 |
| 1.2274 | 23.81 | 3500 | 1.3617 | 0.7301 |
| 1.1787 | 27.21 | 4000 | 1.2068 | 0.7010 |
| 1.1467 | 30.61 | 4500 | 1.2416 | 0.6946 |
| 1.0801 | 34.01 | 5000 | 1.2312 | 0.6990 |
| 1.0709 | 37.41 | 5500 | 1.2984 | 0.7138 |
| 1.0307 | 40.81 | 6000 | 1.2049 | 0.6871 |
| 1.0003 | 44.22 | 6500 | 1.1956 | 0.6841 |
| 1.004 | 47.62 | 7000 | 1.2101 | 0.6793 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
samitizerxu/wav2vec2-xls-r-300m-fr
|
samitizerxu
| 2022-03-23T18:33:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"fr",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- fr
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-cls-r-300m-fr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 56.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 58.22
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-cls-r-300m-fr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6521
- Wer: 0.4330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6773 | 0.8 | 500 | 1.3907 | 0.9864 |
| 0.9526 | 1.6 | 1000 | 0.7760 | 0.6448 |
| 0.6418 | 2.4 | 1500 | 0.7605 | 0.6194 |
| 0.5028 | 3.2 | 2000 | 0.6516 | 0.5322 |
| 0.4133 | 4.0 | 2500 | 0.6303 | 0.5097 |
| 0.3285 | 4.8 | 3000 | 0.6422 | 0.5062 |
| 0.2764 | 5.6 | 3500 | 0.5936 | 0.4748 |
| 0.2361 | 6.4 | 4000 | 0.6486 | 0.4683 |
| 0.2049 | 7.2 | 4500 | 0.6321 | 0.4532 |
| 0.176 | 8.0 | 5000 | 0.6230 | 0.4482 |
| 0.1393 | 8.8 | 5500 | 0.6595 | 0.4403 |
| 0.1141 | 9.6 | 6000 | 0.6552 | 0.4348 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
infinitejoy/wav2vec2-large-xls-r-300m-basaa-cv8
|
infinitejoy
| 2022-03-23T18:32:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bas",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- bas
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Basaa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bas
metrics:
- name: Test WER
type: wer
value: 38.057
- name: Test CER
type: cer
value: 11.233
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-basaa-cv8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4648
- Wer: 0.5472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9421 | 12.82 | 500 | 2.8894 | 1.0 |
| 1.1872 | 25.64 | 1000 | 0.6688 | 0.7460 |
| 0.8894 | 38.46 | 1500 | 0.4868 | 0.6516 |
| 0.769 | 51.28 | 2000 | 0.4960 | 0.6507 |
| 0.6936 | 64.1 | 2500 | 0.4781 | 0.5384 |
| 0.624 | 76.92 | 3000 | 0.4643 | 0.5430 |
| 0.5966 | 89.74 | 3500 | 0.4530 | 0.5591 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
comodoro/wav2vec2-xls-r-300m-cs
|
comodoro
| 2022-03-23T18:32:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"xlsr-fine-tuning-week",
"cs",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- cs
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
- xlsr-fine-tuning-week
datasets:
- common_voice
model-index:
- name: Czech comodoro Wav2Vec2 XLSR 300M CV6.1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1
type: common_voice
args: cs
metrics:
- name: Test WER
type: wer
value: 22.2
- name: Test CER
type: cer
value: 5.1
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: cs
metrics:
- name: Test WER
type: wer
value: 66.78
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: cs
metrics:
- name: Test WER
type: wer
value: 57.52
---
# Wav2Vec2-Large-XLSR-53-Czech
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Czech test data of Common Voice 6.1
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "cs", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\/\"\“\„\%\”\�\–\'\`\«\»\—\’\…]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 22.20 %
## Training
The Common Voice `train` and `validation` datasets were used for training
# TODO The script used for training can be found [here](...)
|
AlexN/xls-r-300m-fr
|
AlexN
| 2022-03-23T18:32:43Z | 56 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language:
- fr
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-300m-fr
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0 fr
type: mozilla-foundation/common_voice_8_0
args: fr
metrics:
- name: Test WER
type: wer
value: 21.58
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 36.03
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 38.86
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2700
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
sammy786/wav2vec2-xlsr-tatar
|
sammy786
| 2022-03-23T18:32:40Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"tt",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- tt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- tt
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: sammy786/wav2vec2-xlsr-tatar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: tt
metrics:
- name: Test WER
type: wer
value: 16.87
- name: Test CER
type: cer
value: 3.64
---
# sammy786/wav2vec2-xlsr-tatar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - tt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
- Loss: 7.66
- Wer: 7.08
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Finnish train.tsv, dev.tsv and other.tsv
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000045637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|-------|---------------|-----------------|----------|
| 200 | 4.849400 | 1.874908 | 0.995232 |
| 400 | 1.105700 | 0.257292 | 0.367658 |
| 600 | 0.723000 | 0.181150 | 0.250513 |
| 800 | 0.660600 | 0.167009 | 0.226078 |
| 1000 | 0.568000 | 0.135090 | 0.177339 |
| 1200 | 0.721200 | 0.117469 | 0.166413 |
| 1400 | 0.416300 | 0.115142 | 0.153765 |
| 1600 | 0.346000 | 0.105782 | 0.153963 |
| 1800 | 0.279700 | 0.102452 | 0.146149 |
| 2000 | 0.273800 | 0.095818 | 0.128468 |
| 2200 | 0.252900 | 0.102302 | 0.133766 |
| 2400 | 0.255100 | 0.096592 | 0.121316 |
| 2600 | 0.229600 | 0.091263 | 0.124561 |
| 2800 | 0.213900 | 0.097748 | 0.125687 |
| 3000 | 0.210700 | 0.091244 | 0.125422 |
| 3200 | 0.202600 | 0.084076 | 0.106284 |
| 3400 | 0.200900 | 0.093809 | 0.113238 |
| 3600 | 0.192700 | 0.082918 | 0.108139 |
| 3800 | 0.182000 | 0.084487 | 0.103371 |
| 4000 | 0.167700 | 0.091847 | 0.104960 |
| 4200 | 0.183700 | 0.085223 | 0.103040 |
| 4400 | 0.174400 | 0.083862 | 0.100589 |
| 4600 | 0.163100 | 0.086493 | 0.099728 |
| 4800 | 0.162000 | 0.081734 | 0.097543 |
| 5000 | 0.153600 | 0.077223 | 0.092974 |
| 5200 | 0.153700 | 0.086217 | 0.090789 |
| 5400 | 0.140200 | 0.093256 | 0.100457 |
| 5600 | 0.142900 | 0.086903 | 0.097742 |
| 5800 | 0.131400 | 0.083068 | 0.095225 |
| 6000 | 0.126000 | 0.086642 | 0.091252 |
| 6200 | 0.135300 | 0.083387 | 0.091186 |
| 6400 | 0.126100 | 0.076479 | 0.086352 |
| 6600 | 0.127100 | 0.077868 | 0.086153 |
| 6800 | 0.118000 | 0.083878 | 0.087676 |
| 7000 | 0.117600 | 0.085779 | 0.091054 |
| 7200 | 0.113600 | 0.084197 | 0.084233 |
| 7400 | 0.112000 | 0.078688 | 0.081319 |
| 7600 | 0.110200 | 0.082534 | 0.086087 |
| 7800 | 0.106400 | 0.077245 | 0.080988 |
| 8000 | 0.102300 | 0.077497 | 0.079332 |
| 8200 | 0.109500 | 0.079083 | 0.088339 |
| 8400 | 0.095900 | 0.079721 | 0.077809 |
| 8600 | 0.094700 | 0.079078 | 0.079730 |
| 8800 | 0.097400 | 0.078785 | 0.079200 |
| 9000 | 0.093200 | 0.077445 | 0.077015 |
| 9200 | 0.088700 | 0.078207 | 0.076617 |
| 9400 | 0.087200 | 0.078982 | 0.076485 |
| 9600 | 0.089900 | 0.081209 | 0.076021 |
| 9800 | 0.081900 | 0.078158 | 0.075757 |
| 10000 | 0.080200 | 0.078074 | 0.074498 |
| 10200 | 0.085000 | 0.078830 | 0.073373 |
| 10400 | 0.080400 | 0.078144 | 0.073373 |
| 10600 | 0.078200 | 0.077163 | 0.073902 |
| 10800 | 0.080900 | 0.076394 | 0.072446 |
| 11000 | 0.080700 | 0.075955 | 0.071585 |
| 11200 | 0.076800 | 0.077031 | 0.072313 |
| 11400 | 0.076300 | 0.077401 | 0.072777 |
| 11600 | 0.076700 | 0.076613 | 0.071916 |
| 11800 | 0.076000 | 0.076672 | 0.071916 |
| 12000 | 0.077200 | 0.076490 | 0.070989 |
| 12200 | 0.076200 | 0.076688 | 0.070856 |
| 12400 | 0.074400 | 0.076780 | 0.071055 |
| 12600 | 0.076300 | 0.076768 | 0.071320 |
| 12800 | 0.077600 | 0.076727 | 0.071055 |
| 13000 | 0.077700 | 0.076714 | 0.071254 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id sammy786/wav2vec2-xlsr-tatar --dataset mozilla-foundation/common_voice_8_0 --config tt --split test
```
|
infinitejoy/wav2vec2-large-xls-r-300m-urdu
|
infinitejoy
| 2022-03-23T18:30:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ur",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_7_0
- robust-speech-event
- ur
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Urdu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ur
metrics:
- name: Test WER
type: wer
value: 105.66
- name: Test CER
type: cer
value: 434.011
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
infinitejoy/wav2vec2-large-xls-r-300m-urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - -UR dataset.
It achieves the following results on the evaluation set:
- Loss: NA
- Wer: NA
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py \
--model_id infinitejoy/wav2vec2-large-xls-r-300m-urdu --dataset speech-recognition-community-v2/dev_data \
--config ur --split validation --chunk_length_s 10 --stride_length_s 1
```
### Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "infinitejoy/wav2vec2-large-xls-r-300m-urdu"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "ur", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
```
### Eval results on Common Voice 7 "test" (WER):
|
infinitejoy/wav2vec2-large-xls-r-300m-bashkir
|
infinitejoy
| 2022-03-23T18:30:18Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ba
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Bashkir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ba
metrics:
- name: Test WER
type: wer
value: 24.2
- name: Test CER
type: cer
value: 5.08
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bashkir
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1892
- Wer: 0.2421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4792 | 0.5 | 2000 | 0.4598 | 0.5404 |
| 1.449 | 1.0 | 4000 | 0.4650 | 0.5610 |
| 1.3742 | 1.49 | 6000 | 0.4001 | 0.4977 |
| 1.3375 | 1.99 | 8000 | 0.3916 | 0.4894 |
| 1.2961 | 2.49 | 10000 | 0.3641 | 0.4569 |
| 1.2714 | 2.99 | 12000 | 0.3491 | 0.4488 |
| 1.2399 | 3.48 | 14000 | 0.3151 | 0.3986 |
| 1.2067 | 3.98 | 16000 | 0.3081 | 0.3923 |
| 1.1842 | 4.48 | 18000 | 0.2875 | 0.3703 |
| 1.1644 | 4.98 | 20000 | 0.2840 | 0.3670 |
| 1.161 | 5.48 | 22000 | 0.2790 | 0.3597 |
| 1.1303 | 5.97 | 24000 | 0.2552 | 0.3272 |
| 1.0874 | 6.47 | 26000 | 0.2405 | 0.3142 |
| 1.0613 | 6.97 | 28000 | 0.2352 | 0.3055 |
| 1.0498 | 7.47 | 30000 | 0.2249 | 0.2910 |
| 1.021 | 7.96 | 32000 | 0.2118 | 0.2752 |
| 1.0002 | 8.46 | 34000 | 0.2046 | 0.2662 |
| 0.9762 | 8.96 | 36000 | 0.1969 | 0.2530 |
| 0.9568 | 9.46 | 38000 | 0.1917 | 0.2449 |
| 0.953 | 9.96 | 40000 | 0.1893 | 0.2425 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|
vitouphy/wav2vec2-xls-r-300m-japanese
|
vitouphy
| 2022-03-23T18:30:07Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"doi:10.57967/hf/0124",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- ja
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Japanese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ja
metrics:
- name: Test WER
type: wer
value: 54.05
- name: Test CER
type: cer
value: 27.54
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ja
metrics:
- name: Validation WER
type: wer
value: 48.77
- name: Validation CER
type: cer
value: 24.87
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ja
metrics:
- name: Test CER
type: cer
value: 27.36
---
#
This model is for transcribing audio into Hiragana, one format of Japanese language.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:
- Modify `eval.py` to suit the use case.
- Since kanji and katakana shares the same sound as hiragana, we convert all texts to hiragana using [pykakasi](https://pykakasi.readthedocs.io) and tokenize them using [fugashi](https://github.com/polm/fugashi).
It achieves the following results on the evaluation set:
- Loss: 0.7751
- Cer: 0.2227
# Evaluation results (Running ./eval.py):
| Model | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data |
|:--------:|:------:|:-------------------:|:------------------------------------------:|
| w/o LM | WER | 0.5964 | 0.5532 |
| | CER | 0.2944 | 0.2629 |
| w/ LM | WER | 0.5405 | 0.4877 |
| | CER | **0.2754** | **0.2487** |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.4081 | 1.6 | 500 | 4.0983 | 1.0 |
| 3.303 | 3.19 | 1000 | 3.3563 | 1.0 |
| 3.1538 | 4.79 | 1500 | 3.2066 | 0.9239 |
| 2.1526 | 6.39 | 2000 | 1.1597 | 0.3355 |
| 1.8726 | 7.98 | 2500 | 0.9023 | 0.2505 |
| 1.7817 | 9.58 | 3000 | 0.8219 | 0.2334 |
| 1.7488 | 11.18 | 3500 | 0.7915 | 0.2222 |
| 1.7039 | 12.78 | 4000 | 0.7751 | 0.2227 |
| Stop & Train | | | | |
| 1.6571 | 15.97 | 5000 | 0.6788 | 0.1685 |
| 1.520400 | 19.16 | 6000 | 0.6095 | 0.1409 |
| 1.448200 | 22.35 | 7000 | 0.5843 | 0.1430 |
| 1.385400 | 25.54 | 8000 | 0.5699 | 0.1263 |
| 1.354200 | 28.73 | 9000 | 0.5686 | 0.1219 |
| 1.331500 | 31.92 | 10000 | 0.5502 | 0.1144 |
| 1.290800 | 35.11 | 11000 | 0.5371 | 0.1140 |
| Stop & Train | | | | |
| 1.235200 | 38.30 | 12000 | 0.5394 | 0.1106 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
phantomcoder1996/wav2vec2-large-xls-r-300m-arabic-colab
|
phantomcoder1996
| 2022-03-23T18:30:02Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"robust-speech-event",
"ar",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ar
thumbnail: wav2vec2-large-xls-r fine tuned on common voice data for Modern Standard
Arabic
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- WER
model-index:
- name: wav2vec2-large-xls-r-300m-arabic-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ar
metrics:
- name: Test WER
type: wer
value: 64.38
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 96.15
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ar
metrics:
- name: Test WER
type: wer
value: 94.96
---
|
jsnfly/wav2vec2-large-xlsr-53-german-gpt2
|
jsnfly
| 2022-03-23T18:29:57Z | 21 | 2 |
transformers
|
[
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"de",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2-Large-XLSR-53-German-GPT2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: de
metrics:
- name: Test WER
type: wer
value: 10.02
- name: Test CER
type: cer
value: 4.7
---
# Wav2Vec2-Large-XLSR-53-German-GPT2
This is an encoder-decoder model for automatic speech recognition trained on on the
MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset. The encoder was initialized from
[jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) and
the decoder from [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2).
It was trained using a two step process:
* fine-tuning only the cross-attention weights and the decoder using the pre-computed outputs of the Wav2Vec-Modell
* relatively fast training
* also works on small GPU (eg. 8 GB)
* but may take a lot of disk space
* should already yield decent results
* fine-tuning the model end-to-end
* much slower
* needs a bigger GPU
There is also one trick, which seemed to improve performance significantly: adding position embeddings to the
encoder outputs and initializing them with the pre-trained position embeddings of the GPT2 model (See `eval.py`).
The training notebooks are still early drafts. Also results can probably improved a lot by using for example a learning
rate schedule.
|
ubamba98/wav2vec2-xls-r-300m-CV8-ro
|
ubamba98
| 2022-03-23T18:29:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"ro",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-xls-r-300m-CV8-ro
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-CV8-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1578
- Wer: 0.6040
- Cer: 0.0475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 2.9736 | 3.62 | 500 | 2.9508 | 1.0 | 1.0 |
| 1.3293 | 7.25 | 1000 | 0.3330 | 0.8407 | 0.0862 |
| 0.956 | 10.87 | 1500 | 0.2042 | 0.6872 | 0.0602 |
| 0.9509 | 14.49 | 2000 | 0.2184 | 0.7088 | 0.0652 |
| 0.9272 | 18.12 | 2500 | 0.2312 | 0.7211 | 0.0703 |
| 0.8561 | 21.74 | 3000 | 0.2158 | 0.6838 | 0.0631 |
| 0.8258 | 25.36 | 3500 | 0.1970 | 0.6844 | 0.0601 |
| 0.7993 | 28.98 | 4000 | 0.1895 | 0.6698 | 0.0577 |
| 0.7525 | 32.61 | 4500 | 0.1845 | 0.6453 | 0.0550 |
| 0.7211 | 36.23 | 5000 | 0.1781 | 0.6274 | 0.0531 |
| 0.677 | 39.85 | 5500 | 0.1732 | 0.6188 | 0.0514 |
| 0.6517 | 43.48 | 6000 | 0.1691 | 0.6177 | 0.0503 |
| 0.6326 | 47.1 | 6500 | 0.1619 | 0.6045 | 0.0479 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
shivam/xls-r-300m-marathi
|
shivam
| 2022-03-23T18:29:32Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"mr",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- mr
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- mr
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: ''
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice Corpus 8.0
type: mozilla-foundation/common_voice_8_0
args: mr
metrics:
- name: Test WER
type: wer
value: 38.27
- name: Test CER
type: cer
value: 8.91
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset.
It achieves the following results on the mozilla-foundation/common_voice_8_0 mr test set:
- Without LM
+ WER: 48.53
+ CER: 10.63
- With LM
+ WER: 38.27
+ CER: 8.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 400.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.2706 | 22.73 | 500 | 4.0174 | 1.0 |
| 3.2492 | 45.45 | 1000 | 3.2309 | 0.9908 |
| 1.9709 | 68.18 | 1500 | 1.0651 | 0.8440 |
| 1.4088 | 90.91 | 2000 | 0.5765 | 0.6550 |
| 1.1326 | 113.64 | 2500 | 0.4842 | 0.5760 |
| 0.9709 | 136.36 | 3000 | 0.4785 | 0.6013 |
| 0.8433 | 159.09 | 3500 | 0.5048 | 0.5419 |
| 0.7404 | 181.82 | 4000 | 0.5052 | 0.5339 |
| 0.6589 | 204.55 | 4500 | 0.5237 | 0.5897 |
| 0.5831 | 227.27 | 5000 | 0.5166 | 0.5447 |
| 0.5375 | 250.0 | 5500 | 0.5292 | 0.5487 |
| 0.4784 | 272.73 | 6000 | 0.5480 | 0.5596 |
| 0.4421 | 295.45 | 6500 | 0.5682 | 0.5467 |
| 0.4047 | 318.18 | 7000 | 0.5681 | 0.5447 |
| 0.3779 | 340.91 | 7500 | 0.5783 | 0.5347 |
| 0.3525 | 363.64 | 8000 | 0.5856 | 0.5367 |
| 0.3393 | 386.36 | 8500 | 0.5960 | 0.5359 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
|
anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm
|
anuragshas
| 2022-03-23T18:29:27Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"sl",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- sl
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M - Slovenian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sl
metrics:
- name: Test WER
type: wer
value: 12.736
- name: Test CER
type: cer
value: 3.605
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sl
metrics:
- name: Test WER
type: wer
value: 45.587
- name: Test CER
type: cer
value: 20.886
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: sl
metrics:
- name: Test WER
type: wer
value: 45.42
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R-300M - Slovenian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2578
- Wer: 0.2273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1829 | 4.88 | 400 | 3.1228 | 1.0 |
| 2.8675 | 9.76 | 800 | 2.8616 | 0.9993 |
| 1.583 | 14.63 | 1200 | 0.6392 | 0.6239 |
| 1.1959 | 19.51 | 1600 | 0.3602 | 0.3651 |
| 1.0276 | 24.39 | 2000 | 0.3021 | 0.2981 |
| 0.9671 | 29.27 | 2400 | 0.2872 | 0.2739 |
| 0.873 | 34.15 | 2800 | 0.2593 | 0.2459 |
| 0.8513 | 39.02 | 3200 | 0.2617 | 0.2473 |
| 0.8132 | 43.9 | 3600 | 0.2548 | 0.2426 |
| 0.7935 | 48.78 | 4000 | 0.2637 | 0.2353 |
| 0.7565 | 53.66 | 4400 | 0.2629 | 0.2322 |
| 0.7359 | 58.54 | 4800 | 0.2579 | 0.2253 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config sl --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-300m-sl-cv8-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sl", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "zmago je divje od letel s helikopterjem visoko vzrak"
```
### Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 19.938 | 12.736 |
|
anantoj/wav2vec2-xls-r-1b-korean
|
anantoj
| 2022-03-23T18:29:13Z | 37 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ko",
"dataset:kresnik/zeroth_korean",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ko
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- kresnik/zeroth_korean
model-index:
- name: Wav2Vec2 XLS-R 1B Korean
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ko
metrics:
- name: Test WER
type: wer
value: 82.07
- name: Test CER
type: cer
value: 42.12
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ko
metrics:
- name: Test WER
type: wer
value: 82.09
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
- Wer: 0.0449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.603 | 0.72 | 500 | 4.6572 | 0.9985 |
| 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 |
| 2.2708 | 2.16 | 1500 | 0.9889 | 0.6989 |
| 2.1769 | 2.88 | 2000 | 0.8366 | 0.6312 |
| 2.1142 | 3.6 | 2500 | 0.7555 | 0.5998 |
| 2.0084 | 4.32 | 3000 | 0.7144 | 0.6003 |
| 1.9272 | 5.04 | 3500 | 0.6311 | 0.5461 |
| 1.8687 | 5.75 | 4000 | 0.6252 | 0.5430 |
| 1.8186 | 6.47 | 4500 | 0.5491 | 0.4988 |
| 1.7364 | 7.19 | 5000 | 0.5463 | 0.4959 |
| 1.6809 | 7.91 | 5500 | 0.4724 | 0.4484 |
| 1.641 | 8.63 | 6000 | 0.4679 | 0.4461 |
| 1.572 | 9.35 | 6500 | 0.4387 | 0.4236 |
| 1.5256 | 10.07 | 7000 | 0.3970 | 0.4003 |
| 1.5044 | 10.79 | 7500 | 0.3690 | 0.3893 |
| 1.4563 | 11.51 | 8000 | 0.3752 | 0.3875 |
| 1.394 | 12.23 | 8500 | 0.3386 | 0.3567 |
| 1.3641 | 12.95 | 9000 | 0.3290 | 0.3467 |
| 1.2878 | 13.67 | 9500 | 0.2893 | 0.3135 |
| 1.2602 | 14.39 | 10000 | 0.2723 | 0.3029 |
| 1.2302 | 15.11 | 10500 | 0.2603 | 0.2989 |
| 1.1865 | 15.83 | 11000 | 0.2440 | 0.2794 |
| 1.1491 | 16.55 | 11500 | 0.2500 | 0.2788 |
| 1.093 | 17.27 | 12000 | 0.2279 | 0.2629 |
| 1.0367 | 17.98 | 12500 | 0.2076 | 0.2443 |
| 0.9954 | 18.7 | 13000 | 0.1844 | 0.2259 |
| 0.99 | 19.42 | 13500 | 0.1794 | 0.2179 |
| 0.9385 | 20.14 | 14000 | 0.1765 | 0.2122 |
| 0.8952 | 20.86 | 14500 | 0.1706 | 0.1974 |
| 0.8841 | 21.58 | 15000 | 0.1791 | 0.1969 |
| 0.847 | 22.3 | 15500 | 0.1780 | 0.2060 |
| 0.8669 | 23.02 | 16000 | 0.1608 | 0.1862 |
| 0.8066 | 23.74 | 16500 | 0.1447 | 0.1626 |
| 0.7908 | 24.46 | 17000 | 0.1457 | 0.1655 |
| 0.7459 | 25.18 | 17500 | 0.1350 | 0.1445 |
| 0.7218 | 25.9 | 18000 | 0.1276 | 0.1421 |
| 0.703 | 26.62 | 18500 | 0.1177 | 0.1302 |
| 0.685 | 27.34 | 19000 | 0.1147 | 0.1305 |
| 0.6811 | 28.06 | 19500 | 0.1128 | 0.1244 |
| 0.6444 | 28.78 | 20000 | 0.1120 | 0.1213 |
| 0.6323 | 29.5 | 20500 | 0.1137 | 0.1166 |
| 0.5998 | 30.22 | 21000 | 0.1051 | 0.1107 |
| 0.5706 | 30.93 | 21500 | 0.1035 | 0.1037 |
| 0.5555 | 31.65 | 22000 | 0.1031 | 0.0927 |
| 0.5389 | 32.37 | 22500 | 0.0997 | 0.0900 |
| 0.5201 | 33.09 | 23000 | 0.0920 | 0.0912 |
| 0.5146 | 33.81 | 23500 | 0.0929 | 0.0947 |
| 0.515 | 34.53 | 24000 | 0.1000 | 0.0953 |
| 0.4743 | 35.25 | 24500 | 0.0922 | 0.0892 |
| 0.4707 | 35.97 | 25000 | 0.0852 | 0.0808 |
| 0.4456 | 36.69 | 25500 | 0.0855 | 0.0779 |
| 0.443 | 37.41 | 26000 | 0.0843 | 0.0738 |
| 0.4388 | 38.13 | 26500 | 0.0816 | 0.0699 |
| 0.4162 | 38.85 | 27000 | 0.0752 | 0.0645 |
| 0.3979 | 39.57 | 27500 | 0.0761 | 0.0621 |
| 0.3889 | 40.29 | 28000 | 0.0771 | 0.0625 |
| 0.3923 | 41.01 | 28500 | 0.0755 | 0.0598 |
| 0.3693 | 41.73 | 29000 | 0.0730 | 0.0578 |
| 0.3642 | 42.45 | 29500 | 0.0739 | 0.0598 |
| 0.3532 | 43.17 | 30000 | 0.0712 | 0.0553 |
| 0.3513 | 43.88 | 30500 | 0.0762 | 0.0516 |
| 0.3349 | 44.6 | 31000 | 0.0731 | 0.0504 |
| 0.3305 | 45.32 | 31500 | 0.0725 | 0.0507 |
| 0.3285 | 46.04 | 32000 | 0.0709 | 0.0489 |
| 0.3179 | 46.76 | 32500 | 0.0667 | 0.0467 |
| 0.3158 | 47.48 | 33000 | 0.0653 | 0.0494 |
| 0.3033 | 48.2 | 33500 | 0.0638 | 0.0456 |
| 0.3023 | 48.92 | 34000 | 0.0644 | 0.0464 |
| 0.2975 | 49.64 | 34500 | 0.0643 | 0.0455 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|
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