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facebook_wav2vec2-xls-r-300m_meet_tr_p_10_character

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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facebook_wav2vec2-xls-r-300m_meet_tr_p_10_character / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: facebook_wav2vec2-xls-r-300m_meet_tr_p_10_character
    results: []

facebook_wav2vec2-xls-r-300m_meet_tr_p_10_character

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6125
  • Cer: 0.1445

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: 7
  • eval_batch_size: 7
  • 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: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Cer Validation Loss
42.2505 0.03 300 1.0 49.1511
13.6348 0.05 600 1.0 5.7557
5.1009 0.08 900 1.0 5.2280
4.9349 0.1 1200 0.9830 5.0621
4.8724 0.13 1500 0.9740 4.9526
4.7966 0.15 1800 0.9595 4.9397
4.7368 0.18 2100 0.9560 4.7570
4.613 0.2 2400 0.9387 4.5097
4.1484 0.23 2700 0.6766 3.6760
3.4652 0.25 3000 0.5760 3.1567
3.0993 0.28 3300 0.5257 2.7454
2.8765 0.3 3600 0.4912 2.6302
2.6016 0.33 3900 0.4584 2.4138
2.4859 0.35 4200 0.4390 2.1625
2.3619 0.38 4500 0.4184 2.0083
2.2449 0.41 4800 0.4043 1.8993
2.2346 0.43 5100 0.3931 1.8297
2.0855 0.46 5400 0.3815 1.7839
2.0646 0.48 5700 0.3632 1.7346
1.9853 0.51 6000 0.3549 1.6617
1.9231 0.53 6300 0.3483 1.5524
1.8599 0.56 6600 0.3447 1.5524
1.8234 0.58 6900 0.3329 1.5756
1.8244 0.61 7200 0.3303 1.4651
1.7709 0.63 7500 0.3151 1.4303
1.7174 0.66 7800 0.3211 1.4079
1.7407 0.68 8100 0.3067 1.3984
1.7047 0.71 8400 0.3140 1.3826
1.6523 0.73 8700 0.2988 1.3349
1.6664 0.76 9000 0.2981 1.3117
1.6511 0.78 9300 0.2916 1.2932
1.5999 0.81 9600 0.2880 1.2659
1.6036 0.84 9900 0.2884 1.2867
1.5836 0.86 10200 0.2878 1.2605
1.5701 0.89 10500 0.2752 1.2592
1.5447 0.91 10800 0.2733 1.3209
1.537 0.94 11100 0.2694 1.2138
1.5456 0.96 11400 0.2727 1.2115
1.5075 0.99 11700 0.2658 1.1827
1.4665 1.01 12000 0.2643 1.1708
1.3995 1.04 12300 0.2590 1.1326
1.3602 1.06 12600 0.2613 1.1597
1.3734 1.09 12900 0.2619 1.1165
1.4103 1.11 13200 0.2607 1.1200
1.3684 1.14 13500 0.2516 1.1350
1.3654 1.16 13800 0.2516 1.0959
1.317 1.19 14100 0.2492 1.1201
1.3467 1.22 14400 0.2470 1.1138
1.3656 1.24 14700 0.2498 1.0575
1.3532 1.27 15000 0.2416 1.0771
1.3109 1.29 15300 0.2426 1.0389
1.2722 1.32 15600 0.2423 1.0465
1.2786 1.34 15900 0.2435 1.0547
1.3118 1.37 16200 0.2418 1.0417
1.2774 1.39 16500 0.2396 1.0232
1.2686 1.42 16800 0.2376 1.0082
1.2974 1.44 17100 0.2360 1.0424
1.2286 1.47 17400 0.2362 0.9912
1.2505 1.49 17700 0.2335 1.0350
1.2401 1.52 18000 0.2287 1.0426
1.2683 1.54 18300 0.2353 0.9930
1.2632 1.57 18600 0.2269 0.9945
1.2464 1.59 18900 0.2248 0.9810
1.2565 1.62 19200 0.2241 0.9859
1.2462 1.65 19500 0.2263 1.0128
1.244 1.67 19800 0.2256 1.0231
1.1923 1.7 20100 0.2189 0.9952
1.1993 1.72 20400 0.2191 0.9601
1.1992 1.75 20700 0.2171 0.9660
1.1902 1.77 21000 0.2165 0.9466
1.1929 1.8 21300 0.2176 0.9196
1.1703 1.82 21600 0.2137 0.9248
1.1667 1.85 21900 0.2131 0.9491
1.1401 1.87 22200 0.2128 0.9040
1.1689 1.9 22500 0.2113 0.9453
1.1515 1.92 22800 0.2116 0.9191
1.1553 1.95 23100 0.2095 0.9255
1.1657 1.97 23400 0.2102 0.9070
1.1371 2.0 23700 0.2123 0.9225
1.0175 2.03 24000 0.2090 0.9125
1.0356 2.05 24300 0.2060 0.8881
1.0307 2.08 24600 0.2037 0.9103
1.0044 2.1 24900 0.2057 0.8796
1.0662 2.13 25200 0.2022 0.8735
0.9837 2.15 25500 0.2025 0.8667
1.0106 2.18 25800 0.2024 0.8756
1.0179 2.2 26100 0.2038 0.8836
1.0049 2.23 26400 0.1987 0.8721
0.9742 2.25 26700 0.2005 0.8609
0.9918 2.28 27000 0.1985 0.8611
0.9956 2.3 27300 0.1994 0.8532
1.0048 2.33 27600 0.1963 0.8572
0.9873 2.35 27900 0.1954 0.8666
1.003 2.38 28200 0.1952 0.8549
0.9405 2.4 28500 0.1947 0.8589
0.9762 2.43 28800 0.6396 0.1527
0.9279 2.45 29100 0.6245 0.1535
0.954 2.48 29400 0.6344 0.1502
1.0043 2.5 29700 0.6290 0.1517
0.9289 2.53 30000 0.6228 0.1507
0.9303 2.56 30300 0.6330 0.1504
0.9703 2.58 30600 0.6346 0.1508
0.9247 2.61 30900 0.6222 0.1486
0.9083 2.63 31200 0.6288 0.1480
0.914 2.66 31500 0.6279 0.1478
0.9218 2.68 31800 0.6299 0.1485
0.9497 2.71 32100 0.6266 0.1480
0.9258 2.73 32400 0.6207 0.1469
0.9159 2.76 32700 0.6097 0.1456
0.9388 2.78 33000 0.6127 0.1470
0.9427 2.81 33300 0.6156 0.1454
0.9376 2.83 33600 0.6152 0.1464
0.8933 2.86 33900 0.6144 0.1442
0.9053 2.88 34200 0.6092 0.1440
0.9055 2.91 34500 0.6147 0.1439
0.9067 2.93 34800 0.6146 0.1439
0.9103 2.96 35100 0.6135 0.1442
0.9227 2.99 35400 0.6125 0.1445

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu113
  • Datasets 1.18.3
  • Tokenizers 0.15.2