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exp17-F03-both
This model is a fine-tuned version of yongjian/wav2vec2-large-a on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9268
- Wer: 0.9485
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: 4
- 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: 1000
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
47.4704 | 0.36 | 500 | 3.3075 | 1.0131 |
3.1649 | 0.71 | 1000 | 3.3442 | 1.0 |
2.9674 | 1.07 | 1500 | 2.6986 | 1.0 |
2.7514 | 1.42 | 2000 | 2.5789 | 1.1299 |
2.6045 | 1.78 | 2500 | 2.3025 | 1.2529 |
2.373 | 2.14 | 3000 | 2.2169 | 1.2698 |
2.1632 | 2.49 | 3500 | 1.9883 | 1.2667 |
2.0942 | 2.85 | 4000 | 1.9294 | 1.2567 |
1.9239 | 3.2 | 4500 | 1.9799 | 1.2467 |
1.7549 | 3.56 | 5000 | 1.7485 | 1.2252 |
1.6973 | 3.91 | 5500 | 1.6799 | 1.2283 |
1.5823 | 4.27 | 6000 | 1.6847 | 1.2267 |
1.4761 | 4.63 | 6500 | 1.6971 | 1.1968 |
1.4381 | 4.98 | 7000 | 1.6280 | 1.2052 |
1.2509 | 5.34 | 7500 | 1.6657 | 1.2060 |
1.3112 | 5.69 | 8000 | 1.5618 | 1.1783 |
1.1851 | 6.05 | 8500 | 1.6555 | 1.1783 |
1.1112 | 6.41 | 9000 | 1.6586 | 1.1752 |
1.0463 | 6.76 | 9500 | 1.6135 | 1.1683 |
1.041 | 7.12 | 10000 | 1.5444 | 1.1522 |
0.9451 | 7.47 | 10500 | 1.5561 | 1.1622 |
0.9454 | 7.83 | 11000 | 1.5044 | 1.1483 |
0.8496 | 8.19 | 11500 | 1.6724 | 1.1330 |
0.825 | 8.54 | 12000 | 1.5950 | 1.1414 |
0.8291 | 8.9 | 12500 | 1.6023 | 1.1384 |
0.7279 | 9.25 | 13000 | 1.6319 | 1.1314 |
0.7394 | 9.61 | 13500 | 1.5478 | 1.1337 |
0.7079 | 9.96 | 14000 | 1.7564 | 1.1453 |
0.609 | 10.32 | 14500 | 1.7671 | 1.1245 |
0.6639 | 10.68 | 15000 | 1.7471 | 1.1314 |
0.648 | 11.03 | 15500 | 1.7694 | 1.2160 |
0.577 | 11.39 | 16000 | 1.6149 | 1.1760 |
0.577 | 11.74 | 16500 | 1.9288 | 1.1238 |
0.5695 | 12.1 | 17000 | 1.7503 | 1.1253 |
0.5326 | 12.46 | 17500 | 1.5635 | 1.1376 |
0.5423 | 12.81 | 18000 | 1.7083 | 1.1668 |
0.4775 | 13.17 | 18500 | 1.7054 | 1.1245 |
0.4772 | 13.52 | 19000 | 1.6455 | 1.1045 |
0.4737 | 13.88 | 19500 | 1.5996 | 1.0968 |
0.4529 | 14.23 | 20000 | 1.9847 | 1.1653 |
0.4461 | 14.59 | 20500 | 1.6845 | 1.1084 |
0.4497 | 14.95 | 21000 | 1.6465 | 1.0938 |
0.4096 | 15.3 | 21500 | 1.5919 | 1.0769 |
0.3897 | 15.66 | 22000 | 1.5637 | 1.0761 |
0.4234 | 16.01 | 22500 | 1.6360 | 1.0953 |
0.3659 | 16.37 | 23000 | 1.7573 | 1.0830 |
0.3352 | 16.73 | 23500 | 1.8474 | 1.0976 |
0.3886 | 17.08 | 24000 | 1.9115 | 1.0953 |
0.3255 | 17.44 | 24500 | 1.8820 | 1.0815 |
0.3405 | 17.79 | 25000 | 1.6862 | 1.0346 |
0.3205 | 18.15 | 25500 | 1.6912 | 1.0500 |
0.322 | 18.51 | 26000 | 1.6253 | 1.0615 |
0.296 | 18.86 | 26500 | 1.7924 | 1.0546 |
0.2869 | 19.22 | 27000 | 1.8204 | 1.0899 |
0.269 | 19.57 | 27500 | 1.7558 | 1.0292 |
0.2844 | 19.93 | 28000 | 1.6038 | 1.0131 |
0.2543 | 20.28 | 28500 | 1.7935 | 1.0161 |
0.3025 | 20.64 | 29000 | 1.8706 | 1.0423 |
0.2707 | 21.0 | 29500 | 2.0011 | 1.0208 |
0.2401 | 21.35 | 30000 | 1.9058 | 1.0161 |
0.2609 | 21.71 | 30500 | 1.7555 | 1.0015 |
0.2403 | 22.06 | 31000 | 1.9301 | 1.0085 |
0.2538 | 22.42 | 31500 | 1.8586 | 0.9969 |
0.2334 | 22.78 | 32000 | 1.8588 | 0.9985 |
0.2013 | 23.13 | 32500 | 1.9307 | 1.0108 |
0.2122 | 23.49 | 33000 | 1.8830 | 0.9908 |
0.2242 | 23.84 | 33500 | 1.8133 | 0.9754 |
0.188 | 24.2 | 34000 | 1.8435 | 0.9800 |
0.2142 | 24.56 | 34500 | 1.8491 | 0.9792 |
0.2059 | 24.91 | 35000 | 1.8005 | 0.9754 |
0.1794 | 25.27 | 35500 | 1.8845 | 0.9700 |
0.185 | 25.62 | 36000 | 1.8620 | 0.9731 |
0.1843 | 25.98 | 36500 | 1.8461 | 0.9539 |
0.1717 | 26.33 | 37000 | 1.8100 | 0.9639 |
0.164 | 26.69 | 37500 | 1.8192 | 0.9547 |
0.1888 | 27.05 | 38000 | 1.8005 | 0.9470 |
0.1792 | 27.4 | 38500 | 1.8901 | 0.9562 |
0.1708 | 27.76 | 39000 | 1.8306 | 0.9547 |
0.1508 | 28.11 | 39500 | 1.8934 | 0.9508 |
0.1751 | 28.47 | 40000 | 1.8956 | 0.9523 |
0.1541 | 28.83 | 40500 | 1.9360 | 0.9416 |
0.1611 | 29.18 | 41000 | 1.9346 | 0.9454 |
0.1684 | 29.54 | 41500 | 1.9247 | 0.9470 |
0.1463 | 29.89 | 42000 | 1.9268 | 0.9485 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2