wav2vec2-large-xlsr-korean-demo-test2
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0566
- Wer: 0.5224
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
31.2541 | 0.3 | 400 | 5.4002 | 1.0 |
4.9419 | 0.59 | 800 | 5.3336 | 1.0 |
4.8926 | 0.89 | 1200 | 5.0531 | 1.0 |
4.7218 | 1.19 | 1600 | 4.5172 | 1.0 |
4.0218 | 1.49 | 2000 | 3.1418 | 0.9518 |
3.0654 | 1.78 | 2400 | 2.4376 | 0.9041 |
2.6226 | 2.08 | 2800 | 2.0151 | 0.8643 |
2.2944 | 2.38 | 3200 | 1.8025 | 0.8290 |
2.1872 | 2.67 | 3600 | 1.6469 | 0.7962 |
2.0747 | 2.97 | 4000 | 1.5165 | 0.7714 |
1.8479 | 3.27 | 4400 | 1.4281 | 0.7694 |
1.8288 | 3.57 | 4800 | 1.3791 | 0.7326 |
1.801 | 3.86 | 5200 | 1.3328 | 0.7177 |
1.6723 | 4.16 | 5600 | 1.2954 | 0.7192 |
1.5925 | 4.46 | 6000 | 1.3137 | 0.6953 |
1.5709 | 4.75 | 6400 | 1.2086 | 0.6973 |
1.5294 | 5.05 | 6800 | 1.1811 | 0.6730 |
1.3844 | 5.35 | 7200 | 1.2053 | 0.6769 |
1.3906 | 5.65 | 7600 | 1.1287 | 0.6556 |
1.4088 | 5.94 | 8000 | 1.1251 | 0.6466 |
1.2989 | 6.24 | 8400 | 1.1577 | 0.6546 |
1.2523 | 6.54 | 8800 | 1.0643 | 0.6377 |
1.2651 | 6.84 | 9200 | 1.0865 | 0.6417 |
1.2209 | 7.13 | 9600 | 1.0981 | 0.6272 |
1.1435 | 7.43 | 10000 | 1.1195 | 0.6317 |
1.1616 | 7.73 | 10400 | 1.0672 | 0.6327 |
1.1272 | 8.02 | 10800 | 1.0413 | 0.6248 |
1.043 | 8.32 | 11200 | 1.0555 | 0.6233 |
1.0523 | 8.62 | 11600 | 1.0372 | 0.6178 |
1.0208 | 8.92 | 12000 | 1.0170 | 0.6128 |
0.9895 | 9.21 | 12400 | 1.0354 | 0.5934 |
0.95 | 9.51 | 12800 | 1.1019 | 0.6039 |
0.9705 | 9.81 | 13200 | 1.0229 | 0.5855 |
0.9202 | 10.1 | 13600 | 1.0364 | 0.5919 |
0.8644 | 10.4 | 14000 | 1.0721 | 0.5984 |
0.8641 | 10.7 | 14400 | 1.0383 | 0.5905 |
0.8924 | 11.0 | 14800 | 0.9947 | 0.5760 |
0.7914 | 11.29 | 15200 | 1.0270 | 0.5885 |
0.7882 | 11.59 | 15600 | 1.0271 | 0.5741 |
0.8116 | 11.89 | 16000 | 0.9937 | 0.5741 |
0.7584 | 12.18 | 16400 | 0.9924 | 0.5626 |
0.7051 | 12.48 | 16800 | 1.0023 | 0.5572 |
0.7232 | 12.78 | 17200 | 1.0479 | 0.5512 |
0.7149 | 13.08 | 17600 | 1.0475 | 0.5765 |
0.6579 | 13.37 | 18000 | 1.0218 | 0.5552 |
0.6615 | 13.67 | 18400 | 1.0339 | 0.5631 |
0.6629 | 13.97 | 18800 | 1.0239 | 0.5621 |
0.6221 | 14.26 | 19200 | 1.0331 | 0.5537 |
0.6159 | 14.56 | 19600 | 1.0640 | 0.5532 |
0.6032 | 14.86 | 20000 | 1.0192 | 0.5567 |
0.5748 | 15.16 | 20400 | 1.0093 | 0.5507 |
0.5614 | 15.45 | 20800 | 1.0458 | 0.5472 |
0.5626 | 15.75 | 21200 | 1.0318 | 0.5398 |
0.5429 | 16.05 | 21600 | 1.0112 | 0.5278 |
0.5407 | 16.34 | 22000 | 1.0120 | 0.5278 |
0.511 | 16.64 | 22400 | 1.0335 | 0.5249 |
0.5316 | 16.94 | 22800 | 1.0146 | 0.5348 |
0.4949 | 17.24 | 23200 | 1.0287 | 0.5388 |
0.496 | 17.53 | 23600 | 1.0229 | 0.5348 |
0.4986 | 17.83 | 24000 | 1.0094 | 0.5313 |
0.4787 | 18.13 | 24400 | 1.0620 | 0.5234 |
0.4508 | 18.42 | 24800 | 1.0401 | 0.5323 |
0.4754 | 18.72 | 25200 | 1.0543 | 0.5303 |
0.4584 | 19.02 | 25600 | 1.0433 | 0.5194 |
0.4431 | 19.32 | 26000 | 1.0597 | 0.5249 |
0.4448 | 19.61 | 26400 | 1.0548 | 0.5229 |
0.4475 | 19.91 | 26800 | 1.0566 | 0.5224 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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