metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ssc-koo-model
results: []
ssc-koo-model
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2968
- Cer: 0.6783
- Wer: 0.9996
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 | Cer | Wer |
|---|---|---|---|---|---|
| 5.3204 | 0.1813 | 100 | 3.0099 | 0.9947 | 1.0 |
| 3.0302 | 0.3626 | 200 | 2.9541 | 0.9947 | 1.0 |
| 2.9391 | 0.5440 | 300 | 2.9475 | 0.9891 | 1.0 |
| 2.8252 | 0.7253 | 400 | 2.8337 | 0.9449 | 1.0 |
| 2.6387 | 0.9066 | 500 | 2.8135 | 0.8428 | 1.0 |
| 2.4564 | 1.0870 | 600 | 2.4902 | 0.7996 | 1.0 |
| 2.2494 | 1.2684 | 700 | 2.3222 | 0.7157 | 1.0 |
| 2.0578 | 1.4497 | 800 | 2.2174 | 0.6497 | 1.0 |
| 1.96 | 1.6310 | 900 | 2.1959 | 0.6285 | 1.0 |
| 1.9028 | 1.8123 | 1000 | 2.0725 | 0.6030 | 1.0 |
| 1.861 | 1.9937 | 1100 | 2.1194 | 0.5821 | 1.0 |
| 1.7362 | 2.1741 | 1200 | 2.0431 | 0.5334 | 1.0034 |
| 1.7346 | 2.3554 | 1300 | 1.9258 | 0.5612 | 0.9989 |
| 1.6872 | 2.5367 | 1400 | 2.0139 | 0.5290 | 0.9994 |
| 1.6921 | 2.7180 | 1500 | 1.8111 | 0.5355 | 0.9975 |
| 1.6316 | 2.8994 | 1600 | 1.8895 | 0.5355 | 0.9979 |
| 1.6122 | 3.0798 | 1700 | 1.7664 | 0.5184 | 0.9977 |
| 1.5547 | 3.2611 | 1800 | 1.7894 | 0.5413 | 0.9987 |
| 1.522 | 3.4424 | 1900 | 1.8288 | 0.5181 | 0.9981 |
| 1.5581 | 3.6238 | 2000 | 1.7491 | 0.5186 | 0.9977 |
| 1.5435 | 3.8051 | 2100 | 1.6929 | 0.4941 | 0.9958 |
| 1.4729 | 3.9864 | 2200 | 1.7429 | 0.5246 | 0.9989 |
| 1.4626 | 4.1668 | 2300 | 1.8013 | 0.4950 | 1.0098 |
| 1.4534 | 4.3481 | 2400 | 1.7123 | 0.4895 | 0.9938 |
| 1.4282 | 4.5295 | 2500 | 1.6176 | 0.5090 | 0.9977 |
| 1.4085 | 4.7108 | 2600 | 1.7041 | 0.4888 | 0.9945 |
| 1.4031 | 4.8921 | 2700 | 1.6435 | 0.4764 | 0.9915 |
| 1.3729 | 5.0725 | 2800 | 1.8482 | 0.4806 | 1.0034 |
| 1.3016 | 5.2539 | 2900 | 1.6509 | 0.4884 | 0.9932 |
| 1.3634 | 5.4352 | 3000 | 1.6329 | 0.4900 | 0.9920 |
| 1.3245 | 5.6165 | 3100 | 1.6840 | 0.4913 | 1.0036 |
| 1.3025 | 5.7978 | 3200 | 1.7043 | 0.5168 | 1.0015 |
| 1.4095 | 5.9791 | 3300 | 1.6779 | 0.5539 | 0.9966 |
| 1.2452 | 6.1596 | 3400 | 1.7308 | 0.4870 | 0.9934 |
| 1.2521 | 6.3409 | 3500 | 1.5969 | 0.4836 | 0.9903 |
| 1.2549 | 6.5222 | 3600 | 1.6128 | 0.4952 | 0.9907 |
| 1.2759 | 6.7035 | 3700 | 1.5583 | 0.4781 | 0.9924 |
| 1.2093 | 6.8849 | 3800 | 1.6189 | 0.4503 | 1.0220 |
| 1.2453 | 7.0653 | 3900 | 1.5713 | 0.4635 | 1.0824 |
| 1.147 | 7.2466 | 4000 | 1.6146 | 0.5098 | 1.0038 |
| 1.1986 | 7.4279 | 4100 | 1.6173 | 0.4708 | 1.0062 |
| 1.1709 | 7.6092 | 4200 | 1.7183 | 0.4605 | 1.0021 |
| 1.1371 | 7.7906 | 4300 | 1.5477 | 0.5110 | 0.9932 |
| 1.2075 | 7.9719 | 4400 | 1.5412 | 0.4962 | 0.9913 |
| 1.1176 | 8.1523 | 4500 | 1.5634 | 0.4670 | 0.9936 |
| 1.107 | 8.3336 | 4600 | 1.5297 | 0.5283 | 0.9883 |
| 1.0861 | 8.5150 | 4700 | 1.5289 | 0.4419 | 0.9775 |
| 1.1011 | 8.6963 | 4800 | 1.5835 | 0.4770 | 0.9890 |
| 1.0864 | 8.8776 | 4900 | 1.5043 | 0.4636 | 0.9841 |
| 1.0748 | 9.0580 | 5000 | 1.6871 | 0.5933 | 0.9962 |
| 1.0034 | 9.2393 | 5100 | 1.5045 | 0.4699 | 0.9879 |
| 1.0074 | 9.4207 | 5200 | 1.5527 | 0.4470 | 1.0078 |
| 1.02 | 9.6020 | 5300 | 1.5285 | 0.4432 | 0.9835 |
| 1.0147 | 9.7833 | 5400 | 1.5470 | 0.4755 | 0.9911 |
| 1.0409 | 9.9646 | 5500 | 1.5250 | 0.4645 | 0.9852 |
| 0.9529 | 10.1451 | 5600 | 1.5696 | 0.4458 | 1.0038 |
| 0.9414 | 10.3264 | 5700 | 1.5319 | 0.4810 | 0.9858 |
| 0.941 | 10.5077 | 5800 | 1.5790 | 0.4538 | 1.0044 |
| 0.9145 | 10.6890 | 5900 | 1.5246 | 0.4837 | 0.9873 |
| 0.9824 | 10.8704 | 6000 | 1.4837 | 0.4579 | 0.9890 |
| 0.9397 | 11.0508 | 6100 | 1.4778 | 0.4493 | 0.9797 |
| 0.8304 | 11.2321 | 6200 | 1.5285 | 0.4179 | 1.0068 |
| 0.8759 | 11.4134 | 6300 | 1.5563 | 0.4266 | 0.9871 |
| 0.8738 | 11.5947 | 6400 | 1.5636 | 0.4260 | 0.9938 |
| 0.8908 | 11.7761 | 6500 | 1.5436 | 0.4385 | 0.9792 |
| 0.8985 | 11.9574 | 6600 | 1.5019 | 0.4239 | 0.9818 |
| 0.8332 | 12.1378 | 6700 | 1.5872 | 0.4421 | 1.0106 |
| 0.7945 | 12.3191 | 6800 | 1.5605 | 0.4375 | 0.9915 |
| 0.815 | 12.5005 | 6900 | 1.5049 | 0.4306 | 0.9737 |
| 0.8124 | 12.6818 | 7000 | 1.5192 | 0.4348 | 0.9778 |
| 0.8422 | 12.8631 | 7100 | 1.5167 | 0.4165 | 0.9748 |
| 0.7935 | 13.0435 | 7200 | 1.5640 | 0.4264 | 0.9826 |
| 0.7438 | 13.2248 | 7300 | 1.6165 | 0.4271 | 0.9881 |
| 0.7894 | 13.4062 | 7400 | 1.5718 | 0.4120 | 0.9663 |
| 0.7721 | 13.5875 | 7500 | 1.6259 | 0.4228 | 0.9818 |
| 0.7704 | 13.7688 | 7600 | 1.5615 | 0.4129 | 0.9716 |
| 0.77 | 13.9501 | 7700 | 1.5601 | 0.4167 | 0.9756 |
| 0.693 | 14.1306 | 7800 | 1.8324 | 0.4425 | 1.1045 |
| 0.7443 | 14.3119 | 7900 | 1.6445 | 0.4302 | 1.0028 |
| 0.731 | 14.4932 | 8000 | 1.7441 | 0.4224 | 0.9839 |
| 0.7405 | 14.6745 | 8100 | 1.6907 | 0.4266 | 1.0367 |
| 0.7987 | 14.8558 | 8200 | 1.6870 | 0.4337 | 1.0163 |
| 0.7793 | 15.0363 | 8300 | 1.6622 | 0.4282 | 0.9716 |
| 0.7963 | 15.2176 | 8400 | 1.6690 | 0.4499 | 1.0193 |
| 0.8667 | 15.3989 | 8500 | 1.7338 | 0.4621 | 1.1327 |
| 0.9406 | 15.5802 | 8600 | 1.9090 | 0.4674 | 0.9909 |
| 1.026 | 15.7616 | 8700 | 1.9508 | 0.4935 | 0.9879 |
| 1.082 | 15.9429 | 8800 | 1.8690 | 0.4628 | 0.9896 |
| 1.0817 | 16.1233 | 8900 | 1.9416 | 0.5220 | 0.9911 |
| 1.129 | 16.3046 | 9000 | 1.9048 | 0.4781 | 1.0136 |
| 1.1041 | 16.4859 | 9100 | 1.9063 | 0.4870 | 1.0098 |
| 1.0897 | 16.6673 | 9200 | 1.6682 | 0.4857 | 0.9833 |
| 0.8812 | 16.8486 | 9300 | 1.5728 | 0.4479 | 0.9947 |
| 0.8284 | 17.0290 | 9400 | 1.7669 | 0.4586 | 1.1358 |
| 0.8431 | 17.2103 | 9500 | 1.6971 | 0.4425 | 1.0203 |
| 0.8755 | 17.3917 | 9600 | 1.7615 | 0.4822 | 0.9879 |
| 1.062 | 17.5730 | 9700 | 1.7330 | 0.5176 | 0.9869 |
| 1.2399 | 17.7543 | 9800 | 1.9085 | 0.5983 | 0.9943 |
| 1.397 | 17.9356 | 9900 | 1.9057 | 0.6441 | 1.0 |
| 1.471 | 18.1160 | 10000 | 1.9789 | 0.6564 | 1.0 |
| 1.5444 | 18.2974 | 10100 | 2.0396 | 0.7628 | 1.0 |
| 1.6848 | 18.4787 | 10200 | 2.2329 | 0.8280 | 1.0 |
| 1.9732 | 18.6600 | 10300 | 2.4373 | 0.9427 | 1.0 |
| 2.0928 | 18.8413 | 10400 | 2.6932 | 0.9935 | 1.0 |
| 2.3268 | 19.0218 | 10500 | 2.7731 | 0.9921 | 1.0 |
| 2.3669 | 19.2031 | 10600 | 2.7349 | 0.9937 | 1.0 |
| 2.3452 | 19.3844 | 10700 | 2.6973 | 0.9898 | 1.0 |
| 2.3012 | 19.5657 | 10800 | 2.6725 | 0.9906 | 1.0 |
| 2.2682 | 19.7471 | 10900 | 2.6361 | 0.9805 | 1.0 |
| 2.2306 | 19.9284 | 11000 | 2.6518 | 0.9856 | 1.0 |
| 2.1568 | 20.1088 | 11100 | 2.6378 | 0.9823 | 1.0 |
| 2.0884 | 20.2901 | 11200 | 2.6507 | 0.9789 | 1.0 |
| 2.043 | 20.4714 | 11300 | 2.5891 | 0.9679 | 1.0 |
| 1.9813 | 20.6528 | 11400 | 2.5639 | 0.9523 | 1.0 |
| 1.9409 | 20.8341 | 11500 | 2.5460 | 0.9400 | 1.0 |
| 1.8849 | 21.0145 | 11600 | 2.6583 | 0.9198 | 1.0 |
| 1.8339 | 21.1958 | 11700 | 2.5035 | 0.8808 | 1.0 |
| 1.88 | 21.3772 | 11800 | 2.4978 | 0.8844 | 1.0 |
| 1.8623 | 21.5585 | 11900 | 2.4886 | 0.8847 | 1.0 |
| 1.8547 | 21.7398 | 12000 | 2.4982 | 0.8833 | 1.0 |
| 1.8228 | 21.9211 | 12100 | 2.4950 | 0.8875 | 1.0 |
| 1.7964 | 22.1015 | 12200 | 2.5017 | 0.8840 | 1.0 |
| 1.8215 | 22.2829 | 12300 | 2.4629 | 0.8650 | 1.0 |
| 1.7828 | 22.4642 | 12400 | 2.4534 | 0.8535 | 1.0 |
| 1.7645 | 22.6455 | 12500 | 2.4305 | 0.8543 | 1.0 |
| 1.7831 | 22.8268 | 12600 | 2.4408 | 0.8496 | 1.0 |
| 1.7373 | 23.0073 | 12700 | 2.4332 | 0.8323 | 1.0 |
| 1.7207 | 23.1886 | 12800 | 2.4330 | 0.8385 | 1.0 |
| 1.745 | 23.3699 | 12900 | 2.4564 | 0.8564 | 1.0 |
| 1.711 | 23.5512 | 13000 | 2.4077 | 0.8317 | 1.0 |
| 1.6738 | 23.7325 | 13100 | 2.4319 | 0.8411 | 1.0 |
| 1.6978 | 23.9139 | 13200 | 2.3909 | 0.8197 | 1.0 |
| 1.6858 | 24.0943 | 13300 | 2.4286 | 0.8360 | 1.0 |
| 1.6762 | 24.2756 | 13400 | 2.3942 | 0.8206 | 1.0 |
| 1.6632 | 24.4569 | 13500 | 2.4042 | 0.8250 | 1.0 |
| 1.6843 | 24.6383 | 13600 | 2.3702 | 0.8026 | 1.0 |
| 1.6441 | 24.8196 | 13700 | 2.3766 | 0.8090 | 1.0 |
| 1.645 | 25.0 | 13800 | 2.3549 | 0.7979 | 1.0 |
| 1.6432 | 25.1813 | 13900 | 2.3644 | 0.8056 | 1.0 |
| 1.6348 | 25.3626 | 14000 | 2.3551 | 0.7923 | 1.0 |
| 1.6151 | 25.5440 | 14100 | 2.3470 | 0.7952 | 1.0 |
| 1.6106 | 25.7253 | 14200 | 2.3524 | 0.7957 | 1.0 |
| 1.6091 | 25.9066 | 14300 | 2.3157 | 0.7692 | 1.0 |
| 1.6046 | 26.0870 | 14400 | 2.3130 | 0.7698 | 1.0 |
| 1.6114 | 26.2684 | 14500 | 2.3034 | 0.7634 | 1.0 |
| 1.6161 | 26.4497 | 14600 | 2.2931 | 0.7479 | 1.0 |
| 1.5859 | 26.6310 | 14700 | 2.2782 | 0.7385 | 1.0 |
| 1.5979 | 26.8123 | 14800 | 2.2741 | 0.7380 | 1.0 |
| 1.6055 | 26.9937 | 14900 | 2.2754 | 0.7348 | 1.0 |
| 1.5975 | 27.1741 | 15000 | 2.2700 | 0.7280 | 1.0 |
| 1.5867 | 27.3554 | 15100 | 2.2661 | 0.7300 | 1.0 |
| 1.5912 | 27.5367 | 15200 | 2.2641 | 0.7236 | 1.0 |
| 1.5794 | 27.7180 | 15300 | 2.2615 | 0.7243 | 1.0 |
| 1.6202 | 27.8994 | 15400 | 2.2637 | 0.7218 | 1.0 |
| 1.6007 | 28.0798 | 15500 | 2.2634 | 0.7072 | 1.0 |
| 1.5792 | 28.2611 | 15600 | 2.2635 | 0.7044 | 1.0 |
| 1.6341 | 28.4424 | 15700 | 2.2641 | 0.7010 | 1.0 |
| 1.5974 | 28.6238 | 15800 | 2.2650 | 0.6961 | 1.0 |
| 1.5844 | 28.8051 | 15900 | 2.2757 | 0.6931 | 0.9998 |
| 1.6409 | 28.9864 | 16000 | 2.2818 | 0.6873 | 0.9998 |
| 1.6391 | 29.1668 | 16100 | 2.2872 | 0.6828 | 0.9996 |
| 1.6373 | 29.3481 | 16200 | 2.2886 | 0.6816 | 0.9996 |
| 1.6202 | 29.5295 | 16300 | 2.2917 | 0.6805 | 0.9996 |
| 1.6358 | 29.7108 | 16400 | 2.2953 | 0.6781 | 0.9996 |
| 1.6427 | 29.8921 | 16500 | 2.2968 | 0.6783 | 0.9996 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0