Wav2vec2-xlsr-Shemo-Ravdess
This model is a fine-tuned version of makhataei/emotion_recog on the minoosh/shEMO dataset. It achieves the following results on the evaluation set:
- Loss: 1.1736
- Accuracy: 0.6080
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: 0.003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1422 | 1.0 | 224 | 1.1661 | 0.6246 |
| 1.0217 | 2.0 | 449 | 1.1937 | 0.6257 |
| 1.0559 | 3.0 | 674 | 1.2977 | 0.5856 |
| 1.0936 | 4.0 | 899 | 1.3626 | 0.5939 |
| 1.1228 | 5.0 | 1123 | 1.2982 | 0.5880 |
| 1.0318 | 6.0 | 1348 | 1.2040 | 0.6175 |
| 1.0672 | 7.0 | 1573 | 1.2251 | 0.6045 |
| 1.0632 | 8.0 | 1798 | 1.4452 | 0.5797 |
| 1.1499 | 9.0 | 2022 | 1.3601 | 0.5868 |
| 1.0777 | 10.0 | 2247 | 1.3385 | 0.5832 |
| 1.0781 | 11.0 | 2472 | 1.1732 | 0.5915 |
| 1.0678 | 12.0 | 2697 | 1.2847 | 0.5821 |
| 1.1707 | 13.0 | 2921 | 1.3588 | 0.6033 |
| 1.1455 | 14.0 | 3146 | 1.3306 | 0.5632 |
| 1.1142 | 15.0 | 3371 | 1.5357 | 0.5289 |
| 1.1206 | 16.0 | 3596 | 1.4326 | 0.5702 |
| 1.1422 | 17.0 | 3820 | 1.7375 | 0.5018 |
| 1.0812 | 18.0 | 4045 | 1.5080 | 0.5325 |
| 1.1203 | 19.0 | 4270 | 1.3482 | 0.5809 |
| 1.1119 | 20.0 | 4495 | 1.2645 | 0.5632 |
| 1.1494 | 21.0 | 4719 | 1.4513 | 0.5608 |
| 1.1657 | 22.0 | 4944 | 1.4032 | 0.5632 |
| 1.1381 | 23.0 | 5169 | 1.2126 | 0.5773 |
| 1.0927 | 24.0 | 5394 | 1.3380 | 0.5655 |
| 1.015 | 25.0 | 5618 | 1.2219 | 0.5927 |
| 1.0204 | 26.0 | 5843 | 1.2501 | 0.5939 |
| 1.008 | 27.0 | 6068 | 1.2044 | 0.5891 |
| 1.0234 | 28.0 | 6293 | 1.2707 | 0.5832 |
| 0.9675 | 29.0 | 6517 | 1.1381 | 0.6009 |
| 0.9978 | 30.0 | 6742 | 1.2326 | 0.5797 |
| 1.0122 | 31.0 | 6967 | 1.2166 | 0.5797 |
| 0.9453 | 32.0 | 7192 | 1.1262 | 0.6104 |
| 1.0142 | 33.0 | 7416 | 1.1846 | 0.6104 |
| 0.9738 | 34.0 | 7641 | 1.1193 | 0.6175 |
| 0.9176 | 35.0 | 7866 | 1.1634 | 0.5950 |
| 1.0274 | 36.0 | 8091 | 1.1692 | 0.6187 |
| 1.0281 | 37.0 | 8315 | 1.1404 | 0.6045 |
| 0.9439 | 38.0 | 8540 | 1.1726 | 0.5927 |
| 0.9435 | 39.0 | 8765 | 1.0951 | 0.6234 |
| 0.9281 | 40.0 | 8990 | 1.1170 | 0.6068 |
| 0.9538 | 41.0 | 9214 | 1.3379 | 0.5785 |
| 0.9625 | 42.0 | 9439 | 1.2126 | 0.5915 |
| 0.912 | 43.0 | 9664 | 1.3407 | 0.5679 |
| 0.9173 | 44.0 | 9889 | 1.1140 | 0.6269 |
| 0.8946 | 45.0 | 10113 | 1.2278 | 0.5998 |
| 0.9263 | 46.0 | 10338 | 1.4053 | 0.5490 |
| 0.9285 | 47.0 | 10563 | 1.2031 | 0.6116 |
| 0.9143 | 48.0 | 10788 | 1.3764 | 0.5927 |
| 0.9136 | 49.0 | 11012 | 1.2281 | 0.6033 |
| 0.8833 | 50.0 | 11237 | 1.1829 | 0.5962 |
| 0.9111 | 51.0 | 11462 | 1.2174 | 0.5797 |
| 0.9125 | 52.0 | 11687 | 1.2228 | 0.6068 |
| 0.9345 | 53.0 | 11911 | 1.1174 | 0.6187 |
| 0.9006 | 54.0 | 12136 | 1.2127 | 0.5998 |
| 0.9047 | 55.0 | 12361 | 1.1860 | 0.5974 |
| 0.8726 | 56.0 | 12586 | 1.3472 | 0.5608 |
| 0.8383 | 57.0 | 12810 | 1.3870 | 0.5596 |
| 0.8635 | 58.0 | 13035 | 1.2173 | 0.5962 |
| 0.8488 | 59.0 | 13260 | 1.1971 | 0.5962 |
| 0.9105 | 60.0 | 13485 | 1.2144 | 0.5986 |
| 0.8562 | 61.0 | 13709 | 1.2294 | 0.5880 |
| 0.8683 | 62.0 | 13934 | 1.1234 | 0.6092 |
| 0.8944 | 63.0 | 14159 | 1.2112 | 0.5868 |
| 0.8509 | 64.0 | 14384 | 1.5180 | 0.5537 |
| 0.8356 | 65.0 | 14608 | 1.2713 | 0.5726 |
| 0.854 | 66.0 | 14833 | 1.2937 | 0.5797 |
| 0.8977 | 67.0 | 15058 | 1.2866 | 0.5844 |
| 0.8984 | 68.0 | 15283 | 1.2556 | 0.5880 |
| 0.8935 | 69.0 | 15507 | 1.3198 | 0.5868 |
| 0.8521 | 70.0 | 15732 | 1.2408 | 0.5986 |
| 0.8971 | 71.0 | 15957 | 1.2107 | 0.5986 |
| 0.8621 | 72.0 | 16182 | 1.2558 | 0.5797 |
| 0.8549 | 73.0 | 16406 | 1.2017 | 0.6021 |
| 0.8362 | 74.0 | 16631 | 1.3402 | 0.5915 |
| 0.8826 | 75.0 | 16856 | 1.2788 | 0.6009 |
| 0.8518 | 76.0 | 17081 | 1.2311 | 0.6198 |
| 0.8495 | 77.0 | 17305 | 1.1504 | 0.6340 |
| 0.8547 | 78.0 | 17530 | 1.2002 | 0.6080 |
| 0.8794 | 79.0 | 17755 | 1.2842 | 0.5821 |
| 0.893 | 80.0 | 17980 | 1.2378 | 0.5962 |
| 0.8451 | 81.0 | 18204 | 1.1525 | 0.6175 |
| 0.8368 | 82.0 | 18429 | 1.2184 | 0.6068 |
| 0.836 | 83.0 | 18654 | 1.1771 | 0.6033 |
| 0.8364 | 84.0 | 18879 | 1.2725 | 0.5844 |
| 0.7702 | 85.0 | 19103 | 1.2220 | 0.6057 |
| 0.8539 | 86.0 | 19328 | 1.1542 | 0.6104 |
| 0.8075 | 87.0 | 19553 | 1.2943 | 0.5927 |
| 0.7758 | 88.0 | 19778 | 1.2257 | 0.5868 |
| 0.782 | 89.0 | 20002 | 1.1406 | 0.6246 |
| 0.7952 | 90.0 | 20227 | 1.2678 | 0.5998 |
| 0.7911 | 91.0 | 20452 | 1.1540 | 0.6222 |
| 0.7845 | 92.0 | 20677 | 1.1804 | 0.6187 |
| 0.8066 | 93.0 | 20901 | 1.1764 | 0.6210 |
| 0.8042 | 94.0 | 21126 | 1.1706 | 0.6068 |
| 0.7981 | 95.0 | 21351 | 1.1516 | 0.6175 |
| 0.7585 | 96.0 | 21576 | 1.1980 | 0.5998 |
| 0.7576 | 97.0 | 21800 | 1.1870 | 0.6092 |
| 0.7908 | 98.0 | 22025 | 1.1770 | 0.6080 |
| 0.7646 | 99.0 | 22250 | 1.1745 | 0.6092 |
| 0.7791 | 99.67 | 22400 | 1.1736 | 0.6080 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3