wav2vec2-base-960h-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9308
- Accuracy: 0.7752
- F1: 0.7749
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.3739 | 0.99 | 35 | 1.3646 | 0.3654 | 0.2858 |
| 1.3444 | 2.0 | 71 | 1.3366 | 0.3833 | 0.3172 |
| 1.3193 | 2.99 | 106 | 1.2654 | 0.4324 | 0.3350 |
| 1.2447 | 4.0 | 142 | 1.2093 | 0.4649 | 0.3611 |
| 1.2087 | 4.99 | 177 | 1.2030 | 0.4582 | 0.3714 |
| 1.1539 | 6.0 | 213 | 1.1419 | 0.4920 | 0.4317 |
| 1.0795 | 6.99 | 248 | 1.1794 | 0.4721 | 0.4207 |
| 1.0525 | 8.0 | 284 | 1.0922 | 0.5020 | 0.4684 |
| 1.0615 | 8.99 | 319 | 1.0459 | 0.5471 | 0.5158 |
| 0.9381 | 10.0 | 355 | 1.0080 | 0.5656 | 0.5464 |
| 0.8945 | 10.99 | 390 | 1.1166 | 0.5378 | 0.5108 |
| 0.8497 | 12.0 | 426 | 1.0068 | 0.5855 | 0.5772 |
| 0.7729 | 12.99 | 461 | 1.1214 | 0.5517 | 0.5406 |
| 0.6984 | 14.0 | 497 | 1.0416 | 0.5889 | 0.5729 |
| 0.6856 | 14.99 | 532 | 1.0135 | 0.6180 | 0.6185 |
| 0.6095 | 16.0 | 568 | 1.0088 | 0.6320 | 0.6299 |
| 0.5899 | 16.99 | 603 | 0.9208 | 0.6585 | 0.6612 |
| 0.5922 | 18.0 | 639 | 0.8657 | 0.6757 | 0.6749 |
| 0.537 | 18.99 | 674 | 0.8910 | 0.6850 | 0.6892 |
| 0.4767 | 20.0 | 710 | 1.0544 | 0.6525 | 0.6499 |
| 0.4864 | 20.99 | 745 | 0.8024 | 0.7255 | 0.7232 |
| 0.3546 | 22.0 | 781 | 0.8628 | 0.7168 | 0.7205 |
| 0.3567 | 22.99 | 816 | 0.8921 | 0.7168 | 0.7177 |
| 0.381 | 24.0 | 852 | 0.9130 | 0.7069 | 0.7081 |
| 0.3031 | 24.99 | 887 | 1.0026 | 0.7023 | 0.7039 |
| 0.412 | 26.0 | 923 | 0.8413 | 0.7420 | 0.7430 |
| 0.3175 | 26.99 | 958 | 0.8705 | 0.7294 | 0.7335 |
| 0.2581 | 28.0 | 994 | 0.8628 | 0.7387 | 0.7431 |
| 0.328 | 28.99 | 1029 | 0.9022 | 0.7414 | 0.7417 |
| 0.263 | 30.0 | 1065 | 0.9787 | 0.7248 | 0.7251 |
| 0.249 | 30.99 | 1100 | 0.8658 | 0.7454 | 0.7481 |
| 0.2242 | 32.0 | 1136 | 0.9386 | 0.7354 | 0.7380 |
| 0.2848 | 32.99 | 1171 | 0.8553 | 0.7633 | 0.7639 |
| 0.2457 | 34.0 | 1207 | 0.8789 | 0.7692 | 0.7674 |
| 0.1557 | 34.99 | 1242 | 0.8542 | 0.7553 | 0.7594 |
| 0.169 | 36.0 | 1278 | 0.9132 | 0.7573 | 0.7600 |
| 0.171 | 36.99 | 1313 | 0.9550 | 0.7467 | 0.7481 |
| 0.2209 | 38.0 | 1349 | 0.9843 | 0.7407 | 0.7408 |
| 0.1674 | 38.99 | 1384 | 0.9523 | 0.7460 | 0.7468 |
| 0.1998 | 40.0 | 1420 | 0.8683 | 0.7686 | 0.7697 |
| 0.1101 | 40.99 | 1455 | 1.0123 | 0.7354 | 0.7370 |
| 0.1466 | 42.0 | 1491 | 0.9332 | 0.7633 | 0.7651 |
| 0.1376 | 42.99 | 1526 | 0.9193 | 0.7739 | 0.7743 |
| 0.0939 | 44.0 | 1562 | 0.9234 | 0.7626 | 0.7634 |
| 0.1333 | 44.99 | 1597 | 0.9308 | 0.7752 | 0.7749 |
| 0.1183 | 46.0 | 1633 | 0.9375 | 0.7706 | 0.7712 |
| 0.1031 | 46.99 | 1668 | 0.9298 | 0.7739 | 0.7750 |
| 0.1154 | 48.0 | 1704 | 0.9373 | 0.7739 | 0.7745 |
| 0.1317 | 48.99 | 1739 | 0.9611 | 0.7646 | 0.7654 |
| 0.1132 | 49.3 | 1750 | 0.9606 | 0.7626 | 0.7635 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0