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whisper-kor3_de_all
This model is a fine-tuned version of openai/whisper-small on the whisper-kor3_de_all dataset. It achieves the following results on the evaluation set:
- Loss: 0.2446
- Wer: 17.5909
- Cer: 7.8655
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.2987 | 0.05 | 100 | 0.2906 | 19.7898 | 9.3285 |
| 0.2658 | 0.09 | 200 | 0.2795 | 19.3371 | 9.6942 |
| 0.2748 | 0.14 | 300 | 0.2774 | 19.4341 | 8.9980 |
| 0.279 | 0.18 | 400 | 0.2767 | 22.5061 | 10.6901 |
| 0.2634 | 0.23 | 500 | 0.2837 | 19.7736 | 8.9319 |
| 0.2816 | 0.28 | 600 | 0.2826 | 19.8868 | 9.2315 |
| 0.2698 | 0.32 | 700 | 0.2826 | 19.8222 | 8.9759 |
| 0.2728 | 0.37 | 800 | 0.2794 | 19.9030 | 8.9187 |
| 0.2951 | 0.42 | 900 | 0.2752 | 20.1778 | 9.2271 |
| 0.2853 | 0.46 | 1000 | 0.2754 | 19.6281 | 9.3637 |
| 0.264 | 0.51 | 1100 | 0.2769 | 19.8222 | 9.1434 |
| 0.2684 | 0.55 | 1200 | 0.2745 | 19.8545 | 9.1390 |
| 0.286 | 0.6 | 1300 | 0.2731 | 19.6766 | 8.9627 |
| 0.2636 | 0.65 | 1400 | 0.2725 | 19.3048 | 8.7512 |
| 0.262 | 0.69 | 1500 | 0.2690 | 19.6281 | 8.9848 |
| 0.262 | 0.74 | 1600 | 0.2698 | 19.9515 | 9.1610 |
| 0.2788 | 0.78 | 1700 | 0.2693 | 19.7251 | 9.2491 |
| 0.2606 | 0.83 | 1800 | 0.2636 | 18.7065 | 8.6807 |
| 0.2601 | 0.88 | 1900 | 0.2626 | 18.9329 | 8.9231 |
| 0.249 | 0.92 | 2000 | 0.2649 | 19.0137 | 8.7777 |
| 0.2594 | 0.97 | 2100 | 0.2598 | 18.0922 | 8.1519 |
| 0.1764 | 1.02 | 2200 | 0.2565 | 17.8658 | 8.1123 |
| 0.1603 | 1.06 | 2300 | 0.2556 | 18.3508 | 8.2401 |
| 0.1572 | 1.11 | 2400 | 0.2561 | 19.1269 | 9.3549 |
| 0.1536 | 1.15 | 2500 | 0.2564 | 18.1568 | 8.1872 |
| 0.1719 | 1.2 | 2600 | 0.2543 | 18.0598 | 8.2665 |
| 0.1543 | 1.25 | 2700 | 0.2557 | 17.9143 | 8.1431 |
| 0.1636 | 1.29 | 2800 | 0.2519 | 17.8173 | 8.0991 |
| 0.1672 | 1.34 | 2900 | 0.2507 | 18.3670 | 8.6851 |
| 0.1519 | 1.39 | 3000 | 0.2528 | 18.8844 | 8.8834 |
| 0.1582 | 1.43 | 3100 | 0.2502 | 17.9143 | 8.1387 |
| 0.164 | 1.48 | 3200 | 0.2507 | 18.1083 | 8.3238 |
| 0.1464 | 1.52 | 3300 | 0.2487 | 18.1407 | 8.2973 |
| 0.1492 | 1.57 | 3400 | 0.2473 | 18.0760 | 8.2929 |
| 0.149 | 1.62 | 3500 | 0.2467 | 17.9143 | 8.1343 |
| 0.1592 | 1.66 | 3600 | 0.2457 | 17.9628 | 8.2753 |
| 0.1533 | 1.71 | 3700 | 0.2449 | 17.8173 | 7.9933 |
| 0.1597 | 1.75 | 3800 | 0.2454 | 17.8011 | 8.1475 |
| 0.1293 | 1.8 | 3900 | 0.2448 | 17.6233 | 7.8655 |
| 0.1499 | 1.85 | 4000 | 0.2446 | 17.5909 | 7.8655 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
