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whisper-kor3_de_all_pure
This model is a fine-tuned version of openai/whisper-small on the whisper-kor3_de_all_pure dataset. It achieves the following results on the evaluation set:
- Loss: 0.4036
- Wer: 24.4454
- Cer: 10.9931
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 |
|---|---|---|---|---|---|
| 1.1711 | 0.05 | 100 | 0.9951 | 36.7906 | 18.3518 |
| 0.7306 | 0.09 | 200 | 0.7148 | 32.9732 | 15.0468 |
| 0.5337 | 0.14 | 300 | 0.5650 | 32.4402 | 15.4036 |
| 0.492 | 0.18 | 400 | 0.5397 | 32.4546 | 16.1485 |
| 0.4779 | 0.23 | 500 | 0.5196 | 30.6108 | 14.1530 |
| 0.4501 | 0.28 | 600 | 0.5130 | 31.3166 | 14.6783 |
| 0.4574 | 0.32 | 700 | 0.4941 | 32.4978 | 15.6153 |
| 0.4215 | 0.37 | 800 | 0.4920 | 28.4500 | 13.0396 |
| 0.4574 | 0.42 | 900 | 0.4829 | 30.4379 | 14.8704 |
| 0.3928 | 0.46 | 1000 | 0.4750 | 28.1619 | 12.7024 |
| 0.4207 | 0.51 | 1100 | 0.4673 | 29.3287 | 13.8550 |
| 0.4026 | 0.55 | 1200 | 0.4654 | 28.7957 | 13.8393 |
| 0.4004 | 0.6 | 1300 | 0.4581 | 27.9458 | 14.1726 |
| 0.3808 | 0.65 | 1400 | 0.4498 | 26.7214 | 12.2319 |
| 0.3994 | 0.69 | 1500 | 0.4483 | 26.7790 | 12.0751 |
| 0.4087 | 0.74 | 1600 | 0.4373 | 26.3901 | 12.1496 |
| 0.3761 | 0.78 | 1700 | 0.4343 | 26.6926 | 11.9771 |
| 0.3889 | 0.83 | 1800 | 0.4334 | 27.3840 | 12.9651 |
| 0.4135 | 0.88 | 1900 | 0.4296 | 25.9723 | 12.5417 |
| 0.3559 | 0.92 | 2000 | 0.4257 | 25.3097 | 11.5537 |
| 0.433 | 0.97 | 2100 | 0.4211 | 25.6410 | 11.5615 |
| 0.2988 | 1.02 | 2200 | 0.4213 | 25.0648 | 12.0790 |
| 0.221 | 1.06 | 2300 | 0.4241 | 25.3529 | 11.2832 |
| 0.2657 | 1.11 | 2400 | 0.4218 | 25.2953 | 11.4949 |
| 0.2538 | 1.15 | 2500 | 0.4205 | 25.3961 | 12.1731 |
| 0.2398 | 1.2 | 2600 | 0.4202 | 25.1945 | 12.0281 |
| 0.268 | 1.25 | 2700 | 0.4152 | 24.8776 | 11.2165 |
| 0.2683 | 1.29 | 2800 | 0.4129 | 24.9496 | 11.1734 |
| 0.2688 | 1.34 | 2900 | 0.4118 | 25.0936 | 11.2793 |
| 0.2713 | 1.39 | 3000 | 0.4135 | 24.5030 | 11.0754 |
| 0.2572 | 1.43 | 3100 | 0.4103 | 24.7479 | 11.0793 |
| 0.2565 | 1.48 | 3200 | 0.4106 | 24.5462 | 11.6360 |
| 0.253 | 1.52 | 3300 | 0.4116 | 24.5462 | 10.9068 |
| 0.2549 | 1.57 | 3400 | 0.4092 | 24.5895 | 11.0283 |
| 0.2859 | 1.62 | 3500 | 0.4080 | 24.8920 | 11.2087 |
| 0.236 | 1.66 | 3600 | 0.4062 | 24.8632 | 11.1499 |
| 0.2422 | 1.71 | 3700 | 0.4051 | 24.4454 | 11.0127 |
| 0.2441 | 1.75 | 3800 | 0.4050 | 24.4166 | 10.9735 |
| 0.2694 | 1.8 | 3900 | 0.4041 | 24.5030 | 11.0440 |
| 0.2311 | 1.85 | 4000 | 0.4036 | 24.4454 | 10.9931 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
