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whisper-large-v2-mn-13
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1689
- Wer: 20.0240
- Cer: 6.6010
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: 8
- eval_batch_size: 4
- 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: 25000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|---|---|---|---|---|---|
| 0.3921 | 0.09 | 1000 | 15.7845 | 0.4101 | 46.9030 |
| 0.3115 | 0.17 | 2000 | 14.2911 | 0.3353 | 41.8451 |
| 0.2659 | 0.26 | 3000 | 11.8131 | 0.2800 | 34.6406 |
| 0.2477 | 0.35 | 4000 | 10.6659 | 0.2578 | 32.0024 |
| 0.2274 | 0.43 | 5000 | 10.0460 | 0.2463 | 30.3419 |
| 0.2059 | 0.52 | 6000 | 9.9264 | 0.2305 | 28.5558 |
| 0.2092 | 0.61 | 7000 | 9.4277 | 0.2196 | 27.8785 |
| 0.1956 | 0.69 | 8000 | 9.2745 | 0.2093 | 26.8353 |
| 0.195 | 0.78 | 9000 | 8.9485 | 0.2042 | 26.6168 |
| 0.195 | 0.87 | 10000 | 8.5324 | 0.2001 | 25.6718 |
| 0.1795 | 0.95 | 11000 | 8.1786 | 0.1936 | 24.1698 |
| 0.1575 | 1.04 | 12000 | 7.8653 | 0.1915 | 23.8912 |
| 0.1358 | 1.13 | 13000 | 7.6749 | 0.1918 | 23.3778 |
| 0.1509 | 1.21 | 14000 | 7.7221 | 0.1852 | 23.1811 |
| 0.1474 | 1.3 | 15000 | 7.3246 | 0.1764 | 22.4984 |
| 0.1461 | 1.39 | 16000 | 7.3187 | 0.1793 | 22.4110 |
| 0.134 | 1.47 | 17000 | 7.1123 | 0.1737 | 21.9412 |
| 0.1289 | 1.56 | 18000 | 7.4593 | 0.1727 | 22.0614 |
| 0.1287 | 1.65 | 19000 | 7.0230 | 0.1701 | 21.4223 |
| 0.1196 | 1.73 | 20000 | 6.9447 | 0.1666 | 21.2475 |
| 0.1275 | 1.82 | 21000 | 6.7956 | 0.1653 | 20.8106 |
| 0.1329 | 1.91 | 22000 | 6.7729 | 0.1622 | 20.3354 |
| 0.1294 | 1.99 | 23000 | 6.6448 | 0.1606 | 20.2207 |
| 0.1043 | 2.08 | 24000 | 6.6010 | 0.1689 | 20.0240 |
| 0.079 | 2.17 | 25000 | 6.6246 | 0.1687 | 20.1005 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
