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whisper-small-mn-12
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2949
- Wer: 32.3301
- Cer: 13.3493
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: 32
- eval_batch_size: 32
- 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 | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.3012 | 1.05 | 1000 | 0.3749 | 43.2379 | 17.6739 |
| 0.2171 | 2.11 | 2000 | 0.3012 | 36.7435 | 15.2029 |
| 0.1732 | 3.16 | 3000 | 0.2823 | 33.4225 | 13.7561 |
| 0.145 | 4.21 | 4000 | 0.2822 | 32.4995 | 13.2436 |
| 0.1159 | 5.27 | 5000 | 0.2949 | 32.3301 | 13.3493 |
| 0.0863 | 6.32 | 6000 | 0.3116 | 32.7234 | 13.3892 |
| 0.0685 | 7.38 | 7000 | 0.3343 | 32.4776 | 13.3077 |
| 0.0506 | 8.43 | 8000 | 0.3584 | 33.3952 | 13.7736 |
| 0.0336 | 9.48 | 9000 | 0.3861 | 33.7011 | 13.8493 |
| 0.0215 | 10.54 | 10000 | 0.4193 | 33.7011 | 14.0140 |
| 0.0141 | 11.59 | 11000 | 0.4463 | 34.0343 | 14.0298 |
| 0.0089 | 12.64 | 12000 | 0.4660 | 33.6137 | 13.8052 |
| 0.0057 | 13.7 | 13000 | 0.4913 | 33.9797 | 13.9849 |
| 0.0039 | 14.75 | 14000 | 0.5078 | 33.9906 | 14.0656 |
| 0.0033 | 15.81 | 15000 | 0.5244 | 33.7721 | 13.9192 |
| 0.0024 | 16.86 | 16000 | 0.5358 | 33.7612 | 13.7910 |
| 0.0018 | 17.91 | 17000 | 0.5469 | 33.6465 | 13.8468 |
| 0.0013 | 18.97 | 18000 | 0.5614 | 33.6683 | 13.7553 |
| 0.0014 | 20.02 | 19000 | 0.5707 | 33.6574 | 13.8884 |
| 0.0006 | 21.07 | 20000 | 0.5835 | 34.0671 | 14.0764 |
| 0.0007 | 22.13 | 21000 | 0.5927 | 33.9742 | 14.0772 |
| 0.0005 | 23.18 | 22000 | 0.5994 | 34.0398 | 14.0290 |
| 0.0004 | 24.24 | 23000 | 0.6067 | 33.9469 | 13.9217 |
| 0.0003 | 25.29 | 24000 | 0.6109 | 33.9688 | 13.9591 |
| 0.0003 | 26.34 | 25000 | 0.6130 | 33.8267 | 13.8360 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
