whisper-a-normal-ls
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: 0.0292
- Wer: 3.1854
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: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 132
- num_epochs: 11
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 1.0 | 70 | 0.2501 | 18.9989 |
| 0.8254 | 2.0 | 140 | 0.1896 | 25.1422 |
| 0.1344 | 3.0 | 210 | 0.0928 | 29.1240 |
| 0.1344 | 4.0 | 280 | 0.0523 | 17.8612 |
| 0.054 | 5.0 | 350 | 0.0681 | 33.4471 |
| 0.0174 | 6.0 | 420 | 0.0446 | 11.9454 |
| 0.0174 | 7.0 | 490 | 0.0394 | 7.0535 |
| 0.0121 | 8.0 | 560 | 0.0314 | 5.6883 |
| 0.0004 | 9.0 | 630 | 0.0292 | 3.2992 |
| 0.0 | 10.0 | 700 | 0.0293 | 3.1854 |
| 0.0 | 10.8489 | 759 | 0.0292 | 3.1854 |
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Base model
openai/whisper-small