Whisper Small En - Sridhar Vanga
This model is a fine-tuned version of openai/whisper-small on the sridhar1ga/UCLASS dataset. It achieves the following results on the evaluation set:
- Loss: 2.0974
- Wer: 41.4951
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: 8
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0513 | 18.5185 | 500 | 1.5771 | 61.3218 |
| 0.0056 | 37.0370 | 1000 | 1.8013 | 42.1452 |
| 0.0089 | 55.5556 | 1500 | 1.8905 | 65.0054 |
| 0.0054 | 74.0741 | 2000 | 1.7860 | 44.9621 |
| 0.0016 | 92.5926 | 2500 | 1.9571 | 41.9285 |
| 0.0001 | 111.1111 | 3000 | 2.0281 | 41.0618 |
| 0.0001 | 129.6296 | 3500 | 2.0595 | 41.9285 |
| 0.0001 | 148.1481 | 4000 | 2.0805 | 41.3868 |
| 0.0001 | 166.6667 | 4500 | 2.0927 | 41.4951 |
| 0.0001 | 185.1852 | 5000 | 2.0974 | 41.4951 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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