Whisper small Tonga - Tonga ASR
This model is a fine-tuned version of openai/whisper-small on the csikasote/Tonga dataset. It achieves the following results on the evaluation set:
- Loss: 0.5598
- Wer: 51.1127
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: Use OptimizerNames.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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5631 | 0.9579 | 500 | 0.5425 | 56.3746 |
| 0.3865 | 1.9157 | 1000 | 0.4621 | 57.9972 |
| 0.243 | 2.8736 | 1500 | 0.4462 | 46.4070 |
| 0.1604 | 3.8314 | 2000 | 0.4600 | 47.2415 |
| 0.0831 | 4.7893 | 2500 | 0.4872 | 48.2151 |
| 0.0423 | 5.7471 | 3000 | 0.5048 | 56.5369 |
| 0.0195 | 6.7050 | 3500 | 0.5273 | 51.3213 |
| 0.0084 | 7.6628 | 4000 | 0.5476 | 48.9801 |
| 0.0051 | 8.6207 | 4500 | 0.5553 | 50.3245 |
| 0.0036 | 9.5785 | 5000 | 0.5598 | 51.1127 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2