UG Speech Data ASR - Ewe nornmaliser
This model is a fine-tuned version of openai/whisper-small on the ugspeechdata-ewe dataset. It achieves the following results on the evaluation set:
- Loss: 0.5275
- Wer Ortho: 46.3552
- Wer: 38.6876
- Cer: 13.2130
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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | Wer Ortho |
|---|---|---|---|---|---|---|
| 0.5021 | 0.4785 | 400 | 15.1787 | 0.5774 | 44.6759 | 52.4914 |
| 0.4833 | 0.9569 | 800 | 13.7387 | 0.5141 | 40.5820 | 48.5622 |
| 0.3765 | 1.4354 | 1200 | 13.0650 | 0.4926 | 38.5423 | 46.8196 |
| 0.3626 | 1.9139 | 1600 | 12.9516 | 0.4771 | 37.9238 | 46.1237 |
| 0.3109 | 2.3923 | 2000 | 12.3654 | 0.4750 | 37.0070 | 44.9041 |
| 0.3048 | 2.8708 | 2400 | 12.9748 | 0.4719 | 37.5137 | 45.5116 |
| 0.2446 | 3.3493 | 2800 | 0.4953 | 45.7020 | 37.8667 | 12.8493 |
| 0.2362 | 3.8278 | 3200 | 0.4882 | 45.9007 | 38.0896 | 13.0340 |
| 0.1642 | 4.3062 | 3600 | 0.5249 | 46.3910 | 38.3491 | 12.8627 |
| 0.1611 | 4.7847 | 4000 | 0.5275 | 46.3552 | 38.6876 | 13.2130 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2