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: 0.5273
- Wer Ortho: 46.0461
- Wer: 38.3491
- Cer: 13.0384
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 |
|---|---|---|---|---|---|---|
| 0.5022 | 0.4785 | 400 | 15.0475 | 0.5773 | 44.5732 | 52.3734 |
| 0.4835 | 0.9569 | 800 | 13.6924 | 0.5142 | 40.5166 | 48.4899 |
| 0.3764 | 1.4354 | 1200 | 13.2187 | 0.4926 | 38.7241 | 47.1020 |
| 0.3624 | 1.9139 | 1600 | 12.8324 | 0.4770 | 37.8553 | 46.0811 |
| 0.3165 | 2.3923 | 2000 | 0.4770 | 45.1081 | 37.1660 | 12.5025 |
| 0.3058 | 2.8708 | 2400 | 0.4728 | 45.5634 | 37.5822 | 12.8574 |
| 0.2386 | 3.3493 | 2800 | 0.4945 | 45.8291 | 38.0272 | 12.8462 |
| 0.2334 | 3.8278 | 3200 | 0.4874 | 45.7743 | 38.0440 | 12.8868 |
| 0.1662 | 4.3062 | 3600 | 0.5242 | 46.6003 | 38.5020 | 12.9679 |
| 0.1615 | 4.7847 | 4000 | 0.5273 | 46.0461 | 38.3491 | 13.0384 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4