Whisper base AR - BA
This model is a fine-tuned version of openai/whisper-base on the quran-ayat-speech-to-text dataset. It achieves the following results on the evaluation set:
- Loss: 0.0892
- Wer: 0.1918
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.406 | 1.0 | 625 | 0.0887 | 0.1907 |
| 1.3322 | 2.0 | 1250 | 0.0906 | 0.1874 |
| 1.2587 | 3.0 | 1875 | 0.0903 | 0.1844 |
| 1.1135 | 4.0 | 2500 | 0.0892 | 0.1954 |
| 1.0444 | 5.0 | 3125 | 0.0879 | 0.1883 |
| 0.9344 | 6.0 | 3750 | 0.0867 | 0.1802 |
| 0.9135 | 7.0 | 4375 | 0.0874 | 0.1854 |
| 0.8567 | 8.0 | 5000 | 0.0861 | 0.1882 |
| 0.7738 | 9.0 | 5625 | 0.0857 | 0.1951 |
| 0.7419 | 10.0 | 6250 | 0.0852 | 0.1958 |
| 0.7167 | 11.0 | 6875 | 0.0854 | 0.1933 |
| 0.6929 | 12.0 | 7500 | 0.0850 | 0.1874 |
| 0.6539 | 13.0 | 8125 | 0.0847 | 0.1908 |
| 0.6448 | 14.0 | 8750 | 0.0845 | 0.1883 |
| 0.5887 | 15.0 | 9375 | 0.0846 | 0.1892 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1