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: 0.1077
- Wer: 0.2309
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: 8
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 3.3412 | 1.0 | 157 | 0.1041 | 0.2149 |
| 3.0121 | 2.0 | 314 | 0.1054 | 0.2123 |
| 2.6811 | 3.0 | 471 | 0.1033 | 0.2079 |
| 2.2468 | 4.0 | 628 | 0.1062 | 0.2163 |
| 2.1438 | 5.0 | 785 | 0.1029 | 0.2168 |
| 1.8098 | 6.0 | 942 | 0.1035 | 0.2131 |
| 1.7488 | 7.0 | 1099 | 0.1023 | 0.2190 |
| 1.52 | 8.0 | 1256 | 0.1020 | 0.2116 |
| 1.431 | 9.0 | 1413 | 0.1013 | 0.2112 |
| 1.3151 | 10.0 | 1570 | 0.1005 | 0.2168 |
| 1.2219 | 11.0 | 1727 | 0.1011 | 0.2107 |
| 1.1879 | 12.0 | 1884 | 0.1003 | 0.2097 |
| 1.1158 | 13.0 | 2041 | 0.1007 | 0.2098 |
| 1.0995 | 14.0 | 2198 | 0.0998 | 0.2095 |
| 1.0596 | 14.9088 | 2340 | 0.1001 | 0.2107 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1