Whisper Small ar-quran
This model is a fine-tuned version of openai/whisper-small on the Quran dataset. It achieves the following results on the evaluation set:
- Loss: 0.0003
- Wer: 0.0288
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- training_steps: 20905
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0499 | 0.0478 | 1000 | 0.0595 | 4.9726 |
| 0.0072 | 0.0957 | 2000 | 0.0255 | 2.2505 |
| 0.0518 | 0.1435 | 3000 | 0.0147 | 1.3491 |
| 0.0045 | 0.1913 | 4000 | 0.0107 | 0.9546 |
| 0.0035 | 0.2392 | 5000 | 0.0084 | 0.7682 |
| 0.001 | 0.2870 | 6000 | 0.0061 | 0.5717 |
| 0.0036 | 0.3348 | 7000 | 0.0052 | 0.4668 |
| 0.0035 | 0.3827 | 8000 | 0.0040 | 0.3609 |
| 0.0007 | 0.4305 | 9000 | 0.0036 | 0.3273 |
| 0.0009 | 1.0071 | 10000 | 0.0028 | 0.2727 |
| 0.0007 | 1.0550 | 11000 | 0.0023 | 0.2305 |
| 0.0005 | 1.1028 | 12000 | 0.0019 | 0.1883 |
| 0.0002 | 1.1506 | 13000 | 0.0018 | 0.1629 |
| 0.0004 | 1.1985 | 14000 | 0.0012 | 0.1217 |
| 0.0004 | 1.2463 | 15000 | 0.0011 | 0.1289 |
| 0.0002 | 1.2941 | 16000 | 0.0009 | 0.0935 |
| 0.0002 | 1.3420 | 17000 | 0.0007 | 0.0666 |
| 0.0002 | 1.3898 | 18000 | 0.0005 | 0.0537 |
| 0.0003 | 1.4376 | 19000 | 0.0004 | 0.0417 |
| 0.0001 | 2.0148 | 20000 | 0.0003 | 0.0288 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
Citation
Please cite the model using the following BibTeX entry:
@misc{deepdml/whisper-small-ar-quran-mix-norm,
title={Fine-tuned Whisper small ASR model for speech recognition in Arabic},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-small-ar-quran-mix-norm}},
year={2025}
}