whisper-base-ar-quran
This model is a fine-tuned version of openai/whisper-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0839
- Wer: 5.7544
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: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1092 | 0.05 | 250 | 0.1969 | 13.3890 |
| 0.0361 | 0.1 | 500 | 0.1583 | 10.6375 |
| 0.0192 | 0.15 | 750 | 0.1109 | 8.8468 |
| 0.0144 | 0.2 | 1000 | 0.1157 | 7.9754 |
| 0.008 | 0.25 | 1250 | 0.1000 | 7.5360 |
| 0.0048 | 1.03 | 1500 | 0.0933 | 6.8227 |
| 0.0113 | 1.08 | 1750 | 0.0955 | 6.9638 |
| 0.0209 | 1.13 | 2000 | 0.0824 | 6.3586 |
| 0.0043 | 1.18 | 2250 | 0.0830 | 6.3444 |
| 0.002 | 1.23 | 2500 | 0.1015 | 6.3025 |
| 0.0013 | 2.01 | 2750 | 0.0863 | 6.0639 |
| 0.0014 | 2.06 | 3000 | 0.0905 | 6.0213 |
| 0.0018 | 2.11 | 3250 | 0.0864 | 6.0293 |
| 0.0008 | 2.16 | 3500 | 0.0887 | 5.9308 |
| 0.0029 | 2.21 | 3750 | 0.0777 | 5.9159 |
| 0.0022 | 2.26 | 4000 | 0.0847 | 5.8749 |
| 0.0005 | 3.05 | 4250 | 0.0827 | 5.8352 |
| 0.0003 | 3.1 | 4500 | 0.0826 | 5.7800 |
| 0.0006 | 3.15 | 4750 | 0.0833 | 5.7625 |
| 0.0003 | 3.2 | 5000 | 0.0839 | 5.7544 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2