Whisper Small for Quran Recognition
This model is a fine-tuned version of openai/whisper-small on the Quran_Reciters dataset. It achieves the following results on the evaluation set:
- Loss: 0.0179
- Wer: 3.1288
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0073 | 1.62 | 500 | 0.0249 | 5.0026 |
| 0.0014 | 3.24 | 1000 | 0.0214 | 4.1086 |
| 0.0008 | 4.85 | 1500 | 0.0221 | 3.9883 |
| 0.0 | 6.47 | 2000 | 0.0180 | 2.9740 |
| 0.0 | 8.09 | 2500 | 0.0177 | 3.0944 |
| 0.0 | 9.71 | 3000 | 0.0178 | 3.0944 |
| 0.0 | 11.33 | 3500 | 0.0179 | 3.1288 |
| 0.0 | 12.94 | 4000 | 0.0179 | 3.1288 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
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Base model
openai/whisper-small