Whisper Medium Mnong
This model is a fine-tuned version of openai/whisper-medium on the MnongAudio-v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2773
- Wer: 16.8874
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 |
|---|---|---|---|---|
| 2.1592 | 0.2915 | 200 | 2.0293 | 117.3459 |
| 1.3786 | 0.5831 | 400 | 1.3853 | 90.0408 |
| 0.9096 | 0.8746 | 600 | 1.0002 | 77.8146 |
| 0.5869 | 1.1662 | 800 | 0.7615 | 64.7733 |
| 0.4996 | 1.4577 | 1000 | 0.5799 | 52.1141 |
| 0.3741 | 1.7493 | 1200 | 0.4811 | 60.9781 |
| 0.1899 | 2.0408 | 1400 | 0.4078 | 35.3031 |
| 0.1792 | 2.3324 | 1600 | 0.3690 | 34.2588 |
| 0.1514 | 2.6239 | 1800 | 0.3361 | 31.5079 |
| 0.1758 | 2.9155 | 2000 | 0.3069 | 30.9730 |
| 0.0619 | 3.2070 | 2200 | 0.3031 | 28.2731 |
| 0.047 | 3.4985 | 2400 | 0.2952 | 22.1600 |
| 0.0472 | 3.7901 | 2600 | 0.2914 | 24.8344 |
| 0.0246 | 4.0816 | 2800 | 0.2799 | 20.0458 |
| 0.0255 | 4.3732 | 3000 | 0.2849 | 23.4590 |
| 0.0246 | 4.6647 | 3200 | 0.2773 | 19.5619 |
| 0.0235 | 4.9563 | 3400 | 0.2736 | 20.4279 |
| 0.0088 | 5.2478 | 3600 | 0.2795 | 19.9440 |
| 0.0076 | 5.5394 | 3800 | 0.2786 | 17.0657 |
| 0.0057 | 5.8309 | 4000 | 0.2773 | 16.8874 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
openai/whisper-medium