Whisper Base Mnong
This model is a fine-tuned version of openai/whisper-base on the MnongAudio-v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7611
- Wer: 77.7127
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 |
|---|---|---|---|---|
| 2.7389 | 0.2915 | 200 | 2.6665 | 373.6628 |
| 2.2233 | 0.5831 | 400 | 2.2426 | 189.4549 |
| 1.8164 | 0.8746 | 600 | 1.8990 | 131.6353 |
| 1.5731 | 1.1662 | 800 | 1.6678 | 124.3760 |
| 1.4459 | 1.4577 | 1000 | 1.4828 | 95.8227 |
| 1.3009 | 1.7493 | 1200 | 1.3453 | 96.9689 |
| 1.0242 | 2.0408 | 1400 | 1.2264 | 89.9898 |
| 0.9227 | 2.3324 | 1600 | 1.1492 | 80.0815 |
| 0.9111 | 2.6239 | 1800 | 1.0539 | 83.2399 |
| 0.8831 | 2.9155 | 2000 | 0.9899 | 88.1814 |
| 0.5906 | 3.2070 | 2200 | 0.9452 | 84.5899 |
| 0.54 | 3.4985 | 2400 | 0.9017 | 79.6740 |
| 0.542 | 3.7901 | 2600 | 0.8713 | 72.2364 |
| 0.4606 | 4.0816 | 2800 | 0.8320 | 72.9241 |
| 0.4879 | 4.3732 | 3000 | 0.8172 | 75.4712 |
| 0.4033 | 4.6647 | 3200 | 0.7940 | 75.9552 |
| 0.4235 | 4.9563 | 3400 | 0.7737 | 73.2552 |
| 0.3638 | 5.2478 | 3600 | 0.7704 | 79.2155 |
| 0.383 | 5.5394 | 3800 | 0.7641 | 77.7382 |
| 0.3714 | 5.8309 | 4000 | 0.7611 | 77.7127 |
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-base