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Large v3 turbo 0.6s 200mspad augmentedv1 - Alperitoo
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Cagrilar-full-relabeled dataset. It achieves the following results on the evaluation set:
- Loss: 0.4556
- Wer: 29.4969
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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3879 | 1.0 | 477 | 0.3409 | 28.6979 |
| 0.2692 | 2.0 | 954 | 0.3272 | 27.9853 |
| 0.1854 | 3.0 | 1431 | 0.3403 | 28.0285 |
| 0.1384 | 4.0 | 1908 | 0.3524 | 28.2444 |
| 0.0976 | 5.0 | 2385 | 0.3778 | 28.3740 |
| 0.0707 | 6.0 | 2862 | 0.3889 | 28.3092 |
| 0.0523 | 7.0 | 3339 | 0.4196 | 28.9786 |
| 0.0385 | 8.0 | 3816 | 0.4418 | 29.3241 |
| 0.0324 | 9.0 | 4293 | 0.4532 | 29.8424 |
| 0.0268 | 10.0 | 4770 | 0.4556 | 29.4969 |
Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0
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