miosipof/whisper-small-ft-balbus-sep28k-v1.6
This model is a fine-tuned version of openai/whisper-small on the Apple dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.1091
- Accuracy: 0.8101
- Precision: 0.8184
- Recall: 0.7261
- F1: 0.7695
- Roc-auc: 0.8006
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: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 1200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc-auc |
|---|---|---|---|---|---|---|---|---|
| 0.1683 | 0.2506 | 200 | 0.1682 | 0.5730 | 0.7364 | 0.0341 | 0.0652 | 0.5123 |
| 0.1494 | 0.5013 | 400 | 0.1446 | 0.7084 | 0.6603 | 0.6838 | 0.6718 | 0.7056 |
| 0.1212 | 0.7519 | 600 | 0.1236 | 0.7629 | 0.6917 | 0.8245 | 0.7523 | 0.7699 |
| 0.1088 | 1.0025 | 800 | 0.1107 | 0.8062 | 0.8337 | 0.6945 | 0.7578 | 0.7936 |
| 0.0955 | 1.2531 | 1000 | 0.1106 | 0.8081 | 0.8036 | 0.7416 | 0.7713 | 0.8006 |
| 0.0997 | 1.5038 | 1200 | 0.1091 | 0.8101 | 0.8184 | 0.7261 | 0.7695 | 0.8006 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.20.3