whisper-base.en-fsc
This model is a fine-tuned version of openai/whisper-base.en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0437
- Accuracy: 0.9950
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: 0.0005
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9959 | 120 | 0.0862 | 0.9739 |
| No log | 2.0 | 241 | 0.0422 | 0.9866 |
| No log | 2.9959 | 361 | 0.0630 | 0.9823 |
| No log | 4.0 | 482 | 0.0630 | 0.9805 |
| No log | 4.9959 | 602 | 0.0626 | 0.9821 |
| No log | 6.0 | 723 | 0.0339 | 0.9905 |
| No log | 6.9959 | 843 | 0.0452 | 0.9897 |
| No log | 8.0 | 964 | 0.0527 | 0.9834 |
| 0.1514 | 8.9959 | 1084 | 0.0637 | 0.9868 |
| 0.1514 | 10.0 | 1205 | 0.0443 | 0.9921 |
| 0.1514 | 10.9959 | 1325 | 0.0306 | 0.9937 |
| 0.1514 | 12.0 | 1446 | 0.0416 | 0.9897 |
| 0.1514 | 12.9959 | 1566 | 0.0363 | 0.9910 |
| 0.1514 | 14.0 | 1687 | 0.0413 | 0.9924 |
| 0.1514 | 14.9959 | 1807 | 0.0344 | 0.9945 |
| 0.1514 | 16.0 | 1928 | 0.0508 | 0.9924 |
| 0.0161 | 16.9959 | 2048 | 0.0436 | 0.9937 |
| 0.0161 | 18.0 | 2169 | 0.0435 | 0.9931 |
| 0.0161 | 18.9959 | 2289 | 0.0428 | 0.9945 |
| 0.0161 | 20.0 | 2410 | 0.0425 | 0.9947 |
| 0.0161 | 20.9959 | 2530 | 0.0432 | 0.9947 |
| 0.0161 | 22.0 | 2651 | 0.0438 | 0.9947 |
| 0.0161 | 22.9959 | 2771 | 0.0437 | 0.9950 |
| 0.0161 | 24.0 | 2892 | 0.0438 | 0.9950 |
| 0.0011 | 24.8963 | 3000 | 0.0438 | 0.9950 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1