Whisper Small Java
This model is a fine-tuned version of openai/whisper-small on the SLR Javanenese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9356
- Wer: 38.3731
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
| 0.8832 |
0.1 |
100 |
0.9373 |
51.7965 |
| 0.3579 |
1.075 |
200 |
0.9986 |
51.4516 |
| 0.2348 |
2.05 |
300 |
0.9892 |
46.0765 |
| 0.1397 |
3.025 |
400 |
1.0404 |
47.0250 |
| 0.0836 |
3.125 |
500 |
0.9862 |
46.9531 |
| 0.0515 |
4.1 |
600 |
1.0148 |
42.2248 |
| 0.0222 |
5.075 |
700 |
0.9917 |
40.2846 |
| 0.0191 |
6.05 |
800 |
0.9665 |
39.3360 |
| 0.0078 |
7.025 |
900 |
0.9541 |
39.0486 |
| 0.0009 |
7.125 |
1000 |
0.9356 |
38.3731 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
