WhisperTiny15hCommonVoice5hAugmentV2
This model is a fine-tuned version of OpenAI/whisper-tiny on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9025
- Wer: 0.4359
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: 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: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8054 | 0.6693 | 500 | 0.9866 | 0.4886 |
| 0.5822 | 1.3387 | 1000 | 0.9179 | 0.4688 |
| 0.4991 | 2.0080 | 1500 | 0.8808 | 0.4333 |
| 0.3864 | 2.6774 | 2000 | 0.8846 | 0.4295 |
| 0.2676 | 3.3467 | 2500 | 0.8738 | 0.4407 |
| 0.2598 | 4.0161 | 3000 | 0.8789 | 0.4324 |
| 0.1893 | 4.6854 | 3500 | 0.8829 | 0.4276 |
| 0.1514 | 5.3548 | 4000 | 0.9006 | 0.4256 |
| 0.1451 | 6.0241 | 4500 | 0.9021 | 0.4345 |
| 0.137 | 6.6934 | 5000 | 0.9025 | 0.4359 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0