WhisperTiny15hCommonVoice45hAugmentV2
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: 0.8412
- Wer: 0.4180
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 |
|---|---|---|---|---|
| 0.9528 | 0.2233 | 500 | 0.9953 | 0.4696 |
| 0.7794 | 0.4466 | 1000 | 0.9161 | 0.4818 |
| 0.6888 | 0.6699 | 1500 | 0.8845 | 0.4304 |
| 0.5905 | 0.8933 | 2000 | 0.8664 | 0.4301 |
| 0.4504 | 1.1166 | 2500 | 0.8553 | 0.4267 |
| 0.4521 | 1.3399 | 3000 | 0.8520 | 0.4231 |
| 0.4061 | 1.5632 | 3500 | 0.8458 | 0.4194 |
| 0.39 | 1.7865 | 4000 | 0.8475 | 0.4179 |
| 0.3882 | 2.0098 | 4500 | 0.8440 | 0.4168 |
| 0.3237 | 2.2331 | 5000 | 0.8412 | 0.4180 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0