WhisperTiny15hCommonVoice30hAugment
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: 0.8565
- Wer: 0.4172
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 |
|---|---|---|---|---|
| 0.98 | 0.2976 | 500 | 1.0271 | 0.4861 |
| 0.8044 | 0.5952 | 1000 | 0.9400 | 0.4785 |
| 0.7403 | 0.8929 | 1500 | 0.9092 | 0.4431 |
| 0.575 | 1.1905 | 2000 | 0.8876 | 0.4370 |
| 0.5335 | 1.4881 | 2500 | 0.8789 | 0.4309 |
| 0.5165 | 1.7857 | 3000 | 0.8660 | 0.4287 |
| 0.4262 | 2.0833 | 3500 | 0.8623 | 0.4259 |
| 0.4143 | 2.3810 | 4000 | 0.8622 | 0.4222 |
| 0.4144 | 2.6786 | 4500 | 0.8545 | 0.4201 |
| 0.4047 | 2.9762 | 5000 | 0.8565 | 0.4172 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0