WhisperTiny20hCommonVoice10hAugment
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: 0.8526
- Wer: 0.4173
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 |
|---|---|---|---|---|
| 0.8867 | 0.4444 | 500 | 1.0354 | 0.4892 |
| 0.7794 | 0.8889 | 1000 | 0.9536 | 0.4646 |
| 0.6075 | 1.3333 | 1500 | 0.9055 | 0.4392 |
| 0.5813 | 1.7778 | 2000 | 0.8904 | 0.4338 |
| 0.4598 | 2.2222 | 2500 | 0.8724 | 0.4262 |
| 0.4365 | 2.6667 | 3000 | 0.8647 | 0.4259 |
| 0.3813 | 3.1111 | 3500 | 0.8568 | 0.4188 |
| 0.3842 | 3.5556 | 4000 | 0.8605 | 0.4251 |
| 0.3735 | 4.0 | 4500 | 0.8560 | 0.4221 |
| 0.3411 | 4.4444 | 5000 | 0.8526 | 0.4173 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0