Whisper Small Ori vi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4021
- Wer: 15.2519
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 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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.458 | 0.2222 | 100 | 0.4649 | 16.8154 |
| 0.4314 | 0.4444 | 200 | 0.4266 | 16.4319 |
| 0.4275 | 0.6667 | 300 | 0.4166 | 15.5542 |
| 0.3946 | 0.8889 | 400 | 0.4107 | 15.5764 |
| 0.2151 | 1.1111 | 500 | 0.4051 | 15.5616 |
| 0.2383 | 1.3333 | 600 | 0.4014 | 15.3551 |
| 0.2176 | 1.5556 | 700 | 0.3979 | 15.5395 |
| 0.2271 | 1.7778 | 800 | 0.3996 | 15.2371 |
| 0.222 | 2.0 | 900 | 0.3966 | 15.4141 |
| 0.1469 | 2.2222 | 1000 | 0.4021 | 15.2519 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0