whisper-small-sukuma
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1995
- Wer: 13.27
- Cer: 3.53
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_FUSED 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.2402 | 1.2920 | 500 | 0.2913 | 29.22 | 7.38 |
| 0.1006 | 2.5840 | 1000 | 0.1916 | 17.51 | 4.71 |
| 0.0549 | 3.8760 | 1500 | 0.1726 | 14.94 | 4.06 |
| 0.0110 | 5.1680 | 2000 | 0.1825 | 13.78 | 3.68 |
| 0.0050 | 6.4599 | 2500 | 0.1896 | 13.66 | 3.61 |
| 0.0019 | 7.7519 | 3000 | 0.1931 | 13.49 | 3.55 |
| 0.0019 | 9.0439 | 3500 | 0.1964 | 13.19 | 3.51 |
| 0.0011 | 10.3359 | 4000 | 0.1995 | 13.27 | 3.53 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2