leenag/Norm_KLuke_Med
This model is a fine-tuned version of openai/whisper-small on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set:
- Loss: 0.5144
- Wer: 44.9541
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0275 | 11.3636 | 250 | 0.3935 | 48.5092 |
| 0.0091 | 22.7273 | 500 | 0.4626 | 49.0252 |
| 0.0012 | 34.0909 | 750 | 0.4798 | 47.1330 |
| 0.0002 | 45.4545 | 1000 | 0.5015 | 46.3876 |
| 0.0001 | 56.8182 | 1250 | 0.4947 | 45.5849 |
| 0.0 | 68.1818 | 1500 | 0.5088 | 45.0688 |
| 0.0 | 79.5455 | 1750 | 0.5129 | 44.9541 |
| 0.0 | 90.9091 | 2000 | 0.5144 | 44.9541 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.0
- Tokenizers 0.19.1