whisper-small-sc
This model is a fine-tuned version of openai/whisper-small on janaab/supreme-court-speech dataset. It achieves the following results on the evaluation set:
- Loss: 0.2992
- Wer Ortho: 10.7269
- Wer: 10.2317
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: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.2799 | 0.7704 | 250 | 0.2614 | 12.1130 | 11.6261 |
| 0.1454 | 1.5408 | 500 | 0.2367 | 11.0523 | 10.6016 |
| 0.072 | 2.3112 | 750 | 0.2428 | 10.6736 | 10.2154 |
| 0.0488 | 3.0817 | 1000 | 0.2638 | 10.8335 | 10.3623 |
| 0.0294 | 3.8521 | 1250 | 0.2689 | 11.2821 | 10.7793 |
| 0.012 | 4.6225 | 1500 | 0.2992 | 10.7269 | 10.2317 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1