Whisper-Small-Inbrowser-Proctor
This model is a fine-tuned version of openai/whisper-small on the Inbrowser Procotor Dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.3093
- Wer: 16.9481
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: 5e-06
- train_batch_size: 8
- 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: 20
- training_steps: 250
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2855 | 0.4545 | 25 | 0.4320 | 24.4186 |
| 0.1728 | 0.9091 | 50 | 0.3271 | 17.4896 |
| 0.0925 | 1.3636 | 75 | 0.3101 | 14.5428 |
| 0.1021 | 1.8182 | 100 | 0.3059 | 16.8366 |
| 0.054 | 2.2727 | 125 | 0.3039 | 15.1641 |
| 0.083 | 2.7273 | 150 | 0.3050 | 14.6703 |
| 0.0355 | 3.1818 | 175 | 0.3055 | 14.7818 |
| 0.0502 | 3.6364 | 200 | 0.3074 | 15.6897 |
| 0.0287 | 4.0909 | 225 | 0.3089 | 17.0596 |
| 0.0347 | 4.5455 | 250 | 0.3093 | 16.9481 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0