whisper-medium-aeb_TunCS
This model is a fine-tuned version of openai/whisper-medium on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5447
- Wer: 29.5377
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
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 1.0 | 163 | 0.6260 | 44.0925 |
| No log | 2.0 | 326 | 0.4777 | 42.5942 |
| No log | 3.0 | 489 | 0.4481 | 39.5548 |
| 0.6281 | 4.0 | 652 | 0.4537 | 39.7260 |
| 0.6281 | 5.0 | 815 | 0.4666 | 40.4538 |
| 0.6281 | 6.0 | 978 | 0.5000 | 33.3904 |
| 0.0853 | 7.0 | 1141 | 0.5104 | 29.7517 |
| 0.0853 | 8.0 | 1304 | 0.5232 | 30.6079 |
| 0.0853 | 9.0 | 1467 | 0.5309 | 30.0514 |
| 0.0114 | 10.0 | 1630 | 0.5447 | 29.5377 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0