moore_small_V1
This model is a fine-tuned version of openai/whisper-small on the abdouaziiz/moore_clean dataset. It achieves the following results on the evaluation set:
- Loss: 0.0515
- Wer: 0.1004
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-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 50
- training_steps: 48000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8667 | 0.5770 | 500 | 0.2067 | 0.2110 |
| 0.3227 | 1.1541 | 1000 | 0.1456 | 0.1656 |
| 0.2201 | 1.7311 | 1500 | 0.1188 | 0.1238 |
| 0.1564 | 2.3081 | 2000 | 0.1009 | 0.1232 |
| 0.1213 | 2.8852 | 2500 | 0.0778 | 0.1123 |
| 0.0779 | 3.4622 | 3000 | 0.0749 | 0.1027 |
| 0.0694 | 4.0392 | 3500 | 0.0689 | 0.1069 |
| 0.0434 | 4.6163 | 4000 | 0.0639 | 0.1254 |
| 0.0413 | 5.1933 | 4500 | 0.0611 | 0.1055 |
| 0.0332 | 5.7703 | 5000 | 0.0570 | 0.1110 |
| 0.0287 | 6.3474 | 5500 | 0.0583 | 0.1050 |
| 0.0257 | 6.9244 | 6000 | 0.0515 | 0.1004 |
| 0.0217 | 7.5014 | 6500 | 0.0538 | 0.0964 |
| 0.021 | 8.0785 | 7000 | 0.0537 | 0.0902 |
| 0.018 | 8.6555 | 7500 | 0.0567 | 0.0850 |
| 0.0172 | 9.2325 | 8000 | 0.0533 | 0.0792 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.20.3