whisper-small-moore-v2
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5124
- Wer: 40.4014
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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 300
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0637 | 0.9470 | 500 | 0.5086 | 48.3004 |
| 0.6753 | 1.8939 | 1000 | 0.4204 | 41.2431 |
| 0.3738 | 2.8409 | 1500 | 0.4010 | 40.6280 |
| 0.2418 | 3.7879 | 2000 | 0.4068 | 39.0741 |
| 0.1319 | 4.7348 | 2500 | 0.4307 | 39.2036 |
| 0.0722 | 5.6818 | 3000 | 0.4523 | 39.0741 |
| 0.0410 | 6.6288 | 3500 | 0.4703 | 39.9482 |
| 0.0203 | 7.5758 | 4000 | 0.4912 | 40.2396 |
| 0.0088 | 8.5227 | 4500 | 0.5065 | 39.7216 |
| 0.0062 | 9.4697 | 5000 | 0.5124 | 40.4014 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2