whisper-small-10DB-3r6
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Cer: 27.9030
- Loss: 0.3175
- Wer: 74.6757
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: 3e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|---|---|---|---|---|---|
| 4.2071 | 1.2675 | 100 | 60.5454 | 1.0186 | 100.1235 |
| 2.3919 | 2.5350 | 200 | 36.2347 | 0.6153 | 85.4231 |
| 1.8048 | 3.8025 | 300 | 31.8374 | 0.4921 | 80.2965 |
| 1.3156 | 5.0637 | 400 | 31.7468 | 0.3902 | 78.5053 |
| 0.9425 | 6.3312 | 500 | 29.7243 | 0.3259 | 76.7758 |
| 0.8272 | 7.5987 | 600 | 28.2753 | 0.3199 | 75.9728 |
| 0.7388 | 8.8662 | 700 | 27.9030 | 0.3175 | 74.6757 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2