composite_eu+faktoria+Dialektoak_54378
This model is a fine-tuned version of openai/whisper-large on the composite_eu, faktoria, Dialektoak dataset. It achieves the following results on the evaluation set:
- Loss: 0.2164
- Wer: 10.9775
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: 2e-05
- train_batch_size: 256
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1619 | 0.42 | 500 | 0.2754 | 15.5930 |
| 0.1307 | 0.84 | 1000 | 0.2378 | 13.6187 |
| 0.089 | 1.26 | 1500 | 0.2186 | 12.2006 |
| 0.0867 | 1.68 | 2000 | 0.2166 | 11.5952 |
| 0.0512 | 2.1 | 2500 | 0.2170 | 11.0001 |
| 0.0519 | 2.52 | 3000 | 0.2164 | 10.9775 |
| 0.0511 | 2.94 | 3500 | 0.2175 | 11.0144 |
| 0.027 | 3.36 | 4000 | 0.2344 | 11.0719 |
| 0.0265 | 3.78 | 4500 | 0.2365 | 10.9713 |
| 0.0144 | 4.2 | 5000 | 0.2529 | 11.2525 |
Framework versions
- Transformers 4.38.0
- Pytorch 2.1.1+cu121
- Datasets 2.8.0
- Tokenizers 0.15.2