composite_eu+faktoria+Dialektoak_54379
This model is a fine-tuned version of openai/whisper-large-v2 on the composite_eu, faktoria, Dialektoak dataset. It achieves the following results on the evaluation set:
- Loss: 0.2286
- Wer: 11.6794
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 |
|---|---|---|---|---|
| 0.1707 | 0.42 | 500 | 0.2822 | 18.4744 |
| 0.1363 | 0.84 | 1000 | 0.2362 | 15.2975 |
| 0.1037 | 1.26 | 1500 | 0.2257 | 13.1672 |
| 0.0992 | 1.68 | 2000 | 0.2141 | 12.4654 |
| 0.0685 | 2.1 | 2500 | 0.2094 | 12.1473 |
| 0.0688 | 2.52 | 3000 | 0.2091 | 12.0570 |
| 0.0672 | 2.94 | 3500 | 0.2047 | 11.5891 |
| 0.0458 | 3.36 | 4000 | 0.2173 | 12.0919 |
| 0.045 | 3.78 | 4500 | 0.2156 | 11.8169 |
| 0.0316 | 4.2 | 5000 | 0.2286 | 11.6794 |
Framework versions
- Transformers 4.38.0
- Pytorch 2.1.1+cu121
- Datasets 2.8.0
- Tokenizers 0.15.2