whisper-es-pr-pe-qlora-v5
This model is a fine-tuned version of openai/whisper-small on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.1012
- Wer: 0.9873
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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
- training_steps: 1200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1171 | 0.3333 | 400 | 0.1185 | 0.9853 |
| 0.0634 | 1.0983 | 800 | 0.1018 | 0.9778 |
| 0.0582 | 1.4317 | 1200 | 0.1012 | 0.9873 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1