Whisper openai-whisper-large-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the llamadas ecu911 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2832
- Wer: 19.8947
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5257 | 1.0 | 422 | 0.3596 | 35.3561 |
| 0.2108 | 2.0 | 844 | 0.2888 | 27.7362 |
| 0.1153 | 3.0 | 1266 | 0.2694 | 25.3256 |
| 0.0674 | 4.0 | 1688 | 0.2810 | 32.9731 |
| 0.0435 | 5.0 | 2110 | 0.2817 | 20.3380 |
| 0.0301 | 6.0 | 2532 | 0.2910 | 23.0673 |
| 0.0245 | 7.0 | 2954 | 0.2817 | 20.5043 |
| 0.021 | 8.0 | 3376 | 0.2734 | 18.4400 |
| 0.0173 | 9.0 | 3798 | 0.2784 | 23.9263 |
| 0.0167 | 10.0 | 4220 | 0.2832 | 19.8947 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1