metadata
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-basque
results: []
whisper-large-basque
This model is a fine-tuned version of openai/whisper-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2281
- Wer: 11.8969
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: 1e-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 |
|---|---|---|---|---|
| 0.1763 | 0.42 | 500 | 0.2915 | 17.9244 |
| 0.1415 | 0.84 | 1000 | 0.2468 | 15.0738 |
| 0.1182 | 1.26 | 1500 | 0.2483 | 14.3144 |
| 0.1234 | 1.68 | 2000 | 0.2520 | 14.1380 |
| 0.0961 | 2.1 | 2500 | 0.2397 | 13.6557 |
| 0.0952 | 2.52 | 3000 | 0.2360 | 12.7999 |
| 0.0912 | 2.94 | 3500 | 0.2264 | 12.4284 |
| 0.0717 | 3.36 | 4000 | 0.2295 | 12.0590 |
| 0.0698 | 3.78 | 4500 | 0.2260 | 12.2581 |
| 0.0582 | 4.2 | 5000 | 0.2281 | 11.8969 |
Framework versions
- Transformers 4.38.0
- Pytorch 2.1.1+cu121
- Datasets 2.8.0
- Tokenizers 0.15.2