metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v3-ft-btb-cv-ca-cy
results: []
whisper-large-v3-ft-btb-cv-ca-cy
This model is a fine-tuned version of openai/whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3666
- Wer: 0.2773
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5072 | 0.4068 | 1000 | 0.5020 | 0.3567 |
| 0.3708 | 0.8137 | 2000 | 0.4260 | 0.3258 |
| 0.2599 | 1.2205 | 3000 | 0.3973 | 0.3003 |
| 0.2618 | 1.6273 | 4000 | 0.3783 | 0.2905 |
| 0.1846 | 2.0342 | 5000 | 0.3666 | 0.2773 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3