Whisper LARGE PT 425
This model is a fine-tuned version of openai/whisper-large-v3 on the 425 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1162
- Wer: 3.9720
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: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0037 | 7.5989 | 10000 | 0.1162 | 3.9720 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
Citation
BibTeX:
@inproceedings{camoes,
title={{CAMÕES: A Comprehensive Automatic Speech Recognition Benchmark for European Portuguese}},
author={Carlos Carvalho, Francisco Teixeira, Catarina Botelho, Anna Pompili, Rubén Solera-Ureña, Sérgio Paulo, Mariana Julião, Thomas Rolland, John Mendonça, Diogo Pereira, Isabel Trancoso, Alberto Abad},
booktitle={Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
year={2025},
}