HiTZ/whisper-tiny-eu

Whisper Tiny Basque

This model is a fine-tuned version of openai/whisper-tiny on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:

• Loss: 0.6522
• Wer: 32.2694

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: 3.75e-05
• train_batch_size: 256
• eval_batch_size: 128
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0096	19.0	1000	0.5796	32.8952
0.0011	38.0	2000	0.6522	32.2694
0.0005	57.01	3000	0.6949	33.1403
0.0003	76.01	4000	0.7217	33.0734
0.0003	96.0	5000	0.7321	33.1585

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3

Citation

If you use these models in your research, please cite:

@misc{dezuazo2025whisperlmimprovingasrmodels,
      title={Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages}, 
      author={Xabier de Zuazo and Eva Navas and Ibon Saratxaga and Inma Hernáez Rioja},
      year={2025},
      eprint={2503.23542},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.23542}, 
}

Please, check the related paper preprint in arXiv:2503.23542 for more details.

Licensing

This model is available under the Apache-2.0 License. You are free to use, modify, and distribute this model as long as you credit the original creators.