HiTZ/whisper-large-eu

Whisper Large Basque

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

• Loss: 0.4369
• Wer: 12.2342

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: 32
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_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: 20000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0196	4.01	1000	0.2825	15.4725
0.0039	9.01	2000	0.3072	14.2270
0.0031	14.01	3000	0.3170	13.7652
0.0023	19.0	4000	0.3310	13.6640
0.0014	24.0	5000	0.3384	13.5749
0.0034	29.0	6000	0.3425	13.7450
0.0011	33.01	7000	0.3476	13.0990
0.001	38.01	8000	0.3432	13.0990
0.0004	43.01	9000	0.3524	12.8033
0.0017	48.01	10000	0.3620	13.3946
0.0003	53.0	11000	0.3564	12.6190
0.0001	58.0	12000	0.3675	12.6352
0.0	63.0	13000	0.3878	12.4286
0.0	67.01	14000	0.3996	12.3577
0.0	72.01	15000	0.4088	12.3456
0.0	77.01	16000	0.4167	12.3091
0.0	82.01	17000	0.4241	12.3112
0.0	87.0	18000	0.4302	12.3193
0.0	92.0	19000	0.4351	12.2565
0.0	97.0	20000	0.4369	12.2342

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.