deepdml/whisper-large-v3-turbo-ar-mix-norm

Whisper Turbo ar

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1973
• Wer: 18.8998
• Cer: 5.0561

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
• 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: 0.04
• training_steps: 18000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.5358	0.0556	1000	0.3047	26.8192	8.1187
0.3875	0.1111	2000	0.2829	27.2654	7.5340
0.2546	0.1667	3000	0.2629	24.2008	6.7543
0.1702	0.2222	4000	0.2628	23.4884	6.5769
0.1075	0.2778	5000	0.2584	23.9566	6.6370
0.0859	0.3333	6000	0.2569	24.5221	6.6761
0.06	0.3889	7000	0.2479	22.1828	6.1018
0.0539	0.4444	8000	0.2461	22.6143	6.2866
0.0427	0.5	9000	0.2402	23.1083	6.3401
0.0341	0.5556	10000	0.2356	22.2012	6.0513
0.0275	0.6111	11000	0.2338	20.7378	5.6669
0.0204	0.6667	12000	0.2296	21.1381	5.7997
0.0156	0.7222	13000	0.2324	21.9037	5.8359
0.0162	0.7778	14000	0.2214	20.4825	5.5345
0.0163	0.8333	15000	0.2131	21.0426	5.6430
0.0127	0.8889	16000	0.2093	19.5791	5.2782
0.006	0.9444	17000	0.2083	19.8197	5.2719
0.0072	1.0	18000	0.1973	18.8998	5.0561

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.5.1+cu121
• Datasets 3.6.0
• Tokenizers 0.21.0

Citation

Please cite the model using the following BibTeX entry:

@misc{deepdml/whisper-large-v3-turbo-ar-mix-norm,
      title={Fine-tuned Whisper turbo ASR model for speech recognition in Arabic},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-large-v3-turbo-ar-mix-norm}},
      year={2026}
    }