whisper-large-arabic-dialects-v5

This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3185
• Wer: 18.5555
• Cer: 9.6321

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: 8e-06
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 8000
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.383	0.1390	5000	0.3887	29.3404	16.0198
0.3528	0.2780	10000	0.3820	26.1219	12.6343
0.3443	0.4170	15000	0.3583	25.1779	11.8628
0.3422	0.5559	20000	0.3410	23.6417	11.4832
0.3103	0.6949	25000	0.3280	22.2940	10.9650
0.2942	0.8339	30000	0.3199	22.1227	11.0281
0.3076	0.9729	35000	0.3111	21.6923	10.8906
0.2502	1.1119	40000	0.3059	21.1143	10.5784
0.243	1.2509	45000	0.2973	20.7546	10.4810
0.2236	1.3899	50000	0.2958	20.3886	10.3351
0.2341	1.5288	55000	0.2906	20.1640	10.0796
0.2161	1.6678	60000	0.2840	19.7242	9.9189
0.232	1.8068	65000	0.2781	19.6928	9.9768
0.2296	1.9458	70000	0.2747	19.0896	9.5917
0.161	2.0848	75000	0.2826	19.1571	9.6719
0.1626	2.2238	80000	0.2798	19.2042	9.8972
0.1429	2.3628	85000	0.2832	18.9545	9.5847
0.1539	2.5017	90000	0.2766	18.6718	9.4762
0.1544	2.6407	95000	0.2724	18.4471	9.3818
0.1553	2.7797	100000	0.2715	18.4471	9.4508
0.1546	2.9187	105000	0.2683	18.2147	9.3637
0.0886	3.0577	110000	0.2911	18.6372	9.5890
0.0847	3.1967	115000	0.2894	18.4943	9.4961
0.0792	3.3356	120000	0.2909	18.2524	9.4680
0.0809	3.4746	125000	0.2901	18.3168	9.4424
0.0719	3.6136	130000	0.2929	18.3827	9.4867
0.074	3.7526	135000	0.2914	18.1518	9.3884
0.0802	3.8916	140000	0.2890	18.1220	9.3788
0.0383	4.0306	145000	0.3115	18.5068	9.6366
0.0425	4.1696	150000	0.3164	18.4000	9.5148
0.0367	4.3085	155000	0.3168	18.6529	9.6903
0.0343	4.4475	160000	0.3184	18.5272	9.5974
0.0329	4.5865	165000	0.3182	18.4660	9.5148
0.0352	4.7255	170000	0.3189	18.6388	9.6954
0.0392	4.8645	175000	0.3185	18.5555	9.6321

Framework versions

• Transformers 4.55.4
• Pytorch 2.7.1+cu128
• Datasets 3.6.0
• Tokenizers 0.21.4