120min_wav2vec_xls-r53_FT

This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-arabic on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.1684
• Wer: 0.6547
• Cer: 0.2177

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: 0.0001
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 300
• num_epochs: 12
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.8782	0.1908	100	1.3708	0.9188	0.3620
1.3601	0.3817	200	1.1605	0.8503	0.3078
1.3002	0.5725	300	1.0615	0.8377	0.2959
1.2639	0.7634	400	1.0643	0.8093	0.2844
1.1603	0.9542	500	1.0018	0.7723	0.2685
1.1299	1.1450	600	1.0108	0.7599	0.2646
0.9707	1.3359	700	0.9639	0.7449	0.2558
1.0436	1.5267	800	0.9838	0.7850	0.2675
0.9747	1.7176	900	1.0015	0.7400	0.2527
0.9765	1.9084	1000	0.9177	0.7264	0.2475
0.9291	2.0992	1100	0.9657	0.7285	0.2469
0.8804	2.2901	1200	0.9735	0.7219	0.2446
0.7731	2.4809	1300	0.9996	0.7102	0.2401
0.8597	2.6718	1400	1.0208	0.7053	0.2398
0.8478	2.8626	1500	0.9148	0.6933	0.2322
0.7741	3.0534	1600	1.0133	0.6992	0.2352
0.6887	3.2443	1700	0.9603	0.6912	0.2309
0.712	3.4351	1800	1.0089	0.6882	0.2311
0.7073	3.6260	1900	0.9700	0.6855	0.2323
0.7667	3.8168	2000	1.0454	0.6954	0.2311
0.7134	4.0076	2100	0.9957	0.6866	0.2257
0.5991	4.1985	2200	0.9846	0.6862	0.2266
0.622	4.3893	2300	1.0161	0.6748	0.2256
0.6062	4.5802	2400	1.0280	0.6881	0.2292
0.6094	4.7710	2500	0.9586	0.6694	0.2240
0.6701	4.9618	2600	0.9728	0.6706	0.2239
0.586	5.1527	2700	1.0369	0.6667	0.2241
0.6001	5.3435	2800	1.1335	0.6670	0.2243
0.4735	5.5344	2900	1.0438	0.6733	0.2233
0.5801	5.7252	3000	1.0320	0.6722	0.2234
0.5684	5.9160	3100	0.9592	0.6664	0.2222
0.4922	6.1069	3200	1.0551	0.6647	0.2203
0.4317	6.2977	3300	1.1491	0.6581	0.2200
0.5079	6.4885	3400	1.0728	0.6565	0.2203
0.4498	6.6794	3500	1.0398	0.6579	0.2178
0.5414	6.8702	3600	1.0863	0.6616	0.2195
0.4707	7.0611	3700	1.1589	0.6540	0.2190
0.4554	7.2519	3800	1.1677	0.6591	0.2201
0.4644	7.4427	3900	1.0799	0.6555	0.2181
0.4021	7.6336	4000	1.1018	0.6570	0.2185
0.458	7.8244	4100	1.1470	0.6475	0.2154
0.4341	8.0153	4200	1.1655	0.6519	0.2183
0.3751	8.2061	4300	1.2057	0.6568	0.2190
0.3804	8.3969	4400	1.1528	0.6473	0.2157
0.3895	8.5878	4500	1.1330	0.6493	0.2184
0.4174	8.7786	4600	1.1791	0.6479	0.2171
0.4163	8.9695	4700	1.2067	0.6620	0.2172
0.3946	9.1603	4800	1.1451	0.6549	0.2201
0.36	9.3511	4900	1.1684	0.6547	0.2177

Framework versions

• Transformers 4.41.1
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.19.1