wav2vec2-darija

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4265
• Wer: 0.4101
• Cer: 0.1327

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: 8
• 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: 1000
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
10.5265	0.49	500	3.2082	1.0	0.9971
2.9629	0.98	1000	2.9619	1.0000	0.9357
2.6527	1.46	1500	1.4934	1.0974	0.4550
1.2946	1.95	2000	0.6904	0.7809	0.2566
0.9059	2.44	2500	0.5906	0.7002	0.2190
0.8133	2.93	3000	0.5199	0.6382	0.2049
0.7026	3.41	3500	0.4570	0.5986	0.1919
0.6572	3.9	4000	0.4238	0.5792	0.1848
0.5904	4.39	4500	0.4116	0.5637	0.1807
0.5593	4.88	5000	0.3850	0.5414	0.1734
0.5084	5.37	5500	0.3951	0.5409	0.1733
0.5033	5.85	6000	0.4449	0.5176	0.1649
0.4712	6.34	6500	0.5485	0.5172	0.1658
0.4459	6.83	7000	0.5259	0.5061	0.1623
0.4246	7.32	7500	0.3686	0.4991	0.1605
0.4261	7.8	8000	0.3663	0.4898	0.1589
0.4078	8.29	8500	0.3740	0.4858	0.1564
0.3783	8.78	9000	0.3907	0.4824	0.1566
0.3647	9.27	9500	0.3424	0.4750	0.1525
0.3527	9.76	10000	0.3444	0.4692	0.1513
0.3482	10.24	10500	0.3856	0.4692	0.1507
0.3338	10.73	11000	0.3650	0.4664	0.1512
0.3198	11.22	11500	0.3516	0.4628	0.1492
0.3218	11.71	12000	0.3660	0.4644	0.1491
0.3115	12.2	12500	0.3490	0.4545	0.1475
0.2977	12.68	13000	0.3555	0.4504	0.1451
0.2958	13.17	13500	0.3425	0.4571	0.1449
0.278	13.66	14000	0.4035	0.4520	0.1446
0.2716	14.15	14500	0.3552	0.4492	0.1437
0.2729	14.63	15000	0.3665	0.4470	0.1432
0.2691	15.12	15500	0.3700	0.4498	0.1444
0.2563	15.61	16000	0.3658	0.4423	0.1421
0.2511	16.1	16500	0.4152	0.4408	0.1425
0.2348	16.59	17000	0.4889	0.4375	0.1416
0.2437	17.07	17500	0.4209	0.4382	0.1413
0.2388	17.56	18000	0.6032	0.4359	0.1408
0.2235	18.05	18500	0.4831	0.4369	0.1402
0.2197	18.54	19000	0.4989	0.4345	0.1402
0.2285	19.02	19500	0.5929	0.4342	0.1393
0.2224	19.51	20000	0.4098	0.4317	0.1398
0.2183	20.0	20500	0.3547	0.4254	0.1384
0.2113	20.49	21000	0.3926	0.4324	0.1385
0.2125	20.98	21500	0.3982	0.4299	0.1386
0.201	21.46	22000	0.3929	0.4293	0.1389
0.2002	21.95	22500	0.4047	0.4218	0.1372
0.2029	22.44	23000	0.5153	0.4235	0.1375
0.195	22.93	23500	0.5601	0.4198	0.1364
0.182	23.41	24000	0.4596	0.4168	0.1355
0.1889	23.9	24500	0.4165	0.4209	0.1353
0.1795	24.39	25000	0.4096	0.4185	0.1352
0.1809	24.88	25500	0.4732	0.4126	0.1341
0.1762	25.37	26000	0.4324	0.4146	0.1347
0.1764	25.85	26500	0.4462	0.4160	0.1347
0.1805	26.34	27000	0.3955	0.4107	0.1333
0.1733	26.83	27500	0.4182	0.4135	0.1336
0.1651	27.32	28000	0.4111	0.4104	0.1330
0.1713	27.8	28500	0.4426	0.4126	0.1332
0.1766	28.29	29000	0.4426	0.4085	0.1328
0.1631	28.78	29500	0.4248	0.4083	0.1328
0.1608	29.27	30000	0.4334	0.4096	0.1327
0.1688	29.76	30500	0.4265	0.4101	0.1327

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1