aradia-ctc-v1

This model is a fine-tuned version of /l/users/abdulwahab.sahyoun/aradia/aradia-ctc-v1 on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_300HRS - NA dataset. It achieves the following results on the evaluation set:

• Loss: 0.7171
• Wer: 0.3336

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: 0.0003
• train_batch_size: 32
• eval_batch_size: 32
• 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
• num_epochs: 20.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.22	100	5.1889	1.0
No log	0.43	200	3.1129	1.0
No log	0.65	300	3.0503	1.0
No log	0.87	400	3.0279	1.0
6.2756	1.09	500	2.9965	1.0
6.2756	1.3	600	2.3618	0.9993
6.2756	1.52	700	1.2715	0.8758
6.2756	1.74	800	0.9971	0.7156
6.2756	1.96	900	0.8927	0.6382
1.712	2.17	1000	0.8252	0.5926
1.712	2.39	1100	0.7794	0.5434
1.712	2.61	1200	0.7557	0.5092
1.712	2.83	1300	0.7347	0.5203
1.712	3.04	1400	0.7189	0.4929
0.9305	3.26	1500	0.6820	0.4595
0.9305	3.48	1600	0.6792	0.4504
0.9305	3.69	1700	0.6596	0.4442
0.9305	3.91	1800	0.6756	0.4432
0.9305	4.13	1900	0.6663	0.4392
0.737	4.35	2000	0.6479	0.4372
0.737	4.56	2100	0.6353	0.4203
0.737	4.78	2200	0.6251	0.4088
0.737	5.0	2300	0.6209	0.4177
0.737	5.22	2400	0.6639	0.4094
0.6247	5.43	2500	0.6408	0.3970
0.6247	5.65	2600	0.6373	0.3932
0.6247	5.87	2700	0.6411	0.3928
0.6247	6.09	2800	0.6378	0.3897
0.6247	6.3	2900	0.6396	0.3929
0.5443	6.52	3000	0.6544	0.3864
0.5443	6.74	3100	0.6218	0.3786
0.5443	6.96	3200	0.6200	0.3784
0.5443	7.17	3300	0.6157	0.3791
0.5443	7.39	3400	0.6317	0.3798
0.4845	7.61	3500	0.6540	0.3771
0.4845	7.83	3600	0.6436	0.3670
0.4845	8.04	3700	0.6335	0.3695
0.4845	8.26	3800	0.6579	0.3610
0.4845	8.48	3900	0.6170	0.3613
0.4279	8.69	4000	0.6523	0.3617
0.4279	8.91	4100	0.6349	0.3577
0.4279	9.13	4200	0.6344	0.3673
0.4279	9.35	4300	0.6215	0.3641
0.4279	9.56	4400	0.6513	0.3608
0.3825	9.78	4500	0.6386	0.3605
0.3825	10.0	4600	0.6724	0.3549
0.3825	10.22	4700	0.6776	0.3602
0.3825	10.43	4800	0.6739	0.3544
0.3825	10.65	4900	0.6688	0.3557
0.3477	10.87	5000	0.6674	0.3564
0.3477	11.09	5100	0.6786	0.3476
0.3477	11.3	5200	0.6818	0.3478
0.3477	11.52	5300	0.6874	0.3470
0.3477	11.74	5400	0.6993	0.3424
0.3101	11.96	5500	0.6950	0.3404
0.3101	12.17	5600	0.6872	0.3406
0.3101	12.39	5700	0.6846	0.3424
0.3101	12.61	5800	0.7051	0.3405
0.3101	12.83	5900	0.7051	0.3378
0.2859	13.04	6000	0.6955	0.3403
0.2859	13.26	6100	0.7115	0.3390
0.2859	13.48	6200	0.7074	0.3384
0.2859	13.69	6300	0.7002	0.3376
0.2859	13.91	6400	0.7171	0.3360
0.2714	14.13	6500	0.7193	0.3341
0.2714	14.35	6600	0.7132	0.3347
0.2714	14.56	6700	0.7184	0.3353
0.2714	14.78	6800	0.7171	0.3331

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.10.2+cu113
• Datasets 1.18.4
• Tokenizers 0.11.6