aradia-ctc-hubert-ft

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

• Loss: 0.8536
• Wer: 0.3737

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: 30.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.43	100	3.6934	1.0
No log	0.87	200	3.0763	1.0
No log	1.3	300	2.9737	1.0
No log	1.74	400	2.5734	1.0
5.0957	2.17	500	1.1900	0.9011
5.0957	2.61	600	0.9726	0.7572
5.0957	3.04	700	0.8960	0.6209
5.0957	3.48	800	0.7851	0.5515
5.0957	3.91	900	0.7271	0.5115
1.0312	4.35	1000	0.7053	0.4955
1.0312	4.78	1100	0.6823	0.4737
1.0312	5.22	1200	0.6768	0.4595
1.0312	5.65	1300	0.6635	0.4488
1.0312	6.09	1400	0.6602	0.4390
0.6815	6.52	1500	0.6464	0.4310
0.6815	6.95	1600	0.6455	0.4394
0.6815	7.39	1700	0.6630	0.4312
0.6815	7.82	1800	0.6521	0.4126
0.6815	8.26	1900	0.6282	0.4284
0.544	8.69	2000	0.6248	0.4178
0.544	9.13	2100	0.6510	0.4104
0.544	9.56	2200	0.6527	0.4013
0.544	10.0	2300	0.6511	0.4064
0.544	10.43	2400	0.6734	0.4061
0.4478	10.87	2500	0.6756	0.4145
0.4478	11.3	2600	0.6727	0.3990
0.4478	11.74	2700	0.6619	0.4007
0.4478	12.17	2800	0.6614	0.4019
0.4478	12.61	2900	0.6695	0.4004
0.3919	13.04	3000	0.6778	0.3966
0.3919	13.48	3100	0.6872	0.3971
0.3919	13.91	3200	0.6882	0.3945
0.3919	14.35	3300	0.7177	0.4010
0.3919	14.78	3400	0.6888	0.4043
0.3767	15.22	3500	0.7124	0.4202
0.3767	15.65	3600	0.7276	0.4120
0.3767	16.09	3700	0.7265	0.4034
0.3767	16.52	3800	0.7392	0.4077
0.3767	16.95	3900	0.7403	0.3965
0.3603	17.39	4000	0.7445	0.4016
0.3603	17.82	4100	0.7579	0.4012
0.3603	18.26	4200	0.7225	0.3963
0.3603	18.69	4300	0.7355	0.3951
0.3603	19.13	4400	0.7482	0.3925
0.3153	19.56	4500	0.7723	0.3972
0.3153	20.0	4600	0.7469	0.3898
0.3153	20.43	4700	0.7800	0.3944
0.3153	20.87	4800	0.7827	0.3897
0.3153	21.3	4900	0.7935	0.3914
0.286	21.74	5000	0.7984	0.3750
0.286	22.17	5100	0.7945	0.3830
0.286	22.61	5200	0.8011	0.3775
0.286	23.04	5300	0.7978	0.3824
0.286	23.48	5400	0.8161	0.3833
0.2615	23.91	5500	0.7823	0.3858
0.2615	24.35	5600	0.8312	0.3863
0.2615	24.78	5700	0.8427	0.3819
0.2615	25.22	5800	0.8432	0.3802
0.2615	25.65	5900	0.8286	0.3794
0.2408	26.09	6000	0.8224	0.3824
0.2408	26.52	6100	0.8228	0.3823
0.2408	26.95	6200	0.8324	0.3795
0.2408	27.39	6300	0.8564	0.3744
0.2408	27.82	6400	0.8629	0.3774
0.2254	28.26	6500	0.8545	0.3778
0.2254	28.69	6600	0.8492	0.3767
0.2254	29.13	6700	0.8511	0.3751
0.2254	29.56	6800	0.8491	0.3753
0.2254	30.0	6900	0.8536	0.3737

Framework versions

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