learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
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: linear
num_epochs: 24
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.274	0.2584	100	2.9770	1.0
2.6375	0.5168	200	2.5025	0.9899
2.0193	0.7752	300	1.7123	0.9546
1.2825	1.0336	400	1.1216	0.7681
1.1884	1.2920	500	0.8725	0.6704
0.935	1.5504	600	0.6984	0.6106
0.8219	1.8088	700	0.6194	0.5110
0.947	2.0672	800	0.5441	0.4978
0.7252	2.3256	900	0.5025	0.4827
0.6017	2.5840	1000	0.4948	0.4436
0.5668	2.8424	1100	0.4635	0.4279
0.5788	3.1008	1200	0.4433	0.4159
0.6228	3.3592	1300	0.4221	0.3882
0.5826	3.6176	1400	0.4101	0.3819
0.5042	3.8760	1500	0.4021	0.3724
0.5962	4.1344	1600	0.3826	0.3699
0.4774	4.3928	1700	0.3832	0.3472
0.3943	4.6512	1800	0.3757	0.3554
0.5177	4.9096	1900	0.3702	0.3636
0.4252	5.1680	2000	0.3784	0.3239
0.4051	5.4264	2100	0.3765	0.3233
0.4709	5.6848	2200	0.3668	0.3352
0.5013	5.9432	2300	0.3479	0.3258
0.4049	6.2016	2400	0.3518	0.3151
0.396	6.4599	2500	0.3450	0.3157
0.3179	6.7183	2600	0.3462	0.3012
0.4026	6.9767	2700	0.3413	0.2987
0.3065	7.2351	2800	0.3466	0.3012
0.3578	7.4935	2900	0.3485	0.2936
0.3801	7.7519	3000	0.3481	0.2917
0.2889	8.0103	3100	0.3349	0.3006
0.3422	8.2687	3200	0.3473	0.2905
0.2781	8.5271	3300	0.3429	0.3018
0.2766	8.7855	3400	0.3447	0.3031
0.2966	9.0439	3500	0.3416	0.3043
0.3177	9.3023	3600	0.3366	0.2892
0.3245	9.5607	3700	0.3421	0.2924
0.3776	9.8191	3800	0.3477	0.2962
0.2868	10.0775	3900	0.3309	0.2899
0.3264	10.3359	4000	0.3317	0.2905
0.3352	10.5943	4100	0.3363	0.2911
0.2949	10.8527	4200	0.3304	0.2823
0.2962	11.1111	4300	0.3335	0.2829
0.2903	11.3695	4400	0.3364	0.2817
0.2602	11.6279	4500	0.3380	0.2823
0.3312	11.8863	4600	0.3309	0.2760
0.2659	12.1447	4700	0.3316	0.2798
0.3107	12.4031	4800	0.3421	0.2842
0.3231	12.6615	4900	0.3328	0.2766
0.2178	12.9199	5000	0.3308	0.2710
0.2772	13.1783	5100	0.3382	0.2691
0.2842	13.4367	5200	0.3348	0.2773
0.2871	13.6951	5300	0.3346	0.2678
0.3136	13.9535	5400	0.3345	0.2703
0.2265	14.2119	5500	0.3362	0.2703
0.2409	14.4703	5600	0.3358	0.2716
0.3904	14.7287	5700	0.3365	0.2697
0.2499	14.9871	5800	0.3338	0.2691
0.2285	15.2455	5900	0.3347	0.2653
0.2518	15.5039	6000	0.3372	0.2653
0.2218	15.7623	6100	0.3374	0.2672
0.3185	16.0207	6200	0.3371	0.2710
0.2119	16.2791	6300	0.3409	0.2741
0.2565	16.5375	6400	0.3428	0.2741
0.2296	16.7959	6500	0.3402	0.2892
0.2233	17.0543	6600	0.3412	0.2722
0.3201	17.3127	6700	0.3524	0.2665
0.2023	17.5711	6800	0.3445	0.2754
0.2259	17.8295	6900	0.3432	0.2728
0.228	18.0879	7000	0.3419	0.2754
0.2231	18.3463	7100	0.3533	0.2760
0.2493	18.6047	7200	0.3381	0.2691
0.2648	18.8630	7300	0.3357	0.2735
0.26	19.1214	7400	0.3446	0.2754
0.1977	19.3798	7500	0.3476	0.2754
0.205	19.6382	7600	0.3399	0.2728
0.3084	19.8966	7700	0.3485	0.2760
0.2097	20.1550	7800	0.3446	0.2735
0.3635	20.4134	7900	0.3441	0.2710
0.2044	20.6718	8000	0.3551	0.2798
0.1887	20.9302	8100	0.3510	0.2779
0.2081	21.1886	8200	0.3541	0.2842
0.2571	21.4470	8300	0.3620	0.2735
0.2718	21.7054	8400	0.3512	0.2760
0.1865	21.9638	8500	0.3492	0.2735
0.204	22.2222	8600	0.3496	0.2785
0.1769	22.4806	8700	0.3501	0.2716
0.2081	22.7390	8800	0.3537	0.2779
0.185	22.9974	8900	0.3507	0.2697
0.1785	23.2558	9000	0.3524	0.2754
0.3433	23.5142	9100	0.3521	0.2773
0.2692	23.7726	9200	0.3514	0.2773

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

vorew/wav2vec2-devnagari

wav2vec2-devnagari

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vorew/
wav2vec2-devnagari