Training Loss	Epoch	Step	Validation Loss	Wer
10.903	0.06	100	3.7302	1.0
3.6693	0.11	200	3.6193	1.0
3.6908	0.17	300	3.6678	1.0
3.6565	0.22	400	3.6365	1.0
3.6348	0.28	500	3.6443	1.0
3.6878	0.33	600	3.6583	1.0
3.572	0.39	700	3.6304	1.0
3.6749	0.44	800	3.6420	1.0
3.6872	0.5	900	3.6469	1.0
3.6594	0.56	1000	3.6278	1.0
3.6131	0.61	1100	3.6169	1.0
3.5748	0.67	1200	3.6234	1.0
3.6181	0.72	1300	3.6494	1.0
3.6164	0.78	1400	3.6248	1.0
3.6688	0.83	1500	3.6610	1.0
4.1978	0.89	1600	3.6903	1.0
3.7485	0.94	1700	3.6275	1.0
3.649	1.0	1800	3.6139	1.0
3.5834	1.06	1900	3.6161	1.0
3.6338	1.11	2000	3.6647	1.0
3.5427	1.17	2100	3.6129	1.0
3.6117	1.22	2200	3.6084	1.0
3.6726	1.28	2300	3.6149	1.0
3.6278	1.33	2400	3.6342	1.0
3.6746	1.39	2500	3.6102	1.0
3.574	1.44	2600	3.7048	1.0
3.5892	1.5	2700	3.6126	1.0
3.6575	1.56	2800	3.6163	1.0
3.592	1.61	2900	3.6610	1.0
3.6506	1.67	3000	3.6127	1.0
3.5823	1.72	3100	3.6071	1.0
3.6674	1.78	3200	3.6032	1.0
3.6017	1.83	3300	3.6236	1.0
3.5865	1.89	3400	3.6208	1.0
3.646	1.94	3500	3.6074	1.0
3.6042	2.0	3600	3.6442	1.0
3.56	2.06	3700	3.6076	1.0
3.6241	2.11	3800	3.6051	1.0
3.6245	2.17	3900	3.6074	1.0
3.5764	2.22	4000	3.6238	1.0
3.6168	2.28	4100	3.6192	1.0
3.6143	2.33	4200	3.6093	1.0
3.613	2.39	4300	3.6123	1.0
3.6178	2.44	4400	3.6135	1.0
3.6234	2.5	4500	3.6161	1.0
3.5833	2.56	4600	3.6064	1.0
3.5759	2.61	4700	3.6077	1.0
3.6747	2.67	4800	3.6123	1.0
3.5914	2.72	4900	3.6041	1.0
3.6342	2.78	5000	3.6208	1.0
3.5883	2.83	5100	3.6056	1.0
3.5563	2.89	5200	3.6159	1.0
3.6213	2.94	5300	3.6173	1.0
3.6507	3.0	5400	3.6031	1.0
3.549	3.06	5500	3.6371	1.0
3.5712	3.11	5600	3.6049	1.0
3.5731	3.17	5700	3.6273	1.0
3.6232	3.22	5800	3.6012	1.0
3.6406	3.28	5900	3.6020	1.0
3.6456	3.33	6000	3.6015	1.0
3.6268	3.39	6100	3.6047	1.0
3.6286	3.44	6200	3.6023	1.0
3.609	3.5	6300	3.6053	1.0
3.6256	3.56	6400	3.6040	1.0
3.5537	3.61	6500	3.6075	1.0
3.5214	3.67	6600	3.6055	1.0
3.6031	3.72	6700	3.6156	1.0
3.6624	3.78	6800	3.6037	1.0
3.5813	3.83	6900	3.6030	1.0
3.6514	3.89	7000	3.6043	1.0
3.5535	3.94	7100	3.6091	1.0
3.5954	4.0	7200	3.6089	1.0

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Revolt47/wav2vec2-urdufinetuned

wav2vec2-urdufinetuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Revolt47/
wav2vec2-urdufinetuned