Training Loss	Epoch	Step	Validation Loss	Wer
0.7517	0.0449	15	0.5024	40.5912
0.4299	0.0898	30	0.3868	34.1310
0.363	0.1347	45	0.3704	25.2001
0.3744	0.1796	60	0.3537	22.5218
0.3731	0.2246	75	0.3554	24.5736
0.3743	0.2695	90	0.3388	22.7566
0.3001	0.3144	105	0.3401	22.3597
0.3382	0.3593	120	0.3262	33.1556
0.3353	0.4042	135	0.3266	28.0469
0.325	0.4491	150	0.3247	26.7473
0.3303	0.4940	165	0.3147	22.8616
0.2925	0.5389	180	0.3147	21.2041
0.3109	0.5838	195	0.3108	23.1859
0.2989	0.6287	210	0.3084	24.3570
0.3111	0.6737	225	0.3018	18.4117
0.2918	0.7186	240	0.3033	17.6076
0.3099	0.7635	255	0.2971	21.7151
0.2997	0.8084	270	0.2987	21.5361
0.2898	0.8533	285	0.2923	21.5828
0.2848	0.8982	300	0.2914	17.6452
0.285	0.9431	315	0.2874	17.7425
0.2624	0.9880	330	0.2861	16.8489
0.169	1.0329	345	0.2948	18.5687
0.1515	1.0778	360	0.2927	26.6540
0.1504	1.1228	375	0.2918	18.9422
0.1484	1.1677	390	0.2916	18.3482
0.1358	1.2126	405	0.2904	17.2198
0.128	1.2575	420	0.2895	17.6764
0.1417	1.3024	435	0.2895	23.2572
0.1561	1.3473	450	0.2876	17.7775
0.1445	1.3922	465	0.2874	17.5415
0.1384	1.4371	480	0.2825	16.1420
0.1488	1.4820	495	0.2857	17.3832
0.1701	1.5269	510	0.2779	22.6826
0.1475	1.5719	525	0.2857	25.9860
0.144	1.6168	540	0.2790	16.3145
0.1402	1.6617	555	0.2874	21.3948
0.1575	1.7066	570	0.2756	15.9786
0.1409	1.7515	585	0.2815	17.0862
0.1388	1.7964	600	0.2792	18.9176
0.1273	1.8413	615	0.2803	23.6165
0.1537	1.8862	630	0.2758	17.5454
0.1537	1.9311	645	0.2764	15.8373
0.1474	1.9760	660	0.2708	16.4935
0.1111	2.0210	675	0.2805	19.4337
0.0745	2.0659	690	0.2924	18.5388
0.0639	2.1108	705	0.2917	15.8269
0.0673	2.1557	720	0.2945	16.9306
0.066	2.2006	735	0.2955	16.3677
0.0714	2.2455	750	0.2933	16.2289
0.0701	2.2904	765	0.2911	20.4558
0.0631	2.3353	780	0.2971	17.1316
0.064	2.3802	795	0.2916	15.3846
0.0659	2.4251	810	0.2971	15.1602
0.0615	2.4701	825	0.2878	20.4480
0.0723	2.5150	840	0.2935	14.7569
0.0695	2.5599	855	0.2846	15.6570
0.0704	2.6048	870	0.2919	19.4000
0.0642	2.6497	885	0.2849	17.7373
0.0684	2.6946	900	0.2888	15.9164
0.077	2.7395	915	0.2828	15.5052
0.0708	2.7844	930	0.2858	17.0538
0.065	2.8293	945	0.2829	20.8617
0.0788	2.8743	960	0.2854	19.5621
0.0677	2.9192	975	0.2825	16.6984
0.0642	2.9641	990	0.2887	16.1537
0.0627	3.0090	1005	0.2828	16.0331
0.0262	3.0539	1020	0.3084	15.0202
0.0266	3.0988	1035	0.3129	16.9708
0.024	3.1437	1050	0.3114	14.9722
0.0271	3.1886	1065	0.3152	14.5416
0.026	3.2335	1080	0.3135	16.4533
0.0281	3.2784	1095	0.3151	17.0123
0.0295	3.3234	1110	0.3160	15.4183
0.0259	3.3683	1125	0.3101	14.8269
0.0276	3.4132	1140	0.3194	14.1175
0.0271	3.4581	1155	0.3172	17.3314
0.0304	3.5030	1170	0.3111	18.0577
0.0268	3.5479	1185	0.3129	14.0928
0.0256	3.5928	1200	0.3083	14.7374
0.0281	3.6377	1215	0.3079	14.9125
0.0274	3.6826	1230	0.3180	14.4586
0.0282	3.7275	1245	0.3091	14.6622
0.0224	3.7725	1260	0.3139	14.4132
0.0254	3.8174	1275	0.3141	14.0747
0.0279	3.8623	1290	0.3110	18.3676
0.0245	3.9072	1305	0.3119	15.0565
0.0256	3.9521	1320	0.3149	16.3560
0.0273	3.9970	1335	0.3128	16.3405
0.0126	4.0419	1350	0.3265	14.9385
0.0087	4.0868	1365	0.3411	14.4547
0.009	4.1317	1380	0.3394	14.6298
0.0093	4.1766	1395	0.3424	14.4547
0.0082	4.2216	1410	0.3457	14.4780
0.0093	4.2665	1425	0.3472	13.8192
0.0072	4.3114	1440	0.3491	15.0189
0.0093	4.3563	1455	0.3490	16.3962
0.0098	4.4012	1470	0.3455	16.3755
0.0077	4.4461	1485	0.3429	16.9410
0.0089	4.4910	1500	0.3452	17.0966
0.0099	4.5359	1515	0.3469	18.3897
0.0066	4.5808	1530	0.3465	19.0083
0.0074	4.6257	1545	0.3455	19.6867
0.0069	4.6707	1560	0.3489	18.5440
0.008	4.7156	1575	0.3502	18.4078
0.0079	4.7605	1590	0.3503	18.1057
0.0077	4.8054	1605	0.3501	18.2574
0.0058	4.8503	1620	0.3492	18.1653
0.0076	4.8952	1635	0.3486	17.7905
0.0064	4.9401	1650	0.3487	17.3858
0.0057	4.9850	1665	0.3489	17.3755

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

golesheed/whisper-v2-Brabantic

Whisper Large V2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

golesheed/
whisper-v2-Brabantic