Training Loss	Epoch	Step	Validation Loss	Wer
4.1356	0.2778	10	4.1201	47.9002
4.0312	0.5556	20	4.0231	47.3319
3.917	0.8333	30	3.8659	46.5425
3.7606	1.1111	40	3.6569	45.8478
3.4823	1.3889	50	3.3969	44.5216
3.0938	1.6667	60	3.0765	41.9324
2.7895	1.9444	70	2.6692	34.9542
2.3101	2.2222	80	2.1389	34.5122
1.6935	2.5	90	1.5546	34.8911
1.1419	2.7778	100	1.0650	36.5330
0.904	3.0556	110	0.8400	29.4601
0.7536	3.3333	120	0.7657	28.9233
0.6857	3.6111	130	0.7202	27.7550
0.6609	3.8889	140	0.6886	26.6814
0.5804	4.1667	150	0.6656	25.6710
0.5611	4.4444	160	0.6465	25.0710
0.5574	4.7222	170	0.6293	24.3448
0.552	5.0	180	0.6135	24.0606
0.4717	5.2778	190	0.6024	24.5974
0.4681	5.5556	200	0.5898	24.0290
0.4679	5.8333	210	0.5778	23.5238
0.4351	6.1111	220	0.5670	23.6501
0.3982	6.3889	230	0.5599	23.2081
0.3892	6.6667	240	0.5520	22.0714
0.3771	6.9444	250	0.5439	21.1872
0.3532	7.2222	260	0.5372	21.6925
0.3435	7.5	270	0.5309	27.5024
0.336	7.7778	280	0.5253	20.9346
0.3088	8.0556	290	0.5201	20.4610
0.3014	8.3333	300	0.5184	20.5242
0.316	8.6111	310	0.5146	20.2400
0.2931	8.8889	320	0.5118	19.9874
0.2228	9.1667	330	0.5079	20.3663
0.2445	9.4444	340	0.5052	20.2716
0.2343	9.7222	350	0.5039	20.2084
0.2893	10.0	360	0.5023	20.0189
0.2014	10.2778	370	0.5030	20.0505
0.2048	10.5556	380	0.5036	19.6400
0.1941	10.8333	390	0.5003	20.1137
0.1601	11.1111	400	0.4992	19.8295
0.1647	11.3889	410	0.5010	19.8926
0.1519	11.6667	420	0.5044	19.6716
0.1747	11.9444	430	0.5005	20.1137
0.1194	12.2222	440	0.5076	20.7452
0.1021	12.5	450	0.5104	19.9242
0.1115	12.7778	460	0.5102	20.7136
0.1355	13.0556	470	0.5068	20.3979
0.0824	13.3333	480	0.5152	20.5557
0.0858	13.6111	490	0.5189	20.3663
0.0786	13.8889	500	0.5225	21.1557
0.0564	14.1667	510	0.5250	20.9031
0.056	14.4444	520	0.5232	20.8715
0.0558	14.7222	530	0.5282	20.5557
0.0657	15.0	540	0.5299	20.7452
0.0369	15.2778	550	0.5342	20.6505
0.0355	15.5556	560	0.5341	20.1137
0.0383	15.8333	570	0.5370	20.4926
0.0333	16.1111	580	0.5401	20.5557
0.027	16.3889	590	0.5455	20.9346
0.0261	16.6667	600	0.5480	20.6189
0.024	16.9444	610	0.5494	20.4294
0.0164	17.2222	620	0.5505	20.3663
0.0159	17.5	630	0.5577	20.7136
0.0168	17.7778	640	0.5549	20.9031
0.015	18.0556	650	0.5555	20.8083
0.0116	18.3333	660	0.5596	20.9978
0.0131	18.6111	670	0.5614	20.9346
0.0121	18.8889	680	0.5634	20.3663
0.009	19.1667	690	0.5643	20.7452
0.0108	19.4444	700	0.5633	20.3031
0.0096	19.7222	710	0.5666	20.3979
0.0123	20.0	720	0.5660	20.4610
0.009	20.2778	730	0.5695	20.5242
0.0099	20.5556	740	0.5684	20.3663
0.0079	20.8333	750	0.5701	20.7768
0.008	21.1111	760	0.5701	20.7136
0.0084	21.3889	770	0.5719	20.7136
0.0076	21.6667	780	0.5724	20.4610
0.0081	21.9444	790	0.5724	20.7136
0.0067	22.2222	800	0.5731	20.6820
0.0076	22.5	810	0.5737	20.4926
0.0079	22.7778	820	0.5748	20.3979
0.0069	23.0556	830	0.5747	20.6820
0.0066	23.3333	840	0.5751	20.7136
0.0062	23.6111	850	0.5755	20.7136
0.0071	23.8889	860	0.5764	20.5873
0.0062	24.1667	870	0.5774	20.7136
0.0059	24.4444	880	0.5769	20.5873
0.0066	24.7222	890	0.5772	20.6189
0.0066	25.0	900	0.5778	20.5873
0.0066	25.2778	910	0.5779	20.5557
0.0062	25.5556	920	0.5781	20.5873
0.006	25.8333	930	0.5787	20.6189
0.0061	26.1111	940	0.5789	20.5873
0.0056	26.3889	950	0.5788	20.5557
0.006	26.6667	960	0.5789	20.5873
0.0055	26.9444	970	0.5790	20.5873
0.0057	27.2222	980	0.5791	20.6189
0.0063	27.5	990	0.5792	20.6820
0.0059	27.7778	1000	0.5792	20.6820

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1.dev0
Tokenizers 0.19.1

edoyin/whisper-tiny-finetune

whisper-tiny-finetune

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

edoyin/
whisper-tiny-finetune