Training Loss	Epoch	Step	Validation Loss	Wer
3.9976	0.2778	10	3.9664	44.1176
3.9022	0.5556	20	3.8748	43.6170
3.7582	0.8333	30	3.7266	43.0538
3.5744	1.1111	40	3.5294	39.1427
3.2991	1.3889	50	3.2828	35.3567
3.0818	1.6667	60	2.9793	34.7935
2.6849	1.9444	70	2.5935	37.0150
2.2809	2.2222	80	2.0883	37.7034
1.6882	2.5	90	1.5027	38.6733
1.2127	2.7778	100	1.0275	39.0488
0.8696	3.0556	110	0.7946	29.4431
0.7336	3.3333	120	0.7152	28.1915
0.6793	3.6111	130	0.6666	27.2215
0.6489	3.8889	140	0.6346	26.0638
0.6353	4.1667	150	0.6115	24.8748
0.583	4.4444	160	0.5928	24.7497
0.5455	4.7222	170	0.5775	23.8110
0.487	5.0	180	0.5647	23.3417
0.4925	5.2778	190	0.5541	22.7159
0.4952	5.5556	200	0.5444	22.6533
0.4481	5.8333	210	0.5359	22.2153
0.4827	6.1111	220	0.5263	22.4030
0.3897	6.3889	230	0.5196	21.8085
0.3834	6.6667	240	0.5121	21.8711
0.3906	6.9444	250	0.5073	21.2140
0.3705	7.2222	260	0.5055	21.3705
0.3518	7.5	270	0.4980	21.2140
0.354	7.7778	280	0.4934	20.7447
0.3202	8.0556	290	0.4914	20.4318
0.2997	8.3333	300	0.4859	20.0563
0.2699	8.6111	310	0.4852	26.9399
0.2724	8.8889	320	0.4809	27.0338
0.2844	9.1667	330	0.4802	26.4393
0.2332	9.4444	340	0.4801	24.6558
0.2337	9.7222	350	0.4810	20.2753
0.2542	10.0	360	0.4731	20.5882
0.1986	10.2778	370	0.4779	20.1189
0.2023	10.5556	380	0.4767	24.6558
0.1864	10.8333	390	0.4763	20.3379
0.1873	11.1111	400	0.4765	20.6195
0.1595	11.3889	410	0.4831	20.4631
0.1581	11.6667	420	0.4872	20.2128
0.1663	11.9444	430	0.4851	20.0563
0.1282	12.2222	440	0.4864	19.9625
0.1138	12.5	450	0.4918	19.9937
0.1283	12.7778	460	0.4931	19.9312
0.0847	13.0556	470	0.4891	20.4944
0.0902	13.3333	480	0.5027	19.8999
0.0719	13.6111	490	0.5056	20.6821
0.1011	13.8889	500	0.5023	19.9937
0.0676	14.1667	510	0.5113	20.4005
0.0632	14.4444	520	0.5154	24.7184
0.0643	14.7222	530	0.5207	20.1502
0.053	15.0	540	0.5184	20.2753
0.0389	15.2778	550	0.5295	20.4631
0.0467	15.5556	560	0.5286	20.3066
0.0414	15.8333	570	0.5403	20.2753
0.0334	16.1111	580	0.5334	20.0876
0.0283	16.3889	590	0.5514	20.2441
0.0282	16.6667	600	0.5415	20.1815
0.0267	16.9444	610	0.5451	20.7447
0.019	17.2222	620	0.5483	20.3379
0.0202	17.5	630	0.5551	19.9625
0.0179	17.7778	640	0.5574	20.3066
0.0186	18.0556	650	0.5621	20.6821
0.0123	18.3333	660	0.5634	20.6195
0.0138	18.6111	670	0.5648	20.2753
0.0133	18.8889	680	0.5655	20.4318
0.0114	19.1667	690	0.5666	20.5569
0.0112	19.4444	700	0.5721	20.3379
0.0108	19.7222	710	0.5714	20.8385
0.0106	20.0	720	0.5744	20.4944
0.0092	20.2778	730	0.5751	20.4318
0.0096	20.5556	740	0.5756	20.3692
0.009	20.8333	750	0.5779	20.1502
0.0084	21.1111	760	0.5790	20.4944
0.0077	21.3889	770	0.5820	20.4005
0.0083	21.6667	780	0.5822	20.4005
0.008	21.9444	790	0.5820	20.4005
0.0077	22.2222	800	0.5829	20.4318
0.0083	22.5	810	0.5843	20.4005
0.0073	22.7778	820	0.5856	20.4005
0.0069	23.0556	830	0.5869	20.4005
0.0067	23.3333	840	0.5886	20.5257
0.007	23.6111	850	0.5882	20.4944
0.0074	23.8889	860	0.5872	20.4631
0.0073	24.1667	870	0.5885	20.4631
0.0066	24.4444	880	0.5896	20.6195
0.0061	24.7222	890	0.5898	20.6195
0.0073	25.0	900	0.5902	20.5882
0.0067	25.2778	910	0.5901	20.6508
0.006	25.5556	920	0.5905	20.5257
0.0061	25.8333	930	0.5911	20.7447
0.0064	26.1111	940	0.5916	20.6821
0.0066	26.3889	950	0.5919	20.6195
0.0071	26.6667	960	0.5924	20.5569
0.006	26.9444	970	0.5923	20.5569
0.0068	27.2222	980	0.5923	20.5257
0.0061	27.5	990	0.5924	20.5257
0.0058	27.7778	1000	0.5924	20.5257

Framework versions

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

ismichel/whisper-tiny-finetune

whisper-tiny-finetune

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ismichel/
whisper-tiny-finetune