optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
training_precision: float32

Training results

Train Loss	Train Accuracy	Train Wermet	Train Wermet Syl	Validation Loss	Validation Accuracy	Validation Wermet	Validation Wermet Syl	Epoch
5.3409	0.0111	1.3547	1.2898	3.9789	0.0114	0.9710	0.9563	0
4.7143	0.0116	0.8622	0.8228	3.9404	0.0113	0.9823	0.9735	1
4.6752	0.0117	0.8472	0.8057	3.9081	0.0114	0.9579	0.9359	2
4.6500	0.0117	0.8382	0.7945	3.8820	0.0115	0.9213	0.8856	3
4.6282	0.0118	0.8286	0.7805	3.8738	0.0114	0.9433	0.9119	4
4.6095	0.0118	0.8190	0.7696	3.8630	0.0115	0.9117	0.8698	5
4.5875	0.0119	0.7976	0.7465	3.8341	0.0116	0.8976	0.8552	6
4.5682	0.0120	0.7753	0.7227	3.8277	0.0116	0.9014	0.8653	7
4.5376	0.0121	0.7528	0.7005	3.7844	0.0118	0.8332	0.7815	8
4.5060	0.0122	0.7392	0.6844	3.7537	0.0118	0.8578	0.8152	9
4.4580	0.0124	0.7221	0.6694	3.7038	0.0120	0.8190	0.7679	10
4.3989	0.0125	0.7156	0.6636	3.6169	0.0122	0.7979	0.7429	11
4.3056	0.0128	0.7069	0.6557	3.5098	0.0125	0.7924	0.7396	12
4.1673	0.0132	0.7054	0.6584	3.3542	0.0128	0.7759	0.7240	13
3.9762	0.0138	0.6987	0.6559	3.1318	0.0133	0.7644	0.7231	14
3.7385	0.0145	0.6835	0.6448	2.9144	0.0138	0.7392	0.6955	15
3.5040	0.0152	0.6644	0.6298	2.7413	0.0142	0.7019	0.6548	16
3.2728	0.0160	0.6408	0.6101	2.5183	0.0149	0.6798	0.6363	17
3.0657	0.0167	0.6188	0.5912	2.3594	0.0153	0.6528	0.6103	18
2.8703	0.0174	0.5936	0.5685	2.2644	0.0156	0.6310	0.5925	19
2.6850	0.0181	0.5680	0.5453	2.1296	0.0160	0.6040	0.5652	20
2.5227	0.0188	0.5423	0.5215	2.0019	0.0165	0.5793	0.5403	21
2.3878	0.0194	0.5199	0.5015	1.8996	0.0169	0.5592	0.5229	22
2.2437	0.0201	0.4959	0.4788	1.8141	0.0172	0.5414	0.5045	23
2.1205	0.0207	0.4752	0.4607	1.7245	0.0175	0.5208	0.4838	24
1.9919	0.0213	0.4533	0.4390	1.6673	0.0178	0.5026	0.4659	25
1.9140	0.0217	0.4355	0.4216	1.6041	0.0181	0.4873	0.4512	26
1.8225	0.0222	0.4184	0.4052	1.6271	0.0179	0.4852	0.4511	27
1.7265	0.0227	0.4016	0.3895	1.5219	0.0184	0.4635	0.4275	28
1.6240	0.0233	0.3833	0.3729	1.4718	0.0186	0.4515	0.4170	29
1.5610	0.0236	0.3697	0.3588	1.4404	0.0188	0.4407	0.4056	30
1.4719	0.0242	0.3540	0.3449	1.4125	0.0189	0.4310	0.3961	31
1.4152	0.0245	0.3421	0.3339	1.3655	0.0191	0.4234	0.3881	32
1.3546	0.0249	0.3277	0.3195	1.3419	0.0192	0.4156	0.3816	33
1.2565	0.0256	0.3135	0.3060	1.3172	0.0194	0.4065	0.3722	34
1.2135	0.0258	0.3026	0.2958	1.3019	0.0194	0.4006	0.3662	35
1.1739	0.0261	0.2923	0.2861	1.3843	0.0190	0.3951	0.3587	36
1.0950	0.0267	0.2782	0.2733	1.2665	0.0197	0.3883	0.3541	37
1.0435	0.0271	0.2673	0.2631	1.2567	0.0197	0.3837	0.3497	38
0.9922	0.0275	0.2580	0.2542	1.2566	0.0197	0.3801	0.3444	39
0.9387	0.0279	0.2464	0.2438	1.2441	0.0198	0.3767	0.3423	40
0.9345	0.0278	0.2393	0.2373	1.2221	0.0199	0.3682	0.3336	41
0.8574	0.0285	0.2268	0.2255	1.2258	0.0199	0.3680	0.3338	42
0.8275	0.0287	0.2183	0.2180	1.2044	0.0201	0.3628	0.3290	43
0.8201	0.0288	0.2114	0.2108	1.2056	0.0201	0.3601	0.3270	44
0.7684	0.0292	0.2020	0.2029	1.1879	0.0202	0.3553	0.3215	45
0.7262	0.0295	0.1938	0.1947	1.2263	0.0200	0.3537	0.3177	46
0.7286	0.0295	0.1876	0.1898	1.1772	0.0203	0.3485	0.3135	47
0.6807	0.0300	0.1775	0.1797	1.1761	0.0203	0.3490	0.3155	48
0.6609	0.0301	0.1713	0.1742	1.1853	0.0203	0.3484	0.3153	49
0.6062	0.0306	0.1615	0.1653	1.1660	0.0204	0.3432	0.3090	50
0.5755	0.0309	0.1547	0.1584	1.1698	0.0204	0.3428	0.3089	51
0.5600	0.0310	0.1482	0.1524	1.1667	0.0204	0.3398	0.3058	52
0.5715	0.0308	0.1449	0.1496	1.1614	0.0205	0.3381	0.3036	53
0.5247	0.0313	0.1358	0.1411	1.1639	0.0205	0.3359	0.3025	54
0.5085	0.0315	0.1301	0.1358	1.2420	0.0202	0.3412	0.3064	55
0.4827	0.0317	0.1239	0.1295	1.1677	0.0205	0.3349	0.3009	56
0.4848	0.0317	0.1207	0.1280	1.1653	0.0205	0.3326	0.2991	57
0.4323	0.0322	0.1109	0.1185	1.1602	0.0206	0.3299	0.2953	58
0.4183	0.0323	0.1057	0.1133	1.1622	0.0206	0.3307	0.2962	59
0.4329	0.0322	0.1028	0.1100	1.1714	0.0206	0.3300	0.2950	60
0.3962	0.0326	0.0964	0.1045	1.1726	0.0206	0.3311	0.2967	61
0.3642	0.0329	0.0898	0.0973	1.1699	0.0206	0.3289	0.2936	62
0.3786	0.0327	0.0884	0.0963	1.1734	0.0206	0.3279	0.2929	63
0.3698	0.0328	0.0842	0.0925	1.1728	0.0207	0.3282	0.2932	64
0.3219	0.0333	0.0765	0.0850	1.1830	0.0207	0.3258	0.2907	65
0.3035	0.0335	0.0725	0.0811	1.1840	0.0207	0.3261	0.2904	66
0.3522	0.0330	0.0745	0.0826	1.2107	0.0206	0.3299	0.2955	67
0.3001	0.0335	0.0663	0.0749	1.1810	0.0207	0.3264	0.2909	68
0.2729	0.0338	0.0595	0.0677	1.1911	0.0207	0.3247	0.2886	69
0.2696	0.0338	0.0572	0.0654	1.1950	0.0207	0.3260	0.2905	70
0.2840	0.0337	0.0563	0.0648	1.2094	0.0207	0.3250	0.2887	71
0.2319	0.0342	0.0484	0.0569	1.2107	0.0207	0.3250	0.2878	72
0.2371	0.0342	0.0464	0.0541	1.2059	0.0207	0.3240	0.2880	73
0.2666	0.0338	0.0486	0.0575	1.2036	0.0207	0.3241	0.2887	74
0.2443	0.0340	0.0442	0.0522	1.2106	0.0207	0.3241	0.2877	75
0.2118	0.0344	0.0380	0.0456	1.2172	0.0207	0.3240	0.2871	76
0.1997	0.0346	0.0354	0.0428	1.2247	0.0208	0.3219	0.2852	77
0.2461	0.0341	0.0386	0.0466	1.2257	0.0207	0.3240	0.2874	78
0.2367	0.0342	0.0364	0.0431	1.2173	0.0208	0.3234	0.2870	79
0.1857	0.0347	0.0294	0.0365	1.2287	0.0208	0.3244	0.2876	80
0.1504	0.0351	0.0244	0.0314	1.2425	0.0207	0.3238	0.2871	81
0.1438	0.0352	0.0227	0.0287	1.2495	0.0208	0.3222	0.2861	82
0.1545	0.0350	0.0232	0.0288	1.2612	0.0207	0.3257	0.2898	83
0.2122	0.0345	0.0284	0.0346	1.2518	0.0208	0.3241	0.2884	84
0.1685	0.0349	0.0222	0.0278	1.2466	0.0208	0.3231	0.2868	85
0.1371	0.0352	0.0181	0.0236	1.2606	0.0208	0.3239	0.2869	86
0.1357	0.0352	0.0171	0.0216	1.2675	0.0208	0.3240	0.2874	87
0.1022	0.0356	0.0132	0.0172	1.2887	0.0208	0.3233	0.2875	88
0.0943	0.0356	0.0118	0.0159	1.2876	0.0208	0.3252	0.2884	89

Framework versions

Transformers 4.34.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_4_with_init_sun_syl_wd_0__0090

whisper_4_with_init_sun_syl_wd_0__0090

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bigmorning/
whisper_4_with_init_sun_syl_wd_0__0090