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

Framework versions

Transformers 4.34.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

bigmorning/whisper_4_with_init_sun_syl_wd_0__0055

whisper_4_with_init_sun_syl_wd_0__0055

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__0055