whisper_4_with_init_sun__0100

This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.1327
Train Accuracy: 0.0351
Train Wermet: 0.0150
Validation Loss: 1.3217
Validation Accuracy: 0.0208
Validation Wermet: 0.3240
Epoch: 99

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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': 0.01}
training_precision: float32

Training results

Train Loss	Train Accuracy	Train Wermet	Validation Loss	Validation Accuracy	Validation Wermet	Epoch
5.3333	0.0111	1.3132	3.9675	0.0114	0.9339	0
4.7131	0.0116	0.8607	3.9360	0.0114	0.9503	1
4.6717	0.0117	0.8449	3.9196	0.0113	0.9768	2
4.6474	0.0117	0.8338	3.9039	0.0114	0.9557	3
4.6273	0.0118	0.8243	3.8721	0.0115	0.9414	4
4.6101	0.0118	0.8167	3.8629	0.0116	0.9156	5
4.5912	0.0119	0.7985	3.8361	0.0116	0.8988	6
4.5645	0.0120	0.7753	3.8298	0.0116	0.9045	7
4.5386	0.0121	0.7558	3.7904	0.0118	0.8426	8
4.5075	0.0122	0.7405	3.7472	0.0119	0.8103	9
4.4586	0.0124	0.7255	3.7163	0.0120	0.8189	10
4.3978	0.0126	0.7174	3.6168	0.0122	0.8163	11
4.3031	0.0128	0.7107	3.4956	0.0125	0.7847	12
4.1606	0.0133	0.7025	3.3414	0.0128	0.7897	13
3.9636	0.0138	0.6991	3.1311	0.0133	0.7495	14
3.7290	0.0145	0.6827	2.8892	0.0139	0.7292	15
3.4993	0.0152	0.6643	2.7195	0.0143	0.7129	16
3.2810	0.0159	0.6448	2.5418	0.0148	0.6803	17
3.0604	0.0167	0.6182	2.3572	0.0153	0.6538	18
2.8748	0.0174	0.5946	2.2575	0.0156	0.6337	19
2.6889	0.0181	0.5699	2.0988	0.0162	0.6016	20
2.5493	0.0187	0.5449	1.9878	0.0166	0.5834	21
2.3921	0.0194	0.5207	1.9029	0.0168	0.5597	22
2.2491	0.0201	0.4987	1.8642	0.0169	0.5409	23
2.1254	0.0207	0.4766	1.7354	0.0175	0.5231	24
1.9980	0.0213	0.4552	1.6661	0.0178	0.5049	25
1.9147	0.0217	0.4382	1.6140	0.0180	0.4921	26
1.8008	0.0223	0.4196	1.5652	0.0182	0.4742	27
1.7185	0.0228	0.4028	1.5159	0.0184	0.4632	28
1.6401	0.0232	0.3867	1.4891	0.0185	0.4548	29
1.5786	0.0235	0.3728	1.5141	0.0183	0.4548	30
1.4950	0.0241	0.3582	1.4345	0.0188	0.4340	31
1.4323	0.0244	0.3448	1.3694	0.0191	0.4226	32
1.3495	0.0250	0.3319	1.3780	0.0190	0.4172	33
1.3007	0.0253	0.3187	1.3296	0.0193	0.4109	34
1.2320	0.0257	0.3074	1.3116	0.0194	0.4029	35
1.1836	0.0261	0.2958	1.3025	0.0195	0.3992	36
1.1131	0.0266	0.2842	1.2885	0.0195	0.3894	37
1.0630	0.0269	0.2730	1.2627	0.0197	0.3850	38
1.0189	0.0272	0.2628	1.2633	0.0197	0.3822	39
1.0025	0.0273	0.2550	1.2561	0.0197	0.3760	40
0.9498	0.0277	0.2445	1.2288	0.0199	0.3710	41
0.9027	0.0281	0.2337	1.2188	0.0199	0.3684	42
0.8469	0.0286	0.2240	1.2072	0.0200	0.3637	43
0.8056	0.0289	0.2153	1.2046	0.0201	0.3599	44
0.7761	0.0291	0.2070	1.1989	0.0201	0.3579	45
0.7369	0.0295	0.1982	1.1938	0.0202	0.3528	46
0.7026	0.0298	0.1902	1.1934	0.0202	0.3508	47
0.6976	0.0298	0.1834	1.1803	0.0203	0.3469	48
0.6880	0.0298	0.1765	1.1844	0.0203	0.3470	49
0.6674	0.0300	0.1702	1.1741	0.0203	0.3446	50
0.6099	0.0305	0.1606	1.1753	0.0203	0.3440	51
0.5972	0.0306	0.1549	1.1692	0.0204	0.3401	52
0.5555	0.0310	0.1475	1.1744	0.0204	0.3382	53
0.5275	0.0313	0.1412	1.1743	0.0204	0.3384	54
0.5103	0.0315	0.1344	1.1720	0.0205	0.3355	55
0.5268	0.0313	0.1308	1.1709	0.0205	0.3343	56
0.5060	0.0315	0.1251	1.2090	0.0203	0.3318	57
0.4696	0.0318	0.1172	1.1748	0.0205	0.3321	58
0.4737	0.0318	0.1136	1.1764	0.0205	0.3313	59
0.4749	0.0318	0.1115	1.1684	0.0206	0.3289	60
0.4208	0.0323	0.1015	1.1704	0.0206	0.3275	61
0.3895	0.0326	0.0958	1.1777	0.0206	0.3286	62
0.3721	0.0328	0.0909	1.1754	0.0206	0.3267	63
0.4037	0.0324	0.0912	1.1798	0.0206	0.3272	64
0.3752	0.0327	0.0851	1.1840	0.0206	0.3268	65
0.3589	0.0329	0.0816	1.1819	0.0206	0.3258	66
0.3484	0.0330	0.0781	1.1825	0.0207	0.3261	67
0.3335	0.0332	0.0730	1.1890	0.0207	0.3269	68
0.2810	0.0337	0.0644	1.1991	0.0207	0.3237	69
0.2753	0.0338	0.0620	1.2133	0.0207	0.3251	70
0.3127	0.0334	0.0651	1.2154	0.0206	0.3269	71
0.2941	0.0336	0.0591	1.2063	0.0207	0.3245	72
0.2792	0.0337	0.0564	1.2073	0.0207	0.3253	73
0.2419	0.0341	0.0496	1.2113	0.0207	0.3247	74
0.2508	0.0340	0.0492	1.2158	0.0207	0.3244	75
0.2655	0.0339	0.0484	1.2171	0.0207	0.3240	76
0.2318	0.0342	0.0428	1.2267	0.0207	0.3263	77
0.2063	0.0345	0.0384	1.2268	0.0207	0.3263	78
0.2292	0.0343	0.0394	1.2309	0.0207	0.3242	79
0.1941	0.0346	0.0342	1.2372	0.0207	0.3264	80
0.2112	0.0345	0.0362	1.2365	0.0207	0.3270	81
0.1866	0.0347	0.0325	1.2414	0.0207	0.3244	82
0.1731	0.0348	0.0295	1.2579	0.0207	0.3324	83
0.1888	0.0346	0.0323	1.2551	0.0207	0.3284	84
0.1704	0.0348	0.0268	1.2662	0.0207	0.3270	85
0.1662	0.0348	0.0252	1.2662	0.0207	0.3259	86
0.2004	0.0346	0.0280	1.2666	0.0207	0.3260	87
0.1514	0.0350	0.0219	1.2709	0.0207	0.3265	88
0.1456	0.0350	0.0215	1.2787	0.0207	0.3280	89
0.1475	0.0351	0.0200	1.2800	0.0208	0.3270	90
0.1025	0.0355	0.0149	1.2893	0.0208	0.3260	91
0.1094	0.0355	0.0149	1.2986	0.0207	0.3272	92
0.0926	0.0356	0.0129	1.3074	0.0207	0.3246	93
0.1614	0.0350	0.0213	1.3017	0.0208	0.3251	94
0.1140	0.0353	0.0150	1.3106	0.0208	0.3264	95
0.1600	0.0348	0.0191	1.3071	0.0207	0.3277	96
0.1070	0.0354	0.0123	1.3157	0.0208	0.3249	97
0.0928	0.0356	0.0104	1.3235	0.0208	0.3281	98
0.1327	0.0351	0.0150	1.3217	0.0208	0.3240	99

Framework versions

Transformers 4.34.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

whisper_4_with_init_sun__0100

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js