whisper_input_decoder_no_lob__0145

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: 0.0321
Train Accuracy: 0.0359
Train Wermet: 0.0347
Validation Loss: 1.4821
Validation Accuracy: 0.0209
Validation Wermet: 0.3368
Epoch: 144

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
5.4122	0.0107	0.9328	3.9759	0.0114	0.9606	0
4.7176	0.0116	0.8683	3.9404	0.0114	0.9334	1
4.6750	0.0117	0.8478	3.9211	0.0115	0.9237	2
4.6511	0.0117	0.8413	3.8864	0.0115	0.9331	3
4.6294	0.0118	0.8270	3.8729	0.0115	0.9228	4
4.6134	0.0118	0.8199	3.8690	0.0114	0.9451	5
4.5980	0.0118	0.8102	3.8491	0.0115	0.9152	6
4.5759	0.0119	0.7890	3.8366	0.0116	0.8691	7
4.5518	0.0120	0.7694	3.8081	0.0116	0.9013	8
4.5219	0.0121	0.7591	3.7734	0.0118	0.8383	9
4.4761	0.0122	0.7400	3.7156	0.0120	0.8125	10
4.4139	0.0125	0.7257	3.6311	0.0121	0.8188	11
4.3113	0.0128	0.7127	3.5089	0.0124	0.8008	12
4.1608	0.0132	0.7088	3.3587	0.0127	0.7742	13
3.9595	0.0138	0.7012	3.1493	0.0132	0.7718	14
3.7188	0.0145	0.6820	2.8784	0.0139	0.7292	15
3.4775	0.0153	0.6678	2.6716	0.0144	0.7074	16
3.2575	0.0160	0.6481	2.4980	0.0149	0.6764	17
3.0615	0.0167	0.6314	2.3456	0.0153	0.6476	18
2.8715	0.0174	0.6094	2.2090	0.0158	0.6210	19
2.6930	0.0181	0.5931	2.0918	0.0162	0.5992	20
2.5383	0.0187	0.5739	1.9769	0.0166	0.5791	21
2.3952	0.0193	0.5512	1.9042	0.0168	0.5589	22
2.2427	0.0201	0.5333	1.8028	0.0172	0.5394	23
2.1236	0.0206	0.5174	1.7434	0.0174	0.5240	24
2.0315	0.0211	0.4978	1.6755	0.0177	0.5084	25
1.9066	0.0217	0.4773	1.6534	0.0178	0.4947	26
1.8279	0.0221	0.4596	1.5606	0.0182	0.4788	27
1.7325	0.0227	0.4412	1.5173	0.0184	0.4667	28
1.6416	0.0232	0.4199	1.4733	0.0186	0.4511	29
1.5702	0.0236	0.4028	1.4519	0.0187	0.4442	30
1.4787	0.0241	0.3839	1.4213	0.0188	0.4322	31
1.4238	0.0244	0.3700	1.3971	0.0190	0.4272	32
1.3561	0.0249	0.3594	1.3499	0.0192	0.4171	33
1.2828	0.0254	0.3431	1.3555	0.0192	0.4097	34
1.2318	0.0257	0.3277	1.3183	0.0194	0.4035	35
1.1668	0.0262	0.3201	1.3068	0.0195	0.3978	36
1.1571	0.0261	0.3105	1.2901	0.0195	0.3916	37
1.0812	0.0267	0.2989	1.2720	0.0197	0.3860	38
1.0134	0.0273	0.2863	1.2593	0.0197	0.3777	39
0.9986	0.0273	0.2769	1.2629	0.0198	0.3754	40
0.9322	0.0279	0.2653	1.2320	0.0199	0.3694	41
0.9021	0.0281	0.2552	1.2308	0.0200	0.3651	42
0.8583	0.0284	0.2444	1.2199	0.0200	0.3614	43
0.8101	0.0288	0.2355	1.2120	0.0200	0.3597	44
0.8045	0.0288	0.2299	1.2023	0.0201	0.3567	45
0.7823	0.0290	0.2213	1.2075	0.0201	0.3529	46
0.7186	0.0296	0.2107	1.1917	0.0202	0.3530	47
0.6949	0.0298	0.2028	1.1926	0.0202	0.3465	48
0.6669	0.0300	0.1943	1.1902	0.0203	0.3446	49
0.6125	0.0305	0.1842	1.1892	0.0203	0.3437	50
0.5926	0.0307	0.1778	1.2058	0.0203	0.3450	51
0.6055	0.0305	0.1738	1.1859	0.0203	0.3394	52
0.5828	0.0307	0.1653	1.1921	0.0203	0.3379	53
0.5507	0.0311	0.1569	1.1906	0.0204	0.3385	54
0.5050	0.0315	0.1485	1.1834	0.0205	0.3361	55
0.4878	0.0316	0.1447	1.1815	0.0205	0.3329	56
0.4825	0.0317	0.1410	1.2096	0.0204	0.3359	57
0.4987	0.0315	0.1374	1.2000	0.0204	0.3352	58
0.4576	0.0319	0.1305	1.1868	0.0205	0.3329	59
0.4185	0.0323	0.1215	1.2043	0.0205	0.3322	60
0.3889	0.0326	0.1156	1.1853	0.0206	0.3302	61
0.3790	0.0327	0.1101	1.2028	0.0205	0.3316	62
0.4072	0.0324	0.1110	1.2502	0.0203	0.3309	63
0.3519	0.0330	0.1020	1.1959	0.0206	0.3284	64
0.3861	0.0326	0.1034	1.1885	0.0206	0.3271	65
0.3789	0.0326	0.0961	1.1969	0.0206	0.3298	66
0.3233	0.0332	0.0905	1.1922	0.0207	0.3280	67
0.2956	0.0335	0.0854	1.2003	0.0207	0.3296	68
0.2666	0.0339	0.0796	1.2141	0.0207	0.3252	69
0.3181	0.0333	0.0813	1.2133	0.0207	0.3302	70
0.3032	0.0335	0.0770	1.2170	0.0207	0.3315	71
0.2746	0.0337	0.0741	1.2180	0.0207	0.3299	72
0.2549	0.0339	0.0705	1.2496	0.0206	0.3308	73
0.2529	0.0339	0.0685	1.2239	0.0207	0.3321	74
0.2427	0.0340	0.0671	1.2351	0.0207	0.3292	75
0.2166	0.0343	0.0623	1.2361	0.0207	0.3313	76
0.2030	0.0345	0.0585	1.2462	0.0207	0.3312	77
0.2126	0.0344	0.0566	1.2441	0.0207	0.3313	78
0.2166	0.0343	0.0569	1.2506	0.0207	0.3334	79
0.2088	0.0344	0.0562	1.2557	0.0207	0.3389	80
0.2212	0.0342	0.0560	1.2652	0.0207	0.3334	81
0.2256	0.0343	0.0543	1.2543	0.0207	0.3393	82
0.1915	0.0346	0.0501	1.2540	0.0207	0.3299	83
0.1544	0.0350	0.0443	1.2676	0.0207	0.3347	84
0.1567	0.0350	0.0435	1.2740	0.0207	0.3375	85
0.1329	0.0352	0.0405	1.2833	0.0207	0.3398	86
0.1261	0.0353	0.0392	1.3088	0.0206	0.3397	87
0.1547	0.0350	0.0438	1.2933	0.0207	0.3279	88
0.1288	0.0352	0.0377	1.2985	0.0208	0.3371	89
0.1400	0.0351	0.0391	1.3020	0.0208	0.3329	90
0.1077	0.0355	0.0351	1.3118	0.0207	0.3344	91
0.0982	0.0355	0.0348	1.3274	0.0207	0.3352	92
0.1041	0.0355	0.0356	1.3251	0.0208	0.3362	93
0.1573	0.0350	0.0364	1.3173	0.0208	0.3362	94
0.1478	0.0350	0.0365	1.3125	0.0208	0.3367	95
0.0929	0.0356	0.0336	1.3256	0.0208	0.3351	96
0.1106	0.0354	0.0341	1.3289	0.0208	0.3428	97
0.0916	0.0355	0.0322	1.3384	0.0208	0.3383	98
0.0708	0.0358	0.0313	1.3525	0.0208	0.3333	99
0.1271	0.0352	0.0326	1.3447	0.0208	0.3336	100
0.0963	0.0355	0.0329	1.3481	0.0208	0.3288	101
0.0698	0.0358	0.0294	1.3748	0.0207	0.3341	102
0.0844	0.0356	0.0313	1.3615	0.0208	0.3380	103
0.1045	0.0354	0.0319	1.3519	0.0208	0.3357	104
0.0877	0.0355	0.0326	1.3760	0.0207	0.3402	105
0.0713	0.0357	0.0303	1.3742	0.0208	0.3392	106
0.0686	0.0358	0.0292	1.3920	0.0207	0.3353	107
0.0466	0.0360	0.0287	1.3964	0.0208	0.3342	108
0.0353	0.0361	0.0258	1.3977	0.0208	0.3339	109
0.0369	0.0361	0.0269	1.4132	0.0208	0.3298	110
0.0601	0.0359	0.0304	1.4104	0.0208	0.3438	111
0.1244	0.0352	0.0332	1.3939	0.0207	0.3349	112
0.1102	0.0353	0.0301	1.3826	0.0208	0.3421	113
0.1199	0.0352	0.0311	1.3783	0.0208	0.3368	114
0.0576	0.0358	0.0281	1.3972	0.0208	0.3340	115
0.0363	0.0360	0.0255	1.3984	0.0208	0.3314	116
0.0260	0.0362	0.0260	1.4108	0.0208	0.3327	117
0.0291	0.0361	0.0265	1.4222	0.0208	0.3376	118
0.0252	0.0361	0.0249	1.4481	0.0208	0.3327	119
0.0855	0.0356	0.0283	1.4200	0.0208	0.3303	120
0.0947	0.0355	0.0287	1.4148	0.0208	0.3291	121
0.0561	0.0358	0.0250	1.4208	0.0208	0.3292	122
0.0675	0.0357	0.0272	1.4145	0.0208	0.3402	123
0.0340	0.0360	0.0276	1.4617	0.0207	0.3460	124
0.0213	0.0362	0.0285	1.4502	0.0208	0.3374	125
0.0369	0.0360	0.0287	1.4512	0.0208	0.3483	126
0.0717	0.0357	0.0310	1.4271	0.0208	0.3314	127
0.0434	0.0359	0.0242	1.4545	0.0208	0.3355	128
0.0541	0.0358	0.0281	1.4461	0.0208	0.3298	129
0.0281	0.0361	0.0265	1.4912	0.0207	0.3358	130
0.0438	0.0359	0.0260	1.4805	0.0208	0.3327	131
0.1140	0.0352	0.0304	1.4342	0.0208	0.3370	132
0.0611	0.0358	0.0330	1.4374	0.0209	0.3405	133
0.0319	0.0360	0.0320	1.4453	0.0209	0.3382	134
0.0208	0.0361	0.0290	1.4533	0.0209	0.3382	135
0.0242	0.0361	0.0311	1.4630	0.0209	0.3409	136
0.0185	0.0361	0.0315	1.4696	0.0209	0.3468	137
0.0416	0.0359	0.0294	1.4782	0.0209	0.3362	138
0.0844	0.0355	0.0333	1.4643	0.0208	0.3339	139
0.0708	0.0356	0.0316	1.4601	0.0208	0.3416	140
0.0509	0.0358	0.0303	1.4652	0.0209	0.3417	141
0.0279	0.0360	0.0314	1.4720	0.0209	0.3376	142
0.0467	0.0358	0.0314	1.4730	0.0209	0.3361	143
0.0321	0.0359	0.0347	1.4821	0.0209	0.3368	144

Framework versions

Transformers 4.33.0.dev0
TensorFlow 2.13.0
Tokenizers 0.13.3

whisper_input_decoder_no_lob__0145

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js