whisper_charsplit_new_0099

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: 0.0022
• Train Accuracy: 0.0795
• Train Wermet: 8.7907
• Validation Loss: 0.5556
• Validation Accuracy: 0.0766
• Validation Wermet: 7.7851
• Epoch: 98

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
0.8733	0.0602	13.0686	0.6470	0.0676	11.4066	0
0.5740	0.0666	12.7778	0.5113	0.0706	11.1022	1
0.4553	0.0692	12.2404	0.4371	0.0723	10.9105	2
0.3813	0.0708	11.9157	0.3935	0.0733	9.4615	3
0.3292	0.0720	11.5732	0.3630	0.0740	9.9885	4
0.2886	0.0729	11.5171	0.3403	0.0745	9.8042	5
0.2561	0.0736	11.3173	0.3256	0.0749	9.9431	6
0.2282	0.0743	11.7308	0.3159	0.0752	9.2086	7
0.2036	0.0748	11.4503	0.3071	0.0754	9.5236	8
0.1820	0.0754	11.7175	0.3005	0.0756	10.0755	9
0.1628	0.0758	11.7056	0.2993	0.0757	9.9497	10
0.1450	0.0762	11.7637	0.2971	0.0758	10.1481	11
0.1287	0.0766	11.8509	0.3029	0.0759	10.2042	12
0.1140	0.0770	12.1100	0.3004	0.0760	10.3873	13
0.0998	0.0773	11.9502	0.3025	0.0761	10.7066	14
0.0872	0.0777	12.3196	0.3129	0.0759	10.7707	15
0.0760	0.0779	12.2637	0.3142	0.0761	10.2638	16
0.0651	0.0782	12.1215	0.3192	0.0761	10.0750	17
0.0547	0.0785	12.0551	0.3294	0.0761	10.4732	18
0.0463	0.0787	11.9677	0.3402	0.0760	10.2814	19
0.0386	0.0789	11.6855	0.3517	0.0760	10.0599	20
0.0318	0.0790	11.6314	0.3628	0.0760	9.6652	21
0.0262	0.0792	11.4603	0.3728	0.0760	10.0035	22
0.0224	0.0792	11.4330	0.3824	0.0760	9.1995	23
0.0181	0.0793	11.3124	0.3982	0.0759	9.8710	24
0.0142	0.0794	11.3562	0.4057	0.0760	9.6831	25
0.0118	0.0794	11.0532	0.4207	0.0759	9.7227	26
0.0101	0.0794	11.2963	0.4282	0.0760	9.5792	27
0.0114	0.0794	11.3093	0.4431	0.0758	9.5545	28
0.0109	0.0794	11.4214	0.4419	0.0760	9.4377	29
0.0084	0.0794	10.9143	0.4474	0.0760	9.3668	30
0.0043	0.0795	10.9497	0.4525	0.0761	9.3202	31
0.0036	0.0795	10.7759	0.4667	0.0761	9.0385	32
0.0047	0.0795	10.7613	0.4788	0.0759	9.4065	33
0.0130	0.0793	11.1022	0.4748	0.0760	9.4521	34
0.0074	0.0794	10.9738	0.4730	0.0760	9.3348	35
0.0032	0.0795	10.6370	0.4750	0.0762	8.8298	36
0.0020	0.0795	10.7428	0.4835	0.0762	9.0566	37
0.0014	0.0795	10.6908	0.4937	0.0761	9.2445	38
0.0035	0.0795	10.6833	0.5276	0.0757	8.9798	39
0.0120	0.0793	10.4810	0.4963	0.0760	8.9194	40
0.0045	0.0795	10.2251	0.5014	0.0761	8.5737	41
0.0028	0.0795	10.3174	0.4968	0.0762	8.8525	42
0.0023	0.0795	10.4871	0.5027	0.0762	8.6712	43
0.0024	0.0795	10.3731	0.5055	0.0762	8.6347	44
0.0041	0.0795	10.2751	0.5242	0.0760	8.3671	45
0.0070	0.0794	10.2166	0.5169	0.0760	8.8409	46
0.0037	0.0795	10.0455	0.5174	0.0762	8.2514	47
0.0023	0.0795	9.9201	0.5167	0.0763	8.9537	48
0.0008	0.0795	10.0022	0.5166	0.0764	8.4855	49
0.0006	0.0795	9.9494	0.5233	0.0763	8.5719	50
0.0069	0.0794	10.2037	0.5434	0.0759	8.5399	51
0.0083	0.0794	9.9557	0.5173	0.0762	8.2406	52
0.0032	0.0795	10.0283	0.5240	0.0763	9.0101	53
0.0018	0.0795	10.0694	0.5247	0.0763	8.5717	54
0.0008	0.0795	10.1079	0.5217	0.0764	8.5608	55
0.0005	0.0795	10.0546	0.5286	0.0764	8.8830	56
0.0007	0.0795	10.2557	0.5328	0.0764	8.5665	57
0.0006	0.0795	10.2165	0.5412	0.0763	8.4623	58
0.0124	0.0792	10.2304	0.5284	0.0762	9.1194	59
0.0044	0.0795	10.3884	0.5223	0.0764	8.8152	60
0.0015	0.0795	9.8557	0.5227	0.0764	8.3774	61
0.0005	0.0795	9.8123	0.5233	0.0765	8.5043	62
0.0003	0.0795	9.7631	0.5282	0.0765	8.3860	63
0.0003	0.0795	9.7593	0.5320	0.0765	8.4815	64
0.0002	0.0795	9.7663	0.5357	0.0765	8.4281	65
0.0034	0.0795	9.8382	0.5771	0.0758	8.8051	66
0.0123	0.0792	10.2575	0.5261	0.0763	9.3701	67
0.0027	0.0795	10.3802	0.5272	0.0764	8.8216	68
0.0011	0.0795	10.1683	0.5291	0.0764	8.5736	69
0.0012	0.0795	10.1305	0.5336	0.0765	8.6648	70
0.0008	0.0795	10.2545	0.5315	0.0765	9.0617	71
0.0006	0.0795	10.4562	0.5369	0.0765	9.6485	72
0.0032	0.0795	10.2347	0.5569	0.0763	8.4947	73
0.0062	0.0794	10.1654	0.5471	0.0763	8.8666	74
0.0029	0.0795	10.1320	0.5376	0.0765	8.7713	75
0.0012	0.0795	10.2943	0.5406	0.0765	8.6959	76
0.0006	0.0795	10.1888	0.5371	0.0767	8.9689	77
0.0005	0.0795	10.2138	0.5398	0.0766	8.7470	78
0.0016	0.0795	10.2173	0.5497	0.0764	8.9675	79
0.0065	0.0794	10.2806	0.5559	0.0763	9.4487	80
0.0028	0.0795	10.7728	0.5394	0.0766	8.9716	81
0.0012	0.0795	10.3247	0.5453	0.0765	8.9986	82
0.0013	0.0795	10.3174	0.5535	0.0765	8.9229	83
0.0011	0.0795	10.2846	0.5452	0.0766	9.1239	84
0.0007	0.0795	10.1996	0.5491	0.0766	8.9308	85
0.0034	0.0795	10.5048	0.5578	0.0764	8.9920	86
0.0038	0.0795	10.1430	0.5538	0.0765	9.1635	87
0.0019	0.0795	10.3176	0.5492	0.0766	8.5812	88
0.0007	0.0795	10.2569	0.5488	0.0766	8.9133	89
0.0006	0.0795	10.2538	0.5541	0.0766	8.7676	90
0.0029	0.0795	10.1412	0.5666	0.0764	9.0822	91
0.0042	0.0795	9.5603	0.5582	0.0765	7.6837	92
0.0015	0.0795	9.4004	0.5495	0.0766	7.7859	93
0.0008	0.0795	9.5417	0.5503	0.0767	7.8876	94
0.0005	0.0795	9.3473	0.5590	0.0766	7.8967	95
0.0016	0.0795	9.1740	0.5746	0.0765	7.8469	96
0.0044	0.0794	8.8948	0.5589	0.0765	7.4085	97
0.0022	0.0795	8.7907	0.5556	0.0766	7.7851	98

Framework versions

• Transformers 4.32.0.dev0
• TensorFlow 2.12.0
• Tokenizers 0.13.3