golesheed/whisper-v2-WestFlemish

Whisper Large V2

This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4599
• Wer: 24.2092

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:

• learning_rate: 3e-05
• train_batch_size: 12
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 20
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.8662	0.0833	15	0.5819	33.0573
0.5255	0.1667	30	0.4760	37.3151
0.4103	0.25	45	0.4493	28.9104
0.4344	0.3333	60	0.4362	25.2990
0.3882	0.4167	75	0.4279	37.7798
0.402	0.5	90	0.4190	40.5043
0.4105	0.5833	105	0.4136	42.3254
0.4126	0.6667	120	0.3959	24.2446
0.3672	0.75	135	0.3956	36.5367
0.4401	0.8333	150	0.3805	24.8744
0.3698	0.9167	165	0.3834	44.2526
0.3728	1.0	180	0.3742	42.0447
0.1642	1.0833	195	0.3942	27.6555
0.1904	1.1667	210	0.3793	25.7967
0.1801	1.25	225	0.3859	23.6879
0.1693	1.3333	240	0.3934	25.1928
0.1839	1.4167	255	0.3853	29.6629
0.19	1.5	270	0.3763	27.3489
0.1977	1.5833	285	0.3764	21.0436
0.1922	1.6667	300	0.3719	30.1040
0.185	1.75	315	0.3716	25.9736
0.1873	1.8333	330	0.3671	22.8127
0.1802	1.9167	345	0.3621	21.2582
0.1931	2.0	360	0.3662	24.4262
0.0848	2.0833	375	0.3989	34.8949
0.0823	2.1667	390	0.3888	23.3718
0.0817	2.25	405	0.3914	22.8057
0.0952	2.3333	420	0.3784	23.8530
0.0961	2.4167	435	0.3917	33.5315
0.0954	2.5	450	0.3822	20.7959
0.0909	2.5833	465	0.3877	22.4282
0.084	2.6667	480	0.3878	26.7025
0.0769	2.75	495	0.3890	21.8597
0.0879	2.8333	510	0.3899	24.0724
0.0835	2.9167	525	0.3788	20.9327
0.0845	3.0	540	0.3807	26.7379
0.0383	3.0833	555	0.4227	24.4333
0.0408	3.1667	570	0.4173	31.6868
0.0393	3.25	585	0.4202	21.5413
0.035	3.3333	600	0.4141	23.4355
0.034	3.4167	615	0.4193	24.1927
0.0383	3.5	630	0.4160	27.6319
0.0295	3.5833	645	0.4243	26.9644
0.0323	3.6667	660	0.4201	25.1221
0.0337	3.75	675	0.4195	26.5445
0.0328	3.8333	690	0.4229	23.7775
0.0344	3.9167	705	0.4213	23.9025
0.0257	4.0	720	0.4209	23.6643
0.0134	4.0833	735	0.4392	22.7372
0.0113	4.1667	750	0.4556	21.2511
0.0122	4.25	765	0.4596	21.6899
0.0117	4.3333	780	0.4652	21.7890
0.0111	4.4167	795	0.4637	21.6946
0.0115	4.5	810	0.4627	23.1571
0.0127	4.5833	825	0.4567	24.0040
0.0108	4.6667	840	0.4592	23.0415
0.0107	4.75	855	0.4610	23.4661
0.0094	4.8333	870	0.4602	25.0112
0.0104	4.9167	885	0.4599	24.6621
0.0125	5.0	900	0.4599	24.2092

Framework versions

• Transformers 4.45.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1