golesheed/whisper-2-dutch

Whisper Large V2

This model is a fine-tuned version of openai/whisper-large-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3047
• Wer: 10.4756

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
0.5862	0.09	30	0.3770	15.4837
0.3186	0.19	60	0.3302	13.7743
0.2867	0.28	90	0.3126	13.5958
0.288	0.38	120	0.2984	12.1001
0.2647	0.47	150	0.2963	14.9480
0.2578	0.57	180	0.2984	13.6251
0.2943	0.66	210	0.2910	15.0124
0.2584	0.76	240	0.2758	14.6729
0.2741	0.85	270	0.2724	11.9040
0.2595	0.95	300	0.2743	14.1753
0.2164	1.04	330	0.2688	12.1469
0.1197	1.14	360	0.2665	12.0006
0.1275	1.23	390	0.2690	11.4035
0.1342	1.33	420	0.2742	12.2025
0.1271	1.42	450	0.2695	12.0972
0.1335	1.52	480	0.2728	11.3508
0.1385	1.61	510	0.2669	11.5908
0.1326	1.71	540	0.2631	11.8045
0.1245	1.8	570	0.2621	12.0884
0.1232	1.9	600	0.2597	11.6611
0.1325	1.99	630	0.2576	11.6054
0.0615	2.09	660	0.2724	12.8055
0.0615	2.18	690	0.2703	12.1908
0.0575	2.28	720	0.2699	12.0474
0.0568	2.37	750	0.2722	11.8425
0.0562	2.47	780	0.2734	12.9987
0.0568	2.56	810	0.2696	11.2630
0.0567	2.66	840	0.2749	10.9557
0.058	2.75	870	0.2783	11.6025
0.0608	2.85	900	0.2733	11.1605
0.0586	2.94	930	0.2678	11.9830
0.044	3.04	960	0.2753	11.2601
0.0236	3.13	990	0.2814	10.8825
0.0235	3.23	1020	0.2853	11.0376
0.0229	3.32	1050	0.2865	10.7654
0.0217	3.42	1080	0.2848	10.6776
0.0233	3.51	1110	0.2838	10.6600
0.0223	3.61	1140	0.2867	10.6981
0.0208	3.7	1170	0.2791	10.3761
0.0195	3.8	1200	0.2832	10.5020
0.02	3.89	1230	0.2841	10.9176
0.0204	3.99	1260	0.2817	10.4610
0.0092	4.08	1290	0.2933	10.5312
0.0078	4.18	1320	0.2992	10.4727
0.0068	4.27	1350	0.3026	10.3264
0.0076	4.37	1380	0.3064	10.7361
0.0077	4.46	1410	0.3070	10.5752
0.0073	4.56	1440	0.3070	10.5459
0.0078	4.65	1470	0.3053	10.5254
0.0083	4.75	1500	0.3035	10.4317
0.009	4.84	1530	0.3042	10.4669
0.0074	4.94	1560	0.3047	10.4756

Framework versions

• Transformers 4.38.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.0