whisper-large-v3-atco2-asr-atcosim

This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1039
• Wer: 22.2698

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 64
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 250
• training_steps: 12644
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.049	1.97	250	0.0613	41.3521
0.0168	3.94	500	0.0656	25.3775
0.0076	5.91	750	0.0703	16.7505
0.0028	7.87	1000	0.0722	23.0540
0.001	9.84	1250	0.0727	21.6365
0.0008	11.81	1500	0.0728	24.0815
0.0012	13.78	1750	0.0712	36.9653
0.0025	15.75	2000	0.0701	21.1248
0.0005	17.72	2250	0.0745	10.2458
0.0006	19.69	2500	0.0781	26.3169
0.0013	21.65	2750	0.0760	15.4127
0.0073	23.62	3000	0.0790	85.4764
0.0038	25.59	3250	0.0724	44.4682
0.0003	27.56	3500	0.0772	37.4056
0.0003	29.53	3750	0.0778	31.2238
0.0	31.5	4000	0.0806	22.4040
0.0	33.46	4250	0.0831	20.6886
0.0	35.43	4500	0.0847	20.3322
0.0	37.4	4750	0.0860	20.7935
0.0	39.37	5000	0.0871	20.3657
0.0	41.34	5250	0.0880	20.5293
0.0	43.31	5500	0.0889	20.7977
0.0	45.28	5750	0.0898	20.4957
0.0	47.24	6000	0.0906	20.9612
0.0	49.21	6250	0.0914	20.8564
0.0	51.18	6500	0.0921	21.1919
0.0	53.15	6750	0.0928	20.7809
0.0	55.12	7000	0.0934	21.1793
0.0	57.09	7250	0.0941	21.2087
0.0	59.06	7500	0.0947	21.2255
0.0	61.02	7750	0.0953	21.4142
0.0	62.99	8000	0.0959	21.1961
0.0	64.96	8250	0.0966	21.1080
0.0	66.93	8500	0.0972	21.0955
0.0	68.9	8750	0.0978	21.4226
0.0	70.87	9000	0.0983	21.3681
0.0	72.83	9250	0.0988	21.6532
0.0	74.8	9500	0.0994	21.6155
0.0	76.77	9750	0.0999	21.5107
0.0	78.74	10000	0.1005	21.3974
0.0	80.71	10250	0.1010	21.6407
0.0	82.68	10500	0.1014	21.7120
0.0	84.65	10750	0.1019	21.8755
0.0	86.61	11000	0.1023	21.9510
0.0	88.58	11250	0.1027	21.9636
0.0	90.55	11500	0.1030	22.0223
0.0	92.52	11750	0.1033	22.0265
0.0	94.49	12000	0.1036	22.3536
0.0	96.46	12250	0.1038	22.3956
0.0	98.43	12500	0.1039	22.2698

Framework versions

• Transformers 4.35.0
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.14.1