output2

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7711
• Wer: 0.3693

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: 0.0001
• train_batch_size: 8
• 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: 1000
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.9858	0.5	500	0.8322	0.6842
0.7867	1.0	1000	0.6777	0.6137
0.6252	1.5	1500	0.6082	0.5503
0.5833	2.0	2000	0.5441	0.5066
0.4611	2.5	2500	0.5498	0.4922
0.4511	3.0	3000	0.5262	0.4654
0.37	3.5	3500	0.5422	0.4554
0.375	4.0	4000	0.6414	0.4659
0.3149	4.5	4500	0.5149	0.4353
0.3103	5.0	5000	0.5329	0.4423
0.2735	5.5	5500	0.9014	0.4359
0.2711	6.0	6000	3.1838	0.4374
0.26	6.5	6500	0.5987	0.4288
0.2451	7.0	7000	0.5245	0.4206
0.2184	7.5	7500	0.5627	0.4138
0.2115	8.0	8000	0.6408	0.4245
0.187	8.5	8500	0.5788	0.4093
0.1955	9.0	9000	0.5591	0.4214
0.1725	9.5	9500	0.5812	0.4135
0.1758	10.0	10000	0.5863	0.4051
0.1592	10.5	10500	0.6263	0.4116
0.1576	11.0	11000	0.5829	0.4028
0.1427	11.5	11500	0.6378	0.4016
0.1476	12.0	12000	0.5706	0.3988
0.1289	12.5	12500	0.6381	0.4104
0.1366	13.0	13000	0.6326	0.3975
0.1183	13.5	13500	0.6256	0.3916
0.1225	14.0	14000	0.6376	0.3971
0.1083	14.5	14500	0.6493	0.3905
0.1134	15.0	15000	0.6686	0.3951
0.1003	15.5	15500	0.6983	0.3967
0.104	16.0	16000	0.6324	0.3927
0.0928	16.5	16500	0.6482	0.3907
0.0944	17.0	17000	0.6790	0.3912
0.0925	17.5	17500	0.6877	0.3902
0.0847	18.0	18000	0.6572	0.3845
0.0808	18.5	18500	0.6551	0.3910
0.0836	19.0	19000	0.6832	0.3859
0.0757	19.5	19500	0.7594	0.3905
0.0751	20.0	20000	0.6960	0.3880
0.0715	20.5	20500	0.7244	0.3840
0.07	21.0	21000	0.7233	0.3848
0.0654	21.5	21500	0.7428	0.3833
0.0657	22.0	22000	0.7014	0.3842
0.0641	22.5	22500	0.7357	0.3796
0.0624	23.0	23000	0.7338	0.3796
0.0575	23.5	23500	0.7375	0.3804
0.0578	24.0	24000	0.7386	0.3782
0.0542	24.5	24500	0.7405	0.3758
0.0509	25.0	25000	0.7719	0.3774
0.0495	25.5	25500	0.7505	0.3763
0.0521	26.0	26000	0.7345	0.3742
0.0477	26.5	26500	0.7776	0.3740
0.0442	27.0	27000	0.7742	0.3738
0.0473	27.5	27500	0.7695	0.3719
0.0452	28.0	28000	0.7737	0.3705
0.0425	28.5	28500	0.7937	0.3702
0.0415	29.0	29000	0.7970	0.3713
0.0432	29.5	29500	0.7714	0.3700
0.041	30.0	30000	0.7711	0.3693

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0