wavlm-large-timit-punctuation

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

• Loss: 0.3368
• Wer: 0.2601

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
5.2379	1.0	500	3.1228	1.0
2.5847	2.01	1000	1.1550	0.9147
1.0034	3.01	1500	0.5856	0.5180
0.5868	4.02	2000	0.4238	0.4229
0.3892	5.02	2500	0.3356	0.3665
0.2926	6.02	3000	0.3196	0.3360
0.2294	7.03	3500	0.3046	0.3170
0.1976	8.03	4000	0.3032	0.3111
0.1644	9.04	4500	0.2946	0.2954
0.1574	10.04	5000	0.3211	0.2998
0.1391	11.04	5500	0.2986	0.2922
0.1124	12.05	6000	0.2948	0.2837
0.1003	13.05	6500	0.2928	0.2788
0.1031	14.06	7000	0.3230	0.2805
0.0901	15.06	7500	0.3081	0.2749
0.0842	16.06	8000	0.3075	0.2726
0.0809	17.07	8500	0.3215	0.2717
0.0747	18.07	9000	0.3272	0.2721
0.0735	19.08	9500	0.3242	0.2684
0.0631	20.08	10000	0.3216	0.2640
0.0632	21.08	10500	0.3149	0.2646
0.0625	22.09	11000	0.3196	0.2630
0.0611	23.09	11500	0.3244	0.2638
0.0532	24.1	12000	0.3271	0.2641
0.0503	25.1	12500	0.3368	0.2636
0.0534	26.1	13000	0.3393	0.2627
0.049	27.11	13500	0.3389	0.2626
0.0441	28.11	14000	0.3375	0.2605
0.0522	29.12	14500	0.3368	0.2601

Framework versions

• Transformers 4.19.2
• Pytorch 1.8.2+cu111
• Datasets 1.17.0
• Tokenizers 0.11.6