wav2vec2_base_vietnamese_control_dataset

This model is a fine-tuned version of nguyenvulebinh/wav2vec2-base-vi on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0491
• Wer: 0.2007

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
19.8101	2.05	500	17.9020	1.0
12.7254	4.1	1000	13.4585	1.0
8.6927	6.15	1500	8.4217	1.0
5.6269	8.2	2000	5.1617	1.0
3.9616	10.25	2500	3.6167	1.0
3.2881	12.3	3000	3.2056	1.0
3.1381	14.34	3500	3.1174	1.0
3.092	16.39	4000	3.1044	1.0
3.0222	18.44	4500	2.9461	1.0
2.8394	20.49	5000	2.7133	1.0
2.6096	22.54	5500	2.3871	1.0
2.3244	24.59	6000	2.0311	1.0
1.9785	26.64	6500	1.6117	1.0
1.6346	28.69	7000	1.2173	1.0
1.3122	30.74	7500	0.9547	1.0
1.0927	32.79	8000	0.7738	1.0
0.9261	34.84	8500	0.6172	0.8970
0.7336	36.89	9000	0.4357	0.3654
0.5754	38.93	9500	0.3304	0.3071
0.4791	40.98	10000	0.2668	0.2785
0.4212	43.03	10500	0.2240	0.2548
0.3439	45.08	11000	0.1852	0.2329
0.3048	47.13	11500	0.1607	0.2119
0.2684	49.18	12000	0.1376	0.2105
0.2298	51.23	12500	0.1227	0.2071
0.2192	53.28	13000	0.1092	0.2055
0.2063	55.33	13500	0.0990	0.2039
0.1875	57.38	14000	0.0895	0.2039
0.1692	59.43	14500	0.0822	0.2039
0.1421	61.48	15000	0.0766	0.2029
0.1505	63.52	15500	0.0710	0.2031
0.1796	65.57	16000	0.0682	0.2019
0.1265	67.62	16500	0.0641	0.2015
0.1172	69.67	17000	0.0617	0.2019
0.1173	71.72	17500	0.0586	0.2011
0.1226	73.77	18000	0.0568	0.2015
0.1165	75.82	18500	0.0567	0.2011
0.1098	77.87	19000	0.0547	0.2007
0.0996	79.92	19500	0.0537	0.2009
0.1024	81.97	20000	0.0521	0.2009
0.0992	84.02	20500	0.0508	0.2009
0.1008	86.07	21000	0.0510	0.2009
0.1147	88.11	21500	0.0501	0.2007
0.1138	90.16	22000	0.0500	0.2005
0.0939	92.21	22500	0.0493	0.2007
0.1021	94.26	23000	0.0492	0.2005
0.1009	96.31	23500	0.0488	0.2009
0.0935	98.36	24000	0.0491	0.2007

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3