wav2vec2-xlsr-1B-NPSC-NN

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4562
• Wer: 0.1531

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: 6e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.6894	1.08	500	1.2423	0.8619
0.7543	2.15	1000	0.5956	0.3817
0.5481	3.23	1500	0.5043	0.3246
0.4661	4.3	2000	0.4813	0.2793
0.3901	5.38	2500	0.4371	0.2592
0.3512	6.45	3000	0.4216	0.2458
0.3016	7.53	3500	0.3814	0.2257
0.278	8.6	4000	0.4151	0.2145
0.2435	9.68	4500	0.4816	0.2130
0.2122	10.75	5000	0.4489	0.2137
0.1949	11.83	5500	0.3978	0.2063
0.1929	12.9	6000	0.3823	0.2026
0.1757	13.98	6500	0.3409	0.1965
0.1771	15.05	7000	0.3844	0.1936
0.1452	16.13	7500	0.3749	0.1900
0.1341	17.2	8000	0.4407	0.2026
0.13	18.28	8500	0.4253	0.1883
0.1183	19.35	9000	0.4311	0.1880
0.118	20.43	9500	0.4431	0.1882
0.1123	21.51	10000	0.4753	0.1820
0.1037	22.58	10500	0.4087	0.1834
0.1066	23.66	11000	0.4151	0.1845
0.0977	24.73	11500	0.4367	0.1783
0.0968	25.81	12000	0.4237	0.1756
0.0835	26.88	12500	0.4729	0.1781
0.0919	27.96	13000	0.4153	0.1701
0.0677	29.03	13500	0.4317	0.1693
0.0726	30.11	14000	0.4380	0.1736
0.066	31.18	14500	0.4384	0.1681
0.0713	32.26	15000	0.4215	0.1629
0.0605	33.33	15500	0.4574	0.1714
0.0632	34.41	16000	0.4343	0.1642
0.0567	35.48	16500	0.4231	0.1601
0.0556	36.56	17000	0.4404	0.1667
0.0426	37.63	17500	0.4459	0.1625
0.0445	38.71	18000	0.4484	0.1629
0.0463	39.78	18500	0.4508	0.1596
0.0448	40.86	19000	0.4395	0.1605
0.0434	41.94	19500	0.4490	0.1607
0.0347	43.01	20000	0.4772	0.1582
0.0332	44.09	20500	0.4729	0.1582
0.037	45.16	21000	0.4559	0.1573
0.0328	46.24	21500	0.4664	0.1560
0.0366	47.31	22000	0.4543	0.1543
0.0377	48.39	22500	0.4507	0.1560
0.0331	49.46	23000	0.4567	0.1533

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0