HarrisDePerceptron/xls-r-300m-ur-cv7

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It achieves the following results on the evaluation set:

• Loss: 1.2924
• Wer: 0.7201

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: 7.5e-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: 100
• num_epochs: 200.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
11.2783	4.17	100	4.6409	1.0
3.5578	8.33	200	3.1649	1.0
3.1279	12.5	300	3.0335	1.0
2.9944	16.67	400	2.9526	0.9983
2.9275	20.83	500	2.9291	1.0009
2.8077	25.0	600	2.5633	0.9895
2.4438	29.17	700	1.9045	0.9564
1.9659	33.33	800	1.4114	0.7960
1.7092	37.5	900	1.2584	0.7637
1.517	41.67	1000	1.2040	0.7507
1.3966	45.83	1100	1.1273	0.7463
1.3197	50.0	1200	1.1054	0.6957
1.2476	54.17	1300	1.1035	0.7001
1.1796	58.33	1400	1.0890	0.7097
1.1237	62.5	1500	1.0883	0.7167
1.0777	66.67	1600	1.1067	0.7219
1.0051	70.83	1700	1.1115	0.7236
0.9521	75.0	1800	1.0867	0.7132
0.9147	79.17	1900	1.0852	0.7210
0.8798	83.33	2000	1.1411	0.7097
0.8317	87.5	2100	1.1634	0.7018
0.7946	91.67	2200	1.1621	0.7201
0.7594	95.83	2300	1.1482	0.7036
0.729	100.0	2400	1.1493	0.7062
0.7055	104.17	2500	1.1726	0.6931
0.6622	108.33	2600	1.1938	0.7001
0.6583	112.5	2700	1.1832	0.7149
0.6299	116.67	2800	1.1996	0.7175
0.5903	120.83	2900	1.1986	0.7132
0.5816	125.0	3000	1.1909	0.7010
0.5583	129.17	3100	1.2079	0.6870
0.5392	133.33	3200	1.2109	0.7228
0.5412	137.5	3300	1.2353	0.7245
0.5136	141.67	3400	1.2390	0.7254
0.5007	145.83	3500	1.2273	0.7123
0.4883	150.0	3600	1.2773	0.7289
0.4835	154.17	3700	1.2678	0.7289
0.4568	158.33	3800	1.2592	0.7350
0.4525	162.5	3900	1.2705	0.7254
0.4379	166.67	4000	1.2717	0.7306
0.4198	170.83	4100	1.2618	0.7219
0.4216	175.0	4200	1.2909	0.7158
0.4305	179.17	4300	1.2808	0.7167
0.399	183.33	4400	1.2750	0.7193
0.3937	187.5	4500	1.2719	0.7149
0.3905	191.67	4600	1.2816	0.7158
0.3892	195.83	4700	1.2951	0.7210
0.3932	200.0	4800	1.2924	0.7201

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0