wav2vec2-xls-r-300m-gn-cv8

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

• Loss: 0.9392
• Wer: 0.7033

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
• 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
• training_steps: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
20.0601	5.54	100	5.1622	1.0
3.7052	11.11	200	3.2869	1.0
3.3275	16.65	300	3.2162	1.0
3.2984	22.22	400	3.1638	1.0
3.1111	27.76	500	2.5541	1.0
2.238	33.32	600	1.2198	0.9616
1.5284	38.86	700	0.9571	0.8593
1.2735	44.43	800	0.8719	0.8363
1.1269	49.97	900	0.8334	0.7954
1.0427	55.54	1000	0.7700	0.7749
1.0152	61.11	1100	0.7747	0.7877
0.943	66.65	1200	0.7151	0.7442
0.9132	72.22	1300	0.7224	0.7289
0.8397	77.76	1400	0.7354	0.7059
0.8577	83.32	1500	0.7285	0.7263
0.7931	88.86	1600	0.7863	0.7084
0.7995	94.43	1700	0.7562	0.6880
0.799	99.97	1800	0.7905	0.7059
0.7373	105.54	1900	0.7791	0.7161
0.749	111.11	2000	0.8125	0.7161
0.6925	116.65	2100	0.7722	0.6905
0.7034	122.22	2200	0.8989	0.7136
0.6745	127.76	2300	0.8270	0.6982
0.6837	133.32	2400	0.8569	0.7161
0.6689	138.86	2500	0.8339	0.6982
0.6471	144.43	2600	0.8441	0.7110
0.615	149.97	2700	0.9038	0.7212
0.6477	155.54	2800	0.9089	0.7059
0.6047	161.11	2900	0.9149	0.7059
0.5613	166.65	3000	0.8582	0.7263
0.6017	172.22	3100	0.8787	0.7084
0.5546	177.76	3200	0.8753	0.6957
0.5747	183.32	3300	0.9167	0.7212
0.5535	188.86	3400	0.8448	0.6905
0.5331	194.43	3500	0.8644	0.7161
0.5428	199.97	3600	0.8730	0.7033
0.5219	205.54	3700	0.9047	0.6982
0.5158	211.11	3800	0.8706	0.7033
0.5107	216.65	3900	0.9139	0.7084
0.4903	222.22	4000	0.9456	0.7315
0.4772	227.76	4100	0.9475	0.7161
0.4713	233.32	4200	0.9237	0.7059
0.4743	238.86	4300	0.9305	0.6957
0.4705	244.43	4400	0.9561	0.7110
0.4908	249.97	4500	0.9389	0.7084
0.4717	255.54	4600	0.9234	0.6982
0.4462	261.11	4700	0.9323	0.6957
0.4556	266.65	4800	0.9432	0.7033
0.4691	272.22	4900	0.9389	0.7059
0.4601	277.76	5000	0.9392	0.7033

Framework versions

• Transformers 4.16.0
• Pytorch 1.10.0+cu111
• Datasets 1.18.1
• Tokenizers 0.11.0