wav2vec2-xls-r-300m-gn-cv8-3

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: 0.9517
• Wer: 0.8542

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
19.9125	5.54	100	5.4279	1.0
3.8031	11.11	200	3.3070	1.0
3.3783	16.65	300	3.2450	1.0
3.3472	22.22	400	3.2424	1.0
3.2714	27.76	500	3.1100	1.0
3.2367	33.32	600	3.1091	1.0
3.1968	38.86	700	3.1013	1.0
3.2004	44.43	800	3.1173	1.0
3.1656	49.97	900	3.0682	1.0
3.1563	55.54	1000	3.0457	1.0
3.1356	61.11	1100	3.0139	1.0
3.086	66.65	1200	2.8108	1.0
2.954	72.22	1300	2.3238	1.0
2.6125	77.76	1400	1.6461	1.0
2.3296	83.32	1500	1.2834	0.9744
2.1345	88.86	1600	1.1091	0.9693
2.0346	94.43	1700	1.0273	0.9233
1.9611	99.97	1800	0.9642	0.9182
1.9066	105.54	1900	0.9590	0.9105
1.8178	111.11	2000	0.9679	0.9028
1.7799	116.65	2100	0.9007	0.8619
1.7726	122.22	2200	0.9689	0.8951
1.7389	127.76	2300	0.8876	0.8593
1.7151	133.32	2400	0.8716	0.8542
1.6842	138.86	2500	0.9536	0.8772
1.6449	144.43	2600	0.9296	0.8542
1.5978	149.97	2700	0.8895	0.8440
1.6515	155.54	2800	0.9162	0.8568
1.6586	161.11	2900	0.9039	0.8568
1.5966	166.65	3000	0.8627	0.8542
1.5695	172.22	3100	0.9549	0.8824
1.5699	177.76	3200	0.9332	0.8517
1.5297	183.32	3300	0.9163	0.8338
1.5367	188.86	3400	0.8822	0.8312
1.5586	194.43	3500	0.9217	0.8363
1.5429	199.97	3600	0.9564	0.8568
1.5273	205.54	3700	0.9508	0.8542
1.5043	211.11	3800	0.9374	0.8542
1.4724	216.65	3900	0.9622	0.8619
1.4794	222.22	4000	0.9550	0.8363
1.4843	227.76	4100	0.9577	0.8465
1.4781	233.32	4200	0.9543	0.8440
1.4507	238.86	4300	0.9553	0.8491
1.4997	244.43	4400	0.9728	0.8491
1.4371	249.97	4500	0.9543	0.8670
1.4825	255.54	4600	0.9636	0.8619
1.4187	261.11	4700	0.9609	0.8440
1.4363	266.65	4800	0.9567	0.8593
1.4463	272.22	4900	0.9581	0.8542
1.4117	277.76	5000	0.9517	0.8542

Framework versions

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