wav2vec2-xls-r-300m-gn-cv8-4

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: 1.5805
• Wer: 0.7545

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: 13000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
9.2216	16.65	300	3.2771	1.0
3.1804	33.32	600	2.2869	1.0
1.5856	49.97	900	0.9573	0.8772
1.0299	66.65	1200	0.9044	0.8082
0.8916	83.32	1500	0.9478	0.8056
0.8451	99.97	1800	0.8814	0.8107
0.7649	116.65	2100	0.9897	0.7826
0.7185	133.32	2400	0.9988	0.7621
0.6595	149.97	2700	1.0607	0.7749
0.6211	166.65	3000	1.1826	0.7877
0.59	183.32	3300	1.1060	0.7826
0.5383	199.97	3600	1.1826	0.7852
0.5205	216.65	3900	1.2148	0.8261
0.4786	233.32	4200	1.2710	0.7928
0.4482	249.97	4500	1.1943	0.7980
0.4149	266.65	4800	1.2449	0.8031
0.3904	283.32	5100	1.3100	0.7928
0.3619	299.97	5400	1.3125	0.7596
0.3496	316.65	5700	1.3699	0.7877
0.3277	333.32	6000	1.4344	0.8031
0.2958	349.97	6300	1.4093	0.7980
0.2883	366.65	6600	1.3296	0.7570
0.2598	383.32	6900	1.4026	0.7980
0.2564	399.97	7200	1.4847	0.8031
0.2408	416.65	7500	1.4896	0.8107
0.2266	433.32	7800	1.4232	0.7698
0.224	449.97	8100	1.5560	0.7903
0.2038	466.65	8400	1.5355	0.7724
0.1948	483.32	8700	1.4624	0.7621
0.1995	499.97	9000	1.5808	0.7724
0.1864	516.65	9300	1.5653	0.7698
0.18	533.32	9600	1.4868	0.7494
0.1689	549.97	9900	1.5379	0.7749
0.1624	566.65	10200	1.5936	0.7749
0.1537	583.32	10500	1.6436	0.7801
0.1455	599.97	10800	1.6401	0.7673
0.1437	616.65	11100	1.6069	0.7673
0.1452	633.32	11400	1.6041	0.7519
0.139	649.97	11700	1.5758	0.7545
0.1299	666.65	12000	1.5559	0.7545
0.127	683.32	12300	1.5776	0.7596
0.1264	699.97	12600	1.5790	0.7519
0.1209	716.65	12900	1.5805	0.7545

Framework versions

• Transformers 4.16.1
• Pytorch 1.10.0+cu111
• Datasets 1.18.2
• Tokenizers 0.11.0