wav2vec2-large-xls-r-300m-ar

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: 0.4819
• Wer: 0.4244

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: 3e-05
• train_batch_size: 32
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 400
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
11.0435	0.67	400	4.3104	1.0
3.4451	1.34	800	3.1566	1.0
3.1399	2.01	1200	3.0532	0.9990
2.8538	2.68	1600	1.6994	0.9238
1.7195	3.35	2000	0.8867	0.6727
1.326	4.02	2400	0.6603	0.5834
1.1561	4.69	2800	0.5809	0.5479
1.0764	5.36	3200	0.5943	0.5495
1.0144	6.03	3600	0.5344	0.5251
0.965	6.7	4000	0.4844	0.4936
0.927	7.37	4400	0.5048	0.5019
0.8985	8.04	4800	0.5809	0.5267
0.8684	8.71	5200	0.4740	0.4753
0.8581	9.38	5600	0.4813	0.4834
0.8334	10.05	6000	0.4515	0.4545
0.8134	10.72	6400	0.4370	0.4543
0.8002	11.39	6800	0.4225	0.4384
0.7884	12.06	7200	0.4593	0.4565
0.7675	12.73	7600	0.4752	0.4680
0.7607	13.4	8000	0.4950	0.4771
0.7475	14.07	8400	0.4373	0.4391
0.7397	14.74	8800	0.4506	0.4541
0.7289	15.41	9200	0.4840	0.4691
0.722	16.08	9600	0.4701	0.4571
0.7067	16.75	10000	0.4561	0.4461
0.7033	17.42	10400	0.4384	0.4347
0.6915	18.09	10800	0.4424	0.4290
0.6854	18.76	11200	0.4635	0.4360
0.6813	19.43	11600	0.4280	0.4147
0.6776	20.1	12000	0.4610	0.4344
0.67	20.77	12400	0.4540	0.4367
0.6653	21.44	12800	0.4509	0.4234
0.6609	22.11	13200	0.4874	0.4444
0.6541	22.78	13600	0.4542	0.4230
0.6528	23.45	14000	0.4732	0.4373
0.6463	24.12	14400	0.4483	0.4188
0.6399	24.79	14800	0.4731	0.4341
0.6353	25.46	15200	0.5031	0.4412
0.6358	26.13	15600	0.4986	0.4397
0.6317	26.8	16000	0.5000	0.4360
0.6262	27.47	16400	0.4958	0.4318
0.6317	28.14	16800	0.4738	0.4234
0.6205	28.81	17200	0.4853	0.4262
0.6205	29.48	17600	0.4819	0.4244

Framework versions

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