wav2vec2-large-xls-r-300m-gn-pt

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6822
• Wer: 0.5432

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: 0.0003
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 35
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.1972	0.79	400	1.9288	1.0045
0.9928	1.58	800	0.8247	0.9452
0.6075	2.36	1200	0.7675	0.8451
0.4724	3.15	1600	0.5485	0.7111
0.3879	3.94	2000	0.5885	0.7433
0.3152	4.73	2400	0.7606	0.7695
0.2872	5.52	2800	0.5723	0.6608
0.258	6.31	3200	0.5971	0.6820
0.2317	7.09	3600	0.5845	0.6471
0.2137	7.88	4000	0.7690	0.7198
0.193	8.67	4400	0.6219	0.6614
0.1795	9.46	4800	0.6203	0.6703
0.1768	10.25	5200	0.5645	0.6164
0.1637	11.03	5600	0.5804	0.6412
0.1573	11.82	6000	0.5914	0.5896
0.1467	12.61	6400	0.6517	0.6200
0.141	13.4	6800	0.6376	0.6310
0.135	14.19	7200	0.6343	0.6042
0.1279	14.98	7600	0.6680	0.6325
0.1222	15.76	8000	0.7109	0.6617
0.1169	16.55	8400	0.7067	0.6361
0.114	17.34	8800	0.7143	0.6144
0.1085	18.13	9200	0.6871	0.6081
0.0996	18.92	9600	0.8332	0.6569
0.0952	19.7	10000	0.7076	0.5992
0.0929	20.49	10400	0.6946	0.6078
0.0871	21.28	10800	0.6197	0.5822
0.0823	22.07	11200	0.6969	0.5876
0.0776	22.86	11600	0.6285	0.5619
0.0758	23.65	12000	0.7098	0.6010
0.0728	24.43	12400	0.6618	0.5905
0.0664	25.22	12800	0.6484	0.5536
0.0656	26.01	13200	0.6417	0.5593
0.0603	26.8	13600	0.7287	0.5813
0.0571	27.59	14000	0.6727	0.5700
0.0559	28.37	14400	0.6775	0.5631
0.0555	29.16	14800	0.7849	0.5968
0.0506	29.95	15200	0.8266	0.6185
0.0485	30.74	15600	0.7347	0.5747
0.0461	31.53	16000	0.6836	0.5432
0.0423	32.32	16400	0.6913	0.5396
0.0407	33.1	16800	0.6655	0.5328
0.04	33.89	17200	0.6873	0.5399
0.0396	34.68	17600	0.6822	0.5432

Framework versions

• Transformers 4.35.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1