wav2vec2-large-multilang-cv-ru

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 0.9734
• Wer: 0.7037

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: 0.0005
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.0328	0.79	500	3.0713	1.0
1.9426	1.58	1000	1.2048	0.9963
1.1285	2.37	1500	0.9825	0.9282
0.9462	3.15	2000	0.8836	0.8965
0.8274	3.94	2500	0.8134	0.8661
0.7106	4.73	3000	0.8033	0.8387
0.6545	5.52	3500	0.8309	0.8366
0.6013	6.31	4000	0.7667	0.8240
0.5599	7.1	4500	0.7740	0.8160
0.5027	7.89	5000	0.7796	0.8188
0.4588	8.68	5500	0.8204	0.7968
0.4448	9.46	6000	0.8277	0.7738
0.4122	10.25	6500	0.8292	0.7776
0.3816	11.04	7000	0.8548	0.7907
0.3587	11.83	7500	0.8245	0.7805
0.3374	12.62	8000	0.8371	0.7701
0.3214	13.41	8500	0.8311	0.7822
0.3072	14.2	9000	0.8940	0.7674
0.2929	14.98	9500	0.8788	0.7604
0.257	15.77	10000	0.8911	0.7633
0.2592	16.56	10500	0.8673	0.7604
0.2392	17.35	11000	0.9582	0.7810
0.232	18.14	11500	0.9340	0.7423
0.2252	18.93	12000	0.8874	0.7320
0.2079	19.72	12500	0.9436	0.7483
0.2003	20.5	13000	0.9573	0.7638
0.194	21.29	13500	0.9361	0.7308
0.188	22.08	14000	0.9704	0.7221
0.1754	22.87	14500	0.9668	0.7265
0.1688	23.66	15000	0.9680	0.7246
0.162	24.45	15500	0.9443	0.7066
0.1617	25.24	16000	0.9664	0.7265
0.1504	26.03	16500	0.9505	0.7189
0.1425	26.81	17000	0.9536	0.7112
0.134	27.6	17500	0.9674	0.7047
0.1301	28.39	18000	0.9852	0.7066
0.1314	29.18	18500	0.9766	0.7073
0.1219	29.97	19000	0.9734	0.7037

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0
• Datasets 2.2.2
• Tokenizers 0.12.1