anantoj/wav2vec2-xls-r-1b-korean

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set:

• Loss: 0.0639
• Wer: 0.0449

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: 7.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.603	0.72	500	4.6572	0.9985
2.6314	1.44	1000	2.0424	0.9256
2.2708	2.16	1500	0.9889	0.6989
2.1769	2.88	2000	0.8366	0.6312
2.1142	3.6	2500	0.7555	0.5998
2.0084	4.32	3000	0.7144	0.6003
1.9272	5.04	3500	0.6311	0.5461
1.8687	5.75	4000	0.6252	0.5430
1.8186	6.47	4500	0.5491	0.4988
1.7364	7.19	5000	0.5463	0.4959
1.6809	7.91	5500	0.4724	0.4484
1.641	8.63	6000	0.4679	0.4461
1.572	9.35	6500	0.4387	0.4236
1.5256	10.07	7000	0.3970	0.4003
1.5044	10.79	7500	0.3690	0.3893
1.4563	11.51	8000	0.3752	0.3875
1.394	12.23	8500	0.3386	0.3567
1.3641	12.95	9000	0.3290	0.3467
1.2878	13.67	9500	0.2893	0.3135
1.2602	14.39	10000	0.2723	0.3029
1.2302	15.11	10500	0.2603	0.2989
1.1865	15.83	11000	0.2440	0.2794
1.1491	16.55	11500	0.2500	0.2788
1.093	17.27	12000	0.2279	0.2629
1.0367	17.98	12500	0.2076	0.2443
0.9954	18.7	13000	0.1844	0.2259
0.99	19.42	13500	0.1794	0.2179
0.9385	20.14	14000	0.1765	0.2122
0.8952	20.86	14500	0.1706	0.1974
0.8841	21.58	15000	0.1791	0.1969
0.847	22.3	15500	0.1780	0.2060
0.8669	23.02	16000	0.1608	0.1862
0.8066	23.74	16500	0.1447	0.1626
0.7908	24.46	17000	0.1457	0.1655
0.7459	25.18	17500	0.1350	0.1445
0.7218	25.9	18000	0.1276	0.1421
0.703	26.62	18500	0.1177	0.1302
0.685	27.34	19000	0.1147	0.1305
0.6811	28.06	19500	0.1128	0.1244
0.6444	28.78	20000	0.1120	0.1213
0.6323	29.5	20500	0.1137	0.1166
0.5998	30.22	21000	0.1051	0.1107
0.5706	30.93	21500	0.1035	0.1037
0.5555	31.65	22000	0.1031	0.0927
0.5389	32.37	22500	0.0997	0.0900
0.5201	33.09	23000	0.0920	0.0912
0.5146	33.81	23500	0.0929	0.0947
0.515	34.53	24000	0.1000	0.0953
0.4743	35.25	24500	0.0922	0.0892
0.4707	35.97	25000	0.0852	0.0808
0.4456	36.69	25500	0.0855	0.0779
0.443	37.41	26000	0.0843	0.0738
0.4388	38.13	26500	0.0816	0.0699
0.4162	38.85	27000	0.0752	0.0645
0.3979	39.57	27500	0.0761	0.0621
0.3889	40.29	28000	0.0771	0.0625
0.3923	41.01	28500	0.0755	0.0598
0.3693	41.73	29000	0.0730	0.0578
0.3642	42.45	29500	0.0739	0.0598
0.3532	43.17	30000	0.0712	0.0553
0.3513	43.88	30500	0.0762	0.0516
0.3349	44.6	31000	0.0731	0.0504
0.3305	45.32	31500	0.0725	0.0507
0.3285	46.04	32000	0.0709	0.0489
0.3179	46.76	32500	0.0667	0.0467
0.3158	47.48	33000	0.0653	0.0494
0.3033	48.2	33500	0.0638	0.0456
0.3023	48.92	34000	0.0644	0.0464
0.2975	49.64	34500	0.0643	0.0455

Framework versions

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