Korean_STT_v0

yoon1000/Korean_STT_v0

Korean_STT_v0 is a automatic speech recognition model from yoon1000. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Feb 13, 2024
Updated: Feb 15, 2024
SHA: f5824b2b

Base models

facebook/wav2vec2-xls-r-300m

Model card

From the Hugging Face model README

ft_0213_korean

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6093
Cer: 0.0958

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: 4
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: 500
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
24.3697	0.17	500	5.0804	1.0
4.8016	0.34	1000	5.1173	1.0
4.6791	0.51	1500	4.7037	1.0000
4.562	0.68	2000	4.6273	0.9779
4.4539	0.84	2500	4.2212	0.9370
3.5358	1.01	3000	2.7001	0.5326
2.6771	1.18	3500	2.1532	0.4519
2.2226	1.35	4000	1.7409	0.3787
1.9143	1.52	4500	1.4978	0.3372
1.6892	1.69	5000	1.3429	0.3112
1.5503	1.86	5500	1.1997	0.2812
1.4184	2.03	6000	1.1011	0.2624
1.2758	2.19	6500	1.0286	0.2551
1.2045	2.36	7000	0.9572	0.2373
1.1666	2.53	7500	0.9170	0.2251
1.1007	2.7	8000	0.8521	0.2142
1.0391	2.87	8500	0.8260	0.2140
0.9761	3.04	9000	0.8005	0.2071
0.9166	3.21	9500	0.7572	0.1941
0.864	3.38	10000	0.7375	0.1935
0.8579	3.54	10500	0.7404	0.1933
0.8442	3.71	11000	0.7080	0.1799
0.8114	3.88	11500	0.6816	0.1766
0.7863	4.05	12000	0.6921	0.1753
0.7454	4.22	12500	0.6831	0.1759
0.7077	4.39	13000	0.6610	0.1689
0.6974	4.56	13500	0.6864	0.1687
0.7001	4.73	14000	0.6450	0.1641
0.6636	4.9	14500	0.6303	0.1585
0.6423	5.06	15000	0.6465	0.1597
0.5828	5.23	15500	0.6224	0.1550
0.6085	5.4	16000	0.6154	0.1534
0.5877	5.57	16500	0.6112	0.1510
0.586	5.74	17000	0.6022	0.1485
0.5656	5.91	17500	0.6022	0.1491
0.5366	6.08	18000	0.5894	0.1468
0.5134	6.25	18500	0.5779	0.1435
0.5217	6.41	19000	0.5960	0.1449
0.5049	6.58	19500	0.5813	0.1408
0.4961	6.75	20000	0.5582	0.1382
0.5089	6.92	20500	0.5898	0.1385
0.4769	7.09	21000	0.5739	0.1361
0.4552	7.26	21500	0.5700	0.1369
0.4552	7.43	22000	0.5956	0.1367
0.4476	7.6	22500	0.5885	0.1342
0.4449	7.77	23000	0.5501	0.1314
0.4333	7.93	23500	0.5474	0.1302
0.3946	8.1	24000	0.6018	0.1327
0.3993	8.27	24500	0.5680	0.1295
0.3892	8.44	25000	0.5575	0.1309
0.3936	8.61	25500	0.5666	0.1288
0.3957	8.78	26000	0.5546	0.1262
0.4006	8.95	26500	0.5702	0.1264
0.3456	9.12	27000	0.5614	0.1247
0.3459	9.28	27500	0.5608	0.1242
0.3511	9.45	28000	0.5527	0.1236
0.3504	9.62	28500	0.5479	0.1201
0.3529	9.79	29000	0.5525	0.1200
0.3397	9.96	29500	0.5451	0.1201
0.314	10.13	30000	0.5549	0.1184
0.3048	10.3	30500	0.5616	0.1180
0.3021	10.47	31000	0.5634	0.1184
0.3136	10.63	31500	0.5753	0.1166
0.3116	10.8	32000	0.5410	0.1149
0.3098	10.97	32500	0.5354	0.1143
0.2852	11.14	33000	0.5482	0.1144
0.2807	11.31	33500	0.5465	0.1126
0.2771	11.48	34000	0.5452	0.1147
0.2865	11.65	34500	0.5538	0.1128
0.2783	11.82	35000	0.5374	0.1118
0.2775	11.99	35500	0.5418	0.1121
0.2649	12.15	36000	0.5468	0.1104
0.2558	12.32	36500	0.5498	0.1108
0.2632	12.49	37000	0.5699	0.1118
0.2488	12.66	37500	0.5523	0.1088
0.2552	12.83	38000	0.5532	0.1090
0.2577	13.0	38500	0.5480	0.1078
0.2334	13.17	39000	0.5716	0.1078
0.2387	13.34	39500	0.5740	0.1080
0.2364	13.5	40000	0.5587	0.1066
0.2253	13.67	40500	0.5544	0.1071
0.2536	13.84	41000	0.5680	0.1055
0.2254	14.01	41500	0.5605	0.1058
0.2207	14.18	42000	0.5776	0.1049
0.2127	14.35	42500	0.5762	0.1046
0.2121	14.52	43000	0.5637	0.1043
0.2048	14.69	43500	0.5647	0.1048
0.2085	14.85	44000	0.5658	0.1032
0.2031	15.02	44500	0.5789	0.1026
0.1923	15.19	45000	0.5627	0.1011
0.1956	15.36	45500	0.5698	0.1016
0.1989	15.53	46000	0.5950	0.1016
0.1996	15.7	46500	0.5833	0.1003
0.1895	15.87	47000	0.5872	0.1003
0.1893	16.04	47500	0.5861	0.1001
0.1837	16.21	48000	0.5947	0.0998
0.1875	16.37	48500	0.5898	0.0994
0.1773	16.54	49000	0.5885	0.1001
0.1834	16.71	49500	0.5964	0.0995
0.1787	16.88	50000	0.5935	0.0994
0.1719	17.05	50500	0.5990	0.0987
0.1697	17.22	51000	0.5917	0.0987
0.1736	17.39	51500	0.5988	0.0988
0.1695	17.56	52000	0.5988	0.0978
0.1663	17.72	52500	0.6062	0.0979
0.1621	17.89	53000	0.5993	0.0976
0.1653	18.06	53500	0.6049	0.0973
0.1639	18.23	54000	0.6169	0.0976
0.1574	18.4	54500	0.6063	0.0973
0.1557	18.57	55000	0.5953	0.0959
0.1608	18.74	55500	0.5943	0.0963
0.1621	18.91	56000	0.5966	0.0961
0.1534	19.07	56500	0.6086	0.0961
0.1441	19.24	57000	0.6128	0.0962
0.169	19.41	57500	0.6053	0.0957
0.1516	19.58	58000	0.6066	0.0960
0.1474	19.75	58500	0.6080	0.0958
0.1478	19.92	59000	0.6093	0.0958

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Feb 13, 2024
Updated: Feb 15, 2024