ft_0112_korean

molto/ft_0112_korean

ft_0112_korean is a automatic speech recognition model from molto. 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: Jan 12, 2024
Updated: Jan 15, 2024
SHA: 81966de9

Base models

facebook/wav2vec2-xls-r-300m

Model card

From the Hugging Face model README

ft_0112_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: 0.6163
Cer: 0.1655

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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
66.0473	0.03	100	126.2500	1.0
39.2751	0.05	200	76.4439	1.0
24.2617	0.07	300	36.6274	1.0
10.2253	0.1	400	7.8025	1.0
4.9219	0.12	500	5.8257	1.0
4.7709	0.15	600	5.2597	1.0
4.7545	0.17	700	5.3516	1.0
4.701	0.2	800	5.2238	1.0
4.6753	0.23	900	5.1713	1.0
4.6339	0.25	1000	5.1546	1.0
4.6107	0.28	1100	5.0488	1.0
4.6086	0.3	1200	4.8149	1.0
4.5324	0.33	1300	4.7533	1.0
4.4797	0.35	1400	4.6892	1.0
4.4485	0.38	1500	4.5327	1.0
4.3794	0.4	1600	4.3797	0.9999
4.1549	0.42	1700	4.2075	0.9838
3.9647	0.45	1800	3.8729	0.9647
3.621	0.47	1900	3.3229	0.6854
3.3163	0.5	2000	2.9646	0.5646
3.0668	0.53	2100	2.7178	0.5608
2.8248	0.55	2200	2.4843	0.4937
2.7238	0.57	2300	2.3321	0.4736
2.614	0.6	2400	2.2513	0.4650
2.4994	0.62	2500	2.1655	0.4538
2.4431	0.65	2600	2.0785	0.4355
2.3307	0.68	2700	1.9603	0.4169
2.2495	0.7	2800	1.9026	0.4134
2.1647	0.72	2900	1.8152	0.4009
2.1075	0.75	3000	1.7521	0.3849
2.0577	0.78	3100	1.7004	0.3781
1.9935	0.8	3200	1.6226	0.3666
1.9391	0.82	3300	1.6097	0.3604
1.9295	0.85	3400	1.5416	0.3526
1.8759	0.88	3500	1.5227	0.3583
1.8316	0.9	3600	1.4791	0.3484
1.7531	0.93	3700	1.4472	0.3415
1.7413	0.95	3800	1.4178	0.3363
1.6609	0.97	3900	1.3587	0.3256
1.6986	1.0	4000	1.3396	0.3208
1.6189	1.02	4100	1.3253	0.3187
1.5853	1.05	4200	1.2929	0.3109
1.5153	1.07	4300	1.2691	0.3106
1.5259	1.1	4400	1.2500	0.3012
1.4916	1.12	4500	1.2151	0.2977
1.4113	1.15	4600	1.1796	0.2930
1.452	1.18	4700	1.1857	0.2928
1.3879	1.2	4800	1.1830	0.2915
1.4164	1.23	4900	1.1725	0.2920
1.4692	1.25	5000	1.1171	0.2794
1.346	1.27	5100	1.0858	0.2745
1.3964	1.3	5200	1.0644	0.2712
1.3359	1.32	5300	1.0585	0.2694
1.2769	1.35	5400	1.0290	0.2614
1.2741	1.38	5500	1.0356	0.2604
1.2257	1.4	5600	1.0167	0.2607
1.2416	1.43	5700	1.0074	0.2558
1.2376	1.45	5800	0.9889	0.2524
1.2048	1.48	5900	0.9649	0.2464
1.1335	1.5	6000	0.9580	0.2488
1.1946	1.52	6100	0.9503	0.2471
1.1926	1.55	6200	0.9467	0.2494
1.1451	1.57	6300	0.9202	0.2408
1.1426	1.6	6400	0.9018	0.2359
1.1569	1.62	6500	0.9216	0.2362
1.1093	1.65	6600	0.9433	0.2414
1.1258	1.68	6700	0.8986	0.2291
1.1024	1.7	6800	0.8838	0.2305
1.0567	1.73	6900	0.8916	0.2298
1.0928	1.75	7000	0.8855	0.2294
1.0526	1.77	7100	0.8592	0.2237
1.0236	1.8	7200	0.8433	0.2209
1.0454	1.82	7300	0.8382	0.2214
1.0252	1.85	7400	0.8252	0.2173
1.0404	1.88	7500	0.8190	0.2148
1.0326	1.9	7600	0.8067	0.2155
1.0008	1.93	7700	0.8081	0.2161
0.9814	1.95	7800	0.8061	0.2152
0.9664	1.98	7900	0.8147	0.2155
1.0032	2.0	8000	0.8232	0.2128
0.9274	2.02	8100	0.7951	0.2118
0.9115	2.05	8200	0.7857	0.2105
0.9339	2.08	8300	0.7722	0.2069
0.8553	2.1	8400	0.7603	0.2070
0.8671	2.12	8500	0.7927	0.2099
0.9067	2.15	8600	0.7511	0.2013
0.8507	2.17	8700	0.7763	0.2029
0.899	2.2	8800	0.7579	0.2026
0.8061	2.23	8900	0.7561	0.2014
0.8191	2.25	9000	0.7590	0.2024
0.8084	2.27	9100	0.7394	0.1972
0.8163	2.3	9200	0.7404	0.1941
0.8189	2.33	9300	0.7340	0.1955
0.8639	2.35	9400	0.7331	0.1950
0.8218	2.38	9500	0.7347	0.1959
0.8221	2.4	9600	0.7098	0.1922
0.7725	2.42	9700	0.7264	0.1923
0.7882	2.45	9800	0.7079	0.1875
0.7786	2.48	9900	0.7131	0.1913
0.7734	2.5	10000	0.7079	0.1912
0.7834	2.52	10100	0.6944	0.1896
0.78	2.55	10200	0.6980	0.1879
0.7602	2.58	10300	0.7076	0.1894
0.7415	2.6	10400	0.6946	0.1857
0.7791	2.62	10500	0.7025	0.1887
0.7357	2.65	10600	0.6949	0.1885
0.7102	2.67	10700	0.6978	0.1895
0.7395	2.7	10800	0.6893	0.1859
0.7301	2.73	10900	0.6847	0.1857
0.7492	2.75	11000	0.7063	0.1863
0.7372	2.77	11100	0.6917	0.1857
0.7474	2.8	11200	0.6843	0.1845
0.6727	2.83	11300	0.6628	0.1775
0.7342	2.85	11400	0.6729	0.1797
0.6599	2.88	11500	0.6631	0.1797
0.7209	2.9	11600	0.6658	0.1795
0.7222	2.92	11700	0.6741	0.1807
0.7124	2.95	11800	0.6722	0.1828
0.7304	2.98	11900	0.6606	0.1782
0.7234	3.0	12000	0.6499	0.1753
0.6857	3.02	12100	0.6547	0.1751
0.6238	3.05	12200	0.6615	0.1771
0.6495	3.08	12300	0.6499	0.1764
0.6219	3.1	12400	0.6558	0.1752
0.6684	3.12	12500	0.6479	0.1752
0.6455	3.15	12600	0.6574	0.1741
0.6414	3.17	12700	0.6489	0.1755
0.6619	3.2	12800	0.6527	0.1754
0.6303	3.23	12900	0.6462	0.1743
0.6525	3.25	13000	0.6505	0.1731
0.6347	3.27	13100	0.6432	0.1713
0.6206	3.3	13200	0.6495	0.1746
0.6445	3.33	13300	0.6328	0.1706
0.6097	3.35	13400	0.6329	0.1689
0.6151	3.38	13500	0.6473	0.1730
0.5948	3.4	13600	0.6413	0.1714
0.5949	3.42	13700	0.6377	0.1712
0.6402	3.45	13800	0.6295	0.1692
0.6607	3.48	13900	0.6287	0.1694
0.6219	3.5	14000	0.6357	0.1704
0.61	3.52	14100	0.6392	0.1715
0.5974	3.55	14200	0.6315	0.1687
0.5839	3.58	14300	0.6359	0.1689
0.6017	3.6	14400	0.6316	0.1673
0.6091	3.62	14500	0.6284	0.1686
0.6565	3.65	14600	0.6304	0.1684
0.6179	3.67	14700	0.6259	0.1661
0.5813	3.7	14800	0.6310	0.1672
0.5802	3.73	14900	0.6250	0.1667
0.6035	3.75	15000	0.6284	0.1666
0.5569	3.77	15100	0.6203	0.1651
0.5712	3.8	15200	0.6207	0.1660
0.546	3.83	15300	0.6246	0.1661
0.5602	3.85	15400	0.6206	0.1656
0.591	3.88	15500	0.6179	0.1650
0.5972	3.9	15600	0.6164	0.1653
0.6168	3.92	15700	0.6174	0.1660
0.5957	3.95	15800	0.6164	0.1657
0.5754	3.98	15900	0.6163	0.1657
0.5686	4.0	16000	0.6163	0.1655

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.13.0
Tokenizers 0.15.0

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Jan 12, 2024
Updated: Jan 15, 2024