korean-small_t35

This model is a fine-tuned version of openai/whisper-small on the korean_samll_dataset4 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1385
• Cer: 5.2553

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: 1e-05
• train_batch_size: 16
• 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: 3.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.2501	0.06	200	0.2322	8.7579
0.217	0.12	400	0.2118	8.6072
0.1947	0.18	600	0.2011	7.4035
0.1938	0.24	800	0.1941	7.3665
0.1878	0.3	1000	0.1826	7.0860
0.192	0.36	1200	0.1786	6.8894
0.1768	0.42	1400	0.1739	6.5072
0.1777	0.48	1600	0.1708	6.4205
0.1714	0.54	1800	0.1675	6.6288
0.171	0.6	2000	0.1637	6.3026
0.1678	0.66	2200	0.1638	6.4964
0.1606	0.72	2400	0.1604	6.4205
0.1541	0.78	2600	0.1580	6.1524
0.1578	0.84	2800	0.1550	5.8736
0.1524	0.9	3000	0.1535	5.9458
0.153	0.96	3200	0.1512	5.8205
0.112	1.02	3400	0.1492	5.7590
0.0833	1.08	3600	0.1491	5.7022
0.0928	1.14	3800	0.1495	5.6578
0.1005	1.2	4000	0.1480	6.0906
0.0918	1.26	4200	0.1475	5.8175
0.0929	1.32	4400	0.1470	5.7632
0.091	1.38	4600	0.1460	5.6557
0.0858	1.44	4800	0.1445	5.6947
0.0889	1.5	5000	0.1435	5.6632
0.0903	1.56	5200	0.1442	5.6412
0.0894	1.61	5400	0.1426	5.5711
0.0842	1.67	5600	0.1426	5.4424
0.0926	1.73	5800	0.1419	5.4171
0.0801	1.79	6000	0.1400	5.3960
0.0843	1.85	6200	0.1397	5.5648
0.0909	1.91	6400	0.1386	5.4677
0.0816	1.97	6600	0.1384	5.6586
0.0484	2.03	6800	0.1421	5.4541
0.0506	2.09	7000	0.1408	5.4424
0.0475	2.15	7200	0.1410	5.5565
0.0477	2.21	7400	0.1406	5.5453
0.0465	2.27	7600	0.1407	5.3383
0.0487	2.33	7800	0.1404	5.4192
0.0438	2.39	8000	0.1400	5.4088
0.0432	2.45	8200	0.1404	5.4022
0.0457	2.51	8400	0.1410	5.3852
0.0468	2.57	8600	0.1398	5.2881
0.0456	2.63	8800	0.1390	5.2789
0.0426	2.69	9000	0.1390	5.3619
0.0437	2.75	9200	0.1385	5.2553
0.0467	2.81	9400	0.1386	5.3404
0.044	2.87	9600	0.1383	5.3180
0.0445	2.93	9800	0.1382	5.3072
0.0453	2.99	10000	0.1380	5.3267

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2