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KYAGABA/whisper-small-sagale_dataset-ormo-10hrs-v5

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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whisper-small-sagale_dataset-ormo-10hrs-v5

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

—Wer: 0.3355
—Cer: 0.1622
—Loss: 0.1740

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: 5e-05
—trainbatchsize: 16
—evalbatchsize: 8
—seed: 42
—optimizer: Use adamwbnb8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.05
—num_epochs: 100
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Wer	Cer	Validation Loss
1.6671	1.0	196	0.9903	0.6375	0.8798
0.2728	2.0	392	0.4929	0.2124	0.1525
0.1106	3.0	588	0.4350	0.1909	0.1279
0.076	4.0	784	0.4198	0.1929	0.1284
0.062	5.0	980	0.4264	0.1937	0.1381
0.0525	6.0	1176	0.3942	0.1791	0.1289
0.0399	7.0	1372	0.4073	0.1897	0.1321
0.032	8.0	1568	0.3698	0.1702	0.1303
0.0246	9.0	1764	0.3831	0.1746	0.1446
0.0214	10.0	1960	0.3674	0.1705	0.1477
0.0164	11.0	2156	0.3711	0.1713	0.1485
0.0133	12.0	2352	0.3617	0.1707	0.1479
0.0128	13.0	2548	0.3609	0.1722	0.1496
0.0117	14.0	2744	0.3628	0.1739	0.1486
0.0089	15.0	2940	0.3526	0.1674	0.1521
0.0088	16.0	3136	0.3543	0.1687	0.1598
0.007	17.0	3332	0.3588	0.1684	0.1673
0.0069	18.0	3528	0.3556	0.1671	0.1722
0.0065	19.0	3724	0.3583	0.1672	0.1751
0.0062	20.0	3920	0.3564	0.1660	0.1612
0.0056	21.0	4116	0.3473	0.1649	0.1659
0.0043	22.0	4312	0.3545	0.1659	0.1755
0.0049	23.0	4508	0.3499	0.1664	0.1664
0.0049	24.0	4704	0.3615	0.1686	0.1666
0.0043	25.0	4900	0.3528	0.1656	0.1720
0.0036	26.0	5096	0.3418	0.1638	0.1650
0.0035	27.0	5292	0.3490	0.1634	0.1826
0.0037	28.0	5488	0.3509	0.1679	0.1696
0.0027	29.0	5684	0.3439	0.1633	0.1814
0.0028	30.0	5880	0.3416	0.1651	0.1776
0.0032	31.0	6076	0.3473	0.1650	0.1674
0.0028	32.0	6272	0.3414	0.1632	0.1771
0.0028	33.0	6468	0.3357	0.1624	0.1840
0.0026	34.0	6664	0.3416	0.1647	0.1787
0.0031	35.0	6860	0.3482	0.1650	0.1855
0.0025	36.0	7056	0.3454	0.1631	0.1821
0.0025	37.0	7252	0.3475	0.1653	0.1707
0.0022	38.0	7448	0.3374	0.1617	0.1713
0.0016	39.0	7644	0.3414	0.1627	0.1781
0.0023	40.0	7840	0.3444	0.1630	0.1767
0.0022	41.0	8036	0.3374	0.1638	0.1697
0.0014	42.0	8232	0.3397	0.1632	0.1907
0.0018	43.0	8428	0.3355	0.1622	0.1740

Framework versions

—Transformers 4.49.0
—Pytorch 2.5.1+cu121
—Datasets 2.20.0
—Tokenizers 0.21.0