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tartuNLP/xls-r-300m-et-children

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xls-r-300m-et-children

This model is a fine-tuned version of TalTechNLP/xls-r-300m-et on an Estonian children's speech dataset.

More information about the model's performance and the data used for evaluation and training:

Luhtaru, Agnes; Jaaska, Rauno; Kruusamäe, Karl; Fishel, Mark (2023). Automatic Transcription for Estonian Children’s Speech. In: Proceedings of the 24th Nordic Conference on Computational Linguistics. https://openreview.net/forum?id=xbPTfBIUby

Training hyperparameters

The following hyperparameters were used during training:

—learning_rate: 2e-05
—trainbatchsize: 32
—evalbatchsize: 8
—seed: 42
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 64
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_steps: 200
—num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3485	1.61	100	0.2034	0.1782
0.1914	3.22	200	0.1818	0.1606
0.1641	4.83	300	0.1770	0.1572
0.1479	6.45	400	0.1745	0.1546
0.1374	8.06	500	0.1751	0.1538
0.1244	9.67	600	0.1734	0.1518
0.1211	11.29	700	0.1753	0.1508
0.1096	12.9	800	0.1758	0.1483
0.1059	14.51	900	0.1771	0.1469
0.0991	16.13	1000	0.1776	0.1469
0.0965	17.74	1100	0.1759	0.1469
0.0944	19.35	1200	0.1784	0.1459
0.0902	20.96	1300	0.1799	0.1469
0.0867	22.58	1400	0.1814	0.1440
0.0817	24.19	1500	0.1828	0.1438
0.0802	25.8	1600	0.1845	0.1438
0.0762	27.42	1700	0.1843	0.1431
0.0774	29.03	1800	0.1839	0.1432
0.0741	30.64	1900	0.1843	0.1442
0.0718	32.26	2000	0.1846	0.1429
0.07	33.86	2100	0.1852	0.1429
0.0692	35.48	2200	0.1872	0.1435
0.0671	37.1	2300	0.1874	0.1433
0.0667	38.7	2400	0.1887	0.1435
0.066	40.32	2500	0.1880	0.1422
0.0628	41.93	2600	0.1897	0.1426
0.0643	43.54	2700	0.1910	0.1428
0.0643	45.16	2800	0.1900	0.1431
0.0638	46.77	2900	0.1900	0.1427
0.0601	48.38	3000	0.1911	0.1431
0.0593	49.99	3100	0.1914	0.1432
0.0606	51.61	3200	0.1912	0.1433
0.0609	53.22	3300	0.1912	0.1431
0.0587	54.83	3400	0.1921	0.1429
0.0567	56.45	3500	0.1924	0.1430
0.0611	58.06	3600	0.1927	0.1431
0.0581	59.67	3700	0.1926	0.1430

Framework versions

—Transformers 4.26.0.dev0
—Pytorch 1.12.1+rocm5.1.1
—Datasets 2.7.1.dev0
—Tokenizers 0.13.2