xls-r-300m-et-children

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

xls-r-300m-et-children is a automatic speech recognition model from tartuNLP. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as cc-by-4.0.

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

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: cc-by-4.0
Model type: wav2vec2
Access: Public
Created: Jan 30, 2023
Updated: May 26, 2025
SHA: 35e024d2

Base models

TalTechNLP/xls-r-300m-et

Model card

From the Hugging Face model README

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
train_batch_size: 32
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_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

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: cc-by-4.0
Languages: et
Created: Jan 30, 2023
Updated: May 26, 2025