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	---
	language:
	- nl
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- mozilla-foundation/common_voice_8_0
	- generated_from_trainer
	- nl
	- robust-speech-event
	- model_for_talk
	datasets:
	- mozilla-foundation/common_voice_8_0
	model-index:
	- name: wav2vec2-large-xls-r-1b-nl
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 8
	type: mozilla-foundation/common_voice_8_0
	args: nl
	metrics:
	- name: Test WER
	type: wer
	value: 11.12
	- name: Test CER
	type: cer
	value: 3.20
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Robust Speech Event - Dev Data
	type: speech-recognition-community-v2/dev_data
	args: nl
	metrics:
	- name: Test WER
	type: wer
	value: 31.92
	- name: Test CER
	type: cer
	value: 13.87
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	#

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. This model is also available with a language model which improves these results. This model can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl-lm. The Common Voice 8 Dutch test Wer is 9.73 of that model.
	It achieves the following results on the evaluation set:
	- Loss: 0.1479
	- Wer: 0.1156

	## Model description

	Model fine-tuned using the wav2vec-als-r-1b model architecture

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	Model has been trained on Common Voice 8 Dutch

	## Training procedure

	### Training hyperparameters

	Model parameters can be found under Files and versions in the run.sh file.

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 1.2223 \| 0.52 \| 500 \| 0.3866 \| 0.3425 \|
	\| 1.0748 \| 1.03 \| 1000 \| 0.2574 \| 0.2169 \|
	\| 1.0416 \| 1.55 \| 1500 \| 0.2177 \| 0.1946 \|
	\| 0.9951 \| 2.06 \| 2000 \| 0.2008 \| 0.1760 \|
	\| 0.975 \| 2.58 \| 2500 \| 0.1961 \| 0.1751 \|
	\| 0.9461 \| 3.1 \| 3000 \| 0.1989 \| 0.1782 \|
	\| 0.9381 \| 3.61 \| 3500 \| 0.1928 \| 0.1699 \|
	\| 0.934 \| 4.13 \| 4000 \| 0.1923 \| 0.1633 \|
	\| 0.9322 \| 4.64 \| 4500 \| 0.1871 \| 0.1634 \|
	\| 0.9012 \| 5.16 \| 5000 \| 0.1890 \| 0.1702 \|
	\| 0.9045 \| 5.68 \| 5500 \| 0.1882 \| 0.1740 \|
	\| 0.8826 \| 6.19 \| 6000 \| 0.1856 \| 0.1575 \|
	\| 0.8848 \| 6.71 \| 6500 \| 0.1861 \| 0.1617 \|
	\| 0.8723 \| 7.22 \| 7000 \| 0.1927 \| 0.1646 \|
	\| 0.8725 \| 7.74 \| 7500 \| 0.1798 \| 0.1531 \|
	\| 0.8573 \| 8.26 \| 8000 \| 0.1781 \| 0.1587 \|
	\| 0.8633 \| 8.77 \| 8500 \| 0.1852 \| 0.1628 \|
	\| 0.8603 \| 9.29 \| 9000 \| 0.1833 \| 0.1601 \|
	\| 0.8421 \| 9.8 \| 9500 \| 0.1788 \| 0.1543 \|
	\| 0.8404 \| 10.32 \| 10000 \| 0.1844 \| 0.1556 \|
	\| 0.8342 \| 10.84 \| 10500 \| 0.1770 \| 0.1538 \|
	\| 0.8161 \| 11.35 \| 11000 \| 0.1821 \| 0.1567 \|
	\| 0.8371 \| 11.87 \| 11500 \| 0.1909 \| 0.1629 \|
	\| 0.8083 \| 12.38 \| 12000 \| 0.1778 \| 0.1498 \|
	\| 0.806 \| 12.9 \| 12500 \| 0.1802 \| 0.1547 \|
	\| 0.8013 \| 13.42 \| 13000 \| 0.1859 \| 0.1584 \|
	\| 0.7913 \| 13.93 \| 13500 \| 0.1875 \| 0.1517 \|
	\| 0.8063 \| 14.45 \| 14000 \| 0.1799 \| 0.1571 \|
	\| 0.7991 \| 14.96 \| 14500 \| 0.1792 \| 0.1538 \|
	\| 0.7843 \| 15.48 \| 15000 \| 0.1753 \| 0.1464 \|
	\| 0.7905 \| 16.0 \| 15500 \| 0.1784 \| 0.1508 \|
	\| 0.7808 \| 16.51 \| 16000 \| 0.1771 \| 0.1485 \|
	\| 0.7743 \| 17.03 \| 16500 \| 0.1795 \| 0.1491 \|
	\| 0.7833 \| 17.54 \| 17000 \| 0.1722 \| 0.1484 \|
	\| 0.7763 \| 18.06 \| 17500 \| 0.1767 \| 0.1518 \|
	\| 0.7698 \| 18.58 \| 18000 \| 0.1720 \| 0.1460 \|
	\| 0.7571 \| 19.09 \| 18500 \| 0.1735 \| 0.1478 \|
	\| 0.7673 \| 19.61 \| 19000 \| 0.1817 \| 0.1511 \|
	\| 0.7415 \| 20.12 \| 19500 \| 0.1763 \| 0.1481 \|
	\| 0.751 \| 20.64 \| 20000 \| 0.1742 \| 0.1484 \|
	\| 0.7563 \| 21.16 \| 20500 \| 0.1810 \| 0.1611 \|
	\| 0.7423 \| 21.67 \| 21000 \| 0.1817 \| 0.1557 \|
	\| 0.7242 \| 22.19 \| 21500 \| 0.1690 \| 0.1446 \|
	\| 0.7251 \| 22.7 \| 22000 \| 0.1684 \| 0.1446 \|
	\| 0.7302 \| 23.22 \| 22500 \| 0.1735 \| 0.1430 \|
	\| 0.733 \| 23.74 \| 23000 \| 0.1720 \| 0.1454 \|
	\| 0.7128 \| 24.25 \| 23500 \| 0.1668 \| 0.1383 \|
	\| 0.7184 \| 24.77 \| 24000 \| 0.1635 \| 0.1377 \|
	\| 0.7015 \| 25.28 \| 24500 \| 0.1646 \| 0.1389 \|
	\| 0.7198 \| 25.8 \| 25000 \| 0.1775 \| 0.1462 \|
	\| 0.7178 \| 26.32 \| 25500 \| 0.1705 \| 0.1419 \|
	\| 0.7199 \| 26.83 \| 26000 \| 0.1649 \| 0.1416 \|
	\| 0.6981 \| 27.35 \| 26500 \| 0.1724 \| 0.1418 \|
	\| 0.6886 \| 27.86 \| 27000 \| 0.1633 \| 0.1382 \|
	\| 0.6922 \| 28.38 \| 27500 \| 0.1698 \| 0.1420 \|
	\| 0.6833 \| 28.9 \| 28000 \| 0.1611 \| 0.1351 \|
	\| 0.6798 \| 29.41 \| 28500 \| 0.1639 \| 0.1365 \|
	\| 0.6711 \| 29.93 \| 29000 \| 0.1668 \| 0.1358 \|
	\| 0.6762 \| 30.44 \| 29500 \| 0.1682 \| 0.1355 \|
	\| 0.6594 \| 30.96 \| 30000 \| 0.1629 \| 0.1345 \|
	\| 0.6664 \| 31.48 \| 30500 \| 0.1625 \| 0.1321 \|
	\| 0.6838 \| 31.99 \| 31000 \| 0.1597 \| 0.1372 \|
	\| 0.6603 \| 32.51 \| 31500 \| 0.1583 \| 0.1302 \|
	\| 0.6468 \| 33.02 \| 32000 \| 0.1595 \| 0.1322 \|
	\| 0.6464 \| 33.54 \| 32500 \| 0.1609 \| 0.1315 \|
	\| 0.6623 \| 34.06 \| 33000 \| 0.1622 \| 0.1366 \|
	\| 0.6414 \| 34.57 \| 33500 \| 0.1587 \| 0.1330 \|
	\| 0.6242 \| 35.09 \| 34000 \| 0.1614 \| 0.1337 \|
	\| 0.632 \| 35.6 \| 34500 \| 0.1568 \| 0.1272 \|
	\| 0.6346 \| 36.12 \| 35000 \| 0.1583 \| 0.1274 \|
	\| 0.6143 \| 36.64 \| 35500 \| 0.1576 \| 0.1264 \|
	\| 0.6208 \| 37.15 \| 36000 \| 0.1621 \| 0.1263 \|
	\| 0.6185 \| 37.67 \| 36500 \| 0.1623 \| 0.1270 \|
	\| 0.6128 \| 38.18 \| 37000 \| 0.1604 \| 0.1268 \|
	\| 0.6151 \| 38.7 \| 37500 \| 0.1593 \| 0.1246 \|
	\| 0.6082 \| 39.22 \| 38000 \| 0.1532 \| 0.1238 \|
	\| 0.6 \| 39.73 \| 38500 \| 0.1524 \| 0.1224 \|
	\| 0.6032 \| 40.25 \| 39000 \| 0.1521 \| 0.1212 \|
	\| 0.6016 \| 40.76 \| 39500 \| 0.1551 \| 0.1215 \|
	\| 0.6009 \| 41.28 \| 40000 \| 0.1523 \| 0.1215 \|
	\| 0.5875 \| 41.8 \| 40500 \| 0.1541 \| 0.1216 \|
	\| 0.608 \| 42.31 \| 41000 \| 0.1536 \| 0.1209 \|
	\| 0.5876 \| 42.83 \| 41500 \| 0.1567 \| 0.1211 \|
	\| 0.5714 \| 43.34 \| 42000 \| 0.1532 \| 0.1217 \|
	\| 0.5756 \| 43.86 \| 42500 \| 0.1516 \| 0.1196 \|
	\| 0.5719 \| 44.38 \| 43000 \| 0.1491 \| 0.1191 \|
	\| 0.5829 \| 44.89 \| 43500 \| 0.1497 \| 0.1193 \|
	\| 0.5664 \| 45.41 \| 44000 \| 0.1487 \| 0.1173 \|
	\| 0.5707 \| 45.92 \| 44500 \| 0.1470 \| 0.1164 \|
	\| 0.5696 \| 46.44 \| 45000 \| 0.1479 \| 0.1161 \|
	\| 0.5767 \| 46.96 \| 45500 \| 0.1492 \| 0.1175 \|
	\| 0.5573 \| 47.47 \| 46000 \| 0.1471 \| 0.1165 \|
	\| 0.5625 \| 47.99 \| 46500 \| 0.1484 \| 0.1168 \|
	\| 0.5671 \| 48.5 \| 47000 \| 0.1474 \| 0.1162 \|
	\| 0.5484 \| 49.02 \| 47500 \| 0.1479 \| 0.1158 \|
	\| 0.555 \| 49.54 \| 48000 \| 0.1477 \| 0.1157 \|


	### Framework versions

	- Transformers 4.17.0.dev0
	- Pytorch 1.10.2+cu102
	- Datasets 1.18.3
	- Tokenizers 0.11.0

	---
	language:
	- nl
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- mozilla-foundation/common_voice_8_0
	- generated_from_trainer
	- nl
	- robust-speech-event
	- model_for_talk
	datasets:
	- mozilla-foundation/common_voice_8_0
	model-index:
	- name: wav2vec2-large-xls-r-1b-nl
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Common Voice 8
	type: mozilla-foundation/common_voice_8_0
	args: nl
	metrics:
	- name: Test WER
	type: wer
	value: 11.12
	- name: Test CER
	type: cer
	value: 3.20
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Robust Speech Event - Dev Data
	type: speech-recognition-community-v2/dev_data
	args: nl
	metrics:
	- name: Test WER
	type: wer
	value: 31.92
	- name: Test CER
	type: cer
	value: 13.87
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	#

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. This model is also available with a language model which improves these results. This model can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-nl-lm. The Common Voice 8 Dutch test Wer is 9.73 of that model.
	It achieves the following results on the evaluation set:
	- Loss: 0.1479
	- Wer: 0.1156

	## Model description

	Model fine-tuned using the wav2vec-als-r-1b model architecture

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	Model has been trained on Common Voice 8 Dutch

	## Training procedure

	### Training hyperparameters

	Model parameters can be found under Files and versions in the run.sh file.

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 1.2223 \| 0.52 \| 500 \| 0.3866 \| 0.3425 \|
	\| 1.0748 \| 1.03 \| 1000 \| 0.2574 \| 0.2169 \|
	\| 1.0416 \| 1.55 \| 1500 \| 0.2177 \| 0.1946 \|
	\| 0.9951 \| 2.06 \| 2000 \| 0.2008 \| 0.1760 \|
	\| 0.975 \| 2.58 \| 2500 \| 0.1961 \| 0.1751 \|
	\| 0.9461 \| 3.1 \| 3000 \| 0.1989 \| 0.1782 \|
	\| 0.9381 \| 3.61 \| 3500 \| 0.1928 \| 0.1699 \|
	\| 0.934 \| 4.13 \| 4000 \| 0.1923 \| 0.1633 \|
	\| 0.9322 \| 4.64 \| 4500 \| 0.1871 \| 0.1634 \|
	\| 0.9012 \| 5.16 \| 5000 \| 0.1890 \| 0.1702 \|
	\| 0.9045 \| 5.68 \| 5500 \| 0.1882 \| 0.1740 \|
	\| 0.8826 \| 6.19 \| 6000 \| 0.1856 \| 0.1575 \|
	\| 0.8848 \| 6.71 \| 6500 \| 0.1861 \| 0.1617 \|
	\| 0.8723 \| 7.22 \| 7000 \| 0.1927 \| 0.1646 \|
	\| 0.8725 \| 7.74 \| 7500 \| 0.1798 \| 0.1531 \|
	\| 0.8573 \| 8.26 \| 8000 \| 0.1781 \| 0.1587 \|
	\| 0.8633 \| 8.77 \| 8500 \| 0.1852 \| 0.1628 \|
	\| 0.8603 \| 9.29 \| 9000 \| 0.1833 \| 0.1601 \|
	\| 0.8421 \| 9.8 \| 9500 \| 0.1788 \| 0.1543 \|
	\| 0.8404 \| 10.32 \| 10000 \| 0.1844 \| 0.1556 \|
	\| 0.8342 \| 10.84 \| 10500 \| 0.1770 \| 0.1538 \|
	\| 0.8161 \| 11.35 \| 11000 \| 0.1821 \| 0.1567 \|
	\| 0.8371 \| 11.87 \| 11500 \| 0.1909 \| 0.1629 \|
	\| 0.8083 \| 12.38 \| 12000 \| 0.1778 \| 0.1498 \|
	\| 0.806 \| 12.9 \| 12500 \| 0.1802 \| 0.1547 \|
	\| 0.8013 \| 13.42 \| 13000 \| 0.1859 \| 0.1584 \|
	\| 0.7913 \| 13.93 \| 13500 \| 0.1875 \| 0.1517 \|
	\| 0.8063 \| 14.45 \| 14000 \| 0.1799 \| 0.1571 \|
	\| 0.7991 \| 14.96 \| 14500 \| 0.1792 \| 0.1538 \|
	\| 0.7843 \| 15.48 \| 15000 \| 0.1753 \| 0.1464 \|
	\| 0.7905 \| 16.0 \| 15500 \| 0.1784 \| 0.1508 \|
	\| 0.7808 \| 16.51 \| 16000 \| 0.1771 \| 0.1485 \|
	\| 0.7743 \| 17.03 \| 16500 \| 0.1795 \| 0.1491 \|
	\| 0.7833 \| 17.54 \| 17000 \| 0.1722 \| 0.1484 \|
	\| 0.7763 \| 18.06 \| 17500 \| 0.1767 \| 0.1518 \|
	\| 0.7698 \| 18.58 \| 18000 \| 0.1720 \| 0.1460 \|
	\| 0.7571 \| 19.09 \| 18500 \| 0.1735 \| 0.1478 \|
	\| 0.7673 \| 19.61 \| 19000 \| 0.1817 \| 0.1511 \|
	\| 0.7415 \| 20.12 \| 19500 \| 0.1763 \| 0.1481 \|
	\| 0.751 \| 20.64 \| 20000 \| 0.1742 \| 0.1484 \|
	\| 0.7563 \| 21.16 \| 20500 \| 0.1810 \| 0.1611 \|
	\| 0.7423 \| 21.67 \| 21000 \| 0.1817 \| 0.1557 \|
	\| 0.7242 \| 22.19 \| 21500 \| 0.1690 \| 0.1446 \|
	\| 0.7251 \| 22.7 \| 22000 \| 0.1684 \| 0.1446 \|
	\| 0.7302 \| 23.22 \| 22500 \| 0.1735 \| 0.1430 \|
	\| 0.733 \| 23.74 \| 23000 \| 0.1720 \| 0.1454 \|
	\| 0.7128 \| 24.25 \| 23500 \| 0.1668 \| 0.1383 \|
	\| 0.7184 \| 24.77 \| 24000 \| 0.1635 \| 0.1377 \|
	\| 0.7015 \| 25.28 \| 24500 \| 0.1646 \| 0.1389 \|
	\| 0.7198 \| 25.8 \| 25000 \| 0.1775 \| 0.1462 \|
	\| 0.7178 \| 26.32 \| 25500 \| 0.1705 \| 0.1419 \|
	\| 0.7199 \| 26.83 \| 26000 \| 0.1649 \| 0.1416 \|
	\| 0.6981 \| 27.35 \| 26500 \| 0.1724 \| 0.1418 \|
	\| 0.6886 \| 27.86 \| 27000 \| 0.1633 \| 0.1382 \|
	\| 0.6922 \| 28.38 \| 27500 \| 0.1698 \| 0.1420 \|
	\| 0.6833 \| 28.9 \| 28000 \| 0.1611 \| 0.1351 \|
	\| 0.6798 \| 29.41 \| 28500 \| 0.1639 \| 0.1365 \|
	\| 0.6711 \| 29.93 \| 29000 \| 0.1668 \| 0.1358 \|
	\| 0.6762 \| 30.44 \| 29500 \| 0.1682 \| 0.1355 \|
	\| 0.6594 \| 30.96 \| 30000 \| 0.1629 \| 0.1345 \|
	\| 0.6664 \| 31.48 \| 30500 \| 0.1625 \| 0.1321 \|
	\| 0.6838 \| 31.99 \| 31000 \| 0.1597 \| 0.1372 \|
	\| 0.6603 \| 32.51 \| 31500 \| 0.1583 \| 0.1302 \|
	\| 0.6468 \| 33.02 \| 32000 \| 0.1595 \| 0.1322 \|
	\| 0.6464 \| 33.54 \| 32500 \| 0.1609 \| 0.1315 \|
	\| 0.6623 \| 34.06 \| 33000 \| 0.1622 \| 0.1366 \|
	\| 0.6414 \| 34.57 \| 33500 \| 0.1587 \| 0.1330 \|
	\| 0.6242 \| 35.09 \| 34000 \| 0.1614 \| 0.1337 \|
	\| 0.632 \| 35.6 \| 34500 \| 0.1568 \| 0.1272 \|
	\| 0.6346 \| 36.12 \| 35000 \| 0.1583 \| 0.1274 \|
	\| 0.6143 \| 36.64 \| 35500 \| 0.1576 \| 0.1264 \|
	\| 0.6208 \| 37.15 \| 36000 \| 0.1621 \| 0.1263 \|
	\| 0.6185 \| 37.67 \| 36500 \| 0.1623 \| 0.1270 \|
	\| 0.6128 \| 38.18 \| 37000 \| 0.1604 \| 0.1268 \|
	\| 0.6151 \| 38.7 \| 37500 \| 0.1593 \| 0.1246 \|
	\| 0.6082 \| 39.22 \| 38000 \| 0.1532 \| 0.1238 \|
	\| 0.6 \| 39.73 \| 38500 \| 0.1524 \| 0.1224 \|
	\| 0.6032 \| 40.25 \| 39000 \| 0.1521 \| 0.1212 \|
	\| 0.6016 \| 40.76 \| 39500 \| 0.1551 \| 0.1215 \|
	\| 0.6009 \| 41.28 \| 40000 \| 0.1523 \| 0.1215 \|
	\| 0.5875 \| 41.8 \| 40500 \| 0.1541 \| 0.1216 \|
	\| 0.608 \| 42.31 \| 41000 \| 0.1536 \| 0.1209 \|
	\| 0.5876 \| 42.83 \| 41500 \| 0.1567 \| 0.1211 \|
	\| 0.5714 \| 43.34 \| 42000 \| 0.1532 \| 0.1217 \|
	\| 0.5756 \| 43.86 \| 42500 \| 0.1516 \| 0.1196 \|
	\| 0.5719 \| 44.38 \| 43000 \| 0.1491 \| 0.1191 \|
	\| 0.5829 \| 44.89 \| 43500 \| 0.1497 \| 0.1193 \|
	\| 0.5664 \| 45.41 \| 44000 \| 0.1487 \| 0.1173 \|
	\| 0.5707 \| 45.92 \| 44500 \| 0.1470 \| 0.1164 \|
	\| 0.5696 \| 46.44 \| 45000 \| 0.1479 \| 0.1161 \|
	\| 0.5767 \| 46.96 \| 45500 \| 0.1492 \| 0.1175 \|
	\| 0.5573 \| 47.47 \| 46000 \| 0.1471 \| 0.1165 \|
	\| 0.5625 \| 47.99 \| 46500 \| 0.1484 \| 0.1168 \|
	\| 0.5671 \| 48.5 \| 47000 \| 0.1474 \| 0.1162 \|
	\| 0.5484 \| 49.02 \| 47500 \| 0.1479 \| 0.1158 \|
	\| 0.555 \| 49.54 \| 48000 \| 0.1477 \| 0.1157 \|


	### Framework versions

	- Transformers 4.17.0.dev0
	- Pytorch 1.10.2+cu102
	- Datasets 1.18.3
	- Tokenizers 0.11.0