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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	datasets:
	- common_voice_8_0
	metrics:
	- wer
	model-index:
	- name: wav2vec2-large-xls-r-2b-frisian-cv-8
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: common_voice_8_0
	type: common_voice_8_0
	config: fy-NL
	split: validation
	args: fy-NL
	metrics:
	- name: Wer
	type: wer
	value: 0.040494215112126836
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: common_voice_8_0
	type: common_voice_8_0
	config: fy-NL
	split: test
	args: fy-NL
	metrics:
	- name: Wer
	type: wer
	value: 0.04223876282699812
	---

	<!-- 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. -->

	# wav2vec2-large-xls-r-2b-frisian-cv-8

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the common_voice_8_0 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0465
	- Wer: 0.0405

	And on the test set:

	- Wer: 0.0422

	## Model description

	This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 7 where
	I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each).
	The number of training hours adds up to 41 hours of Frisian speech. This varies from experiment 2 because I fine-tune on the 2B parameters version of XLS-R.

	## Intended uses & limitations

	The intended use is for recognizing Frisian speech.

	Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.

	## Training and evaluation data

	The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split corresponds to all of the validated data except for the recordings found in the evaluation and test splits.

	## Training procedure

	The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/).

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 3e-05
	- train_batch_size: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 20
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 6.3316 \| 0.21 \| 400 \| 2.9773 \| 1.0 \|
	\| 2.7465 \| 0.43 \| 800 \| 1.2564 \| 0.9352 \|
	\| 1.4576 \| 0.64 \| 1200 \| 0.6275 \| 0.5809 \|
	\| 1.2245 \| 0.86 \| 1600 \| 0.4438 \| 0.4244 \|
	\| 0.9928 \| 1.07 \| 2000 \| 0.3058 \| 0.3247 \|
	\| 0.8768 \| 1.29 \| 2400 \| 0.2656 \| 0.2618 \|
	\| 0.8686 \| 1.5 \| 2800 \| 0.2155 \| 0.2289 \|
	\| 0.8325 \| 1.72 \| 3200 \| 0.1924 \| 0.2016 \|
	\| 0.8495 \| 1.93 \| 3600 \| 0.1748 \| 0.1853 \|
	\| 0.7069 \| 2.14 \| 4000 \| 0.1792 \| 0.1682 \|
	\| 0.7381 \| 2.36 \| 4400 \| 0.1540 \| 0.1524 \|
	\| 0.6648 \| 2.57 \| 4800 \| 0.1397 \| 0.1477 \|
	\| 0.7471 \| 2.79 \| 5200 \| 0.1372 \| 0.1389 \|
	\| 0.7219 \| 3.0 \| 5600 \| 0.1296 \| 0.1308 \|
	\| 0.5894 \| 3.22 \| 6000 \| 0.1167 \| 0.1287 \|
	\| 0.585 \| 3.43 \| 6400 \| 0.1194 \| 0.1264 \|
	\| 0.5486 \| 3.65 \| 6800 \| 0.1159 \| 0.1248 \|
	\| 0.5001 \| 3.86 \| 7200 \| 0.1107 \| 0.1160 \|
	\| 0.4838 \| 4.08 \| 7600 \| 0.1079 \| 0.1212 \|
	\| 0.4213 \| 4.29 \| 8000 \| 0.1065 \| 0.1145 \|
	\| 0.4493 \| 4.5 \| 8400 \| 0.0998 \| 0.1098 \|
	\| 0.4003 \| 4.72 \| 8800 \| 0.0975 \| 0.1027 \|
	\| 0.4034 \| 4.93 \| 9200 \| 0.0947 \| 0.1023 \|
	\| 0.3699 \| 5.15 \| 9600 \| 0.0927 \| 0.1006 \|
	\| 0.3748 \| 5.36 \| 10000 \| 0.0955 \| 0.0994 \|
	\| 0.3681 \| 5.58 \| 10400 \| 0.0923 \| 0.0952 \|
	\| 0.3416 \| 5.79 \| 10800 \| 0.0902 \| 0.0968 \|
	\| 0.3594 \| 6.01 \| 11200 \| 0.0848 \| 0.0935 \|
	\| 0.3303 \| 6.22 \| 11600 \| 0.0889 \| 0.0921 \|
	\| 0.3205 \| 6.43 \| 12000 \| 0.0843 \| 0.0893 \|
	\| 0.3267 \| 6.65 \| 12400 \| 0.0884 \| 0.0882 \|
	\| 0.33 \| 6.86 \| 12800 \| 0.0859 \| 0.0936 \|
	\| 0.3023 \| 7.08 \| 13200 \| 0.0830 \| 0.0851 \|
	\| 0.3057 \| 7.29 \| 13600 \| 0.0826 \| 0.0860 \|
	\| 0.3007 \| 7.51 \| 14000 \| 0.0841 \| 0.0836 \|
	\| 0.2981 \| 7.72 \| 14400 \| 0.0790 \| 0.0817 \|
	\| 0.282 \| 7.94 \| 14800 \| 0.0761 \| 0.0779 \|
	\| 0.2758 \| 8.15 \| 15200 \| 0.0767 \| 0.0776 \|
	\| 0.275 \| 8.36 \| 15600 \| 0.0788 \| 0.0781 \|
	\| 0.283 \| 8.58 \| 16000 \| 0.0728 \| 0.0775 \|
	\| 0.2684 \| 8.79 \| 16400 \| 0.0722 \| 0.0742 \|
	\| 0.2701 \| 9.01 \| 16800 \| 0.0742 \| 0.0720 \|
	\| 0.248 \| 9.22 \| 17200 \| 0.0711 \| 0.0729 \|
	\| 0.2467 \| 9.44 \| 17600 \| 0.0698 \| 0.0711 \|
	\| 0.2588 \| 9.65 \| 18000 \| 0.0688 \| 0.0710 \|
	\| 0.2566 \| 9.87 \| 18400 \| 0.0699 \| 0.0708 \|
	\| 0.2425 \| 10.08 \| 18800 \| 0.0699 \| 0.0683 \|
	\| 0.2292 \| 10.29 \| 19200 \| 0.0697 \| 0.0662 \|
	\| 0.2317 \| 10.51 \| 19600 \| 0.0670 \| 0.0663 \|
	\| 0.2381 \| 10.72 \| 20000 \| 0.0649 \| 0.0648 \|
	\| 0.2281 \| 10.94 \| 20400 \| 0.0619 \| 0.0621 \|
	\| 0.2329 \| 11.15 \| 20800 \| 0.0648 \| 0.0627 \|
	\| 0.2197 \| 11.37 \| 21200 \| 0.0630 \| 0.0632 \|
	\| 0.2406 \| 11.58 \| 21600 \| 0.0611 \| 0.0609 \|
	\| 0.2221 \| 11.8 \| 22000 \| 0.0621 \| 0.0601 \|
	\| 0.2316 \| 12.01 \| 22400 \| 0.0637 \| 0.0596 \|
	\| 0.202 \| 12.23 \| 22800 \| 0.0622 \| 0.0592 \|
	\| 0.2071 \| 12.44 \| 23200 \| 0.0603 \| 0.0589 \|
	\| 0.2119 \| 12.65 \| 23600 \| 0.0589 \| 0.0581 \|
	\| 0.2072 \| 12.87 \| 24000 \| 0.0586 \| 0.0588 \|
	\| 0.1948 \| 13.08 \| 24400 \| 0.0576 \| 0.0562 \|
	\| 0.1967 \| 13.3 \| 24800 \| 0.0573 \| 0.0543 \|
	\| 0.1981 \| 13.51 \| 25200 \| 0.0582 \| 0.0567 \|
	\| 0.1869 \| 13.73 \| 25600 \| 0.0550 \| 0.0533 \|
	\| 0.1929 \| 13.94 \| 26000 \| 0.0530 \| 0.0540 \|
	\| 0.1837 \| 14.16 \| 26400 \| 0.0550 \| 0.0519 \|
	\| 0.1823 \| 14.37 \| 26800 \| 0.0535 \| 0.0521 \|
	\| 0.1756 \| 14.58 \| 27200 \| 0.0552 \| 0.0515 \|
	\| 0.1769 \| 14.8 \| 27600 \| 0.0553 \| 0.0502 \|
	\| 0.1769 \| 15.01 \| 28000 \| 0.0516 \| 0.0493 \|
	\| 0.1781 \| 15.23 \| 28400 \| 0.0519 \| 0.0485 \|
	\| 0.1763 \| 15.44 \| 28800 \| 0.0511 \| 0.0482 \|
	\| 0.1705 \| 15.66 \| 29200 \| 0.0513 \| 0.0471 \|
	\| 0.1696 \| 15.87 \| 29600 \| 0.0484 \| 0.0467 \|
	\| 0.1668 \| 16.09 \| 30000 \| 0.0492 \| 0.0464 \|
	\| 0.1635 \| 16.3 \| 30400 \| 0.0492 \| 0.0470 \|
	\| 0.1597 \| 16.51 \| 30800 \| 0.0505 \| 0.0471 \|
	\| 0.152 \| 16.73 \| 31200 \| 0.0495 \| 0.0471 \|
	\| 0.1589 \| 16.94 \| 31600 \| 0.0478 \| 0.0456 \|
	\| 0.1586 \| 17.16 \| 32000 \| 0.0490 \| 0.0441 \|
	\| 0.1516 \| 17.37 \| 32400 \| 0.0482 \| 0.0448 \|
	\| 0.1506 \| 17.59 \| 32800 \| 0.0485 \| 0.0439 \|
	\| 0.1513 \| 17.8 \| 33200 \| 0.0485 \| 0.0439 \|
	\| 0.1545 \| 18.02 \| 33600 \| 0.0479 \| 0.0432 \|
	\| 0.1472 \| 18.23 \| 34000 \| 0.0479 \| 0.0428 \|
	\| 0.148 \| 18.45 \| 34400 \| 0.0475 \| 0.0424 \|
	\| 0.1446 \| 18.66 \| 34800 \| 0.0477 \| 0.0420 \|
	\| 0.1413 \| 18.87 \| 35200 \| 0.0466 \| 0.0416 \|
	\| 0.1398 \| 19.09 \| 35600 \| 0.0477 \| 0.0407 \|
	\| 0.1431 \| 19.3 \| 36000 \| 0.0466 \| 0.0406 \|
	\| 0.1437 \| 19.52 \| 36400 \| 0.0467 \| 0.0401 \|
	\| 0.1393 \| 19.73 \| 36800 \| 0.0468 \| 0.0404 \|
	\| 0.1416 \| 19.95 \| 37200 \| 0.0465 \| 0.0405 \|


	### Framework versions

	- Transformers 4.28.1
	- Pytorch 2.0.0+cu117
	- Datasets 2.11.0
	- Tokenizers 0.13.3

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	datasets:
	- common_voice_8_0
	metrics:
	- wer
	model-index:
	- name: wav2vec2-large-xls-r-2b-frisian-cv-8
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: common_voice_8_0
	type: common_voice_8_0
	config: fy-NL
	split: validation
	args: fy-NL
	metrics:
	- name: Wer
	type: wer
	value: 0.040494215112126836
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: common_voice_8_0
	type: common_voice_8_0
	config: fy-NL
	split: test
	args: fy-NL
	metrics:
	- name: Wer
	type: wer
	value: 0.04223876282699812
	---

	<!-- 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. -->

	# wav2vec2-large-xls-r-2b-frisian-cv-8

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the common_voice_8_0 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0465
	- Wer: 0.0405

	And on the test set:

	- Wer: 0.0422

	## Model description

	This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 7 where
	I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each).
	The number of training hours adds up to 41 hours of Frisian speech. This varies from experiment 2 because I fine-tune on the 2B parameters version of XLS-R.

	## Intended uses & limitations

	The intended use is for recognizing Frisian speech.

	Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.

	## Training and evaluation data

	The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split corresponds to all of the validated data except for the recordings found in the evaluation and test splits.

	## Training procedure

	The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/).

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 3e-05
	- train_batch_size: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 20
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 6.3316 \| 0.21 \| 400 \| 2.9773 \| 1.0 \|
	\| 2.7465 \| 0.43 \| 800 \| 1.2564 \| 0.9352 \|
	\| 1.4576 \| 0.64 \| 1200 \| 0.6275 \| 0.5809 \|
	\| 1.2245 \| 0.86 \| 1600 \| 0.4438 \| 0.4244 \|
	\| 0.9928 \| 1.07 \| 2000 \| 0.3058 \| 0.3247 \|
	\| 0.8768 \| 1.29 \| 2400 \| 0.2656 \| 0.2618 \|
	\| 0.8686 \| 1.5 \| 2800 \| 0.2155 \| 0.2289 \|
	\| 0.8325 \| 1.72 \| 3200 \| 0.1924 \| 0.2016 \|
	\| 0.8495 \| 1.93 \| 3600 \| 0.1748 \| 0.1853 \|
	\| 0.7069 \| 2.14 \| 4000 \| 0.1792 \| 0.1682 \|
	\| 0.7381 \| 2.36 \| 4400 \| 0.1540 \| 0.1524 \|
	\| 0.6648 \| 2.57 \| 4800 \| 0.1397 \| 0.1477 \|
	\| 0.7471 \| 2.79 \| 5200 \| 0.1372 \| 0.1389 \|
	\| 0.7219 \| 3.0 \| 5600 \| 0.1296 \| 0.1308 \|
	\| 0.5894 \| 3.22 \| 6000 \| 0.1167 \| 0.1287 \|
	\| 0.585 \| 3.43 \| 6400 \| 0.1194 \| 0.1264 \|
	\| 0.5486 \| 3.65 \| 6800 \| 0.1159 \| 0.1248 \|
	\| 0.5001 \| 3.86 \| 7200 \| 0.1107 \| 0.1160 \|
	\| 0.4838 \| 4.08 \| 7600 \| 0.1079 \| 0.1212 \|
	\| 0.4213 \| 4.29 \| 8000 \| 0.1065 \| 0.1145 \|
	\| 0.4493 \| 4.5 \| 8400 \| 0.0998 \| 0.1098 \|
	\| 0.4003 \| 4.72 \| 8800 \| 0.0975 \| 0.1027 \|
	\| 0.4034 \| 4.93 \| 9200 \| 0.0947 \| 0.1023 \|
	\| 0.3699 \| 5.15 \| 9600 \| 0.0927 \| 0.1006 \|
	\| 0.3748 \| 5.36 \| 10000 \| 0.0955 \| 0.0994 \|
	\| 0.3681 \| 5.58 \| 10400 \| 0.0923 \| 0.0952 \|
	\| 0.3416 \| 5.79 \| 10800 \| 0.0902 \| 0.0968 \|
	\| 0.3594 \| 6.01 \| 11200 \| 0.0848 \| 0.0935 \|
	\| 0.3303 \| 6.22 \| 11600 \| 0.0889 \| 0.0921 \|
	\| 0.3205 \| 6.43 \| 12000 \| 0.0843 \| 0.0893 \|
	\| 0.3267 \| 6.65 \| 12400 \| 0.0884 \| 0.0882 \|
	\| 0.33 \| 6.86 \| 12800 \| 0.0859 \| 0.0936 \|
	\| 0.3023 \| 7.08 \| 13200 \| 0.0830 \| 0.0851 \|
	\| 0.3057 \| 7.29 \| 13600 \| 0.0826 \| 0.0860 \|
	\| 0.3007 \| 7.51 \| 14000 \| 0.0841 \| 0.0836 \|
	\| 0.2981 \| 7.72 \| 14400 \| 0.0790 \| 0.0817 \|
	\| 0.282 \| 7.94 \| 14800 \| 0.0761 \| 0.0779 \|
	\| 0.2758 \| 8.15 \| 15200 \| 0.0767 \| 0.0776 \|
	\| 0.275 \| 8.36 \| 15600 \| 0.0788 \| 0.0781 \|
	\| 0.283 \| 8.58 \| 16000 \| 0.0728 \| 0.0775 \|
	\| 0.2684 \| 8.79 \| 16400 \| 0.0722 \| 0.0742 \|
	\| 0.2701 \| 9.01 \| 16800 \| 0.0742 \| 0.0720 \|
	\| 0.248 \| 9.22 \| 17200 \| 0.0711 \| 0.0729 \|
	\| 0.2467 \| 9.44 \| 17600 \| 0.0698 \| 0.0711 \|
	\| 0.2588 \| 9.65 \| 18000 \| 0.0688 \| 0.0710 \|
	\| 0.2566 \| 9.87 \| 18400 \| 0.0699 \| 0.0708 \|
	\| 0.2425 \| 10.08 \| 18800 \| 0.0699 \| 0.0683 \|
	\| 0.2292 \| 10.29 \| 19200 \| 0.0697 \| 0.0662 \|
	\| 0.2317 \| 10.51 \| 19600 \| 0.0670 \| 0.0663 \|
	\| 0.2381 \| 10.72 \| 20000 \| 0.0649 \| 0.0648 \|
	\| 0.2281 \| 10.94 \| 20400 \| 0.0619 \| 0.0621 \|
	\| 0.2329 \| 11.15 \| 20800 \| 0.0648 \| 0.0627 \|
	\| 0.2197 \| 11.37 \| 21200 \| 0.0630 \| 0.0632 \|
	\| 0.2406 \| 11.58 \| 21600 \| 0.0611 \| 0.0609 \|
	\| 0.2221 \| 11.8 \| 22000 \| 0.0621 \| 0.0601 \|
	\| 0.2316 \| 12.01 \| 22400 \| 0.0637 \| 0.0596 \|
	\| 0.202 \| 12.23 \| 22800 \| 0.0622 \| 0.0592 \|
	\| 0.2071 \| 12.44 \| 23200 \| 0.0603 \| 0.0589 \|
	\| 0.2119 \| 12.65 \| 23600 \| 0.0589 \| 0.0581 \|
	\| 0.2072 \| 12.87 \| 24000 \| 0.0586 \| 0.0588 \|
	\| 0.1948 \| 13.08 \| 24400 \| 0.0576 \| 0.0562 \|
	\| 0.1967 \| 13.3 \| 24800 \| 0.0573 \| 0.0543 \|
	\| 0.1981 \| 13.51 \| 25200 \| 0.0582 \| 0.0567 \|
	\| 0.1869 \| 13.73 \| 25600 \| 0.0550 \| 0.0533 \|
	\| 0.1929 \| 13.94 \| 26000 \| 0.0530 \| 0.0540 \|
	\| 0.1837 \| 14.16 \| 26400 \| 0.0550 \| 0.0519 \|
	\| 0.1823 \| 14.37 \| 26800 \| 0.0535 \| 0.0521 \|
	\| 0.1756 \| 14.58 \| 27200 \| 0.0552 \| 0.0515 \|
	\| 0.1769 \| 14.8 \| 27600 \| 0.0553 \| 0.0502 \|
	\| 0.1769 \| 15.01 \| 28000 \| 0.0516 \| 0.0493 \|
	\| 0.1781 \| 15.23 \| 28400 \| 0.0519 \| 0.0485 \|
	\| 0.1763 \| 15.44 \| 28800 \| 0.0511 \| 0.0482 \|
	\| 0.1705 \| 15.66 \| 29200 \| 0.0513 \| 0.0471 \|
	\| 0.1696 \| 15.87 \| 29600 \| 0.0484 \| 0.0467 \|
	\| 0.1668 \| 16.09 \| 30000 \| 0.0492 \| 0.0464 \|
	\| 0.1635 \| 16.3 \| 30400 \| 0.0492 \| 0.0470 \|
	\| 0.1597 \| 16.51 \| 30800 \| 0.0505 \| 0.0471 \|
	\| 0.152 \| 16.73 \| 31200 \| 0.0495 \| 0.0471 \|
	\| 0.1589 \| 16.94 \| 31600 \| 0.0478 \| 0.0456 \|
	\| 0.1586 \| 17.16 \| 32000 \| 0.0490 \| 0.0441 \|
	\| 0.1516 \| 17.37 \| 32400 \| 0.0482 \| 0.0448 \|
	\| 0.1506 \| 17.59 \| 32800 \| 0.0485 \| 0.0439 \|
	\| 0.1513 \| 17.8 \| 33200 \| 0.0485 \| 0.0439 \|
	\| 0.1545 \| 18.02 \| 33600 \| 0.0479 \| 0.0432 \|
	\| 0.1472 \| 18.23 \| 34000 \| 0.0479 \| 0.0428 \|
	\| 0.148 \| 18.45 \| 34400 \| 0.0475 \| 0.0424 \|
	\| 0.1446 \| 18.66 \| 34800 \| 0.0477 \| 0.0420 \|
	\| 0.1413 \| 18.87 \| 35200 \| 0.0466 \| 0.0416 \|
	\| 0.1398 \| 19.09 \| 35600 \| 0.0477 \| 0.0407 \|
	\| 0.1431 \| 19.3 \| 36000 \| 0.0466 \| 0.0406 \|
	\| 0.1437 \| 19.52 \| 36400 \| 0.0467 \| 0.0401 \|
	\| 0.1393 \| 19.73 \| 36800 \| 0.0468 \| 0.0404 \|
	\| 0.1416 \| 19.95 \| 37200 \| 0.0465 \| 0.0405 \|


	### Framework versions

	- Transformers 4.28.1
	- Pytorch 2.0.0+cu117
	- Datasets 2.11.0
	- Tokenizers 0.13.3