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	---
	license: apache-2.0
	base_model: facebook/wav2vec2-large-xlsr-53
	tags:
	- automatic-speech-recognition
	- DewiBrynJones/banc-trawsgrifiadau-bangor-clean-with-ccv
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-xlsr-53-ft-btb-ccv-cy
	results: []
	---

	<!-- 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-xlsr-53-ft-btb-ccv-cy

	This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN-WITH-CCV - DEFAULT dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4122
	- Wer: 0.3223

	## 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: 0.0003
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- training_steps: 10000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|
	\| No log \| 0.0194 \| 100 \| 3.5545 \| 1.0 \|
	\| No log \| 0.0387 \| 200 \| 3.0260 \| 1.0 \|
	\| No log \| 0.0581 \| 300 \| 2.9066 \| 1.0 \|
	\| No log \| 0.0774 \| 400 \| 2.0133 \| 0.9847 \|
	\| 4.0489 \| 0.0968 \| 500 \| 1.4598 \| 0.9004 \|
	\| 4.0489 \| 0.1161 \| 600 \| 1.1772 \| 0.8042 \|
	\| 4.0489 \| 0.1355 \| 700 \| 1.0787 \| 0.7590 \|
	\| 4.0489 \| 0.1549 \| 800 \| 1.0144 \| 0.7212 \|
	\| 4.0489 \| 0.1742 \| 900 \| 0.9339 \| 0.6932 \|
	\| 1.0454 \| 0.1936 \| 1000 \| 0.8806 \| 0.6597 \|
	\| 1.0454 \| 0.2129 \| 1100 \| 0.8644 \| 0.6554 \|
	\| 1.0454 \| 0.2323 \| 1200 \| 0.8454 \| 0.6314 \|
	\| 1.0454 \| 0.2516 \| 1300 \| 0.8093 \| 0.5919 \|
	\| 1.0454 \| 0.2710 \| 1400 \| 0.8076 \| 0.6072 \|
	\| 0.842 \| 0.2904 \| 1500 \| 0.7783 \| 0.5857 \|
	\| 0.842 \| 0.3097 \| 1600 \| 0.7965 \| 0.5941 \|
	\| 0.842 \| 0.3291 \| 1700 \| 0.7415 \| 0.5505 \|
	\| 0.842 \| 0.3484 \| 1800 \| 0.7440 \| 0.5637 \|
	\| 0.842 \| 0.3678 \| 1900 \| 0.7361 \| 0.5865 \|
	\| 0.755 \| 0.3871 \| 2000 \| 0.7314 \| 0.5427 \|
	\| 0.755 \| 0.4065 \| 2100 \| 0.6866 \| 0.5181 \|
	\| 0.755 \| 0.4259 \| 2200 \| 0.6948 \| 0.5426 \|
	\| 0.755 \| 0.4452 \| 2300 \| 0.6796 \| 0.5159 \|
	\| 0.755 \| 0.4646 \| 2400 \| 0.6899 \| 0.5305 \|
	\| 0.6884 \| 0.4839 \| 2500 \| 0.6736 \| 0.5103 \|
	\| 0.6884 \| 0.5033 \| 2600 \| 0.6728 \| 0.5257 \|
	\| 0.6884 \| 0.5226 \| 2700 \| 0.6537 \| 0.5027 \|
	\| 0.6884 \| 0.5420 \| 2800 \| 0.6314 \| 0.4823 \|
	\| 0.6884 \| 0.5614 \| 2900 \| 0.6317 \| 0.4830 \|
	\| 0.6756 \| 0.5807 \| 3000 \| 0.6204 \| 0.4761 \|
	\| 0.6756 \| 0.6001 \| 3100 \| 0.6311 \| 0.4811 \|
	\| 0.6756 \| 0.6194 \| 3200 \| 0.6236 \| 0.4863 \|
	\| 0.6756 \| 0.6388 \| 3300 \| 0.6224 \| 0.4629 \|
	\| 0.6756 \| 0.6581 \| 3400 \| 0.5973 \| 0.4623 \|
	\| 0.6435 \| 0.6775 \| 3500 \| 0.5913 \| 0.4708 \|
	\| 0.6435 \| 0.6969 \| 3600 \| 0.6087 \| 0.4744 \|
	\| 0.6435 \| 0.7162 \| 3700 \| 0.5827 \| 0.4521 \|
	\| 0.6435 \| 0.7356 \| 3800 \| 0.5875 \| 0.4608 \|
	\| 0.6435 \| 0.7549 \| 3900 \| 0.5925 \| 0.4557 \|
	\| 0.6282 \| 0.7743 \| 4000 \| 0.5799 \| 0.4494 \|
	\| 0.6282 \| 0.7937 \| 4100 \| 0.5679 \| 0.4526 \|
	\| 0.6282 \| 0.8130 \| 4200 \| 0.5700 \| 0.4550 \|
	\| 0.6282 \| 0.8324 \| 4300 \| 0.5610 \| 0.4343 \|
	\| 0.6282 \| 0.8517 \| 4400 \| 0.5616 \| 0.4273 \|
	\| 0.5937 \| 0.8711 \| 4500 \| 0.5464 \| 0.4221 \|
	\| 0.5937 \| 0.8904 \| 4600 \| 0.5486 \| 0.4288 \|
	\| 0.5937 \| 0.9098 \| 4700 \| 0.5308 \| 0.4167 \|
	\| 0.5937 \| 0.9292 \| 4800 \| 0.5520 \| 0.4200 \|
	\| 0.5937 \| 0.9485 \| 4900 \| 0.5321 \| 0.4180 \|
	\| 0.5659 \| 0.9679 \| 5000 \| 0.5333 \| 0.4176 \|
	\| 0.5659 \| 0.9872 \| 5100 \| 0.5260 \| 0.4111 \|
	\| 0.5659 \| 1.0066 \| 5200 \| 0.5185 \| 0.3974 \|
	\| 0.5659 \| 1.0259 \| 5300 \| 0.5147 \| 0.3918 \|
	\| 0.5659 \| 1.0453 \| 5400 \| 0.5155 \| 0.3976 \|
	\| 0.4928 \| 1.0647 \| 5500 \| 0.5058 \| 0.3936 \|
	\| 0.4928 \| 1.0840 \| 5600 \| 0.5048 \| 0.3965 \|
	\| 0.4928 \| 1.1034 \| 5700 \| 0.5011 \| 0.3818 \|
	\| 0.4928 \| 1.1227 \| 5800 \| 0.4965 \| 0.3830 \|
	\| 0.4928 \| 1.1421 \| 5900 \| 0.4969 \| 0.3840 \|
	\| 0.4619 \| 1.1614 \| 6000 \| 0.4863 \| 0.3800 \|
	\| 0.4619 \| 1.1808 \| 6100 \| 0.4908 \| 0.3800 \|
	\| 0.4619 \| 1.2002 \| 6200 \| 0.4835 \| 0.3712 \|
	\| 0.4619 \| 1.2195 \| 6300 \| 0.4927 \| 0.3767 \|
	\| 0.4619 \| 1.2389 \| 6400 \| 0.4942 \| 0.3683 \|
	\| 0.4421 \| 1.2582 \| 6500 \| 0.4834 \| 0.3739 \|
	\| 0.4421 \| 1.2776 \| 6600 \| 0.4751 \| 0.3634 \|
	\| 0.4421 \| 1.2969 \| 6700 \| 0.4734 \| 0.3633 \|
	\| 0.4421 \| 1.3163 \| 6800 \| 0.4685 \| 0.3645 \|
	\| 0.4421 \| 1.3357 \| 6900 \| 0.4654 \| 0.3625 \|
	\| 0.4304 \| 1.3550 \| 7000 \| 0.4742 \| 0.3615 \|
	\| 0.4304 \| 1.3744 \| 7100 \| 0.4645 \| 0.3596 \|
	\| 0.4304 \| 1.3937 \| 7200 \| 0.4599 \| 0.3594 \|
	\| 0.4304 \| 1.4131 \| 7300 \| 0.4554 \| 0.3555 \|
	\| 0.4304 \| 1.4324 \| 7400 \| 0.4578 \| 0.3578 \|
	\| 0.4275 \| 1.4518 \| 7500 \| 0.4518 \| 0.3522 \|
	\| 0.4275 \| 1.4712 \| 7600 \| 0.4480 \| 0.3511 \|
	\| 0.4275 \| 1.4905 \| 7700 \| 0.4465 \| 0.3501 \|
	\| 0.4275 \| 1.5099 \| 7800 \| 0.4454 \| 0.3428 \|
	\| 0.4275 \| 1.5292 \| 7900 \| 0.4427 \| 0.3439 \|
	\| 0.4089 \| 1.5486 \| 8000 \| 0.4376 \| 0.3407 \|
	\| 0.4089 \| 1.5679 \| 8100 \| 0.4396 \| 0.3415 \|
	\| 0.4089 \| 1.5873 \| 8200 \| 0.4343 \| 0.3422 \|
	\| 0.4089 \| 1.6067 \| 8300 \| 0.4359 \| 0.3406 \|
	\| 0.4089 \| 1.6260 \| 8400 \| 0.4358 \| 0.3373 \|
	\| 0.4005 \| 1.6454 \| 8500 \| 0.4331 \| 0.3365 \|
	\| 0.4005 \| 1.6647 \| 8600 \| 0.4302 \| 0.3353 \|
	\| 0.4005 \| 1.6841 \| 8700 \| 0.4308 \| 0.3355 \|
	\| 0.4005 \| 1.7034 \| 8800 \| 0.4258 \| 0.3351 \|
	\| 0.4005 \| 1.7228 \| 8900 \| 0.4222 \| 0.3353 \|
	\| 0.3879 \| 1.7422 \| 9000 \| 0.4238 \| 0.3312 \|
	\| 0.3879 \| 1.7615 \| 9100 \| 0.4245 \| 0.3288 \|
	\| 0.3879 \| 1.7809 \| 9200 \| 0.4206 \| 0.3264 \|
	\| 0.3879 \| 1.8002 \| 9300 \| 0.4201 \| 0.3284 \|
	\| 0.3879 \| 1.8196 \| 9400 \| 0.4189 \| 0.3246 \|
	\| 0.369 \| 1.8389 \| 9500 \| 0.4160 \| 0.3258 \|
	\| 0.369 \| 1.8583 \| 9600 \| 0.4142 \| 0.3248 \|
	\| 0.369 \| 1.8777 \| 9700 \| 0.4131 \| 0.3252 \|
	\| 0.369 \| 1.8970 \| 9800 \| 0.4128 \| 0.3228 \|
	\| 0.369 \| 1.9164 \| 9900 \| 0.4122 \| 0.3221 \|
	\| 0.3738 \| 1.9357 \| 10000 \| 0.4122 \| 0.3223 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.3.1+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1

	---
	license: apache-2.0
	base_model: facebook/wav2vec2-large-xlsr-53
	tags:
	- automatic-speech-recognition
	- DewiBrynJones/banc-trawsgrifiadau-bangor-clean-with-ccv
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-xlsr-53-ft-btb-ccv-cy
	results: []
	---

	<!-- 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-xlsr-53-ft-btb-ccv-cy

	This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN-WITH-CCV - DEFAULT dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4122
	- Wer: 0.3223

	## 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: 0.0003
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- training_steps: 10000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|
	\| No log \| 0.0194 \| 100 \| 3.5545 \| 1.0 \|
	\| No log \| 0.0387 \| 200 \| 3.0260 \| 1.0 \|
	\| No log \| 0.0581 \| 300 \| 2.9066 \| 1.0 \|
	\| No log \| 0.0774 \| 400 \| 2.0133 \| 0.9847 \|
	\| 4.0489 \| 0.0968 \| 500 \| 1.4598 \| 0.9004 \|
	\| 4.0489 \| 0.1161 \| 600 \| 1.1772 \| 0.8042 \|
	\| 4.0489 \| 0.1355 \| 700 \| 1.0787 \| 0.7590 \|
	\| 4.0489 \| 0.1549 \| 800 \| 1.0144 \| 0.7212 \|
	\| 4.0489 \| 0.1742 \| 900 \| 0.9339 \| 0.6932 \|
	\| 1.0454 \| 0.1936 \| 1000 \| 0.8806 \| 0.6597 \|
	\| 1.0454 \| 0.2129 \| 1100 \| 0.8644 \| 0.6554 \|
	\| 1.0454 \| 0.2323 \| 1200 \| 0.8454 \| 0.6314 \|
	\| 1.0454 \| 0.2516 \| 1300 \| 0.8093 \| 0.5919 \|
	\| 1.0454 \| 0.2710 \| 1400 \| 0.8076 \| 0.6072 \|
	\| 0.842 \| 0.2904 \| 1500 \| 0.7783 \| 0.5857 \|
	\| 0.842 \| 0.3097 \| 1600 \| 0.7965 \| 0.5941 \|
	\| 0.842 \| 0.3291 \| 1700 \| 0.7415 \| 0.5505 \|
	\| 0.842 \| 0.3484 \| 1800 \| 0.7440 \| 0.5637 \|
	\| 0.842 \| 0.3678 \| 1900 \| 0.7361 \| 0.5865 \|
	\| 0.755 \| 0.3871 \| 2000 \| 0.7314 \| 0.5427 \|
	\| 0.755 \| 0.4065 \| 2100 \| 0.6866 \| 0.5181 \|
	\| 0.755 \| 0.4259 \| 2200 \| 0.6948 \| 0.5426 \|
	\| 0.755 \| 0.4452 \| 2300 \| 0.6796 \| 0.5159 \|
	\| 0.755 \| 0.4646 \| 2400 \| 0.6899 \| 0.5305 \|
	\| 0.6884 \| 0.4839 \| 2500 \| 0.6736 \| 0.5103 \|
	\| 0.6884 \| 0.5033 \| 2600 \| 0.6728 \| 0.5257 \|
	\| 0.6884 \| 0.5226 \| 2700 \| 0.6537 \| 0.5027 \|
	\| 0.6884 \| 0.5420 \| 2800 \| 0.6314 \| 0.4823 \|
	\| 0.6884 \| 0.5614 \| 2900 \| 0.6317 \| 0.4830 \|
	\| 0.6756 \| 0.5807 \| 3000 \| 0.6204 \| 0.4761 \|
	\| 0.6756 \| 0.6001 \| 3100 \| 0.6311 \| 0.4811 \|
	\| 0.6756 \| 0.6194 \| 3200 \| 0.6236 \| 0.4863 \|
	\| 0.6756 \| 0.6388 \| 3300 \| 0.6224 \| 0.4629 \|
	\| 0.6756 \| 0.6581 \| 3400 \| 0.5973 \| 0.4623 \|
	\| 0.6435 \| 0.6775 \| 3500 \| 0.5913 \| 0.4708 \|
	\| 0.6435 \| 0.6969 \| 3600 \| 0.6087 \| 0.4744 \|
	\| 0.6435 \| 0.7162 \| 3700 \| 0.5827 \| 0.4521 \|
	\| 0.6435 \| 0.7356 \| 3800 \| 0.5875 \| 0.4608 \|
	\| 0.6435 \| 0.7549 \| 3900 \| 0.5925 \| 0.4557 \|
	\| 0.6282 \| 0.7743 \| 4000 \| 0.5799 \| 0.4494 \|
	\| 0.6282 \| 0.7937 \| 4100 \| 0.5679 \| 0.4526 \|
	\| 0.6282 \| 0.8130 \| 4200 \| 0.5700 \| 0.4550 \|
	\| 0.6282 \| 0.8324 \| 4300 \| 0.5610 \| 0.4343 \|
	\| 0.6282 \| 0.8517 \| 4400 \| 0.5616 \| 0.4273 \|
	\| 0.5937 \| 0.8711 \| 4500 \| 0.5464 \| 0.4221 \|
	\| 0.5937 \| 0.8904 \| 4600 \| 0.5486 \| 0.4288 \|
	\| 0.5937 \| 0.9098 \| 4700 \| 0.5308 \| 0.4167 \|
	\| 0.5937 \| 0.9292 \| 4800 \| 0.5520 \| 0.4200 \|
	\| 0.5937 \| 0.9485 \| 4900 \| 0.5321 \| 0.4180 \|
	\| 0.5659 \| 0.9679 \| 5000 \| 0.5333 \| 0.4176 \|
	\| 0.5659 \| 0.9872 \| 5100 \| 0.5260 \| 0.4111 \|
	\| 0.5659 \| 1.0066 \| 5200 \| 0.5185 \| 0.3974 \|
	\| 0.5659 \| 1.0259 \| 5300 \| 0.5147 \| 0.3918 \|
	\| 0.5659 \| 1.0453 \| 5400 \| 0.5155 \| 0.3976 \|
	\| 0.4928 \| 1.0647 \| 5500 \| 0.5058 \| 0.3936 \|
	\| 0.4928 \| 1.0840 \| 5600 \| 0.5048 \| 0.3965 \|
	\| 0.4928 \| 1.1034 \| 5700 \| 0.5011 \| 0.3818 \|
	\| 0.4928 \| 1.1227 \| 5800 \| 0.4965 \| 0.3830 \|
	\| 0.4928 \| 1.1421 \| 5900 \| 0.4969 \| 0.3840 \|
	\| 0.4619 \| 1.1614 \| 6000 \| 0.4863 \| 0.3800 \|
	\| 0.4619 \| 1.1808 \| 6100 \| 0.4908 \| 0.3800 \|
	\| 0.4619 \| 1.2002 \| 6200 \| 0.4835 \| 0.3712 \|
	\| 0.4619 \| 1.2195 \| 6300 \| 0.4927 \| 0.3767 \|
	\| 0.4619 \| 1.2389 \| 6400 \| 0.4942 \| 0.3683 \|
	\| 0.4421 \| 1.2582 \| 6500 \| 0.4834 \| 0.3739 \|
	\| 0.4421 \| 1.2776 \| 6600 \| 0.4751 \| 0.3634 \|
	\| 0.4421 \| 1.2969 \| 6700 \| 0.4734 \| 0.3633 \|
	\| 0.4421 \| 1.3163 \| 6800 \| 0.4685 \| 0.3645 \|
	\| 0.4421 \| 1.3357 \| 6900 \| 0.4654 \| 0.3625 \|
	\| 0.4304 \| 1.3550 \| 7000 \| 0.4742 \| 0.3615 \|
	\| 0.4304 \| 1.3744 \| 7100 \| 0.4645 \| 0.3596 \|
	\| 0.4304 \| 1.3937 \| 7200 \| 0.4599 \| 0.3594 \|
	\| 0.4304 \| 1.4131 \| 7300 \| 0.4554 \| 0.3555 \|
	\| 0.4304 \| 1.4324 \| 7400 \| 0.4578 \| 0.3578 \|
	\| 0.4275 \| 1.4518 \| 7500 \| 0.4518 \| 0.3522 \|
	\| 0.4275 \| 1.4712 \| 7600 \| 0.4480 \| 0.3511 \|
	\| 0.4275 \| 1.4905 \| 7700 \| 0.4465 \| 0.3501 \|
	\| 0.4275 \| 1.5099 \| 7800 \| 0.4454 \| 0.3428 \|
	\| 0.4275 \| 1.5292 \| 7900 \| 0.4427 \| 0.3439 \|
	\| 0.4089 \| 1.5486 \| 8000 \| 0.4376 \| 0.3407 \|
	\| 0.4089 \| 1.5679 \| 8100 \| 0.4396 \| 0.3415 \|
	\| 0.4089 \| 1.5873 \| 8200 \| 0.4343 \| 0.3422 \|
	\| 0.4089 \| 1.6067 \| 8300 \| 0.4359 \| 0.3406 \|
	\| 0.4089 \| 1.6260 \| 8400 \| 0.4358 \| 0.3373 \|
	\| 0.4005 \| 1.6454 \| 8500 \| 0.4331 \| 0.3365 \|
	\| 0.4005 \| 1.6647 \| 8600 \| 0.4302 \| 0.3353 \|
	\| 0.4005 \| 1.6841 \| 8700 \| 0.4308 \| 0.3355 \|
	\| 0.4005 \| 1.7034 \| 8800 \| 0.4258 \| 0.3351 \|
	\| 0.4005 \| 1.7228 \| 8900 \| 0.4222 \| 0.3353 \|
	\| 0.3879 \| 1.7422 \| 9000 \| 0.4238 \| 0.3312 \|
	\| 0.3879 \| 1.7615 \| 9100 \| 0.4245 \| 0.3288 \|
	\| 0.3879 \| 1.7809 \| 9200 \| 0.4206 \| 0.3264 \|
	\| 0.3879 \| 1.8002 \| 9300 \| 0.4201 \| 0.3284 \|
	\| 0.3879 \| 1.8196 \| 9400 \| 0.4189 \| 0.3246 \|
	\| 0.369 \| 1.8389 \| 9500 \| 0.4160 \| 0.3258 \|
	\| 0.369 \| 1.8583 \| 9600 \| 0.4142 \| 0.3248 \|
	\| 0.369 \| 1.8777 \| 9700 \| 0.4131 \| 0.3252 \|
	\| 0.369 \| 1.8970 \| 9800 \| 0.4128 \| 0.3228 \|
	\| 0.369 \| 1.9164 \| 9900 \| 0.4122 \| 0.3221 \|
	\| 0.3738 \| 1.9357 \| 10000 \| 0.4122 \| 0.3223 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.3.1+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1