End of training

ddfd34c verified 3 months ago

No virus

7.79 kB

	---
	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-enc-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-enc-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: 0.4095
	- Wer: 0.3271

	## 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 \| 3.5475 \| 1.0 \|
	\| No log \| 0.0387 \| 200 \| 3.0259 \| 1.0 \|
	\| No log \| 0.0581 \| 300 \| 3.0887 \| 1.0 \|
	\| No log \| 0.0774 \| 400 \| 2.3822 \| 0.9972 \|
	\| 4.0938 \| 0.0968 \| 500 \| 1.4547 \| 0.9020 \|
	\| 4.0938 \| 0.1161 \| 600 \| 1.2603 \| 0.8510 \|
	\| 4.0938 \| 0.1355 \| 700 \| 1.0940 \| 0.7655 \|
	\| 4.0938 \| 0.1549 \| 800 \| 1.0705 \| 0.7602 \|
	\| 4.0938 \| 0.1742 \| 900 \| 0.9356 \| 0.6973 \|
	\| 1.0597 \| 0.1936 \| 1000 \| 0.9104 \| 0.6766 \|
	\| 1.0597 \| 0.2129 \| 1100 \| 0.8879 \| 0.6570 \|
	\| 1.0597 \| 0.2323 \| 1200 \| 0.8595 \| 0.6612 \|
	\| 1.0597 \| 0.2516 \| 1300 \| 0.8352 \| 0.6075 \|
	\| 1.0597 \| 0.2710 \| 1400 \| 0.7912 \| 0.6033 \|
	\| 0.8484 \| 0.2904 \| 1500 \| 0.7862 \| 0.6067 \|
	\| 0.8484 \| 0.3097 \| 1600 \| 0.7790 \| 0.6009 \|
	\| 0.8484 \| 0.3291 \| 1700 \| 0.7678 \| 0.5629 \|
	\| 0.8484 \| 0.3484 \| 1800 \| 0.7515 \| 0.5799 \|
	\| 0.8484 \| 0.3678 \| 1900 \| 0.7424 \| 0.5859 \|
	\| 0.764 \| 0.3871 \| 2000 \| 0.7130 \| 0.5521 \|
	\| 0.764 \| 0.4065 \| 2100 \| 0.7114 \| 0.5408 \|
	\| 0.764 \| 0.4259 \| 2200 \| 0.7229 \| 0.5577 \|
	\| 0.764 \| 0.4452 \| 2300 \| 0.6773 \| 0.5160 \|
	\| 0.764 \| 0.4646 \| 2400 \| 0.6784 \| 0.5178 \|
	\| 0.6868 \| 0.4839 \| 2500 \| 0.6720 \| 0.5262 \|
	\| 0.6868 \| 0.5033 \| 2600 \| 0.6804 \| 0.5337 \|
	\| 0.6868 \| 0.5226 \| 2700 \| 0.6599 \| 0.5024 \|
	\| 0.6868 \| 0.5420 \| 2800 \| 0.6287 \| 0.4902 \|
	\| 0.6868 \| 0.5614 \| 2900 \| 0.6304 \| 0.4947 \|
	\| 0.6761 \| 0.5807 \| 3000 \| 0.6258 \| 0.4851 \|
	\| 0.6761 \| 0.6001 \| 3100 \| 0.6311 \| 0.4990 \|
	\| 0.6761 \| 0.6194 \| 3200 \| 0.6172 \| 0.4901 \|
	\| 0.6761 \| 0.6388 \| 3300 \| 0.6187 \| 0.4666 \|
	\| 0.6761 \| 0.6581 \| 3400 \| 0.6045 \| 0.4725 \|
	\| 0.6462 \| 0.6775 \| 3500 \| 0.5950 \| 0.4717 \|
	\| 0.6462 \| 0.6969 \| 3600 \| 0.5903 \| 0.4602 \|
	\| 0.6462 \| 0.7162 \| 3700 \| 0.5865 \| 0.4727 \|
	\| 0.6462 \| 0.7356 \| 3800 \| 0.5820 \| 0.4590 \|
	\| 0.6462 \| 0.7549 \| 3900 \| 0.6026 \| 0.4830 \|
	\| 0.6193 \| 0.7743 \| 4000 \| 0.5807 \| 0.4496 \|
	\| 0.6193 \| 0.7937 \| 4100 \| 0.5621 \| 0.4486 \|
	\| 0.6193 \| 0.8130 \| 4200 \| 0.5730 \| 0.4593 \|
	\| 0.6193 \| 0.8324 \| 4300 \| 0.5592 \| 0.4374 \|
	\| 0.6193 \| 0.8517 \| 4400 \| 0.5621 \| 0.4239 \|
	\| 0.59 \| 0.8711 \| 4500 \| 0.5458 \| 0.4304 \|
	\| 0.59 \| 0.8904 \| 4600 \| 0.5406 \| 0.4271 \|
	\| 0.59 \| 0.9098 \| 4700 \| 0.5269 \| 0.4132 \|
	\| 0.59 \| 0.9292 \| 4800 \| 0.5362 \| 0.4215 \|
	\| 0.59 \| 0.9485 \| 4900 \| 0.5226 \| 0.4163 \|
	\| 0.5636 \| 0.9679 \| 5000 \| 0.5297 \| 0.4148 \|
	\| 0.5636 \| 0.9872 \| 5100 \| 0.5226 \| 0.4136 \|
	\| 0.5636 \| 1.0066 \| 5200 \| 0.5239 \| 0.4054 \|
	\| 0.5636 \| 1.0259 \| 5300 \| 0.5383 \| 0.4058 \|
	\| 0.5636 \| 1.0453 \| 5400 \| 0.5125 \| 0.4067 \|
	\| 0.4924 \| 1.0647 \| 5500 \| 0.5029 \| 0.3953 \|
	\| 0.4924 \| 1.0840 \| 5600 \| 0.5054 \| 0.3932 \|
	\| 0.4924 \| 1.1034 \| 5700 \| 0.4969 \| 0.3894 \|
	\| 0.4924 \| 1.1227 \| 5800 \| 0.4935 \| 0.3851 \|
	\| 0.4924 \| 1.1421 \| 5900 \| 0.4977 \| 0.3817 \|
	\| 0.4602 \| 1.1614 \| 6000 \| 0.4863 \| 0.3874 \|
	\| 0.4602 \| 1.1808 \| 6100 \| 0.4906 \| 0.3777 \|
	\| 0.4602 \| 1.2002 \| 6200 \| 0.4891 \| 0.3764 \|
	\| 0.4602 \| 1.2195 \| 6300 \| 0.4881 \| 0.3801 \|
	\| 0.4602 \| 1.2389 \| 6400 \| 0.4814 \| 0.3727 \|
	\| 0.4407 \| 1.2582 \| 6500 \| 0.4714 \| 0.3772 \|
	\| 0.4407 \| 1.2776 \| 6600 \| 0.4739 \| 0.3706 \|
	\| 0.4407 \| 1.2969 \| 6700 \| 0.4692 \| 0.3714 \|
	\| 0.4407 \| 1.3163 \| 6800 \| 0.4673 \| 0.3728 \|
	\| 0.4407 \| 1.3357 \| 6900 \| 0.4610 \| 0.3678 \|
	\| 0.4284 \| 1.3550 \| 7000 \| 0.4730 \| 0.3653 \|
	\| 0.4284 \| 1.3744 \| 7100 \| 0.4606 \| 0.3640 \|
	\| 0.4284 \| 1.3937 \| 7200 \| 0.4572 \| 0.3620 \|
	\| 0.4284 \| 1.4131 \| 7300 \| 0.4575 \| 0.3630 \|
	\| 0.4284 \| 1.4324 \| 7400 \| 0.4578 \| 0.3590 \|
	\| 0.4299 \| 1.4518 \| 7500 \| 0.4477 \| 0.3569 \|
	\| 0.4299 \| 1.4712 \| 7600 \| 0.4442 \| 0.3552 \|
	\| 0.4299 \| 1.4905 \| 7700 \| 0.4420 \| 0.3546 \|
	\| 0.4299 \| 1.5099 \| 7800 \| 0.4437 \| 0.3483 \|
	\| 0.4299 \| 1.5292 \| 7900 \| 0.4373 \| 0.3486 \|
	\| 0.408 \| 1.5486 \| 8000 \| 0.4336 \| 0.3464 \|
	\| 0.408 \| 1.5679 \| 8100 \| 0.4348 \| 0.3448 \|
	\| 0.408 \| 1.5873 \| 8200 \| 0.4276 \| 0.3418 \|
	\| 0.408 \| 1.6067 \| 8300 \| 0.4294 \| 0.3399 \|
	\| 0.408 \| 1.6260 \| 8400 \| 0.4272 \| 0.3388 \|
	\| 0.3964 \| 1.6454 \| 8500 \| 0.4311 \| 0.3409 \|
	\| 0.3964 \| 1.6647 \| 8600 \| 0.4260 \| 0.3381 \|
	\| 0.3964 \| 1.6841 \| 8700 \| 0.4260 \| 0.3371 \|
	\| 0.3964 \| 1.7034 \| 8800 \| 0.4260 \| 0.3364 \|
	\| 0.3964 \| 1.7228 \| 8900 \| 0.4215 \| 0.3351 \|
	\| 0.3866 \| 1.7422 \| 9000 \| 0.4234 \| 0.3330 \|
	\| 0.3866 \| 1.7615 \| 9100 \| 0.4210 \| 0.3319 \|
	\| 0.3866 \| 1.7809 \| 9200 \| 0.4156 \| 0.3301 \|
	\| 0.3866 \| 1.8002 \| 9300 \| 0.4158 \| 0.3303 \|
	\| 0.3866 \| 1.8196 \| 9400 \| 0.4155 \| 0.3294 \|
	\| 0.37 \| 1.8389 \| 9500 \| 0.4137 \| 0.3292 \|
	\| 0.37 \| 1.8583 \| 9600 \| 0.4120 \| 0.3284 \|
	\| 0.37 \| 1.8777 \| 9700 \| 0.4109 \| 0.3301 \|
	\| 0.37 \| 1.8970 \| 9800 \| 0.4100 \| 0.3279 \|
	\| 0.37 \| 1.9164 \| 9900 \| 0.4095 \| 0.3267 \|
	\| 0.371 \| 1.9357 \| 10000 \| 0.4095 \| 0.3271 \|


	### 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-enc-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-enc-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: 0.4095
	- Wer: 0.3271

	## 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 \| 3.5475 \| 1.0 \|
	\| No log \| 0.0387 \| 200 \| 3.0259 \| 1.0 \|
	\| No log \| 0.0581 \| 300 \| 3.0887 \| 1.0 \|
	\| No log \| 0.0774 \| 400 \| 2.3822 \| 0.9972 \|
	\| 4.0938 \| 0.0968 \| 500 \| 1.4547 \| 0.9020 \|
	\| 4.0938 \| 0.1161 \| 600 \| 1.2603 \| 0.8510 \|
	\| 4.0938 \| 0.1355 \| 700 \| 1.0940 \| 0.7655 \|
	\| 4.0938 \| 0.1549 \| 800 \| 1.0705 \| 0.7602 \|
	\| 4.0938 \| 0.1742 \| 900 \| 0.9356 \| 0.6973 \|
	\| 1.0597 \| 0.1936 \| 1000 \| 0.9104 \| 0.6766 \|
	\| 1.0597 \| 0.2129 \| 1100 \| 0.8879 \| 0.6570 \|
	\| 1.0597 \| 0.2323 \| 1200 \| 0.8595 \| 0.6612 \|
	\| 1.0597 \| 0.2516 \| 1300 \| 0.8352 \| 0.6075 \|
	\| 1.0597 \| 0.2710 \| 1400 \| 0.7912 \| 0.6033 \|
	\| 0.8484 \| 0.2904 \| 1500 \| 0.7862 \| 0.6067 \|
	\| 0.8484 \| 0.3097 \| 1600 \| 0.7790 \| 0.6009 \|
	\| 0.8484 \| 0.3291 \| 1700 \| 0.7678 \| 0.5629 \|
	\| 0.8484 \| 0.3484 \| 1800 \| 0.7515 \| 0.5799 \|
	\| 0.8484 \| 0.3678 \| 1900 \| 0.7424 \| 0.5859 \|
	\| 0.764 \| 0.3871 \| 2000 \| 0.7130 \| 0.5521 \|
	\| 0.764 \| 0.4065 \| 2100 \| 0.7114 \| 0.5408 \|
	\| 0.764 \| 0.4259 \| 2200 \| 0.7229 \| 0.5577 \|
	\| 0.764 \| 0.4452 \| 2300 \| 0.6773 \| 0.5160 \|
	\| 0.764 \| 0.4646 \| 2400 \| 0.6784 \| 0.5178 \|
	\| 0.6868 \| 0.4839 \| 2500 \| 0.6720 \| 0.5262 \|
	\| 0.6868 \| 0.5033 \| 2600 \| 0.6804 \| 0.5337 \|
	\| 0.6868 \| 0.5226 \| 2700 \| 0.6599 \| 0.5024 \|
	\| 0.6868 \| 0.5420 \| 2800 \| 0.6287 \| 0.4902 \|
	\| 0.6868 \| 0.5614 \| 2900 \| 0.6304 \| 0.4947 \|
	\| 0.6761 \| 0.5807 \| 3000 \| 0.6258 \| 0.4851 \|
	\| 0.6761 \| 0.6001 \| 3100 \| 0.6311 \| 0.4990 \|
	\| 0.6761 \| 0.6194 \| 3200 \| 0.6172 \| 0.4901 \|
	\| 0.6761 \| 0.6388 \| 3300 \| 0.6187 \| 0.4666 \|
	\| 0.6761 \| 0.6581 \| 3400 \| 0.6045 \| 0.4725 \|
	\| 0.6462 \| 0.6775 \| 3500 \| 0.5950 \| 0.4717 \|
	\| 0.6462 \| 0.6969 \| 3600 \| 0.5903 \| 0.4602 \|
	\| 0.6462 \| 0.7162 \| 3700 \| 0.5865 \| 0.4727 \|
	\| 0.6462 \| 0.7356 \| 3800 \| 0.5820 \| 0.4590 \|
	\| 0.6462 \| 0.7549 \| 3900 \| 0.6026 \| 0.4830 \|
	\| 0.6193 \| 0.7743 \| 4000 \| 0.5807 \| 0.4496 \|
	\| 0.6193 \| 0.7937 \| 4100 \| 0.5621 \| 0.4486 \|
	\| 0.6193 \| 0.8130 \| 4200 \| 0.5730 \| 0.4593 \|
	\| 0.6193 \| 0.8324 \| 4300 \| 0.5592 \| 0.4374 \|
	\| 0.6193 \| 0.8517 \| 4400 \| 0.5621 \| 0.4239 \|
	\| 0.59 \| 0.8711 \| 4500 \| 0.5458 \| 0.4304 \|
	\| 0.59 \| 0.8904 \| 4600 \| 0.5406 \| 0.4271 \|
	\| 0.59 \| 0.9098 \| 4700 \| 0.5269 \| 0.4132 \|
	\| 0.59 \| 0.9292 \| 4800 \| 0.5362 \| 0.4215 \|
	\| 0.59 \| 0.9485 \| 4900 \| 0.5226 \| 0.4163 \|
	\| 0.5636 \| 0.9679 \| 5000 \| 0.5297 \| 0.4148 \|
	\| 0.5636 \| 0.9872 \| 5100 \| 0.5226 \| 0.4136 \|
	\| 0.5636 \| 1.0066 \| 5200 \| 0.5239 \| 0.4054 \|
	\| 0.5636 \| 1.0259 \| 5300 \| 0.5383 \| 0.4058 \|
	\| 0.5636 \| 1.0453 \| 5400 \| 0.5125 \| 0.4067 \|
	\| 0.4924 \| 1.0647 \| 5500 \| 0.5029 \| 0.3953 \|
	\| 0.4924 \| 1.0840 \| 5600 \| 0.5054 \| 0.3932 \|
	\| 0.4924 \| 1.1034 \| 5700 \| 0.4969 \| 0.3894 \|
	\| 0.4924 \| 1.1227 \| 5800 \| 0.4935 \| 0.3851 \|
	\| 0.4924 \| 1.1421 \| 5900 \| 0.4977 \| 0.3817 \|
	\| 0.4602 \| 1.1614 \| 6000 \| 0.4863 \| 0.3874 \|
	\| 0.4602 \| 1.1808 \| 6100 \| 0.4906 \| 0.3777 \|
	\| 0.4602 \| 1.2002 \| 6200 \| 0.4891 \| 0.3764 \|
	\| 0.4602 \| 1.2195 \| 6300 \| 0.4881 \| 0.3801 \|
	\| 0.4602 \| 1.2389 \| 6400 \| 0.4814 \| 0.3727 \|
	\| 0.4407 \| 1.2582 \| 6500 \| 0.4714 \| 0.3772 \|
	\| 0.4407 \| 1.2776 \| 6600 \| 0.4739 \| 0.3706 \|
	\| 0.4407 \| 1.2969 \| 6700 \| 0.4692 \| 0.3714 \|
	\| 0.4407 \| 1.3163 \| 6800 \| 0.4673 \| 0.3728 \|
	\| 0.4407 \| 1.3357 \| 6900 \| 0.4610 \| 0.3678 \|
	\| 0.4284 \| 1.3550 \| 7000 \| 0.4730 \| 0.3653 \|
	\| 0.4284 \| 1.3744 \| 7100 \| 0.4606 \| 0.3640 \|
	\| 0.4284 \| 1.3937 \| 7200 \| 0.4572 \| 0.3620 \|
	\| 0.4284 \| 1.4131 \| 7300 \| 0.4575 \| 0.3630 \|
	\| 0.4284 \| 1.4324 \| 7400 \| 0.4578 \| 0.3590 \|
	\| 0.4299 \| 1.4518 \| 7500 \| 0.4477 \| 0.3569 \|
	\| 0.4299 \| 1.4712 \| 7600 \| 0.4442 \| 0.3552 \|
	\| 0.4299 \| 1.4905 \| 7700 \| 0.4420 \| 0.3546 \|
	\| 0.4299 \| 1.5099 \| 7800 \| 0.4437 \| 0.3483 \|
	\| 0.4299 \| 1.5292 \| 7900 \| 0.4373 \| 0.3486 \|
	\| 0.408 \| 1.5486 \| 8000 \| 0.4336 \| 0.3464 \|
	\| 0.408 \| 1.5679 \| 8100 \| 0.4348 \| 0.3448 \|
	\| 0.408 \| 1.5873 \| 8200 \| 0.4276 \| 0.3418 \|
	\| 0.408 \| 1.6067 \| 8300 \| 0.4294 \| 0.3399 \|
	\| 0.408 \| 1.6260 \| 8400 \| 0.4272 \| 0.3388 \|
	\| 0.3964 \| 1.6454 \| 8500 \| 0.4311 \| 0.3409 \|
	\| 0.3964 \| 1.6647 \| 8600 \| 0.4260 \| 0.3381 \|
	\| 0.3964 \| 1.6841 \| 8700 \| 0.4260 \| 0.3371 \|
	\| 0.3964 \| 1.7034 \| 8800 \| 0.4260 \| 0.3364 \|
	\| 0.3964 \| 1.7228 \| 8900 \| 0.4215 \| 0.3351 \|
	\| 0.3866 \| 1.7422 \| 9000 \| 0.4234 \| 0.3330 \|
	\| 0.3866 \| 1.7615 \| 9100 \| 0.4210 \| 0.3319 \|
	\| 0.3866 \| 1.7809 \| 9200 \| 0.4156 \| 0.3301 \|
	\| 0.3866 \| 1.8002 \| 9300 \| 0.4158 \| 0.3303 \|
	\| 0.3866 \| 1.8196 \| 9400 \| 0.4155 \| 0.3294 \|
	\| 0.37 \| 1.8389 \| 9500 \| 0.4137 \| 0.3292 \|
	\| 0.37 \| 1.8583 \| 9600 \| 0.4120 \| 0.3284 \|
	\| 0.37 \| 1.8777 \| 9700 \| 0.4109 \| 0.3301 \|
	\| 0.37 \| 1.8970 \| 9800 \| 0.4100 \| 0.3279 \|
	\| 0.37 \| 1.9164 \| 9900 \| 0.4095 \| 0.3267 \|
	\| 0.371 \| 1.9357 \| 10000 \| 0.4095 \| 0.3271 \|


	### Framework versions

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