wav2vec2-xlsr-53-ft-btb-cy / README.md

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	---
	license: apache-2.0
	base_model: facebook/wav2vec2-large-xlsr-53
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-xlsr-53-ft-btb-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-cy

	This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9669
	- Wer: 0.6125

	## 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: 16
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 15.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|
	\| No log \| 0.1414 \| 100 \| 3.7427 \| 1.0 \|
	\| No log \| 0.2829 \| 200 \| 2.9179 \| 1.0 \|
	\| No log \| 0.4243 \| 300 \| 2.8036 \| 1.0 \|
	\| No log \| 0.5658 \| 400 \| 1.2196 \| 0.8934 \|
	\| 3.574 \| 0.7072 \| 500 \| 0.9860 \| 0.7276 \|
	\| 3.574 \| 0.8487 \| 600 \| 0.8392 \| 0.6504 \|
	\| 3.574 \| 0.9901 \| 700 \| 0.7804 \| 0.6029 \|
	\| 3.574 \| 1.1315 \| 800 \| 0.6122 \| 0.4909 \|
	\| 3.574 \| 1.2730 \| 900 \| 0.5901 \| 0.4883 \|
	\| 0.811 \| 1.4144 \| 1000 \| 0.5500 \| 0.4508 \|
	\| 0.811 \| 1.5559 \| 1100 \| 0.5232 \| 0.4142 \|
	\| 0.811 \| 1.6973 \| 1200 \| 0.5186 \| 0.4065 \|
	\| 0.811 \| 1.8388 \| 1300 \| 0.4953 \| 0.3929 \|
	\| 0.811 \| 1.9802 \| 1400 \| 0.4880 \| 0.3928 \|
	\| 0.6459 \| 2.1216 \| 1500 \| 0.4645 \| 0.3692 \|
	\| 0.6459 \| 2.2631 \| 1600 \| 0.4666 \| 0.3586 \|
	\| 0.6459 \| 2.4045 \| 1700 \| 0.4502 \| 0.3593 \|
	\| 0.6459 \| 2.5460 \| 1800 \| 0.4528 \| 0.3638 \|
	\| 0.6459 \| 2.6874 \| 1900 \| 0.4665 \| 0.3926 \|
	\| 0.5306 \| 2.8289 \| 2000 \| 0.4329 \| 0.3505 \|
	\| 0.5306 \| 2.9703 \| 2100 \| 0.4245 \| 0.3374 \|
	\| 0.5306 \| 3.1117 \| 2200 \| 0.4377 \| 0.3340 \|
	\| 0.5306 \| 3.2532 \| 2300 \| 0.4272 \| 0.3337 \|
	\| 0.5306 \| 3.3946 \| 2400 \| 0.4335 \| 0.3326 \|
	\| 0.4628 \| 3.5361 \| 2500 \| 0.4268 \| 0.3275 \|
	\| 0.4628 \| 3.6775 \| 2600 \| 0.4502 \| 0.3409 \|
	\| 0.4628 \| 3.8190 \| 2700 \| 0.6345 \| 0.4390 \|
	\| 0.4628 \| 3.9604 \| 2800 \| 1.0203 \| 0.6403 \|
	\| 0.4628 \| 4.1018 \| 2900 \| 1.2208 \| 0.7922 \|
	\| 0.8685 \| 4.2433 \| 3000 \| 1.1018 \| 0.7387 \|
	\| 0.8685 \| 4.3847 \| 3100 \| 1.2497 \| 0.8062 \|
	\| 0.8685 \| 4.5262 \| 3200 \| 1.6165 \| 0.9616 \|
	\| 0.8685 \| 4.6676 \| 3300 \| 1.4655 \| 0.9217 \|
	\| 0.8685 \| 4.8091 \| 3400 \| 1.0288 \| 0.7465 \|
	\| 1.3918 \| 4.9505 \| 3500 \| 0.9067 \| 0.5948 \|
	\| 1.3918 \| 5.0919 \| 3600 \| 0.9486 \| 0.6353 \|
	\| 1.3918 \| 5.2334 \| 3700 \| 0.8674 \| 0.5428 \|
	\| 1.3918 \| 5.3748 \| 3800 \| 0.9403 \| 0.5793 \|
	\| 1.3918 \| 5.5163 \| 3900 \| 0.9481 \| 0.5764 \|
	\| 1.0402 \| 5.6577 \| 4000 \| 1.0176 \| 0.8257 \|
	\| 1.0402 \| 5.7992 \| 4100 \| 0.9857 \| 0.6343 \|
	\| 1.0402 \| 5.9406 \| 4200 \| 1.3289 \| 0.9014 \|
	\| 1.0402 \| 6.0820 \| 4300 \| 2.0891 \| 0.7125 \|
	\| 1.0402 \| 6.2235 \| 4400 \| 1.2563 \| 0.7696 \|
	\| 1.2886 \| 6.3649 \| 4500 \| 1.1441 \| 0.6927 \|
	\| 1.2886 \| 6.5064 \| 4600 \| 1.0626 \| 0.6573 \|
	\| 1.2886 \| 6.6478 \| 4700 \| 0.9997 \| 0.6423 \|
	\| 1.2886 \| 6.7893 \| 4800 \| 0.9814 \| 0.6380 \|
	\| 1.2886 \| 6.9307 \| 4900 \| 1.0955 \| 0.7651 \|
	\| 1.0984 \| 7.0721 \| 5000 \| 0.9213 \| 0.5883 \|
	\| 1.0984 \| 7.2136 \| 5100 \| 0.8885 \| 0.5933 \|
	\| 1.0984 \| 7.3550 \| 5200 \| 0.9001 \| 0.5899 \|
	\| 1.0984 \| 7.4965 \| 5300 \| 0.8784 \| 0.5859 \|
	\| 1.0984 \| 7.6379 \| 5400 \| 0.9072 \| 0.5898 \|
	\| 0.9659 \| 7.7793 \| 5500 \| 0.8812 \| 0.5841 \|
	\| 0.9659 \| 7.9208 \| 5600 \| 0.8912 \| 0.5855 \|
	\| 0.9659 \| 8.0622 \| 5700 \| 0.8816 \| 0.5807 \|
	\| 0.9659 \| 8.2037 \| 5800 \| 0.8914 \| 0.5803 \|
	\| 0.9659 \| 8.3451 \| 5900 \| 0.8956 \| 0.5810 \|
	\| 0.9679 \| 8.4866 \| 6000 \| 0.9162 \| 0.5780 \|
	\| 0.9679 \| 8.6280 \| 6100 \| 0.9409 \| 0.5810 \|
	\| 0.9679 \| 8.7694 \| 6200 \| 0.9371 \| 0.5781 \|
	\| 0.9679 \| 8.9109 \| 6300 \| 0.9417 \| 0.5790 \|
	\| 0.9679 \| 9.0523 \| 6400 \| 0.9664 \| 0.5784 \|
	\| 1.0241 \| 9.1938 \| 6500 \| 0.9720 \| 0.5775 \|
	\| 1.0241 \| 9.3352 \| 6600 \| 0.9841 \| 0.5784 \|
	\| 1.0241 \| 9.4767 \| 6700 \| 0.9574 \| 0.5887 \|
	\| 1.0241 \| 9.6181 \| 6800 \| 1.0725 \| 0.6068 \|
	\| 1.0241 \| 9.7595 \| 6900 \| 1.0362 \| 0.6000 \|
	\| 1.0797 \| 9.9010 \| 7000 \| 1.0117 \| 0.5914 \|
	\| 1.0797 \| 10.0424 \| 7100 \| 0.9563 \| 0.6058 \|
	\| 1.0797 \| 10.1839 \| 7200 \| 0.9664 \| 0.5978 \|
	\| 1.0797 \| 10.3253 \| 7300 \| 1.0209 \| 0.6022 \|
	\| 1.0797 \| 10.4668 \| 7400 \| 0.9849 \| 0.5975 \|
	\| 1.0701 \| 10.6082 \| 7500 \| 0.9719 \| 0.6057 \|
	\| 1.0701 \| 10.7496 \| 7600 \| 0.9670 \| 0.6123 \|
	\| 1.0701 \| 10.8911 \| 7700 \| 0.9669 \| 0.6125 \|
	\| 1.0701 \| 11.0325 \| 7800 \| 0.9669 \| 0.6125 \|
	\| 1.0701 \| 11.1740 \| 7900 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.3154 \| 8000 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.4569 \| 8100 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.5983 \| 8200 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.7397 \| 8300 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.8812 \| 8400 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.0226 \| 8500 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.1641 \| 8600 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.3055 \| 8700 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.4470 \| 8800 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.5884 \| 8900 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 12.7298 \| 9000 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 12.8713 \| 9100 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 13.0127 \| 9200 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 13.1542 \| 9300 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 13.2956 \| 9400 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.4371 \| 9500 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.5785 \| 9600 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.7199 \| 9700 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.8614 \| 9800 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 14.0028 \| 9900 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.1443 \| 10000 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.2857 \| 10100 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.4272 \| 10200 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.5686 \| 10300 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.7100 \| 10400 \| 0.9669 \| 0.6125 \|
	\| 1.0585 \| 14.8515 \| 10500 \| 0.9669 \| 0.6125 \|
	\| 1.0585 \| 14.9929 \| 10600 \| 0.9669 \| 0.6125 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.3.0+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1

	---
	license: apache-2.0
	base_model: facebook/wav2vec2-large-xlsr-53
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-xlsr-53-ft-btb-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-cy

	This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9669
	- Wer: 0.6125

	## 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: 16
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 15.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|
	\| No log \| 0.1414 \| 100 \| 3.7427 \| 1.0 \|
	\| No log \| 0.2829 \| 200 \| 2.9179 \| 1.0 \|
	\| No log \| 0.4243 \| 300 \| 2.8036 \| 1.0 \|
	\| No log \| 0.5658 \| 400 \| 1.2196 \| 0.8934 \|
	\| 3.574 \| 0.7072 \| 500 \| 0.9860 \| 0.7276 \|
	\| 3.574 \| 0.8487 \| 600 \| 0.8392 \| 0.6504 \|
	\| 3.574 \| 0.9901 \| 700 \| 0.7804 \| 0.6029 \|
	\| 3.574 \| 1.1315 \| 800 \| 0.6122 \| 0.4909 \|
	\| 3.574 \| 1.2730 \| 900 \| 0.5901 \| 0.4883 \|
	\| 0.811 \| 1.4144 \| 1000 \| 0.5500 \| 0.4508 \|
	\| 0.811 \| 1.5559 \| 1100 \| 0.5232 \| 0.4142 \|
	\| 0.811 \| 1.6973 \| 1200 \| 0.5186 \| 0.4065 \|
	\| 0.811 \| 1.8388 \| 1300 \| 0.4953 \| 0.3929 \|
	\| 0.811 \| 1.9802 \| 1400 \| 0.4880 \| 0.3928 \|
	\| 0.6459 \| 2.1216 \| 1500 \| 0.4645 \| 0.3692 \|
	\| 0.6459 \| 2.2631 \| 1600 \| 0.4666 \| 0.3586 \|
	\| 0.6459 \| 2.4045 \| 1700 \| 0.4502 \| 0.3593 \|
	\| 0.6459 \| 2.5460 \| 1800 \| 0.4528 \| 0.3638 \|
	\| 0.6459 \| 2.6874 \| 1900 \| 0.4665 \| 0.3926 \|
	\| 0.5306 \| 2.8289 \| 2000 \| 0.4329 \| 0.3505 \|
	\| 0.5306 \| 2.9703 \| 2100 \| 0.4245 \| 0.3374 \|
	\| 0.5306 \| 3.1117 \| 2200 \| 0.4377 \| 0.3340 \|
	\| 0.5306 \| 3.2532 \| 2300 \| 0.4272 \| 0.3337 \|
	\| 0.5306 \| 3.3946 \| 2400 \| 0.4335 \| 0.3326 \|
	\| 0.4628 \| 3.5361 \| 2500 \| 0.4268 \| 0.3275 \|
	\| 0.4628 \| 3.6775 \| 2600 \| 0.4502 \| 0.3409 \|
	\| 0.4628 \| 3.8190 \| 2700 \| 0.6345 \| 0.4390 \|
	\| 0.4628 \| 3.9604 \| 2800 \| 1.0203 \| 0.6403 \|
	\| 0.4628 \| 4.1018 \| 2900 \| 1.2208 \| 0.7922 \|
	\| 0.8685 \| 4.2433 \| 3000 \| 1.1018 \| 0.7387 \|
	\| 0.8685 \| 4.3847 \| 3100 \| 1.2497 \| 0.8062 \|
	\| 0.8685 \| 4.5262 \| 3200 \| 1.6165 \| 0.9616 \|
	\| 0.8685 \| 4.6676 \| 3300 \| 1.4655 \| 0.9217 \|
	\| 0.8685 \| 4.8091 \| 3400 \| 1.0288 \| 0.7465 \|
	\| 1.3918 \| 4.9505 \| 3500 \| 0.9067 \| 0.5948 \|
	\| 1.3918 \| 5.0919 \| 3600 \| 0.9486 \| 0.6353 \|
	\| 1.3918 \| 5.2334 \| 3700 \| 0.8674 \| 0.5428 \|
	\| 1.3918 \| 5.3748 \| 3800 \| 0.9403 \| 0.5793 \|
	\| 1.3918 \| 5.5163 \| 3900 \| 0.9481 \| 0.5764 \|
	\| 1.0402 \| 5.6577 \| 4000 \| 1.0176 \| 0.8257 \|
	\| 1.0402 \| 5.7992 \| 4100 \| 0.9857 \| 0.6343 \|
	\| 1.0402 \| 5.9406 \| 4200 \| 1.3289 \| 0.9014 \|
	\| 1.0402 \| 6.0820 \| 4300 \| 2.0891 \| 0.7125 \|
	\| 1.0402 \| 6.2235 \| 4400 \| 1.2563 \| 0.7696 \|
	\| 1.2886 \| 6.3649 \| 4500 \| 1.1441 \| 0.6927 \|
	\| 1.2886 \| 6.5064 \| 4600 \| 1.0626 \| 0.6573 \|
	\| 1.2886 \| 6.6478 \| 4700 \| 0.9997 \| 0.6423 \|
	\| 1.2886 \| 6.7893 \| 4800 \| 0.9814 \| 0.6380 \|
	\| 1.2886 \| 6.9307 \| 4900 \| 1.0955 \| 0.7651 \|
	\| 1.0984 \| 7.0721 \| 5000 \| 0.9213 \| 0.5883 \|
	\| 1.0984 \| 7.2136 \| 5100 \| 0.8885 \| 0.5933 \|
	\| 1.0984 \| 7.3550 \| 5200 \| 0.9001 \| 0.5899 \|
	\| 1.0984 \| 7.4965 \| 5300 \| 0.8784 \| 0.5859 \|
	\| 1.0984 \| 7.6379 \| 5400 \| 0.9072 \| 0.5898 \|
	\| 0.9659 \| 7.7793 \| 5500 \| 0.8812 \| 0.5841 \|
	\| 0.9659 \| 7.9208 \| 5600 \| 0.8912 \| 0.5855 \|
	\| 0.9659 \| 8.0622 \| 5700 \| 0.8816 \| 0.5807 \|
	\| 0.9659 \| 8.2037 \| 5800 \| 0.8914 \| 0.5803 \|
	\| 0.9659 \| 8.3451 \| 5900 \| 0.8956 \| 0.5810 \|
	\| 0.9679 \| 8.4866 \| 6000 \| 0.9162 \| 0.5780 \|
	\| 0.9679 \| 8.6280 \| 6100 \| 0.9409 \| 0.5810 \|
	\| 0.9679 \| 8.7694 \| 6200 \| 0.9371 \| 0.5781 \|
	\| 0.9679 \| 8.9109 \| 6300 \| 0.9417 \| 0.5790 \|
	\| 0.9679 \| 9.0523 \| 6400 \| 0.9664 \| 0.5784 \|
	\| 1.0241 \| 9.1938 \| 6500 \| 0.9720 \| 0.5775 \|
	\| 1.0241 \| 9.3352 \| 6600 \| 0.9841 \| 0.5784 \|
	\| 1.0241 \| 9.4767 \| 6700 \| 0.9574 \| 0.5887 \|
	\| 1.0241 \| 9.6181 \| 6800 \| 1.0725 \| 0.6068 \|
	\| 1.0241 \| 9.7595 \| 6900 \| 1.0362 \| 0.6000 \|
	\| 1.0797 \| 9.9010 \| 7000 \| 1.0117 \| 0.5914 \|
	\| 1.0797 \| 10.0424 \| 7100 \| 0.9563 \| 0.6058 \|
	\| 1.0797 \| 10.1839 \| 7200 \| 0.9664 \| 0.5978 \|
	\| 1.0797 \| 10.3253 \| 7300 \| 1.0209 \| 0.6022 \|
	\| 1.0797 \| 10.4668 \| 7400 \| 0.9849 \| 0.5975 \|
	\| 1.0701 \| 10.6082 \| 7500 \| 0.9719 \| 0.6057 \|
	\| 1.0701 \| 10.7496 \| 7600 \| 0.9670 \| 0.6123 \|
	\| 1.0701 \| 10.8911 \| 7700 \| 0.9669 \| 0.6125 \|
	\| 1.0701 \| 11.0325 \| 7800 \| 0.9669 \| 0.6125 \|
	\| 1.0701 \| 11.1740 \| 7900 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.3154 \| 8000 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.4569 \| 8100 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.5983 \| 8200 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.7397 \| 8300 \| 0.9669 \| 0.6125 \|
	\| 1.0518 \| 11.8812 \| 8400 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.0226 \| 8500 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.1641 \| 8600 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.3055 \| 8700 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.4470 \| 8800 \| 0.9669 \| 0.6125 \|
	\| 1.0594 \| 12.5884 \| 8900 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 12.7298 \| 9000 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 12.8713 \| 9100 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 13.0127 \| 9200 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 13.1542 \| 9300 \| 0.9669 \| 0.6125 \|
	\| 1.0584 \| 13.2956 \| 9400 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.4371 \| 9500 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.5785 \| 9600 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.7199 \| 9700 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 13.8614 \| 9800 \| 0.9669 \| 0.6125 \|
	\| 1.0556 \| 14.0028 \| 9900 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.1443 \| 10000 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.2857 \| 10100 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.4272 \| 10200 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.5686 \| 10300 \| 0.9669 \| 0.6125 \|
	\| 1.0511 \| 14.7100 \| 10400 \| 0.9669 \| 0.6125 \|
	\| 1.0585 \| 14.8515 \| 10500 \| 0.9669 \| 0.6125 \|
	\| 1.0585 \| 14.9929 \| 10600 \| 0.9669 \| 0.6125 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.3.0+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1