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	---
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- kresnik/zeroth_korean
	- generated_from_trainer
	datasets:
	- zeroth_korean_asr
	model-index:
	- name: ''
	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. -->

	#

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0639
	- Wer: 0.0449

	## 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: 7.5e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- 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: 2000
	- num_epochs: 50.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 4.603 \| 0.72 \| 500 \| 4.6572 \| 0.9985 \|
	\| 2.6314 \| 1.44 \| 1000 \| 2.0424 \| 0.9256 \|
	\| 2.2708 \| 2.16 \| 1500 \| 0.9889 \| 0.6989 \|
	\| 2.1769 \| 2.88 \| 2000 \| 0.8366 \| 0.6312 \|
	\| 2.1142 \| 3.6 \| 2500 \| 0.7555 \| 0.5998 \|
	\| 2.0084 \| 4.32 \| 3000 \| 0.7144 \| 0.6003 \|
	\| 1.9272 \| 5.04 \| 3500 \| 0.6311 \| 0.5461 \|
	\| 1.8687 \| 5.75 \| 4000 \| 0.6252 \| 0.5430 \|
	\| 1.8186 \| 6.47 \| 4500 \| 0.5491 \| 0.4988 \|
	\| 1.7364 \| 7.19 \| 5000 \| 0.5463 \| 0.4959 \|
	\| 1.6809 \| 7.91 \| 5500 \| 0.4724 \| 0.4484 \|
	\| 1.641 \| 8.63 \| 6000 \| 0.4679 \| 0.4461 \|
	\| 1.572 \| 9.35 \| 6500 \| 0.4387 \| 0.4236 \|
	\| 1.5256 \| 10.07 \| 7000 \| 0.3970 \| 0.4003 \|
	\| 1.5044 \| 10.79 \| 7500 \| 0.3690 \| 0.3893 \|
	\| 1.4563 \| 11.51 \| 8000 \| 0.3752 \| 0.3875 \|
	\| 1.394 \| 12.23 \| 8500 \| 0.3386 \| 0.3567 \|
	\| 1.3641 \| 12.95 \| 9000 \| 0.3290 \| 0.3467 \|
	\| 1.2878 \| 13.67 \| 9500 \| 0.2893 \| 0.3135 \|
	\| 1.2602 \| 14.39 \| 10000 \| 0.2723 \| 0.3029 \|
	\| 1.2302 \| 15.11 \| 10500 \| 0.2603 \| 0.2989 \|
	\| 1.1865 \| 15.83 \| 11000 \| 0.2440 \| 0.2794 \|
	\| 1.1491 \| 16.55 \| 11500 \| 0.2500 \| 0.2788 \|
	\| 1.093 \| 17.27 \| 12000 \| 0.2279 \| 0.2629 \|
	\| 1.0367 \| 17.98 \| 12500 \| 0.2076 \| 0.2443 \|
	\| 0.9954 \| 18.7 \| 13000 \| 0.1844 \| 0.2259 \|
	\| 0.99 \| 19.42 \| 13500 \| 0.1794 \| 0.2179 \|
	\| 0.9385 \| 20.14 \| 14000 \| 0.1765 \| 0.2122 \|
	\| 0.8952 \| 20.86 \| 14500 \| 0.1706 \| 0.1974 \|
	\| 0.8841 \| 21.58 \| 15000 \| 0.1791 \| 0.1969 \|
	\| 0.847 \| 22.3 \| 15500 \| 0.1780 \| 0.2060 \|
	\| 0.8669 \| 23.02 \| 16000 \| 0.1608 \| 0.1862 \|
	\| 0.8066 \| 23.74 \| 16500 \| 0.1447 \| 0.1626 \|
	\| 0.7908 \| 24.46 \| 17000 \| 0.1457 \| 0.1655 \|
	\| 0.7459 \| 25.18 \| 17500 \| 0.1350 \| 0.1445 \|
	\| 0.7218 \| 25.9 \| 18000 \| 0.1276 \| 0.1421 \|
	\| 0.703 \| 26.62 \| 18500 \| 0.1177 \| 0.1302 \|
	\| 0.685 \| 27.34 \| 19000 \| 0.1147 \| 0.1305 \|
	\| 0.6811 \| 28.06 \| 19500 \| 0.1128 \| 0.1244 \|
	\| 0.6444 \| 28.78 \| 20000 \| 0.1120 \| 0.1213 \|
	\| 0.6323 \| 29.5 \| 20500 \| 0.1137 \| 0.1166 \|
	\| 0.5998 \| 30.22 \| 21000 \| 0.1051 \| 0.1107 \|
	\| 0.5706 \| 30.93 \| 21500 \| 0.1035 \| 0.1037 \|
	\| 0.5555 \| 31.65 \| 22000 \| 0.1031 \| 0.0927 \|
	\| 0.5389 \| 32.37 \| 22500 \| 0.0997 \| 0.0900 \|
	\| 0.5201 \| 33.09 \| 23000 \| 0.0920 \| 0.0912 \|
	\| 0.5146 \| 33.81 \| 23500 \| 0.0929 \| 0.0947 \|
	\| 0.515 \| 34.53 \| 24000 \| 0.1000 \| 0.0953 \|
	\| 0.4743 \| 35.25 \| 24500 \| 0.0922 \| 0.0892 \|
	\| 0.4707 \| 35.97 \| 25000 \| 0.0852 \| 0.0808 \|
	\| 0.4456 \| 36.69 \| 25500 \| 0.0855 \| 0.0779 \|
	\| 0.443 \| 37.41 \| 26000 \| 0.0843 \| 0.0738 \|
	\| 0.4388 \| 38.13 \| 26500 \| 0.0816 \| 0.0699 \|
	\| 0.4162 \| 38.85 \| 27000 \| 0.0752 \| 0.0645 \|
	\| 0.3979 \| 39.57 \| 27500 \| 0.0761 \| 0.0621 \|
	\| 0.3889 \| 40.29 \| 28000 \| 0.0771 \| 0.0625 \|
	\| 0.3923 \| 41.01 \| 28500 \| 0.0755 \| 0.0598 \|
	\| 0.3693 \| 41.73 \| 29000 \| 0.0730 \| 0.0578 \|
	\| 0.3642 \| 42.45 \| 29500 \| 0.0739 \| 0.0598 \|
	\| 0.3532 \| 43.17 \| 30000 \| 0.0712 \| 0.0553 \|
	\| 0.3513 \| 43.88 \| 30500 \| 0.0762 \| 0.0516 \|
	\| 0.3349 \| 44.6 \| 31000 \| 0.0731 \| 0.0504 \|
	\| 0.3305 \| 45.32 \| 31500 \| 0.0725 \| 0.0507 \|
	\| 0.3285 \| 46.04 \| 32000 \| 0.0709 \| 0.0489 \|
	\| 0.3179 \| 46.76 \| 32500 \| 0.0667 \| 0.0467 \|
	\| 0.3158 \| 47.48 \| 33000 \| 0.0653 \| 0.0494 \|
	\| 0.3033 \| 48.2 \| 33500 \| 0.0638 \| 0.0456 \|
	\| 0.3023 \| 48.92 \| 34000 \| 0.0644 \| 0.0464 \|
	\| 0.2975 \| 49.64 \| 34500 \| 0.0643 \| 0.0455 \|


	### Framework versions

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

	---
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- kresnik/zeroth_korean
	- generated_from_trainer
	datasets:
	- zeroth_korean_asr
	model-index:
	- name: ''
	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. -->

	#

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0639
	- Wer: 0.0449

	## 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: 7.5e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- 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: 2000
	- num_epochs: 50.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|
	\| 4.603 \| 0.72 \| 500 \| 4.6572 \| 0.9985 \|
	\| 2.6314 \| 1.44 \| 1000 \| 2.0424 \| 0.9256 \|
	\| 2.2708 \| 2.16 \| 1500 \| 0.9889 \| 0.6989 \|
	\| 2.1769 \| 2.88 \| 2000 \| 0.8366 \| 0.6312 \|
	\| 2.1142 \| 3.6 \| 2500 \| 0.7555 \| 0.5998 \|
	\| 2.0084 \| 4.32 \| 3000 \| 0.7144 \| 0.6003 \|
	\| 1.9272 \| 5.04 \| 3500 \| 0.6311 \| 0.5461 \|
	\| 1.8687 \| 5.75 \| 4000 \| 0.6252 \| 0.5430 \|
	\| 1.8186 \| 6.47 \| 4500 \| 0.5491 \| 0.4988 \|
	\| 1.7364 \| 7.19 \| 5000 \| 0.5463 \| 0.4959 \|
	\| 1.6809 \| 7.91 \| 5500 \| 0.4724 \| 0.4484 \|
	\| 1.641 \| 8.63 \| 6000 \| 0.4679 \| 0.4461 \|
	\| 1.572 \| 9.35 \| 6500 \| 0.4387 \| 0.4236 \|
	\| 1.5256 \| 10.07 \| 7000 \| 0.3970 \| 0.4003 \|
	\| 1.5044 \| 10.79 \| 7500 \| 0.3690 \| 0.3893 \|
	\| 1.4563 \| 11.51 \| 8000 \| 0.3752 \| 0.3875 \|
	\| 1.394 \| 12.23 \| 8500 \| 0.3386 \| 0.3567 \|
	\| 1.3641 \| 12.95 \| 9000 \| 0.3290 \| 0.3467 \|
	\| 1.2878 \| 13.67 \| 9500 \| 0.2893 \| 0.3135 \|
	\| 1.2602 \| 14.39 \| 10000 \| 0.2723 \| 0.3029 \|
	\| 1.2302 \| 15.11 \| 10500 \| 0.2603 \| 0.2989 \|
	\| 1.1865 \| 15.83 \| 11000 \| 0.2440 \| 0.2794 \|
	\| 1.1491 \| 16.55 \| 11500 \| 0.2500 \| 0.2788 \|
	\| 1.093 \| 17.27 \| 12000 \| 0.2279 \| 0.2629 \|
	\| 1.0367 \| 17.98 \| 12500 \| 0.2076 \| 0.2443 \|
	\| 0.9954 \| 18.7 \| 13000 \| 0.1844 \| 0.2259 \|
	\| 0.99 \| 19.42 \| 13500 \| 0.1794 \| 0.2179 \|
	\| 0.9385 \| 20.14 \| 14000 \| 0.1765 \| 0.2122 \|
	\| 0.8952 \| 20.86 \| 14500 \| 0.1706 \| 0.1974 \|
	\| 0.8841 \| 21.58 \| 15000 \| 0.1791 \| 0.1969 \|
	\| 0.847 \| 22.3 \| 15500 \| 0.1780 \| 0.2060 \|
	\| 0.8669 \| 23.02 \| 16000 \| 0.1608 \| 0.1862 \|
	\| 0.8066 \| 23.74 \| 16500 \| 0.1447 \| 0.1626 \|
	\| 0.7908 \| 24.46 \| 17000 \| 0.1457 \| 0.1655 \|
	\| 0.7459 \| 25.18 \| 17500 \| 0.1350 \| 0.1445 \|
	\| 0.7218 \| 25.9 \| 18000 \| 0.1276 \| 0.1421 \|
	\| 0.703 \| 26.62 \| 18500 \| 0.1177 \| 0.1302 \|
	\| 0.685 \| 27.34 \| 19000 \| 0.1147 \| 0.1305 \|
	\| 0.6811 \| 28.06 \| 19500 \| 0.1128 \| 0.1244 \|
	\| 0.6444 \| 28.78 \| 20000 \| 0.1120 \| 0.1213 \|
	\| 0.6323 \| 29.5 \| 20500 \| 0.1137 \| 0.1166 \|
	\| 0.5998 \| 30.22 \| 21000 \| 0.1051 \| 0.1107 \|
	\| 0.5706 \| 30.93 \| 21500 \| 0.1035 \| 0.1037 \|
	\| 0.5555 \| 31.65 \| 22000 \| 0.1031 \| 0.0927 \|
	\| 0.5389 \| 32.37 \| 22500 \| 0.0997 \| 0.0900 \|
	\| 0.5201 \| 33.09 \| 23000 \| 0.0920 \| 0.0912 \|
	\| 0.5146 \| 33.81 \| 23500 \| 0.0929 \| 0.0947 \|
	\| 0.515 \| 34.53 \| 24000 \| 0.1000 \| 0.0953 \|
	\| 0.4743 \| 35.25 \| 24500 \| 0.0922 \| 0.0892 \|
	\| 0.4707 \| 35.97 \| 25000 \| 0.0852 \| 0.0808 \|
	\| 0.4456 \| 36.69 \| 25500 \| 0.0855 \| 0.0779 \|
	\| 0.443 \| 37.41 \| 26000 \| 0.0843 \| 0.0738 \|
	\| 0.4388 \| 38.13 \| 26500 \| 0.0816 \| 0.0699 \|
	\| 0.4162 \| 38.85 \| 27000 \| 0.0752 \| 0.0645 \|
	\| 0.3979 \| 39.57 \| 27500 \| 0.0761 \| 0.0621 \|
	\| 0.3889 \| 40.29 \| 28000 \| 0.0771 \| 0.0625 \|
	\| 0.3923 \| 41.01 \| 28500 \| 0.0755 \| 0.0598 \|
	\| 0.3693 \| 41.73 \| 29000 \| 0.0730 \| 0.0578 \|
	\| 0.3642 \| 42.45 \| 29500 \| 0.0739 \| 0.0598 \|
	\| 0.3532 \| 43.17 \| 30000 \| 0.0712 \| 0.0553 \|
	\| 0.3513 \| 43.88 \| 30500 \| 0.0762 \| 0.0516 \|
	\| 0.3349 \| 44.6 \| 31000 \| 0.0731 \| 0.0504 \|
	\| 0.3305 \| 45.32 \| 31500 \| 0.0725 \| 0.0507 \|
	\| 0.3285 \| 46.04 \| 32000 \| 0.0709 \| 0.0489 \|
	\| 0.3179 \| 46.76 \| 32500 \| 0.0667 \| 0.0467 \|
	\| 0.3158 \| 47.48 \| 33000 \| 0.0653 \| 0.0494 \|
	\| 0.3033 \| 48.2 \| 33500 \| 0.0638 \| 0.0456 \|
	\| 0.3023 \| 48.92 \| 34000 \| 0.0644 \| 0.0464 \|
	\| 0.2975 \| 49.64 \| 34500 \| 0.0643 \| 0.0455 \|


	### Framework versions

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