Librarian Bot: Add base_model information to model

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	---
	language: ko
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- generated_from_trainer
	- hf-asr-leaderboard
	- robust-speech-event
	datasets:
	- kresnik/zeroth_korean
	base_model: Wav2Vec2-XLS-R-300M
	model-index:
	- name: Wav2Vec2 XLS-R 300M Korean
	results:
	- task:
	type: automatic-speech-recognition
	name: Automatic Speech Recognition
	dataset:
	name: Zeroth Korean
	type: kresnik/zeroth_korean
	args: clean
	metrics:
	- type: wer
	value: 29.54
	name: Test WER
	- type: cer
	value: 9.53
	name: Test CER
	- task:
	type: automatic-speech-recognition
	name: Automatic Speech Recognition
	dataset:
	name: Robust Speech Event - Dev Data
	type: speech-recognition-community-v2/dev_data
	args: ko
	metrics:
	- type: wer
	value: 76.26
	name: Test WER
	- type: cer
	value: 38.67
	name: Test CER
	- task:
	type: automatic-speech-recognition
	name: Automatic Speech Recognition
	dataset:
	name: Robust Speech Event - Test Data
	type: speech-recognition-community-v2/eval_data
	args: ko
	metrics:
	- type: wer
	value: 73.18
	name: Test WER
	---

	# Wav2Vec2 XLS-R 300M Korean

	Wav2Vec2 XLS-R 300M Korean is an automatic speech recognition model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [Wav2Vec2-XLS-R-300M](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Zeroth Korean](https://huggingface.co/datasets/kresnik/zeroth_korean) dataset.

	This model was trained using HuggingFace's PyTorch framework and is part of the [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by HuggingFace. All training was done on a Tesla V100, sponsored by OVH.

	All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean/tree/main) tab, as well as the [Training metrics](https://huggingface.co/w11wo/wav2vec2-xls-r-300m-korean/tensorboard) logged via Tensorboard.

	## Model

	\| Model \| #params \| Arch. \| Training/Validation data (text) \|
	\| ---------------------------- \| ------- \| ----- \| ------------------------------- \|
	\| `wav2vec2-xls-r-300m-korean` \| 300M \| XLS-R \| `Zeroth Korean` Dataset \|

	## Evaluation Results

	The model achieves the following results on evaluation:

	\| Dataset \| Loss \| WER \| CER \|
	\| -------------------------------- \| ------ \| ------ \| ------ \|
	\| `Zeroth Korean` \| 0.2089 \| 29.54% \| 9.53% \|
	\| `Robust Speech Event - Dev Data` \| N/A \| 76.26% \| 38.67% \|

	## 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 \| Cer \|
	\| :-----------: \| :---: \| :---: \| :-------------: \| :----: \| :----: \|
	\| 19.7138 \| 0.72 \| 500 \| 19.6427 \| 1.0 \| 1.0 \|
	\| 4.8039 \| 1.44 \| 1000 \| 4.7842 \| 1.0 \| 1.0 \|
	\| 4.5619 \| 2.16 \| 1500 \| 4.5608 \| 0.9992 \| 0.9598 \|
	\| 4.254 \| 2.88 \| 2000 \| 4.2729 \| 0.9955 \| 0.9063 \|
	\| 4.1905 \| 3.6 \| 2500 \| 4.2257 \| 0.9903 \| 0.8758 \|
	\| 4.0683 \| 4.32 \| 3000 \| 3.9294 \| 0.9937 \| 0.7911 \|
	\| 3.486 \| 5.04 \| 3500 \| 2.7045 \| 1.0012 \| 0.5934 \|
	\| 2.946 \| 5.75 \| 4000 \| 1.9691 \| 0.9425 \| 0.4634 \|
	\| 2.634 \| 6.47 \| 4500 \| 1.5212 \| 0.8807 \| 0.3850 \|
	\| 2.4066 \| 7.19 \| 5000 \| 1.2551 \| 0.8177 \| 0.3601 \|
	\| 2.2651 \| 7.91 \| 5500 \| 1.0423 \| 0.7650 \| 0.3039 \|
	\| 2.1828 \| 8.63 \| 6000 \| 0.9599 \| 0.7273 \| 0.3106 \|
	\| 2.1023 \| 9.35 \| 6500 \| 0.9482 \| 0.7161 \| 0.3063 \|
	\| 2.0536 \| 10.07 \| 7000 \| 0.8242 \| 0.6767 \| 0.2860 \|
	\| 1.9803 \| 10.79 \| 7500 \| 0.7643 \| 0.6563 \| 0.2637 \|
	\| 1.9468 \| 11.51 \| 8000 \| 0.7319 \| 0.6441 \| 0.2505 \|
	\| 1.9178 \| 12.23 \| 8500 \| 0.6937 \| 0.6320 \| 0.2489 \|
	\| 1.8515 \| 12.95 \| 9000 \| 0.6443 \| 0.6053 \| 0.2196 \|
	\| 1.8083 \| 13.67 \| 9500 \| 0.6286 \| 0.6122 \| 0.2148 \|
	\| 1.819 \| 14.39 \| 10000 \| 0.6015 \| 0.5986 \| 0.2074 \|
	\| 1.7684 \| 15.11 \| 10500 \| 0.5682 \| 0.5741 \| 0.1982 \|
	\| 1.7195 \| 15.83 \| 11000 \| 0.5385 \| 0.5592 \| 0.2007 \|
	\| 1.7044 \| 16.55 \| 11500 \| 0.5362 \| 0.5524 \| 0.2097 \|
	\| 1.6879 \| 17.27 \| 12000 \| 0.5119 \| 0.5489 \| 0.2083 \|
	\| 1.656 \| 17.98 \| 12500 \| 0.4990 \| 0.5362 \| 0.1968 \|
	\| 1.6122 \| 18.7 \| 13000 \| 0.4561 \| 0.5092 \| 0.1900 \|
	\| 1.5919 \| 19.42 \| 13500 \| 0.4778 \| 0.5225 \| 0.1975 \|
	\| 1.5896 \| 20.14 \| 14000 \| 0.4563 \| 0.5098 \| 0.1859 \|
	\| 1.5589 \| 20.86 \| 14500 \| 0.4362 \| 0.4940 \| 0.1725 \|
	\| 1.5353 \| 21.58 \| 15000 \| 0.4140 \| 0.4826 \| 0.1580 \|
	\| 1.5441 \| 22.3 \| 15500 \| 0.4031 \| 0.4742 \| 0.1550 \|
	\| 1.5116 \| 23.02 \| 16000 \| 0.3916 \| 0.4748 \| 0.1545 \|
	\| 1.4731 \| 23.74 \| 16500 \| 0.3841 \| 0.4810 \| 0.1542 \|
	\| 1.4647 \| 24.46 \| 17000 \| 0.3752 \| 0.4524 \| 0.1475 \|
	\| 1.4328 \| 25.18 \| 17500 \| 0.3587 \| 0.4476 \| 0.1461 \|
	\| 1.4129 \| 25.9 \| 18000 \| 0.3429 \| 0.4242 \| 0.1366 \|
	\| 1.4062 \| 26.62 \| 18500 \| 0.3450 \| 0.4251 \| 0.1355 \|
	\| 1.3928 \| 27.34 \| 19000 \| 0.3297 \| 0.4145 \| 0.1322 \|
	\| 1.3906 \| 28.06 \| 19500 \| 0.3210 \| 0.4185 \| 0.1336 \|
	\| 1.358 \| 28.78 \| 20000 \| 0.3131 \| 0.3970 \| 0.1275 \|
	\| 1.3445 \| 29.5 \| 20500 \| 0.3069 \| 0.3920 \| 0.1276 \|
	\| 1.3159 \| 30.22 \| 21000 \| 0.3035 \| 0.3961 \| 0.1255 \|
	\| 1.3044 \| 30.93 \| 21500 \| 0.2952 \| 0.3854 \| 0.1242 \|
	\| 1.3034 \| 31.65 \| 22000 \| 0.2966 \| 0.3772 \| 0.1227 \|
	\| 1.2963 \| 32.37 \| 22500 \| 0.2844 \| 0.3706 \| 0.1208 \|
	\| 1.2765 \| 33.09 \| 23000 \| 0.2841 \| 0.3567 \| 0.1173 \|
	\| 1.2438 \| 33.81 \| 23500 \| 0.2734 \| 0.3552 \| 0.1137 \|
	\| 1.2487 \| 34.53 \| 24000 \| 0.2703 \| 0.3502 \| 0.1118 \|
	\| 1.2249 \| 35.25 \| 24500 \| 0.2650 \| 0.3484 \| 0.1142 \|
	\| 1.2229 \| 35.97 \| 25000 \| 0.2584 \| 0.3374 \| 0.1097 \|
	\| 1.2374 \| 36.69 \| 25500 \| 0.2568 \| 0.3337 \| 0.1095 \|
	\| 1.2153 \| 37.41 \| 26000 \| 0.2494 \| 0.3327 \| 0.1071 \|
	\| 1.1925 \| 38.13 \| 26500 \| 0.2518 \| 0.3366 \| 0.1077 \|
	\| 1.1908 \| 38.85 \| 27000 \| 0.2437 \| 0.3272 \| 0.1057 \|
	\| 1.1858 \| 39.57 \| 27500 \| 0.2396 \| 0.3265 \| 0.1044 \|
	\| 1.1808 \| 40.29 \| 28000 \| 0.2373 \| 0.3156 \| 0.1028 \|
	\| 1.1842 \| 41.01 \| 28500 \| 0.2356 \| 0.3152 \| 0.1026 \|
	\| 1.1668 \| 41.73 \| 29000 \| 0.2319 \| 0.3188 \| 0.1025 \|
	\| 1.1448 \| 42.45 \| 29500 \| 0.2293 \| 0.3099 \| 0.0995 \|
	\| 1.1327 \| 43.17 \| 30000 \| 0.2265 \| 0.3047 \| 0.0979 \|
	\| 1.1307 \| 43.88 \| 30500 \| 0.2222 \| 0.3078 \| 0.0989 \|
	\| 1.1419 \| 44.6 \| 31000 \| 0.2215 \| 0.3038 \| 0.0981 \|
	\| 1.1231 \| 45.32 \| 31500 \| 0.2193 \| 0.3013 \| 0.0972 \|
	\| 1.139 \| 46.04 \| 32000 \| 0.2162 \| 0.3007 \| 0.0968 \|
	\| 1.1114 \| 46.76 \| 32500 \| 0.2122 \| 0.2982 \| 0.0960 \|
	\| 1.111 \| 47.48 \| 33000 \| 0.2125 \| 0.2946 \| 0.0948 \|
	\| 1.0982 \| 48.2 \| 33500 \| 0.2099 \| 0.2957 \| 0.0953 \|
	\| 1.109 \| 48.92 \| 34000 \| 0.2092 \| 0.2955 \| 0.0955 \|
	\| 1.0905 \| 49.64 \| 34500 \| 0.2088 \| 0.2954 \| 0.0953 \|

	## Disclaimer

	Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

	## Authors

	Wav2Vec2 XLS-R 300M Korean was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on OVH Cloud.

	## Framework versions

	- Transformers 4.17.0.dev0
	- Pytorch 1.10.2+cu102
	- Datasets 1.18.2.dev0
	- Tokenizers 0.10.3