Update: KenLM추가로 README 수정

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	---
	language:
	- ko # Example: fr
	license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
	library_name: transformers # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
	tags:
	- audio
	- automatic-speech-recognition
	datasets:
	- KsponSpeech
	metrics:
	- wer # Example: wer. Use metric id from https://hf.co/metrics
	---

	# ko-spelling-wav2vec2-conformer-del-1s

	## Table of Contents
	- [ko-spelling-wav2vec2-conformer-del-1s](#ko-spelling-wav2vec2-conformer-del-1s)
	- [Table of Contents](#table-of-contents)
	- [Model Details](#model-details)
	- [Evaluation](#evaluation)
	- [How to Get Started With the Model](#how-to-get-started-with-the-model)

	## Model Details
	- Model Description:
	해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다. <br />
	Wav2Vec2ConformerForCTC를 이용하여 KsponSpeech에 대한 Fine-Tuning 모델입니다. <br />

	- Dataset use [AIHub KsponSpeech](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123) <br />
	Datasets는 해당 Data를 전처리하여 임의로 만들어 사용하였습니다. <br />
	del-1s의 의미는 1초 이하의 데이터 필터링을 의미합니다. <br />
	해당 모델은 철자전사 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 각 표기법을 따름) <br />

	- Developed by: TADev (@lIlBrother, @ddobokki, @jp42maru)
	- Language(s): Korean
	- License: apache-2.0
	- Parent Model: See the [wav2vec2-conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer) for more information about the pre-trained base model. (해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다.)

	## Evaluation
	Just using `load_metric("wer")` and `load_metric("wer")` in huggingface `datasets` library <br />

	## How to Get Started With the Model
	KenLM과 혼용된 Wav2Vec2ProcessorWithLM 예제를 보시려면 [42maru-kenlm 예제](https://huggingface.co/42MARU/ko-ctc-kenlm-spelling-only-wiki)를 참고하세요
	```python
	import librosa
	from pyctcdecode import build_ctcdecoder
	from transformers import (
	AutoConfig,
	AutoFeatureExtractor,
	AutoModelForCTC,
	AutoTokenizer,
	Wav2Vec2ProcessorWithLM,
	)
	from transformers.pipelines import AutomaticSpeechRecognitionPipeline

	audio_path = ""

	# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
	model = AutoModelForCTC.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
	feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
	tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
	beamsearch_decoder = build_ctcdecoder(
	labels=list(tokenizer.encoder.keys()),
	kenlm_model_path=None,
	)
	processor = Wav2Vec2ProcessorWithLM(
	feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=beamsearch_decoder
	)

	# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
	asr_pipeline = AutomaticSpeechRecognitionPipeline(
	model=model,
	tokenizer=processor.tokenizer,
	feature_extractor=processor.feature_extractor,
	decoder=processor.decoder,
	device=-1,
	)

	# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
	raw_data, _ = librosa.load(audio_path, sr=16000)
	kwargs = {"decoder_kwargs": {"beam_width": 100}}
	pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
	# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
	result = unicodedata.normalize("NFC", pred)
	print(result)
	# 안녕하세요 123 테스트입니다.
	```
	Beam-100 Result (WER):
	\| "clean" \| "other" \|
	\| ------- \| ------- \|
	\| 22.01 \| 27.34 \|