Update: readme 수정

README.md

@@ -1,3 +1,86 @@
 ---
-license: apache-2.0
+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 />
+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
+```python
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoTokenizer,
+    Wav2Vec2ProcessorWithLM,
+)
+from transformers.pipelines import AutomaticSpeechRecognitionPipeline
+import librosa
+
+# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
+config = AutoConfig.from_pretrained(model_config_path)
+model = AutoModelForCTC.from_pretrained(
+    model_name_or_path,
+    config=config,
+)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path)
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+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.
+```