Update README.md

README.md

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----
+language:
+- kr
 license: cc-by-4.0
+library_name: nemo
+datasets:
+- RealCallData
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- Citrinet1024
+- NeMo
+- pytorch
+model-index:
+- name: stt_kr_citrinet1024_PublicCallCenter_1000H_0.26
+  results: []
 ---
+
+## Model Overview
+
+<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
+
+## NVIDIA NeMo: Training
+
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
+```
+pip install nemo_toolkit['all']
+``` 
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+
+### Automatically instantiate the model
+
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained("ypluit/stt_kr_citrinet1024_PublicCallCenter_1000H_0.26")
+```
+
+
+### Transcribing using Python
+First, let's get a sample
+```
+get any korean telephone voice wave file
+```
+Then simply do:
+```
+asr_model.transcribe(['sample-kr.wav'])
+```
+
+### Transcribing many audio files
+
+```shell
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="model"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+```
+
+### Input
+
+This model accepts 16000Hz Mono-channel Audio (wav files) as input.
+
+### Output
+
+This model provides transcribed speech as a string for a given audio sample.
+
+
+## Model Architecture
+
+See nemo toolkit and reference papers.
+## Training
+
+Learned about 20 days on 2 A6000
+
+### Datasets
+
+Private call center real data (1200hour)
+
+## Performance
+
+0.26 WER
+
+## Limitations
+
+This model was trained with 1200 hours of Korean telephone voice data for customer service in a call center. might be Poor performance for general-purpose dialogue and specific accents.
+
+## References
+
+
+[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)