readme: ---
  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)