readme: ---
  language:
  - kr
  license: cc-by-4.0
  library_name: nemo
  datasets:
  - Ksponspeech
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - transducer
  - Conformer
  - Transformer
  - NeMo
  - pytorch
  model-index:
  - name: stt_kr_conformer_transducer_large
    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("eesungkim/stt_kr_conformer_transducer_large")
  ```
  
  ### Transcribing using Python
  First, let's get a sample
  ```
  wget https://dldata-public.s3.us-east-2.amazonaws.com/sample-kor.wav
  ```
  Then simply do:
  ```
  asr_model.transcribe(['sample-kor.wav'])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="eesungkim/stt_kr_conformer_transducer_large"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
  ```
  
  ### Input
  
  This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
  
  ### Output
  
  This model provides transcribed speech as a string for a given audio sample.
  
  ## Model Architecture
  
  Conformer-Transducer model is an autoregressive variant of Conformer model [2] for Automatic Speech Recognition which uses Transducer loss/decoding. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).
  
  ## Training
  
  The model was finetuned based on the pre-trained English Model for over several epochs. 
  
  There are several transcribing and sub-word modeling methods for Korean speech recognition. This model uses sentencepiece subwords of Hangul characters based on phonetic transcription using Google Sentencepiece Tokenizer [3].
  
  ### Datasets
  
  All the models in this collection are trained on [Ksponspeech](https://aihub.or.kr/aidata/105/download) dataset, which is an open-domain dialog corpus recorded by 2,000 native Korean speakers in a controlled and quiet environment. The standard split dataset consists of 965 hours of training set, 4 hours of development set, 3 hours of test-clean, and 4 hours of test-other.
  
  ## Performance
  
  Version | Tokenizer | eval_clean CER | eval_other CER | eval_clean WER | eval_other WER
  --- | --- | --- | --- |--- |---
  v1.7.0rc | SentencePiece Char | 6.94% | 7.38% | 19.49% | 22.73%
  
  ## Limitations
  
  Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which including technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
  
  This model produces a spoken-form token sequence. If you want to have a written form, you can consider applying inverse text normalization.
  
  
  ## References
  
  [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
  
  [2] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
  
  [3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
  
  
  
  
  