readme: ---
  language: "kr"
  thumbnail:
  tags:
  - ASR
  - CTC
  - Attention
  - Conformer
  - pytorch
  - speechbrain
  license: "apache-2.0"
  datasets:
  - ksponspeech
  metrics:
  - wer
  - cer
  ---
  
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  <br/><br/>
  
  # Conformer for KsponSpeech (with Transformer LM)
  
  This repository provides all the necessary tools to perform automatic speech
  recognition from an end-to-end system pretrained on KsponSpeech (Kr) within
  SpeechBrain. For a better experience, we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io). 
  The performance of the model is the following:
  
  | Release  | eval clean CER | eval other CER |    GPUs     |
  | :------: | :------------: | :------------: | :---------: |
  | 01-23-23 |     7.33%      |     7.99%      | 6xA100 80GB |
  
  ## Pipeline description
  
  This ASR system is composed of 3 different but linked blocks:
  - Tokenizer (unigram) that transforms words into subword units and trained with
  the train transcriptions of KsponSpeech.
  - Neural language model (Transformer LM) trained on the train transcriptions of KsponSpeech
  - Acoustic model made of a conformer encoder and a joint decoder with CTC +
  transformer. Hence, the decoding also incorporates the CTC probabilities.
  ## Install SpeechBrain
  First of all, please install SpeechBrain with the following command:
  ```
  !pip install git+https://github.com/speechbrain/speechbrain.git
  ```
  Please notice that we encourage you to read our tutorials and learn more about
  [SpeechBrain](https://speechbrain.github.io).
  ### Transcribing your own audio files (in Korean)
  ```python
  from speechbrain.pretrained import EncoderDecoderASR
  asr_model = EncoderDecoderASR.from_hparams(source="ddwkim/asr-conformer-transformerlm-ksponspeech", savedir="pretrained_models/asr-conformer-transformerlm-ksponspeech",  run_opts={"device":"cuda"})
  asr_model.transcribe_file("ddwkim/asr-conformer-transformerlm-ksponspeech/record_0_16k.wav")
  ```
  
  ### Inference on GPU
  
  To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
  
  ## Parallel Inference on a Batch
  
  Please, [see this Colab notebook](https://colab.research.google.com/drive/1finp9pfmGRzWHCAPNkqAH2yGH6k_BbPA?usp=sharing) on using the pretrained model
  
  ### Training
  
  The model was trained with SpeechBrain (Commit hash: 'c762107').
  To train it from scratch follow these steps:
  1. Clone SpeechBrain:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```bash
  cd speechbrain
  pip install -r requirements.txt
  pip install .
  ```
  3. Run Training:
  ```bash
  cd recipes/KsponSpeech/ASR/transformer
  python train.py hparams/conformer_medium.yaml --data_folder=your_data_folder
  ```
  You can find our training results (models, logs, etc) at the subdirectories.
  
  ### Limitations
  
  The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
  
  # **About SpeechBrain**
  
  - Website: https://speechbrain.github.io/
  - Code: https://github.com/speechbrain/speechbrain/
  - HuggingFace: https://huggingface.co/speechbrain/
  
  # **Citing SpeechBrain**
  
  Please, cite SpeechBrain if you use it for your research or business.
  ```bibtex
  @misc{speechbrain,
    title={{SpeechBrain}: A General-Purpose Speech Toolkit},
    author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
    year={2021},
    eprint={2106.04624},
    archivePrefix={arXiv},
    primaryClass={eess.AS},
    note={arXiv:2106.04624}
  }
  ```
  
  # Citing the model
  ```bibtex
  @misc{returnzero,
    title = {ReturnZero Conformer Korean ASR model},
    author = {Dongwon Kim and Dongwoo Kim and Jeongkyu Roh},
    year = {2021},
    howpublished = {\url{https://huggingface.co/ddwkim/asr-conformer-transformerlm-ksponspeech}},
  }
  ```
  
  # Citing KsponSpeech dataset
  ```bibtex
  @Article{app10196936,
  AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
  TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition},
  JOURNAL = {Applied Sciences},
  VOLUME = {10},
  YEAR = {2020},
  NUMBER = {19},
  ARTICLE-NUMBER = {6936},
  URL = {https://www.mdpi.com/2076-3417/10/19/6936},
  ISSN = {2076-3417},
  DOI = {10.3390/app10196936}
  }
  ```
  