This official implementation of "K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables"

• PyTorch version >= 1.7.1
• Python version >= 3.6
• To install K-wav2vec and develop locally:

git clone https://github.com/Yoon-SeokJin/K-wav2vec
cd K-wav2vec

## install essential library
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

## install locally
python setup.py develop

• We only test this implementation in Ubuntu 18.04.
• DockerFile is also supported in this repo.

• We support script examples to execute code easily(check script folder)
• Following this instruction give you exact matched results.

# Guilde to make multi-model with Ksponspeech(orthographic transcription) 

# [1] preprocess dataset & make manifest
bash script/preprocess/make_ksponspeech_script_for_mulitmodel.sh

# [2] further pre-train the model
bash script/pretrain/run_further_pretrain.sh
 
# [3] fine-tune the model
bash script/finetune/run_ksponspeech_multimodel.sh

# [4] inference the model
bash script/inference/evaluate_multimodel.sh

• E-Wav2vec 2.0 : Wav2vec 2.0 pretrained on Englsih dataset released by Fairseq(-py)
• K-Wav2vec 2.0 : The model further pretrained on Ksponspeech by using Englsih model
  • Fairseq Version : If you want to fine-tune your model with fairseq framework, you can download with this LINK
  • Huggingface Version : If you want to fine-tune your model with huggingface framework, you can download with this LINK

• Ksponspeech : Open-domain dialog corpus
• Clovacall : Call-based speech corpus for reservation

• Our code was modified from fairseq codebase. We use the same license as fairseq.
• The preprocessing code was developed with reference to Kospeech.

Our implementation code(-py) is MIT-licensed. The license applies to the pre-trained models as well.