This is a PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition paper

In order to train the model on your data follow the steps below

• prepare your data and make sure the data is formatted in an CSV format as below

audio_path,text,duration
file/to/file.wav,the text in that file,3.2 

• make sure the audios are MONO if not make the proper conversion to meet this condition

• create enviroment

python -m venv env

• activate the enviroment

source env/bin/activate

• install the required dependencies

pip install -r requirements.txt

• update the config file if needed

• train the model

  • from scratch

  python train.py

  • from checkpoint

  python train.py checkpoint=path/to/checkpoint tokenizer.tokenizer_file=path/to/tokenizer.json
  

  Referances

  @misc{https://doi.org/10.48550/arxiv.2005.08100,
  doi = {10.48550/ARXIV.2005.08100},
  
  url = {https://arxiv.org/abs/2005.08100},
  
  author = {Gulati, Anmol and Qin, James and Chiu, Chung-Cheng and Parmar, Niki and Zhang, Yu and Yu, Jiahui and Han, Wei and Wang, Shibo and Zhang, Zhengdong and Wu, Yonghui and Pang, Ruoming},
  
  keywords = {Audio and Speech Processing (eess.AS), Machine Learning (cs.LG), Sound (cs.SD), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Conformer: Convolution-augmented Transformer for Speech Recognition},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
  

}