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
@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}
}