Lyric_ASR

Implementation of "Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing"

How to use

Dependencies

• kaldi

• srilm (can be built with kaldi/tools/install_srilm.sh)

Path

• Modify path.sh with your path of kaldi and srilm.

• Relink utils and steps to kaldi/egs/wsj/s5/utils and kaldi/egs/wsj/s5/steps.

Preprocess acoustic data

• Vocal data can be obtained from another repository.

• Modify path of vocal_data in progress/prepare_data.sh

• Then run following commands:

$ bash progress/prepare_data.sh

Train LMs

• Modify paths in progress/process_lm.sh

$ bash  progress/process_lm.sh

Train HMM-GMM models

$ bash progress/run.sh

Train blstm models

$ bash progress/nnet3/run_blstm_wsj.sh 10 500 125 2

Train tdnn-lstm models

$ bash progress/nnet3/run_tdnn_lstm_1a_wsj.sh 10 130 65

Files and Directories

• conf : configuration files (Ex: number of bins in extracting mfcc)

• local : training scripts for librispeech

• progress : our training scripts put in here

• pyutils : python codes (often used by scripts) written by Tsai

• more_tools : other python codes (independent to the scripts) written by Tuan

• path.sh : specified paths for kaldi, srilm...etc

• steps : One should soft link steps directory in wsj to it.(ln -s $wsj_steps ./)

• utils : One should soft link utils directory in wsj to it (ln -s $wsj_utils ./)

Citation

@inproceedings{tsai2018transcribing,
  title={Transcribing lyrics from commercial song audio: the first step towards singing content processing},
  author={Tsai, Che-Ping and Tuan, Yi-Lin and Lee, Lin-shan},
  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={5749--5753},
  year={2018},
  organization={IEEE}
}