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automaticsunglyricsannotation_ismir2018's Introduction

README: Automatic Sung-Lyrics Data Annotation

Created by: Chitralekha Gupta

Affiliation: NUS, Singapore

Last edited on: 12th June 2018

Last edited by: Chitralekha Gupta

This is the code base for automatically obtaining aligned lyrics for solo-singing in Smule's Sing! karaoke DAMP dataset. It provides a cleaner annotated dataset.

Please refer to the following paper for details:

Gupta, C., Tong R., Li, H. and Wang, Y., 2018, September, "SEMI-SUPERVISED LYRICS AND SOLO-SINGING ALIGNMENT". Accepted for ISMIR 2018, Paris.(http://ismir2018.ircam.fr/doc/pdfs/30_Paper.pdf)

Contents

This consists of the following:

  • Audio folder (folder: audio), where an example audio (.m4a) from DAMP dataset is provided
  • Converted .m4a to .wav are in the folder: wavfiles
  • All ~10 seconds segments are in the folder: wavsegments_initial
  • Cleaned up subset of segments are in the folder wavsegments_final, the corresponding automatically obtained lyrics annotations are in "resultant.txt"
  • fulloutput.txt contains all segment names, lyric window transcript, ASR transcript, and %correct
  • Lyrics folder (folder: lyrics), where lyrics are extracted from Smule Sing! website
  • 'perfs20.csv' is a meta-data file from Kruspe's dataset, given here: http://www.music-ir.org/mirex/wiki/2017:Automatic_Lyrics-to-Audio_Alignment
  • Rest are python scripts and other dependency files.

Dependencies

  • This program is designed for monophonic (without background music) audio files.

How to run?

  • The python scripts main.py is the main file.
  • Start with main.py, and follow the instructions in the file header.
  • The script currently runs for one example file present in the folder "audio"
  • You can add more files from the DAMP dataset to the "audio" folder

Contact

  • Chitralekha Gupta: chitralekha[at]u[dot]nus[dot]edu
  • Haizhou Li: haizhou[dot]li[at]nus[dot]edu[dot]sg
  • Ye Wang: wangye[at]comp[dot]nus[dot]edu[dot]sg

automaticsunglyricsannotation_ismir2018's People

Contributors

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Watchers

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