DSing

DAMP Sing

DSing (Roa-Dabike & Barker, 2019) is an unaccompanied singing dataset created to fill a gap in automatic lyrics transcription datasets. It is derived from the Smule DAMP-MVP 300x30x2 dataset, a collection of thousands of solo-singing karaoke recordings.

Repository structure

The DSing repository contains the following directories:

  • DSing-Kaldi-Recipe/: Kaldi recipe for the DSing ASR task (the baseline system).
  • DSing-preconstructed/: the DSing dataset segmentation.

Getting started

  • Get the audio dataset: Request permission and download the audio from Smule DAMP-MVP 300x30x2.
  • Get the dataset segmentation: The train/dev/test splits used in our paper are in DSing-preconstructed/.
  • Run the baseline system: See DSing-Kaldi-Recipe/ for instructions on training and evaluating the baseline Kaldi ASR system.

References

2019

  1. Conference
    interspeech2019.png
    Automatic Lyric Transcription from Karaoke Vocal Tracks: Resources and a Baseline System
    Gerardo Roa-Dabike and Jon P. Barker
    In Interspeech 2019, 2019
    DOI Bib Code
    46 Google Scholar citations