In 2015, the Institute of Museum and Library Services (IMLS) awarded WGBH on behalf of the American Archive of Public Broadcasting a grant to address the challenges faced by many libraries and archives trying to provide better access to their media collections through online discoverability. Through a collaboration with Pop Up Archive and HiPSTAS at the University of Texas at Austin, our project has supported the creation of speech-to-transcripts for the initial 40,000 hours of historic public broadcasting preserved in the AAPB, the launch of a free open-source speech-to-text tool, and FIX IT, a game that allows the public to help correct our transcripts.
Now, our colleagues at HiPSTAS are debuting a new machine learning toolkit and DIY techniques for labeling speakers in “unheard” audio — audio that is not documented in a machine-generated transcript. The toolkit was developed through a massive effort using machine learning to identify notable speakers’ voices (such as Martin Luther King, Jr. and John F. Kennedy) from within the AAPB’s 40,000 hour collection of historic public broadcasting content.
This effort has vast potential for archivists, researchers, and other organizations seeking to discover and make accessible sound at scale — sound that otherwise would require a human to listen and identify in every digital file.
Read more about the audio labeling toolkit here, and stay tuned for more posts in this series.