Currently, only BBC teams and partners can access our Kaldi toolkit - email us and we'll get back to you within two working days. For those outside the BBC, we're interested in partnerships on projects with experimental or public service value - contact us to register your interest in becoming a partner. The MakerBox team will use that email to start a conversation. There is also an open source version of Kaldi available online.
How does it work?
- 1Download and run the debian package
- 2Upload video/speech files into the tool, using the instructions provided
- 3Edit the transcription, as required
What is Kaldi?
Kaldi is a speech recognition toolkit, built upon the open source software originally developed for use by speech recognition researchers. The quickest way to search through a piece of audio or video is via a transcript, but transcription by hand is a costly and time consuming endeavour. Kaldi has been designed as a means of automating the same process for free, requiring only a small amount of installation effort from a software developer. The BBC-Kaldi component provides a machine learning model built using the tools in the Open Source Kaldi toolkit and audio / text data from BBC programme and an easy to use interface that makes it simple to get up and running.
Top tips to get you ready
Transcription quality will depend on things like audio quality, accents, background noises, cross-talking etc - so transcribe clean audio where possible
If you record speech and atmospherics on separate channels, export the speech channel only
What have we learned from Kaldi?
Kaldi has been used extensively within the BBC, with a variety of learnings from each project. BBC Newslabs' Window On the Newsroom project showed that speech-to-text is a tremendous timesaver for journalists who need to be across a wide number of video feeds. Their Audiogram project proved that creating subtitles for viral videoclips can be done much quicker than was previously imagined. BBC Rewind also used speech-to-text to open up almost a million hours of material from the BBC Archive.