Recently, Spotify open-sourced an AI framework at the 2020 International Society for Music Information Retrieval Conference, known as Klio. Klio is an ecosystem which allows developers to process audio files or any other binary files at any scale.
Built by Spotify, Klio runs large-scale audio intelligence systems at the digital music platform and is used by teams of engineers and audio researchers to help develop and deploy next-generation audio algorithms.
Behind Klio
Klio is built upon Apache Beam, and the jobs are opinionated data pipelines in Python. Tuned for audio and binary file processing, the goal of this framework is to create smarter data pipelines for audio.
Spotify built Klio as a standardised framework for running audio analysis allowing teams to share a common useful foundation instead of duplicating each others’ work. Klio reduces the operational and backend engineering load on these teams, allowing them to focus on doing the processing, machine learning and other algorithmic work. Klio is meant for processing media and can be used on cloud infrastructure, or locally on one’s computer. Klio focuses Beam on analysing, manipulating, and transforming large binary media (e.g. images, audio, video) where the content in its native form can’t really fit or be analysed in a database in any meaningful way. The Klio ecosystem is made up of multiple, separate Python packages. Besides the internal packages, there are some packages that are user-facing, which are klio-cli, klio and klio-audio.
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