Leveraging your content for structured audio data

AudioShake’s advanced stem separation technology lets you isolate various kinds of dialogue, music, and effects from your recordings—turning finished audio into structured, usable data that can power a wide range of machine learning applications.

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AudioShake can help you transform your audio data into training-ready datasets—or even build a custom model just for you. Say you’re sitting on a library of older, mixed-down audio: a legendary concert series, an early film catalog, or the archives of a long-running podcast. With AudioShake, you can separate those recordings into their component stems, enabling the creation of specialized models tailored to your material—models that understand your style, your sound, and your sonic DNA.

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For speech and video applications, our AudioShake can separate clean, isolated dialogue—even in challenging, naturalistic environments. This makes it possible to train high-performance models on real conversations and multi-speaker interactions–even when there is significant background noise in the source audio.

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AudioShake is the gold standard for AI audio separation, with our stem outputs used daily in high-stakes workflows like music and film releases, commercials, spatial audio, and sports clips–as well as new audio training paradigms for realistic audio. If you want to take your content further, AudioShake is here to help.