Audio Data Services for AI Training
Companies aiming to train custom models or generate high-quality datasets from their own content can leverage AudioShake’s best-in-class separation technology to isolate individual audio components—such as dialogue, music, effects, or other overlapping elements—from existing recordings. This isn't synthetic or generative AI—it’s authentic, real-world audio, intelligently separated for precision and control.
What are AudioShake's Data Services?
Best-in-class audio separation infrastructure
The separation models behind your data
Depending on your content and use case, we apply the right combination of separation models to get to clean, training-ready audio.
Isolates spoken dialogue from complex mixed audio. Handles noisy on-location recordings, crowd environments, and mixed broadcast content where speech clarity is the priority.
When background music, including lyrics, intefereres with the quality of a speech input, AudioShake's music removal all music leaving a pure speaker stem for training inputs.
Even on low-quality or degraded recordings, AudioShake's Speech Recovery models can remove background noise, unwanted speech, and bleed to recover clean, isolated dialogue—even in challenging, naturalistic environments.
Separates individual speakers from recordings with multiple voices into distinct tracks for training. Used for interview content, unscripted television, and any production where speaker-level control matters.
Confidence scores accompany our multi-speaker separation outputs to provide meaningful signals to users on the level to which an output contains a correct, consistent speaker throughout.
LEARN MORE ABOUT CONFIDENCE SCORES →Frequently Asked Questions
Yes. AudioShake's API is designed for high-volume, automated processing — enterprise AI and technology teams use it to convert large audio archives into structured training datasets. The pipeline supports consistent, repeatable output across large volumes, which matters for training data workflows where distribution stability is critical.
Models trained on mixed or noisy audio learn the interference alongside the intended signal, which hurts generalization, inflates the data volume needed to hit performance targets, and destabilizes evaluation benchmarks. Clean stems give models unambiguous signal boundaries — reducing training data requirements, improving real-world generalization, and stabilizing benchmarks. AudioShake's processing is consistent and repeatable, which matters for pipelines sensitive to distribution shifts from inconsistent preprocessing.
