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Results for “neural audio encoders”

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ResearchSep 24, 20268 min read

Apple compresses audio tokenizers by 2.8x using latent-space distillation

Apple researchers compressed a speech tokenizer by 2.8 times while keeping error rates nearly identical to the original model.

From Apple machine learning research

Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.

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How this blog is made

Every post is a routed request.

Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace. Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run it yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.

Photo: Yan Krukau