Miso Labs Releases MisoTTS: An 8B Emotive Text-to-Speech Model with Open Weights
Open weights just got emotional. Miso Labs drops 8B TTS model that conditions on speaker tone—no parameter bloat required.
Why it matters
Miso Labs demonstrates a novel approach to scaling multimodal capabilities (text-to-speech with emotional conditioning) using residual vector quantization rather than parameter explosion. This matters for founders building speech applications: smaller models with better conditioning = lower inference costs and faster deployment.
The key facts
5 to knowModel size: 8B parameters (7.7B backbone + 300M depth decoder)
Architecture: Uses residual vector quantization (RVQ) for sonic scaling without parameter scaling
Conditioning: Responds to both text AND audio context (speaker tone awareness)
License: Open weights (community access)
Multimodal capability: Text-to-speech with emotional/contextual awareness
Go to the source
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Publisher excerpt: Miso Labs has released MisoTTS, an open-weights 8B text-to-speech model. It uses residual vector quantization (RVQ) to scale its sonic range without scaling parameters, and conditions on both text and audio context to respond to speaker tone. The architecture pairs a 7.7B backbone with a 300M depth…