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Miso Labs has released MisoTTS, an 8B-parameter text-to-speech model that leverages residual vector quantization to expand sonic range without increasing parameter count. By pairing a Llama 3.2-style backbone with a depth decoder, the architecture achieves 110ms latency while conditioning on both text and prior audio to mitigate the uncanny valley. This approach offers a scalable alternative to flat vocabulary expansion for generating expressive speech from text and audio context.
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Miso Labs Releases MisoTTS: An 8B Emotive Text-to-Speech Model with Open Weights

MarkTechPost6 min read

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 decoder. The post Miso Labs Releases MisoTTS: An 8B Emotive Text-to-Speech Model with Open Weights appeared first on MarkTechPost.

Miso Labs has released MisoTTS, an open-weights 8-billion-parameter text-to-speech model. It generates expressive speech from both text and audio context. The model uses residual vector quantization (RVQ) to widen its sonic range. This avoids scaling a single flat vocabulary while keeping parameter count fixed. MisoTTS is an 8B-parameter text-to-dialogue RVQ Transformer. It is inspired by the Sesame CSM architecture. It pairs a Llama 3.2-style backbone with a smaller audio decoder. It generates Mimi audio codes from text and optional audio context. The model conditions on both text and prior audio. That second input…

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