# Miso One Miso One (shipped as `MisoTTS`) is Miso Labs' 8B [[Text-to-Speech (TTS)|text-to-speech]] model, released with open weights on 3 June 2026. Miso Labs is a Y Combinator company building what they call emotive foundation voice models. The pitch is emotional range, not raw intelligibility. ## What's different **It conditions on audio, not just text.** Most TTS takes a string and a speaker embedding. Miso One takes text *and* an audio context, so the output tracks the tone of what came before rather than reading each sentence in a vacuum. That's the whole bet: emotion is a property of the conversation, not of the sentence. Architecture, as published: - Residual vector quantization, 32 codebook indices over 2048-entry codebooks - 7.7B temporal backbone plus a 300M depth decoder - One-shot voice cloning from roughly a 10-second sample - ~110ms latency claim - English only at launch Weights are on [[HuggingFace]] as `MisoLabs/MisoTTS`, code on GitHub (~3.2k stars). Licensed under a modified MIT for the weights, with training data and pipeline unreleased. Open-weight, not open-source. See [[AI Open Weight Models]]. ## What I'd want before trusting it The claims are unusually unaccompanied by evidence. - The 110ms latency figure ships without a methodology, hardware spec, or batch size. Treat it as an orientation point. - No published word error rate, speaker-similarity score, or MOS naturalness comparison against [[ElevenLabs]], [[Orpheus TTS]], [[Qwen3-TTS]] or anything else. For a model whose entire claim is expressiveness, the absence of a MOS number is conspicuous. - Full-duplex and turn-taking are listed as future work, so this is a synthesis model, not a conversational stack. - Hosted API was announced as coming, not shipped. - It needs a high-VRAM GPU for interactive use. 8B params of audio tokens is not a laptop workload. As of August 2026 there's no successor in the family. The GitHub org holds `MisoTTS` and a docs repo, nothing else. ## Why it's still interesting On-premise plus one-shot cloning plus permissive weights is a combination the hosted incumbents don't offer. If you're building voice into a product where the audio can't leave your infrastructure, the shortlist is short, and this joins it alongside [[VibeVoice]] and Orpheus. The audio-conditioning idea is the part likely to outlive this particular checkpoint. Once a model can hear the previous turn, "read this text with feeling" stops being a prompt-engineering problem. ## References - Repository: https://github.com/MisoLabsAI/MisoTTS - Weights: https://huggingface.co/MisoLabs/MisoTTS - Miso Labs: https://www.misolabs.ai/blog/miso-tts-8b - Y Combinator profile: https://www.ycombinator.com/companies/miso-labs - MarkTechPost coverage: https://www.marktechpost.com/2026/06/04/miso-labs-releases-misotts-an-8b-emotive-text-to-speech-model-with-open-weights/ - Launch video: https://www.youtube.com/watch?v=HizlJgDbac8 - Elvis Saravia's post: https://x.com/omarsar0/status/2062227352416067984 ## Related - [[Text-to-Speech (TTS)]] - [[Voice Cloning]] - [[VibeVoice]] - [[Orpheus TTS]] - [[Qwen3-TTS]] - [[LuxTTS]] - [[Gemini 3.1 Flash TTS]] - [[ElevenLabs]] - [[AI Open Weight Models]] - [[HuggingFace]] - [[Y Combinator]] - [[Knowii Voice AI]]