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Cover image for miso-tts-docker: local Miso TTS 8B on Windows, Docker Desktop, and RTX 50-series
Stephen Phillips
Stephen Phillips

Posted on • Originally published at happymonkey.ai

miso-tts-docker: local Miso TTS 8B on Windows, Docker Desktop, and RTX 50-series

#ai #docker #tts #gpu

High-quality local speech is finally useful for agents and demos — if you can get the GPU stack to boot. miso-tts-docker is our unofficial Docker Compose pack for running Miso TTS 8B on NVIDIA GPUs, with a special focus on Windows, Docker Desktop, and RTX 50-series (Blackwell) cards.

Not affiliated with Miso Labs — we wrap upstream inference with GPU-ready containers, Hugging Face caching, and Windows-friendly launchers.

Why we packaged it

Raw research repos rarely survive first contact with Docker Desktop on Windows or brand-new GPU arch flags. Builders hit tokenizer gatekeeping, torchcodec gaps, cache churn, and “works on my Linux box” launch scripts. We wanted a clone-and-run path that still respects the upstream model.

What you get

  • Docker Compose with NVIDIA GPU passthrough
  • PyTorch 2.11 + CUDA 12.8 oriented at RTX 5090 / Blackwell (sm_120)
  • Full bfloat16 Miso path by default (~24 GB VRAM recommended)
  • Persistent Hugging Face cache volumes
  • Preflight checks for gated Llama 3.2 tokenizer access
  • soundfile audio I/O patch so PyTorch 2.11 does not depend on torchcodec
  • Windows .cmd launchers (no PowerShell execution-policy drama)
  • Web voice demo: Whisper STT, optional LLM replies, dual TTS backends
  • Fast mode via Pocket TTS on CPU for low-latency replies
  • HTTPS via Caddy for mic access from phones/tablets on the LAN

Two speeds of voice

Quality mode runs Miso 8B on GPU for the high-end voice. Fast mode runs Pocket TTS on CPU so demos stay snappy without monopolising VRAM. You can also run both and A/B engines in the web UI.

Honest requirements

  • ≥24 GB VRAM recommended for quality mode
  • ~40 GB disk for first-run downloads
  • Hugging Face token + accepted Llama 3.2-1B license for the gated tokenizer path

Why it fits HappyMonkey

Voice is becoming a first-class agent interface. Packaging local TTS well is the difference between a LinkedIn demo and something staff can actually use on a desk GPU without a research internship.

Repo: github.com/HappyMonkeyAI/miso-tts-docker

Originally published on HappyMonkey.ai.

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