Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@midasheng-gen-mcp Make a thunderstorm at night in a forest"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
One call in Claude, Cursor or opencode - "a comedy club scene with a punchline, crowd laughter and a jazz sting" - and you get a single 16 kHz WAV with all of it. Speech lands close to dedicated TTS quality in 9 languages with emotion control, and the whole ~6 GB model stays on your GPU until you unload it.
MiDashengLM-Gen - the first end-to-end trained general text-to-audio model (per the paper): an LLM drives per-token flow matching to generate variable-length audio scenes with speech intelligibility approaching dedicated TTS (Seed-TTS WER 12.15% -> 2.79% vs 1.24% for dedicated TTS). Supports 9 languages and emotion control. Checkpoint auto-downloads from Hugging Face (~6 GB). Model weights are never bundled - see docs/WRAPPEE.md.
Related MCP server: MMAudio MCP
How it runs: a local FastMCP 3.4 server (stdio for Claude Desktop, HTTP
/mcp for Cursor/webapp) with a React dashboard. The model loads lazily on
first generate and stays on the GPU until unloaded.
Direction | Artifacts | Notes |
Hands-in | structured caption (caption/asr/speech/sfx/music/env views), guidance + seed params | Via MCP tool, REST, or webapp |
Hands-out | 16 kHz mono WAV scenes, indexed in SQLite, browsable/exportable |
|
Generate whole soundscapes in one call - crowd laughter + jazz sting + comedy speech, or rain + thunder + forest ambience
Speech intelligibility near dedicated TTS quality, 9 languages, emotion
control through the speech view
Scene library with pagination, audio playback, and export in the webapp
Async job API for long generations; Prefab UI cards in chat
Variable-length output via the learned stop head - no fixed-duration cuts
The fastest path is the .mcpb bundle for Claude Desktop (see INSTALL.md for all options):
Download midasheng-gen-mcp-v0.1.0.mcpb from
Releases
Drag it onto Claude Desktop
First use will download the ~6 GB checkpoint automatically
Or clone and double-click start.bat for the full stack (backend + webapp).
"Generate a comedy club scene: a punchline, crowd laughter, and a jazz
band sting" -> audio_scene(operation="generate", caption="A comedian delivering a punchline followed by uproarious crowd laughter", asr="And that is why I never buy cheap luggage anymore!", speech="expressive comedic male voice", music="sudden upbeat jazz band sting", sfx="crowd laughter", env="intimate comedy club")
"Make a thunderstorm at night in a forest" -> audio_scene(operation="generate", caption="A rolling thunderstorm in a forest at night", sfx="distant thunder and heavy rain", env="dense forest", seed=42)
"What's the model state?" -> audio_scene(operation="status")
Doc | Contents |
All install methods, prerequisites | |
First-run model download, GPU checks, pitfalls | |
MiDashengLM-Gen paper, weights, license, demo | |
System architecture, ports, data flow | |
Env vars, config options | |
All available tools | |
Contributing, local setup | |
Common issues |
Windows/Linux/macOS with a CUDA GPU (RTX 4090 class: ~12 GB VRAM (fp32); CPU inference works but is slow)
Python 3.12+ via uv, Node.js 20+, bun
(auto-installed by start.ps1 on naked PCs)
~10 GB free disk (checkpoint ~6 GB + torch stack)
Apache-2.0 (model and this wrapper). See the use-restrictions section in the upstream repo: no unlawful/military use, no harm to minors or groups.
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