This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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: audiolla
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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