midasheng-gen

name	midasheng-gen
description	Unified audio scene generation - speech, music, SFX, and ambience in one 16 kHz mix from structured text captions

MiDashengLM-Gen - Audio Scene Generation

What this server does

MiDashengLM-Gen MCP generates coherent 16 kHz mixed audio scenes from text: speech, music, sound effects, and environmental acoustics in a single autoregressive pass (LLM backbone + per-token flow matching). Apache-2.0, fully local on CUDA (RTX 4090 class: ~12 GB VRAM (fp32)).

Tool surface

audio_scene (portmanteau)

Operations:

• status: model state, GPU info, torch/transformers versions
• generate: synchronous scene generation from structured views
• list: paginated scene browser (limit/offset, has_more)
• get: single scene metadata
• delete: remove scene + WAV (requires confirm=True)
• export: copy a scene WAV to a destination path
• samples: built-in example captions
• download_model: fetch the checkpoint from Hugging Face (idempotent)
• load_model / unload_model: GPU memory management

midasheng_help

Multi-level help: topics overview | generate | prompt_format | model_state | examples.

Prefab cards

• show_scene_status_card: model + library status
• show_scene_card: one scene's metadata

Structured caption format

The model consumes tagged views; absent views become <|unknown|>.

<|caption|> overall scene <|asr|> transcript to be spoken <|speech|> speaker characteristics (voice, emotion, style) <|sfx|> sound effects <|music|> music description <|env|> environment / ambience

Best practices

1. Check audio_scene(operation="status") before generating - the state machine tells you what is missing (not_installed / model_missing / ready) and never fakes success.
2. Keep the caption view concrete and the asr view clean prose for maximum speech intelligibility.
3. Use seed for reproducible scenes; raise eval_cfg toward 3.0 for more assertive adherence, lower toward 1.5 for softer interpretation.
4. List is paginated: pass offset from the previous response.
5. delete is destructive: confirm=True is required.

Notes

• Generation blocks up to ~3 minutes on a 4090; the webapp uses the REST job API for async progress.
• Output: 16 kHz mono WAV, variable length via the learned stop head.
• 9 languages supported by the checkpoint; emotion control via the speech view.