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How to generate a seamless BGM loop for a game scene with Audio Studio MCP

Describe a scene in one sentence and get candidate music loops sized for game BGM.

  • Top pick
  • Content
  • Intermediate
  • 1 hour 15 minutes
  • From the README
  • Claude Code
  • Cursor

Who it is forAn indie game developer or hobby game maker who needs a looping background track for a level, menu or cafe scene and has no budget for a composer.

The problemFinding or commissioning a looping BGM track that matches a mood, tempo and length takes time and money.

The outcomeThree candidate BGM WAV files, each with a JSON sidecar, processed into a seamless loop at -16 LUFS and exported as OGG.

What you need first

  • An Apple Silicon Mac (M1 or later) or a 64-bit Windows PC with an NVIDIA GPU (8 GB VRAM or more)
  • git and ffmpeg installed
  • About 6 GB of free disk on macOS or about 14 GB on Windows for the model and PyTorch
  • An MCP client such as Claude Code, Claude Desktop or Cursor
  • On Windows only: a free Hugging Face account with the Stable Audio 3 license accepted

Steps

  1. Run git clone https://github.com/kanaworksai-hue/audio-studio-mcp.git and then cd audio-studio-mcp.
  2. Run brew install ffmpeg if ffmpeg is not installed, then run ./install.sh.
  3. Run claude mcp add --scope user audio-studio -- /path/to/audio-studio-mcp/.venv/bin/audio-studio-mcp to register the server.
  4. Ask the client: "Make a 60-second lo-fi BGM loop at 85 BPM for a cafe scene, give me 3 candidates."
  5. Ask the client to run process_audio with a seamless loop and loudness normalization to -16 LUFS, exporting OGG, then listen to the candidates and keep the best one.

Prompt for your agent

Using the audio-studio MCP server from the audio-studio-mcp repo, call generate_audio for a 60-second lo-fi BGM loop at 85 BPM for a cafe scene with variations set to 3. Then call process_audio on the chosen file to make a seamless loop, normalize to -16 LUFS and export OGG. Verify that the output file exists in outputs/ with a matching .json sidecar.

Caveat. The first generation on macOS may download the medium model (about 5.9 GB), so the first run is much slower than later ones.

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