AutoMixMaster

Version 0.4.1

FIXED-RULE AUDIO WORKFLOW FOR MIXING AND MASTERING MUSIC STEMS

Designed for amateur music producers and hobbyists

Overview • Feature Set • First Session • Quick Start • System Requirements • Build + Install • Licensing

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Overview

AutoMixMaster helps you turn raw stems into a cleaner, release-ready track with fewer manual steps.

It focuses on predictable, fixed-rule processing so results are repeatable and beginner-friendly, with optional extras like AI stem separation, batch mode, and bundled renderer integrations.

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Feature Set

Core capabilities at a glance

Module	Description
Auto Mix + Auto Master	Deterministic stem balancing, gain staging, and limiting workflow.
One-Click Pipeline	Mix + Master (Ctrl+Shift+M) runs Auto Mix → Auto Master → Export.
AI Stem Separation (Optional)	Splits a single full-mix import into stems before processing when enabled.
Task-Scoped Model Browser	Install/uninstall models and set active packs per task (mix, master, analysis, separation) from Hugging Face or GitHub Releases.
Batch Processing	Queue folders, auto-group stems by filename role patterns, and render one mastered song per group (<song>_AutoMixMaster_YYYYMMDD_XX.<ext>). Supports recursive discovery via UI toggle or AUTOMIX_BATCH_RECURSIVE=1.
Renderer Integrations	Built-in discovery for PhaseLimiter, FFmpeg, SoX, and rsgain; only available tools are shown (*_BIN env overrides supported).
Verification Reporting	Export verification report plus batch completion summary; optional per-export .report.json sidecar.
Task Center + Logs	Real-time progress tracking with timestamped activity log and copy-log utility.
Analysis Meters	Live LUFS and peak metering via GlowMeters.

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First Session

Welcome to your first mixing and mastering session. AutoMixMaster simplifies the process into a few core steps:

1. Import audio: Drag and drop files onto the waveform area, or click Import. Supported formats: WAV, AIFF, FLAC, MP3, OGG.
2. (Optional) enable AI Stem Separation: Toggle AI Stem Separation and check the badge beside it (Separation model: <name/none>).
   • If exactly one full-mix track is loaded, separation runs before Auto Mix.
   • If multiple files are loaded, they are treated as regular stems and separation is skipped.
3. Manage models in Model Browser: Open Models to fetch catalog entries, install/uninstall models, and set active packs per task (mix, master, analysis, separation) using Set Active or Use Selected for Task.
4. Auto Mix: Click Auto Mix to analyze stems and apply deterministic balancing rules.
5. Auto Master: Click Auto Master to apply mastering strategy and limiting.
6. One-click pipeline: Click Mix + Master (Ctrl+Shift+M) to run Auto Mix → Auto Master → Export. If AI Stem Separation is enabled and one full mix is loaded, separation is performed first, then the pipeline continues automatically.
7. Export: Use Export (Ctrl+E) for manual output control, or rely on pipeline export.

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Quick Start

Getting started with AutoMixMaster is simple.

⚠️ Testing disclaimer: only the Windows version has been manually tested end-to-end so far.
Linux, macOS, and ARM64 artifacts are currently provided as best-effort builds.

Windows (Pre-compiled Executable)

Windows users can download the portable release zip (AutoMixMaster-windows-<arch>.zip), extract it, and run AutoMixMaster.exe.

macOS (Pre-compiled App Bundle)

macOS users can download the release zip (AutoMixMaster-macos-<arch>.zip), extract it, and open AutoMixMaster.app.

Linux (Prebuilt Packages)

Linux users can download either:

• AppImage (AutoMixMaster-<version>-<arch>.AppImage) for a portable one-file launch.
• Debian package (automixmaster_<version>_<arch>.deb) for Ubuntu/Debian install.
• Flatpak bundle (AutoMixMaster-linux-<arch>.flatpak) for Flatpak-based installs.

Build From Source

If you are on Linux, or prefer to build the application from source on Windows, refer to the Build + Install section below for verified instructions.

Estimated System Requirements

These are practical estimates for AI-heavy workflows (especially ONNX-based separation/mix/master inference), not strict hard limits.

AutoMixMaster is designed to benefit from GPU acceleration via ONNX Runtime providers.

Minimum OS requirements (release artifacts)

• Windows: Windows 10 or Windows 11 (x64 or ARM64)
• macOS (Apple Silicon / ARM64): macOS 14+
• macOS (Intel / x64): macOS 15+
• Linux: Ubuntu 24.04 LTS+ for current prebuilt .deb/AppImage artifacts

Note: Ubuntu 22.04 may still work if you build from source on 22.04 with compatible dependencies, but official CI/release packaging currently targets Ubuntu 24.04.

Minimum workable

• CPU: modern 6-core / 12-thread desktop CPU (Ryzen 5 5600 / Core i5-12400 class)
• RAM: 16 GB minimum
• GPU: compatible acceleration path with ~6 GB VRAM
  • Windows DirectML path: DirectX 12-capable GPU
  • CUDA path: NVIDIA CUDA-capable GPU
• Storage: ~10 GB free (models, temp files, exports)

Recommended (smoother)

• CPU: 8 cores / 16 threads or better (Ryzen 7 / Core i7 class)
• RAM: 32 GB
• GPU: 8–12 GB VRAM

Heavy batch / long sessions

• CPU: 12 cores+ strongly recommended
• RAM: 32–64 GB
• GPU: 12 GB+ VRAM

Why these estimates

• GPU acceleration matters most: DirectML needs a DirectX 12 GPU and CUDA needs an NVIDIA CUDA-capable GPU (DirectML, CUDA).
• Demucs notes roughly 3 GB minimum and around 7 GB typical GPU memory, so 8 GB+ VRAM is a safer real-world target; CPU-only runs work but are slower (Demucs README).

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Build + Install

Windows (Visual Studio 2026)

1. Configure

cmake -S . -B build -G "Visual Studio 18 2026" -A x64

1. Build

cmake --build build --config Release --parallel

Ubuntu Linux (24.04+)

1. Install dependencies

sudo apt-get install -y \
  libasound2-dev libfreetype6-dev libx11-dev libxcomposite-dev \
  libxcursor-dev libxext-dev libxinerama-dev libxrandr-dev \
  libxrender-dev libwebkit2gtk-4.1-dev libglu1-mesa-dev mesa-common-dev

1. Configure + build

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --parallel

1. Run tests (optional but recommended)

ctest --test-dir build --output-on-failure

macOS (Apple Silicon + Intel)

1. Install build tools

xcode-select --install
brew install cmake ninja

1. Configure + build (pick one architecture)

# Apple Silicon (arm64)
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_OSX_ARCHITECTURES=arm64 -DBUILD_TESTING=OFF -DBUILD_TOOLS=OFF

# Intel (x64)
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_OSX_ARCHITECTURES=x86_64 -DBUILD_TESTING=OFF -DBUILD_TOOLS=OFF

cmake --build build --target AutoMixMasterApp --parallel

1. Configure + build (universal binary: arm64 + x86_64)

cmake -S . -B build-universal \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
  -DBUILD_TESTING=OFF \
  -DBUILD_TOOLS=OFF

cmake --build build-universal --target AutoMixMasterApp --parallel

1. Install app bundle

APP_BUNDLE="$(find build build-universal -maxdepth 6 -type d -name 'AutoMixMaster.app' 2>/dev/null | head -n 1)"
cp -R assets "$APP_BUNDLE/Contents/MacOS/assets"
sudo cp -R "$APP_BUNDLE" /Applications/
open /Applications/AutoMixMaster.app

Linux Package Builds (.deb + AppImage)

After building, create distributable Linux packages with:

./tools/package_linux.sh

Output artifacts are written to dist/linux/.

Flatpak

Manifest path:

packaging/flatpak/io.automixmaster.AutoMixMaster.yml

Note: the Flatpak manifest prefetches JUCE/nlohmann/libebur128 sources and passes FETCHCONTENT_SOURCE_DIR_* flags so CMake does not need live GitHub access inside the Flatpak sandbox.

Install Flatpak tooling:

sudo apt-get install -y flatpak flatpak-builder

Add Flathub and install required runtime/SDK:

flatpak remote-add --user --if-not-exists flathub https://flathub.org/repo/flathub.flatpakrepo
flatpak --user install -y flathub org.freedesktop.Platform//24.08 org.freedesktop.Sdk//24.08

Build bundle:

./tools/build_flatpak.sh

Output:

• dist/flatpak/AutoMixMaster.flatpak

Bundled Renderer Tools (Optional)

The renderer registry can auto-discover optional CLI tools if you place binaries under:

• assets/ffmpeg/bin/ffmpeg(.exe) or set FFMPEG_BIN
• assets/sox/bin/sox(.exe) or set SOX_BIN
• assets/rsgain/bin/rsgain(.exe) or set RSGAIN_BIN

If a tool is missing, it is hidden from selectable available renderers automatically.

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Licensing

AutoMixMaster is distributed under the GNU General Public License v3 (GPLv3).

Component	License	Role
JUCE 8.0.8	AGPLv3 / Commercial	Framework
libebur128	MIT	Metering
nlohmann/json	MIT	Metadata
Catch2 3.7.1	BSL-1.0	Testing
PhaseLimiter	GPL / Custom	Limiting
FFmpeg	GPL-compatible	Optional renderer
SoX	GPL-2.0-or-later	Optional renderer
rsgain	BSD-2-Clause	Optional ReplayGain tagging stage