AudioMaster

AI-powered music mastering tool built with Rust, Tauri, and Python.

Overview

AudioMaster is a comprehensive music mastering application that combines Rust's performance for audio processing with Python's machine learning ecosystem. It features a Tauri-based desktop application, a CLI tool, and a web frontend for AI-driven audio mastering workflows.

Features

• AI-Powered Mastering — Neural network-based audio mastering and enhancement
• Rust Core — High-performance audio processing with mastering-core library
• CLI Tool — Command-line interface for batch and scriptable mastering
• Desktop App — Tauri-based cross-platform desktop application
• Python ML Pipeline — Machine learning inference and FX processing in Python
• Web Frontend — Vite-powered web interface for mastering control
• Documentation — User guides, developer docs, and runbooks
• Website — Project landing page with screenshots and information

Tech Stack

Category	Technology
Core (Rust)	Rust 2021, serde, tokio, symphonia, hound
CLI	clap (derive), tracing, indicatif
Desktop App	Tauri (^2.x)
Web Frontend	Vue 3, Vite
Python Backend	PyTorch, torchaudio, numpy
Build	Cargo workspace, Vite
License	GPL-3.0-or-later
Version	1.2.0

Project Structure

AudioMaster/
├── Cargo.toml                     # Rust workspace definition
├── Cargo.lock                     # Dependency lock file
├── package.json                   # Node dependencies (Vite/frontend)
├── vite.config.js                 # Vite configuration
├── index.html                     # Web frontend entry
├── LICENSE                        # GPL-3.0 license
├── README.md                      # This file
├── CONTRIBUTING.md                # Contribution guidelines
├── crates/                        # Rust workspace crates
│   ├── mastering-core/            # Core audio processing library
│   │   └── Cargo.toml
│   │   └── src/                   # Library source
│   └── mastering-cli/             # CLI binary
│       └── Cargo.toml
│       └── src/main.rs
├── src-tauri/                     # Tauri desktop app
│   ├── Cargo.toml                 # Tauri Rust deps
│   ├── tauri.conf.json            # Tauri configuration
│   ├── build.rs                   # Build script
│   ├── capabilities/              # Tauri capabilities
│   ├── gen/                       # Generated Tauri files
│   └── icons/                     # App icons
├── python/                        # Python ML pipeline
│   ├── requirements.txt           # Python dependencies
│   ├── matchering_bridge.py       # Matchering integration
│   ├── ml_inference.py            # ML inference engine
│   └── apply_fx.py                # Audio FX application
├── docs/                          # Documentation
│   ├── runbooks/                  # Operational runbooks
│   ├── user/                      # User documentation
│   └── dev/                       # Developer documentation
├── website/                       # Project website
│   ├── index.html                 # Landing page
│   ├── style.css                  # Website styles
│   ├── icon.png / icon.svg        # Branding assets
│   ├── sitemap.xml                # SEO sitemap
│   └── robots.txt                 # SEO robots
├── screenshot.png                 # Application screenshot
├── dist/                          # Build output
└── .github/                       # GitHub workflows
    └── workflows/                 # CI/CD pipelines

Installation

Prerequisites

• Rust — Latest stable toolchain (rustup)
• Python 3.10+ — For ML inference pipeline
• Node.js 18+ — For web frontend
• Cargo — Rust package manager

Rust Core

# Clone and build the Rust workspace
cd AudioMaster
cargo build --release

# Run the CLI tool
cargo run -p mastering-cli -- --help

Python ML Pipeline

cd python
pip install -r requirements.txt

Desktop App (Tauri)

# Install Tauri CLI
cargo install tauri-cli --version "^2"

# Build the desktop app
cd src-tauri
cargo tauri dev      # Development
cargo tauri build    # Production build

Web Frontend

npm install
npm run dev    # Start on port 1421
npm run build  # Production build

Usage

CLI

# Master an audio file
cargo run -p mastering-cli -- input.wav -o output.wav

# Dry run (preview settings)
cargo run -p mastering-cli -- input.wav --dry-run

# Apply custom preset
cargo run -p mastering-cli -- input.wav --preset pop

Desktop App

Launch the Tauri desktop application for a GUI-based mastering experience with real-time preview.

Python API

from python.ml_inference import MLModel
from python.apply_fx import apply_effects

# Load model and process audio
model = MLModel.load("path/to/model")
result = model.infer(audio_data)
processed = apply_fx(result, fx_params)

Development

Building

# Build all workspace crates
cargo build

# Run tests
cargo test

# Run with coverage
cargo llvm-cov

# Build frontend
npm run build

Contributing

See CONTRIBUTING.md for contribution guidelines.

Documentation

• docs/user/ — User documentation
• docs/dev/ — Developer documentation
• docs/runbooks/ — Operational runbooks

License

GPL-3.0-or-later