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Installation

Step 1: Install Python 3.11+

The preprocessor requires Python 3.11 or later.

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brew install python@3.12

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sudo apt update && sudo apt install python3.12 python3.12-venv python3-pip

Download the installer from python.org and check "Add Python to PATH" during setup. Or use winget:

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winget install Python.Python.3.12

Verify your version:

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python3 --version   # should print 3.11.x or later

Step 2: Install ffmpeg

Used for audio normalization, sample-rate conversion, and format conversion.

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brew install ffmpeg

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sudo apt update && sudo apt install ffmpeg

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winget install Gyan.FFmpeg

Or download from ffmpeg.org and add to PATH.

Verify it's available:

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ffmpeg -version

Step 3: Install uv

Install uv to manage the virtualenv, dependencies, and commands.

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curl -LsSf https://astral.sh/uv/install.sh | sh

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brew install uv

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winget install astral-sh.uv

Verify it's available:

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uv --version

Step 4: Clone and install the preprocessor

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git clone https://github.com/steven-ahfu/suno-to-ableton.git
cd suno-to-ableton
uv sync

This installs everything needed for the default processing pipeline: stem discovery, naming, sample-rate conversion, silence trimming, BPM detection, grid alignment, MIDI cleanup, and report generation.

Optional extras

TUI (interactive terminal interface)

Adds a point-and-click terminal UI for browsing projects, toggling options, and running the pipeline.

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uv sync --extra tui

Installs Textual automatically.

Stem separation (CPU)

Adds AI-powered stem separation via Demucs (Meta) and UVR (Ultimate Vocal Remover).

Install PyTorch first

The separation extras depend on PyTorch for inference. You must install PyTorch before installing the separation extras.

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# 1. Install PyTorch (CPU-only)
uv pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu

# 2. Install separation extras
uv sync --extra separation

Stem separation (NVIDIA GPU)

CUDA acceleration significantly speeds up stem separation. Requires an NVIDIA GPU with CUDA support.

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# 1. Install PyTorch with CUDA 12.1 support
uv pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121

# 2. Install GPU separation extras
uv sync --extra separation-gpu

Tip

Visit pytorch.org/get-started to find the install command matching your platform and CUDA version.

Everything at once

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uv pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
uv sync --extra tui --extra separation

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uv pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121
uv sync --extra tui --extra separation-gpu

Verify the installation

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# Should print usage info
uv run suno-to-ableton --help

# Check ffmpeg
ffmpeg -version

# (Optional) Check PyTorch — only needed for stem separation
uv run python -c "import torch; print(f'PyTorch {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

Updating

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cd suno-to-ableton
git pull
uv sync

Uninstalling

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rm -rf .venv

Dependency reference

Package Role
librosa BPM detection, onset analysis, section detection
pretty_midi MIDI reading, writing, and cleanup
soundfile Audio metadata probing
scipy Signal processing for feature analysis
ffmpeg Audio normalization and format conversion (external)
rich Terminal output formatting
typer CLI framework
pydantic Data models and serialization
textual TUI (optional — .[tui])
demucs AI stem separation — Meta's model (optional — .[separation])
audio-separator UVR stem separation backend (optional — .[separation])
onnxruntime / onnxruntime-gpu ONNX inference for UVR models (optional)
PyTorch Neural network inference for Demucs (install separately before separation extras)