Stem Separator

Separates audio into vocals, drums, bass, and other instruments using Demucs by Meta Research.

Features

• AI-Powered Separation: Uses state-of-the-art Demucs models for high-quality stem separation
• Multiple Models: Support for 4-stem and 6-stem (guitar + piano) models
• Multiple Formats: Output as WAV, MP3, FLAC, OGG, or AAC
• YouTube Support: Download and process YouTube videos directly, including playlists
• Spotify Support: Download and process Spotify tracks (requires spotdl)
• Batch Processing: Process entire directories of audio files
• Custom Remixing: Mix stems with custom volume levels
• Audio Preview: Preview separated stems before saving
• GPU Acceleration: Automatic GPU detection for faster processing
• Metadata Preservation: Preserve ID3 tags from source files
• Configuration File: Save your preferences in ~/.stem-separator.yaml

Installation

Quick Install

Windows:

install.bat

Linux/macOS:

chmod +x install.sh && ./install.sh

Manual Install

1. Install FFmpeg (required):

   # Windows
   winget install FFmpeg.FFmpeg
   
   # macOS
   brew install ffmpeg
   
   # Linux (Debian/Ubuntu)
   sudo apt install ffmpeg

2. Install Python packages:

   pip install demucs yt-dlp soundfile scipy pyyaml rich

3. Optional dependencies:

   # Spotify support
   pip install spotdl
   
   # Audio preview
   pip install sounddevice
   
   # Enhanced metadata handling
   pip install mutagen

Install as Package (Recommended)

pip install -e .

This enables:

• Running as stem-separator song.mp3 or stems song.mp3
• Running as python -m stem_separator song.mp3

Docker (Recommended for Easy Setup)

Run with Docker for the easiest setup - no dependencies to install:

# CPU mode (works on any machine)
docker compose --profile cpu up -d

# GPU mode (requires NVIDIA GPU + Container Toolkit)
docker compose --profile gpu up -d

# Access the web UI at http://localhost:8080

See DOCKER.md for complete Docker documentation, including:

• Building custom images
• Volume mounting for input/output
• Model caching
• CLI usage in Docker
• API documentation

Usage

Basic Usage

# Process a local file
python stem_separator.py song.mp3

# YouTube URL
python stem_separator.py "https://www.youtube.com/watch?v=VIDEO_ID"

# Specify output folder
python stem_separator.py song.mp3 -o ./output

# Use 6-stem model (adds guitar + piano separation)
python stem_separator.py song.mp3 --model htdemucs_6s

# Export as MP3
python stem_separator.py song.mp3 --format mp3

# Extract only specific stems
python stem_separator.py song.mp3 --stems vocals,drums

# Create karaoke version (everything except vocals)
python stem_separator.py song.mp3 --stems karaoke --format mp3

# Extract acapella (vocals only)
python stem_separator.py song.mp3 --stems acapella

Batch Processing

# Process all audio files in a directory
python stem_separator.py ./music_folder --batch -o ./stems

# Process recursively
python stem_separator.py ./music_folder --batch --recursive

# Dry run (see what would be processed)
python stem_separator.py ./music_folder --batch --dry-run

YouTube Playlists

# Process an entire playlist
python stem_separator.py "https://youtube.com/playlist?list=..." --playlist

# Resume interrupted playlist download
python stem_separator.py "https://youtube.com/playlist?list=..." --playlist

Spotify Support

# Single track
python stem_separator.py "https://open.spotify.com/track/..."

# Playlist or album
python stem_separator.py "https://open.spotify.com/playlist/..." --playlist

Custom Remixing

# Create a custom mix with volume control
python stem_separator.py song.mp3 --remix "vocals:0.5,drums:1.0,bass:0.8"

# Process then remix
python stem_separator.py song.mp3 --remix "vocals:0,drums:1.5,bass:1.2,other:0.5"

Audio Preview

# Preview stems interactively after processing
python stem_separator.py song.mp3 --preview

Quality Analysis

# Analyze separation quality
python stem_separator.py song.mp3 --quality

Options Reference

Output Options

Option	Description
-o, --output	Output directory (default: current)
--format	Output format: wav, mp3, flac, ogg, aac (default: wav)
--naming	Output naming template (default: {name}_{stem})

Model Options

Option	Description
--model	AI model: htdemucs, htdemucs_ft, htdemucs_6s (default: htdemucs)
--stems	Stems to export: comma-separated or preset

Processing Options

Option	Description
--cpu	Force CPU mode (skip GPU)
--normalize	Normalize audio levels in output stems
--low-memory	Low memory mode for very long tracks
--quality	Analyze and report stem separation quality

Batch Options

Option	Description
--batch	Process directory of files
-r, --recursive	Search directories recursively
-j, --parallel N	Number of parallel jobs

Source Options

Option	Description
--playlist	Process YouTube/Spotify playlist
--browser	Use browser cookies (chrome, firefox, edge, safari)

Other Options

Option	Description
--remix	Remix stems with volume control
--preview	Preview stems interactively
--no-metadata	Don't preserve metadata
-v, --verbose	Verbose output
-q, --quiet	Quiet mode
--dry-run	Show what would be done
--version	Show version

Models

Model	Stems	Description
htdemucs	4	Default model (vocals, drums, bass, other)
htdemucs_ft	4	Fine-tuned version (better quality)
htdemucs_6s	6	Adds guitar and piano separation

Stem Presets

Preset	Description
all	All stems (default)
karaoke	Everything except vocals (instrumental)
acapella	Vocals only
instrumental	Same as karaoke

Configuration File

Create ~/.stem-separator.yaml to set defaults:

# Default settings
model: htdemucs
format: wav
output_dir: .
normalize: false
naming_template: "{name}_{stem}"

# Processing
cpu: false
low_memory: false

# YouTube
browser: chrome

# Output
verbose: false
quiet: false

Generate a sample config:

python stem_separator.py --generate-config > ~/.stem-separator.yaml

Output

Creates a folder named {song}_stems containing the separated stems.

4-stem model output:

• vocals - Singing/voice
• drums - Drums and percussion
• bass - Bass
• other - Guitar, piano, synths, etc.

6-stem model output (htdemucs_6s):

• All of the above, plus:
• guitar - Guitar
• piano - Piano

GPU Support

The script automatically tries GPU first and falls back to CPU if needed.

NVIDIA GPUs

# Standard CUDA support
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu121

# RTX 5090 / Blackwell GPUs (requires nightly)
pip install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128

Apple Silicon (M1/M2/M3)

pip install torch torchaudio
# MPS backend is used automatically

Performance

Setup	Time per Song
GPU (CUDA)	~30 seconds
Apple Silicon (MPS)	~1 minute
CPU	2-4 minutes

Web UI

The Docker container includes a modern web interface at http://localhost:8080 with:

• File Upload: Drag & drop audio files (MP3, WAV, FLAC, OGG, AAC, M4A)
• URL Processing: Paste YouTube or Spotify URLs directly
• Playlist Support: Process entire playlists
• All CLI Options: Model selection, output format, stem selection, etc.
• Real-time Progress: Live progress updates via WebSocket
• Easy Downloads: Download individual stems or all as ZIP

API Usage

from stem_separator import separate_audio, StemSeparator, remix_stems

# Simple usage
result = separate_audio(
    input_file="song.mp3",
    output_dir="./output",
    model_name="htdemucs",
    output_format="mp3",
)

# With model pre-loading for batch
separator = StemSeparator(model_name="htdemucs_6s")
separator.load_model()

for song in songs:
    result = separator.separate(song, output_dir)

separator.unload_model()

# Remix stems
result = remix_stems(
    stems_dir="./output/song_stems",
    output_path="./remix.mp3",
    mix_components="vocals:0.5,drums:1.0,bass:0.8",
)

Troubleshooting

YouTube 403 Errors

python stem_separator.py URL --browser edge

GPU Not Working

1. Check CUDA is installed: nvidia-smi
2. Check PyTorch CUDA: python -c "import torch; print(torch.cuda.is_available())"
3. For newest GPUs, install nightly PyTorch

Memory Issues

# Use low-memory mode for long tracks
python stem_separator.py long_song.mp3 --low-memory

License

MIT License

Acknowledgments

• Demucs by Meta Research
• yt-dlp for YouTube downloads
• spotdl for Spotify downloads