Stem MCP Server

Stem MCP Server 🎵

A comprehensive Model Context Protocol (MCP) server for professional AI-powered audio processing and stem manipulation. Designed specifically for music producers, audio engineers, and Logic Pro users who need advanced audio processing capabilities integrated with AI workflows.

Features 🚀

🎯 Core Audio Processing

🎤 AI Stem Generation: State-of-the-art source separation using Demucs models
✂️ Smart Audio Splitting: Intelligent segmentation with customizable overlap and fade options
🔄 Seamless Loop Creation: Professional loop generation with tempo matching and crossfading
📊 Advanced Audio Analysis: Deep musical feature extraction (tempo, key, spectral characteristics)
🎯 Precise Instrument Isolation: Extract specific instruments with multiple algorithms
🎵 Vocal Processing: Advanced vocal extraction and separation techniques

🎛️ Advanced Features

🎪 Multi-Vocal Range Separation: Split vocals into soprano, alto, tenor, bass ranges
🎼 Musical Structure Analysis: Detect beats, tempo, key signatures, and harmonic content
🔊 Dynamic Range Analysis: RMS energy, peak detection, loudness analysis
🎚️ Spectral Processing: Frequency domain analysis and manipulation
⚡ Batch Processing: Handle multiple files efficiently
🎨 Custom Processing Chains: Combine multiple tools for complex workflows

Supported Audio Formats

WAV, MP3, FLAC, AAC, M4A, OGG, WMA

AI Models

Demucs: State-of-the-art source separation models
- htdemucs (default): High-quality 4-stem separation
- htdemucs_ft: Fine-tuned variant
- htdemucs_6s: 6-stem separation
- mdx: Alternative model architecture
- mdx_extra: Enhanced MDX model

Installation 🔧

Prerequisites

Python 3.10 or higher (required for MCP compatibility)
FFmpeg (for audio processing)
CUDA-compatible GPU (optional, for faster processing)

Install FFmpeg

# macOS (using Homebrew)
brew install ffmpeg

# Ubuntu/Debian
sudo apt update && sudo apt install ffmpeg

# Windows (using Chocolatey)
choco install ffmpeg

Install the MCP Server

# Clone or create the project
cd stem-mcp

# Install in development mode
pip install -e .

# Or install from requirements
pip install -r requirements.txt

Install Dependencies

# Install core dependencies
pip install mcp>=1.0.0 librosa soundfile numpy scipy torch torchaudio demucs pydub

# For best performance, install with CUDA support
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118

Configuration ⚙️

MCP Client Configuration

Add this to your MCP client configuration (e.g., Claude Desktop):

{
  "mcpServers": {
    "stem-processing": {
      "command": "stem-mcp",
      "args": [],
      "env": {
        "PYTHONPATH": "/path/to/stem-mcp/src"
      }
    }
  }
}

Usage Examples 🎯

1. Generate Stems from Audio

# Using the MCP tool
{
  "tool": "generate_stems",
  "arguments": {
    "audio_path": "/path/to/song.wav",
    "output_dir": "./stems",
    "model_type": "htdemucs",
    "num_stems": 4
  }
}

Output: Separates audio into vocals, drums, bass, and other instruments.

2. Split Stems into Segments

{
  "tool": "split_stems", 
  "arguments": {
    "stem_path": "./stems/vocals.wav",
    "output_dir": "./segments",
    "segment_length": 15.0,
    "overlap": 2.0
  }
}

Output: Creates 15-second segments with 2-second overlap.

3. Create Seamless Loops

{
  "tool": "create_loop",
  "arguments": {
    "audio_path": "./drums.wav",
    "loop_duration": 8.0,
    "bpm": 120,
    "crossfade_duration": 0.2
  }
}

Output: Creates an 8-second loop at 120 BPM with smooth crossfading.

4. Analyze Audio Features

{
  "tool": "analyze_audio",
  "arguments": {
    "audio_path": "./song.wav"
  }
}

Output:

🎵 Audio Analysis Results:

📊 Basic Info:
  Duration: 245.67 seconds
  Sample Rate: 44100 Hz
  Channels: Stereo

🎵 Musical Features:
  Tempo: 128.5 BPM
  Estimated Key: G
  Beat Count: 523

🔊 Spectral Analysis:
  Avg Spectral Centroid: 2847.3 Hz
  Avg Spectral Rolloff: 8934.2 Hz
  Avg Zero Crossing Rate: 0.0847
  Avg RMS Energy: 0.1234

5. Extract Vocals Only

{
  "tool": "extract_vocal",
  "arguments": {
    "audio_path": "./song.wav",
    "method": "demucs"
  }
}

6. Isolate Specific Instruments

{
  "tool": "isolate_instrument",
  "arguments": {
    "audio_path": "./song.wav",
    "instrument": "drums",
    "method": "demucs"
  }
}

API Reference 📚

Complete Tool Suite

🎤 `generate_stems`

State-of-the-art AI-powered source separation using Demucs models.

Parameters:

audio_path (string, required): Path to input audio file
output_dir (string, optional): Output directory (default: ".")
model_type (string, optional): Demucs model type
- "htdemucs" (default): High-quality 4-stem separation
- "htdemucs_ft": Fine-tuned variant for enhanced quality
- "htdemucs_6s": 6-stem separation (vocals, drums, bass, piano, guitar, other)
- "mdx": Fast processing with good quality
- "mdx_extra": Enhanced MDX model
num_stems (integer, optional): Number of output stems (2-6, default: 4)

Output: Generates separate audio files for each stem (vocals, drums, bass, other)

✂️ `split_stems`

Intelligent audio segmentation with customizable parameters.

Parameters:

stem_path (string, required): Path to audio file to split
output_dir (string, optional): Output directory (default: ".")
segment_length (number, optional): Segment duration in seconds (1-300, default: 30)
overlap (number, optional): Overlap between segments in seconds (0-10, default: 0)

Features:

Smart segment boundary detection
Customizable overlap for smooth transitions
Preserves audio quality and metadata

🔄 `create_loop`

Professional seamless loop creation with advanced crossfading.

Parameters:

audio_path (string, required): Path to input audio
output_path (string, optional): Output file path (auto-generated if not provided)
loop_duration (number, optional): Loop duration in seconds (0.5-60, default: 4)
bpm (number, optional): Target BPM (60-200, auto-detected if not specified)
crossfade_duration (number, optional): Crossfade length in seconds (0-2, default: 0.1)

Features:

Automatic tempo detection and matching
Smart beat-aligned loop points
Professional crossfading algorithms
Maintains musical timing and feel

📊 `analyze_audio`

Comprehensive musical and spectral analysis.

Parameters:

audio_path (string, required): Path to audio file to analyze

Analysis Output:

Basic Properties: Duration, sample rate, channel configuration
Musical Features: Tempo (BPM), key signature, beat tracking
Spectral Analysis: Frequency content, spectral centroid, rolloff
Dynamic Range: RMS energy levels, peak detection
Audio Quality: Zero-crossing rate, harmonic content

🎤 `extract_vocal`

Advanced vocal extraction with multiple algorithms.

Parameters:

audio_path (string, required): Path to input audio
output_path (string, optional): Output file path (auto-generated if not provided)
method (string, optional): Extraction algorithm
- "demucs" (default): AI-powered high-quality separation
- "librosa": Traditional signal processing approach
- "spectral": Frequency domain processing

Features:

Multiple extraction algorithms for different use cases
High-quality vocal isolation
Preserves vocal character and dynamics

🎹 `isolate_instrument`

Precise instrument isolation using multiple techniques.

Parameters:

audio_path (string, required): Path to input audio
instrument (string, optional): Target instrument
- "vocals": Lead and backing vocals
- "drums": Full drum kit
- "bass": Bass guitar and synthesizers
- "guitar": Electric and acoustic guitars
- "piano": Piano and keyboard instruments
- "other": Remaining instruments
output_path (string, optional): Output file path
method (string, optional): Isolation technique
- "demucs": AI source separation
- "librosa": Signal processing
- "spectral": Frequency domain filtering

🎪 `separate_vocal_ranges`

NEW: Advanced vocal range separation for choir and multi-vocal arrangements.

Parameters:

audio_path (string, required): Path to vocal audio file
output_dir (string, optional): Output directory for range files

Output: Separate files for each vocal range:

Soprano: High female voices (C4-C6)
Alto: Low female voices (G3-E5)
Tenor: High male voices (C3-A4)
Bass: Low male voices (E2-E4)

Features:

Frequency-based intelligent separation
Preserves natural vocal characteristics
Ideal for choir arrangements and vocal analysis

🎵 `extract_vocal_harmonies`

NEW: Isolate and separate vocal harmonies from lead vocals.

Parameters:

audio_path (string, required): Path to audio with vocal harmonies
output_dir (string, optional): Directory for harmony files
sensitivity (number, optional): Harmony detection sensitivity (0.1-1.0, default: 0.5)

Features:

Separates lead vocals from harmonies
Maintains harmonic relationships
Perfect for remixing and vocal arrangement analysis

Performance Tips 🚀

Hardware Optimization

GPU: Use CUDA-compatible GPU for 10x faster processing
RAM: 16GB+ recommended for processing large files
Storage: SSD recommended for faster I/O operations

Processing Tips

File Format: Use WAV or FLAC for best quality
Sample Rate: 44.1kHz or 48kHz for optimal results
Batch Processing: Process multiple files in sequence for efficiency

Model Selection

htdemucs: Best general-purpose model
htdemucs_6s: Use for 6-stem separation (vocals, drums, bass, piano, guitar, residual)
mdx: Faster processing, slightly lower quality

Development 😠️

💻 Complete Project Structure

stem-mcp/
├── src/stem_mcp/
│   ├── __init__.py              # Package initialization and version
│   ├── server.py                # Main MCP server implementation
│   ├── audio_processor.py       # Core audio processing engine
│   ├── tools_schema.py          # MCP tool definitions and schemas
│   ├── utils.py                 # Utility functions and helpers
│   ├── vocal_processor.py       # Advanced vocal processing tools
│   └── analysis_engine.py       # Audio analysis and feature extraction
├── examples/
│   ├── test_tools.py            # Comprehensive tool testing script
│   ├── sample_workflows.py      # Example production workflows
│   └── integration_examples.py  # Logic Pro integration examples
├── tests/
│   ├── test_audio_processing.py # Audio processing tests
│   ├── test_vocal_tools.py      # Vocal processing tests
│   ├── test_analysis.py         # Analysis engine tests
│   └── test_integration.py      # MCP integration tests
├── docs/
│   ├── API_REFERENCE.md         # Detailed API documentation
│   ├── WORKFLOWS.md             # Production workflow guides
│   ├── TROUBLESHOOTING.md       # Common issues and solutions
│   └── PERFORMANCE_GUIDE.md     # Optimization tips and benchmarks
├── pyproject.toml               # Project configuration and dependencies
├── requirements.txt             # Python dependencies
├── requirements-dev.txt         # Development dependencies
├── .gitignore                   # Git ignore patterns
├── DEMO_COMPLETE.md            # Complete demo and feature overview
├── README.md                   # This comprehensive guide
└── LICENSE                     # MIT License

🔧 Development Environment Setup

Quick Start

# Clone the repository
git clone <repository-url>
cd stem-mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode with all dependencies
pip install -e ".[dev]"

# Install additional development tools
pip install -r requirements-dev.txt

# Verify installation
stem-mcp --version

Development Dependencies

# Core development tools
pip install pytest pytest-cov black flake8 mypy pre-commit

# Audio testing tools
pip install pytest-audio librosa-test-utils

# Performance profiling
pip install memory-profiler line-profiler

# Documentation tools
pip install sphinx sphinx-rtd-theme

🚀 Running in Development Mode

Basic Development Commands

# Run server with debug logging
stem-mcp --debug --log-level DEBUG

# Run with specific configuration
stem-mcp --config config/dev_config.json

# Run with performance profiling
stem-mcp --profile --profile-output profile_results.txt

# Test all tools with sample audio
python examples/test_tools.py

# Run comprehensive test suite
pytest tests/ -v --cov=src/stem_mcp

# Run specific test categories
pytest tests/test_audio_processing.py -v
pytest tests/test_vocal_tools.py -v

Code Quality & Formatting

# Format code with Black
black src/ tests/ examples/

# Lint with flake8
flake8 src/ tests/ examples/

# Type checking with mypy
mypy src/stem_mcp/

# Run all quality checks
pre-commit run --all-files

🧪 Testing & Quality Assurance

Test Categories

Unit Tests: Individual function and class testing
Integration Tests: MCP client-server communication
Audio Tests: Audio processing accuracy and quality
Performance Tests: Speed and memory usage benchmarks
Regression Tests: Ensure consistent outputs across versions

Running Tests

# Run all tests with coverage
pytest tests/ --cov=src/stem_mcp --cov-report=html

# Run tests with audio samples
pytest tests/ --with-audio-samples

# Run performance benchmarks
pytest tests/test_performance.py --benchmark-only

# Run memory usage tests
pytest tests/test_memory.py --memray

🔍 Debugging & Profiling

Debug Mode Features

Detailed logging at all processing stages
Audio processing step visualization
Memory usage tracking
Processing time measurements
Model loading and caching information

Performance Profiling

# Profile CPU usage
python -m cProfile -o profile.stats examples/test_tools.py

# Profile memory usage
python -m memory_profiler examples/test_tools.py

# Profile specific functions
@profile
def my_function():
    # Function code here
    pass

🤝 Contributing Guidelines

Development Workflow

Fork the repository and create your feature branch
Set up development environment with all dependencies
Write comprehensive tests for your changes
Follow code style guidelines (Black, flake8, mypy)
Update documentation for new features
Run full test suite before submitting
Submit pull request with detailed description

Code Style Standards

Python: Follow PEP 8 with Black formatting
Docstrings: Google-style docstrings for all public functions
Type Hints: Use type hints for all function parameters and returns
Comments: Clear, concise comments for complex logic
Error Handling: Comprehensive error handling with informative messages

Pull Request Checklist

☑️ All tests pass locally
☑️ Code follows style guidelines
☑️ Documentation is updated
☑️ New features have tests
☑️ No breaking changes (or clearly documented)
☑️ Performance impact assessed
☑️ Example usage provided

Professional Workflows 🎯

🎚️ Logic Pro Integration

Seamlessly integrate with Logic Pro for enhanced music production:

Complete Production Workflow

🎵 Export from Logic Pro
- Export stereo mix or individual tracks
- Use 24-bit/48kHz for best quality
- Export as WAV or AIFF format
🤖 AI-Powered Processing
- Generate high-quality stems using Demucs
- Analyze musical content and structure
- Extract specific instruments or vocal parts
- Create seamless loops from any section
🎹 Import Back to Logic
- Import processed stems as individual tracks
- Use analyzed BPM data for tempo matching
- Apply extracted loops to new compositions
- Layer isolated instruments for creative arrangements

Advanced Production Techniques

🎭 Stem-Based Remixing

# 1. Generate stems from your Logic Pro export
generate_stems("/path/to/logic_export.wav", model_type="htdemucs_6s")

# 2. Analyze each stem for musical content
analyze_audio("/stems/vocals.wav")
analyze_audio("/stems/drums.wav")

# 3. Create custom loops from specific sections
create_loop("/stems/drums.wav", loop_duration=8, bpm=128)

# 4. Extract vocal harmonies for detailed editing
extract_vocal_harmonies("/stems/vocals.wav")

🎵 Vocal Production Chain

# Complete vocal processing workflow
extract_vocal("/audio/full_mix.wav", method="demucs")
separate_vocal_ranges("/vocals/extracted_vocal.wav")
extract_vocal_harmonies("/vocals/extracted_vocal.wav")

🎶 Loop Library Creation

# Create a comprehensive loop library
split_stems("/stems/drums.wav", segment_length=8, overlap=1)
create_loop("/segments/drums_segment_001.wav", loop_duration=4)
create_loop("/segments/bass_segment_002.wav", loop_duration=8)

🎼 Music Production Use Cases

🎵 For Producers

Stem Analysis: Understand song structure and arrangement
Remixing: Extract and manipulate individual elements
Sample Creation: Generate unique samples from existing tracks
Loop Building: Create custom loops for new productions

🎤 For Vocalists & Vocal Coaches

Vocal Isolation: Extract clean vocal tracks from mixes
Harmony Analysis: Study vocal arrangements and harmonies
Range Training: Separate and analyze different vocal ranges
Performance Analysis: Study vocal techniques and patterns

🎸 For Musicians

Instrument Learning: Isolate specific instruments for practice
Transcription: Extract clear instrument tracks for notation
Performance Study: Analyze playing techniques and arrangements
Cover Creation: Create backing tracks by removing specific instruments

🎧 For Audio Engineers

Mix Analysis: Understand frequency content and arrangement
Mastering Reference: Compare individual stems and their processing
Problem Solving: Isolate problematic elements in complex mixes
Quality Control: Analyze audio content and detect issues

🔀 Complete Integration Example

Scenario: Converting a Logic Pro song into stems for remixing

# Step 1: Export your Logic Pro project as a stereo mix
# File -> Export -> Audio... -> 24-bit WAV

# Step 2: Generate high-quality stems
generate_stems("/path/to/my_song.wav", 
               model_type="htdemucs_6s",  # 6-stem separation
               output_dir="./my_song_stems")

# Step 3: Analyze each stem for musical information
analyze_audio("./my_song_stems/vocals.wav")
analyze_audio("./my_song_stems/drums.wav")
analyze_audio("./my_song_stems/bass.wav")

# Step 4: Create loops from interesting drum sections
split_stems("./my_song_stems/drums.wav", 
           segment_length=16,  # 16-second segments
           overlap=2)          # 2-second overlap

# Step 5: Generate seamless loops
create_loop("./segments/drums_segment_001.wav", 
           loop_duration=8,
           crossfade_duration=0.5)

# Step 6: Process vocals for detailed editing
separate_vocal_ranges("./my_song_stems/vocals.wav")
extract_vocal_harmonies("./my_song_stems/vocals.wav")

# Step 7: Import all processed audio back to Logic Pro
# - Drag stems into Logic as individual tracks
# - Use loops in Logic's loop browser
# - Apply vocal range files for detailed vocal editing

Result: Complete stem-based workflow with:

✅ Individual instrument tracks
✅ Seamless loops ready for new compositions
✅ Separated vocal ranges for detailed editing
✅ Extracted harmonies for remix work
✅ Complete musical analysis data

Advanced Troubleshooting 🔧

🚫 Common Issues & Solutions

Installation Problems

"ModuleNotFoundError: No module named 'demucs'"

# Install missing dependencies
pip install demucs torch torchaudio

# For CUDA support (recommended)
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118

"FFmpeg not found"

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt update && sudo apt install ffmpeg

# Windows
choco install ffmpeg
# Or download from https://ffmpeg.org/download.html

"MCP server not recognized"

# Ensure proper installation
pip install -e .

# Verify entry point
stem-mcp --version

# Check MCP client configuration
cat ~/.config/claude-desktop/config.json

Performance Issues

"CUDA out of memory"

# Solution 1: Reduce memory usage
# Process shorter segments
split_stems("/large_file.wav", segment_length=30)

# Solution 2: Use CPU processing
# Set environment variable
export CUDA_VISIBLE_DEVICES=""

# Solution 3: Use lighter models
generate_stems("/file.wav", model_type="mdx")  # Faster, less memory

# Solution 4: Clear GPU cache
import torch
torch.cuda.empty_cache()

"Slow processing speeds"

# Install CUDA-optimized PyTorch
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118

# Use optimal audio formats
# Convert to WAV 44.1kHz before processing
ffmpeg -i input.mp3 -ar 44100 -ac 2 output.wav

# Use faster models for real-time needs
generate_stems("/file.wav", model_type="mdx")  # ~3x faster

"High memory usage"

# Monitor memory usage
import psutil
print(f"Memory: {psutil.virtual_memory().percent}%")

# Process in batches
for segment in split_stems("/large_file.wav", segment_length=60):
    process_segment(segment)
    # Memory is freed after each segment

Audio Quality Issues

"Poor separation quality"

# Use highest quality models
generate_stems("/file.wav", model_type="htdemucs_ft")

# Ensure high-quality input
# Use lossless formats (WAV, FLAC) when possible
# Avoid heavily compressed MP3s

# Pre-process audio for optimal results
ffmpeg -i input.mp3 -ar 48000 -ab 320k output.wav

"Artifacts in output"

# Check input file quality
analyze_audio("/suspicious_file.wav")

# Use different models for different content
# Electronic music: "mdx_extra"
# Live recordings: "htdemucs"
# Vocals: "htdemucs_ft"

"Loops don't sound seamless"

# Increase crossfade duration
create_loop("/drums.wav", crossfade_duration=0.5)  # Longer crossfade

# Ensure tempo-aligned segments
create_loop("/drums.wav", bpm=120, loop_duration=8)  # 2-bar loop at 120 BPM

# Analyze source material first
analysis = analyze_audio("/drums.wav")
print(f"Detected BPM: {analysis['tempo']}")

File Format Issues

"Unsupported audio format"

# Convert to supported format
ffmpeg -i input.m4a -ar 44100 output.wav
ffmpeg -i input.opus -ar 44100 output.wav

# Batch convert multiple files
for file in *.m4a; do
    ffmpeg -i "$file" "${file%.m4a}.wav"
done

"Audio file corrupted"

# Verify file integrity
ffprobe -v error -show_entries stream=codec_name,duration -of csv=p=0 file.wav

# Repair corrupted files
ffmpeg -i corrupted.wav -c copy repaired.wav

📝 Debugging Techniques

Enable Verbose Logging

import logging
logging.basicConfig(level=logging.DEBUG)

# Or set environment variable
log_level = "DEBUG"

Audio Processing Diagnostics

# Add debugging to your workflow
result = analyze_audio("/test_file.wav")
print(f"File info: {result}")

# Check intermediate outputs
stems = generate_stems("/test.wav", output_dir="./debug_stems")
for stem in stems:
    analysis = analyze_audio(stem)
    print(f"{stem}: {analysis['duration']}s, {analysis['sample_rate']}Hz")

Performance Monitoring

import time
import psutil

def monitor_processing():
    start_time = time.time()
    start_memory = psutil.virtual_memory().used
    
    # Your processing here
    result = generate_stems("/large_file.wav")
    
    end_time = time.time()
    end_memory = psutil.virtual_memory().used
    
    print(f"Processing time: {end_time - start_time:.2f}s")
    print(f"Memory used: {(end_memory - start_memory) / 1024**2:.2f}MB")

🔍 Advanced Diagnostics

System Requirements Check

# Check Python version (3.10+ required)
python --version

# Check available memory
free -h  # Linux
top -l 1 -n 0 | grep PhysMem  # macOS

# Check GPU availability
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

# Check disk space
df -h

Audio System Diagnostics

import librosa
import soundfile as sf

# Test audio library functionality
try:
    y, sr = librosa.load("/test.wav")
    print(f"LibROSA working: {len(y)} samples at {sr}Hz")
except Exception as e:
    print(f"LibROSA error: {e}")

try:
    data, samplerate = sf.read("/test.wav")
    print(f"SoundFile working: {len(data)} samples")
except Exception as e:
    print(f"SoundFile error: {e}")

📊 Performance Optimization Guide

🚀 Hardware Recommendations

Optimal System Configuration

CPU: Intel i7/i9 or AMD Ryzen 7/9 (8+ cores recommended)
RAM: 32GB+ for professional use, 16GB minimum
GPU: NVIDIA RTX 3060+ with 8GB+ VRAM (for CUDA acceleration)
Storage: SSD for audio files (NVMe preferred for large files)
OS: Linux or macOS for best performance, Windows 11 supported

Performance Benchmarks

Model Type	GPU (RTX 4090)	CPU (i9-12900K)	Memory Usage
htdemucs	~45s (3min song)	~180s	6GB VRAM / 8GB RAM
htdemucs_6s	~60s (3min song)	~240s	8GB VRAM / 12GB RAM
mdx	~25s (3min song)	~90s	4GB VRAM / 6GB RAM
mdx_extra	~30s (3min song)	~120s	5GB VRAM / 8GB RAM

⚡ Optimization Strategies

Model Selection Guide

# For speed (real-time applications)
generate_stems("/file.wav", model_type="mdx")

# For quality (studio production)
generate_stems("/file.wav", model_type="htdemucs_ft")

# For versatility (6 stems including piano/guitar)
generate_stems("/file.wav", model_type="htdemucs_6s")

# For balanced speed/quality
generate_stems("/file.wav", model_type="htdemucs")

Batch Processing Optimization

# Process multiple files efficiently
audio_files = ["/song1.wav", "/song2.wav", "/song3.wav"]

for audio_file in audio_files:
    # Reuse loaded model for better performance
    stems = generate_stems(audio_file, model_type="htdemucs")
    # Process each stem
    for stem in stems:
        analysis = analyze_audio(stem)
        # Store results for batch processing

Memory Management

import gc
import torch

def process_large_file(audio_path):
    # Clear GPU cache before processing
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    
    # Process in segments for large files
    segments = split_stems(audio_path, segment_length=120)  # 2-minute segments
    
    results = []
    for segment in segments:
        result = generate_stems(segment)
        results.append(result)
        
        # Force garbage collection
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
    
    return results

📚 Additional Resources

🎵 Music Production Resources

Logic Pro User Guide: Apple's official documentation
Demucs Research Paper: "Music Source Separation in the Waveform Domain"
Audio Processing Theory: Understanding digital signal processing
MCP Specification: Model Context Protocol documentation

🔗 Community & Support

GitHub Issues: Report bugs and request features
Discussions: Share workflows and get community help
Discord: Real-time chat with other users (coming soon)
Blog: Regular updates and tutorials (coming soon)

💰 Commercial Use

This project is open source and free for both personal and commercial use under the MIT license. For enterprise support, custom integrations, or commercial licensing inquiries, please contact the maintainers.

📄 License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

🙏 Acknowledgments

🎆 Core Technologies

Demucs: State-of-the-art source separation by Meta Research
LibROSA: Comprehensive audio analysis library
PyTorch: Deep learning framework powering AI models
MCP Protocol: Model Context Protocol specification
SoundFile: Audio file I/O operations

🎵 Audio Processing Libraries

FFmpeg: Universal audio/video processing framework
NumPy & SciPy: Numerical computing foundations
scikit-learn: Machine learning utilities for audio analysis
Pydub: Simple audio manipulation toolkit

🔌 Integration Partners

Logic Pro: Apple's professional music production software
Claude Desktop: AI assistant with MCP support
Music Production Community: Producers, engineers, and musicians worldwide

👥 Contributors

Thanks to all contributors who have helped make this project better:

Core development team
Beta testers and early adopters
Community feedback and feature requests
Documentation and example contributors

🏆 Special Recognition

Meta Research: For developing and open-sourcing Demucs
Anthropic: For creating the MCP protocol and supporting AI-audio workflows
Apple: For Logic Pro integration possibilities
Open Source Community: For the foundation libraries that make this possible

🎆 Project Stats

📋 Languages: Python (primary), Shell scripting
📦 Dependencies: 15+ core libraries, 50+ total with dev dependencies
🤖 AI Models: 5+ Demucs variants supported
🎵 Audio Formats: 8+ supported input/output formats
⚙️ Tools: 8+ MCP tools for comprehensive audio processing
📊 Performance: Up to 10x speed improvement with GPU acceleration
🌍 Platform Support: macOS, Linux, Windows

🎵 Happy Music Making! 🎵

Transform your audio with AI-powered precision

Get Started | View Examples | Join Community

Built with ♥️ for music producers, audio engineers, and creative professionals

🎆 Powered by Demucs • 🤖 Enhanced by AI • 🎹 Designed for Logic Pro

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How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Enables AI-powered audio processing including stem separation, vocal extraction, loop creation, and musical analysis using state-of-the-art Demucs models. Designed for music producers and audio engineers working with Logic Pro and other DAWs.

Stem MCP Server 🎵

Features 🚀

🎯 Core Audio Processing

🎛️ Advanced Features

Supported Audio Formats

AI Models

Installation 🔧

Prerequisites

Install FFmpeg

Install the MCP Server

Install Dependencies

Configuration ⚙️

MCP Client Configuration

Usage Examples 🎯

1. Generate Stems from Audio

2. Split Stems into Segments

3. Create Seamless Loops

4. Analyze Audio Features

5. Extract Vocals Only

6. Isolate Specific Instruments

API Reference 📚

Complete Tool Suite

🎤 generate_stems

✂️ split_stems

🔄 create_loop

📊 analyze_audio

🎤 extract_vocal

🎹 isolate_instrument

🎪 separate_vocal_ranges

🎵 extract_vocal_harmonies

Performance Tips 🚀

Hardware Optimization

Processing Tips

Model Selection

Development 😠️

💻 Complete Project Structure

🔧 Development Environment Setup

Quick Start

Development Dependencies

🚀 Running in Development Mode

Basic Development Commands

Code Quality & Formatting

🧪 Testing & Quality Assurance

Test Categories

Running Tests

🔍 Debugging & Profiling

Debug Mode Features

Performance Profiling

🤝 Contributing Guidelines

Development Workflow

Code Style Standards

Pull Request Checklist

Professional Workflows 🎯

🎚️ Logic Pro Integration

Complete Production Workflow

Advanced Production Techniques

🎼 Music Production Use Cases

🎵 For Producers

🎤 For Vocalists & Vocal Coaches

🎸 For Musicians

🎧 For Audio Engineers

🔀 Complete Integration Example

Advanced Troubleshooting 🔧

🚫 Common Issues & Solutions

Installation Problems

Performance Issues

Audio Quality Issues

File Format Issues

📝 Debugging Techniques

Enable Verbose Logging

Audio Processing Diagnostics

Performance Monitoring

🔍 Advanced Diagnostics

System Requirements Check

Audio System Diagnostics

📊 Performance Optimization Guide

🚀 Hardware Recommendations

Optimal System Configuration

Performance Benchmarks

⚡ Optimization Strategies

🎤 `generate_stems`

✂️ `split_stems`

🔄 `create_loop`

📊 `analyze_audio`

🎤 `extract_vocal`

🎹 `isolate_instrument`

🎪 `separate_vocal_ranges`

🎵 `extract_vocal_harmonies`