https://github.com/deepakness/mp3detective
🎵 AI-powered MP3 metadata updater that automatically identifies and updates song details (title, artist, album, year) using OpenAI's GPT.
https://github.com/deepakness/mp3detective
mp3 mp3-tags openai songs
Last synced: 3 months ago
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🎵 AI-powered MP3 metadata updater that automatically identifies and updates song details (title, artist, album, year) using OpenAI's GPT.
- Host: GitHub
- URL: https://github.com/deepakness/mp3detective
- Owner: deepakness
- License: mit
- Created: 2025-04-06T02:38:41.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-06T03:21:12.000Z (about 1 year ago)
- Last Synced: 2025-08-02T23:04:04.788Z (11 months ago)
- Topics: mp3, mp3-tags, openai, songs
- Language: Python
- Homepage: https://youtu.be/sS35tyAsnYE
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MP3Detective
MP3Detective is a powerful tool that automatically updates audio file metadata using AI language models. It supports both OpenAI's GPT models and local LLM models via Ollama. The tool supports multiple audio formats and can identify song details and update metadata tags for any music genre and language.
## ✨ Features
- **🤖 Dual LLM Support**: Choose between OpenAI GPT models or local Ollama models
- **🎵 Multi-format Support**: MP3, FLAC, M4A, MP4, OGG, OPUS
- **🌍 Universal Music Knowledge**: Supports all genres and languages
- **🔄 Batch Processing**: Process multiple files with progress tracking
- **🛡️ Privacy Options**: Use local models to keep data private
- **💰 Cost Control**: Avoid API costs with local LLM inference
- **📝 Smart Metadata**: Automatically identifies title, artist, album, year, composer, genre, language
- **🔒 Safe Processing**: Preserves original files by creating copies
- **📊 Detailed Logging**: Comprehensive logs for troubleshooting
## 📋 Prerequisites
### Basic Requirements
- **Python 3.8 or higher**
- **Git** (for cloning the repository)
- **8GB RAM minimum** (16GB+ recommended for larger models)
### LLM Provider Options
**Choose ONE of the following:**
#### Option A: OpenAI (Cloud-based) ☁️
- **OpenAI API key** ([Get one here](https://platform.openai.com/api-keys))
- **Internet connection** required
- **Pay-per-use** pricing model
#### Option B: Ollama (Local LLM) 🏠
**System Requirements for Ollama:**
| Component | Minimum | Recommended | Notes |
|-----------|---------|-------------|-------|
| **RAM** | 8GB | 16GB+ | More RAM = larger models |
| **Storage** | 10GB | 25GB+ | Models range from 2GB-40GB+ |
| **CPU** | Any modern CPU | Intel 11th Gen+ / AMD Zen4+ / Apple M-series | AVX512 support helps |
| **GPU** | None (CPU only) | NVIDIA GTX 1060+ (8GB+ VRAM) | Dramatically improves speed |
| **OS** | Windows 10+, macOS 11+, Linux | Latest versions | Better compatibility |
**Model Size vs RAM Requirements:**
- **Small models (4B params)**: 8GB+ RAM → gemma3:4b
- **Medium models (7-8B params)**: 12-16GB+ RAM → mistral:7b, llama3.1:8b
- **Large models (12B params)**: 16GB+ RAM → gemma3:12b
## 🚀 Quick Start
### Step 1: Clone and Setup
```bash
# Clone the repository
git clone https://github.com/deepakness/mp3detective.git
cd mp3detective
# Create and activate virtual environment
python -m venv venv
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
### Step 2A: OpenAI Setup (Cloud)
```bash
# Edit app.py and configure:
LLM_PROVIDER = "openai"
OPENAI_API_KEY = "your-actual-api-key-here"
OPENAI_MODEL = "gpt-4o" # or gpt-4o-mini, gpt-3.5-turbo
```
### Step 2B: Ollama Setup (Local)
#### Install Ollama
**Windows:**
```bash
# Download from https://ollama.ai
# Or use package managers:
winget install Ollama.Ollama
# OR
choco install ollama
```
**macOS:**
```bash
# Download from https://ollama.ai
# Or use Homebrew:
brew install ollama
```
**Linux:**
```bash
curl -fsSL https://ollama.ai/install.sh | sh
```
#### Download a Model
```bash
# Start with a recommended model (choose one):
# Best performance - Most capable model
ollama pull gemma3:12b # 8GB model, requires ~16GB RAM
# Good balance - Recommended for most users
ollama pull gemma3:4b # 3.5GB model, requires ~8GB RAM
# Excellent general purpose - Meta's latest
ollama pull llama3.1:8b # 5GB model, requires ~12GB RAM
# Fast and efficient - Great for beginners
ollama pull mistral:7b # 5GB model, requires ~12GB RAM
# Check downloaded models
ollama list
```
#### Configure MP3Detective for Ollama
```bash
# Edit app.py and configure:
LLM_PROVIDER = "ollama"
OLLAMA_BASE_URL = "http://localhost:11434" # Default Ollama URL
OLLAMA_MODEL = "gemma3:4b" # Use the model you downloaded
```
### Step 3: Process Your Audio Files
```bash
# Place audio files in the input/ directory
# Supported formats: MP3, FLAC, M4A, MP4, OGG, OPUS
# Run the application
python app.py
```
## ⚙️ Configuration Options
Edit the configuration section in `app.py`:
### LLM Provider Settings
```python
# Choose your LLM provider
LLM_PROVIDER = "openai" # Options: "openai", "ollama"
# OpenAI Configuration (only if using OpenAI)
OPENAI_API_KEY = "your-api-key-here"
OPENAI_MODEL = "gpt-4o" # gpt-4o, gpt-4o-mini, gpt-3.5-turbo
# Ollama Configuration (only if using Ollama)
OLLAMA_BASE_URL = "http://localhost:11434"
OLLAMA_MODEL = "gemma3:4b" # gemma3:12b, gemma3:4b, llama3.1:8b, mistral:7b
```
### Processing Settings
```python
INPUT_FOLDER = "input" # Source audio files directory
OUTPUT_FOLDER = "output" # Processed files directory
BATCH_SIZE = 10 # Progress update frequency
RATE_LIMIT_DELAY = 1.0 # Delay between API calls (seconds)
OVERWRITE = True # Overwrite existing metadata
```
## 🎯 Model Recommendations
### OpenAI Models
| Model | Best For | Speed | Quality | Cost |
|-------|----------|-------|---------|------|
| `gpt-4o` | Best overall quality | Medium | ⭐⭐⭐⭐⭐ | $$$ |
| `gpt-4o-mini` | Balanced performance | Fast | ⭐⭐⭐⭐ | $$ |
| `gpt-3.5-turbo` | Speed and economy | Very Fast | ⭐⭐⭐ | $ |
### Ollama Models
| Model | Size | RAM Needed | Best For | Performance |
|-------|------|------------|----------|-------------|
| `gemma3:12b` | ~8GB | 16GB+ | Best quality & accuracy | ⭐⭐⭐⭐⭐ |
| `gemma3:4b` | ~3.5GB | 8GB+ | Balanced performance | ⭐⭐⭐⭐ |
| `llama3.1:8b` | ~5GB | 12GB+ | General purpose excellence | ⭐⭐⭐⭐⭐ |
| `mistral:7b` | ~5GB | 12GB+ | Fast & efficient | ⭐⭐⭐⭐ |
## 📁 Directory Structure
```
mp3detective/
├── .gitignore # Git ignore rules
├── app.py # Main application file
├── requirements.txt # Python dependencies
├── README.md # This documentation
├── input/ # Place your audio files here
└── output/ # Updated files will appear here
```
## 📖 Usage Guide
1. **Prepare Audio Files**: Place your audio files (MP3, FLAC, M4A, MP4, OGG, OPUS) in the `input/` folder
2. **Configure LLM Provider**: Choose between OpenAI or Ollama in `app.py`
3. **Run the Application**:
```bash
python app.py
```
4. **Monitor Progress**: Watch the console for processing updates
5. **Review Results**:
- Check `output/` folder for processed files
- Review `metadata_updater.log` for detailed information
## 🔧 Troubleshooting
### Common Issues
#### 1. Installation Problems
```bash
# If you get module errors:
pip install -r requirements.txt
# If you're in wrong directory:
cd mp3detective
# If Python environment issues:
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
```
#### 2. OpenAI Issues
- **Invalid API Key**: Verify your key at [OpenAI Dashboard](https://platform.openai.com/api-keys)
- **Insufficient Credits**: Check your [OpenAI billing](https://platform.openai.com/account/billing)
- **Network Issues**: Ensure stable internet connection
- **Rate Limits**: Increase `RATE_LIMIT_DELAY` in configuration
#### 3. Ollama Issues
**Installation Problems:**
```bash
# Check if Ollama is running
ollama serve
# Verify installation
ollama --version
# List available models
ollama list
```
**Model Download Issues:**
```bash
# Download specific model
ollama pull gemma3:4b
# Check download status
ollama list
# Remove and re-download if corrupted
ollama rm gemma3:4b
ollama pull gemma3:4b
```
**Connection Issues:**
- **Server not running**: Start Ollama with `ollama serve`
- **Wrong URL**: Verify `OLLAMA_BASE_URL` matches your Ollama server
- **Model not found**: Ensure model name matches exactly (case-sensitive)
- **Memory issues**: Close other applications to free up RAM
#### 4. Audio File Issues
- **Unsupported format**: Convert to MP3, FLAC, M4A, MP4, OGG, or OPUS
- **Corrupted files**: Try with different audio files
- **Permission errors**: Ensure write permissions for `output/` directory
## 🔒 Privacy & Security
### Data Privacy
- **OpenAI**: Data sent to OpenAI servers (subject to their privacy policy)
- **Ollama**: All processing stays on your local machine (completely private)
### Security Best Practices
- Never commit API keys to version control
- Keep your OpenAI API key secure and rotate regularly
- Use environment variables for sensitive configuration
- Backup your original audio files before processing
## 💡 Best Practices
### File Organization
- Keep original files backed up separately
- Use descriptive filenames for better AI recognition
- Process files in small batches initially to test configuration
### Model Selection
- **For accuracy**: Use OpenAI GPT-4o or gemma3:12b/llama3.1:8b
- **For speed**: Use OpenAI GPT-3.5-turbo or gemma3:4b/mistral:7b
- **For privacy**: Always use Ollama models
- **For cost**: Use Ollama models or OpenAI GPT-3.5-turbo
### Performance Optimization
- Close unnecessary applications when using Ollama
- Use GPU acceleration if available
- Adjust `RATE_LIMIT_DELAY` based on your setup
- Process files during off-peak hours for better performance
## 🤝 Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📄 License
This project is licensed under the MIT License.
## 🙏 Acknowledgments
- **OpenAI** for providing powerful GPT models via API
- **Ollama** for making local LLM inference accessible
- **Mutagen** for comprehensive audio metadata manipulation
- **tqdm** for beautiful progress bars
- **requests** for reliable HTTP communication
## 📞 Support
If you encounter any issues or have questions:
1. Check the [Issues](https://github.com/deepakness/mp3detective/issues) page
2. Create a new issue if your problem isn't already reported
3. Include relevant logs from `metadata_updater.log`
4. Specify your OS, Python version, and LLM provider
## 🎯 Real-world Accuracy
Based on testing with diverse music collections:
- **Overall accuracy**: ~95% for mainstream music
- **Genre variations**: Pop/Rock (98%) > Classical (92%) > Experimental (85%)
- **Language support**: Excellent for major languages, good for regional languages
- **Era coverage**: Recent music (98%) > Vintage music (90%) > Obscure tracks (80%)
**Note**: Accuracy depends on the LLM model used and the clarity of filenames. Always verify metadata for critical collections.
---
**🎵 Happy metadata organizing! 🎵**