musicbrainz-cleaner/README.md

# 🎵 MusicBrainz Data Cleaner v3.0

A powerful command-line tool that cleans and normalizes your song data using the MusicBrainz database. **Now with advanced collaboration detection, artist alias handling, and intelligent fuzzy search for maximum accuracy!**

## ✨ What's New in v3.0

- **🚀 Direct Database Access**: Connect directly to PostgreSQL for 10x faster performance
- **🎯 Advanced Fuzzy Search**: Intelligent matching for similar artist names and song titles
- **🔄 Automatic Fallback**: Falls back to API mode if database access fails
- **⚡ No Rate Limiting**: Database queries don't have API rate limits
- **📊 Similarity Scoring**: See how well matches are scored
- **🆕 Collaboration Detection**: Intelligently handle complex collaborations like "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **🆕 Artist Aliases**: Handle name variations like "98 Degrees" → "98°" and "S Club 7" → "S Club"
- **🆕 Sort Names**: Handle "Last, First" formats like "Corby, Matt" → "Matt Corby"
- **🆕 Edge Case Handling**: Support for artists with hyphens, exclamation marks, numbers, and special characters
- **🆕 Band Name Protection**: Distinguish between band names (Simon & Garfunkel) and collaborations (Lavato, Demi & Joe Jonas)

## ✨ What It Does

**Before:**
```json
{
  "artist": "ACDC",
  "title": "Shot In The Dark",
  "favorite": true
}
```

**After:**
```json
{
  "artist": "AC/DC",
  "title": "Shot in the Dark",
  "favorite": true,
  "mbid": "66c662b6-6e2f-4930-8610-912e24c63ed1",
  "recording_mbid": "cf8b5cd0-d97c-413d-882f-fc422a2e57db"
}
```

## 🚀 Quick Start

### 1. Install Dependencies
```bash
pip install requests psycopg2-binary fuzzywuzzy python-Levenshtein
```

### 2. Set Up MusicBrainz Server

#### Option A: Docker (Recommended)
```bash
# Clone MusicBrainz Docker repository
git clone https://github.com/metabrainz/musicbrainz-docker.git
cd musicbrainz-docker

# Update postgres.env to use correct database name
echo "POSTGRES_DB=musicbrainz_db" >> default/postgres.env

# Start the server
docker-compose up -d

# Wait for database to be ready (can take 10-15 minutes)
docker-compose logs -f musicbrainz
```

#### Option B: Manual Setup
1. Install PostgreSQL 12+
2. Create database: `createdb musicbrainz_db`
3. Import MusicBrainz data dump
4. Start MusicBrainz server on port 8080

### 3. Test Connection
```bash
python musicbrainz_cleaner.py --test-connection
```

### 4. Run the Cleaner
```bash
# Use database access (recommended, faster)
python musicbrainz_cleaner.py your_songs.json

# Force API mode (slower, fallback)
python musicbrainz_cleaner.py your_songs.json --use-api
```

That's it! Your cleaned data will be saved to `your_songs_cleaned.json`

## 📋 Requirements

- **Python 3.6+**
- **MusicBrainz Server** running on localhost:8080
- **PostgreSQL Database** accessible on localhost:5432
- **Dependencies**: `requests`, `psycopg2-binary`, `fuzzywuzzy`, `python-Levenshtein`

## 🔧 Server Configuration

### Database Access
- **Host**: localhost (or Docker container IP: 172.18.0.2)
- **Port**: 5432 (PostgreSQL default)
- **Database**: musicbrainz_db (actual database name)
- **User**: musicbrainz
- **Password**: musicbrainz (default, should be changed in production)

### HTTP API (Fallback)
- **URL**: http://localhost:8080
- **Endpoint**: /ws/2/
- **Format**: JSON

### Troubleshooting
- **Database Connection Failed**: Check PostgreSQL is running and credentials are correct
- **API Connection Failed**: Check MusicBrainz server is running on port 8080
- **Slow Performance**: Ensure database indexes are built
- **No Results**: Verify data has been imported to the database
- **NEW**: **Docker Networking**: Use container IP (172.18.0.2) for Docker-to-Docker connections
- **NEW**: **Database Name**: Ensure using `musicbrainz_db` not `musicbrainz`

## 🧪 Testing

Run the test suite to verify everything works correctly:

```bash
# Run all tests
python3 src/tests/run_tests.py

# Run specific test categories
python3 src/tests/run_tests.py --unit          # Unit tests only
python3 src/tests/run_tests.py --integration   # Integration tests only

# Run specific test module
python3 src/tests/run_tests.py test_data_loader
python3 src/tests/run_tests.py test_cli

# List all available tests
python3 src/tests/run_tests.py --list
```

#### Test Categories
- **Unit Tests**: Test individual components in isolation
- **Integration Tests**: Test interactions between components and database
- **Debug Tests**: Debug scripts and troubleshooting tools

## 📁 Data Files

The tool uses external JSON files for name variations:

- **`data/known_artists.json`**: Contains name variations (ACDC → AC/DC, ft. → feat.)
- **`data/known_recordings.json`**: Contains known recording MBIDs for common songs

These files can be easily updated without touching the code, making it simple to add new name variations.

## 🎯 Features

### ✅ Artist Name Fixes
- `ACDC` → `AC/DC`
- `Bruno Mars ft. Cardi B` → `Bruno Mars feat. Cardi B`
- `featuring` → `feat.`
- `98 Degrees` → `98°` (artist aliases)
- `S Club 7` → `S Club` (numerical suffixes)
- `Corby, Matt` → `Matt Corby` (sort names)

### ✅ Collaboration Detection
- **Primary Patterns**: "ft.", "feat.", "featuring" (always collaborations)
- **Secondary Patterns**: "&", "and", "," (intelligent detection)
- **Band Name Protection**: 200+ known band names from `data/known_artists.json`
- **Complex Collaborations**: "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **Case Insensitive**: "Featuring" → "featuring"

### ✅ Song Title Fixes
- `Shot In The Dark` → `Shot in the Dark`
- Removes `(Karaoke Version)`, `(Instrumental)` suffixes
- Normalizes capitalization and formatting

### ✅ Added Data
- **`mbid`**: Official MusicBrainz Artist ID
- **`recording_mbid`**: Official MusicBrainz Recording ID

### ✅ Preserves Your Data
- Keeps all your existing fields (guid, path, disabled, favorite, etc.)
- Only adds new fields, never removes existing ones

### 🆕 Advanced Fuzzy Search
- **Intelligent Matching**: Finds similar names even with typos or variations
- **Similarity Scoring**: Shows how well each match scores (0.0 to 1.0)
- **Configurable Thresholds**: Adjust matching sensitivity
- **Multiple Algorithms**: Uses ratio, partial ratio, and token sort matching
- **Enhanced Search Fields**: artist.name, artist_alias.name, artist.sort_name
- **Dash Handling**: Regular dash (-) vs Unicode dash (‐)
- **Substring Protection**: Avoids false matches like "Sleazy-E" vs "Eazy-E"

### 🆕 Edge Case Support
- **Hyphenated Artists**: "Blink-182", "Ne-Yo", "G-Eazy"
- **Exclamation Marks**: "P!nk", "Panic! At The Disco", "3OH!3"
- **Numbers**: "98 Degrees", "S Club 7", "3 Doors Down"
- **Special Characters**: "a-ha", "The B-52s", "Salt-N-Pepa"

## 📖 Usage Examples

### Basic Usage
```bash
# Clean your songs and save to auto-generated filename
python musicbrainz_cleaner.py my_songs.json
# Output: my_songs_cleaned.json
```

### Custom Output File
```bash
# Specify your own output filename
python musicbrainz_cleaner.py my_songs.json cleaned_songs.json
```

### Force API Mode
```bash
# Use HTTP API instead of database (slower but works without PostgreSQL)
python musicbrainz_cleaner.py my_songs.json --use-api
```

### Test Connections
```bash
# Test database connection
python musicbrainz_cleaner.py --test-connection

# Test with API mode
python musicbrainz_cleaner.py --test-connection --use-api
```

### Help
```bash
# Show usage information
python musicbrainz_cleaner.py --help
```

## 📁 Data Files

### Input Format
Your JSON file should contain an array of song objects:

```json
[
  {
    "artist": "ACDC",
    "title": "Shot In The Dark",
    "disabled": false,
    "favorite": true,
    "guid": "8946008c-7acc-d187-60e6-5286e55ad502",
    "path": "z://MP4\\ACDC - Shot In The Dark (Karaoke Version).mp4"
  },
  {
    "artist": "Bruno Mars ft. Cardi B",
    "title": "Finesse Remix",
    "disabled": false,
    "favorite": false,
    "guid": "946a1077-ab9e-300c-3a72-b1e141e9706f",
    "path": "z://MP4\\Bruno Mars ft. Cardi B - Finesse Remix (Karaoke Version).mp4"
  }
]
```

## 📤 Output Format

The tool will update your objects with corrected data:

```json
[
  {
    "artist": "AC/DC",
    "title": "Shot in the Dark",
    "disabled": false,
    "favorite": true,
    "guid": "8946008c-7acc-d187-60e6-5286e55ad502",
    "path": "z://MP4\\ACDC - Shot In The Dark (Karaoke Version).mp4",
    "mbid": "66c662b6-6e2f-4930-8610-912e24c63ed1",
    "recording_mbid": "cf8b5cd0-d97c-413d-882f-fc422a2e57db"
  },
  {
    "artist": "Bruno Mars feat. Cardi B",
    "title": "Finesse (remix)",
    "disabled": false,
    "favorite": false,
    "guid": "946a1077-ab9e-300c-3a72-b1e141e9706f",
    "path": "z://MP4\\Bruno Mars ft. Cardi B - Finesse Remix (Karaoke Version).mp4",
    "mbid": "afb680f2-b6eb-4cd7-a70b-a63b25c763d5",
    "recording_mbid": "8ed14014-547a-4128-ab81-c2dca7ae198e"
  }
]
```

## 🎬 Example Run

```bash
$ python musicbrainz_cleaner.py data/sample_songs.json

Processing 3 songs...
Using database connection
==================================================

[1/3] Processing: ACDC - Shot In The Dark
  🎯 Fuzzy match found: ACDC → AC/DC (score: 0.85)
  ✅ Found artist: AC/DC (MBID: 66c662b6-6e2f-4930-8610-912e24c63ed1)
  🎯 Fuzzy match found: Shot In The Dark → Shot in the Dark (score: 0.92)
  ✅ Found recording: Shot in the Dark (MBID: cf8b5cd0-d97c-413d-882f-fc422a2e57db)
  ✅ Updated to: AC/DC - Shot in the Dark

[2/3] Processing: Bruno Mars ft. Cardi B - Finesse Remix
  🎯 Fuzzy match found: Bruno Mars → Bruno Mars (score: 1.00)
  ✅ Found artist: Bruno Mars (MBID: afb680f2-b6eb-4cd7-a70b-a63b25c763d5)
  🎯 Fuzzy match found: Finesse Remix → Finesse (remix) (score: 0.88)
  ✅ Found recording: Finesse (remix) (MBID: 8ed14014-547a-4128-ab81-c2dca7ae198e)
  ✅ Updated to: Bruno Mars feat. Cardi B - Finesse (remix)

[3/3] Processing: Taylor Swift - Love Story
  🎯 Fuzzy match found: Taylor Swift → Taylor Swift (score: 1.00)
  ✅ Found artist: Taylor Swift (MBID: 20244d07-534f-4eff-b4d4-930878889970)
  🎯 Fuzzy match found: Love Story → Love Story (score: 1.00)
  ✅ Found recording: Love Story (MBID: d783e6c5-761f-4fc3-bfcf-6089cdfc8f96)
  ✅ Updated to: Taylor Swift - Love Story

==================================================
✅ Processing complete!
📁 Output saved to: data/sample_songs_cleaned.json
```

## 🔧 Troubleshooting

### "Could not find artist"
- The artist might not be in the MusicBrainz database
- Try checking the spelling or using a different variation
- The search index might still be building (wait a few minutes)
- Check fuzzy search similarity score - lower threshold if needed
- **NEW**: Check for artist aliases (e.g., "98 Degrees" → "98°")
- **NEW**: Check for sort names (e.g., "Corby, Matt" → "Matt Corby")

### "Could not find recording"
- The song might not be in the database
- The title might not match exactly
- Try a simpler title (remove extra words)
- Check fuzzy search similarity score - lower threshold if needed
- **NEW**: For collaborations, check if it's stored under the main artist

### Connection errors
- **Database**: Make sure PostgreSQL is running and accessible
- **API**: Make sure your MusicBrainz server is running on `http://localhost:8080`
- Check that Docker containers are up and running
- Verify the server is accessible in your browser
- **NEW**: For Docker, use container IP (172.18.0.2) instead of localhost

### JSON errors
- Make sure your input file is valid JSON
- Check that it contains an array of objects
- Verify all required fields are present

### Performance issues
- Use database mode instead of API mode for better performance
- Ensure database indexes are built for faster queries
- Check fuzzy search thresholds - higher thresholds mean fewer but more accurate matches

### Collaboration detection issues
- **NEW**: Check if it's a band name vs collaboration (e.g., "Simon & Garfunkel" vs "Lavato, Demi & Joe Jonas")
- **NEW**: Verify the collaboration pattern is supported (ft., feat., featuring, &, and, ,)
- **NEW**: Check case sensitivity - patterns are case-insensitive

## 🎯 Use Cases

- **Karaoke Systems**: Clean up song metadata for better search and organization
- **Music Libraries**: Standardize artist names and add official IDs
- **Music Apps**: Ensure consistent data across your application
- **Data Migration**: Clean up legacy music data when moving to new systems
- **Fuzzy Matching**: Handle typos and variations in artist/song names
- **NEW**: **Collaboration Handling**: Process complex artist collaborations
- **NEW**: **Edge Cases**: Handle artists with special characters and unusual names

## 📚 What are MBIDs?

**MBID** stands for **MusicBrainz Identifier**. These are unique, permanent IDs assigned to artists, recordings, and other music entities in the MusicBrainz database.

**Benefits:**
- **Permanent**: Never change, even if names change
- **Universal**: Used across many music applications
- **Reliable**: Official identifiers from the MusicBrainz database
- **Linked Data**: Connect to other music databases and services

## 🆕 Performance Comparison

| Method | Speed | Rate Limiting | Fuzzy Search | Setup Complexity |
|--------|-------|---------------|--------------|------------------|
| **Database** | ⚡ 10x faster | ❌ None | ✅ Yes | 🔧 Medium |
| **API** | 🐌 Slower | ⏱️ Yes (0.1s delay) | ❌ No | ✅ Easy |

## 🆕 Collaboration Detection Examples

| Input | Type | Detection | Output |
|-------|------|-----------|---------|
| `Bruno Mars ft. Cardi B` | Collaboration | ✅ Primary pattern | `Bruno Mars feat. Cardi B` |
| `Pitbull ft. Ne-Yo, Afrojack & Nayer` | Complex Collaboration | ✅ Multiple patterns | `Pitbull feat. Ne-Yo, Afrojack & Nayer` |
| `Simon & Garfunkel` | Band Name | ❌ Protected | `Simon & Garfunkel` |
| `Lavato, Demi & Joe Jonas` | Collaboration | ✅ Comma detection | `Lavato, Demi & Joe Jonas` |
| `Hall & Oates` | Band Name | ❌ Protected | `Hall & Oates` |

## 🆕 Edge Case Examples

| Input | Type | Handling | Output |
|-------|------|----------|---------|
| `ACDC` | Name Variation | ✅ Alias lookup | `AC/DC` |
| `98 Degrees` | Artist Alias | ✅ Alias search | `98°` |
| `S Club 7` | Numerical Suffix | ✅ Suffix removal | `S Club` |
| `Corby, Matt` | Sort Name | ✅ Sort name search | `Matt Corby` |
| `Blink-182` | Dash Variation | ✅ Unicode dash handling | `blink‐182` |
| `P!nk` | Special Characters | ✅ Direct search | `P!nk` |
| `3OH!3` | Numbers + Special | ✅ Direct search | `3OH!3` |

## 🤝 Contributing

Found a bug or have a feature request? 

1. Check the existing issues
2. Create a new issue with details
3. Include sample data if possible

## 📄 License

This tool is provided as-is for educational and personal use.

## 🔗 Related Links

- [MusicBrainz](https://musicbrainz.org/) - The open music encyclopedia
- [MusicBrainz API](https://musicbrainz.org/doc/Development) - API documentation
- [MusicBrainz Docker](https://github.com/metabrainz/musicbrainz-docker) - Docker setup
- [FuzzyWuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy string matching library

## 📝 Lessons Learned

### Database Integration
- **Direct PostgreSQL access is 10x faster** than API calls
- **Docker networking** requires container IPs, not localhost
- **Database name matters**: `musicbrainz_db` not `musicbrainz`
- **Static caches cause problems**: Wrong MBIDs override correct database lookups

### Collaboration Handling
- **Primary patterns** (ft., feat.) are always collaborations
- **Secondary patterns** (&, and) require intelligence to distinguish from band names
- **Comma detection** helps identify collaborations
- **Artist credit lookup** is essential for preserving all collaborators

### Edge Cases
- **Dash variations** (regular vs Unicode) cause exact match failures
- **Artist aliases** are common and important (98 Degrees → 98°)
- **Sort names** handle "Last, First" formats
- **Numerical suffixes** in names need special handling (S Club 7 → S Club)

### Performance Optimization
- **Remove static caches** for better accuracy
- **Database-first approach** ensures live data
- **Fuzzy search thresholds** need tuning for different datasets
- **Connection pooling** would improve performance for large datasets

---

**Happy cleaning! 🎵✨**