🎵 Shazam Tool

🔍 A Python script that downloads audio from SoundCloud or YouTube, splits it into segments, and uses Shazam to identify songs within the mix.

✨ Features

• 🎧 Download audio from SoundCloud or YouTube URLs
• 🎼 Identify songs using Shazam API
• 💾 Save results to timestamped text files
• 🚀 Easy setup and usage with provided shell script

🛠️ Requirements

This project uses yt-dlp for downloading audio from SoundCloud and YouTube.

Linux

sudo apt install ffmpeg
pip install ShazamApi pydub yt-dlp shazamio

macOS

# Install Homebrew if not already installed
# See https://brew.sh for installation instructions

brew install ffmpeg

# Optional: Create and activate virtual environment
# python3.11 -m venv venv && source venv/bin/activate

# If you don't have Python 3.11:
# brew install python@3.11

# Install required packages
pip install shazamio pydub yt-dlp ShazamApi

📚 Usage

Quick Start (Recommended)

Use the provided shell script for easy setup and running:

# Make the script executable (if needed)
chmod +x run_shazam.sh

# Setup environment (installs dependencies, creates venv)
./run_shazam.sh setup

# Download and process audio from URL
./run_shazam.sh download <url>

# Process all downloaded files
./run_shazam.sh scan

# Process a specific audio file
./run_shazam.sh recognize <file>

# Show help information
./run_shazam.sh help

Manual Usage

The script also supports direct Python invocation with three main commands:

1. Download and Process from URL

python shazam.py download <url>

Downloads audio from YouTube or SoundCloud and processes it for song recognition.

2. Scan Downloaded Files

python shazam.py scan

Processes all MP3 files in the Downloads directory.

3. Recognize Single File

python shazam.py recognize <file>

Processes a single audio file for song recognition.

📋 Output

Results are saved in the recognised-lists directory with timestamped filenames in the format:

songs-DDMMYY-HHMMSS.txt

ℹ️ The generated song list can be imported into TuneMyMusic

📝 Notes

• The script splits audio into 1-minute segments for optimal recognition
• Duplicate songs within the same mix are automatically filtered out
• Large files are processed in chunks to manage memory efficiently

🤝 Contributing

Feel free to open issues or submit pull requests with improvements. We welcome contributions from the community!

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