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Uses the Shazam API to identify songs playing around you.
Records audio snippets and matches them against Shazam’s database.
Song History Tracking:
Keeps a detailed history of all identified songs, including:
Song title and artist
Date and time of recognition
Time category (morning, afternoon, evening, night)
Day of the week
Song Downloading:
Automatically downloads identified songs using yt-dlp.
Organizes downloaded songs into a dedicated folder.
Listening Trends Chart (Desktop App)
Top Songs Chart (A chart that displays the total listening duration for each song & visualizes the top 20 songs based on listening duration.)
Top Artists Chart (A chart that displays the number of unique songs identified for each artist & Visualizes the top 20 artists based on the number of unique songs identified)
Filtering, Sorting & Searching (only for Desktop app)
Allows users to filter the song history based on specific parameters (Title, Artist, Date, Time, Day of Week, Time Category).
Allows users to sort the song history in ascending or descending order based on a selected parameter.
Allows user to search for a specific song
Listening Trends Dashboard (Web Interface):
A Flask-based web dashboard to visualize your listening habits:
Unique songs and artists
Most active day of the week
Songs You've Listened to by Artist
Daily Count of Fresh Tracks
Your Music Hotspots Throughout the Month (Time Slot when you discover the most tracks)
Installation & Setup (Python)
Clone the Repository
git clone https://github.com/rishabhc9/Music-Collector.git
cd Music-Collector
Run the program (Desktop App with GUI)
python3 MusicCollector.py
Run the program (Desktop CLI App with auto-downloading)
python3 MusicCollector(CLI Auto-Download).py
Run the program (Web Interface, cd inside rpi_music_collector folder and run)