A python and HTML/CSS/JS based tool to extract chords from any song, which creates a database to analyze and show chord-key data by different criteria as title, artist, by chord type, number of times a chord appears; by song, by a group of songs or globally.
- Python 3.9.x
- sudo apt install ffmpeg
- pip install numpy librosa chord-extractor json yt-dlp
- Run an analisys:
$ python Chordy.py
- Generate a chord list
$ python Library.py
- Manage your database:
$ ./chords_database.html
- Chord extracting from any MP3, WAV, MIDI file, or directly a Youtube link.
- Chord Visualizer with GUITAR diagrams, HTML5-webkit compatible.
- Chord carts transposing with live changing diagrams.
- Bar/Time Signatures counting, Tempo/BPM analyzer tool & live rhythm change.
- Keynote analysis on each bar, A4 referencial frequency identifier.
- A database generation for analysing purposses capable of sorting chords, how many times used, the most and least used chords; by song, globally, ....
- Modular functions, python based, can be easilly added ... my framework works over numbers, and numbers, and their analyse.
- The fewer external libraries and dependences used here, the better. Only chord-extractor and librosa for the audio analysing and controls. NumPy for anything numbers related. jQuery, Chart.js are also used for scripting database support.
The program generates:
- Artist_Title.html file. The visualizer of each song.
- chord_database.html and chord_db.json files. A full database of all the song's with data analysis tools. Both files are updated automatically.
In order to generatethe files both, you are told to provide some info as:
- ARTIST NAME
- SONG TITLE
(Development in progress)
2024 September, 16th.