Automated Music Mastering through an Optimization-Based Approach.
Can either be used through python or as webapp through Docker or Python.
A short comparison video between different mastering services:
https://youtu.be/6kvGeVtv1Cw?feature=shared
Clone Repo
git clone https://github.com/Wieland3/reference-master.git
Move into directory
cd reference-master
Run shell script to build the image
sh docker-build-tag.sh
Run docker image
docker run -p 8080:8080 reference-master:latest
Navigate to
http://0.0.0.0:8080
pip install -r requirements.txtcd webapp
python3 app.py
Put the Song you want to master in the "songs/raw" folder
Put your reference Song in the "songs/references" folder
from reference_master.mastering.master import masterMaster and use loudness of reference Track
master("your-track.wav", "Bohemian Rhapsody.mp3")Alternatively, you can manually specify the target loudness of the song to be mastered
master("your-track.wav", "Bohemian Rhapsody.mp3", -7.0)The mastered song will be automatically saved in the "songs/mastered" folder.
Depending on how many epochs are set in the settings.yaml the process can take a couple of minutes. You can change the number of epochs and other parameters for the mastering in the settings.yaml file.
During the mastering process the current distance between the songs is printed to the console. A distance somewhere close to 1 is a hint that the mastering was successful.