DEPRECATED Please use Bliss Analyser instead.

Music Similarity

This is a python3 script to analyse your music collection with Bliss, Essentia, and Musly, and to provide a simple HTTP API allowing the creation of mix of similar music for LMS.

You may configure which anlysers are used to analyse your music collection, and which of these is used for the similarity score. By default Bliss is used for Linux, and Musly for Mac and Windows. Essentia support is disabled unless you download the relevant models and extractor - detailed with the `INSTALL.md located within the relevant OS folder.

Bliss

This script can be configured to use the Bliss music analyser (which is a wrapper around the Bliss library) to analyse tracks, and use this analysis to locate similar tracks. The bliss binary is only required for analysis, and is not used for locating similar tracks.

This project contains the following pre-built versions of this app:

1. Linux 64-bit - linux/x86-64/bliss-analyse
2. macOS i386 (compiled by AF-1) - mac/i386/bliss-analyse

Musly

This script can be configured to use the Musly audio music similarity library to analyse tracks, and to locate similar tracks based upon timbre. This project contains the following pre-built versions of this library:

1. Linux 64-bit - linux/x86-64/libmusly.so
2. Raspbian 32-bit, not linked to libav, therefore cannot be used for analysis - linux/armv7l/libmusly.so
3. Raspbian 32-bit, linked against libav - linux/armv7l/raspbian-buster/libav/libmusly.so
4. Windows built with MinGW32, requires 32-bit Python - windows/mingw32/libmusly.dll
5. Windows built with MinGW64, requires 64-bit Python - windows/mingw64/libmusly.dll
6. macOS i386/64-bit, compiled by AF-1 - mac/i386/libmusly.dylib

Essentia

Essentia is used to extract certain attributes about each track - e.g. which key its in, bpm, etc - and the similarity API can then be configured to use these attributes to filter the results (e.g. only accept tracks in a similar key, etc.)

The Essentia binaries and models are only required for analysing tracks, and are not required for locating similar tracks.

Essentia binaries may be downloaded from acousticbrainz.org. However, please note that these only support low-level analysis (bpm, key). To support high-level analysis (danceability, aggresiveness, etc.) you will need to compile your own build of Essentia.

• Linux i386
• Linux x86_64
• Mac 64 bit
• Windows 32

If you download the extractor, place this within linux, windows, or mac sub-folder.

To be able to use Essentia for similarity scoring you must have a build of Essentia that supports the high-level models. A Linux version of Essentia that supports high-level analysis, and the models for this, may downloaded from:

• Essentia
• Models

Analysing Tracks

Before starting the server your music needs to be analysed with you enabled analysers. This is accomplished via:

./music-similarity.py --analyse /path/to/music/folder

The time taken for this depends upon which analysers you are using. Bliss takes roughly 2 hours for 25k tracks, Essentia about 20 hours, and Musly 1 hour. The process analyses tracks with the enabled anlysers, extracts certain tags, and (if Musly is enabled) initialises Musly's 'jukebox' style with 1000 random tracks. If re-run new tracks will be added, and old (non-existent) will be removed. Pass --keep-old to keep these old tracks.

To analyse the Music path stored in the config file, the following shortcut can be used:

./music-similarity.py --analyse m

CUE files

If the analysis locates a music file with a similarly named CUE file (e.g. artist/album/album name.flac and artist/album/album name.cue) then it will read the track listing from the LMS db file and use ffmpeg to split the music file into temporary 128kbps MP3 files for analysis. The files are removed once analysis is complete.

Testing Musly Analysis

Musly has a bug where sometimes it gives the same similarity to all tracks, which will obviously break the music similarity feature. To test if the analysis is correct you can run the script in test mode:

./music-similarity.py --log-level INFO --test

This will query Musly for the 50 most similar tracks to the 1st analysed track. It then checks that there are different similarities, and if not an error message is shown.

If this script states that there is an error you can try simply removing the jukebox file and re-running this script - it will recreate the jukebox with random tracks. If this keeps failing it might be better to adjust the styletracks config item, delete the jukebox, and test again.

Alternatively, you can have this script run until it receives valid similarities from Musly. In this mode the script will test the jukebox, remove it if the test fails, recreate jukebox, test the jukebox, ...

./music-similarity.py --log-level INFO --test --repeat

Similarity service

music-similarity can be installed as a system service, or started manually, to provide a Music similarity service that can be called from the LMS plugin, etc.

./music-similarity.py

...when the service starts, it will confirm that the number of tracks in its SQLite database is the same as the number in the 'jukebox'. If the number differs, the jukebox is recreated.

Configuration

Configuration is read from a JSON file (default name is config.json). This has the following basic items:

{
 "paths":{
  "db":"/home/user/.local/share/music-similarity/",
  "local":"/home/Music/"
 }
}

• paths.db should be the path where the SQLite and jukebox files created by this app can be written to / read from.
• paths.local should be the path where this script can access your music files. This can be different to the path that LMS uses if you are running analysis on a different machine to where you would run the script as the similarity server. This script will only store the paths relative to this location - eg. if paths.local=/home/music/ then /home/music/A/b.mp3 will be stored as A/b.mp3. Only required if analysing tracks.

The linux/x86-64, linux/armv7l, mac, and windows folders contain example config files for each system.

Please refer to docs/OtherConfig.md for all configuration items.

Installation

Please read the INSTALL.md file within the relevant OS (linux, mac, windows) folder for installation instructions.

Credits

lib/musly.py (which is used as a python interface to the Musly library) is taken, and modified, from Roland0's Musly Integration for LMS

The Linux Essentia binary mentioned above is from Roland0's LMS Essentia Integration

The macOS build of the Musly library has been built by AF-1 and a copy taken from their github repo.