This is an alpha release of a DSTM mixer that uses 'Musly' (https://www.musly.org) to obtain 'similar' tracks, and then (optionally) uses 'Essentia' (https://essentia.upf.edu/) to filter these 'similar' tracks by BPM, Key (using 'Camelot Wheell' (https://mixedinkey.com/camelot-wheel/)), loudness, and (if tracks analysed on Linux) other attributes (such as danceability, aggressiveness, etc.).
There are two parts to this plugin: - The DSTM plugin, which is a standard LMS Plugin - https://github.com/CDrummond/lms-musicsimilarity - A python script (music-similarity) used to analyse music tracks, create a 'similarity' database, and provide access to query track similarity via a simple HTTP API - https://github.com/CDrummond/music-similarity The 'music-similarity' script is used to analyse your tracks, and saves its results to an SQLite database and a Musly 'jukebox' file. (This 'jukebox' is what Musly uses to compute similarities, and contains binary data taken from the SQLite database. This jukebox can be created 'on the fly' but this can take sometime, hence a cached version is written to, and read from, disk). Musly itself is *very* fast to analyse tracks (by default it (my modified version) analyses the middle 2 minutes (starting no later than 3 1/2 minutes into track) of each track). Essentia, however, is *much* slower. As a rough guide, using both Musly and Essentia, my 7 year old i7, 8-core, SSD, laptop analyses around 1140 tracks/hour. Essentia is used to extract attributes of each track - its BPM, key, and loudness. If Essentia has been compiled with Gaia/SVM (or Tensorflow) it can also extract other attributes - such as danceability, aggressiveness, etc. Supporting these extra attributes on non-Linux systems will require rebuilding of Essentia. These attributes are stored in the SQLite database, therefore the Essentia binary is only required when analysing tracks and is not required when providing the similarity service. The script is run in anlysis mode via: Code: -------------------- ./music-similarity/music-similarity.py -a m -l DEBUG -------------------- Once your tracks have been analysed the script needs to be run in 'server' mode to allow the LMS plugin to query for similar tracks. Code: -------------------- ./music-similarity/music-similarity.py -l DEBUG -------------------- Further details may be found on the 'Music Similarity github page.' (https://github.com/CDrummond/music-similarity) I've uploaded an alpha release of the music-similarity analyser and API server to my Google drive, and created a 0.0.3 release of the LMS plugin. Links below: - 'music-similarity-all-alpha1.zip' (https://drive.google.com/file/d/1PeQOcPdeTYgXcvMOoWnPLD7M0hJXx-tK/view?usp=sharing) All music-similarity files, apart from Essentia extractor, ffmpeg, and ffprobe for windows. This is basically a 'release' snapshot of the github repo. - 'music-similarity-linux-x86-64-alpha1.zip' (https://drive.google.com/file/d/1qg5weyrWKs3Dwr2ie_lmTth1OZTqklg3/view?usp=sharing) music-similarity for x86 Linux. Musly has been compiled on Fedora, but should (not tested) work on other Linux distributions. Essentia extractor binary supports extra attributes, and is taken from Roland0's LMS Essentia page. - 'music-similarity-pi-alpha1.zip' (https://drive.google.com/file/d/1vlG5gmYFn7Lt_2CS-fnuCccBO_EXyBcP/view?usp=sharing) Cut down build for running the similarity API service on a Raspberry Pi. This does not include the Essentia extractor, and the build of Musly does not support analysing tracks. - 'music-similarity-windows-alpha1.zip' (https://drive.google.com/file/d/1bBy5TcxVOYOYzUUdLIOsG-Oi6714FgtK/view?usp=sharing) Windows version contains pre-built Musly library and a basic Essentia extractor (taken from acousticbrainz.org). Musly uses the ffmpeg/libav libraries to decode audio files. However, building this on Windows was too much hassle. Therefore, the build of Musly in this ZIP uses the commandline ffmpeg and ffprobe binaries (bundled in this ZIP) to decode audio files. - 'LMS Music Similarity plugin' (https://github.com/CDrummond/lms-musicsimilarity/releases/download/0.0.3/lms-musicsimilarity-0.0.3.zip) This plugin can be installed manually, or you can install from my 'repo' (https://raw.githubusercontent.com/CDrummond/lms-plugins/master/repo.xml) Example configs are within each ZIP, these will need to be updated with your specific paths. You can also define "Genre groups" in the LMS plugin - so that only tracks from similar genres are used. On my personal setup I analyse tracks on my Linux laptop (e.g. using 2) and then copy the SQLite database and jukebox files to my Raspberry Pi4 - where LMS (using 5) and the similarity API server (using 3) run. As this is a alpha release I fully expect there to be issues - especially on the Windows side, as I'm a 100% Linux user and have not fully tested this setup. However, I'm interested to get feedback on how well this works for others, perhaps even what are the best default settings to use for the plugin side. The LMS plugin adds "Create Similarity Mix", etc, items to LMS's context menus. These were added at the request of "afriend", but are not something I actively use and I'm more interested in feedback on the DSTM side (which is my main use case). I have no access to a mac, so currently the code does not explicitly support this - however Musly can be compiled for macOS and acousticbrainz.org does have pre-built mac binaries. For Linux and Windows the code attempts to set the correct config - e.g. libmusy location, essentia extractor path, etc. For mac you will need to update config.json for these manually. Or, you can look to editing lib/config.py and uncomment the mac sections. If you are going to test, I suggest you create a small subset (e.g. 500 tracks) of your Library - so that you are sure the config works, etc. before spending hours analysing 1000s of tracks. *Material debug:* 1. Launch via http: //SERVER:9000/material/?debug=json (Use http: //SERVER:9000/material/?debug=json,cometd to also see update messages, e.g. play queue) 2. Open browser's developer tools 3. Open console tab in developer tools 4. REQ/RESP messages sent to/from LMS will be logged here. ------------------------------------------------------------------------ cpd73's Profile: http://forums.slimdevices.com/member.php?userid=66686 View this thread: http://forums.slimdevices.com/showthread.php?t=115609 _______________________________________________ plugins mailing list plugins@lists.slimdevices.com http://lists.slimdevices.com/mailman/listinfo/plugins
