srasher wrote: > Then lmsmusly.py works from the command line. Great! > > Next step will be to include it into LMS, will try this tomorrow. > > Cheers, > Seb
Well, I got mixed results for that: 1. Installation on the ODroid: Getting it to run the playlist generation on the Odroid wasn't possible for me with medium effort. python3-requests could only be installed on Ubuntu 14.04 (max2play's latest available version base for the U3) in a compatible version (> 2.4.2) by using PIP. That was somehow OK. After several minor issues, I reached a dead end due to some other command used inside lmsmusly.py only working with >= Python 3.5. Trying to install that beside the "default" 3.4 version on the Odroid lead to dependency issues in apt, so at some point I gave up. 2. Generated playlist contents: My next try was to let lmsmusly.py run on the x86_64 machine. Not a viable solution for everyday use but OK for trials in the beginning or for the "Party only" use case. Well, it doesn't show exactly the results that I had hoped for. First it took rather minutes than seconds to generate a 20 song static playlist addition based on the first song of the current playlist (one of the examples on the LMSmusly web page). It generates them, but the mix is rather strange. Within those 20 songs (and I tried 5 or 6 times with always different seed tracks) it never had less than 5 songs from the same album as the seed track. The additional songs then most of the time had a majority of songs from the same artist. And then there always where one or two songs that were totally "off". Example: when starting a playlist with Alice Cooper, I got 14 Alice Cooper songs, 7 from the same album, one from The Pogues, one from AC/DC, one from Deep Purple, one from Aerosmith (that's all fine), one song from Cradle of Filth (Black Metal, totally off genre-wise) and one from Cindy Lauper (well, discussable ;-)). Similar mix results with other seed tracks. While the Cradle of Filth song might be something like a glitch, maybe caused by the 30 seconds analysis time window or something, the general distribution of the tracks was not what I had expected. Statistics: I have analyzed ~25000 songs from 2200 albums, wide variety of genres from Pop to Death Metal. All of the seed tracks "main genre's" have at least 100 albums within the collection, mostly from not less than 20 artists. I know that the generation isn't based on metadata at all, still the genre and artist distribution is something I would look at to judge the "quality" of the mix ;-) Maybe something went strange in my analysis of the tracks (left everything at default in the related config.py). I think it is a very interesting tool, especially given the hassle that MusicIP usage nowadays generates. Will do more trials soon. Thanks for your efforts anyway, Roland! Much appreciated. Cheers, Seb "The only word I know is 'Grunt' - and I can't spell it" (R.I.P. D.A.) ------------------------------------------------------------------------ srasher's Profile: http://forums.slimdevices.com/member.php?userid=6209 View this thread: http://forums.slimdevices.com/showthread.php?t=108495 _______________________________________________ plugins mailing list [email protected] http://lists.slimdevices.com/mailman/listinfo/plugins
