afriend wrote: 
> The value (analysis result) for a track in the musly.db is different
> from the one Roland0's script (LMSmusly-0.1.1) generates. I don't know
> much about the underlying models used to compute the similarity score.
> Do you use different settings? Also, could it be that Roland0's script
> is a bit faster or is yours just more thorough?

Not sure, never used Roland0's script. I used (well copied and modified)
Roland0's musly.py which interfaces Python->libmusy.so Musly does not
process the whole track, but only a portion. One of the changes I made
was how much to analyse - Roland0's is 30seconds, mine 2 minutes. My
script also performs concurrent analysis of multiple tracks  - Roland0's
might do this, I have no idea. Whose is better, I'm not sure - they
should be pretty similar. My aim was using Musy for DSTM.

afriend wrote: 
> If I put all the paths into the configure.json I just need "-a m" to
> analyze the music files, right?

Yes.

afriend wrote: 
> And what does "*only* updating the *metadata* database" do as opposed to
> "-a m"? Just remove dead tracks musly couldn't find in the LMS database?

My code does not touch the LMS db, it creates its own SQLite database.
Therefore, I wanted a quick way to update this database to reflect and
metadata changes in my files. When doing this the script just reads the
tags and updates the DB for any changes.

The script only needs access to the LMS DB if you have CUE files where 1
file has multiple tracks. The script will get the metadata, start, and
duration, of each track from LMS. It then uses ffmpeg to create
temporary MP3s for each track and have musly anylyse these.

afriend wrote: 
> About jukebox and LMS/musly startup time: your script seems to
> (auto-)create a jukebox. Does it use the jukebox automatically? In
> general, how long does the start of (lms)musly take? I'm only asking
> because Roland0 said that with a large number of tracks in the database
> the startup time of lmsmusly would increase significantly.

My script will create an SQLite database file and a Musly jukebox file.
If the jukebox file does not exist it will be created - but this can
take sometime (around 10mins on an i7 laptop for ~20k tracks). I analyse
my files on my laptop, and the copy the SQLite DB and jukebox onto my
Pi4 (where LMS runs, and musly-server is also run to provided a backend
for LMS).

afriend wrote: 
> And finally, I've notice that "create similarity mix" doesn't show up in
> the LMS default web UI. Is this by design?

Nope, not by design. Not sure why its not there, but then I've never
created this sort of menu - so I just copied/modifed the one from LMS's
Music IP mixer. Its not a feature I really use.

afriend wrote: 
> If it all works out I'll leave the macOS binaries and some instructions
> 'here' (https://github.com/AF-1/sobras).

Thanks for trying this. Please let me know if it works, and what (if
any) changes needed to be made and I'll update the github page to point
to your binaries.

p.s. Personally I think the Essentia backend provided better mixes, but
MuscIP is still better than either. But, it'd be great to have another
user's opinion.



*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.
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