ldarieut wrote: 
> Got this running on WSL2 in windows, at 1490 tracks/hour so far over the
> network with a freshly compiled musly.

Is that 1490 with just Musly? If so, that's slow - I used to get
~20k/hour with just Musly, and ~1100/hour with Musly+Essentia. Also, why
recompile Musly? And if you did please use my fork - simply becasue I
merged in some pull request from the Musly repo.

ldarieut wrote: 
> Anyway, I was reading about the tensorflow version of essentia, and thus
> I compiled the latest version with tensorflow support.

I've never actually got very far with Tensorflow. Looked into it, but
the models (AFAIR) are hundreds of megs. Just seemed overkill. Plus
Tensorflow, AFAIK, is slower - more accurate, but slower.

ldarieut wrote: 
> However, how am I supposed to use essentia-tensorflow in
> music-similarity? It doesn't look like I can just replace the binary
> essentia-extractor, as the tensorflow version doesn't seem to work that
> way, it looks like more of a python package. I haven't looked at
> LMS-essentia in details though.

First you will need to download the models you want to use. I'm not sure
if Essentia+Tensorflow produces the same attribute types as
Essentia+Gaia/SVM - probably does, but I can't be sure.

ldarieut wrote: 
> Can I have some pointers as to how essentia is used (technically) in
> music-similarity?

MusicSimilarity just calls the music extractor, supplying a profile to
tell Essentia what models to use. Essentia then outputs a response in
JSON format (which MusicSimilarity optionally saves to disk). This JSON
is then parsed and the attributes extracted.



*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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