Merge branch 'clients/inter_client_comms' of https://bitbucket.org/flucoma/flucoma-supercollider into clients/inter_client_comms
Gerard
6 years ago
commit
d5f692cf0e
@ -1,149 +0,0 @@
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// TB2 SC Playground V0
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/*
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Current stinkers:
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1) Producing flat datapoints for FluidDataSet (i.e. flattening and cherry picking a multichannel buffer) takes bloody ages due to all the server syncing. I can't work out how to do it reliably outwith a Routine (which would certainly be quicker, to my mind)
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2) Functions from the classes don't yet directly return things, and you have to access their return data through actions. This is partly because I don't know what the correct thing to do w/r/t blocking is, so I'm hoping GR will do it properly
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*/
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//STEP 0: start server
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s.reboot;
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if(s.hasBooted.not){"Warning: server not running".postln};
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//STEP 1: Get some files
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Buffer.freeAll;
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(
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FileDialog.new(fileMode:2,okFunc:{|x| ~path = x[0];
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~audioBuffers = SoundFile.collectIntoBuffers(~path+/+'*',s);
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~lookup = Dictionary(n:~audioBuffers.size);
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~audioBuffers.do{|b| ~lookup.add(b.path->b)};
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});
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)
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//STEP 2: Make a FluidDataSet
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~dataset = FluidDataSet.new(s,"mfccs");
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//STEP 3A: EITHER populate the dataset like so (and cry about how long the data point assembly takes)
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(
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Routine{
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var tmpMFCCs = Buffer.new(s);
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var tmpStats = Buffer.new(s);
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var tmpFlat = Buffer.alloc(s,12 * 4 * 2, 1);//12 dims * 4 stats * 2 derivatives
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s.sync;
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~audioBuffers.do{|b|
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("Analyzing" + b.path).postln;
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FluidBufMFCC.process(s,b,features: tmpMFCCs);
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FluidBufStats.process(s,source:tmpMFCCs, stats: tmpStats,numDerivs:1);
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"stats".postln;
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12.do{|i|
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//This takes ages becayse of server syncing :-(
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FluidBufCompose.process(s,tmpStats,0,2, i+1,1, destination: tmpFlat, destStartFrame: (i*8));
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FluidBufCompose.process(s,tmpStats,4,1, i+1,1, destination: tmpFlat, destStartFrame: (i*8) + 2);
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FluidBufCompose.process(s,tmpStats,6,3, i+1,1, destination:tmpFlat, destStartFrame: (i*8) + 3);
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FluidBufCompose.process(s,tmpStats,11,1, i+1,1, destination: tmpFlat, destStartFrame: (i*8) + 6);
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FluidBufCompose.process(s,tmpStats,13,1, i+1,1, destination:tmpFlat, destStartFrame: (i*8) + 7);
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};
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~dataset.addPoint(b.path,tmpFlat);
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};
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s.sync;
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"Done".postln;
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tmpFlat.free;
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tmpStats.free;
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tmpMFCCs.free;
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}.play
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)
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//STEP 3B: OR populate the dataset with the flattening happening in langage side (much faster for now)
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(
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Routine{
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var tmpMFCCs = Buffer.new(s);
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var tmpStats = Buffer.new(s);
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var langStats;
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var langFlat;
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var tmpFlat = Buffer.alloc(s,12 * 4 * 2, 1); //12 dims * 4 stats * 2 derivatives
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s.sync;
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~audioBuffers.do{|b|
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("Analyzing" + b.path).postln;
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FluidBufMFCC.process(s,b,features: tmpMFCCs);
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FluidBufStats.process(s,source:tmpMFCCs, stats: tmpStats,numDerivs:1);
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tmpStats.getn(0,182,{|y| langStats = y;});
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s.sync;
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"stats".postln;
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langFlat = Array.new();
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//taking the mean, std, min and max, and the mean, std, min and max of the first derivative, of each MFCCs except coeff 0 to dismiss amplitude)
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[0,1,4,6,7,8,11,13].do({|i| var j,k; j =((i*13)+1); k = j + 11;langFlat = langFlat ++ langStats[j..k]});
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tmpFlat.setn(0,langFlat);
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s.sync;
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~dataset.addPoint(b.path,tmpFlat);
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};
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s.sync;
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"Done".postln;
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tmpStats.free;
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tmpMFCCs.free;
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tmpFlat.free;
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}.play
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)
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//check
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~dataset.size({|x| x.postln})
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//save
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(
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FileDialog.new(fileMode: 0, acceptMode: 1, okFunc:{|x| var file = x[0];
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//if the file exists and is a json, delete it
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if ((file.splitext[1] == "json") && (File.existsCaseSensitive(file)), {File.delete(file);"File Overwritten".postln;});
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//if not json, make it so
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if (file.splitext[1] != "json", {file = file ++ ".json";});
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// then write
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~dataset.write(file);
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});
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)
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//STEP 3C: OR load in one you rolled earlier
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FileDialog.new(fileMode: 0, acceptMode: 0, okFunc:{|x| ~dataset.read(x[0])});
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//peek
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c = Buffer.new(s)
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~dataset.getPoint(~audioBuffers[3].path,c, { c.getn(0,96,{|x| x.postln})})
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/*************************************/
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//FluidKDTree
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~kdtree = FluidKDTree.new(s)
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~kdtree.fit(~dataset,action:{"fit".postln})
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//match
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~kdtree.kNearest(c,5,{|x| ~matches = x;})
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~kdtree.kNearestDist(c,5,{|x| x.postln})
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~lookup[~matches[4]].postln
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/*************************************/
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//FluidKMeans
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~kMeans= FluidKMeans.new(s)
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~kMeans.fit(~dataset,k:5,action:{"fit".postln})
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// predicts in which cluster a point would be
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~kMeans.predictPoint(c,{|x|x.postln})
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// predicts which cluster each points of a dataset would be in, as a label
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~labels = FluidLabelSet.new(s,"clusters")
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~kMeans.predict(~dataset,~labels, {|x| x.postln})
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~labels.getLabel(~audioBuffers[2].path,action:{|c| c.postln})
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//query each item
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(
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Routine{
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~labels.size({|x|x.do {|i|
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~audioBuffers[i].path.postln;
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~labels.getLabel(~audioBuffers[i].path,action:{|c| c.postln});
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s.sync;
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}
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});
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}.play
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)
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//labelset can be written as json
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~labels.write(~path+/+"labels.json")
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