relective merge branch 'nested-iterator-with-condition' into refactor/NRTSynths
Pierre Alexandre Tremblay
5 years ago
commit
50b59a39cf
@ -1,122 +1,223 @@
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// load a source
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b = Buffer.read(s,"/Volumes/machins/projets/newsfeed/sons/textes/Audio/synth/fromtexttospeech-AmE-George.wav")
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b.play
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// load a source folder
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~loader = FluidLoadFolder("/Volumes/machins/projets/newsfeed/sons/textes/Audio/synth");
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~loader.play;
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//slightly oversegment with novelty
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//segments should still make sense but might cut a few elements in 2 or 3
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~originalslices = Buffer(s);
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FluidBufNoveltySlice.process(s, b, indices: ~originalslices, feature: 1, kernelSize: 29, threshold: 0.05, filterSize: 5, hopSize: 128, action: {~originalslices.numFrames.postln;})
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~slicer = FluidSliceCorpus({ |src,start,num,dest| FluidBufNoveltySlice.kr(src,start,num,indices:dest, feature: 1, kernelSize: 29, threshold: 0.05, filterSize: 5, hopSize: 128)});
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~slicer.play(s, ~loader.buffer,~loader.index);
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//test the segmentation by looping them
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(
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{
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BufRd.ar(1, b,
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Phasor.ar(0,1,
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BufRd.kr(1, ~originalslices,
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MouseX.kr(0, BufFrames.kr(~originalslices) - 1), 0, 1),
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BufRd.kr(1, ~originalslices,
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MouseX.kr(1, BufFrames.kr(~originalslices)), 0, 1),
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BufRd.kr(1,~originalslices,
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MouseX.kr(0, BufFrames.kr(~originalslices) - 1), 0, 1)), 0, 1);
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~originalindices = Array.newFrom(~slicer.index.keys).sort{|a,b| ~slicer.index[a][\bounds][0]< ~slicer.index[b][\bounds][0]}.collect{|x|~slicer.index[x][\bounds]};
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d = {arg start=0, end = 44100;
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BufRd.ar(1, ~loader.buffer, Phasor.ar(0,1,start,end,start),0,1);
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}.play;
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)
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//analyse each segment with MFCCs in a dataset
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~originalslices.getn(0,~originalslices.numFrames, {|x|~originalslicesarray = x; if ((x.last != b.numFrames), {~originalslicesarray = ~originalslicesarray ++ (b.numFrames)}); });//retrieve the indices and add the file boundary at the end if not there already
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w = Window.new.front;
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b = ControlSpec(0, ~originalindices.size - 1, \linear, 1); // min, max, mapping, step
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c = StaticText(w, Rect(340, 20, 50, 20)).align_(\center);
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a = Slider(w, Rect(10, 20, 330, 20))
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.action_({var val = b.map(a.value).asInteger;
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c.string_(val.asString);
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d.set(\start,~originalindices[val][0], \end, ~originalindices[val][1]);
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});
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)
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//iterates through the
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//a few buffers and our dataset - with back and forth from the language
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//analyse each segment with 20 MFCCs in a dataset and spectralshapes in another one
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(
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~mfccs = Buffer(s);
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~stats = Buffer(s);
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~flat = Buffer(s);
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~slices = FluidDataSet(s,\slices);
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Routine{
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s.sync;
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(~originalslicesarray.size - 1).do{|i|
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FluidBufMFCC.process(s, b, startFrame: ~originalslicesarray[i], numFrames: (~originalslicesarray[i+1] - ~originalslicesarray[i]), numChans: 1,features: ~mfccs, numCoeffs: 20, action: {
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FluidBufStats.process(s, ~mfccs, startChan: 1, stats: ~stats, action: {
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FluidBufFlatten.process(s, ~stats, ~flat, action: {
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~slices.addPoint(i.asSymbol, ~flat);
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});
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});
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~featuresbuf = 4.collect{Buffer.new};
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~statsbuf = 4.collect{Buffer.new};
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~flatbuf = 4.collect{Buffer.new};
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~slicesMFCC = FluidDataSet(s,\slicesM);
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~slicesShapes = FluidDataSet(s,\slicesS);
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~extractor = FluidProcessSlices({|src,start,num,data|
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var features, stats, writer, flatten,mfccBuf, statsBuf, flatBuf, label, voice;
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label = data.key;
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voice = data.value[\voice];
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features = FluidBufMFCC.kr(src,startFrame:start,numFrames:num,numChans:1, numCoeffs: 20, features:~featuresbuf[voice],trig:1,blocking: 1);
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stats = FluidBufStats.kr(~featuresbuf[voice],stats:~statsbuf[voice],trig:Done.kr(features),blocking: 1);
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flatten = FluidBufFlatten.kr(~statsbuf[voice],~flatbuf[voice],trig:Done.kr(stats),blocking: 1);
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writer = FluidDataSetWr.kr(~slicesMFCC,label, -1, ~flatbuf[voice], Done.kr(flatten),blocking: 1);
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features = FluidBufSpectralShape.kr(src,startFrame:start,numFrames:num,numChans:1, features:~featuresbuf[voice],trig:Done.kr(writer),blocking: 1);
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stats = FluidBufStats.kr(~featuresbuf[voice],stats:~statsbuf[voice],trig:Done.kr(features),blocking: 1);
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flatten = FluidBufFlatten.kr(~statsbuf[voice],~flatbuf[voice],trig:Done.kr(stats),blocking: 1);
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writer = FluidDataSetWr.kr(~slicesShapes,label, -1, ~flatbuf[voice], Done.kr(flatten),blocking: 1);
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});
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};
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}.play;
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)
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~extractor.play(s,~loader.buffer, ~slicer.index);
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~slices.print
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~slicesMFCC.print
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~slicesShapes.print
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//run a window over consecutive segments, forcing them in 2 classes, and merging the consecutive segments of similar class
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//we overlap the analysis with the last (original) slice to check for continuity
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(
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~winSize = 4;//the number of consecutive items to split in 2 classes;
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~curated = FluidDataSet(s,\curatedDS);
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~query = FluidDataSetQuery(s);
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~kmeans = FluidKMeans(s,2,100);
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~stan = FluidStandardize(s);
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~kmeans = FluidKMeans(s,2,1000);
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~windowDS = FluidDataSet(s,\windowDS);
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~windowLS = FluidLabelSet(s,\windowLS);
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)
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//curate stats (MFCCs)
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~query.clear
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~query.addRange((0*20)+1,10);
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~query.transform(~slicesMFCC,~curated);
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//OR
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//curate stats (moments)
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~query.clear
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~query.addRange(0,3);
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~query.transform(~slicesShapes,~curated);
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//OR
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//curate both
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~query.clear
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~query.addColumn(0);//add col 0 (mean of mfcc0 as 'loudness')
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~query.transform(~slicesMFCC,~curated);//mfcc0 as loudness
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~query.clear;
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~query.addRange(0,3);//add some spectral moments
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~query.transformJoin(~slicesShapes, ~curated, ~curated);//join in centroids
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~stan.fitTransform(~curated, ~curated);
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~curated.print
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~curated.dump{|x|~sliceDict = x;};
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~originalslicesarray = (~originalindices.flatten ++ ~loader.buffer.numFrames).asSet.asArray.sort
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~orginalkeys = Array.newFrom(~slicer.index.keys).sort{|a,b| ~slicer.index[a][\bounds][0]< ~slicer.index[b][\bounds][0]}
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//the windowed function
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(
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Routine{
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~indices = [0];
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~head = 0;
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~sliceDict = Dictionary.new(4);
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~tempDict = Dictionary.new(4);
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~slices.dump{|x|~sliceDict = x;};
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s.sync;
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while ( {~head <= (~originalslicesarray.size - ~winSize)},
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{
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var step = ~winSize - 1;
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var nbass = [];
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//run a process on ~winSize items from ~head (with an overlap of 1)
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//copy the items to a subdataset
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~winSize.do{|i|
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~tempDict.put((i.asString), ~sliceDict["data"][(i+~head).asString]);//here one could curate which stats to take
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"whichslices:%\n".postf(i+~head);
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~windowedFunct = {arg head, winSize, overlap;
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var nbass = [], assignments = [], tempDict = ();
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//check the size of everything to not overrun
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winSize = (~originalslicesarray.size - head).min(winSize);
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//copy the items to a subdataset from hear
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winSize.do{|i|
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tempDict.put((i.asString), ~sliceDict["data"][(~orginalkeys[(i+head)]).asString]);//here one could curate which stats to take
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"whichslices:%\n".postf(i+head);
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};
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~windowDS.load(Dictionary.newFrom([\cols, 133, \data, ~tempDict]));
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s.sync;
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"% - loaded\n".postf(~head);
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~windowDS.load(Dictionary.newFrom([\cols, ~sliceDict["cols"].asInteger, \data, tempDict]), action: {
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"% - loaded\n".postf(head);
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//kmeans 2 and retrieve ordered array of class assignations
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~kmeans.fitPredict(~windowDS, ~windowLS, {|x| nbass = x;});
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s.sync;
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"% - fitted1: ".postf(~head); nbass.postln;
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~kmeans.fitPredict(~windowDS, ~windowLS, action: {|x|
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nbass = x;
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"% - fitted1: ".postf(head); nbass.postln;
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if (nbass.includes(winSize.asFloat), {
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~kmeans.fitPredict(~windowDS, ~windowLS, {|x|
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nbass = x; "% - fitted2: ".postf(head); nbass.postln;
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if (nbass.includes(winSize.asFloat), {
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~kmeans.fitPredict(~windowDS, ~windowLS, {|x|
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nbass = x; "% - fitted3: ".postf(head); nbass.postln;
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});
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});
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});
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});
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// if (nbass.includes(0.0), { Routine{~kmeans.fitPredict(~windowDS, ~windowLS, {|x| nbass = x; "% - fitted2: ".postf(~head); nbass.postln; s.sync;});}.play; });
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~windowLS.dump{|x|
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var assignments = x.at("data").asSortedArray.flop[1].flatten;
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"% - assigned ".postf(head);
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~windowLS.dump{|x|~assignments = x.at("data").asSortedArray.flop[1].flatten;};
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s.sync;
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"% - assigned ".postf(~head);
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assignments.postln;
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~assignments.postln;
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(winSize-1).do{|i|
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if (assignments[i+1] != assignments[i], {
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~newindices= ~newindices ++ (~originalslicesarray[head+i+1]).asInteger;
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~newkeys = ~newkeys ++ (~orginalkeys[head+i+1]);
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});
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step.do{|i|
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if (~assignments[i+1] != ~assignments[i], {~indices= ~indices ++ (~originalslicesarray[~head+i+1])});
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};
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~head = ~head + step;
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//if we still have some frames to do, do them
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if (((winSize + head) < ~originalslicesarray.size), {
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"-----------------".postln;
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});
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//leftovers
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if ( (~originalslicesarray.size - ~head) > 1, {
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//run a process on (a.size - ~head) items from ~head
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(~originalslicesarray.size - ~head - 1).do{|i|
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if (~assignments[i+1] != ~assignments[i], {~indices= ~indices ++ (~originalslicesarray[~head+i+1])});
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// (~head+i).postln;
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~windowedFunct.value(head + winSize - overlap, winSize, overlap);
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}, {~newindices = (~newindices ++ ~loader.buffer.numFrames); "done".postln;});//if we're done close the books
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};
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});
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~indices.postln;
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}.play;
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});
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}
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)
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{var i = 8;BufRd.ar(1,b,Line.ar(~originalslicesarray[i],~originalslicesarray[i+1],(~originalslicesarray[i+1] - ~originalslicesarray[i])/b.sampleRate, doneAction: 2))}.play;
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{var i = 4;BufRd.ar(1,b,Line.ar(~indices[i],~indices[i+1],(~indices[i+1] - ~indices[i])/b.sampleRate, doneAction: 2))}.play;
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//the job
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~newindices = [~originalslicesarray[0]]; ~newkeys = [~orginalkeys[0]];
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~windowedFunct.value(0, 4, 1);
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//try again with more clusters
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~newindices = [~originalslicesarray[0]]; ~newkeys = [~orginalkeys[0]];
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~kmeans.numClusters = 3;
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~windowedFunct.value(0,6,2);
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~newindices.postln;
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~newkeys.postln;
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~newindices.size;
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~newkeys.size;
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~newindices.last;
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~newkeys.last;
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~slicer.index[~orginalkeys[0]]
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//export to reaper
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(
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//first create a new file that ends with rpp - it will overwrite if the file exists
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f = File.new("/tmp/clusteredslices-" ++ Date.getDate.stamp ++".rpp","w+");
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if (f.isOpen , {
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var path, prevpath ="", sr, count, dur;
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//write the header
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f.write("<REAPER_PROJECT 0.1 \"5.99/OSX64\" 1603037150\n\n");
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//a first track with the originalslicearray
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//write the track header
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f.write("<TRACK\nNAME \"novelty output\"\n");
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// iterate through the items in the track
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~orginalkeys.do{|v, i|
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path = ~slicer.index[v][\path];
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if (path != prevpath, {
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sr = ~slicer.index[v][\sr];
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prevpath = path;
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count = 0;
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});
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dur = ~originalslicesarray[i+1] - ~originalslicesarray[i];
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if ( dur > 0, {
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f.write("<ITEM\nPOSITION " ++ (~originalslicesarray[i] / sr) ++ "\nLENGTH " ++ (dur / sr) ++ "\nNAME \"" ++ v ++ "\"\nSOFFS " ++ (count / sr) ++ "\n<SOURCE WAVE\nFILE \"" ++ path ++ "\"\n>\n>\n");
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});
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count = count + dur;
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};
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//write the track footer
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f.write(">\n");
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// a second track with the new ~indices
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prevpath = "";
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//write the track header
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f.write("<TRACK\nNAME \"clustered output\"\n");
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// iterate through the items in the track
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~newkeys.do{|v, i|
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path = ~slicer.index[v][\path];
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if (path != prevpath, {
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sr = ~slicer.index[v][\sr];
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prevpath = path;
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count = 0;
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});
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dur = ~newindices[i+1] - ~newindices[i];
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if (dur > 0, {
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f.write("<ITEM\nPOSITION " ++ (~newindices[i] / sr) ++ "\nLENGTH " ++ (dur / sr) ++ "\nNAME \"" ++ v ++ "\"\nSOFFS " ++ (count / sr) ++ "\n<SOURCE WAVE\nFILE \"" ++ path ++ "\"\n>\n>\n");
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});
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count = count + dur;
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};
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//write the track footer
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f.write(">\n");
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//write the footer
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f.write(">\n");
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f.close;
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});
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)
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@ -0,0 +1,161 @@
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// Make:
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// - A kmeans
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// - a datasetquery
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// - a normalizer
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// - a standardizer
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// - 3 DataSets of example points R-G-B descriptions
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// - 3 DataSets for the scaled versions
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// - 1 summative dataset and a LabelSet for predicted labels
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(
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~classifier = FluidKMeans(s,5, 1000);
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~query = FluidDataSetQuery(s);
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~stan = FluidStandardize(s);
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~norm = FluidNormalize(s);
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~sourceR = FluidDataSet(s,\a3DsourceR);
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~sourceG = FluidDataSet(s,\a3DsourceG);
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~sourceB = FluidDataSet(s,\a3DsourceB);
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~scaledR = FluidDataSet(s,\a3DscaledR);
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~scaledG = FluidDataSet(s,\a3DscaledG);
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~scaledB = FluidDataSet(s,\a3DscaledB);
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~composited = FluidDataSet(s,\a3Dcomposited);
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~labels = FluidLabelSet(s,\a3Dlabels);
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)
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//Make some random, but clustered test points, each descriptor category in a separate dataset
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(
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~sourceR.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, 1.0.sum3rand]}.flatten))]));
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~sourceG.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, 1.0.rand2]}.flatten))]));
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~sourceB.load(Dictionary.newFrom([\cols, 1, \data, (Dictionary.newFrom(40.collect{|x| [x, (0.5.sum3rand).squared + [0.75,-0.1].choose]}.flatten))]));
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)
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//here we manipulate
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//assemble the scaled dataset
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(
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~query.addColumn(0, {
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~query.transformJoin(~sourceB, ~sourceG, ~composited, {
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~query.transformJoin(~sourceR, ~composited, ~composited);
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});
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});
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)
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~composited.print
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//Fit the classifier to the example DataSet and labels, and then run prediction on the test data into our mapping label set
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~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict = x;};~composited.dump{|x|~compodict=x;};});
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//Visualise:
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(
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w = Window("sourceClasses", Rect(128, 64, 820, 120));
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w.drawFunc = {
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Pen.use{
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~compodict["data"].keysValuesDo{|key, colour|
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Pen.fillColor = Color.fromArray((colour * 0.5 + 0.5 ).clip(0,1) ++ 1);
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Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict["data"].at(key).asInteger[0] * 20 + 10),15,15));
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};
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};
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};
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w.refresh;
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w.front;
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)
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// standardize our colours and rerun
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(
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~stan.fitTransform(~sourceR, ~scaledR, {
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~stan.fitTransform(~sourceG, ~scaledG, {
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~stan.fitTransform(~sourceB, ~scaledB, {
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//assemble
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~query.addColumn(0, {
|
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~query.transformJoin(~scaledB, ~scaledG, ~composited, {
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~query.transformJoin(~scaledR, ~composited, ~composited, {
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//fit
|
||||
~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
)
|
||||
|
||||
//Visualise:
|
||||
(
|
||||
w = Window("stanClasses", Rect(128, 64, 820, 120));
|
||||
w.drawFunc = {
|
||||
Pen.use{
|
||||
~compodict2["data"].keysValuesDo{|key, colour|
|
||||
Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
|
||||
Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
|
||||
};
|
||||
};
|
||||
};
|
||||
w.refresh;
|
||||
w.front;
|
||||
)
|
||||
|
||||
//now let's normalise instead
|
||||
(
|
||||
~norm.fitTransform(~sourceR, ~scaledR, {
|
||||
~norm.fitTransform(~sourceG, ~scaledG, {
|
||||
~norm.fitTransform(~sourceB, ~scaledB, {
|
||||
//assemble
|
||||
~query.addColumn(0, {
|
||||
~query.transformJoin(~scaledB, ~scaledG, ~composited, {
|
||||
~query.transformJoin(~scaledR, ~composited, ~composited, {
|
||||
//fit
|
||||
~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
)
|
||||
|
||||
//Visualise:
|
||||
(
|
||||
w = Window("normClasses", Rect(128, 64, 820, 120));
|
||||
w.drawFunc = {
|
||||
Pen.use{
|
||||
~compodict2["data"].keysValuesDo{|key, colour|
|
||||
Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
|
||||
Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
|
||||
};
|
||||
};
|
||||
};
|
||||
w.refresh;
|
||||
w.front;
|
||||
)
|
||||
|
||||
// let's mess up with the scaling of one dimension: let's multiply the range of Red by 10
|
||||
~norm.min = -10;
|
||||
~norm.max = 10;
|
||||
(
|
||||
~norm.fitTransform(~sourceR, ~scaledR, {
|
||||
//assemble
|
||||
~query.addColumn(0, {
|
||||
~query.transformJoin(~scaledB, ~scaledG, ~composited, {
|
||||
~query.transformJoin(~scaledR, ~composited, ~composited, {
|
||||
//fit
|
||||
~classifier.fitPredict(~composited,~labels,{~labels.dump{|x|~labeldict2 = x;};~composited.dump{|x|~compodict2=x;};});
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
)
|
||||
|
||||
//Visualise:
|
||||
(
|
||||
w = Window("norm10rClasses", Rect(128, 64, 820, 120));
|
||||
w.drawFunc = {
|
||||
Pen.use{
|
||||
~compodict2["data"].keysValuesDo{|key, colour|
|
||||
Pen.fillColor = Color.fromArray((colour * 0.25 + 0.5 ).clip(0,1) ++ 1);
|
||||
Pen.fillRect( Rect( (key.asFloat * 20 + 10), (~labeldict2["data"].at(key).asInteger[0] * 20 + 10),15,15));
|
||||
};
|
||||
};
|
||||
};
|
||||
w.refresh;
|
||||
w.front;
|
||||
)
|
||||
Loading…
Reference in New Issue