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321 lines
8.6 KiB
Plaintext
321 lines
8.6 KiB
Plaintext
(
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Window.closeAll;
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s.options.inDevice_("MacBook Pro Microphone");
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s.options.outDevice_("External Headphones");
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// s.options.sampleRate_(48000);
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s.options.sampleRate_(44100);
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s.waitForBoot{
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Task{
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var win = Window(bounds:Rect(100,100,1000,800));
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var label_width = 120;
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var item_width = 300;
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var mfcc_multslider;
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var nMFCCs = 13;
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var mfccbuf = Buffer.alloc(s,nMFCCs);
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var parambuf = Buffer.alloc(s,3);
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var id_counter = 0;
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var continuous_training = false;
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var mfcc_ds = FluidDataSet(s);
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var param_ds = FluidDataSet(s);
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var mfcc_ds_norm = FluidDataSet(s);
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var param_ds_norm = FluidDataSet(s);
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var scaler_params = FluidNormalize(s);
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var scaler_mfcc = FluidNormalize(s);
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var nn = FluidMLPRegressor(s,[3,3],FluidMLPRegressor.sigmoid,FluidMLPRegressor.sigmoid,learnRate:0.05,batchSize:5,validation:0);
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var synth, loss_st;
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var param_sliders = Array.newClear(3);
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var statsWinSl, hidden_tf, batchSize_nb, momentum_nb, learnRate_nb, maxIter_nb, outAct_pum, act_pum;
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var add_point = {
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var id = "point-%".format(id_counter);
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mfcc_ds.addPoint(id,mfccbuf,{mfcc_ds.print});
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param_ds.addPoint(id,parambuf,{param_ds.print});
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id_counter = id_counter + 1;
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};
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var train = {
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scaler_mfcc.fitTransform(mfcc_ds,mfcc_ds_norm,{
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scaler_params.fitTransform(param_ds,param_ds_norm,{
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// mfcc_ds.print;
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// param_ds.print;
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nn.fit(mfcc_ds_norm,param_ds_norm,{
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arg loss;
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// loss.postln;
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defer{loss_st.string_("loss: %".format(loss))};
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if(continuous_training,{
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train.value;
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});
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});
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});
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});
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};
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var open_mlp = {
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arg path;
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// nn.prGetParams.postln;
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nn.read(path,{
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var params = nn.prGetParams;
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var n_layers = params[1];
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var layers_string = "";
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// params.postln;
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n_layers.do({
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arg i;
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if(i > 0,{layers_string = "% ".format(layers_string)});
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layers_string = "%%".format(layers_string,params[2+i]);
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});
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nn.maxIter_(maxIter_nb.value);
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nn.learnRate_(learnRate_nb.value);
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nn.momentum_(momentum_nb.value);
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nn.batchSize_(batchSize_nb.value);
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defer{
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hidden_tf.string_(layers_string);
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act_pum.value_(nn.activation);
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outAct_pum.value_(nn.outputActivation);
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/* maxIter_nb.value_(nn.maxIter);
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learnRate_nb.value_(nn.learnRate);
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momentum_nb.value_(nn.momentum);
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batchSize_nb.value_(nn.batchSize);*/
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};
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});
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};
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s.sync;
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synth = {
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arg vol = -15, isPredicting = 0, avg_win = 0, smooth_params = 0;
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var params = FluidStats.kr(FluidBufToKr.kr(parambuf),ControlRate.ir * smooth_params * isPredicting)[0];
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var msig = SinOsc.ar(params[1],0,params[2] * params[1]);
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var csig = SinOsc.ar(params[0] + msig);
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var sound_in = SoundIn.ar(0);
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var analysis_sig, mfccs, trig, mfccbuf_norm, parambuf_norm;
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csig = BLowPass4.ar(csig,16000);
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csig = BHiPass4.ar(csig,40);
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analysis_sig = Select.ar(isPredicting,[csig,sound_in]);
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mfccs = FluidMFCC.kr(analysis_sig,nMFCCs,startCoeff:1,maxNumCoeffs:nMFCCs);
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trig = Impulse.kr(30);
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mfccbuf_norm = LocalBuf(nMFCCs);
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parambuf_norm = LocalBuf(3);
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mfccs = FluidStats.kr(mfccs,ControlRate.ir * avg_win)[0];
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FluidKrToBuf.kr(mfccs,mfccbuf);
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scaler_mfcc.kr(trig * isPredicting,mfccbuf,mfccbuf_norm);
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nn.kr(trig * isPredicting,mfccbuf_norm,parambuf_norm);
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scaler_params.kr(trig * isPredicting,parambuf_norm,parambuf,invert:1);
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SendReply.kr(trig * isPredicting,"/params",params);
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SendReply.kr(trig,"/mfccs",mfccs);
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csig = csig.dup;
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csig * Select.kr(isPredicting,[vol.dbamp,Amplitude.kr(sound_in)]);
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}.play;
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s.sync;
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win.view.decorator_(FlowLayout(Rect(0,0,win.bounds.width,win.bounds.height)));
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param_sliders[0] = EZSlider(win,Rect(0,0,item_width,20),"carrier freq",\freq.asSpec,{arg sl; parambuf.set(0,sl.value)},440,true,label_width);
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win.view.decorator.nextLine;
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param_sliders[1] = EZSlider(win,Rect(0,0,item_width,20),"mod freq",\freq.asSpec,{arg sl; parambuf.set(1,sl.value)},100,true,label_width);
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win.view.decorator.nextLine;
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param_sliders[2] = EZSlider(win,Rect(0,0,item_width,20),"index",ControlSpec(0,20),{arg sl; parambuf.set(2,sl.value)},10,true,label_width);
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win.view.decorator.nextLine;
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EZSlider(win,Rect(0,0,item_width,20),"params avg smooth",nil.asSpec,{arg sl; synth.set(\avg_win,sl.value)},0,true,label_width);
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win.view.decorator.nextLine;
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StaticText(win,Rect(0,0,label_width,20)).string_("% MFCCs".format(nMFCCs));
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win.view.decorator.nextLine;
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statsWinSl = EZSlider(win,Rect(0,0,item_width,20),"fmcc avg smooth",nil.asSpec,{arg sl; synth.set(\avg_win,sl.value)},0,true,label_width);
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win.view.decorator.nextLine;
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mfcc_multslider = MultiSliderView(win,Rect(0,0,item_width,200))
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.size_(nMFCCs)
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.elasticMode_(true);
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win.view.decorator.nextLine;
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Button(win,Rect(0,0,100,20))
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.states_([["Add Point"]])
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.action_{
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add_point.value;
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};
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win.view.decorator.nextLine;
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// spacer
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StaticText(win,Rect(0,0,label_width,20));
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win.view.decorator.nextLine;
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// MLP Parameters
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StaticText(win,Rect(0,0,label_width,20)).align_(\right).string_("hidden layers");
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hidden_tf = TextField(win,Rect(0,0,item_width - label_width,20))
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.string_(nn.hidden.asString.replace(", "," ")[2..(nn.hidden.asString.size-3)])
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.action_{
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arg tf;
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var hidden_ = "[%]".format(tf.string.replace(" ",",")).interpret;
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nn.hidden_(hidden_);
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// nn.prGetParams.postln;
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};
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win.view.decorator.nextLine;
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StaticText(win,Rect(0,0,label_width,20)).align_(\right).string_("activation");
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act_pum = PopUpMenu(win,Rect(0,0,item_width - label_width,20))
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.items_(["identity","sigmoid","relu","tanh"])
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.value_(nn.activation)
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.action_{
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arg pum;
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nn.activation_(pum.value);
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// nn.prGetParams.postln;
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};
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win.view.decorator.nextLine;
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StaticText(win,Rect(0,0,label_width,20)).align_(\right).string_("output activation");
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outAct_pum = PopUpMenu(win,Rect(0,0,item_width - label_width,20))
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.items_(["identity","sigmoid","relu","tanh"])
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.value_(nn.outputActivation)
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.action_{
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arg pum;
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nn.outputActivation_(pum.value);
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// nn.prGetParams.postln;
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};
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win.view.decorator.nextLine;
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maxIter_nb = EZNumber(win,Rect(0,0,item_width,20),"max iter",ControlSpec(1,10000,step:1),{
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arg nb;
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nn.maxIter_(nb.value.asInteger);
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// nn.prGetParams.postln;
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},nn.maxIter,false,label_width);
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win.view.decorator.nextLine;
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learnRate_nb = EZNumber(win,Rect(0,0,item_width,20),"learn rate",ControlSpec(0.001,1.0),{
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arg nb;
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nn.learnRate_(nb.value);
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// nn.prGetParams.postln;
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},nn.learnRate,false,label_width);
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win.view.decorator.nextLine;
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momentum_nb = EZNumber(win,Rect(0,0,item_width,20),"momentum",ControlSpec(0,1),{
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arg nb;
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nn.momentum_(nb.value);
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// nn.prGetParams.postln;
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},nn.momentum,false,label_width);
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win.view.decorator.nextLine;
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batchSize_nb = EZNumber(win,Rect(0,0,item_width,20),"batch size",ControlSpec(1,1000,step:1),{
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arg nb;
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nn.batchSize_(nb.value.asInteger);
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// nn.prGetParams.postln;
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},nn.batchSize,false,label_width);
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win.view.decorator.nextLine;
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Button(win,Rect(0,0,100,20))
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.states_([["Train"]])
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.action_{
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train.value;
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};
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Button(win,Rect(0,0,200,20))
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.states_([["Continuous Training Off"],["Continuous Training On"]])
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.action_{
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arg but;
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continuous_training = but.value.asBoolean;
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train.value;
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};
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win.view.decorator.nextLine;
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loss_st = StaticText(win,Rect(0,0,item_width,20)).string_("loss:");
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win.view.decorator.nextLine;
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Button(win,Rect(0,0,100,20))
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.states_([["Not Predicting"],["Predicting"]])
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.action_{
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arg but;
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synth.set(\isPredicting,but.value);
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};
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win.view.decorator.nextLine;
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Button(win,Rect(0,0,100,20))
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.states_([["Save MLP"]])
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.action_{
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Dialog.savePanel({
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arg path;
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nn.write(path);
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});
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};
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Button(win,Rect(0,0,100,20))
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.states_([["Open MLP"]])
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.action_{
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Dialog.openPanel({
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arg path;
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open_mlp.(path);
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});
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};
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win.bounds_(win.view.decorator.used);
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win.front;
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OSCdef(\mfccs,{
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arg msg;
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// msg.postln;
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defer{
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mfcc_multslider.value_(msg[3..].linlin(-40,40,0,1));
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};
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},"/mfccs");
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OSCdef(\params,{
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arg msg;
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// msg.postln;
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defer{
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param_sliders.do{
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arg sl, i;
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sl.value_(msg[3 + i]);
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};
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};
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},"/params");
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s.sync;
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/* statsWinSl.valueAction_(0.1);
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100.do{
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var cfreq = exprand(20,20000);
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var mfreq = exprand(20,20000);
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var index = rrand(0.0,20);
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parambuf.setn(0,[cfreq,mfreq,index]);
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0.2.wait;
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add_point.value;
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0.05.wait;
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};*/
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/* 100.do{
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var cfreq = exprand(60,4000);
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var mfreq = exprand(60,1000).clip(0,cfreq);
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var index = rrand(0.0,20);
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parambuf.setn(0,[cfreq,mfreq,index]);
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0.2.wait;
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add_point.value;
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0.05.wait;
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};*/
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}.play(AppClock);
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};
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) |