@magenta/music
Version:
Make music with machine learning, in the browser.
1 lines • 168 kB
JavaScript
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a{constructor(e={}){this.trained=!1,this.initialized=!1,this.config={encoder_layers:e.encoder_layers||[1024,256,64],decoder_layers:e.decoder_layers||[64,256,1024],input_size:e.input_size||256,latent_size:e.latent_size||4,beta:e.beta||1,epochs:e.epochs||10}}dispose(){this.initialized&&(this.encoder.dispose(),this.decoder.dispose(),this.vae.dispose(),this.initialized=!1)}initialize(){this.dispose();const e=i.d.now(),t=r.input({shape:[this.config.input_size]});this.encoder=this.getEncoder(t);const[n,,]=this.encoder.apply(t);this.decoder=this.getDecoder(n.shape.slice(1));const s=this.decoder.apply(n);this.vae=r.model({inputs:t,outputs:s,name:"vae"}),this.initialized=!0,o.logWithDuration("Initialized model",e,"MidiMe")}async train(e,t){const n=i.d.now();this.trained=!1;let s=.001;!1===r.ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")&&!1===r.ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&1===r.ENV.get("WEBGL_VERSION")&&(s=5e-5);const a=r.train.adam(s);for(let n=0;n<this.config.epochs;n++)await r.nextFrame(),await a.minimize(()=>r.tidy(()=>{const[,r,o]=this.encoder.predict(e),i=this.vae.predict(e),s=this.loss(r,o,i,e);return t&&t(n,{y:i,total:s.totalLoss.arraySync(),losses:[s.reconLoss.arraySync(),s.latentLoss.arraySync()]}),s.totalLoss})),await r.nextFrame();o.logWithDuration("Training finished",n,"MidiMe"),this.trained=!0,a.dispose()}async sample(e=1){return this.initialized||await this.initialize(),r.tidy(()=>{const t=r.randomNormal([e,this.config.latent_size]);return this.decoder.predict(t)})}async decode(e){return this.initialized||await this.initialize(),this.decoder.predict(e)}async encode(e){this.initialized||await this.initialize();const[t,,]=this.encoder.predict(e);return t}predict(e){return this.vae.predict(e)}getEncoder(e){let t=e;for(let e=0;e<this.config.encoder_layers.length;e++)t=r.layers.dense({units:this.config.encoder_layers[e],activation:"relu"}).apply(t);const n=this.getAffineLayers(t,this.config.latent_size,e,!1),o=this.getAffineLayers(t,this.config.latent_size,e,!0),i=(new s).apply([n,o]);return r.model({inputs:e,outputs:[i,n,o],name:"encoder"})}getDecoder(e){const t=r.input({shape:e});let n=t;for(let e=0;e<this.config.decoder_layers.length;e++)n=r.layers.dense({units:this.config.decoder_layers[e],activation:"relu"}).apply(n);const o=this.getAffineLayers(n,this.config.input_size,t,!1);return r.model({inputs:t,outputs:o,name:"decoder"})}loss(e,t,n,o){return r.tidy(()=>{const i=this.klLoss(e,t),s=this.reconstructionLoss(o,n);return{latentLoss:i,reconLoss:s,totalLoss:r.add(s,r.mul(i,this.config.beta))}})}reconstructionLoss(e,t){return r.tidy(()=>{const n=r.pow(r.sub(e,t),2),o=r.div(n,r.mul(2,r.pow(r.ones([1]),2)));return r.mean(r.sum(o,-1))})}klLoss(e,t){return r.tidy(()=>{const n=r.pow(e,2),o=r.pow(t,2),i=r.add(1,r.log(o)),s=r.add(n,o),a=r.sub(i,s),u=r.div(r.mean(r.sum(a,-1)),2);return r.mul(-1,u)})}getAffineLayers(e,t,n,o){const i=r.layers.dense({units:t}).apply(e);return o?r.layers.activation({activation:"softplus"}).apply(i):i}}var u=n(37),c=n(8),l=n(36);class p{constructor(e,t){if(void 0===e)throw Error("`kernel` is undefined.");if(void 0===t)throw Error("`bias` is undefined.");this.kernel=e,this.bias=t}}function m(e,t){return t.matMul(e.kernel).add(e.bias)}class d{}class h{constructor(e,t){this.lstmFwVars=e,this.lstmBwVars=t}process(e){return r.tidy(()=>[this.singleDirection(e,!0),this.singleDirection(e,!1)])}singleDirection(e,t){const n=e.shape[0],o=e.shape[1],i=t?this.lstmFwVars:this.lstmBwVars;let s=[r.zeros([n,i.bias.shape[0]/4]),r.zeros([n,i.bias.shape[0]/4])];const a=r.scalar(1),u=(e,t)=>r.basicLSTMCell(a,i.kernel,i.bias,e,t[0],t[1]),c=r.split(e.toFloat(),o,1),l=[];for(const e of t?c:c.reverse())s=u(e.squeeze([1]),s),l.push(s[1]);return t?l:l.reverse()}}class f extends d{constructor(e,t,n){super(),this.bidirectionalLstm=new h(e,t),this.muVars=n,this.zDims=n?this.muVars.bias.shape[0]:null}encode(e,t){if(t)throw new Error("Variable-length segments not supported in flat encoder");return r.tidy(()=>{const[t,n]=this.bidirectionalLstm.process(e),o=r.concat([t[t.length-1],n[0]],1);return this.muVars?m(this.muVars,o):o})}}class g extends d{constructor(e,t,n){super(),this.baseEncoders=e,this.numSteps=t,this.muVars=n,this.zDims=this.muVars.bias.shape[0]}encode(e,t){if(t){if(1!==e.shape[0])throw new Error("When using variable-length segments, batch size must be 1.");if(t.length!==this.numSteps[0])throw new Error("Must provide length for all variable-length segments.")}return r.tidy(()=>{let n=e;for(let e=0;e<this.baseEncoders.length;++e){const o=this.numSteps[e],i=r.split(n,o,1),s=[];for(let n=0;n<o;++n)s.push(this.baseEncoders[e].encode(0===e&&t?r.slice3d(i[n],[0,0,0],[1,t[n],-1]):i[n]));n=r.stack(s,1)}return m(this.muVars,n.squeeze([1]))})}}function S(e,t,n){const o=[],i=[],s=[],a=r.split(m(n,e).tanh(),2*t.length,1);for(let e=0;e<t.length;++e){const n=t[e],u=r.scalar(1);o.push((e,t,o)=>r.basicLSTMCell(u,n.kernel,n.bias,e,t,o)),i.push(a[2*e]),s.push(a[2*e+1])}return{cell:o,c:i,h:s}}class y{}class P extends y{constructor(e,t,n,r,o,i){super(),this.lstmCellVars=e,this.zToInitStateVars=t,this.outputProjectVars=n,this.zDims=this.zToInitStateVars.kernel.shape[0],this.outputDims=r||n.bias.shape[0],o&&i&&(this.controlBidirectionalLstm=new h(o,i))}decode(e,t,n,o,i){const s=e.shape[0];return r.tidy(()=>{const a=S(e,this.lstmCellVars,this.zToInitStateVars);let u=i?r.expandDims(i,0):void 0;const c=[];let l=n||r.zeros([s,this.outputDims]);if(this.controlBidirectionalLstm){const[e,t]=this.controlBidirectionalLstm.process(u);u=r.concat([r.stack(e,1),r.stack(t,1)],2)}const p=u?r.split(r.tile(u,[s,1,1]),i.shape[0],1):void 0;for(let n=0;n<t;++n){const t=p?[l,e,r.squeeze(p[n],[1])]:[l,e];[a.c,a.h]=r.multiRNNCell(a.cell,r.concat(t,1),a.c,a.h);const i=m(this.outputProjectVars,a.h[a.h.length-1]);l=this.sample(i,o),c.push(l)}return r.stack(c,1)})}}class b extends P{sample(e,t){const n=e;return(t?r.greaterEqual(r.sigmoid(n.div(r.scalar(t))),r.randomUniform(n.shape)):r.greaterEqual(n,0)).toFloat()}}class w extends P{sample(e,t){const n=e,o=t?r.multinomial(n.div(r.scalar(t)),1).as1D():n.argMax(1).as1D();return r.oneHot(o,this.outputDims).toFloat()}}class M extends P{constructor(e,t,n,r,o,i){super(e,t,n,r.numDims,o,i),this.nade=r}sample(e,t){const[n,o]=r.split(e,[this.nade.numHidden,this.nade.numDims],1);return this.nade.sample(n,o)}}class N extends P{sample(e,t){let[n,o,i]=r.split(e,3,1);if(o=r.sigmoid(o),i=r.tanh(i),t){n=r.sigmoid(n.div(r.scalar(t)));const e=r.randomUniform(n.shape,0,1);n=r.greater(n,e).toFloat()}else n=r.greater(r.sigmoid(n),.5).toFloat();return r.concat([n,o,i],1)}}class q extends y{constructor(e){super(),this.coreDecoders=e,this.numDecoders=this.coreDecoders.length,this.zDims=this.coreDecoders[0].zDims,this.outputDims=this.coreDecoders.reduce((e,t)=>e+t.outputDims,0)}decodeSeparately(e,t,n,r,o){const i=[];for(let s=0;s<this.coreDecoders.length;++s)i.push(this.coreDecoders[s].decode(e,t,n[s],r,o));return i}decode(e,t,n,o,i){return r.tidy(()=>{const s=this.decodeSeparately(e,t,this.coreDecoders.map(e=>n),o,i);return r.concat(s,-1)})}}class T extends y{constructor(e,t,n,r){super(),this.splitDecoder=new q(e),this.lstmCellVars=t,this.zToInitStateVars=n,this.numSteps=r,this.zDims=this.zToInitStateVars.kernel.shape[0],this.outputDims=this.splitDecoder.outputDims}decode(e,t,n,o,i){const s=e.shape[0];return r.tidy(()=>{const n=S(e,this.lstmCellVars,this.zToInitStateVars),a=[];let u=new Array(this.splitDecoder.numDecoders).fill(void 0);const c=r.zeros([s,1]),l=i?r.split(i,this.numSteps):void 0;for(let e=0;e<this.numSteps;++e){[n.c,n.h]=r.multiRNNCell(n.cell,c,n.c,n.h);const i=this.splitDecoder.decodeSeparately(n.h[n.h.length-1],t/this.numSteps,u,o,l?l[e]:void 0);a.push(r.concat(i,-1)),u=i.map(e=>e.slice([0,e.shape[1]-1,0],[s,1,e.shape[e.rank-1]]).squeeze([1]).toFloat())}return r.concat(a,1)})}}class v{constructor(e,t){this.numDims=e.shape[0],this.numHidden=e.shape[2],this.encWeights=e.as2D(this.numDims,this.numHidden),this.decWeightsT=t.as2D(this.numDims,this.numHidden)}sample(e,t){const n=e.shape[0];return r.tidy(()=>{const o=[];let i=e.clone();for(let e=0;e<this.numDims;e++){const s=r.sigmoid(i),a=this.encWeights.slice([e,0],[1,this.numHidden]).as1D(),u=this.decWeightsT.slice([e,0],[1,this.numHidden]),c=t.slice([0,e],[n,1]).add(r.matMul(s,u,!1,!0)).sigmoid().greaterEqual(r.scalar(.5)).toFloat().as1D();e<this.numDims-1&&(i=i.add(r.outerProduct(c.toFloat(),a))),o.push(c)}return r.stack(o,1)})}}class A{constructor(e,t){this.initialized=!1,this.checkpointURL=e,this.spec=t}instantiateFromSpec(){this.dataConverter=l.converterFromSpec(this.spec.dataConverter),this.chordEncoder=this.spec.chordEncoder?u.chordEncoderFromType(this.spec.chordEncoder):void 0}dispose(){void 0!==this.rawVars&&Object.keys(this.rawVars).forEach(e=>this.rawVars[e].dispose()),this.encoder=void 0,this.decoder=void 0,this.initialized=!1}getLstmLayers(e,t){const n=[];let r=0;for(;;){const o=e.replace("%d",r.toString());if(!(o+"kernel"in t))break;n.push(new p(t[o+"kernel"],t[o+"bias"])),++r}return n}async initialize(){this.dispose();const e=i.d.now();this.spec||await Object(i.a)(this.checkpointURL+"/config.json").then(e=>e.json()).then(e=>{if("MusicVAE"!==e.type)throw new Error("Attempted to instantiate MusicVAE model with incorrect type:\n "+e.type);this.spec=e}),this.instantiateFromSpec();const t="cell_%d/bidirectional_rnn/%s/multi_rnn_cell/cell_0/lstm_cell/",n="encoder/"+t,s="encoder/hierarchical_level_%d/"+t.replace("%d","0"),a="control_preprocessing/"+t,u=await Object(i.a)(this.checkpointURL+"/weights_manifest.json").then(e=>e.json()).then(e=>r.io.loadWeights(e,this.checkpointURL));this.rawVars=u;const c=new p(u["encoder/mu/kernel"],u["encoder/mu/bias"]);if(this.dataConverter.numSegments){const e=this.getLstmLayers(s.replace("%s","fw"),u),t=this.getLstmLayers(s.replace("%s","bw"),u);if(e.length!==t.length||2!==e.length)throw Error(`Only 2 hierarchical encoder levels are supported. Got ${e.length} forward and ${t.length} backward.`);const n=[0,1].map(n=>new f(e[n],t[n]));this.encoder=new g(n,[this.dataConverter.numSegments,1],c)}else{const e=this.getLstmLayers(n.replace("%s","fw"),u),t=this.getLstmLayers(n.replace("%s","bw"),u);if(e.length!==t.length||1!==e.length)throw Error(`Only single-layer bidirectional encoders are supported. Got ${e.length} forward and ${t.length} backward.`);this.encoder=new f(e[0],t[0],c)}const l=a.replace("%s","fw").replace("%d","0")+"kernel"in u,m=this.dataConverter.numSegments?"core_decoder/":"",d=[];if(this.dataConverter.NUM_SPLITS)for(let e=0;e<this.dataConverter.NUM_SPLITS;++e)d.push(`${m}core_decoder_${e}/decoder/`);else d.push(m+"decoder/");let h=[null],S=[null];if(l&&(h=this.getLstmLayers(a.replace("%s","fw"),u),S=this.getLstmLayers(a.replace("%s","bw"),u),h.length!==S.length||1!==h.length))throw Error(`Only single-layer bidirectional control preprocessing is supported. Got ${h.length} forward and ${S.length} backward.`);const y=d.map(e=>{const t=this.getLstmLayers(e+"multi_rnn_cell/cell_%d/lstm_cell/",u),n=new p(u[e+"z_to_initial_state/kernel"],u[e+"z_to_initial_state/bias"]),r=new p(u[e+"output_projection/kernel"],u[e+"output_projection/bias"]);return e+"nade/w_enc"in u?new M(t,n,r,new v(u[e+"nade/w_enc"],u[e+"nade/w_dec_t"]),h[0],S[0]):"GrooveConverter"===this.spec.dataConverter.type?new N(t,n,r,void 0,h[0],S[0]):this.spec.useBooleanDecoder?new b(t,n,r,void 0,h[0],S[0]):new w(t,n,r,void 0,h[0],S[0])});if(this.dataConverter.numSegments){const e=this.getLstmLayers("decoder/hierarchical_level_0/cell_%d/lstm_cell/",u),t=new p(u["decoder/hierarchical_level_0/initial_state/kernel"],u["decoder/hierarchical_level_0/initial_state/bias"]);this.decoder=new T(y,e,t,this.dataConverter.numSegments)}else 1===y.length?this.decoder=y[0]:this.decoder=new q(y);this.zDims=this.decoder.zDims,this.initialized=!0,o.logWithDuration("Initialized model",e,"MusicVAE")}isInitialized(){return this.initialized}checkControlArgs(e){const t=(e=e||{}).extraControls||{};if(this.chordEncoder&&!e.chordProgression)throw new Error("Chord progression expected but not provided.");if(!this.chordEncoder&&e.chordProgression)throw new Error("Unexpected chord progression provided.");if(this.chordEncoder&&this.dataConverter.endTensor&&e.chordProgression.length>1)throw new Error("Multiple chords not supported when using variable-length segments.");if(this.spec.conditionOnKey&&null==e.key)throw new Error("Key expected but not provided.");if(!this.spec.conditionOnKey&&null!=e.key)throw new Error("Unexpected key provided.");if(this.spec.extraControls)for(const e of this.spec.extraControls){if(!(e.name in t))throw new Error("Missing control signal: "+e.name);if(t[e.name].shape[1]!==e.depth)throw new Error(`Control signal ${e.name} has invalid depth: ${t[e.name].shape[1]} != ${e.depth}`)}const n=this.spec.extraControls?new Set(this.spec.extraControls.map(e=>e.name)):new Set;for(const e in t)n.has(e)||o.log("Unspecified control signal provided: "+e,"MusicVAE",5)}controlArgsToTensor(e){return e=e||{},r.tidy(()=>{const t=[];if(e.chordProgression){const n=this.encodeChordProgression(e.chordProgression);t.push(n)}if(null!=e.key){const n=r.oneHot(r.fill([this.dataConverter.numSteps],e.key,"int32"),12);t.push(n)}if(e.extraControls)for(const n of this.spec.extraControls)t.push(e.extraControls[n.name]);return t.length?r.concat2d(t,1):void 0})}async interpolateTensors(e,t,n,o){this.initialized||await this.initialize();const i=await this.encodeTensors(e,o),s=r.tidy(()=>this.getInterpolatedZs(i,t));i.dispose();const a=await this.decodeTensors(s,n,o);return s.dispose(),a}async interpolate(e,t,n,i){this.checkControlArgs(i),this.initialized||await this.initialize();const s=await this.encode(e,i),a=r.tidy(()=>this.getInterpolatedZs(s,t));s.dispose();const u=this.decode(a,n,i);return a.dispose(),u.then(()=>o.logWithDuration("Interpolation completed",0,"MusicVAE",20)),u}async getSegmentLengths(e){if(e.shape[0]>1)throw new Error("Variable-length segments not supported for batch size > 1.");const t=this.dataConverter.numSteps,n=this.dataConverter.numSegments,o=r.tidy(()=>r.min(r.equal(e.squeeze([0]),this.dataConverter.endTensor.expandDims(0)),1)),i=await o.data();o.dispose();const s=t/n,a=[];let u=0,c=i.indexOf(1);for(;-1!==c;)a.push(c-u+1),u+=s,c=i.indexOf(1,u);if(a.length!==n)throw new Error(`Incorrect number of segments: ${a.length} != ${n}`);return a}encodeChordProgression(e){const t=this.dataConverter.numSteps,n=this.dataConverter.numSegments,o=this.dataConverter.SEGMENTED_BY_TRACK?t/n:t,i=this.dataConverter.SEGMENTED_BY_TRACK?r.concat2d([this.chordEncoder.encode(c.NO_CHORD).expandDims(0),this.chordEncoder.encodeProgression(e,o-1)],0):this.chordEncoder.encodeProgression(e,o);return this.dataConverter.SEGMENTED_BY_TRACK?r.tile(i,[n,1]):i}async encodeTensors(e,t){this.checkControlArgs(t),this.initialized||await this.initialize();const n=this.dataConverter.endTensor?await this.getSegmentLengths(e):void 0;return r.tidy(()=>{const o=this.controlArgsToTensor(t),i=[e];if(o){const t=r.tile(r.expandDims(o,0),[e.shape[0],1,1]);i.push(t)}const s=r.concat3d(i,2);return this.encoder.encode(s,n)})}async encode(e,t){this.initialized||await this.initialize();const n=i.d.now(),s=r.tidy(()=>r.stack(e.map(e=>this.dataConverter.toTensor(e)))),a=await this.encodeTensors(s,t);return s.dispose(),o.logWithDuration("Encoding completed",n,"MusicVAE",20),a}async decodeTensors(e,t,n){return this.checkControlArgs(n),this.initialized||await this.initialize(),r.tidy(()=>{const r=this.controlArgsToTensor(n);return this.decoder.decode(e,this.dataConverter.numSteps,void 0,t,r)})}async decode(e,t,n,s=c.DEFAULT_STEPS_PER_QUARTER,a=c.DEFAULT_QUARTERS_PER_MINUTE){this.initialized||await this.initialize();const u=i.d.now(),l=await this.decodeTensors(e,t,n),p=r.tidy(()=>r.split(l,l.shape[0]).map(e=>e.squeeze([0]))),m=[];for(const e of p)m.push(await this.dataConverter.toNoteSequence(e,s,a)),e.dispose();return l.dispose(),o.logWithDuration("Decoding completed",u,"MusicVAE",20),m}getInterpolatedZs(e,t){if("number"==typeof t&&(t=[t]),2!==e.shape[0]&&4!==e.shape[0])throw new Error("Invalid number of input sequences. Requires length 2, or 4");if(1!==t.length&&2!==t.length)throw new Error("Invalid number of dimensions. Requires length 1, or 2.");const n=t[0],o=2===t.length?t[1]:n;return r.tidy(()=>{const t=r.linspace(0,1,n),i=e.slice([0,0],[1,e.shape[1]]).as1D(),s=e.slice([1,0],[1,e.shape[1]]).as1D();if(2===e.shape[0]){const e=s.sub(i);return r.outerProduct(t,e).add(i)}if(4===e.shape[0]){const a=r.linspace(0,1,o),u=e.slice([2,0],[1,e.shape[1]]).as1D(),c=e.slice([3,0],[1,e.shape[1]]).as1D(),l=r.scalar(1).sub(t),p=r.scalar(1).sub(a);let m=i.mul(r.outerProduct(p,l).as3D(o,n,1));return m=r.addStrict(m,s.mul(r.outerProduct(a,l).as3D(o,n,1))),m=r.addStrict(m,u.mul(r.outerProduct(p,t).as3D(o,n,1))),m=r.addStrict(m,c.mul(r.outerProduct(a,t).as3D(o,n,1))),m.as2D(n*o,e.shape[1])}throw new Error("Invalid number of note sequences. Requires length 2, or 4")})}async sampleTensors(e,t=.5,n){this.checkControlArgs(n),this.initialized||await this.initialize();const o=r.tidy(()=>r.randomNormal([e,this.decoder.zDims])),i=await this.decodeTensors(o,t,n);return o.dispose(),i}async sample(e,t=.5,n,s=c.DEFAULT_STEPS_PER_QUARTER,a=c.DEFAULT_QUARTERS_PER_MINUTE){this.checkControlArgs(n),this.initialized||await this.initialize();const u=i.d.now(),l=r.tidy(()=>r.randomNormal([e,this.decoder.zDims])),p=this.decode(l,t,n,s,a);return l.dispose(),p.then(()=>o.logWithDuration("Sampling completed",u,"MusicVAE",20)),p}async similarTensors(e,t,n,o,i){if(n<0||n>1)throw new Error("Similarity must be between 0 and 1.");this.initialized||await this.initialize();const s=r.expandDims(e,0),a=await this.encodeTensors(s,i);s.dispose();const u=r.tidy(()=>{const e=r.randomNormal([t,this.decoder.zDims]);return r.add(a.mul(n),e.mul(1-n))});a.dispose();const c=await this.decodeTensors(u,o,i);return u.dispose(),c}async similar(e,t,n,i,s){if(this.checkControlArgs(s),n<0||n>1)throw new Error("Similarity must be between 0 and 1.");this.initialized||await this.initialize();const a=await this.encode([e],s),u=r.tidy(()=>{const e=r.randomNormal([t,this.decoder.zDims]);return r.add(a.mul(n),e.mul(1-n))});a.dispose();const c=this.decode(u,i,s);return u.dispose(),c.then(()=>o.logWithDuration("Similar sequence generation completed",0,"MusicVAE",20)),c}}},2:function(e,t){e.exports=__WEBPACK_EXTERNAL_MODULE__2__},21:function(e,t){var n;n=function(){return this}();try{n=n||new Function("return this")()}catch(e){"object"==typeof window&&(n=window)}e.exports=n},23:function(e,t,n){"use strict";n.r(t),n.d(t,"NO_EVENT",(function(){return s})),n.d(t,"NOTE_OFF",(function(){return a})),n.d(t,"Melody",(function(){return u})),n.d(t,"MelodyRhythm",(function(){return c})),n.d(t,"MelodyShape",(function(){return l})),n.d(t,"MelodyRegister",(function(){return p}));var r=n(2),o=n(10),i=n(13);const s=0,a=1;class 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r&&(r.quantizedEndStep=this.events.length,n.notes.push(r)),n.totalQuantizedSteps=this.events.length,n}}class c{constructor(){this.depth=1}extract(e){const t=e.events.length,n=r.buffer([t,1]);for(let r=0;r<t;++r)n.set(e.events[r]>=2?1:0,r,0);return n.toTensor().as2D(t,1)}}class l{constructor(){this.depth=3}extract(e){const t=e.events.length,n=r.buffer([t,3]);let o=null,i=null;for(let r=0;r<t;++r)if(e.events[r]>=2){if(null!==o){let t;0===n.get(o,0)&&0===n.get(o,1)&&0===n.get(o,2)&&(o=-1),t=e.events[r]<i?0:e.events[r]>i?2:1;for(let e=r;e>o;--e)n.set(1,e,t)}o=r,i=e.events[r]}if(o!==t-1)if(null===o||0===n.get(o,0)&&0===n.get(o,1)&&0===n.get(o,2))for(let e=0;e<t;++e)n.set(1,e,1);else for(let e=t-1;e>o;--e)for(let t=0;t<3;t++)n.set(n.get(o,t),e,t);return n.toTensor().as2D(t,3)}}class p{constructor(e){this.boundaryPitches=e,this.depth=e.length+1}meanMelodyPitch(e){let t=0,n=0,r=null;for(let o=0;o<e.events.length;++o)e.events[o]===a?r=null:e.events[o]>=2&&(r=e.minPitch+e.events[o]-2),null!==r&&(t+=r,n+=1);return n?t/n:null}extract(e){const t=e.events.length,n=this.meanMelodyPitch(e);if(null===n)return r.zeros([t,this.depth]);let o=0;for(;o<this.boundaryPitches.length&&n>=this.boundaryPitches[o];)o++;const i=r.buffer([t,this.depth]);for(let e=0;e<t;++e)i.set(1,e,o);return i.toTensor().as2D(t,this.depth)}}},29:function(e,t,n){"use strict";n.r(t),n.d(t,"Performance",(function(){return a}));var r=n(10),o=n(8),i=n(13),s=n(12);class a{constructor(e,t,n,r,o){this.events=e,this.maxShiftSteps=t,this.numVelocityBins=n,this.program=r,this.isDrum=o}static fromNoteSequence(e,t,n,r){i.assertIsQuantizedSequence(e);const s=e.notes.filter((e,t)=>void 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this.events)switch(c.type){case"note-on":u.get(c.pitch).push([i,a]);break;case"note-off":const l=u.get(c.pitch);if(l.length){const[t,o]=l.shift();i>t?n.notes.push(r.a.Note.create({pitch:c.pitch,velocity:o,instrument:e,quantizedStartStep:t,quantizedEndStep:i,program:this.program,isDrum:this.isDrum})):s.log(`Ignoring zero-length note: (pitch = ${c.pitch}, step = ${i})`,"Performance")}else s.log(`Ignoring note-off with no previous note-on:(pitch = ${c.pitch}, step = ${i})`,"Performance");break;case"time-shift":i+=c.steps;break;case"velocity-change":if(!t)throw new Error("Unexpected velocity change event: "+c);a=o.MIN_MIDI_VELOCITY+(c.velocityBin-1)*t+1;break;default:throw new Error("Unrecognized performance event: "+c)}return u.forEach((t,o)=>{for(const[a,u]of t)i>a?n.notes.push(r.a.Note.create({pitch:o,velocity:u,instrument:e,quantizedStartStep:a,quantizedEndStep:i,program:this.program,isDrum:this.isDrum})):s.log(`Ignoring zero-length note: (pitch = ${o}, step = 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