/
unipen2htk.js
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/
unipen2htk.js
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#!/usr/bin/node
// running with node 0.7.5, installed 'commander' and 'progress'
// uses typed arrays, which landed in node 0.5.5.
var requirejs = require('requirejs');
requirejs(['commander', 'fs', 'progress', './alea', './point', './features', './version'],
function(program, fs, ProgressBar, Alea, Point, Features, version) {
var CANVAS_RESOLUTION = 100;
var RETINA_FACTOR = 1;
var DOT_SIZE = 1/60;
var RESAMPLE_HERTZ = 100; // sample rate written into parameter file
var MAX_ROTATE_AMT = 10 * Math.PI / 180; // 10 degree rotation
var random = new Alea.Random('unipen2htk seed');
program
.version(version)
.usage('[options] <json input file>')
.option('-P, --parmdir <output dir>', 'directory for parameter file output',
null)
.option('-M, --mlf <filename>', 'label file for parameter data',
null)
.option('-H, --html <filename>', 'html file output for previewing data',
null)
.option('-T, --train <number>', 'omit 1 in <number> examples from training set', Number, 0)
.option('-S, --script <filename>', 'list of parameter files for training', null)
.option('-Q, --qualscript <filename>', 'list of parameter files *not* used for training', null)
.option('-A, --allographs <number>', 'Randomly spread the input into <number> allograph classes', Number, 1)
.option('-r, --rotate <number>', 'Generate <number> randomly-rotated variants', Number, 0)
.option('-d, --deltas', 'Include delta and acceleration features')
.option('-c, --codebook <filename>', 'Include VQ data using specified JSON codebook',
null)
.parse(process.argv);
var input_file = program.args[0];
var data = JSON.parse(fs.readFileSync(input_file, 'utf-8'));
var vq_features = null;
if (program.codebook) {
var codebook = JSON.parse(fs.readFileSync(program.codebook, 'utf-8'));
/* Find the <codebook> field, skip <comments> etc. */
while (codebook.length && codebook[0].type!=='<codebook>')
codebook.shift();
console.assert(codebook.length && codebook[0].type==='<codebook>');
codebook = codebook[0].value;
vq_features = Features.make_vq(codebook);
}
var mklogfunc = function(what, opt_filename) {
var fd = -1;
if (opt_filename) {
fd = fs.openSync(opt_filename, 'w');
}
var f = function(s) {
if (fd >= 0) {
fs.writeSync(fd, s+"\n", null, 'utf8');
}
};
f.close = function() {
if (fd >= 0) {
fs.closeSync(fd);
console.log(what+": "+opt_filename);
}
};
return f;
};
var p = mklogfunc('HTML output', program.html);
p("<!DOCTYPE HTML>");
p("<html><head><title>Character Visualization</title>");
p("<style type=\"text/css\">");
p("canvas { width: "+CANVAS_RESOLUTION+"px; height: "+CANVAS_RESOLUTION+"px; border: 1px solid #ccc; }");
p("</style></head>");
p("<body><h1>Character Visualization</h1>");
//p('Version', data.version);
p("<p>" + data.set.length + " characters, ");
p("avg <span id='avglen'></span> samples.</p>");
var m = mklogfunc('Label file', program.mlf);
var ma = mklogfunc('Label file (random allographs)',
(program.allographs>1 && program.mlf) ?
program.mlf + ".allograph" : null);
m("#!MLF!#");
ma('#!MLF!#');
var s = mklogfunc('Script file', program.script);
var q = mklogfunc('Qualification file', program.qualscript);
var canvas_id = 0;
var draw_letter = function(data_set, caption) {
// data_set should be normalized (range [0,1], dups removed)
var id = "c" + (canvas_id++);
p("<canvas id="+JSON.stringify(id)+" title="+JSON.stringify(caption)+"></canvas>");
p("<script type=\"text/javascript\">");
p("(function() { ");
p(" var canvas = document.getElementById("+JSON.stringify(id)+");");
p(" canvas.width = canvas.height = "+CANVAS_RESOLUTION*RETINA_FACTOR+";");
p(" var ctx = canvas.getContext(\"2d\");");
var norm = function(pt) {
return new Point( pt.x *CANVAS_RESOLUTION*RETINA_FACTOR,
(1-pt.y)*CANVAS_RESOLUTION*RETINA_FACTOR,
pt.isUp);
};
// set ctx.stroke style, whatever that property is.
data_set.strokes.forEach(function(stroke) {
p("ctx.beginPath();");
stroke.map(norm).forEach(function(pt, i) {
if (i==0 || pt.isUp) {
p("ctx.moveTo("+pt.x+","+pt.y+");");
} else {
p("ctx.lineTo("+pt.x+","+pt.y+");");
}
});
p("ctx.stroke();");
});
// points
p("ctx.beginPath();");
data_set.strokes.forEach(function(stroke) {
stroke.map(norm).forEach(function(pt) {
p("ctx.arc("+pt.x+","+pt.y+","+
(DOT_SIZE*CANVAS_RESOLUTION*RETINA_FACTOR)+","+
"0,2*Math.PI,true);");
});
});
p("ctx.fill();");
// label data
p(" ctx.fillStyle=\"#008\";");
p(" ctx.font=\""+(10*RETINA_FACTOR)+"px sans-serif\";");
p(" ctx.fillText("+JSON.stringify(data_set.source+" "+data_set.start)+", 0, 10, "+CANVAS_RESOLUTION*RETINA_FACTOR+");");
p("})();");
p("</script>");
};
// okay, draw the letters!
var nvariants = 1 + (program.rotate);
var avg_len = 0, avg_cnt = 0, min_len, max_len;
var bar = new ProgressBar('Writing features: [:bar] :percent :etas',
{ total: data.set.length*nvariants, width: 30 });
var featmin, featmax;
for (var i=0, n=0; i<data.set.length*nvariants; i++, bar.tick()) {
var data_set = data.set[Math.floor(i/nvariants)];
var label = data_set.name;
data_set = JSON.parse(JSON.stringify(data_set)); // clone
Features.normalize(data_set);
if (nvariants > 1 && (i%nvariants) !== 0) {
Features.rotate(data_set, ((random()*2)-1)*MAX_ROTATE_AMT);
//Features.rotate(data_set, MAX_ROTATE_AMT*(i%nvariants)/nvariants);
}
draw_letter(data_set, "Unipen "+Math.floor(i/nvariants)+'-'+(i%nvariants));
Features.smooth(data_set);
Features.singleStroke(data_set);
//draw_letter(data_set, "Smoothed "+Math.floor(i/nvariants)+'-'+(i%nvariants));
Features.equidist(data_set);
draw_letter(data_set, "Resampled "+Math.floor(i/nvariants)+'-'+(i%nvariants));
if (data_set.strokes[0].length===0) continue;
avg_len += data_set.strokes[0].length;
avg_cnt += 1;
Features.features(data_set);
if (data_set.features.length===0) continue;
if (program.deltas || vq_features) {
Features.delta_and_accel(data_set);
}
if (vq_features) {
vq_features(data_set);
}
if (program.deltas) {
Features.merge_delta_and_accel(data_set);
}
if (i===0) {
min_len = data_set.features.length;
max_len = data_set.features.length;
featmax = data_set.features[0].slice(0);
featmin = data_set.features[0].slice(0);
}
min_len = Math.min(min_len, data_set.features.length);
max_len = Math.max(max_len, data_set.features.length);
data_set.features.forEach(function(featvect) {
featvect.forEach(function(f, j) {
if (f > featmax[j]) { featmax[j] = f; }
if (f < featmin[j]) { featmin[j] = f; }
});
});
if (!program.parmdir) continue;
// Make 12-byte header
var nfeat = data_set.features.length;
if (nfeat === 0) continue; // hm, strange.
var featvlen = data_set.features[0].length;
var sampSize = featvlen * 4;
var parmKind = 9 /* USER: user defined sample kind */;
if (program.deltas) {
parmKind += parseInt('001400', 8); /* _D_A */
}
if (vq_features) {
parmKind = 10; /* DISCRETE */
featvlen = data_set.vq[0].length;
sampSize = featvlen * 2;
}
var hbuf = new ArrayBuffer(12);
// nSamples - number of samples in file (4-byte integer)
new Uint32Array(hbuf, 0)[0] = nfeat;
// sampPeriod - sample period in 100ns units (4-byte integer)
new Uint32Array(hbuf, 4)[0] = Math.round(10000000/RESAMPLE_HERTZ);
// sampSize - number of bytes per sample (2-byte integer)
new Uint16Array(hbuf, 8)[0] = sampSize;
// parmKind - a code indicating the sample kind (2-byte integer)
new Uint16Array(hbuf,10)[0] = parmKind;
// Make a Float32 array w/ the feature vector.
var fbuf = new ArrayBuffer(sampSize * nfeat), featv;
if (vq_features) {
featv = new Uint16Array(fbuf);
data_set.vq.forEach(function(fv, j) {
var base = j * featvlen;
fv.forEach(function(v, k) {
featv[base+k] = (v+1); // HTK uses 1-based VQ values.
});
});
} else {
featv = new Float32Array(fbuf);
data_set.features.forEach(function (fv, j) {
featv.set(fv, j*featvlen);
});
}
// convert to node-native buffer type and write file
var filename = "" + Math.floor(i/nvariants);
while (filename.length < 4) { filename = "0" + filename; }
if (nvariants > 1) {
filename += '-' + (i % nvariants);
}
var parm_fd = fs.openSync(program.parmdir+"/"+filename+".htk", 'w');
var w = function(arraybuf) {
var b = new Buffer(new Uint8Array(arraybuf));
fs.writeSync(parm_fd, b, 0, b.length, null);
};
w(hbuf);
w(fbuf);
fs.closeSync(parm_fd);
m('"'+program.parmdir+'/'+filename+'.lab"');
ma('"'+program.parmdir+'/'+filename+'.lab"');
if (program.allographs===1) {
m(label);
} else {
var a = Math.floor((i*program.allographs)/(data.set.length*nvariants));
ma(label+(1+a)+"\t"+label);
for (var j=0; j<program.allographs; j++) {
if (j>0) m('///');
m(label+(j+1)+"\t"+label);
}
}
m('.');
ma('.');
if (program.train === 0 || (Math.floor(n/nvariants) % program.train !== 0)){
s(program.parmdir+'/'+filename+'.htk');
} else {
q(program.parmdir+'/'+filename+'.htk');
}
n++; // keep separate count just in case we disqualify particular files
}
avg_len /= avg_cnt;
p("<script type=\"text/javascript\">");
p("document.getElementById('avglen').innerHTML='"+avg_len+"';");
p("</script>");
// done w/ progress bar.
console.log("\r ");
// some stats
p.close();
m.close();
ma.close();
s.close();
q.close();
if (program.parmdir) {
console.log("Parameter files in: "+program.parmdir);
}
console.log("Min/Avg/Max # features: "+min_len+"/"+Math.round(avg_len)+"/"+max_len);
console.log("Min feat: "+featmin);
console.log("Max feat: "+featmax);
});