def main():
parser = argparse.ArgumentParser()
parser.add_argument("input", #nargs="1" ,
help="Score to be played, without extension.",
#default=config.defaultGenScore
)
parser.add_argument("modelFilename", #nargs="1" ,
help="Model filename",
#default=config.defaultModelFilename
)
parser.add_argument("outputDir", nargs="?" ,
help="Music output directory",
default = config.defaultOutputDir
)
args = parser.parse_args()
genScore = sampleLoader.loadGenScore(args.input)
genFeat = featureManager.extractGenFeat(genScore)
#wrap genFeat by {} because trainFeats is {score1, score2}
featureManager.saveJson([genFeat], config.getGenInFeatFilename(args))
#perfFeats = model.genPerfFeats(config.defaultGenFeatFilename, args.modelFilename)
m = model.getModelObj()
perfFeats = m.gen(args)
#wrap perfFeat by {} because trainFeats is {score1, score2}
featureManager.saveJson([perfFeats], config.getGenOutFeatFilename(args))
musicGenerator.genMusic(genScore, args)
def gen(self, args):
genFeats = featureManager.loadJson(config.getGenInFeatFilename(args))
genFeat = genFeats[0]
scoreName = genFeat['name']
scoreFeats = genFeat['scoreFeats']
#wrap genFeats in [] to match data structure in train
lines = self.formatLineDirect([genFeat])
#logging.printDebug(lines)
allLines = map(lambda l: "0 " + l, lines)
#logging.printDebug(allLines)
#TODO: collect filenames
SVMGenInputFilename = self.getGenInputFilename(args)
with open(SVMGenInputFilename, 'w') as f:
f.writelines(map(lambda x:x+"\n", allLines))
for featName in config.perfFeatsList:
cmd = [self.genBinPath]
cmd.append(SVMGenInputFilename)
singleModelFilename= self.getSingleModelFilename(args, featName)
cmd.append(singleModelFilename)
perfFeatFilename= self.getGenOutputFilename(args, featName)
cmd.append(perfFeatFilename)
#logging.printDebug(" ".join(cmd))
subprocess.call(" ".join(cmd), shell=True)
#TODO: read all perfFeatFilename, transform to dict and return
perfFeats = {}
for featName in config.perfFeatsList:
#TODO: put all filename definition in one place
#perfFeatFilename= self.getGenOutputFilename(args, featName)
with open(perfFeatFilename, 'r') as f:
lines = f.readlines()
#dequantize
q = quantizer.getQuantizerObj(self.getQuantizeFilename(args, featName))
realVals= q.dequantize(lines)
#feat = map(float, lines)
perfFeats[featName]=realVals
#logging.printDebug(perfFeats)
return featureManager.formatFeatFile(scoreName, {}, perfFeats)
def gen(self, args):
genFeats = featureManager.loadJson(config.getGenInFeatFilename(args))
genFeat = genFeats[0]
scoreName = genFeat['name']
scoreFeats = genFeat['scoreFeats']
#wrap genFeats in [] to match data structure in train
lines = self.formatLineDirect([genFeat])
#logging.printDebug(lines)
allLines = map(lambda l: "0 " + l, lines)
#logging.printDebug(allLines)
#TODO: collect filenames
SVMGenInputFilename = self.getGenInputFilename(args)
with open(SVMGenInputFilename, 'w') as f:
f.writelines(map(lambda x: x + "\n", allLines))
for featName in config.perfFeatsList:
cmd = [self.genBinPath]
cmd.append(SVMGenInputFilename)
singleModelFilename = self.getSingleModelFilename(args, featName)
cmd.append(singleModelFilename)
perfFeatFilename = self.getGenOutputFilename(args, featName)
cmd.append(perfFeatFilename)
#logging.printDebug(" ".join(cmd))
subprocess.call(" ".join(cmd), shell=True)
#TODO: read all perfFeatFilename, transform to dict and return
perfFeats = {}
for featName in config.perfFeatsList:
#TODO: put all filename definition in one place
#perfFeatFilename= self.getGenOutputFilename(args, featName)
with open(perfFeatFilename, 'r') as f:
lines = f.readlines()
#dequantize
q = quantizer.getQuantizerObj(
self.getQuantizeFilename(args, featName))
realVals = q.dequantize(lines)
#feat = map(float, lines)
perfFeats[featName] = realVals
#logging.printDebug(perfFeats)
return featureManager.formatFeatFile(scoreName, {}, perfFeats)
