def main():
parser = argparse.ArgumentParser()
parser.add_argument("inputList", #nargs="?" ,
help=("Training sample list."),
#default=config.defaultTrainSampleList
)
parser.add_argument("outputDir", nargs="?" ,
help="Music output directory",
default = config.defaultOutputDir
)
#parser.add_argument("modelName", nargs="?" ,
# help="model filename",
# default=config.defaultModelFilename
# )
args = parser.parse_args()
trainSampList = sampleLoader.parseFileList(args.inputList)
trainFeatsList = []
for trainSampFilename in trainSampList:
trainSamp = sampleLoader.loadTrainSample(trainSampFilename)
trainFeat = featureManager.extractTrainFeat(trainSamp)
trainFeatsList.append(trainFeat)
trainFeatFilename = config.getTrainInFeatFilename(args)
featureManager.saveJson(trainFeatsList, trainFeatFilename);
m = model.getModelObj();
#modelName = config.getModelFilename(args)
m.train(args); #load features from config.getTrainInFeatFilename(args)
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 main():
parser = argparse.ArgumentParser()
parser.add_argument(
"inputList", #nargs="?" ,
help=("Training sample list."),
#default=config.defaultTrainSampleList
)
parser.add_argument("outputDir",
nargs="?",
help="Music output directory",
default=config.defaultOutputDir)
#parser.add_argument("modelName", nargs="?" ,
# help="model filename",
# default=config.defaultModelFilename
# )
args = parser.parse_args()
trainSampList = sampleLoader.parseFileList(args.inputList)
trainFeatsList = []
for trainSampFilename in trainSampList:
trainSamp = sampleLoader.loadTrainSample(trainSampFilename)
trainFeat = featureManager.extractTrainFeat(trainSamp)
trainFeatsList.append(trainFeat)
trainFeatFilename = config.getTrainInFeatFilename(args)
featureManager.saveJson(trainFeatsList, trainFeatFilename)
m = model.getModelObj()
#modelName = config.getModelFilename(args)
m.train(args)
