def main(): parser = argparse.ArgumentParser() parser.add_argument( "inputList", #nargs="1" , help="Testing sample list.", #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() genList = sampleLoader.parseFileList(args.inputList) for genFilename in genList: cmd = ["./musicPupilGen.py"] cmd.append(genFilename) cmd.append(args.modelFilename) cmd.append(args.outputDir) #logging.printDebug(" ".join(cmd)) subprocess.call(" ".join(cmd), shell=True)
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( "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)
def main(): parser = argparse.ArgumentParser() parser.add_argument("inputList", #nargs="1" , help="Testing sample list.", #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() genList= sampleLoader.parseFileList(args.inputList) for genFilename in genList: cmd = ["./musicPupilGen.py"] cmd.append(genFilename) cmd.append(args.modelFilename) cmd.append(args.outputDir) #logging.printDebug(" ".join(cmd)) subprocess.call(" ".join(cmd), shell=True)
import config import sampleLoader import featureManager trainSampList = sampleLoader.parseFileList(config.unittestTrainSampleList) for trainSampFilename in trainSampList: print("[INFO] Testing training sample: " + trainSampFilename) trainSamp = sampleLoader.loadTrainSample(trainSampFilename) print("[INFO] Extracting samples") trainFeat = featureManager.extractTrainFeat(trainSamp) #print(trainFeat) print(trainFeat['scoreFeats']) for key, feat in trainFeat['scoreFeats'].items(): print(key) assert ( len(feat) == 4 ), 'the number of feature points doesn\'t match the test sample' for key, feat in trainFeat['perfFeats'].items(): print(key) assert ( len(feat) == 4 ), 'the number of feature points doesn\'t match the test sample' print('Tests passed')
import config import sampleLoader import featureManager trainSampList = sampleLoader.parseFileList(config.unittestTrainSampleList) for trainSampFilename in trainSampList: print("[INFO] Testing training sample: " + trainSampFilename) trainSamp = sampleLoader.loadTrainSample(trainSampFilename) print("[INFO] Extracting samples") trainFeat = featureManager.extractTrainFeat(trainSamp) #print(trainFeat) print(trainFeat['scoreFeats']) for key,feat in trainFeat['scoreFeats'].items(): print(key) assert(len(feat) == 4), 'the number of feature points doesn\'t match the test sample' for key, feat in trainFeat['perfFeats'].items(): print(key) assert(len(feat) == 4), 'the number of feature points doesn\'t match the test sample' print('Tests passed')