def RhythmMasterLevelPorcess():
# pathname = r'I:\librosa\RhythmMaster\hangengxman\hangengxman_4k_nm.imd'
# level = LevelInfo.LoadRhythmMasterLevel(pathname)
# print(type(level))
# count = 0
# for note in level:
# if note[1] == 3:
# print(count, type(note[2]))
# count += 1
songList = ['feiyuedexin']
songList = ['aiqingmaimai']
x, y = PrepareTrainData(songList, 32, True)
y = y[:, 3]
acceptThrehold = 0.9
pathname = MakeMp3Pathname(songList[0])
# y = y > acceptThrehold
# LevelInfo.SaveSamplesToRegionFile(y, pathname, 'region')
# return
notes = postprocess.TrainDataToLevelData(y, 0, acceptThrehold)
notes = np.asarray(notes)
notes[0]
notes = notes[:, 0]
LevelInfo.SaveInstantValue(notes, pathname, '_slide')
def GenerateLevel(predicts, pathname):
short = predicts[:, 1]
short = postprocess.PickInstanceSample(short, count=400)
notes = postprocess.TrainDataToLevelData(short, 10, 0.1, 0)
notes = notes[:, 0]
LevelInfo.SaveInstantValue(notes, pathname, '_inst_beat')
def GenerateLevel(predicts, pathname):
pre = predicts[:, 1]
# pre = postprocess.PurifyInstanceSample(pre)
# picked = postprocess.PickInstanceSample(pre)
# sam = np.zeros_like(pre)
# sam[picked] = 1
notes = postprocess.TrainDataToLevelData(pre, 10, 0.90, 0)
notes = np.asarray(notes)
notes[0]
notes = notes[:, 0]
LevelInfo.SaveInstantValue(notes, pathname, '_inst_singing')
def GenerateLevel(predicts, pathname):
# #for long note
# print('evaluate shape', evaluate.shape)
# predicts = evaluate[:,1] > acceptThrehold
# LevelInfo.SaveSamplesToRegionFile(predicts, pathname, '_region')
# return
# predicts = postprocess.pick(evaluate, kernelSize=11)
print('predicts', predicts.shape)
# postprocess.SaveResult(predicts, testy, 0, r'I:\work\result.log')
acceptThrehold = 0.8
pred = predicts[:, 1]
notes = postprocess.TrainDataToLevelData(pred, 10, acceptThrehold, 0)
print('gen notes number', len(notes))
notes = notes[:, 0]
LevelInfo.SaveInstantValue(notes, pathname, '_region')
def SaveShortNoteFile(
pathname=r'I:\librosa\RhythmMaster\breakfree\breakfree_4k_nm.imd'):
# 读取节奏大师关卡，生成文件
duration = LevelInfo.ReadRhythmMasterLevelTime(pathname)
level = LevelInfo.LoadRhythmMasterLevel(pathname)
numSample = duration // 10
targetData = ConvertLevelToLables(level, numSample)
beat = np.max(targetData[:, 1:4], axis=1)
# beat[:] = 0
long = np.max(targetData[:, 4:5], axis=1)
for i in range(1, len(long)):
if long[i - 1] == 0 and long[i] > 0:
beat[i] = 1
note = postprocess.TrainDataToLevelData(beat, 10, 0.1)
note = note[:, 0]
print('note count', len(note))
LevelInfo.SaveInstantValue(note, pathname, '_origshort')
def LoadRawData(useSoftmax=True):
songList = ['hangengxman']
songList = ['jilejingtu']
with open('I:/work/evaluate_data.raw', 'rb') as file:
evaluate = pickle.load(file)
print(type(evaluate))
acceptThrehold = 0.4
pathname = MakeMp3Pathname(songList[0])
#for long note
print('evaluate shape', evaluate.shape)
predicts = evaluate[:, 1] > acceptThrehold
LevelInfo.SaveSamplesToRegionFile(predicts, pathname, '_region')
return
predicts = postprocess.pick(evaluate)
# postprocess.SaveResult(predicts, testy, 0, r'I:\work\result.log')
if useSoftmax:
predicts = predicts[:, 1]
acceptThrehold = 0.7
notes = postprocess.TrainDataToLevelData(predicts, 0, acceptThrehold)
notes = np.asarray(notes)
notes[0]
pathname = MakeMp3Pathname(songList[0])
duration, bpm, entertime = LoadMusicInfo(pathname)
levelNotes = postprocess.ConvertToLevelNote(notes, bpm, entertime)
levelFilename = 'I:/work/%s.xml' % (songList[0])
LevelInfo.GenerateIdolLevel(levelFilename, levelNotes, bpm, entertime,
duration)
notes = notes[:, 0]
LevelInfo.SaveInstantValue(notes, pathname, '_predict')