def note_generation(self, input, shape):
starter = numpy.reshape(input, shape)
prediction = self.model.predict(starter, verbose=0)
index = numpy.argmax(prediction)
newNote = Note.Note([], [])
newNote.setFromIndex(index)
return newNote
def edit_note(note_res):
_id = request.query._id
date = request.forms.get('date')
odometr = request.forms.get('odometr')
type_to_do = request.forms.get('type_to_do')
info = request.forms.get('info')
try:
date = date_parse(date)
except DateValueError:
note = note_res.find_one(note_object_to_dict(Note(_id)))
return template('templates/edit_note', note=note, dateerror=True)
note = Note(_id=_id,
date=date,
odometr=odometr,
type_to_do=type_to_do,
info=info)
note_res.save(note_object_to_dict(note))
redirect('/')
def generate(self, starter):
melody = []
for noteIndex in range(50):
input = numpy.reshape(starter, (1, SEQUENCE_LEN, 450))
prediction = self.model.predict(input, verbose=0)
index = numpy.argmax(prediction)
newNote = Note.Note([], [])
newNote.setFromIndex(index)
melody.append(newNote)
starter = starter[1:]
starter.append(newNote.getOneHot())
return melody
def add_note(note_res):
date = request.forms.get('date')
odometr = request.forms.get('odometr')
type_to_do = request.forms.get('type_to_do')
info = request.forms.get('info')
try:
date = date_parse(date)
except DateValueError:
list_of_notes = note_res.list()
return template('templates/home',
list_of_notes=list_of_notes,
dateerror=True)
note = Note(date=date, odometr=odometr, type_to_do=type_to_do, info=info)
note_res.save(note_object_to_dict(note))
redirect('/')
def _changeM21NoteToNote(M21Note):
pitchVector = [0]*45
durationVector = [0]*10
if int(M21Note.duration.quarterLength) > 4:
raise Exception("Duration too long")
minIndex = 0
minVal = 8
for i in range(10):
currentVal = abs(M21Note.duration.quarterLength - QUARTER_LENGTHS[i])
if currentVal < minVal:
minVal = currentVal
minIndex = i
durationVector[minIndex] = 1
if M21Note.isClassOrSubclass((note.Note,)):
interval_ = int(interval.Interval(note.Note('c3'), M21Note).cents / 100)
if interval_ < 0.0 or interval_ > 44.0:
raise Exception("Pitch out of scale")
pitchVector[int(interval_)] = 1
return Note.Note(pitchVector, durationVector)
starter = Tune.Tune("../ABC/ABCChina06")
inputIndex = 5
inputIndex2 = 15
############################################################################
environment.set('musicxmlPath', '/bin/musescore3')
ND_model = NeuralNetwork.NeuralNetwork()
ND_model.load_model(ND_modelFilepath)
starterInput, temp = starter.getTrainData(ND_sequenceLength)
ND_input = starterInput[inputIndex]
ND_inputShape = (1, ND_sequenceLength, 450)
NN_model = NeuralNetwork.NeuralNetwork()
NN_model.load_model(NN_modelFilepath)
starterInput, temp = starter.getTrainData(NN_sequenceLength)
NN_input = starterInput[inputIndex]
NN_inputShape = (1, NN_sequenceLength, 450)
melody = []
for noteIndex in range(newMelodyLength):
duration = ND_model.pod_generation(ND_input, ND_inputShape)
note = NN_model.note_generation(NN_input, NN_inputShape)
melody.append(Note.Note(note.pitchVector, duration))
ND_input = updateNoteInput(ND_input, melody[-1])
NN_input = updateNoteInput(NN_input, melody[-1])
MusicConverter.convert(melody, MUSIC_STREAM).show('musicxml')
compareNewMelody(melody)
NP_modelFilepath = "weights/note-pitch"
NP_sequenceLength = 0
newMelodyLength = 0
starter = Tune.Tune("ABC/ABCChina06")
############################################################################
environment.set('musicxmlPath', '/bin/musescore3')
DD_model = NeuralNetwork.NeuralNetwork()
DD_model.load_model(DD_modelFilepath)
DD_input, temp = starter.getTrainData_noteDuration(DD_sequenceLength)
DD_inputShape = (1, DD_sequenceLength, len(DD_input[0]))
NP_model = NeuralNetwork.NeuralNetwork()
NP_model.load_model(NP_modelFilepath)
NP_input, temp = starter.getTrainData(NP_sequenceLength)
NP_inputShape = (1, NP_sequenceLength, len(NP_input[0]))
melody = []
for noteIndex in range(newMelodyLength):
duration = DD_model.pod_generation(DD_input, DD_inputShape)
pitch = NP_model.pod_generation(NP_input, NP_inputShape)
melody.append(Note.Note(pitch, duration))
updatePoDInput(DD_input, duration)
updateNoteInput(NP_input, melody[-1])
MusicConverter.convert(melody, MUSIC_STREAM).show('musicxml')
compareNewMelody(melody)
def delete_note(note_res):
_id = request.query._id
note_res.remove(Note(_id))
redirect('/')
def edit_note(note_res):
_id = request.query._id
note = note_res.find_one(note_object_to_dict(Note(_id)))
return template('templates/edit_note', note=note, dateerror=None)
inputIndex = 0
inputIndex2 = 0
############################################################################
environment.set('musicxmlPath', '/bin/musescore3' )
ND_model = NeuralNetwork.NeuralNetwork()
ND_model.load_model(ND_modelFilepath)
starterInput, temp = starter.getTrainData(ND_sequenceLength)
ND_input = starterInput[inputIndex]
ND_inputShape = (1, ND_sequenceLength, 450)
NP_model = NeuralNetwork.NeuralNetwork()
NP_model.load_model(NP_modelFilepath)
starterInput, temp = starter.getTrainData(NP_sequenceLength)
NP_input = starterInput[inputIndex2]
NP_inputShape = (1, NP_sequenceLength, 450)
melody = []
for noteIndex in range(newMelodyLength):
duration = ND_model.pod_generation(ND_input, ND_inputShape)
pitch = NP_model.pod_generation(NP_input, NP_inputShape)
melody.append(Note.Note(list(pitch), list(duration)))
ND_input = updateNoteInput(ND_input, melody[-1])
NP_input = updateNoteInput(NP_input, melody[-1])
MusicConverter.convert(melody, MUSIC_STREAM).show('musicxml')
compareNewMelody(melody)