def initialization(dataset_path=None,
metadatas=None,
voice_ids=voice_ids_default,
BACH_DATASET=BACH_DATASET):
from glob import glob
print('Creating dataset')
if dataset_path:
chorale_list = filter_file_list(glob(dataset_path + '/*.mid') +
glob(dataset_path + '/*.xml'),
num_voices=NUM_VOICES)
pickled_dataset = 'datasets/custom_dataset/' + dataset_path.split(
'/')[-1] + '.pickle'
else:
chorale_list = filter_file_list(
corpus.getBachChorales(fileExtensions='xml'))
pickled_dataset = BACH_DATASET
# remove wrong chorales:
min_pitches, max_pitches = compute_min_max_pitches(chorale_list,
voices=voice_ids)
make_dataset(chorale_list,
pickled_dataset,
voice_ids=voice_ids,
transpose=True,
metadatas=metadatas)
def demoBachSearchBrief():
choraleList = corpus.getBachChorales()
results = stream.Stream()
for filePath in choraleList:
fileName = os.path.split(filePath)[1]
pieceName = fileName.replace('.xml', '')
chorale = converter.parse(filePath)
print(fileName)
key = chorale.analyze('key')
if key.mode == 'minor':
lastChordPitches = []
for part in chorale.parts:
lastChordPitches.append(part.flat.pitches[-1])
lastChord = chord.Chord(lastChordPitches)
lastChord.duration.type = "whole"
lastChord.transpose("P8", inPlace=True)
if lastChord.isMinorTriad(
) is False and lastChord.isIncompleteMinorTriad() is False:
continue
lastChord.lyric = pieceName
m = stream.Measure()
m.keySignature = chorale.flat.getElementsByClass('KeySignature')[0]
m.append(lastChord)
results.append(m.makeAccidentals(inPlace=True))
results.show()
def demoBachSearchBrief():
choraleList = corpus.getBachChorales()
results = stream.Stream()
for filePath in choraleList:
fileName = os.path.split(filePath)[1]
pieceName = fileName.replace('.xml', '')
chorale = converter.parse(filePath)
print fileName
key = chorale.analyze('key')
if key.mode == 'minor':
lastChordPitches = []
for part in chorale.parts:
lastChordPitches.append(part.flat.pitches[-1])
lastChord = chord.Chord(lastChordPitches)
lastChord.duration.type = "whole"
lastChord.transpose("P8", inPlace=True)
if lastChord.isMinorTriad() is False and lastChord.isIncompleteMinorTriad() is False:
continue
lastChord.lyric = pieceName
m = stream.Measure()
m.keySignature = chorale.flat.getElementsByClass(
'KeySignature')[0]
m.append(lastChord)
results.append(m.makeAccidentals(inPlace=True))
results.show()
def get_random_choral(log=True):
""" Gets a choral from the J. S. Bach chorals corpus (in Music21). """
choral_file = corpus.getBachChorales()[random.randint(0, 399)]
choral = corpus.parse(choral_file)
if log:
print("Chosen choral:", choral.metadata.title)
return choral
def get_random_choral(log=True):
""" Gets a choral from the J. S. Bach chorals corpus (in Music21). """
choral_file = corpus.getBachChorales()[random.randint(0, 399)]
choral = corpus.parse(choral_file)
if log:
print("Chosen choral:", choral.metadata.title)
return choral
def initialization(dataset_path=None,
metadatas=None,
voice_ids=voice_ids_default,
BACH_DATASET=BACH_DATASET):
from glob import glob
print('Creating dataset')
# Switched the backdash directions, hopefully will fix the error
if dataset_path:
chorale_list = filter_file_list(glob(dataset_path + '/*.mid') +
glob(dataset_path + '/*.xml'),
num_voices=NUM_VOICES)
# hardcode below for now, original code is not working. Try in cloud linux instance next
# pickled_dataset = 'datasets/custom_dataset/' + dataset_path.split('/')[-1] + '.pickle'
pickled_dataset = 'datasets/custom_dataset/custom_dataset.pickle'
else:
chorale_list = filter_file_list(
corpus.getBachChorales(fileExtensions='xml'))
pickled_dataset = BACH_DATASET
# remove wrong chorales:
min_pitches, max_pitches = compute_min_max_pitches(chorale_list,
voices=voice_ids)
make_dataset(chorale_list,
pickled_dataset,
voice_ids=voice_ids,
transpose=True,
metadatas=metadatas)
def demoBachSearch():
import os, random
from music21 import corpus, key
fpList = corpus.getBachChorales('.xml')
random.shuffle(fpList)
results = stream.Stream()
for fp in fpList[:40]:
fn = os.path.split(fp)[1]
print fn
s = converter.parse(fp)
# get key, mode
key, mode = s.analyze('key')[:2]
if mode == 'minor':
pFirst = []
pLast = []
for pStream in s.parts:
# clear accidental display status
pFirst.append(pStream.flat.getElementsByClass('Note')[0].pitch)
pLast.append(pStream.flat.getElementsByClass('Note')[-1].pitch)
cFirst = chord.Chord(pFirst)
cFirst.quarterLength = 2
cFirst.transpose(12, inPlace=True)
cFirst.addLyric(fn)
cFirst.addLyric('%s %s' % (key, mode))
cLast = chord.Chord(pLast)
cLast.quarterLength = 2
cLast.transpose(12, inPlace=True)
if cLast.isMajorTriad():
cLast.addLyric('M')
elif cLast.isMinorTriad():
cLast.addLyric('m')
else:
cLast.addLyric('?')
m = stream.Measure()
m.keySignature = s.flat.getElementsByClass('KeySignature')[0]
print 'got', m.keySignature
m.append(cFirst)
m.append(cLast)
results.append(m.makeAccidentals(inPlace=True))
results.show()
def demoBachSearch():
import random
from music21 import key
fpList = corpus.getBachChorales('.xml')
random.shuffle(fpList)
results = stream.Stream()
for fp in fpList[:40]:
fn = os.path.split(fp)[1]
print(fn)
s = converter.parse(fp)
# get key, mode
key, mode = s.analyze('key')[:2]
if mode == 'minor':
pFirst = []
pLast = []
for pStream in s.parts:
# clear accidental display status
pFirst.append(pStream.flat.getElementsByClass('Note')[0].pitch)
pLast.append(pStream.flat.getElementsByClass('Note')[-1].pitch)
cFirst = chord.Chord(pFirst)
cFirst.quarterLength = 2
cFirst.transpose(12, inPlace=True)
cFirst.addLyric(fn)
cFirst.addLyric('%s %s' % (key, mode))
cLast = chord.Chord(pLast)
cLast.quarterLength = 2
cLast.transpose(12, inPlace=True)
if cLast.isMajorTriad():
cLast.addLyric('M')
elif cLast.isMinorTriad():
cLast.addLyric('m')
else:
cLast.addLyric('?')
m = stream.Measure()
m.keySignature = s.flat.getElementsByClass('KeySignature')[0]
print('got', m.keySignature)
m.append(cFirst)
m.append(cLast)
results.append(m.makeAccidentals(inPlace=True))
results.show()
def demoBachSearch():
import random
from music21 import key
fpList = corpus.getBachChorales(".xml")
random.shuffle(fpList)
results = stream.Stream()
for fp in fpList[:40]:
fn = os.path.split(fp)[1]
print(fn)
s = converter.parse(fp)
# get key, mode
key, mode = s.analyze("key")[:2]
if mode == "minor":
pFirst = []
pLast = []
for pStream in s.parts:
# clear accidental display status
pFirst.append(pStream.flat.getElementsByClass("Note")[0].pitch)
pLast.append(pStream.flat.getElementsByClass("Note")[-1].pitch)
cFirst = chord.Chord(pFirst)
cFirst.quarterLength = 2
cFirst.transpose(12, inPlace=True)
cFirst.addLyric(fn)
cFirst.addLyric("%s %s" % (key, mode))
cLast = chord.Chord(pLast)
cLast.quarterLength = 2
cLast.transpose(12, inPlace=True)
if cLast.isMajorTriad():
cLast.addLyric("M")
elif cLast.isMinorTriad():
cLast.addLyric("m")
else:
cLast.addLyric("?")
m = stream.Measure()
m.keySignature = s.flat.getElementsByClass("KeySignature")[0]
print("got", m.keySignature)
m.append(cFirst)
m.append(cLast)
results.append(m.makeAccidentals(inPlace=True))
results.show()
def testEx03(self):
# What is the most common closing soprano scale degree by key signature
#s in the bach chorales?
#from music21 import graph
results = {}
for fn in corpus.getBachChorales()[:2]:
s = corpus.parse(fn)
ksScale = s.flat.getElementsByClass('KeySignature')[0].getScale()
for p in s.parts:
if p.id.lower() == 'soprano':
n = s.parts['soprano'].flat.getElementsByClass('Note')[-1]
degree = ksScale.getScaleDegreeFromPitch(n.pitch)
if degree not in results.keys():
results[degree] = 0
results[degree] += 1
def testEx03(self):
# What is the most common closing soprano scale degree by key signature
#s in the bach chorales?
#from music21 import graph
results = {}
for fn in corpus.getBachChorales()[:2]:
s = corpus.parse(fn)
ksScale = s.flat.getElementsByClass('KeySignature')[0].getScale()
for p in s.parts:
if p.id.lower() == 'soprano':
n = s.parts['soprano'].flat.getElementsByClass('Note')[-1]
degree = ksScale.getScaleDegreeFromPitch(n.pitch)
if degree not in results.keys():
results[degree] = 0
results[degree] += 1
def initialize_transposition_dataset(dataset_dir=None,
metadatas: List[Metadata] = []):
"""
Create 'datasets/transpose/bach_sop.pickle' or
'datasets/transpose/custom_dataset.pickle'
:param dataset_dir: use Bach chorales if None
:param metadatas:
:type metadatas: Metadata
:return:
:rtype:
"""
from glob import glob
PACKAGE_DIR = os.path.dirname(__file__)
NUM_VOICES = 1
voice_ids = [SOP_INDEX]
print('Creating dataset')
if dataset_dir:
chorale_list = filter_file_list(glob(dataset_dir + '/*.mid') +
glob(dataset_dir + '/*.xml'),
num_voices=NUM_VOICES)
pickled_dataset = os.path.join(PACKAGE_DIR,
'datasets/transpose/' + \
dataset_dir.split(
'/')[
-1] + '.pickle')
else:
chorale_list = filter_file_list(
corpus.getBachChorales(fileExtensions='xml'))
pickled_dataset = os.path.join(PACKAGE_DIR,
'datasets/transpose/bach_sop.pickle')
# remove wrong chorales:
min_pitches, max_pitches = compute_min_max_pitches(chorale_list,
voices=voice_ids)
make_transposition_dataset(chorale_list,
pickled_dataset,
voice_ids=voice_ids,
metadatas=metadatas)
def bachParallels():
'''
find all instances of parallel fifths or octaves in Bach chorales.
Checking the work of George Fitsioris and Darrell Conklin,
"Parallel successions of perfect fifths in the Bach chorales"
Proceedings of the fourth Conference on Interdisciplinary Musicology (CIM08)
Thessaloniki, Greece, 3-6 July 2008, http://web.auth.gr/cim08/
'''
from music21 import corpus
for fn in corpus.getBachChorales():
print(fn)
c = corpus.parse(fn)
displayMe = False
for i in range(len(c.parts) - 1):
iName = c.parts[i].id
if iName.lower() not in ['soprano', 'alto', 'tenor', 'bass']:
continue
ifn = c.parts[i].flat.notesAndRests
omi = ifn.offsetMap
for j in range(i + 1, len(c.parts)):
jName = c.parts[j].id
if jName.lower() not in ['soprano', 'alto', 'tenor', 'bass']:
continue
jfn = c.parts[j].flat.notesAndRests
for k in range(len(omi) - 1):
n1pi = omi[k]['element']
n2pi = omi[k + 1]['element']
n1pj = jfn.getElementsByOffset(
offsetStart=omi[k]['endTime'] - .001,
offsetEnd=omi[k]['endTime'] - .001,
mustBeginInSpan=False)[0]
n2pj = jfn.getElementsByOffset(
offsetStart=omi[k + 1]['offset'],
offsetEnd=omi[k + 1]['offset'],
mustBeginInSpan=False)[0]
if n1pj is n2pj:
continue # no oblique motion
if n1pi.isRest or n2pi.isRest or n1pj.isRest or n2pj.isRest:
continue
if n1pi.isChord or n2pi.isChord or n1pj.isChord or n2pj.isChord:
continue
vlq = voiceLeading.VoiceLeadingQuartet(
n1pi, n2pi, n1pj, n2pj)
if vlq.parallelMotion(
'P8') is False and vlq.parallelMotion(
'P5') is False:
continue
displayMe = True
n1pi.addLyric('par ' + str(vlq.vIntervals[0].name))
n2pi.addLyric(' w/ ' + jName)
# m1 = stream.Measure()
# m1.append(n1pi)
# m1.append(n2pi)
# r1 = note.Rest()
# r1.duration.quarterLength = 8 - m1.duration.quarterLength
# m1.append(r1)
# m2 = stream.Measure()
# m2.append(n1pj)
# m2.append(n2pj)
# r2 = note.Rest()
# r2.duration.quarterLength = 8 - m2.duration.quarterLength
# m2.append(r2)
#
# p1.append(m1)
# p2.append(m2)
# sc.append(p1)
# sc.append(p2)
# sc.show()
if displayMe:
c.show()
def getMusicDataset():
if path.isfile("music_dataset.p"):
print("Loading input data")
music_file = open("music_dataset.p", "rb")
input_data = load(music_file)
music_file.close()
return input_data
else:
bach_files = getBachChorales(fileExtensions='xml')
chorale_files = bach_files[:100]
chorales = list()
for chorale_file in tqdm(chorale_files, desc="Parsing chorale files"):
chorales.append(parse(chorale_file))
alto_chorales = []
for chorale in tqdm(chorales, desc="Extracting alto parts"):
for part in chorale.parts:
if part.partName == "Alto":
alto_chorales.append(part.flat.notesAndRests)
chorale_data = []
for part in tqdm(alto_chorales, desc="Creating note/rest lists"):
data = []
for nor in part:
if nor.isNote or nor.isRest:
data.append((nor.name, nor.duration.quarterLength / 4))
chorale_data.append(data)
print("Finding Minimum Duration")
min_dur = min([nor[1] for chorale in chorale_data for nor in chorale])
input_data = []
def extendNor(p, d):
results = list()
if p == "rest":
p = "R"
elif p == "D-":
p = "C#"
elif p == "F-":
p = "E"
elif p == "G-":
p = "F#"
elif p == "B-":
p = "A#"
elif p == "E-":
p = "D#"
elif p == "A-":
p = "G#"
elif p == "E#":
p = "F"
if d > min_dur:
num_notes = int(d / min_dur)
results.append(p)
for i in range(num_notes - 1):
results.append("H")
else:
results.append(p)
return results
for chorale in tqdm(chorale_data,
desc="Creating regularized note data"):
new_input = []
for (pitch, dur) in chorale:
new_notes = extendNor(pitch, dur)
new_input.extend(new_notes)
input_data.append(new_input)
print("Dumping input data to pickle file")
music_file = open("music_dataset.p", "wb")
dump(input_data, music_file)
music_file.close()
return input_data
def bachParallels():
'''
find all instances of parallel fifths or octaves in Bach chorales.
Checking the work of George Fitsioris and Darrell Conklin,
"Parallel successions of perfect fifths in the Bach chorales"
Proceedings of the fourth Conference on Interdisciplinary Musicology (CIM08)
Thessaloniki, Greece, 3-6 July 2008, http://web.auth.gr/cim08/
'''
from music21 import corpus
for fn in corpus.getBachChorales():
print (fn)
c = corpus.parse(fn)
displayMe = False
for i in range(len(c.parts) - 1):
iName = c.parts[i].id
if iName.lower() not in ['soprano', 'alto', 'tenor', 'bass']:
continue
ifn = c.parts[i].flat.notesAndRests
omi = ifn.offsetMap
for j in range(i+1, len(c.parts)):
jName = c.parts[j].id
if jName.lower() not in ['soprano', 'alto', 'tenor', 'bass']:
continue
jfn = c.parts[j].flat.notesAndRests
for k in range(len(omi) - 1):
n1pi = omi[k]['element']
n2pi = omi[k+1]['element']
n1pj = jfn.getElementsByOffset(offsetStart = omi[k]['endTime'] - .001, offsetEnd = omi[k]['endTime'] - .001, mustBeginInSpan = False)[0]
n2pj = jfn.getElementsByOffset(offsetStart = omi[k+1]['offset'], offsetEnd = omi[k+1]['offset'], mustBeginInSpan = False)[0]
if n1pj is n2pj:
continue # no oblique motion
if n1pi.isRest or n2pi.isRest or n1pj.isRest or n2pj.isRest:
continue
if n1pi.isChord or n2pi.isChord or n1pj.isChord or n2pj.isChord:
continue
vlq = voiceLeading.VoiceLeadingQuartet(n1pi, n2pi, n1pj, n2pj)
if vlq.parallelMotion('P8') is False and vlq.parallelMotion('P5') is False:
continue
displayMe = True
n1pi.addLyric('par ' + str(vlq.vIntervals[0].name))
n2pi.addLyric(' w/ ' + jName)
# m1 = stream.Measure()
# m1.append(n1pi)
# m1.append(n2pi)
# r1 = note.Rest()
# r1.duration.quarterLength = 8 - m1.duration.quarterLength
# m1.append(r1)
# m2 = stream.Measure()
# m2.append(n1pj)
# m2.append(n2pj)
# r2 = note.Rest()
# r2.duration.quarterLength = 8 - m2.duration.quarterLength
# m2.append(r2)
#
# p1.append(m1)
# p2.append(m2)
# sc.append(p1)
# sc.append(p2)
# sc.show()
if displayMe:
c.show()
def ks_mem(freq):
""" Alternative memory for Karplus-Strong """
return (sum(lz.sinusoid(x * freq) for x in [1, 3, 9]) +
lz.white_noise() + lz.Stream(-1, 1)) / 5
# Configuration
rate = 44100
s, Hz = lz.sHz(rate)
ms = 1e-3 * s
beat = 90 # bpm
step = 60. / beat * s
# Open the choral file
choral_file = corpus.getBachChorales()[random.randint(0, 399)]
choral = corpus.parse(choral_file)
print u"Playing", choral.metadata.title
# Creates the score from the music21 data
score = reduce(operator.concat,
[[(pitch.frequency * Hz, # Note
note.offset * step, # Starting time
note.quarterLength * step, # Duration
Fermata in note.expressions) for pitch in note.pitches]
for note in choral.flat.notes]
)
# Mix all notes into song
song = lz.Streamix()
last_start = 0