def generate_note(self, f0_info, n_duration, round_to_sixteenth=True):
f0 = f0_info[0]
a = remap(f0_info[1], self.cqt.min(), self.cqt.max(), 0, 1)
duration = librosa.frames_to_time(n_duration, sr=self.sr, hop_length=self.hop_length)
note_duration = 0.02 * np.around(duration / 0.02) # Round to 2 decimal places for music21 compatibility
midi_duration = second_to_quarter(duration, self.tempo)
midi_velocity = int(round(remap(f0_info[1], self.cqt.min(), self.cqt.max(), 80, 120)))
if round_to_sixteenth:
midi_duration = round(midi_duration * 16) / 16
try:
if f0 is None:
midi_note = None
note_info = Rest(type=self.mm.secondsToDuration(note_duration).type)
f0 = 0
else:
midi_note = round(librosa.hz_to_midi(f0))
note = Note(librosa.midi_to_note(midi_note), type=self.mm.secondsToDuration(note_duration).type)
note.volume.velocity = midi_velocity
note_info = [note]
except DurationException:
if f0 is None:
midi_note = None
note_info = Rest(type='32nd')
f0 = 0
else:
midi_note = round(librosa.hz_to_midi(f0))
note = Note(librosa.midi_to_note(midi_note),
type='eighth')
note.volume.velocity = midi_velocity
note_info = [note]
midi_info = [midi_note, midi_duration, midi_velocity]
n = np.arange(librosa.frames_to_samples(n_duration, hop_length=self.hop_length))
sine_wave = a * np.sin(2 * np.pi * f0 * n / float(self.sr))
return [sine_wave, midi_info, note_info]
def evaluate(model,
wavfile='sample/waltz_for_toutzy.wav',
log=1,
YLIM=[0, 48]):
# log = 1
# if 1:
if hasattr(model, 'model'):
model = model.model
# wavfile = 'sample/waltz_for_toutzy_50.wav'
# wavfile = 'sample/Tamacun.wav'
# wavfile = 'sample/MIDI/composer-bach-edition-bg-genre-cant-work-0002-format-midi1-multi-zip-number-01.wav'
p = util.piece(wavfile)
print p.x0.max()
if p.x0.dtype == 'int16':
p.x0 = p.x0.astype('float32')
p.x0 = p.x0 / 2**15
p.xs = p.x0
# p.xs = p.xs.astype('float32')
p.downsample(16000)
# p.bitrate = 18000
# p.trimto(18,26)
# p.trimto(28,40)
p.trimto(50, 66)
# p.trimto(60,100)
# p.trimto(100,140)
# print len(p.xs)
chunks = to_chunk(p, 20)[:]
chunks = np.array(chunks)
# chunks = util_midi.norm_by_rmsq(chunks,norm = 1)
eps = 1E-8
plt.figure(figsize=[12, 6])
mroll = transcribe(chunks, model, chroma=0, log=log)
ytk = librosa.midi_to_note(range(0, 128))
plt.yticks(np.arange(0, 128) + .5, librosa.midi_to_note(range(0, 128)))
# plt.ylim(40,78)
plt.grid()
# plt.ylim(0,36)
plt.ylim(YLIM)
ipd.display(ipd.Audio(p.xs, rate=16000))
plt.figure(figsize=[12, 6])
# print p.xs.dtype
cqt(p)
# plt.yscale('log')
plt.yticks(np.exp(np.linspace(*np.log(plt.gca().get_ylim()), num=48)),
librosa.midi_to_note(np.arange(48) + 24))
plt.show()
return mroll
def get_chord(self, poly='random', top_k=None):
for i in range(10):
try:
if poly == 'random':
poly = np.random.randint(1, 7)
if poly in [1, 2]:
notes = list(
np.random.randint(
40,
self.chords.midi_notes.values.max()[0] + 1,
size=poly))
else:
chord_df = random.choice(
[self.chords, self.top_chords.iloc[:top_k]])
df = chord_df[chord_df.poly == poly].sample(1)
notes = df.midi_notes.values[0]
if self.verbose:
print(poly)
print('notes', notes, librosa.midi_to_note(notes))
try:
print('TAB:', df.joint.values)
except:
pass
break
except Exception as ex:
print(ex, 'Retry', i + 1)
return notes
def render(self, width, force_chord):
result = np.full((OVERFLOW_WIDTH_LIMIT, 3), '', dtype='<U1')
air = self.air
scale = width / (self.pos_end - self.pos_start)
already_mute = False
for pos in range(self.pos_start, self.pos_end):
render_x = int(np.round((pos - self.pos_start) * scale))
for type in range(3): #for type in ['melody','lyric','chord']
render_str = ''
if (type == 0): #melody
if (air.melody_onset[pos]):
render_str = librosa.midi_to_note(air.melody[pos])[:2]
already_mute = False
if (air.melody[pos] == -1 and not already_mute):
render_str = '0'
already_mute = True
elif (type == 1): #lyric
if (air.lyric[pos] != '' and air.lyric[pos] != '-'):
render_str = air.lyric[pos]
else: #chord
if (pos == 0 or air.chord[pos] != air.chord[pos - 1]
or (force_chord and pos == self.pos_start)):
render_str = air.chord[pos].replace(':', '')
chars = list(render_str)
render_len = min(len(chars), OVERFLOW_WIDTH_LIMIT - render_x)
result[render_x:render_x + render_len,
type] = chars[:render_len]
return result
def transpose(label, n_semitones):
"""Transpose a chord label by some number of semitones
Parameters
----------
label : str
A chord string
n_semitones : float
The number of semitones to move `label`
Returns
-------
label_transpose : str
The transposed chord label
"""
# Otherwise, split off the note from the modifier
match = re.match("(?P<note>[A-G][b#]*)(?P<mod>.*)", label)
if not match:
return label
note = match.group("note")
new_note = librosa.midi_to_note(librosa.note_to_midi(note) + n_semitones, octave=False)
return new_note + match.group("mod")
def init_list():
# 识别音符范围是G3~E7，MIDI Number从53到100，但左右各多取一个音符以便确定频率的截止带宽
for midi in range(54, 102):
pitch = librosa.midi_to_note(midi)
pitch_hz = librosa.note_to_hz(pitch)
pitch_list.append(pitch)
hz_list.append(pitch_hz)
def midi_module(audio_path, y, CQT, sr, plot=True):
midi_path = re.sub(r'.wav', '.mid', audio_path)
n_frames = CQT.shape[1]
# Output definition(y)
#Ground_truth_mat=mm.midi2mat(midi_path,len(y),n_frames,sr)[0]
midi_data = pretty_midi.PrettyMIDI(midi_path)
pianoRoll = midi_data.instruments[0].get_piano_roll(fs=CQT.shape[1] *
44100. / len(y))
Ground_truth_mat = (
pianoRoll[RangeMIDInotes[0]:RangeMIDInotes[1] + 1, :CQT.shape[1]] > 0)
if plot:
plt.figure()
plt.subplot(211)
lb.display.specshow(Ground_truth_mat,
sr=sr,
bins_per_octave=12,
fmin=lb.note_to_hz('A0'),
x_axis='time',
y_axis='cqt_note')
# Label distribution in the sequence
plt.subplot(212)
n_pitch_frame = np.sum(Ground_truth_mat, axis=1)
plt.bar(range(RangeMIDInotes[0], RangeMIDInotes[1] + 1),
n_pitch_frame / np.sum(n_pitch_frame).astype(np.float))
plt.xticks(
range(RangeMIDInotes[0], RangeMIDInotes[1] + 1, 12),
lb.midi_to_note(range(RangeMIDInotes[0], RangeMIDInotes[1] + 1,
12)))
plt.xlabel('Midi note')
plt.ylabel('Note probability')
return Ground_truth_mat #(88, 10979)
def test_cq():
"""
Just for testing, from librosa docu
"""
# Plot one octave of filters in time and frequency
basis, lengths = librosa.filters.constant_q(22050)
plt.figure(figsize=(10, 6))
plt.subplot(2, 1, 1)
notes = librosa.midi_to_note(np.arange(24, 24 + len(basis)))
for i, (f, n) in enumerate(zip(basis, notes[:12])):
f_scale = librosa.util.normalize(f) / 2
plt.plot(i + f_scale.real)
plt.plot(i + f_scale.imag, linestyle=':')
plt.axis('tight')
plt.yticks(np.arange(len(notes[:12])), notes[:12])
plt.ylabel('CQ filters')
plt.title('CQ filters (one octave, time domain)')
plt.xlabel('Time (samples at 22050 Hz)')
plt.legend(['Real', 'Imaginary'], frameon=True, framealpha=0.8)
plt.subplot(2, 1, 2)
F = np.abs(np.fft.fftn(basis, axes=[-1]))
# Keep only the positive frequencies
F = F[:, :(1 + F.shape[1] // 2)]
librosa.display.specshow(F, x_axis='linear')
plt.yticks(np.arange(len(notes))[::12], notes[::12])
plt.ylabel('CQ filters')
plt.title('CQ filter magnitudes (frequency domain)')
plt.tight_layout()
plt.show()
def transpose(label, n_semitones):
'''Transpose a chord label by some number of semitones
Parameters
----------
label : str
A chord string
n_semitones : float
The number of semitones to move `label`
Returns
-------
label_transpose : str
The transposed chord label
'''
# Otherwise, split off the note from the modifier
match = re.match(six.text_type('(?P<note>[A-G][b#]*)(?P<mod>.*)'),
six.text_type(label))
if not match:
return label
note = match.group('note')
new_note = librosa.midi_to_note(librosa.note_to_midi(note) + n_semitones,
octave=False)
return new_note + match.group('mod')
def transpose(label, n_semitones):
'''Transpose a chord label by some number of semitones
Parameters
----------
label : str
A chord string
n_semitones : float
The number of semitones to move `label`
Returns
-------
label_transpose : str
The transposed chord label
'''
# Otherwise, split off the note from the modifier
match = re.match(six.text_type('(?P<note>[A-G][b#]*)(?P<mod>.*)'),
six.text_type(label))
if not match:
return label
note = match.group('note')
new_note = librosa.midi_to_note(librosa.note_to_midi(note) + n_semitones,
octave=False)
return new_note + match.group('mod')
def get_label(self, index):
if index < 0 or index > self.nb_classes:
return None
minor = index >= 12
midi = index + 12
if minor:
midi = index - 12
label = librosa.midi_to_note(midi=midi, octave=False)
if minor:
label += 'm'
return label
def to_major_minor_key(index):
if not np.isscalar(index):
return [to_major_minor_key(x) for x in index]
minor = index >= 12
midi = index + 12
if minor:
midi = index - 12
tonic = librosa.midi_to_note(midi=midi, octave=False)
mode = 'minor' if minor else 'major'
return tonic, mode
def get_components_score_informed(W, H, n_components, signal, pitches):
for n in range(n_components):
# Re-create the STFT of a single NMF component.
Y = scipy.outer(W[:,n], H[n]) * signal.X_phase
# Transform the STFT into the time domain.
y = librosa.istft(Y)
label = pitches[n]
if not isinstance(label, str):
label = librosa.midi_to_note(label)
yield (label, ipd.Audio(y, rate=signal.sr))
def get_components_score_informed_with_onset_templates(W, H, n_components, signal, pitches):
for n in range(n_components*2):
# Re-create the STFT of a single NMF component.
Y = scipy.outer(W[:,n], H[n]) * signal.X_phase
# Transform the STFT into the time domain.
y = librosa.istft(Y)
if n < n_components:
label = "note: "
else:
n = n - n_components
label = "onset: "
label += librosa.midi_to_note(pitches[n])
yield ('Component {} ({}):'.format(n, label), ipd.Audio(y, rate=signal.sr))
def get_notes(self, sequence):
notes = []
for note_obj in sequence:
note = librosa.midi_to_note(note_obj.pitch)
octave = int(note[-1])
if octave < 3:
note = note[0:-1] + '3'
elif octave > 4:
note = note[0:-1] + '4'
duration = math.ceil(
(note_obj.end_time - note_obj.start_time) * 4) / 4
notes.append({'note': note, 'duration': duration})
return notes
def tracktosentences(file, index):
midi = mido.MidiFile(file, clip=True)
matrix = GenPlot.tracktoarray(GenPlot.trackcombine(midi)[0][index])
notez = []
for _ in range(int(len(matrix) / 127)):
playing = librosa.midi_to_note(
GenPlot.removenegative(matrix[_ * 127:(_ + 1) * 127]))
if notez != []:
if notez[len(notez) - 1][0] != playing:
notez.append([playing, 0])
else:
notez[len(notez) - 1][1] += 1
else:
notez.append([playing, 0])
return listtosentences(notez, midi.ticks_per_beat)
def midi_to_note_zeros(annotation):
'''
Special function so that zeros represent silence
Input: Annotation List taken straight from mtrack
Output: 1d np.array containing frequencies instead of note names
'''
new_values = np.array([])
for a in annotation:
new_a = '0'
if a != 0:
new_a = librosa.midi_to_note(a)
new_values = np.append(new_values, new_a)
return new_values
def vocabulary(self):
''' Build the vocabulary for all key_mode strings
Returns
-------
labels : list
list of string labels.
'''
qualities = MODES + list(QUALITY.keys())
tonics = midi_to_note(list(range(12)), octave=False)
labels = ['N']
for key_mode in product(tonics, qualities):
labels.append('{}:{}'.format(*key_mode))
return labels
def plot_weight(model, tokens):
result, attn = model.inference(tokens)
result[:, 1] = 0.0
print('PD:', np.argmax(result, axis=1))
print('GT:', tokens)
print(
'GT:', ' '.join(
midi_to_note(x - 2) if x >= 2 else ['<n>', '<s>'][x]
for x in tokens))
print('PB:', np.max(result, axis=1))
tokens_one_hot = np.eye(result.shape[1])[tokens]
fig, ax = plt.subplots(nrows=2, ncols=1, sharex='all')
ax[0].imshow(result.T, interpolation='nearest', aspect='auto')
ax[0].invert_yaxis()
ax[1].imshow(tokens_one_hot.T, interpolation='nearest', aspect='auto')
ax[1].invert_yaxis()
plt.show()
def create_half_tone_filterbank(N, fs, midi_start_note=43, num_oct=4): """ create half-tone filterbank """ import librosa # midi notes p = np.arange(midi_start_note, midi_start_note + 12 * num_oct) # midi notes of discrete DFT-bins p_fk = np.insert( f_to_midi_scale(np.arange(1, N/2) * fs / N), 0, 0) # differences d = np.abs(p[:, np.newaxis] - p_fk) # half-tone filterbank Hp = 0.5 * np.tanh(np.pi * (1 - 2 * d)) + 0.5 return Hp, get_chroma_labels(start_note=librosa.midi_to_note(midi_start_note, octave=False))
def get_winner(self, notes):
top_chords = self.top_chords.copy()
query = notes_to_chroma(librosa.midi_to_note(notes))
top_chords['chroma_dist'] = np.array([
textdistance.levenshtein(query, chord)
for chord in top_chords.chroma.values
])
top_chords['dist'] = np.array([
textdistance.levenshtein(notes, chord)
for chord in top_chords.midi_notes
])
top_chords['chroma_dist'] = top_chords.chroma_dist.max(
) - top_chords.chroma_dist
# top_chords['dist'] = np.array([len(textdistance.lcsseq(query, chord)) for chord in top_chords.chroma.values])
candidates = top_chords[top_chords.dist ==
top_chords.dist.min()].sort_values(
['dist', 'chroma_dist'], ascending=False)
winner = candidates.iloc[0]
return winner.midi_notes
def analyse_audio(audio_file, midi_file):
x, _ = librosa.load(audio_file, sr=sr)
print("Music file length=%s, sampling_rate=%s" % (x.shape[0],sr))
plt.figure(figsize=(14, 5))
plt.title('Music Sample Waveplot')
librosa.display.waveplot(x, sr=sr)
x_stft_spectrum = lb.stft(x, n_fft=1024,hop_length=512,center=True, dtype=np.complex64)
x_stft = librosa.amplitude_to_db(abs(x_stft_spectrum))
plt.figure(figsize=(14, 5))
librosa.display.specshow(lb.amplitude_to_db(x_stft, ref=np.max), sr=sr, fmin=lb.note_to_hz('A0'), x_axis='time', y_axis='linear',cmap='coolwarm')
plt.title('Power spectrogram')
plt.colorbar(format='%+2.0f dB')
plt.tight_layout()
plt.figure(figsize=(14, 5))
x_cqt = np.abs(librosa.cqt(x, sr=sr, bins_per_octave=bins_per_octave, n_bins=n_bins, fmin=lb.note_to_hz('A0')))
librosa.display.specshow(librosa.amplitude_to_db(x_cqt, ref=np.max), sr=sr, x_axis='time', y_axis='cqt_note',cmap='coolwarm')
print("CQT Matrix shape", x_cqt.shape)
plt.colorbar(format='%+2.0f dB')
plt.title('Constant-Q power spectrum')
plt.tight_layout()
n_frames=x_cqt.shape[1]
midi_data = pretty_midi.PrettyMIDI(midi_file)
plt.figure(figsize=(12, 4))
plot_piano_roll(midi_data, 24, 84)
print('There are {} time signature changes'.format(len(midi_data.time_signature_changes)))
print('There are {} instruments'.format(len(midi_data.instruments)))
print('Instrument 1 has {} notes'.format(len(midi_data.instruments[0].notes)))
pianoRoll = midi_data.instruments[0].get_piano_roll(fs=n_frames * 44100. / len(x))
midi_mat = (pianoRoll[MIDInotes[0]:MIDInotes[1] + 1, :n_frames] > 0)
print("MIDI Matrix shape", midi_mat.shape)
plt.figure()
librosa.display.specshow(midi_mat, sr=sr, bins_per_octave=12, fmin=lb.note_to_hz('A0'), x_axis='time', y_axis='cqt_note')
n_pitch_frame=np.sum(midi_mat, axis=1)
print(n_pitch_frame)
plt.bar(range(MIDInotes[0],MIDInotes[1]+1),n_pitch_frame/np.sum(n_pitch_frame).astype(np.float))
plt.xticks(range(MIDInotes[0],MIDInotes[1]+1,12), lb.midi_to_note(range(MIDInotes[0], MIDInotes[1]+1,12)))
plt.xlabel('Midi note')
plt.ylabel('Note probability')
def enharmonic(self, key_str):
'''Force the tonic spelling to fit our tonic list
by spelling out of vocab keys enharmonically.
Parameters
----------
key_str : str
The key_mode string in jams style.
Returns
-------
key_str : str
The key_mode string spelled enharmonically to fit our vocab.
'''
key_list = key_str.split(':')
# spell the tonic enharmonically if necessary
if key_list[0] != 'N':
key_list[0] = midi_to_note(note_to_midi(key_list[0]), octave=False)
if len(key_list) == 1:
key_list.append('major')
return ':'.join(key_list)
def _glyph_to_note(glyph):
"""Converts a `Glyph` message to a `<note>` tag.
Args:
glyph: A `tensorflow.moonlight.Glyph` message. The glyph type should be
one of `NOTEHEAD_*`.
Returns:
An etree `<note>` tag, or `None` if the glyph is not a notehead.
Raises:
ValueError: If the note duration is not a multiple of `1 / DIVISIONS`.
"""
if not glyph.HasField('note'):
return None
note = etree.Element('note')
etree.SubElement(note, 'voice').text = '1'
if glyph.type == musicscore_pb2.Glyph.NOTEHEAD_EMPTY:
note_type = HALF
elif glyph.type == musicscore_pb2.Glyph.NOTEHEAD_WHOLE:
note_type = WHOLE
else:
index = min(len(FILLED), len(glyph.beam))
note_type = FILLED[index]
etree.SubElement(note, 'type').text = note_type
duration = DIVISIONS * (glyph.note.end_time - glyph.note.start_time)
if not duration.is_integer():
raise ValueError('Duration is not an integer: ' + str(duration))
etree.SubElement(note, 'duration').text = str(int(duration))
pitch_match = re.match('([A-G])([#b]?)([0-9]+)',
librosa.midi_to_note(glyph.note.pitch))
pitch = etree.SubElement(note, 'pitch')
etree.SubElement(pitch, 'step').text = pitch_match.group(1)
etree.SubElement(pitch, 'alter').text = str(
ACCIDENTAL_TO_ALTER[pitch_match.group(2)])
etree.SubElement(pitch, 'octave').text = pitch_match.group(3)
return note
def full_chord(noteMIDI):
new_noteMIDI = noteMIDI
# sort the noteMIDI list
new_noteMIDI.sort()
# the lowest note is the pivot, move other note down octave(s), so that
# all notes are within one octave from the lowest note
for i in range(len(new_noteMIDI) - 1):
while new_noteMIDI[i + 1] >= new_noteMIDI[0] + 12:
new_noteMIDI[i + 1] -= 12 # 12 notes in one octave
# now that all notes are with in one octave of each other, remove duplicate notes, then sort again
new_noteMIDI = list(set(new_noteMIDI))
new_noteMIDI.sort()
# find distance between each note
dis = []
for i in range(len(new_noteMIDI) - 1):
dis.append(new_noteMIDI[i + 1] - new_noteMIDI[i])
# this gives us the quality and the inversion of the chord
result = QUALITY.get(tuple(dis))
if result is None:
return "No chord is found!"
# find the root note of the chord
root = librosa.midi_to_note(new_noteMIDI[result[1]], octave=False)
return root + result[0]
def token_to_note(x):
return midi_to_note(x - 2) if x >= 2 else ['(n)', '(s)'][x]
def __test(midi_num, note, octave, cents):
note_out = librosa.midi_to_note(midi_num, octave=octave, cents=cents)
assert note_out == note
def __init__(
self,
db_cache=config.GIT2MID_DB_ROOT,
db='single_notes.pkl',
sr=44100,
preload_audio=False,
):
'''
There are three main components in this init
1. data frame | pointing to the audio files and holding metadata
2. chord data | base that is used for generating the chordsamples
3. the actual audiofiles | when preload_audio = True a dict will be generated
'''
import pandas as pd
self.db_cache = db_cache
self.db = db
self.df = pd.read_pickle(
os.path.join(self.db_cache, 'fishman/single_notes/', self.db))
# clean with blacklist
self.blacklist = pd.read_table(os.path.join(
self.db_cache, 'fishman/single_notes/blacklist.txt'),
header=None,
index_col=0).index
self.df = self.df.drop(self.blacklist)
# change the file paths in the df
def generate_new_path(old_path, path):
# print(old_path)
new_root = '/'.join(path.split('/')[:-1])
filename = '/'.join(old_path.split('/')[-2:])
new_path = '/'.join((new_root, filename))
return new_path
self.df.index = [
generate_new_path(
x, os.path.join(self.db_cache, 'fishman/single_notes/',
self.db)) for x in self.df.index
]
# chord shapes to use for the chords
self.chords = pd.read_json(
os.path.join(self.db_cache, 'chords/chord_classes.json'))
self.chords['poly'] = [len(x) for x in self.chords.midi_notes]
self.top_chords = pd.read_json(
os.path.join(self.db_cache, 'chords/top_chords.json'))
self.top_chords['poly'] = [len(x) for x in self.top_chords.midi_notes]
self.top_chords['chroma'] = [
notes_to_chroma(note)
for note in librosa.midi_to_note(self.top_chords.midi_notes)
]
self.sr = sr
self.preload_audio = preload_audio
if self.preload_audio:
self.audio_dict = {}
for file in self.df.index:
self.audio_dict[file] = librosa.load(file, sr=44100)[0]
self.verbose = False
def __test(midi_num, note, octave, cents):
note_out = librosa.midi_to_note(midi_num, octave=octave, cents=cents)
assert note_out == note
def midi_to_note(midi):
return 0 if midi == 0 else librosa.midi_to_note(midi)
def imshow_notes(x):
import librosa
imshow(x)
plt.xticks(np.arange(12), [librosa.midi_to_note(i, octave=False)
for i in range(12)])
def __test(midi_num, note, octave, cents):
note_out = librosa.midi_to_note(midi_num, octave=octave, cents=cents)
eq_(note_out, note)
def states_to_pianoroll(states, note_min, note_max, hop_time):
"""
Converts state sequence to an intermediate, internal piano-roll notation
Parameters
----------
states : int
Sequence of states estimated by Viterbi
note_min : string, 'A#4' format
Lowest note supported by this estimator
note_max : string, 'A#4' format
Highest note supported by this estimator
hop_time : float
Time interval between two states.
Returns
-------
output : List of lists
output[i] is the i-th note in the sequence. Each note is a list
described by [onset_time, offset_time, pitch].
"""
midi_min = librosa.note_to_midi(note_min)
midi_max = librosa.note_to_midi(note_max)
states_ = np.hstack((states, np.zeros(1)))
# possible types of states
silence = 0
onset = 1
sustain = 2
my_state = silence
output = []
last_onset = 0
last_offset = 0
last_midi = 0
for i in range(len(states_)):
if my_state == silence:
if int(states_[i] % 2) != 0:
# Found an onset!
last_onset = i * hop_time
last_midi = ((states_[i] - 1) / 2) + midi_min
last_note = librosa.midi_to_note(last_midi)
my_state = onset
elif my_state == onset:
if int(states_[i] % 2) == 0:
my_state = sustain
elif my_state == sustain:
if int(states_[i] % 2) != 0:
# Found an onset.
# Finish last note
last_offset = i * hop_time
my_note = [last_onset, last_offset, last_midi, last_note]
output.append(my_note)
# Start new note
last_onset = i * hop_time
last_midi = ((states_[i] - 1) / 2) + midi_min
last_note = librosa.midi_to_note(last_midi)
my_state = onset
elif states_[i] == 0:
# Found silence. Finish last note.
last_offset = i * hop_time
my_note = [last_onset, last_offset, last_midi, last_note]
output.append(my_note)
my_state = silence
return output
def __test(midi_num, note, octave, cents):
note_out = librosa.midi_to_note(midi_num, octave=octave, cents=cents)
eq_(note_out, note)
def test_midi_to_note_cents_nooctave():
librosa.midi_to_note(24.25, octave=False, cents=True)
