def _reconstruct():
log.info("Choosing a random sample and autoencoding it...")
samples = glob(cfg.Paths.samples + "/*sample*.npz")
sample = np.random.choice(samples)
sample = np.load(sample)
sample = sample['sample']
tracks = []
for track in range(sample.shape[0]):
t = pproll.Track(pianoroll=sample[track, :, :], program=0)
t = t.binarize()
tracks.append(t)
song = pproll.Multitrack(tracks=tracks, resolution=midi_cfg.resolution)
song = song.set_nonzeros(1)
pproll.write(multitrack=song,
path=os.path.join(cfg.Paths.generated, "original.mid"))
sample = dataset.preprocess_single(sample)
e = best_encoder.predict(sample)
d = best_decoder.predict(e)
d = d.reshape((1, 1, midi_cfg.phrase_size, 130))
reconstructed = dataset.postprocess(d)
tracks = []
for sample in range(reconstructed.shape[0]):
for track in range(reconstructed.shape[1]):
t = pproll.Track(pianoroll=reconstructed[sample, track, :, :],
program=0)
t = t.binarize()
tracks.append(t)
song = pproll.Multitrack(tracks=tracks, resolution=midi_cfg.resolution)
song = song.set_nonzeros(1)
pproll.write(multitrack=song,
path=os.path.join(cfg.Paths.generated,
f"reconstructed{sample}.mid"))
def make_musak(model, starting_notes, length):
print("composing...")
previous_state = None
previous_state_u = None
#song composed by taking just the argmax of predited notes at each time setp
song = np.asarray(starting_notes)
#song composed by including all notes with sigmoid over a certain threshold
song_u = np.asarray(starting_notes)
note = song[-1]
note_u = song[-1]
threshold = 0.8
volume = 60
print('length', length)
for i in range(length+1):
logits, previous_state = model.call(np.expand_dims(song[-50: -1], axis=0), previous_state)
logits_u, previous_state_u = model.call(np.expand_dims(song_u[-50: -1], axis=0), previous_state_u)
choice = np.argmax(logits[-1][0].numpy())
note = np.zeros(model.note_range)
note[choice] = volume
note_u = np.where(logits_u[-1][0] > threshold, volume, 0)
song = np.append(song, [note], axis = 0)
song_u = np.append(song_u, [note_u], axis = 0)
song = song[-len(starting_notes):-1]
song_u = song_u[-len(starting_notes):-1]
print("song", song.shape)
print(song)
t = pypianoroll.Track(song)
multi_1 = pypianoroll.Multitrack(name="song", tracks = [t], beat_resolution=4)
multi_1.write('./song.mid')
t = pypianoroll.Track(song_u)
multi_2 = pypianoroll.Multitrack(name="song", tracks = [t], beat_resolution=4)
multi_2.write('./song_u.mid')
fig, ax = multi_1.plot()
plt.show()
fig, ax = multi_2.plot()
plt.show()
def pro_chordlabel_to_midi(chord_label,
chord_dict,
inv_beat_resolution=2,
constant_tempo=120):
VOL = 90
beat_reol_pnrl = 48
step_width = beat_reol_pnrl * inv_beat_resolution
chord_pnrl = np.zeros(
(len(chord_label) * beat_reol_pnrl * inv_beat_resolution, 128))
for idx_label, i_label in enumerate(chord_label):
midi_chord = chord_dict[chord_dict["Label"] ==
i_label]['Midi'].values[0]
if (idx_label % 2) == 0 and (idx_label + 1 < len(chord_label)):
if (chord_label[idx_label] == chord_label[idx_label + 1]):
step_end = step_width
else:
step_end = step_width - 1
else:
step_end = step_width - 1
for i_midi_chord in midi_chord:
for i in range(0, step_end):
chord_pnrl[int(idx_label * step_width +
i)][int(i_midi_chord)] = VOL
mt = pypianoroll.Multitrack(
tracks=[pypianoroll.Track(pianoroll=chord_pnrl)],
beat_resolution=beat_reol_pnrl)
return mt.to_pretty_midi(constant_tempo=constant_tempo,
constant_velocity=60)
def generate_multitrack(pianoroll, instruments, lowest_pitch, n_pitches,
beat_resolution):
instruments = [Instruments[inst.upper()] for inst in instruments]
tempo = 120.0
downbeat = None
pianoroll = pianoroll.astype(np.uint8)
assert (np.all(np.logical_and(pianoroll >= 0, pianoroll < 128)))
full_pitch_pianoroll = np.zeros(
(pianoroll.shape[0], pianoroll.shape[1], 128))
full_pitch_pianoroll[:, :,
lowest_pitch:lowest_pitch + n_pitches] = pianoroll
# create tracks
tracks = []
for i, instrument in enumerate(instruments):
tracks.append(
pp.Track(full_pitch_pianoroll[:, i],
is_drum=instrument.is_drum(),
program=instrument.midi_program(),
name=instrument.name))
# create multitrack piano rolls
return pp.Multitrack(tracks=tracks,
tempo=tempo *
(np.ones(full_pitch_pianoroll.shape[0])),
downbeat=downbeat,
beat_resolution=beat_resolution,
name='generated')
def plot_pianoroll(pianoroll, beat_resolution, fig_name=None):
'''Plot a Single Track Pianoroll image
:param pianoroll: Pianoroll tensor of shape time_steps * pitches
:return:
'''
pypianoroll.plot_track(pypianoroll.Track(pianoroll=pianoroll), fig_name,
beat_resolution)
def save_music(musics, epoch):
multitrack = []
programs = [0, 0, 25, 33, 48]
padding = np.zeros((192, 128), dtype=np.uint8)
x = []
os.makedirs('Gen_numpy', exist_ok=True)
np.save("./Gen_numpy/%d_org.npy" % epoch, musics.cpu().detach().numpy())
for i in range(len(musics[0])):
roll_all = np.zeros((0, 128), dtype=np.uint8)
for music in musics:
roll = music[i]
roll = np.reshape(roll.cpu().detach().numpy(), (192, 84))
roll = np.pad(roll, ((0, 0), (24, 20)), 'constant')
roll = (roll > 0.5) * 1
roll_all = np.concatenate((roll_all, roll), 0)
roll_all = np.concatenate((roll_all, padding), 0)
x.append(roll_all)
track = pypianoroll.Track(pianoroll=roll_all * 100,
program=programs[i],
is_drum=i == 0,
name='')
multitrack.append(track)
multitrack = pypianoroll.Multitrack(tracks=multitrack,
tempo=120.0,
downbeat=[0, 48, 96, 144],
beat_resolution=12)
os.makedirs('Gen_midi', exist_ok=True)
multitrack.write('./Gen_midi/' + str(epoch) + '.mid')
def convert_tensor_to_midi(tensor, tempo, output_file_path):
"""
Writes a pianoroll tensor to a midi file
Parameters
----------
tensor : 2d numpy array
pianoroll to be converted to a midi
tempo : float
tempo to output
output_file_path : str
output midi file path
Returns
-------
None
"""
single_track = pypianoroll.Track(pianoroll=tensor)
multi_track = pypianoroll.Multitrack(
tracks=[single_track],
tempo=tempo,
beat_resolution=Constants.beat_resolution)
output_file_index = 0
while os.path.isfile(output_file_path.format(output_file_index)):
output_file_index += 1
multi_track.write(output_file_path.format(output_file_index))
def to_track(track_np, n=None, is_drum=False, name='piano', program=0):
"""
If n is given, use the values in N2MIDI/N2NAME and ignore name/program
"""
if len(track_np.shape) != 3:
raise ValueError(
"Pass in an array of shape [n_bar, n_timesteps, n_pitches]")
n_bar = track_np.shape[0]
n_timesteps = track_np.shape[1]
tot_timesteps = n_bar * n_timesteps
n_pitches = track_np.shape[2]
track_np_flat = track_np.reshape(tot_timesteps, -1)
padding_amt = (128 - n_pitches) // 2
note_padding = np.zeros((tot_timesteps, padding_amt), dtype=np.bool)
track_np_flat = np.concatenate((note_padding, track_np_flat, note_padding),
axis=1)
if n is not None:
program = N2MIDI[n]
name = N2NAME[n]
is_drum = n == 0
track = ppr.Track(pianoroll=track_np_flat,
is_drum=is_drum,
name=name,
program=program)
return track
def save_pianoroll_as_midi(
pianoroll,
programs=[0, 0, 0, 0],
is_drums=[False, False, False, False],
tempo=100, # in bpm
beat_resolution=4, # number of time steps
destination_path=None):
pianoroll = pianoroll > 0
# Reshape batched pianoroll array to a single pianoroll array
pianoroll_ = pianoroll.reshape(
(-1, pianoroll.shape[2], pianoroll.shape[3]))
# Create the tracks
tracks = []
for idx in range(pianoroll_.shape[2]):
tracks.append(
pypianoroll.Track(pianoroll_[..., idx], programs[idx],
is_drums[idx]))
multitrack = pypianoroll.Multitrack(tracks=tracks,
tempo=tempo,
beat_resolution=beat_resolution)
multitrack.write(destination_path)
print('Midi saved to ', destination_path)
return destination_path
def save(matrix, filename):
track = pr.Track(pianoroll=matrix,
program=0,
is_drum=False,
name='classic music transferred from jazz')
multitrack = pr.Multitrack(tracks=[track])
pr.utilities.write(multitrack, filename)
print("{} saved".format(filename))
def reconstruct(file_path,
model,
start_bar,
end_bar,
temperature=0.5,
smooth_threshold=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.train():
model.eval()
with torch.no_grad():
sample_np = getSlicedPianorollMatrixNp(file_path)
sample_np = transposeNotesHigherLower(sample_np)
sample_np = cutOctaves(sample_np)
sample_np = sample_np[start_bar:end_bar]
sample = torch.from_numpy(sample_np).float()
recon, embed, logvar = model(sample.view(-1, 1, 96, 60).to(device))
recon = torch.softmax(recon, dim=3)
recon = recon.squeeze(1).cpu().numpy()
# recon /= np.abs(np.max(recon))
recon[recon < (1 - temperature)] = 0
sample_play = debinarizeMidi(sample_np, prediction=False)
sample_play = addCuttedOctaves(sample_play)
recon = debinarizeMidi(recon, prediction=True)
recon = addCuttedOctaves(recon)
recon_out = recon[0]
sample_out = sample_play[0]
if recon.shape[0] > 1:
for i in range(recon.shape[0] - 1):
sample_out = np.concatenate((sample_out, sample_play[i + 1]),
axis=0)
recon_out = np.concatenate((recon_out, recon[i + 1]), axis=0)
# plot with pypianoroll
sample_plot = ppr.Track(sample_out)
ppr.plot(sample_plot)
recon_plot = ppr.Track(recon_out)
ppr.plot(recon_plot)
# smooth output
smoother = NoteSmoother(recon_out, threshold=smooth_threshold)
smoothed_seq = smoother.smooth()
smoother_seq_plot = ppr.Track(smoothed_seq)
ppr.plot(smoother_seq_plot)
def write_midi(data, filename, low_lim, high_lim, tempo=120.0, br=2):
"""Save piano roll to a midi file."""
pr = copy.deepcopy(data)
pr[pr > 0] = 127
track = pypianoroll.Track(pad_piano_roll(pr, low_lim, high_lim))
multitrack = pypianoroll.Multitrack(tracks=[track],
tempo=tempo,
beat_resolution=br)
multitrack.write(filename)
def save_trim_pianoroll_seq(seq, min_note, max_note, thepath):
pypianoroll.Multitrack(tracks=[
pypianoroll.Track(
from_trim_pianoroll_to_full(
seq,
min_note,
max_note,
))
],
beat_resolution=4).write(thepath)
def visualize_piano_roll(pianoroll_matrix, fs=5):
""" input: piano roll matrix with shape (number of notes, time steps)
effect: generates a nice graph with the piano roll visualization
"""
if (pianoroll_matrix.shape[0] == 128):
pianoroll_matrix = pianoroll_matrix.T.astype(float)
track = pproll.Track(pianoroll=pianoroll_matrix, program=0, is_drum=False, name='piano roll')
# Plot the piano-roll
fig, ax = track.plot(beat_resolution=fs)
plt.show()
def train(self, epochs, train_data, batch_size=128, sample_interval=50):
# Load and convert the data
#notes = get_notes()
#n_vocab = len(set(notes))
#X_train, y_train = prepare_sequences(notes, n_vocab)
X_train = train_data
# Adversarial ground truths
real = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
# Training the model
for epoch in range(epochs):
# Training the discriminator
# Select a random batch of note sequences
idx = np.random.randint(0, X_train.shape[0], batch_size)
real_seqs = X_train[idx]
# noise = np.random.choice(range(484), (batch_size, self.latent_dim))
# noise = (noise-242)/242
noise = np.random.normal(
0, 1, (batch_size, self.seq_length, self.latent_dim))
# Generate a batch of new note sequences
gen_seqs = self.generator.predict(noise)
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(real_seqs, real)
d_loss_fake = self.discriminator.train_on_batch(gen_seqs, fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# Training the Generator
noise = np.random.normal(
0, 1, (batch_size, self.seq_length, self.latent_dim))
# Train the generator (to have the discriminator label samples as real)
g_loss = self.combined.train_on_batch(noise, real)
# Print the progress and save into loss lists
if epoch % sample_interval == 0:
print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" %
(epoch, d_loss[0], 100 * d_loss[1], g_loss))
self.disc_loss.append(d_loss[0])
self.gen_loss.append(g_loss)
noise = np.random.normal(0, 1, (1, self.seq_length, self.latent_dim))
predictions = self.generator.predict(noise)
track = pypianoroll.Track(pianoroll=predictions,
program=0,
is_drum=False,
name='my awesome piano')
pypianoroll.write(
track,
"C:\\Users\\10413\\Desktop\\deep_learning\\project\\midi-lstm-gan-master\\output"
)
def _generate_samples(decoder: k.Model):
z = np.random.randn(1, 512)
predicted = decoder.predict(z)
x = dataset.postprocess(predicted)
guitar = pproll.Track(pianoroll=predicted[0, :, :], program=0)
guitar = guitar.binarize()
song = pproll.Multitrack(tracks=[guitar], resolution=midi_cfg.resolution)
song = song.set_nonzeros(1)
song.validate()
pproll.write(multitrack=song,
path=os.path.join(cfg.Paths.generated, f"{0}.mid"))
def npy_dg(df,seq_len,bs=8,is_test=False,balance=False,is_tree=False,super_genres=['Pop_Rock','Electronic','Country','RnB','Jazz','Latin','International','Rap','Vocal','New Age','Folk','Reggae','Blues']): while True: batch_all_df = df.sample(n=bs//2).reset_index(drop=True) batch_noprock=noprock.sample(n=bs//2).reset_index(drop=True) xs=list() ys=list() #for batch_df in batch_all_df: x = np.zeros((bs//2,seq_len,128)) if drumz: x_dr = np.zeros((bs/2,seq_len,128)) y = np.zeros((bs//2,len(super_genres))) for i in range(batch_df.shape[0]): try: pi = np.load(_DATA_FOLDER+batch_df['file_id'][i]+'.npy') if drumz: dr = np_file['dr'] except KeyboardInterrupt: sys.exit() except: i-=1 continue if pi.shape[0]<seq_len: x[i,:pi.shape[0]]=pi if drumz: x_dr[i,:pi.shape[0]]=dr else: strt = np.random.randint(pi.shape[0]-seq_len) x[i] = pi[strt:strt+seq_len] if not is_test: transp = np.random.randint(-9,9) tr = pp.Track(pianoroll=x[i]) tr.transpose(transp) x[i] = tr.pianoroll if is_tree: x[i] = du.pianoroll_to_halftree(x[i]) for j in range(len(super_genres)): y[i,j]=batch_df[super_genres[j]][i] x[i] = x[i]/(np.max(x[i],axis=1).reshape((seq_len,1))+10e-7) xs.append(x) ys.append(y) x = np.concatenate(xs,axis=0) y = np.concatenate(ys,axis=0) yield x,y
def pyplot_piano_roll(pr,
cmap="Blues",
br=None,
db=None,
low_lim=21,
high_lim=109,
ax=None):
"""Plot piano roll representation."""
pr = pad_piano_roll(pr, low_lim, high_lim)
pr = pypianoroll.Track(pianoroll=pr)
fig, ax = pypianoroll.plot_track(pr,
cmap=cmap,
beat_resolution=br,
downbeats=db)
return fig, ax
def merge_tracks(multitrack):
# Familia de instrumentos escogidas.
trackInfo = (("Drums", 0), ("Bass", 33), ("Brass", 56), ("Piano", 0), ("Ensemble", 48),)
tracksToMerge = [[], [], [], [], []]
# Observamos a que familia de instrumentos pertenece cada track de la cancion
# original.
for i, track in enumerate(multitrack.tracks):
if track.is_drum:
tracksToMerge[0].append(i)
elif 32 <= track.program < 40:
tracksToMerge[1].append(i)
elif 56 <= track.program < 64:
tracksToMerge[2].append(i)
elif 0 <= track.program < 8:
tracksToMerge[3].append(i)
elif track.program < 96 or 104 <= track.program < 112:
tracksToMerge[4].append(i)
tracks = []
# Una vez que asignamos los tracks a mezclarse, los recorremos, mezclandolos.
for i, trackList in enumerate(tracksToMerge):
# Si hay tracks que se pueden mezclar.
if trackList:
newTracks = []
for trackIndex in trackList:
newTracks.append(multitrack.tracks[trackIndex])
auxM = pypi.Multitrack(tracks=newTracks, tempo=multitrack.tempo, downbeat=multitrack.downbeat, resolution=multitrack.resolution)
merged = auxM.blend("max")
tracks.append(pypi.Track(pianoroll=merged, program=trackInfo[i][1], is_drum=(i == 0), name=trackInfo[i][0]).standardize().binarize())
# Si no hay tracks, se genera un track vacio.
else:
tracks.append(pypi.Track(pianoroll=None, program=trackInfo[i][1], is_drum=(i == 0), name=trackInfo[i][0]).standardize().binarize())
# Multitrack con los 5 tracks a utilizar.
m = pypi.Multitrack(tracks=tracks, tempo=multitrack.tempo[0], downbeat=multitrack.downbeat, resolution=multitrack.resolution, name=multitrack.name)
return m
def create_midi_from_piano_roll(
roll: np.ndarray, midi_path: str, lowest_note: str, tempo: int,
instrument: int, velocity: float
) -> None:
"""
Create MIDI file from array with piano roll.
:param roll:
piano roll
:param midi_path:
path where resulting MIDI file is going to be saved
:param lowest_note:
note that corresponds to the lowest row of piano roll
:param tempo:
number of piano roll's time steps per minute
:param instrument:
ID (number) of instrument according to General MIDI specification
:param velocity:
one common velocity for all notes
:return:
None
"""
notes_order = {
'C': 0, 'C#': 1, 'D': 2, 'D#': 3, 'E': 4, 'F': 5, 'F#': 6,
'G': 7, 'G#': 8, 'A': 9, 'A#': 10, 'B': 11
}
n_semitones_per_octave = 12
n_rows_below = (
n_semitones_per_octave * int(lowest_note[-1])
+ notes_order[lowest_note[:-1]]
)
n_pypianoroll_pitches = 128
n_rows_above = n_pypianoroll_pitches - n_rows_below - roll.shape[0]
resized_roll = np.hstack((
np.zeros((roll.shape[1], n_rows_below)),
roll.T,
np.zeros((roll.shape[1], n_rows_above))
))
track = pypianoroll.Track(velocity * resized_roll, instrument)
multitrack = pypianoroll.Multitrack(
tracks=[track],
tempo=tempo,
beat_resolution=1
)
pypianoroll.write(multitrack, midi_path)
def array2png(roll, filepath):
roll = roll.transpose(0, 1, 4, 2, 3)
roll = roll.reshape(roll.shape[1], -1,
roll.shape[2]) #时间并称1维(t, time, pitch)
multiTracks = ppr.Multitrack()
for i in range(roll.shape[0]):
pianoroll = ((roll[i, :, :] > 0) * 100) #True*100 响度设为100
pianoroll = torch.from_numpy(pianoroll)
pad = nn.ZeroPad2d(padding=(0, 128 - pianoroll.shape[1], 0, 0))
pianoroll = pad(pianoroll)
pianoroll = pianoroll.numpy()
# print("######### show piano roll picture")
# print(pianoroll.shape)
track = ppr.Track(pianoroll=pianoroll)
multiTracks.tracks.append(track)
fig, axs = multiTracks.plot()
plt.savefig(filepath)
def pianoroll_to_midi(snippet, filename="Sampled/sample.midi"):
snippet = np.asarray(snippet, dtype=np.uint8)
snippet = snippet * 127 # sets velocity of notes from 1 to 127 (max MIDI velocity)
if snippet.shape[1] == 89:
snippet = one_hot_pianoroll_to_small_pianoroll(snippet)
snippet = small_to_full_pianoroll(snippet)
elif snippet.shape[1] == 88:
snippet = small_to_full_pianoroll(snippet)
else:
if not snippet.shape[1] == 128:
raise ValueError(
"input shape does not have 128 pitches (or 88, then it will be converted automatically) and cannot be converted to MIDI!"
)
snippet = ppr.Track(pianoroll=snippet)
snippet = ppr.Multitrack(tracks=[snippet], tempo=120, beat_resolution=4)
ppr.write(snippet, path_to_root + filename)
def array_to_pypianoroll(array, tempo=60):
# Order: Piano, Guitar, Strings, Bass, Drums
programs = [
1, # Accoustic Piano
29, # Electric muted guitar
49, # Orchestral Strings
34, # Electric Bass Finger
118, # DrumSet
]
is_drum = [False, False, False, False, True]
tracks = []
for track in range(array.shape[0]):
tracks.append(
pypianoroll.Track(pianoroll=array[track, :, :],
program=programs[track],
is_drum=is_drum[track]))
return pypianoroll.Multitrack(tracks=tracks,
tempo=tempo,
beat_resolution=96 // 4)
def convert_to_npz(generated_phrase, songs_directory, song_name):
padded_phrase = np.pad(
generated_phrase,
[(0, 0), (0, 0),
(LOWEST_NOTE, TOTAL_PIANOROLL_NOTES - LOWEST_NOTE - NUM_NOTES),
(0, 0)],
'constant',
constant_values=False) #repad notes from 84 to 128
padded_reshaped_phrase = np.reshape(
padded_phrase,
(NUM_TRACKS, NUM_BARS, BEATS_PER_BAR,
TOTAL_PIANOROLL_NOTES)) #reshape to be in pypianoroll format
program_list = [0, 0, 24, 32, 48] #list of instruments
is_Drum_list = [True, False, False, False, False]
name_list = ["Drums", "Piano", "Guitar", "Bass", "Strings"]
pianoroll_list = []
for track in range(0, NUM_TRACKS):
track_data = padded_reshaped_phrase[track]
concated_bars = np.empty((0, TOTAL_PIANOROLL_NOTES), dtype=bool)
for bar in range(0, NUM_BARS):
concated_bars = np.concatenate((concated_bars, track_data[bar]),
axis=0)
pianoroll_list.append(
pypianoroll.Track(pianoroll=concated_bars,
program=program_list[track],
is_drum=is_Drum_list[track],
name=name_list[track]))
multitrack = pypianoroll.Multitrack(
tracks=pianoroll_list,
tempo=120.0,
beat_resolution=24,
downbeat=np.asarray([True] + [False] *
(NUM_BARS * BEATS_PER_BAR - 1), ))
pypianoroll.save(join(songs_directory, (song_name + ".npz")), multitrack)
def pianorollMatrixToTempMidi(matrix,
path='../utils/midi_files/temp.mid',
prediction=True,
show=False,
showPlayer=False,
autoplay=False):
# matrix must be of LENGTHxPITCH dimension here: (96 or more,128)
if (prediction):
matrix[-3:, :] = 0
tempTrack = ppr.Track(matrix)
newTrack = ppr.Multitrack()
newTrack.append_track(tempTrack)
newTrack.write(path)
score = music21.converter.parse(path)
if (show):
score.show()
if (showPlayer):
score.show('midi')
if (autoplay):
music21.midi.realtime.StreamPlayer(score).play()
def numpy_to_pianoroll(folder):
#Bass Drums Guitar Piano Strings
programs = [34,0,30,1,51]
names = ['Bass' ,'Drums' ,'Guitar' ,'Piano' ,'Strings']
tempo = np.full((96), 105)
for filename in os.listdir(folder):
multisample = np.load(os.path.join(folder,filename))
for sample,i in zip(multisample,range(multisample.shape[0])):
tracks = []
classes = []
for instrument,program,name in zip(sample,programs,names):
print(instrument.shape)
track = np.vstack(instrument)
print(track.shape)
track[track > 0.5] = 100
track[track < 0.5] = 0
print(track.shape)
track = np.pad(track.astype(int),((0,0),(0,44)),mode='constant')
if name !='Guitar':
print(ppr.metrics.qualified_note_rate((track),2))
print(ppr.metrics.n_pitches_used((track)))
classes.append(ppr.metrics.n_pitches_used((track)))
print(track.shape)
isdrum = False
if program == 0:
isdrum = True
ppr_track = ppr.Track(track,program,isdrum,name)
tracks.append(ppr_track)
ppr_song = ppr.Multitrack(tracks=tracks, tempo=tempo, beat_resolution=24)
for instrument, clasnum in zip(names,classes):
print(instrument+':'+str(clasnum))
print(123)
plot = ppr.plot_multitrack(ppr_song,mode='separate',ytick='off')
plt.savefig('gen_samples/'+filename+str(i)+".png",dpi=400)
ppr.write(ppr_song, 'gen_samples/'+filename+"song")
def save_pianoroll(filename, pianoroll, programs, is_drums, tempo,
beat_resolution, lowest_pitch):
"""Saves a batched pianoroll array to a npz file."""
if not np.issubdtype(pianoroll.dtype, np.bool_):
raise TypeError("Input pianoroll array must have a boolean dtype.")
if pianoroll.ndim != 5:
raise ValueError("Input pianoroll array must have 5 dimensions.")
if pianoroll.shape[-1] != len(programs):
raise ValueError("Length of `programs` does not match the number of "
"tracks for the input array.")
if pianoroll.shape[-1] != len(is_drums):
raise ValueError("Length of `is_drums` does not match the number of "
"tracks for the input array.")
reshaped = pianoroll.reshape(
-1, pianoroll.shape[1] * pianoroll.shape[2], pianoroll.shape[3],
pianoroll.shape[4])
# Pad to the correct pitch range and add silence between phrases
to_pad_pitch_high = 128 - lowest_pitch - pianoroll.shape[3]
padded = np.pad(
reshaped, ((0, 0), (0, pianoroll.shape[2]),
(lowest_pitch, to_pad_pitch_high), (0, 0)), 'constant')
# Reshape the batched pianoroll array to a single pianoroll array
pianoroll_ = padded.reshape(-1, padded.shape[2], padded.shape[3])
print("pianoroll_", np.shape(pianoroll_))
# Create the tracks
tracks = []
for idx in range(pianoroll_.shape[2]):
tracks.append(pypianoroll.Track(
pianoroll_[..., idx], programs[idx], is_drums[idx]))
# Create and save the multitrack
multitrack = pypianoroll.Multitrack(
tracks=tracks, tempo=tempo, beat_resolution=beat_resolution)
multitrack.save(filename)
def main(mono_files, comparison_dir):
np.random.shuffle(mono_files)
found = 0
with tqdm(total=100) as progress:
for f in mono_files:
midi = pypianoroll.Multitrack(f, beat_resolution=4)
roll = midi.tracks[0].pianoroll
# 64 = 4 bars, each bar at 16 timesteps each (4 * 4 (beat_resolution))
roll = roll[:64]
notes_in_roll = np.argmax(roll, axis=1)
notes_non_zero = len(notes_in_roll[np.where(notes_in_roll != 0)])
# minimum 30%
rate = notes_non_zero / 64
if rate >= .3:
new_midi = pypianoroll.Multitrack(
tracks=[pypianoroll.Track(roll)], beat_resolution=4)
unique_name = f.split(os.path.sep)[-1]
new_midi.write(os.path.join(comparison_dir, unique_name))
found += 1
progress.update(1)
if found == 100:
return
import tqdm
import numpy as np
from glob import glob
import os
from matplotlib import pyplot as plt
from my_encoder import encoder
import pypianoroll
DATASET_LOC = os.path.abspath("D:\\data\\folkdataset\\")
DATASET_GLOB = os.path.join(DATASET_LOC, "*.mid")
DATASET_FILES = glob(DATASET_GLOB)
NEW_LOCATION = os.path.abspath("D:\\data\\folkmagenta\\")
files = 200
DATASET_FILES = DATASET_FILES[:files]
lens = []
for fname in tqdm.tqdm(DATASET_FILES):
one_bar = pypianoroll.Multitrack(fname).tracks[0].pianoroll[:96]
midi_one_bar = pypianoroll.Multitrack(tracks=[pypianoroll.Track(one_bar)])
midi_one_bar.write('test.mid')
seq = encoder.encode('test.mid')
lens.append(len(seq))
print(np.mean(lens), np.std(lens))
print(np.quantile(lens, 0.75)) # 24
plt.hist(lens)
plt.show()
def main():
pre_melody = pypianoroll.load(args.input)
pre = []
step = pre_melody.beat_resolution // 4 # 16 beat minimum
pianoroll = np.zeros((pre_melody.get_max_length(),128,len(programs)))
for track in pre_melody.tracks:
if track.is_drum:
dst_index = 0
else:
dst_index = 1
for i in range(1,len(programs),1):
if track.program >= programs[i] and (len(programs) == i+1 or track.program < programs[i+1]):
dst_index = i
break
pianoroll[0:track.pianoroll.shape[0],:,dst_index] += track.pianoroll
pianoroll = pianoroll[:,note_offset:note_offset+note_size,trc_idx]
p = np.where(pianoroll != 0)
current_seq = []
def _current(cur_seq):
cur = []
for c in sorted(cur_seq):
if not (c >= note_size and c < note_size*2):
cur.append(c)
for c in sorted(cur_seq):
if (c >= note_size and c < note_size*2):
cur.append(c)
return cur # Bass, Piano, etc..., Drums
pos = 0
for i in np.argsort(p[0]):
if p[0][i] % step != 0:
continue
if pos < p[0][i]:
for _ in range(pos,p[0][i],step):
pre.extend(_current(current_seq))
pre.append(time_note)
current_seq = []
pos = p[0][i]
j = p[1][i]
t = p[2][i]
note = t*note_size + j
current_seq.append(note)
pre.extend(_current(current_seq))
if len(pre) == 0 or pre[-1] != time_note:
pre.append(time_note)
if len(pre) > 512:
pre = pre[-512:]
cur_top = (0,top_p)
with tf.Session(graph=tf.Graph()) as sess:
context = tf.placeholder(tf.int32, [1, None])
output = model.model(hparams=hparams, X=context)
vars = [v for v in tf.trainable_variables() if 'model' in v.name]
saver = tf.train.Saver(var_list=vars)
ckpt = tf.train.latest_checkpoint(args.model)
saver.restore(sess, ckpt)
pianoroll = np.zeros((trc_len, args.num_bars*16, 128))
seq = get_sequence(sess, context, pre, cur_top)
pos = 0
firstnote = False
print('Generating Melody...')
progress = tqdm(total=pianoroll.shape[1])
while pos < pianoroll.shape[1]:
for note in seq:
if (not firstnote) and note >= time_note:
continue
else:
firstnote = True
pre.append(note)
if note == time_note:
pos += 1
progress.update(1)
if pos >= pianoroll.shape[1]:
break
elif note < time_note:
trc = trc_idx.index(note // note_size)
mid = note % note_size + note_offset
if mid < 128:
pianoroll[trc,pos,mid] = 100
seq = get_sequence(sess, context, pre[-512:], cur_top)
pr = []
for i,(t,p) in enumerate(zip(tracks,programs)):
pr.append(pypianoroll.Track(pianoroll=pianoroll[i], program=p, is_drum=(t=='Drums')))
mt = pypianoroll.Multitrack(tracks=pr, tempo=args.tempo, beat_resolution=4)
mt.write(args.output)