def plot_pianoroll(path="Sampled/4bar_samples/sample_4_0.midi"):
midi = ppr.parse(path, beat_resolution=4) # get Multitrack object
midi = midi.tracks[0] # get first/only track
pianoroll = midi.pianoroll
print(pianoroll.shape)
ppr.plot(midi, filename="testppr.png", beat_resolution=4)
def plot_interpolation_sotw_to_lick():
interpolations = []
for i in range(0, 10, 3):
path = "Sampled/interpolation_examples/SotW_to_lick/1/interpolate_" + str(
i) + ".midi"
midi = ppr.parse(path, beat_resolution=4) # get Multitrack object
midi = midi.tracks[0] # get first/only track
midi.name = ""
if i == 0:
midi.name = "start sequence"
if i == 10:
midi.name = "end sequence"
interpolations.append(midi)
mt = ppr.Multitrack(tracks=interpolations, beat_resolution=4)
p, _ = ppr.plot(mt,
yticklabel="off",
xtick='beat',
xticklabel=True,
grid="both")
#p.set_size_inches((8, 8), forward=True)
filename = "interpolation_SotW_to_lick.png"
p.savefig(filename)
def plot_interpolation_pianorolls(bars=16):
interpolations = []
for i in range(0, 6, 2):
path = "Sampled/" + str(bars) + "bar_interpolation/interpolate_" + str(
i) + ".midi"
midi = ppr.parse(path, beat_resolution=4) # get Multitrack object
midi = midi.tracks[0] # get first/only track
midi.name = ""
if i == 0:
midi.name = "start sequence"
if i == 4:
midi.name = "end sequence"
pr = midi.pianoroll
# padding to full length in case MIDI file ends earlier
if pr.shape[0] != bars * 16:
padding = np.zeros((bars * 16 - pr.shape[0], pr.shape[1]))
pr = np.concatenate((pr, padding), axis=0)
midi.pianoroll = pr
interpolations.append(midi)
mt = ppr.Multitrack(tracks=interpolations, beat_resolution=4)
if bars == 16:
p, _ = ppr.plot(mt,
yticklabel="number",
xtick='beat',
xticklabel=False,
grid="off")
# there seems to be a bug in ppr, despite xticklabel=False, the plot still has the labels for each x-axis value
else:
p, _ = ppr.plot(mt,
yticklabel="number",
xtick='beat',
xticklabel=True,
grid="both")
p.set_size_inches((8, 8), forward=True)
filename = str(bars) + "bar_interpolation.png"
p.savefig(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 plot_loss(self):
plt.plot(self.disc_loss, c='red')
plt.plot(self.gen_loss, c='blue')
plt.title("GAN Loss per Epoch")
plt.legend(['Discriminator', 'Generator'])
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig('GAN_Loss_per_Epoch_final_1000.png', transparent=True)
plt.close()
if __name__ == '__main__':
path = "C:\\Users\\10413\\Desktop\\deep_learning\\project\\midi-lstm-gan-master\\lpd_5\\lpd_5_cleansed" # 文件夹目录
listOfFiles = getListOfFiles(path)
data_train = np.empty([len(listOfFiles), 4 * 96, 128])
i = 0
for files in listOfFiles:
piano_roll = pypianoroll.load(files)
data = piano_roll.tracks[1].pianoroll
data2 = piano_roll.tracks[1]
pypianoroll.plot(data2) # 打开文件
if data.shape[0] > (4 * 96 - 1):
data_train[i, 0:4 * 96 - 1, 0:127] = data[0:4 * 96 - 1, 0:127]
i = i + 1
if i > 10:
break
gan = GAN(rows=4 * 96)
gan.train(epochs=2,
batch_size=32,
train_data=data_train,
sample_interval=1)