def generate(initIn, r, dt, generate_function, filename, show=True):
'''Generate a sample sequence, plot the resulting piano-roll and save
it as a MIDI file.
filename : string
A MIDI file will be created at this location.
show : boolean
If True, a piano-roll of the generated sequence will be shown.'''
piano_roll = numpy.round(generate_function(initIn))
for repeat in range(1):
piano_roll = numpy.concatenate(
(piano_roll, numpy.round(generate_function(piano_roll))), axis=0)
midiwrite(filename, piano_roll, r, dt)
if show:
extent = (0, dt * len(piano_roll)) + r
pylab.figure()
pylab.imshow(piano_roll.T,
origin='lower',
aspect='auto',
interpolation='nearest',
cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
def generate(self, filename, show=True):
'''Generate a sample sequence, plot the resulting piano-roll and save
it as a MIDI file.
filename : string
A MIDI file will be created at this location.
show : boolean
If True, a piano-roll of the generated sequence will be shown.'''
piano_roll = self.generate_function()
print "Sample generated!"
midiwrite(filename, piano_roll, self.r, self.dt)
if show:
extent = (0, self.dt * len(piano_roll)) + self.r
pylab.figure()
pylab.imshow(piano_roll.T,
origin='lower',
aspect='auto',
interpolation='nearest',
cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number (corresponds to piano keys)')
pylab.title('generated piano-roll')
pylab.savefig('Piano_Roll_' + str(filename))
def write_to_midi_file(sample, file_path):
"""
write midi list to midi format file
"""
sample = frequency2key(sample)
sample = convert_key_to_midi_list(sample)
midiwrite(file_path, sample, R, DT)
def ideal_predict(model, data):
inp = data[0, :, :]
inp = np.expand_dims(inp, axis=0)
out = model.predict(inp)
print out.shape
print np.max(out)
midiwrite('./test.midi', np.round(out[0, :, :]), r=(12, 109), dt=64)
def generate(self, init_chord, file_name, LS=False, chord_name=None, chord_file=None, state_file=None, n_steps=80, r=(21,109)):
if(LS):
Lsystem = LSystem(chord_name, init_chord, chord_file, state_file, r)
init_sequence = numpy.zeros((1, n_steps +1, self.maxFeatures))
init_sequence[:, 0, init_chord-self.r[0]] = 1
for i in numpy.arange(n_steps):
probs = self.rnnModel.predict_proba(init_sequence)[:, i, :]
for j in numpy.arange(len(init_sequence)):
if(LS):
ind = Lsystem.getMaxProbs(probs[j,0:(self.maxFeatures-1)],True)
else:
ind = maxProbs(probs[j,0:(self.maxFeatures-1)])
init_sequence[j, i+1, ind] = 1
generate_sq = [sq[:,0:(self.maxFeatures-1)].nonzero()[1] for sq in init_sequence]
print(generate_sq[0] + self.r[0])
if(LS):
print(Lsystem.cur_chord)
print(Lsystem.cur_state)
print(Lsystem.cur_opes)
midiwrite(file_name, init_sequence[0,:,0:(self.maxFeatures-1)], self.r, self.dt)
extent = (0, self.dt * len(init_sequence[0,:,0:(self.maxFeatures-1)])) + self.r
pylab.figure()
pylab.imshow(init_sequence[0,:,0:(self.maxFeatures-1)].T, origin='lower', aspect='auto',interpolation='nearest', cmap=pylab.cm.gray_r,extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
def pr2midi(pianoroll, i=1):
filename = './Test/piano_rolls/pianoroll_' + str(i) + '.mid'
print(pianoroll.shape)
utils.midiwrite(
filename, pianoroll.T, (32, 93), .1
) # 3rd arg is the start and end notes; 4rth arg is time between sample
i += 1
def generate_sample(self, length=200, filename="sample.mid"):
piece = self.valid[0][2]
piece.shape
for i in range(length - self.seq_length):
seq = numpy.array(piece[-self.seq_length:])
#print seq.shape
new = self.generate_function(seq)
sample = numpy.random.random(88)
new_n = new > sample
#while numpy.sum(new_n) == 0:
# new += 0.001
# new_n = new > sample
#print new
piece = numpy.vstack([piece, new_n])
#print piece.shape
piano_roll = piece
midiwrite(filename, piano_roll, self.r, self.dt)
if self.show:
extent = (0, self.dt * len(piano_roll)) + self.r
pylab.figure()
pylab.imshow(
piano_roll.T, origin='lower', aspect='auto',
interpolation='nearest', cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
pylab.savefig('piano_roll')
pylab.show()
def train(self, files, batch_size=100, num_epochs=200):
assert len(files) > 0, 'Training set is empty!' \
' (did you download the data files?)'
dataset = [midiread(f, self.r,
self.dt).piano_roll.astype(theano.config.floatX)
for f in files]
def accuracy (v, v_sample):
accs = []
t, n = v.shape
for time in range(t):
tp = 0 # true positive
fp = 0 # false positive
fn = 0 # false negative
for note in range(n):
if v[time][note] == 1 and v_sample[time][note] == 1:
tp += 1.
if v[time][note] == 0 and v_sample[time][note] == 1:
fp += 1.
if v[time][note] == 1 and v_sample[time][note] == 0:
fn += 1.
if tp + fp + fn != 0:
a = tp / (tp + fp + fn)
else:
a = 0
accs.append(a)
acc = numpy.mean(accs)
return acc
try:
print ('lstm_rbm, dataset=Nottingham, lr=%f, epoch=%i' %(self.lr, num_epochs))
for epoch in xrange(num_epochs):
numpy.random.shuffle(dataset)
costs = []
accs = []
for s, sequence in enumerate(dataset):
for i in xrange(0, len(sequence), batch_size):
v = sequence[i:i + batch_size]
(cost, v_sample) = self.train_function(v)
costs.append(cost)
acc = accuracy(v, v_sample)
accs.append(acc)
p = 'Epoch %i/%i LL %f ACC %f time %s' % (epoch + 1, num_epochs, numpy.mean(costs), numpy.mean(accs), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
print (p)
if (epoch%100 == 0 or epoch==num_epochs-1):
piano_roll = self.generate_function()
midiwrite('sample/lstm_rbm%i.mid' %(epoch), piano_roll, self.r, self.dt)
sys.stdout.flush()
except KeyboardInterrupt:
print 'Interrupted by user.'
def generate(self, filename, show=True):
piano_roll = self.generate_function()
midiwrite(filename, piano_roll, self.r, self.dt)
if show:
extent = (0, self.dt * len(piano_roll)) + self.r
pylab.figure()
pylab.imshow(piano_roll.T, origin='lower', aspect='auto',
interpolation='nearest', cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
def generate(self,
init_chord,
file_name,
LS=False,
chord_name=None,
chord_file=None,
state_file=None,
n_steps=80,
r=(21, 109)):
if (LS):
Lsystem = LSystem(chord_name, init_chord, chord_file, state_file,
r)
init_sequence = numpy.zeros((1, n_steps + 1, self.maxFeatures))
init_sequence[:, 0, init_chord - self.r[0]] = 1
for i in numpy.arange(n_steps):
probs = self.rnnModel.predict_proba(init_sequence)[:, i, :]
for j in numpy.arange(len(init_sequence)):
if (LS):
ind = Lsystem.getMaxProbs(
probs[j, 0:(self.maxFeatures - 1)], True)
else:
ind = maxProbs(probs[j, 0:(self.maxFeatures - 1)])
init_sequence[j, i + 1, ind] = 1
generate_sq = [
sq[:, 0:(self.maxFeatures - 1)].nonzero()[1]
for sq in init_sequence
]
print(generate_sq[0] + self.r[0])
if (LS):
print(Lsystem.cur_chord)
print(Lsystem.cur_state)
print(Lsystem.cur_opes)
midiwrite(file_name, init_sequence[0, :, 0:(self.maxFeatures - 1)],
self.r, self.dt)
extent = (0, self.dt *
len(init_sequence[0, :, 0:(self.maxFeatures - 1)])) + self.r
pylab.figure()
pylab.imshow(init_sequence[0, :, 0:(self.maxFeatures - 1)].T,
origin='lower',
aspect='auto',
interpolation='nearest',
cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
def label_from_file(self, rootLoc, fileLoc, learn_rate, n_iters, threshold):
"""
Given a xml file at fileLoc, harmonizes the melody in the xml file, by
doing gradient descent on the top hidden layer of the network. This
gives us an estimate of the top layer activations that might generate
the melody. We then run the network forwards to get the entire harmony
from the top level activations that we estimate.
"""
noteReader = myparser.LegatoNoteAdder(64)
myparser.read(fileLoc, noteReader.handle)
snippet = noteReader.mtx
mask = melody_blocker(snippet)
linear_snippet = snippet.reshape([88*64])
linear_mask = mask.reshape([88*64])
in_data = numpy.zeros([10, 88*64])
x_mask = numpy.zeros([10, 88*64])
for i in range(10):
in_data[i, :] = linear_snippet
x_mask[i, :] = linear_mask
# Do gradient descent to estimate the activations on layer 1.
new_vals = theano.shared(
value=numpy.random.sample([10, self.layer_sizes[-1]]),
)
f = theano.function(
inputs=[],
updates=[(self.isolated_reverse_input, new_vals)],
)
f()
trainer = self.label(in_data, x_mask, learn_rate)
for i in range(n_iters):
print trainer()
# Then, generate using it.
result = dbn.sample(self.isolated_reverse_input, rootLoc=rootLoc, save=False,
threshold=threshold)
# Add the melody back onto the snippet.
final = result * (1.0 - mask)
final = final + snippet
final[final > 0.5] = 1
midiwrite(path.join(rootLoc, 'test.midi'), final.T, r=(12, 109), dt=64)
return final
def generate(self, filename, show=True):
'''Generate a sample sequence, plot the resulting piano-roll and save
it as a MIDI file.
filename : string
A MIDI file will be created at this location.
show : boolean
If True, a piano-roll of the generated sequence will be shown.'''
piano_roll = self.generate_function()
midiwrite(filename, piano_roll, self.r, self.dt)
if show:
extent = (0, self.dt * len(piano_roll)) + self.r
pylab.figure()
pylab.imshow(piano_roll.T, origin='lower', aspect='auto',
interpolation='nearest', cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
def sample(self, top_level=None, rootLoc='./', save=True, threshold=0.5,
filename='test.midi'):
"""
Generates a sample from the trained neural net. top_level is a 10 x
[size of top layer] matrix whose rows contain values for the top
layer. Most of the time, I only use the first row, but you can only
process data in increments of batch_size.
"""
if top_level is None:
top_level_size = self.layer_sizes[-1]
top_level = numpy.random.randint(2, size=[10, top_level_size])\
.astype(dtype=NUMPY_DTYPE)
output = self.generate(top_level)
output = output.reshape([10, 88*64])
firstIm = output[0, :].reshape([88, 64])
# Makes a little picture of the piano roll.
outIm = Image.fromarray((firstIm*255).astype('uint8'))
outIm.save(path.join(rootLoc, 'test.png'))
if threshold is not None:
firstIm[firstIm > threshold] = 1
firstIm[firstIm <= threshold] = 0
if save:
midiwrite(path.join(rootLoc, filename), firstIm.T, r=(12, 109), dt=64)
return firstIm
def make_note(mid_path, model):
temperature = 0.8
probroll = []
sampleroll = []
pressedroll = []
maxlen = 100
notes = 88
x = np.zeros((1, maxlen, notes))
for i in range(music_length_second):
predi = model.predict(x, verbose=0)[0]
predi[predi < 0] = 1E-20 # Avoid negative props
preds = np.log(predi) / temperature
exp_preds = np.exp(preds)
exp_preds = exp_preds * ((1.**2 + (predi.sum() / exp_preds.sum())**3)**
0.5) # root mean square normalization
exp_preds[exp_preds > 1] = 0.99 # Ensure no propability over 1.
keypressed = np.random.binomial(1, exp_preds)
sampleroll.append(predi)
probroll.append(exp_preds)
pressedroll.append(keypressed)
x[0, :-1] = x[0, 1:] # Roll one forward
x[0, -1] = keypressed # insert new prediction
midiwrite(mid_path, pressedroll)
# Compatible with Python 2.7. not 3.x
# Recursively load midi files, extract piano rolls and save as *.mid (file) and *.npy (matrix)
load_root = './MIDI_Data/'
save_root = './MIDI_Data_PianoRolls/'
for dirpath, dirs, files in os.walk(load_root):
for name in files:
if name.endswith('.mid'):
print dirpath, name
print dirpath.replace(load_root, save_root), name
load_dirpath = dirpath
save_dirpath = dirpath.replace(load_root, save_root)
load_filepath = os.path.join(load_dirpath, name)
save_filepath = os.path.join(save_dirpath, name)
# Read MIDI file
piano_roll = midiread(load_filepath).piano_roll
if not os.path.exists(save_dirpath):
os.makedirs(save_dirpath)
# Save the piano roll as MIDI
midiwrite(save_filepath, piano_roll=piano_roll)
# Save the piano roll as *.npy file
np.save(save_filepath.replace('.mid', '.npy'), piano_roll)
if time == length:
break
sumOfZero = 0
sumOfOne = 0
for note in range(88) :
for noteFrom in range(88) :
valueFrom = int (generated[time-1][noteFrom])
sumOfZero = sumOfZero + count[note][0][noteFrom][valueFrom]
sumOfOne = sumOfOne + count[note][1][noteFrom][valueFrom]
ratio = sumOfOne / sumOfZero
if (ratio/5) > rd.random(): # using orgin ratio directly brings too many notes
generated[time][note] = 1
#print sum(generated[time])
midiwrite("output-bi.mid", generated)
sumOfZero = 0
sumOfOne = 0
for note in range(88) :
for noteFrom in range(88) :
valueFrom = int (generated[time-1][noteFrom])
for notePreFrom in range(88) :
valuePreFrom = int (generated[time-2][notePreFrom])
sumOfZero = sumOfZero + count[note][0][noteFrom][valueFrom][notePreFrom][valuePreFrom]
sumOfOne = sumOfOne + count[note][1][noteFrom][valueFrom][notePreFrom][valuePreFrom]
ratio = sumOfOne / sumOfZero
# print ratio
if (ratio/4) > rd.random(): # using orgin ratio directly brings too many notes
generated[time][note] = 1 #
# print sum(generated[time]) #print number of pressed note
midiwrite("output-tri.mid", generated) # fileout
exp_preds[exp_preds > 1] = 0.99 #Ensure no propability over 1.
#rand = np.random.randn(predi.shape[0])*2
#probas = exp_preds*rand
#preds = exp_preds / np.sum(exp_preds)
#probas = np.random.multinomial(10, preds, 1)
#keypressed = probas > 0.50
#use a binomial distribution with propability as exp_preds
keypressed =np.random.binomial(1,exp_preds)
sampleroll.append(predi)
probroll.append(exp_preds)
pressedroll.append(keypressed)
#print(keypressed)
#Take x one forward
x[0,:-1] = x[0,1:] #Roll one forward
x[0,-1] = keypressed #insert new prediction
#plt.matshow(sampleroll)
#plt.title('Raw Predicted')
#plt.matshow(probroll)
#plt.title('Propability of keypress')
#plt.matshow(pressedroll)
#plt.title('New Keypresses')
##plt.matshow(np.array(pianoroll) > 0.25)
#plt.show()
#Go from the Pianoroll to a midi file which can be played.
midiwrite('2x128_temp%s.mid'%temperature, pressedroll , r, dt)
def train(self, files, batch_size=100, num_epochs=200):
assert len(files) > 0, 'Training set is empty!' \
' (did you download the data files?)'
dataset = [
midiread(f, self.r,
self.dt).piano_roll.astype(theano.config.floatX)
for f in files
]
def accuracy(v, v_sample):
accs = []
t, n = v.shape
for time in range(t):
tp = 0 # true positive
fp = 0 # false positive
fn = 0 # false negative
for note in range(n):
if v[time][note] == 1 and v_sample[time][note] == 1:
tp += 1.
if v[time][note] == 0 and v_sample[time][note] == 1:
fp += 1.
if v[time][note] == 1 and v_sample[time][note] == 0:
fn += 1.
if tp + fp + fn != 0:
a = tp / (tp + fp + fn)
else:
a = 0
accs.append(a)
acc = numpy.mean(accs)
return acc
def sampling(sample): # 01
s = T.matrix()
b = rng.binomial(size=s.shape,
n=1,
p=s,
dtype=theano.config.floatX)
fun = theano.function(inputs=[s], outputs=[b])
return fun(sample)[0]
try:
print('rnn, dataset=Nottingham, lr=%f, epoch=%i' %
(self.lr, num_epochs))
for epoch in range(num_epochs):
numpy.random.shuffle(dataset)
costs = []
monitors = []
accs = []
for s, sequence in enumerate(dataset):
for i in range(0, len(sequence), batch_size):
if i + batch_size + 1 >= len(sequence):
break
v = sequence[i:i + batch_size]
targets = sequence[i + 1:i + batch_size + 1]
(cost, monitor,
sample) = self.train_function(v, targets)
costs.append(cost)
monitors.append(monitor)
sample = sampling(sample)
acc = accuracy(v, sample)
accs.append(acc)
p = 'Epoch %i/%i LL %f ACC %f Cost %f time %s' \
% (epoch + 1, num_epochs, numpy.mean(monitors), numpy.mean(accs), numpy.mean(costs),
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
print(p)
if (epoch % 100 == 0 or epoch == num_epochs - 1):
piano_roll = sampling(self.generate_function()[0])
midiwrite('sample/rnn_%i.mid' % (epoch), piano_roll,
self.r, self.dt)
sys.stdout.flush()
except KeyboardInterrupt:
print('Interrupted by user.')
from midi.utils import midiwrite
import sys
# print "This is the name of the script: ", sys.argv[0]
# print "Number of arguments: ", len(sys.argv)
# print "The arguments are: " , str(sys.argv)
if len(sys.argv) != 2:
print 'song ID as arg'
else:
print 'Song ID:', int(sys.argv[1])
song_num = int(sys.argv[1])
results_dir = 'Results'
new_song = np.load('{}/input_song_{}.npz'.format(results_dir, song_num))
new_song = new_song[new_song.files[0]]
print(new_song.shape)
midiwrite('{}/input_song_{}.mid'.format(results_dir, song_num), new_song.T,
(32, 93), 0.25)
new_song = np.load('{}/output_song_{}.npz'.format(results_dir, song_num))
new_song = new_song[new_song.files[0]]
print(new_song.shape)
midiwrite('{}/output_song_{}.mid'.format(results_dir, song_num),
new_song.T, (32, 93), 0.25)
print '\n\nMiDi Generated...'
for sequence in data_batch
], 0)
train_result = sess.run(
[cost, train_op],
feed_dict={
x: x_batch,
state: numpy.zeros([batch_size, n_hidden]),
output: numpy.zeros([batch_size, n_hidden])
})
total_cost.append(train_result[0])
if len(total_cost) > 0:
loss_per_check += sum(total_cost) / float(len(total_cost))
if epoch % check_epochs == 0:
print 'epoch %d, epoch_cost: %f' % (epoch, loss_per_check)
if len(losses) > 2 and loss_per_check > max(losses):
lr = sess.run(learning_rate_decay_op)
print 'learning rate decay to %f' % lr
losses.append(loss_per_check)
loss_per_check = 0.0
sequence = sess.run(generator, feed_dict={x: x_batch})
for index in xrange(len(sequence)):
sequence[index] = sequence[index].reshape((n_output, ))
print '#############################'
for t_v in sequence[::5]:
print numpy.sum(t_v), t_v
midiwrite(generation_path + filename, sequence, r, dt)
generated = []
# for note in seed:
# generated.append(note)
# Now add seperator note
# generated.append(np.ones_like(seed[0]))
generated.append(seed[0])
for i in range(GEN_LENGTH):
# np.newaxis is needed to make predict work for some reason...
# pred_probs = model.predict(seed[np.newaxis, :], verbose=0)
pred_probs = model.predict(seed_batch, verbose=0)
next_note = np.random.binomial(n=1, p=pred_probs[0], size=pred_probs[0].shape)
### append to MIDI
generated.append(next_note)
# Update the input
# seed = np.vstack((seed[1:], next_note))
seed_batch[0][0] = next_note
### output MIDI
sample_filename = save_dir + "lstm_e10_composition_{}.mid".format(iteration)
print "Saving sample to {}".format(sample_filename)
midiwrite(sample_filename, generated, MIDI_RANGE, DT)
# Clean states again for training
model.reset_states()
def train(self, files, batch_size=128, num_epochs=200):
def downsampling(sample):
# only keep the note on even index position (0, 2, 4 ...)
downsample = []
for i, s in enumerate(sample):
if i % 2 == 0:
downsample.append(s)
return downsample
def upsampling(sample, length):
'''
double each notes in sample
sample: the melody to be upsampled
length: the length of the original melody M, sample=Downsampling(M)
'''
upsample = []
for s in sample:
upsample.append(s)
if (len(upsample) >= length):
# upsampling melody length cannot longer than original melody length
break
upsample.append(s)
return upsample
def sampling(sample):
# to be one hot (to be 0,1)
ixes = []
for i, s in enumerate(sample):
n_step = []
for n in xrange(20):
ix = np.random.choice(range(88), p=s)
n_step.append(ix)
count = Counter(n_step)
ix = count.most_common(1)[0][0]
x = np.zeros((88, ))
x[ix] = 1
ixes.append(x)
return ixes
def accuracy(v, v_sample):
# ACC
accs = []
t = len(v)
n = len(v[0])
for time in range(t):
tp = 0 # true positive
fp = 0 # false positive
fn = 0 # false negative
for note in range(n):
if v[time][note] == 1 and v_sample[time][note] == 1:
tp += 1.
if v[time][note] == 0 and v_sample[time][note] == 1:
fp += 1.
if v[time][note] == 1 and v_sample[time][note] == 0:
fn += 1.
if tp + fp + fn != 0:
a = tp / (tp + fp + fn)
else:
a = 0
accs.append(a)
acc = numpy.mean(accs)
return acc
def generate():
# sampling procedure, generating music
# layer 4
generate_sharp_4 = self.generate_4()[0]
generate_sharp_4 = sampling(generate_sharp_4)
# layer 3
upsample_3 = upsampling(generate_sharp_4,
2 * len(generate_sharp_4) + 1)
generate_sharp_3 = self.generate_3(upsample_3, upsample_3)[0]
generate_sharp_3 = sampling(generate_sharp_3)
# layer 2
upsample_2 = upsampling(generate_sharp_3,
2 * len(generate_sharp_3) + 1)
generate_sharp_2 = self.generate_2(upsample_2, upsample_2)[0]
generate_sharp_2 = sampling(generate_sharp_2)
# layer 1
upsample_1 = upsampling(generate_sharp_2,
2 * len(generate_sharp_2) + 1)
generate_sharp_1 = self.generate_1(upsample_1, upsample_1)[0]
generate_sharp_1 = sampling(generate_sharp_1)
# layer 0
upsample_0 = upsampling(generate_sharp_1,
2 * len(generate_sharp_1) + 1)
generate_sharp_0 = self.generate_0(upsample_0, upsample_0)[0]
generate_sharp_0 = sampling(generate_sharp_0)
return generate_sharp_0
# load data
dataset = [
midiread(f, self.r,
self.dt).piano_roll.astype(theano.config.floatX)
for f in files
]
print('pyramid_mono, dataset=nottingham_mono, lr=%f, epoch=%i' %
(self.lr, num_epochs))
for epoch in range(num_epochs):
numpy.random.shuffle(dataset)
monitors_0 = []
accs_0 = []
monitors_1 = []
accs_1 = []
monitors_2 = []
accs_2 = []
monitors_3 = []
accs_3 = []
monitors_4 = []
accs_4 = []
for s, sequence in enumerate(dataset):
for i in range(0, len(sequence), batch_size):
batch_music = sequence[i:i + batch_size] # 128
# layer 0
downsample_0 = downsampling(batch_music) # 64
upsample_0 = upsampling(downsample_0,
len(batch_music)) # 128
# layer 1
downsample_1 = downsampling(downsample_0) # 32
upsample_1 = upsampling(downsample_1,
len(downsample_0)) # 64
# layer 2
downsample_2 = downsampling(downsample_1) # 16
upsample_2 = upsampling(downsample_2,
len(downsample_1)) # 32
# layer 3
downsample_3 = downsampling(downsample_2) # 8
upsample_3 = upsampling(downsample_3,
len(downsample_2)) # 16
# layer 0
sharp_0, monitor_0 = self.sharp_fun_0(
batch_music, upsample_0)
monitors_0.append(monitor_0)
accs_0.append(accuracy(batch_music, sampling(sharp_0)))
# layer 1
sharp_1, monitor_1 = self.sharp_fun_1(
downsample_0, upsample_1)
monitors_1.append(monitor_1)
accs_1.append(accuracy(downsample_0, sampling(sharp_1)))
# layer 2
sharp_2, monitor_2 = self.sharp_fun_2(
downsample_1, upsample_2)
monitors_2.append(monitor_2)
accs_2.append(accuracy(downsample_1, sampling(sharp_2)))
# layer 3
sharp_3, monitor_3 = self.sharp_fun_3(
downsample_2, upsample_3)
monitors_3.append(monitor_3)
accs_3.append(accuracy(downsample_2, sampling(sharp_3)))
# layer 4
if (len(downsample_3) == 1):
sharp_4, monitor_4 = self.sharp_fun_4(
downsample_3, downsample_3)
accs_4.append(accuracy(downsample_3,
sampling(sharp_4)))
else:
sharp_4, monitor_4 = self.sharp_fun_4(
downsample_3[1:],
downsample_3[:len(downsample_3) - 1])
accs_4.append(
accuracy(downsample_3[1:], sampling(sharp_3)))
monitors_4.append(monitor_4)
p = 'Epoch %i/%i layer0:LL %f ACC %f layer1:LL %f ACC %f layer2:LL %f ACC %f layer3:LL %f ACC %f ' \
'layer4:LL %f ACC %f time %s' % \
(epoch + 1, num_epochs, numpy.mean(monitors_0), numpy.mean(accs_0), numpy.mean(monitors_1), numpy.mean(accs_1),
numpy.mean(monitors_2), numpy.mean(accs_2), numpy.mean(monitors_3), numpy.mean(accs_3), numpy.mean(monitors_4),
numpy.mean(accs_4), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
print(p)
if (epoch % 50 == 0 or epoch == num_epochs - 1):
piano_roll = generate()
midiwrite('sample/pyramid_mono_%i.mid' % (epoch), piano_roll,
self.r, self.dt)
def sample_and_save(model, length=64):
x = sample(model, length)
midiwrite('./test.midi', x, r=(12, 109), dt=64)
def generate(self, filename, show=True):
piano_roll = self.generate_function()
midiwrite(filename, piano_roll, self.r, self.dt)
model = RnnRbm()
model = train_rnnrbm(model, dataset)
cur = sys.getrecursionlimit()
sys.setrecursionlimit(40000)
with open('saved_rnndbn.pkl', mode='w') as f:
pickle.dump(model, f, -1)
sys.setrecursionlimit(cur)
with open('saved_rnndbn.pkl', mode='r') as f:
model = pickle.load(f)
import matplotlib
matplotlib.use('Agg')
sample_files = ["sample1.mid", "sample2.mid"]
for s in sample_files:
piano_roll = model.generate()
midiwrite(s, piano_roll, key_range, dt)
extent = (0, dt * len(piano_roll)) + key_range
import matplotlib.pyplot as plt
plt.imshow(piano_roll.T,
origin='lower',
aspect='auto',
interpolation='nearest',
cmap="gray_r",
extent=extent)
plt.xlabel('time (s)')
plt.ylabel('MIDI note number')
plt.title('generated piano-roll')
plt.savefig(s[:-4] + '.png')
# expt/chkpt-synthetic/DMM_lr-0_0008-dh-200-ds-100-nl-relu-bs-20-ep-20-rs-600-rd-0_1-infm-R-tl-2-el-2-ar-2_0-use_p-approx-rc-lstm-uid-final.h5
# =============================================
params, zvec = DMM_evaluate.sample(dmm_reloaded, T=200)
bin_prob = params[0]
print 'Samples: ', bin_prob.shape
# ===========================================
#Parameters for music sampling taken from http://deeplearning.net/tutorial/rnnrbm.html
rval = (21, 109)
dt = 0.3
SAVEDIR = './samples/'
createIfAbsent(SAVEDIR)
print 'Saving wav...'
for idx, sample in enumerate(bin_prob):
piano_roll = (sample > 0.5) * 1.
filename = SAVEDIR + DATASET + '-' + str(idx) + '.mid'
midiwrite(filename, piano_roll, rval, dt)
print idx,
print 'Converting...'
print os.system('cd ' + SAVEDIR + ';timidity -Ow1S ' + DATASET +
'*.mid;cd ../')
print '\nFiles\n', ', '.join(os.listdir(SAVEDIR))
# ================================================
import IPython
IPython.display.Audio('./samples/jsb-0.wav')
def generate(self, session, filename='001.mid'):
sequence = session.run(self.vg)
midiwrite(self.generation_path + filename, sequence, self.r, self.dt)
# for note in seed:
# generated.append(note)
# Now add seperator note
# generated.append(np.ones_like(seed[0]))
generated.append(seed[0])
for i in range(GEN_LENGTH):
# np.newaxis is needed to make predict work for some reason...
# pred_probs = model.predict(seed[np.newaxis, :], verbose=0)
pred_probs = model.predict(seed_batch, verbose=0)
next_note = np.random.binomial(n=1,
p=pred_probs[0],
size=pred_probs[0].shape)
### append to MIDI
generated.append(next_note)
# Update the input
# seed = np.vstack((seed[1:], next_note))
seed_batch[0][0] = next_note
### output MIDI
sample_filename = save_dir + "lstm_e30_composition_{}.mid".format(
iteration)
print "Saving sample to {}".format(sample_filename)
midiwrite(sample_filename, generated, MIDI_RANGE, DT)
# Clean states again for training
model.reset_states()
def main():
#--- import data ---#
sizeOfMiniBatch = 5 #how many tunes per miniBatch
noOfEpoch = 100
noOfEpochPerMB = 2
lengthOfMB = 100
sparseParam = np.float32(0.01) #increases with no. of cells
path = './Piano-midi.de/train-individual/hpps'
#path = './Piano-midi.de/train'
files = os.listdir(path)
assert len(files) > 0, 'Training set is empty!' \
' (did you download the data files?)'
#pitch range is from 21 to 109
dataset = [midiread((path + "/" + f), (21, 109),0.3).piano_roll.astype(theano.config.floatX) for f in files]
#check number of notes for each tune:
print(str([np.array(dataset[n]).shape[0] for n in np.arange(np.array(dataset).shape[0])]))
# set "silent" to zero in 1-hot format
for k in np.arange(np.array(dataset).shape[0]):
for n in np.arange(0,np.array(dataset[k]).shape[0],1):
if np.sum(dataset[k][n], dtype=theano.config.floatX) == 0 :
dataset[k][n][0] = np.float32(1.0)
#--- training with data ---#
myRNN4Music = RNN4Music(h1_length=176, h2_length=176, h3_length=176, io_length=88, R1=np.float32(0.001), R2=np.float32(0.001), R3=np.float32(0.001), Rout=np.float32(0.001))
#myRNN4Music.loadParameters('120_120_120_0_001_xEn_150epoch_hpps')
#myRNN4Music.loadParameters('120_120_120_0_001_sqr_150epoch_hpps')
myRNN4Music.loadParameters('176_176_176_0_001_xEn_L1_0_01_100epoch_hpps')
#myRNN4Music.saveParameters('176_176_176_0_001_xEn_L1_0_1_300epoch_hpps')
#myRNN4Music.train(dataset, noOfEpochPerMB, noOfEpoch, sizeOfMiniBatch, lengthOfMB, sparseParam)
#myRNN4Music.saveParameters('176_176_176_0_001_xEn_L1_0_01_100epoch_hpps')
#myRNN4Music.train(dataset, noOfEpochPerMB, noOfEpoch, sizeOfMiniBatch, lengthOfMB, sparseParam)
#myRNN4Music.saveParameters('176_176_176_0_001_xEn_L1_0_01_200epoch_hpps')
#myRNN4Music.train(dataset, noOfEpochPerMB, noOfEpoch, sizeOfMiniBatch, lengthOfMB, sparseParam)
#myRNN4Music.saveParameters('176_176_176_0_001_xEn_L1_0_01_300epoch_hpps')
#myRNN4Music.train(dataset, noOfEpochPerMB, noOfEpoch, sizeOfMiniBatch, lengthOfMB, sparseParam)
#myRNN4Music.saveParameters('176_176_176_0_001_xEn_L1_0_01_400epoch_hpps')
#--- plot some genearted tunes ---#
for baseSample in np.array([0, 20, 15, 31]):
exampleLength = 50
myRNN4Music.resetStates()
generatedTune = myRNN4Music.genMusic(np.float32(dataset[baseSample][0:exampleLength]), 300)
midiwrite('176_176_176_0_001_sqr_hpps150_' + str(baseSample) + '.mid', generatedTune[0], (21, 109),0.3)
#generatedTune[0] is the tune, generatedTune[1] is the probability at each iteration
#plot genearted probability
plt.figure(0 + baseSample*100)
plt.imshow(np.array(generatedTune[1][0:50,25:65]), origin = 'lower', extent=[25,65,0,50], aspect=0.5,
interpolation = 'nearest', cmap='gist_stern_r')
plt.title('probability of generated midi note piano-roll')
plt.xlabel('midi note')
plt.ylabel('sample number (time steps)')
plt.colorbar()##
#plot leading example for generation
plt.figure(1 + baseSample*100)
plt.imshow(np.transpose(dataset[baseSample]), origin='lower', aspect='auto',
interpolation='nearest', cmap=pylab.cm.gray_r)
plt.colorbar()
plt.title('original piano-roll')
plt.xlabel('sample number (time steps)')
plt.ylabel('midi note')
#plot generated tune
plt.figure(2 + baseSample*100)
plt.imshow(np.transpose(np.array(generatedTune[0][0:500])), origin='lower', aspect='auto',
interpolation='nearest', cmap=pylab.cm.gray_r)
plt.colorbar()
plt.title('generated piano-roll')
plt.xlabel('sample number (time steps)')
plt.ylabel('midi note')
plt.show()