def interpolation(p0, p1, interpolation_rate, dimensions, model):
dims = dimensions
omega = torch.tensor(np.pi / 2)
t = interpolation_rate
A = torch.sin((1. - t) * omega) / torch.sin(omega)
B = torch.sin(t * omega) / torch.sin(omega)
z0 = p0 + 0
z0[:, dims] *= A
z1 = p1 + 0
z1[:, dims] *= B
z0[:, dims] += z1[:, dims]
output = model.decode(z0)
output = output.cpu().data.numpy()
output = numpy_to_midi(np.concatenate(output, 0), display=True)
IPython.display.display(IPython.display.Audio(output, rate=22050))
out = list()
for i in np.arange(0, 1.1, 0.2):
A = torch.sin((1. - i) * omega) / torch.sin(omega)
B = torch.sin(i * omega) / torch.sin(omega)
z0 = p0 + 0
z0[:, dims] *= A
z1 = p1 + 0
z1[:, dims] *= B
z0[:, dims] += z1[:, dims]
output = model.decode(z0)
output = list(output.cpu().data.numpy())
out.extend(output)
output = numpy_to_midi(np.concatenate(np.array(out), 0),
display=True,
interpolation=True)
IPython.display.display(IPython.display.Audio(output, rate=22050))
plot_interpolation(p0, p1, model)
def z_rhythm_tune(parameters, dimensions, z, model):
param = parameters
dims = dimensions
ratio = np.asarray([1, -0.5770807266235352, -0.6188344955444336, 0.3938814401626587, 0.28745582699775696])
ratio = torch.from_numpy(ratio).float()
if torch.cuda.is_available():
ratio = ratio.cuda()
tuned = z + 0
tuned[:, dims] += ratio[:len(dims)]*param
output = model.decode(tuned)
output = output.cpu().data.numpy()
output = numpy_to_midi(np.concatenate(output, 0), display=True)
IPython.display.display(IPython.display.Audio(output, rate=22050))
def z_rest_tune(parameters, dimensions, z, model):
param = parameters
dims = dimensions
ratio = 2*np.random.rand(128-7) - 1
ratio = torch.from_numpy(ratio).float()
if torch.cuda.is_available():
ratio = ratio.cuda()
tuned = z + 0
tuned[:, dims] += ratio[:len(dims)]*param
output = model.decode(tuned)
output = output.cpu().data.numpy()
output = numpy_to_midi(np.concatenate(output, 0), display=True)
IPython.display.display(IPython.display.Audio(output, rate=22050))
def z_pitch_tune(parameters, dimensions, z, model):
param = parameters
dims = dimensions
ratio = np.asarray([1, -0.46068906784057617, 0.17136303583780924, 0.158604234457016, 0.1399634579817454])
ratio = torch.from_numpy(ratio).float()
if torch.cuda.is_available():
ratio = ratio.cuda()
tuned = z + 0
tuned[:, dims] += ratio[:len(dims)]*(-param)
output = model.decode(tuned)
output = output.cpu().data.numpy()
output = numpy_to_midi(np.concatenate(output, 0), display=True)
IPython.display.display(IPython.display.Audio(output, rate=22050))