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vanilla-generative-model.py.old
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vanilla-generative-model.py.old
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import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
import scipy.io.wavfile as wavfile
def gated(x):
return F.tanh(x[:, :1]) * F.sigmoid(x[:, 1:])
class WaveNet(Chain):
def __init__(self):
super(WaveNet, self).__init__(
dc00=L.Convolution2D(1, 1, (2, 1), pad=(1, 0), use_cudnn=False),
dc01=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc02=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc03=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc04=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc05=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc06=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc07=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc08=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc09=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc10=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc11=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc12=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc13=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc14=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc15=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc16=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc17=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc18=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc19=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc20=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc21=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc22=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc23=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc24=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc25=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc26=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc27=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc28=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
dc29=L.Convolution2D(None, 2, (2, 1), pad=(1, 0), use_cudnn=False),
c101=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c102=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c103=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c104=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c105=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c106=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c107=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c108=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c109=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c110=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c111=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c112=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c113=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c114=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c115=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c116=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c117=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c118=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c119=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c120=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c121=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c122=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c123=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c124=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c125=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c126=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c127=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
c128=L.Convolution2D(None, 1, (1, 1), use_cudnn=False),
conv0=L.Convolution2D(None, 128, (1, 1), use_cudnn=False),
conv1=L.Convolution2D(None, 128, (1, 1), use_cudnn=False),
conv2=L.Convolution2D(None, 254, (1, 1), use_cudnn=False),
)
def __call__(self, x):
x00 = F.reshape(self.dc00(x)[:, :, :-1], (1, 1, -1, 2))
a01 = gated(self.dc01(x00)[:, :, :-1])
x01 = F.reshape(self.c101(a01) + x00, (1, 1, -1, 4))
a02 = gated(self.dc02(x01)[:, :, :-1])
x02 = F.reshape(self.c102(a02) + x01, (1, 1, -1, 8))
a03 = gated(self.dc03(x02)[:, :, :-1])
x03 = F.reshape(self.c103(a03) + x02, (1, 1, -1, 16))
a04 = gated(self.dc04(x03)[:, :, :-1])
x04 = F.reshape(self.c104(a04) + x03, (1, 1, -1, 32))
a05 = gated(self.dc05(x04)[:, :, :-1])
x05 = F.reshape(self.c105(a05) + x04, (1, 1, -1, 64))
a06 = gated(self.dc06(x05)[:, :, :-1])
x06 = F.reshape(self.c106(a06) + x05, (1, 1, -1, 128))
a07 = gated(self.dc07(x06)[:, :, :-1])
x07 = F.reshape(self.c107(a07) + x06, (1, 1, -1, 256))
a08 = gated(self.dc08(x07)[:, :, :-1])
x08 = F.reshape(self.c108(a08) + x07, (1, 1, -1, 512))
a09 = gated(self.dc09(x08)[:, :, :-1])
x09 = F.reshape(self.c109(a09) + x08, (1, 1, -1, 1))
a10 = gated(self.dc10(x09)[:, :, :-1])
x10 = F.reshape(self.c110(a10) + x09, (1, 1, -1, 2))
a11 = gated(self.dc11(x10)[:, :, :-1])
x11 = F.reshape(self.c111(a11) + x10, (1, 1, -1, 4))
a12 = gated(self.dc12(x11)[:, :, :-1])
x12 = F.reshape(self.c112(a12) + x11, (1, 1, -1, 8))
a13 = gated(self.dc13(x12)[:, :, :-1])
x13 = F.reshape(self.c113(a13) + x12, (1, 1, -1, 16))
a14 = gated(self.dc14(x13)[:, :, :-1])
x14 = F.reshape(self.c114(a14) + x13, (1, 1, -1, 32))
a15 = gated(self.dc15(x14)[:, :, :-1])
x15 = F.reshape(self.c115(a15) + x14, (1, 1, -1, 64))
a16 = gated(self.dc16(x15)[:, :, :-1])
x16 = F.reshape(self.c116(a16) + x15, (1, 1, -1, 128))
a17 = gated(self.dc17(x16)[:, :, :-1])
x17 = F.reshape(self.c117(a17) + x16, (1, 1, -1, 256))
a18 = gated(self.dc18(x17)[:, :, :-1])
x18 = F.reshape(self.c118(a18) + x17, (1, 1, -1, 512))
a19 = gated(self.dc19(x18)[:, :, :-1])
x19 = F.reshape(self.c119(a19) + x18, (1, 1, -1, 1))
a20 = gated(self.dc20(x19)[:, :, :-1])
x20 = F.reshape(self.c120(a20) + x19, (1, 1, -1, 2))
a21 = gated(self.dc21(x20)[:, :, :-1])
x21 = F.reshape(self.c121(a21) + x20, (1, 1, -1, 4))
a22 = gated(self.dc22(x21)[:, :, :-1])
x22 = F.reshape(self.c122(a22) + x21, (1, 1, -1, 8))
a23 = gated(self.dc23(x22)[:, :, :-1])
x23 = F.reshape(self.c123(a23) + x22, (1, 1, -1, 16))
a24 = gated(self.dc24(x23)[:, :, :-1])
x24 = F.reshape(self.c124(a24) + x23, (1, 1, -1, 32))
a25 = gated(self.dc25(x24)[:, :, :-1])
x25 = F.reshape(self.c125(a25) + x24, (1, 1, -1, 64))
a26 = gated(self.dc26(x25)[:, :, :-1])
x26 = F.reshape(self.c126(a26) + x25, (1, 1, -1, 128))
a27 = gated(self.dc27(x26)[:, :, :-1])
x27 = F.reshape(self.c127(a27) + x26, (1, 1, -1, 256))
a28 = gated(self.dc28(x27)[:, :, :-1])
x28 = F.reshape(self.c128(a28) + x27, (1, 1, -1, 512))
a29 = gated(self.dc29(x28)[:, :, :-1])
a_list = [a01, a02, a03, a04, a05, a06, a07, a08, a09, a10,
a11, a12, a13, a14, a15, a16, a17, a18, a19, a20,
a21, a22, a23, a24, a25, a26, a27, a28, a29]
sc = F.concat([F.reshape(a, (1, 1, -1, 1)) for a in a_list])
y = self.conv2(F.relu(self.conv1(F.relu(self.conv0(sc)))))
y = F.swapaxes(y, 1, 3)[0, 0]
return y
gen_len = 1024 * 30 # Multiple of 1024
scale = 4096.
with aifc.open('mydataset.aiff') as f:
fs = f.getframerate()
arr = []
while True:
b = f.readframes(1)
if b == b'':
break
arr.append(int.from_bytes(b, 'little', signed=True))
arr = np.array(arr)
arr /= scale
u, indices = np.unique(arr, return_inverse=True)
length = (len(arr) - 1)//1024*1024
x = arr[-length - 1: -1]
x = x.reshape(1, 1, -1, 1).astype(np.float32)
t = indices[-length:].astype(np.int32)
x = cuda.to_gpu(x)
t = cuda.to_gpu(t)
bias = cuda.cupy.array([0, 1], dtype=np.float32)
SCE = lambda y, t: F.softmax_cross_entropy(y, t, use_cudnn=False)
model = L.Classifier(WaveNet(), SCE)
model.to_gpu()
for i in model.predictor.links():
if i.name == 'dc00':
continue
elif 'dc' in i.name:
i.b.data += bias
optimizer = optimizers.Adam()
optimizer.setup(model)
#train
print('loss acc.')
for i in range(50000):
optimizer.update(model, x, t)
if i % 100 == 0:
print('%05.6f %05.6f' % (model.loss.data, model.accuracy.data))
print('saving model and state')
serializers.save_hdf5('model.h5', model)
serializers.save_hdf5('state.h5', optimizer)
# generate
print('generating')
x_sample = Variable(cuda.cupy.zeros((1, 1, gen_len, 1), dtype=np.float32),
volatile=True)
for loc in range(x_sample.data.shape[2])[1024:]:
prob = F.softmax(model.predictor(x_sample[:, :, loc-1024:loc])[-1:],
use_cudnn=False).data
prob = cuda.to_cpu(prob)[0]
x_sample.data[0, 0, loc] = np.random.choice(u, p=prob)
if loc % 200 == 0:
print(loc)
wavfile.write('result.wav', fs,
(cuda.to_cpu(x_sample.data)*scale).astype(np.int16).reshape((-1,
)))
print('done')