/
wavenet_model.py
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wavenet_model.py
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import os
import os.path
import time
from wavenet_modules import *
from audio_data import *
class WaveNetModel(nn.Module):
"""
A Complete Wavenet Model
Args:
layers (Int): Number of layers in each block
blocks (Int): Number of wavenet blocks of this model
dilation_channels (Int): Number of channels for the dilated convolution
residual_channels (Int): Number of channels for the residual connection
skip_channels (Int): Number of channels for the skip connections
classes (Int): Number of possible values each sample can have
output_length (Int): Number of samples that are generated for each input
kernel_size (Int): Size of the dilation kernel
dtype: Parameter type of this model
Shape:
- Input: :math:`(N, C_{in}, L_{in})`
- Output: :math:`()`
L should be the length of the receptive field
"""
def __init__(self,
layers=10,
blocks=4,
dilation_channels=32,
residual_channels=32,
skip_channels=256,
end_channels=256,
classes=256,
output_length=32,
kernel_size=2,
dtype=torch.FloatTensor,
bias=False):
super(WaveNetModel, self).__init__()
self.layers = layers
self.blocks = blocks
self.dilation_channels = dilation_channels
self.residual_channels = residual_channels
self.skip_channels = skip_channels
self.classes = classes
self.kernel_size = kernel_size
self.dtype = dtype
# build model
receptive_field = 1
init_dilation = 1
self.dilations = []
self.dilated_queues = []
# self.main_convs = nn.ModuleList()
self.filter_convs = nn.ModuleList()
self.gate_convs = nn.ModuleList()
self.residual_convs = nn.ModuleList()
self.skip_convs = nn.ModuleList()
# 1x1 convolution to create channels
self.start_conv = nn.Conv1d(in_channels=self.classes,
out_channels=residual_channels,
kernel_size=1,
bias=bias)
for b in range(blocks):
additional_scope = kernel_size - 1
new_dilation = 1
for i in range(layers):
# dilations of this layer
self.dilations.append((new_dilation, init_dilation))
# dilated queues for fast generation
self.dilated_queues.append(DilatedQueue(max_length=(kernel_size - 1) * new_dilation + 1,
num_channels=residual_channels,
dilation=new_dilation,
dtype=dtype))
# dilated convolutions
self.filter_convs.append(nn.Conv1d(in_channels=residual_channels,
out_channels=dilation_channels,
kernel_size=kernel_size,
bias=bias))
self.gate_convs.append(nn.Conv1d(in_channels=residual_channels,
out_channels=dilation_channels,
kernel_size=kernel_size,
bias=bias))
# 1x1 convolution for residual connection
self.residual_convs.append(nn.Conv1d(in_channels=dilation_channels,
out_channels=residual_channels,
kernel_size=1,
bias=bias))
# 1x1 convolution for skip connection
self.skip_convs.append(nn.Conv1d(in_channels=dilation_channels,
out_channels=skip_channels,
kernel_size=1,
bias=bias))
receptive_field += additional_scope
additional_scope *= 2
init_dilation = new_dilation
new_dilation *= 2
self.end_conv_1 = nn.Conv1d(in_channels=skip_channels,
out_channels=end_channels,
kernel_size=1,
bias=True)
self.end_conv_2 = nn.Conv1d(in_channels=end_channels,
out_channels=classes,
kernel_size=1,
bias=True)
# self.output_length = 2 ** (layers - 1)
self.output_length = output_length
self.receptive_field = receptive_field
def wavenet(self, input, dilation_func):
x = self.start_conv(input)
skip = 0
# WaveNet layers
for i in range(self.blocks * self.layers):
# |----------------------------------------| *residual*
# | |
# | |-- conv -- tanh --| |
# -> dilate -|----| * ----|-- 1x1 -- + --> *input*
# |-- conv -- sigm --| |
# 1x1
# |
# ---------------------------------------> + -------------> *skip*
(dilation, init_dilation) = self.dilations[i]
residual = dilation_func(x, dilation, init_dilation, i)
# dilated convolution
filter = self.filter_convs[i](residual)
filter = F.tanh(filter)
gate = self.gate_convs[i](residual)
gate = F.sigmoid(gate)
x = filter * gate
# parametrized skip connection
s = x
if x.size(2) != 1:
s = dilate(x, 1, init_dilation=dilation)
s = self.skip_convs[i](s)
try:
skip = skip[:, :, -s.size(2):]
except:
skip = 0
skip = s + skip
x = self.residual_convs[i](x)
x = x + residual[:, :, (self.kernel_size - 1):]
x = F.relu(skip)
x = F.relu(self.end_conv_1(x))
x = self.end_conv_2(x)
return x
def wavenet_dilate(self, input, dilation, init_dilation, i):
x = dilate(input, dilation, init_dilation)
return x
def queue_dilate(self, input, dilation, init_dilation, i):
queue = self.dilated_queues[i]
queue.enqueue(input.data[0])
x = queue.dequeue(num_deq=self.kernel_size,
dilation=dilation)
x = x.unsqueeze(0)
return x
def forward(self, input):
x = self.wavenet(input,
dilation_func=self.wavenet_dilate)
# reshape output
[n, c, l] = x.size()
l = self.output_length
x = x[:, :, -l:]
x = x.transpose(1, 2).contiguous()
x = x.view(n * l, c)
return x
def generate(self,
num_samples,
first_samples=None,
temperature=1.):
self.eval()
if first_samples is None:
first_samples = self.dtype(1).zero_()
generated = Variable(first_samples, volatile=True)
num_pad = self.receptive_field - generated.size(0)
if num_pad > 0:
generated = constant_pad_1d(generated, self.scope, pad_start=True)
print("pad zero")
for i in range(num_samples):
input = Variable(torch.FloatTensor(1, self.classes, self.receptive_field).zero_())
input = input.scatter_(1, generated[-self.receptive_field:].view(1, -1, self.receptive_field), 1.)
x = self.wavenet(input,
dilation_func=self.wavenet_dilate)[:, :, -1].squeeze()
if temperature > 0:
x /= temperature
prob = F.softmax(x, dim=0)
prob = prob.cpu()
np_prob = prob.data.numpy()
x = np.random.choice(self.classes, p=np_prob)
x = Variable(torch.LongTensor([x]))#np.array([x])
else:
x = torch.max(x, 0)[1].float()
generated = torch.cat((generated, x), 0)
generated = (generated / self.classes) * 2. - 1
mu_gen = mu_law_expansion(generated, self.classes)
self.train()
return mu_gen
def generate_fast(self,
num_samples,
first_samples=None,
temperature=1.,
regularize=0.,
progress_callback=None,
progress_interval=100):
self.eval()
if first_samples is None:
first_samples = torch.LongTensor(1).zero_() + (self.classes // 2)
first_samples = Variable(first_samples)
# reset queues
for queue in self.dilated_queues:
queue.reset()
num_given_samples = first_samples.size(0)
total_samples = num_given_samples + num_samples
input = Variable(torch.FloatTensor(1, self.classes, 1).zero_())
input = input.scatter_(1, first_samples[0:1].view(1, -1, 1), 1.)
# fill queues with given samples
for i in range(num_given_samples - 1):
x = self.wavenet(input,
dilation_func=self.queue_dilate)
input.zero_()
input = input.scatter_(1, first_samples[i + 1:i + 2].view(1, -1, 1), 1.).view(1, self.classes, 1)
# progress feedback
if i % progress_interval == 0:
if progress_callback is not None:
progress_callback(i, total_samples)
# generate new samples
generated = np.array([])
regularizer = torch.pow(Variable(torch.arange(self.classes)) - self.classes / 2., 2)
regularizer = regularizer.squeeze() * regularize
tic = time.time()
for i in range(num_samples):
x = self.wavenet(input,
dilation_func=self.queue_dilate).squeeze()
x -= regularizer
if temperature > 0:
# sample from softmax distribution
x /= temperature
prob = F.softmax(x, dim=0)
prob = prob.cpu()
np_prob = prob.data.numpy()
x = np.random.choice(self.classes, p=np_prob)
x = np.array([x])
else:
# convert to sample value
x = torch.max(x, 0)[1][0]
x = x.cpu()
x = x.data.numpy()
o = (x / self.classes) * 2. - 1
generated = np.append(generated, o)
# set new input
x = Variable(torch.from_numpy(x).type(torch.LongTensor))
input.zero_()
input = input.scatter_(1, x.view(1, -1, 1), 1.).view(1, self.classes, 1)
if (i+1) == 100:
toc = time.time()
print("one generating step does take approximately " + str((toc - tic) * 0.01) + " seconds)")
# progress feedback
if (i + num_given_samples) % progress_interval == 0:
if progress_callback is not None:
progress_callback(i + num_given_samples, total_samples)
self.train()
mu_gen = mu_law_expansion(generated, self.classes)
return mu_gen
def parameter_count(self):
par = list(self.parameters())
s = sum([np.prod(list(d.size())) for d in par])
return s
def cpu(self, type=torch.FloatTensor):
self.dtype = type
for q in self.dilated_queues:
q.dtype = self.dtype
super().cpu()
def load_latest_model_from(location, use_cuda=True):
files = [location + "/" + f for f in os.listdir(location)]
newest_file = max(files, key=os.path.getctime)
print("load model " + newest_file)
if use_cuda:
model = torch.load(newest_file)
else:
model = load_to_cpu(newest_file)
return model
def load_to_cpu(path):
model = torch.load(path, map_location=lambda storage, loc: storage)
model.cpu()
return model