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Ναρουσέ·μ·γιουμεμί·Χινακάννα 2023-07-17 18:39:41 +08:00 committed by GitHub
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import os
import json
from .env import AttrDict
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from .utils import init_weights, get_padding
LRELU_SLOPE = 0.1
def load_model(model_path, device='cuda'):
config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
with open(config_file) as f:
data = f.read()
global h
json_config = json.loads(data)
h = AttrDict(json_config)
generator = Generator(h).to(device)
cp_dict = torch.load(model_path)
generator.load_state_dict(cp_dict['generator'])
generator.eval()
generator.remove_weight_norm()
del cp_dict
return generator, h
class ResBlock1(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.h = h
self.convs1 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2])))
])
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1)))
])
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class ResBlock2(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.h = h
self.convs = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1])))
])
self.convs.apply(init_weights)
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
def padDiff(x):
return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
class SineGen(torch.nn.Module):
""" Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
samp_rate: sampling rate in Hz
harmonic_num: number of harmonic overtones (default 0)
sine_amp: amplitude of sine-wavefrom (default 0.1)
noise_std: std of Gaussian noise (default 0.003)
voiced_thoreshold: F0 threshold for U/V classification (default 0)
flag_for_pulse: this SinGen is used inside PulseGen (default False)
Note: when flag_for_pulse is True, the first time step of a voiced
segment is always sin(np.pi) or cos(0)
"""
def __init__(self, samp_rate, harmonic_num=0,
sine_amp=0.1, noise_std=0.003,
voiced_threshold=0,
flag_for_pulse=False):
super(SineGen, self).__init__()
self.sine_amp = sine_amp
self.noise_std = noise_std
self.harmonic_num = harmonic_num
self.dim = self.harmonic_num + 1
self.sampling_rate = samp_rate
self.voiced_threshold = voiced_threshold
self.flag_for_pulse = flag_for_pulse
def _f02uv(self, f0):
# generate uv signal
uv = (f0 > self.voiced_threshold).type(torch.float32)
return uv
def forward(self, f0, upp=None):
""" sine_tensor, uv = forward(f0)
input F0: tensor(batchsize=1, length, dim=1)
f0 for unvoiced steps should be 0
output sine_tensor: tensor(batchsize=1, length, dim)
output uv: tensor(batchsize=1, length, 1)
"""
with torch.no_grad():
f0 = f0[:, None].transpose(1, 2)
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
# fundamental component
f0_buf[:, :, 0] = f0[:, :, 0]
for idx in np.arange(self.harmonic_num):
f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
idx + 2
) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
rand_ini = torch.rand(
f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
)
rand_ini[:, 0] = 0
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
tmp_over_one *= upp
tmp_over_one = F.interpolate(
tmp_over_one.transpose(2, 1),
scale_factor=upp,
mode="linear",
align_corners=True,
).transpose(2, 1)
rad_values = F.interpolate(
rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
).transpose(
2, 1
) #######
tmp_over_one %= 1
tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
cumsum_shift = torch.zeros_like(rad_values)
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
sine_waves = torch.sin(
torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
)
sine_waves = sine_waves * self.sine_amp
uv = self._f02uv(f0)
uv = F.interpolate(
uv.transpose(2, 1), scale_factor=upp, mode="nearest"
).transpose(2, 1)
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
noise = noise_amp * torch.randn_like(sine_waves)
sine_waves = sine_waves * uv + noise
return sine_waves, uv, noise
class SourceModuleHnNSF(torch.nn.Module):
""" SourceModule for hn-nsf
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0)
sampling_rate: sampling_rate in Hz
harmonic_num: number of harmonic above F0 (default: 0)
sine_amp: amplitude of sine source signal (default: 0.1)
add_noise_std: std of additive Gaussian noise (default: 0.003)
note that amplitude of noise in unvoiced is decided
by sine_amp
voiced_threshold: threhold to set U/V given F0 (default: 0)
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
uv (batchsize, length, 1)
"""
def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
add_noise_std=0.003, voiced_threshod=0):
super(SourceModuleHnNSF, self).__init__()
self.sine_amp = sine_amp
self.noise_std = add_noise_std
# to produce sine waveforms
self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
sine_amp, add_noise_std, voiced_threshod)
# to merge source harmonics into a single excitation
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
self.l_tanh = torch.nn.Tanh()
def forward(self, x, upp=None):
"""
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
F0_sampled (batchsize, length, 1)
Sine_source (batchsize, length, 1)
noise_source (batchsize, length 1)
"""
# source for harmonic branch
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
# source for noise branch, in the same shape as uv
noise = torch.randn_like(uv) * self.sine_amp / 3
return sine_merge, noise, uv
class Generator(torch.nn.Module):
def __init__(self, h):
super(Generator, self).__init__()
self.h = h
self.num_kernels = len(h["resblock_kernel_sizes"])
self.num_upsamples = len(h["upsample_rates"])
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
self.m_source = SourceModuleHnNSF(
sampling_rate=h["sampling_rate"],
harmonic_num=8)
self.noise_convs = nn.ModuleList()
self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
self.ups.append(weight_norm(
ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
k, u, padding=(k - u) // 2)))
if i + 1 < len(h["upsample_rates"]): #
stride_f0 = np.prod(h["upsample_rates"][i + 1:])
self.noise_convs.append(Conv1d(
1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
else:
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = h["upsample_initial_channel"] // (2 ** (i + 1))
for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
self.resblocks.append(resblock(h, ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
self.upp = np.prod(h["upsample_rates"])
def forward(self, x, f0, g=None):
# print(1,x.shape,f0.shape,f0[:, None].shape)
# f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
# print(2,f0.shape)
har_source, noi_source, uv = self.m_source(f0, self.upp)
har_source = har_source.transpose(1, 2)
x = self.conv_pre(x)
x = x + self.cond(g)
# print(124,x.shape,har_source.shape)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, LRELU_SLOPE)
# print(3,x.shape)
x = self.ups[i](x)
x_source = self.noise_convs[i](har_source)
# print(4,x_source.shape,har_source.shape,x.shape)
x = x + x_source
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print('Removing weight norm...')
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post)
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
])
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, periods=None):
super(MultiPeriodDiscriminator, self).__init__()
self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
self.discriminators = nn.ModuleList()
for period in self.periods:
self.discriminators.append(DiscriminatorP(period))
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv1d(1, 128, 15, 1, padding=7)),
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
])
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiScaleDiscriminator(torch.nn.Module):
def __init__(self):
super(MultiScaleDiscriminator, self).__init__()
self.discriminators = nn.ModuleList([
DiscriminatorS(use_spectral_norm=True),
DiscriminatorS(),
DiscriminatorS(),
])
self.meanpools = nn.ModuleList([
AvgPool1d(4, 2, padding=2),
AvgPool1d(4, 2, padding=2)
])
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
if i != 0:
y = self.meanpools[i - 1](y)
y_hat = self.meanpools[i - 1](y_hat)
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg ** 2)
loss += (r_loss + g_loss)
r_losses.append(r_loss.item())
g_losses.append(g_loss.item())
return loss, r_losses, g_losses
def generator_loss(disc_outputs):
loss = 0
gen_losses = []
for dg in disc_outputs:
l = torch.mean((1 - dg) ** 2)
gen_losses.append(l)
loss += l
return loss, gen_losses