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import json | |
import os | |
import numpy as np | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d | |
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm | |
from .env import AttrDict | |
from .utils import get_padding, init_weights | |
LRELU_SLOPE = 0.1 | |
def load_model(model_path, device='cuda'): | |
h = load_config(model_path) | |
generator = Generator(h).to(device) | |
cp_dict = torch.load(model_path, map_location=device) | |
generator.load_state_dict(cp_dict['generator']) | |
generator.eval() | |
generator.remove_weight_norm() | |
del cp_dict | |
return generator, h | |
def load_config(model_path): | |
config_file = os.path.join(os.path.split(model_path)[0], 'config.json') | |
with open(config_file) as f: | |
data = f.read() | |
json_config = json.loads(data) | |
h = AttrDict(json_config) | |
return 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) | |
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): | |
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 | |
def _f02uv(self, f0): | |
# generate uv signal | |
uv = torch.ones_like(f0) | |
uv = uv * (f0 > self.voiced_threshold) | |
return uv | |
def forward(self, f0, upp): | |
""" 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) | |
""" | |
f0 = f0.unsqueeze(-1) | |
fn = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1))) | |
rad_values = (fn / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 | |
rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device) | |
rand_ini[:, 0] = 0 | |
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini | |
is_half = rad_values.dtype is not torch.float32 | |
tmp_over_one = torch.cumsum(rad_values.double(), 1) # % 1 #####%1意味着后面的cumsum无法再优化 | |
if is_half: | |
tmp_over_one = tmp_over_one.half() | |
else: | |
tmp_over_one = tmp_over_one.float() | |
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 | |
rad_values = rad_values.double() | |
cumsum_shift = cumsum_shift.double() | |
sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi) | |
if is_half: | |
sine_waves = sine_waves.half() | |
else: | |
sine_waves = sine_waves.float() | |
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): | |
sine_wavs, uv, _ = self.l_sin_gen(x, upp) | |
sine_merge = self.l_tanh(self.l_linear(sine_wavs)) | |
return sine_merge | |
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.m_source = SourceModuleHnNSF( | |
sampling_rate=h.sampling_rate, | |
harmonic_num=8 | |
) | |
self.noise_convs = nn.ModuleList() | |
self.conv_pre = weight_norm(Conv1d(h.num_mels, 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 = int(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() | |
ch = h.upsample_initial_channel | |
for i in range(len(self.ups)): | |
ch //= 2 | |
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.upp = int(np.prod(h.upsample_rates)) | |
def forward(self, x, f0): | |
har_source = self.m_source(f0, self.upp).transpose(1, 2) | |
x = self.conv_pre(x) | |
for i in range(self.num_upsamples): | |
x = F.leaky_relu(x, LRELU_SLOPE) | |
x = self.ups[i](x) | |
x_source = self.noise_convs[i](har_source) | |
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 is 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 is 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 | |