|
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 |
|
import numpy as np |
|
from stft import TorchSTFT |
|
|
|
LRELU_SLOPE = 0.1 |
|
|
|
|
|
@torch.jit.script |
|
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): |
|
n_channels_int = n_channels[0] |
|
in_act = input_a + input_b |
|
t_act = torch.tanh(in_act[:, :n_channels_int, :]) |
|
s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) |
|
acts = t_act * s_act |
|
return acts |
|
|
|
|
|
class WN(torch.nn.Module): |
|
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): |
|
super(WN, self).__init__() |
|
assert(kernel_size % 2 == 1) |
|
self.hidden_channels =hidden_channels |
|
self.kernel_size = kernel_size, |
|
self.dilation_rate = dilation_rate |
|
self.n_layers = n_layers |
|
self.gin_channels = gin_channels |
|
self.p_dropout = p_dropout |
|
|
|
self.in_layers = torch.nn.ModuleList() |
|
self.res_skip_layers = torch.nn.ModuleList() |
|
self.drop = nn.Dropout(p_dropout) |
|
|
|
if gin_channels != 0: |
|
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) |
|
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') |
|
|
|
for i in range(n_layers): |
|
dilation = dilation_rate ** i |
|
padding = int((kernel_size * dilation - dilation) / 2) |
|
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, |
|
dilation=dilation, padding=padding) |
|
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') |
|
self.in_layers.append(in_layer) |
|
|
|
|
|
if i < n_layers - 1: |
|
res_skip_channels = 2 * hidden_channels |
|
else: |
|
res_skip_channels = hidden_channels |
|
|
|
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) |
|
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') |
|
self.res_skip_layers.append(res_skip_layer) |
|
|
|
def forward(self, x, x_mask, g=None, **kwargs): |
|
output = torch.zeros_like(x) |
|
n_channels_tensor = torch.IntTensor([self.hidden_channels]) |
|
|
|
if g is not None: |
|
g = self.cond_layer(g) |
|
|
|
for i in range(self.n_layers): |
|
x_in = self.in_layers[i](x) |
|
if g is not None: |
|
cond_offset = i * 2 * self.hidden_channels |
|
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] |
|
else: |
|
g_l = torch.zeros_like(x_in) |
|
|
|
acts = fused_add_tanh_sigmoid_multiply( |
|
x_in, |
|
g_l, |
|
n_channels_tensor) |
|
acts = self.drop(acts) |
|
|
|
res_skip_acts = self.res_skip_layers[i](acts) |
|
if i < self.n_layers - 1: |
|
res_acts = res_skip_acts[:,:self.hidden_channels,:] |
|
x = (x + res_acts) * x_mask |
|
output = output + res_skip_acts[:,self.hidden_channels:,:] |
|
else: |
|
output = output + res_skip_acts |
|
return output * x_mask |
|
|
|
def remove_weight_norm(self): |
|
if self.gin_channels != 0: |
|
torch.nn.utils.remove_weight_norm(self.cond_layer) |
|
for l in self.in_layers: |
|
torch.nn.utils.remove_weight_norm(l) |
|
for l in self.res_skip_layers: |
|
torch.nn.utils.remove_weight_norm(l) |
|
|
|
|
|
class Encoder(nn.Module): |
|
def __init__(self, |
|
in_channels, |
|
out_channels, |
|
hidden_channels, |
|
kernel_size, |
|
dilation_rate, |
|
n_layers, |
|
gin_channels=0): |
|
super().__init__() |
|
self.in_channels = in_channels |
|
self.out_channels = out_channels |
|
self.hidden_channels = hidden_channels |
|
self.kernel_size = kernel_size |
|
self.dilation_rate = dilation_rate |
|
self.n_layers = n_layers |
|
self.gin_channels = gin_channels |
|
|
|
self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
|
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) |
|
self.proj = nn.Conv1d(hidden_channels, out_channels, 1) |
|
|
|
def forward(self, x, x_mask=1, g=None): |
|
|
|
x = self.pre(x) * x_mask |
|
x = self.enc(x, x_mask, g=g) |
|
x = self.proj(x) * x_mask |
|
return x |
|
|
|
|
|
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) |
|
|
|
self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))]) |
|
self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))]) |
|
|
|
|
|
def forward(self, x): |
|
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.alpha1, self.alpha2): |
|
xt = x + (1 / a1) * (torch.sin(a1 * x) ** 2) |
|
xt = c1(xt) |
|
xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) |
|
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 ResBlock1_old(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, upsample_scale, 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 |
|
self.upsample_scale = upsample_scale |
|
|
|
def _f02uv(self, f0): |
|
|
|
uv = (f0 > self.voiced_threshold).type(torch.float32) |
|
return uv |
|
|
|
def _f02sine(self, f0_values): |
|
""" f0_values: (batchsize, length, dim) |
|
where dim indicates fundamental tone and overtones |
|
""" |
|
|
|
|
|
rad_values = (f0_values / self.sampling_rate) % 1 |
|
|
|
|
|
rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ |
|
device=f0_values.device) |
|
rand_ini[:, 0] = 0 |
|
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini |
|
|
|
|
|
if not self.flag_for_pulse: |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), |
|
scale_factor=1/self.upsample_scale, |
|
mode="linear").transpose(1, 2) |
|
|
|
|
|
|
|
|
|
|
|
|
|
phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi |
|
phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, |
|
scale_factor=self.upsample_scale, mode="linear").transpose(1, 2) |
|
sines = torch.sin(phase) |
|
|
|
else: |
|
|
|
|
|
|
|
|
|
|
|
uv = self._f02uv(f0_values) |
|
uv_1 = torch.roll(uv, shifts=-1, dims=1) |
|
uv_1[:, -1, :] = 1 |
|
u_loc = (uv < 1) * (uv_1 > 0) |
|
|
|
|
|
tmp_cumsum = torch.cumsum(rad_values, dim=1) |
|
|
|
for idx in range(f0_values.shape[0]): |
|
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] |
|
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] |
|
|
|
|
|
tmp_cumsum[idx, :, :] = 0 |
|
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum |
|
|
|
|
|
|
|
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) |
|
|
|
|
|
sines = torch.cos(i_phase * 2 * np.pi) |
|
return sines |
|
|
|
def forward(self, f0): |
|
""" 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_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, |
|
device=f0.device) |
|
|
|
fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) |
|
|
|
|
|
sine_waves = self._f02sine(fn) * self.sine_amp |
|
|
|
|
|
|
|
|
|
uv = self._f02uv(f0) |
|
|
|
|
|
|
|
|
|
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, upsample_scale, 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 |
|
|
|
|
|
self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num, |
|
sine_amp, add_noise_std, voiced_threshod) |
|
|
|
|
|
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) |
|
self.l_tanh = torch.nn.Tanh() |
|
|
|
def forward(self, x): |
|
""" |
|
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) |
|
F0_sampled (batchsize, length, 1) |
|
Sine_source (batchsize, length, 1) |
|
noise_source (batchsize, length 1) |
|
""" |
|
|
|
with torch.no_grad(): |
|
sine_wavs, uv, _ = self.l_sin_gen(x) |
|
sine_merge = self.l_tanh(self.l_linear(sine_wavs)) |
|
|
|
|
|
noise = torch.randn_like(uv) * self.sine_amp / 3 |
|
return sine_merge, noise, uv |
|
def padDiff(x): |
|
return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) |
|
|
|
|
|
|
|
class Generator(torch.nn.Module): |
|
def __init__(self, h, F0_model): |
|
super(Generator, self).__init__() |
|
self.h = h |
|
self.num_kernels = len(h.resblock_kernel_sizes) |
|
self.num_upsamples = len(h.upsample_rates) |
|
resblock = ResBlock1 if h.resblock == '1' else ResBlock2 |
|
|
|
self.m_source = SourceModuleHnNSF( |
|
sampling_rate=h.sampling_rate, |
|
upsample_scale=np.prod(h.upsample_rates) * h.gen_istft_hop_size, |
|
harmonic_num=8, voiced_threshod=10) |
|
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h.upsample_rates) * h.gen_istft_hop_size) |
|
self.noise_convs = nn.ModuleList() |
|
self.noise_res = nn.ModuleList() |
|
|
|
self.F0_model = F0_model |
|
|
|
self.ups = nn.ModuleList() |
|
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): |
|
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))) |
|
|
|
c_cur = h.upsample_initial_channel // (2 ** (i + 1)) |
|
|
|
if i + 1 < len(h.upsample_rates): |
|
stride_f0 = np.prod(h.upsample_rates[i + 1:]) |
|
self.noise_convs.append(Conv1d( |
|
h.gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2)) |
|
self.noise_res.append(resblock(h, c_cur, 7, [1,3,5])) |
|
else: |
|
self.noise_convs.append(Conv1d(h.gen_istft_n_fft + 2, c_cur, kernel_size=1)) |
|
self.noise_res.append(resblock(h, c_cur, 11, [1,3,5])) |
|
|
|
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.post_n_fft = h.gen_istft_n_fft |
|
self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3)) |
|
self.ups.apply(init_weights) |
|
self.conv_post.apply(init_weights) |
|
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0)) |
|
self.stft = TorchSTFT(filter_length=h.gen_istft_n_fft, hop_length=h.gen_istft_hop_size, win_length=h.gen_istft_n_fft) |
|
|
|
gin_channels = 256 |
|
inter_channels = hidden_channels = h.upsample_initial_channel - gin_channels |
|
|
|
self.embed_spk = nn.Embedding(108, gin_channels) |
|
self.enc = Encoder(768, inter_channels, hidden_channels, 5, 1, 4) |
|
self.dec = Encoder(inter_channels, inter_channels, hidden_channels, 5, 1, 20, gin_channels=gin_channels) |
|
|
|
def forward(self, x, mel, spk_emb, spk_id): |
|
g = self.embed_spk(spk_id).transpose(1, 2) |
|
g = g + spk_emb.unsqueeze(-1) |
|
|
|
f0, _, _ = self.F0_model(mel.unsqueeze(1)) |
|
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) |
|
|
|
har_source, _, _ = self.m_source(f0) |
|
har_source = har_source.transpose(1, 2).squeeze(1) |
|
har_spec, har_phase = self.stft.transform(har_source) |
|
har = torch.cat([har_spec, har_phase], dim=1) |
|
|
|
x = self.enc(x) |
|
x = self.dec(x, g=g) |
|
g = g.repeat(1, 1, x.shape[-1]) |
|
x = torch.cat([x, g], dim=1) |
|
|
|
for i in range(self.num_upsamples): |
|
x = F.leaky_relu(x, LRELU_SLOPE) |
|
x_source = self.noise_convs[i](har) |
|
x_source = self.noise_res[i](x_source) |
|
|
|
x = self.ups[i](x) |
|
if i == self.num_upsamples - 1: |
|
x = self.reflection_pad(x) |
|
|
|
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) |
|
spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) |
|
phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) |
|
|
|
return spec, phase |
|
|
|
def get_f0(self, mel, f0_mean_tgt, voiced_threshold=10): |
|
f0, _, _ = self.F0_model(mel.unsqueeze(1)) |
|
voiced = f0 > voiced_threshold |
|
|
|
lf0 = torch.log(f0) |
|
lf0_ = lf0 * voiced.float() |
|
lf0_mean = lf0_.sum(1) / voiced.float().sum(1) |
|
lf0_mean = lf0_mean.unsqueeze(1) |
|
lf0_adj = lf0 - lf0_mean + torch.log(f0_mean_tgt) |
|
f0_adj = torch.exp(lf0_adj) |
|
|
|
energy = mel.sum(1) |
|
unsilent = energy > -700 |
|
unsilent = unsilent | voiced |
|
f0_adj = f0_adj * unsilent.float() |
|
|
|
return f0_adj |
|
|
|
def get_x(self, x, spk_emb, spk_id): |
|
g = self.embed_spk(spk_id).transpose(1, 2) |
|
g = g + spk_emb.unsqueeze(-1) |
|
|
|
x = self.enc(x) |
|
x = self.dec(x, g=g) |
|
g = g.repeat(1, 1, x.shape[-1]) |
|
x = torch.cat([x, g], dim=1) |
|
|
|
return x |
|
|
|
def infer(self, x, f0): |
|
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) |
|
|
|
har_source, _, _ = self.m_source(f0) |
|
har_source = har_source.transpose(1, 2).squeeze(1) |
|
har_spec, har_phase = self.stft.transform(har_source) |
|
har = torch.cat([har_spec, har_phase], dim=1) |
|
|
|
for i in range(self.num_upsamples): |
|
x = F.leaky_relu(x, LRELU_SLOPE) |
|
x_source = self.noise_convs[i](har) |
|
x_source = self.noise_res[i](x_source) |
|
|
|
x = self.ups[i](x) |
|
if i == self.num_upsamples - 1: |
|
x = self.reflection_pad(x) |
|
|
|
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) |
|
spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) |
|
phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) |
|
|
|
y = self.stft.inverse(spec, phase) |
|
return y |
|
|
|
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_post) |
|
|
|
|
|
def stft(x, fft_size, hop_size, win_length, window): |
|
"""Perform STFT and convert to magnitude spectrogram. |
|
Args: |
|
x (Tensor): Input signal tensor (B, T). |
|
fft_size (int): FFT size. |
|
hop_size (int): Hop size. |
|
win_length (int): Window length. |
|
window (str): Window function type. |
|
Returns: |
|
Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). |
|
""" |
|
x_stft = torch.stft(x, fft_size, hop_size, win_length, window, |
|
return_complex=True) |
|
real = x_stft[..., 0] |
|
imag = x_stft[..., 1] |
|
|
|
|
|
return torch.abs(x_stft).transpose(2, 1) |
|
|
|
class SpecDiscriminator(nn.Module): |
|
"""docstring for Discriminator.""" |
|
|
|
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): |
|
super(SpecDiscriminator, self).__init__() |
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
|
self.fft_size = fft_size |
|
self.shift_size = shift_size |
|
self.win_length = win_length |
|
self.window = getattr(torch, window)(win_length) |
|
self.discriminators = nn.ModuleList([ |
|
norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), |
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), |
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), |
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))), |
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))), |
|
]) |
|
|
|
self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) |
|
|
|
def forward(self, y): |
|
|
|
fmap = [] |
|
y = y.squeeze(1) |
|
y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device())) |
|
y = y.unsqueeze(1) |
|
for i, d in enumerate(self.discriminators): |
|
y = d(y) |
|
y = F.leaky_relu(y, LRELU_SLOPE) |
|
fmap.append(y) |
|
|
|
y = self.out(y) |
|
fmap.append(y) |
|
|
|
return torch.flatten(y, 1, -1), fmap |
|
|
|
class MultiResSpecDiscriminator(torch.nn.Module): |
|
|
|
def __init__(self, |
|
fft_sizes=[1024, 2048, 512], |
|
hop_sizes=[120, 240, 50], |
|
win_lengths=[600, 1200, 240], |
|
window="hann_window"): |
|
|
|
super(MultiResSpecDiscriminator, self).__init__() |
|
self.discriminators = nn.ModuleList([ |
|
SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), |
|
SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), |
|
SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window) |
|
]) |
|
|
|
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 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 = [] |
|
|
|
|
|
b, c, t = x.shape |
|
if t % self.period != 0: |
|
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): |
|
super(MultiPeriodDiscriminator, self).__init__() |
|
self.discriminators = nn.ModuleList([ |
|
DiscriminatorP(2), |
|
DiscriminatorP(3), |
|
DiscriminatorP(5), |
|
DiscriminatorP(7), |
|
DiscriminatorP(11), |
|
]) |
|
|
|
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 |
|
|
|
def discriminator_TPRLS_loss(disc_real_outputs, disc_generated_outputs): |
|
loss = 0 |
|
for dr, dg in zip(disc_real_outputs, disc_generated_outputs): |
|
tau = 0.04 |
|
m_DG = torch.median((dr-dg)) |
|
L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG]) |
|
loss += tau - F.relu(tau - L_rel) |
|
return loss |
|
|
|
def generator_TPRLS_loss(disc_real_outputs, disc_generated_outputs): |
|
loss = 0 |
|
for dg, dr in zip(disc_real_outputs, disc_generated_outputs): |
|
tau = 0.04 |
|
m_DG = torch.median((dr-dg)) |
|
L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG]) |
|
loss += tau - F.relu(tau - L_rel) |
|
return loss |
|
|