import torch
from torch import nn
from torch.nn import functional as F
import modules
import attentions
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from commons import init_weights, get_padding
import torchaudio
from einops import rearrange
import transformers
import math
from styleencoder import StyleEncoder
import commons
from alias_free_torch import *
import activations
class Wav2vec2(torch.nn.Module):
def __init__(self, layer=7, w2v='mms'):
"""we use the intermediate features of mms-300m.
More specifically, we used the output from the 7th layer of the 24-layer transformer encoder.
"""
super().__init__()
if w2v == 'mms':
self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/mms-300m")
else:
self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-xls-r-300m")
for param in self.wav2vec2.parameters():
param.requires_grad = False
param.grad = None
self.wav2vec2.eval()
self.feature_layer = layer
@torch.no_grad()
def forward(self, x):
"""
Args:
x: torch.Tensor of shape (B x t)
Returns:
y: torch.Tensor of shape(B x C x t)
"""
outputs = self.wav2vec2(x.squeeze(1), output_hidden_states=True)
y = outputs.hidden_states[self.feature_layer] # B x t x C(1024)
y = y.permute((0, 2, 1)) # B x t x C -> B x C x t
return y
class ResidualCouplingBlock_Transformer(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers=3,
n_flows=4,
gin_channels=0):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.cond_block = torch.nn.Sequential(torch.nn.Linear(gin_channels, 4 * hidden_channels),
nn.SiLU(), torch.nn.Linear(4 * hidden_channels, hidden_channels))
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(modules.ResidualCouplingLayer_Transformer_simple(channels, hidden_channels, kernel_size, dilation_rate, n_layers, mean_only=True))
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
g = self.cond_block(g.squeeze(2))
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
class PosteriorAudioEncoder(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.down_pre = nn.Conv1d(1, 16, 7, 1, padding=3)
self.resblocks = nn.ModuleList()
downsample_rates = [8,5,4,2]
downsample_kernel_sizes = [17, 10, 8, 4]
ch = [16, 32, 64, 128, 192]
resblock = AMPBlock1
resblock_kernel_sizes = [3,7,11]
resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]]
self.num_kernels = 3
self.downs = nn.ModuleList()
for i, (u, k) in enumerate(zip(downsample_rates, downsample_kernel_sizes)):
self.downs.append(weight_norm(
Conv1d(ch[i], ch[i+1], k, u, padding=(k-1)//2)))
for i in range(4):
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch[i+1], k, d, activation="snakebeta"))
activation_post = activations.SnakeBeta(ch[i+1], alpha_logscale=True)
self.activation_post = Activation1d(activation=activation_post)
self.conv_post = Conv1d(ch[i+1], hidden_channels, 7, 1, padding=3)
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
self.proj = nn.Conv1d(hidden_channels*2, out_channels * 2, 1)
def forward(self, x, x_audio, x_mask, g=None):
x_audio = self.down_pre(x_audio)
for i in range(4):
x_audio = self.downs[i](x_audio)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i*self.num_kernels+j](x_audio)
else:
xs += self.resblocks[i*self.num_kernels+j](x_audio)
x_audio = xs / self.num_kernels
x_audio = self.activation_post(x_audio)
x_audio = self.conv_post(x_audio)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
x_audio = x_audio * x_mask
x = torch.cat([x, x_audio], dim=1)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
return z, m, logs
class PosteriorSFEncoder(nn.Module):
def __init__(self,
src_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
super().__init__()
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_source = nn.Conv1d(src_channels, hidden_channels, 1)
self.pre_filter = nn.Conv1d(1, hidden_channels, kernel_size=9, stride=4, padding=4)
self.source_enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels)
self.filter_enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels)
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers//2, gin_channels=gin_channels)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x_src, x_ftr, x_mask, g=None):
x_src = self.pre_source(x_src) * x_mask
x_ftr = self.pre_filter(x_ftr) * x_mask
x_src = self.source_enc(x_src, x_mask, g=g)
x_ftr = self.filter_enc(x_ftr, x_mask, g=g)
x = self.enc(x_src+x_ftr, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
return z, m, logs
class MelDecoder(nn.Module):
def __init__(self,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
mel_size=20,
gin_channels=0):
super().__init__()
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.conv_pre = Conv1d(hidden_channels, hidden_channels, 3, 1, padding=1)
self.encoder = attentions.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout)
self.proj= nn.Conv1d(hidden_channels, mel_size, 1, bias=False)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, hidden_channels, 1)
def forward(self, x, x_mask, g=None):
x = self.conv_pre(x*x_mask)
if g is not None:
x = x + self.cond(g)
x = self.encoder(x * x_mask, x_mask)
x = self.proj(x) * x_mask
return x
class SourceNetwork(nn.Module):
def __init__(self, upsample_initial_channel=256):
super().__init__()
resblock_kernel_sizes = [3,5,7]
upsample_rates = [2,2]
initial_channel = 192
upsample_initial_channel = upsample_initial_channel
upsample_kernel_sizes = [4,4]
resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]]
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3))
resblock = AMPBlock1
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(weight_norm(
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
k, u, padding=(k-u)//2)))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel//(2**(i+1))
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d, activation="snakebeta"))
activation_post = activations.SnakeBeta(ch, alpha_logscale=True)
self.activation_post = Activation1d(activation=activation_post)
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.cond = Conv1d(256, upsample_initial_channel, 1)
self.ups.apply(init_weights)
def forward(self, x, g):
x = self.conv_pre(x) + self.cond(g)
for i in range(self.num_upsamples):
x = self.ups[i](x)
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 = self.activation_post(x)
## Predictor
x_ = self.conv_post(x)
return x, 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()
class DBlock(nn.Module):
def __init__(self, input_size, hidden_size, factor):
super().__init__()
self.factor = factor
self.residual_dense = weight_norm(Conv1d(input_size, hidden_size, 1))
self.conv = nn.ModuleList([
weight_norm(Conv1d(input_size, hidden_size, 3, dilation=1, padding=1)),
weight_norm(Conv1d(hidden_size, hidden_size, 3, dilation=2, padding=2)),
weight_norm(Conv1d(hidden_size, hidden_size, 3, dilation=4, padding=4)),
])
self.conv.apply(init_weights)
def forward(self, x):
size = x.shape[-1] // self.factor
residual = self.residual_dense(x)
residual = F.interpolate(residual, size=size)
x = F.interpolate(x, size=size)
for layer in self.conv:
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = layer(x)
return x + residual
def remove_weight_norm(self):
for l in self.conv:
remove_weight_norm(l)
class AMPBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
super(AMPBlock1, self).__init__()
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.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
self.activations = nn.ModuleList([
Activation1d(
activation=activations.SnakeBeta(channels, alpha_logscale=True))
for _ in range(self.num_layers)
])
def forward(self, x):
acts1, acts2 = self.activations[::2], self.activations[1::2]
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
xt = a1(x)
xt = c1(xt)
xt = a2(xt)
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 Generator(torch.nn.Module):
def __init__(self, initial_channel, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=256):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = weight_norm(Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3))
resblock = AMPBlock1
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(weight_norm(
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
k, u, padding=(k-u)//2)))
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel//(2**(i+1))
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d, activation="snakebeta"))
activation_post = activations.SnakeBeta(ch, alpha_logscale=True)
self.activation_post = Activation1d(activation=activation_post)
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
self.downs = DBlock(upsample_initial_channel//8, upsample_initial_channel, 4)
self.proj = Conv1d(upsample_initial_channel//8, upsample_initial_channel//2, 7, 1, padding=3)
def forward(self, x, pitch, g=None):
x = self.conv_pre(x) + self.downs(pitch) + self.cond(g)
for i in range(self.num_upsamples):
x = self.ups[i](x)
if i == 0:
pitch = self.proj(pitch)
x = x + pitch
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 = self.activation_post(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()
for l in self.downs:
l.remove_weight_norm()
remove_weight_norm(self.conv_pre)
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
self.use_spectral_norm = use_spectral_norm
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(kernel_size, 1), 0))),
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 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, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorR(torch.nn.Module):
def __init__(self, resolution, use_spectral_norm=False):
super(DiscriminatorR, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
n_fft, hop_length, win_length = resolution
self.spec_transform = torchaudio.transforms.Spectrogram(
n_fft=n_fft, hop_length=hop_length, win_length=win_length, window_fn=torch.hann_window,
normalized=True, center=False, pad_mode=None, power=None)
self.convs = nn.ModuleList([
norm_f(nn.Conv2d(2, 32, (3, 9), padding=(1, 4))),
norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(2,1), padding=(2, 4))),
norm_f(nn.Conv2d(32, 32, (3, 9), stride=(1, 2), dilation=(4,1), padding=(4, 4))),
norm_f(nn.Conv2d(32, 32, (3, 3), padding=(1, 1))),
])
self.conv_post = norm_f(nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
def forward(self, y):
fmap = []
x = self.spec_transform(y) # [B, 2, Freq, Frames, 2]
x = torch.cat([x.real, x.imag], dim=1)
x = rearrange(x, 'b c w t -> b c t w')
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.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, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2,3,5,7,11]
resolutions = [[2048, 512, 2048], [1024, 256, 1024], [512, 128, 512], [256, 64, 256], [128, 32, 128]]
discs = [DiscriminatorR(resolutions[i], use_spectral_norm=use_spectral_norm) for i in range(len(resolutions))]
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
self.discriminators = nn.ModuleList(discs)
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)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(self,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=256,
prosody_size=20,
uncond_ratio=0.,
cfg=False,
**kwargs):
super().__init__()
self.spec_channels = spec_channels
self.inter_channels = inter_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.mel_size = prosody_size
self.enc_p_l = PosteriorSFEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
self.flow_l = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels)
self.enc_p = PosteriorSFEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
self.enc_q = PosteriorAudioEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
self.flow = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels)
self.mel_decoder = MelDecoder(inter_channels,
filter_channels,
n_heads=2,
n_layers=2,
kernel_size=5,
p_dropout=0.1,
mel_size=self.mel_size,
gin_channels=gin_channels)
self.dec = Generator(inter_channels, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
self.sn = SourceNetwork(upsample_initial_channel//2)
self.emb_g = StyleEncoder(in_dim=80, hidden_dim=256, out_dim=gin_channels)
if cfg:
self.emb = torch.nn.Embedding(1, 256)
torch.nn.init.normal_(self.emb.weight, 0.0, 256 ** -0.5)
self.null = torch.LongTensor([0]).cuda()
self.uncond_ratio = uncond_ratio
self.cfg = cfg
@torch.no_grad()
def infer(self, x_mel, w2v, length, f0):
x_mask = torch.unsqueeze(commons.sequence_mask(length, x_mel.size(2)), 1).to(x_mel.dtype)
# Speaker embedding from mel (Style Encoder)
g = self.emb_g(x_mel, x_mask).unsqueeze(-1)
z, _, _ = self.enc_p_l(w2v, f0, x_mask, g=g)
z = self.flow_l(z, x_mask, g=g, reverse=True)
z = self.flow(z, x_mask, g=g, reverse=True)
e, e_ = self.sn(z, g)
o = self.dec(z, e, g=g)
return o, e_
@torch.no_grad()
def voice_conversion(self, src, src_length, trg_mel, trg_length, f0, noise_scale = 0.333, uncond=False):
trg_mask = torch.unsqueeze(commons.sequence_mask(trg_length, trg_mel.size(2)), 1).to(trg_mel.dtype)
g = self.emb_g(trg_mel, trg_mask).unsqueeze(-1)
y_mask = torch.unsqueeze(commons.sequence_mask(src_length, src.size(2)), 1).to(trg_mel.dtype)
z, m_p, logs_p = self.enc_p_l(src, f0, y_mask, g=g)
z = (m_p + torch.randn_like(m_p) * torch.exp(logs_p)*noise_scale) * y_mask
z = self.flow_l(z, y_mask, g=g, reverse=True)
z = self.flow(z, y_mask, g=g, reverse=True)
if uncond:
null_emb = self.emb(self.null) * math.sqrt(256)
g = null_emb.unsqueeze(-1)
e, _ = self.sn(z, g)
o = self.dec(z, e, g=g)
return o
@torch.no_grad()
def voice_conversion_noise_control(self, src, src_length, trg_mel, trg_length, f0, noise_scale = 0.333, uncond=False, denoise_ratio = 0):
trg_mask = torch.unsqueeze(commons.sequence_mask(trg_length, trg_mel.size(2)), 1).to(trg_mel.dtype)
g = self.emb_g(trg_mel, trg_mask).unsqueeze(-1)
g_org, g_denoise = g[:1, :, :], g[1:, :, :]
g_interpolation = (1-denoise_ratio)*g_org + denoise_ratio*g_denoise
y_mask = torch.unsqueeze(commons.sequence_mask(src_length, src.size(2)), 1).to(trg_mel.dtype)
z, m_p, logs_p = self.enc_p_l(src, f0, y_mask, g=g_interpolation)
z = (m_p + torch.randn_like(m_p) * torch.exp(logs_p)*noise_scale) * y_mask
z = self.flow_l(z, y_mask, g=g_interpolation, reverse=True)
z = self.flow(z, y_mask, g=g_interpolation, reverse=True)
if uncond:
null_emb = self.emb(self.null) * math.sqrt(256)
g = null_emb.unsqueeze(-1)
e, _ = self.sn(z, g_interpolation)
o = self.dec(z, e, g=g_interpolation)
return o
@torch.no_grad()
def f0_extraction(self, x_linear, x_mel, length, x_audio, noise_scale = 0.333):
x_mask = torch.unsqueeze(commons.sequence_mask(length, x_mel.size(2)), 1).to(x_mel.dtype)
# Speaker embedding from mel (Style Encoder)
g = self.emb_g(x_mel, x_mask).unsqueeze(-1)
# posterior encoder from linear spec.
_, m_q, logs_q= self.enc_q(x_linear, x_audio, x_mask, g=g)
z = (m_q + torch.randn_like(m_q) * torch.exp(logs_q)*noise_scale)
# Source Networks
_, e_ = self.sn(z, g)
return e_