BlueArchiveTTS / models.py
ORI-Muchim's picture
Update models.py
6f33d96 verified
raw
history blame
58.9 kB
import copy
import math
import torch
from torch import nn
from torch.nn import functional as F
import commons
import modules
import attentions
import monotonic_align
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from commons import init_weights, get_padding
from pqmf import PQMF
from stft import TorchSTFT, OnnxSTFT
AVAILABLE_FLOW_TYPES = ["pre_conv", "pre_conv2", "fft", "mono_layer_inter_residual", "mono_layer_post_residual"]
AVAILABLE_DURATION_DISCRIMINATOR_TYPES = {"dur_disc_1": "DurationDiscriminator", "dur_disc_2": "DurationDiscriminator2"}
class StochasticDurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
super().__init__()
filter_channels = in_channels # it needs to be removed from future version.
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.n_flows = n_flows
self.gin_channels = gin_channels
self.log_flow = modules.Log()
self.flows = nn.ModuleList()
self.flows.append(modules.ElementwiseAffine(2))
for i in range(n_flows):
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
self.flows.append(modules.Flip())
self.post_pre = nn.Conv1d(1, filter_channels, 1)
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
self.post_flows = nn.ModuleList()
self.post_flows.append(modules.ElementwiseAffine(2))
for i in range(4):
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
self.post_flows.append(modules.Flip())
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
x = torch.detach(x)
x = self.pre(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.convs(x, x_mask)
x = self.proj(x) * x_mask
if not reverse:
flows = self.flows
assert w is not None
logdet_tot_q = 0
h_w = self.post_pre(w)
h_w = self.post_convs(h_w, x_mask)
h_w = self.post_proj(h_w) * x_mask
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
z_q = e_q
for flow in self.post_flows:
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
logdet_tot_q += logdet_q
z_u, z1 = torch.split(z_q, [1, 1], 1)
u = torch.sigmoid(z_u) * x_mask
z0 = (w - u) * x_mask
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
logdet_tot = 0
z0, logdet = self.log_flow(z0, x_mask)
logdet_tot += logdet
z = torch.cat([z0, z1], 1)
for flow in flows:
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
logdet_tot = logdet_tot + logdet
nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
return nll + logq # [b]
else:
flows = list(reversed(self.flows))
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
for flow in flows:
z = flow(z, x_mask, g=x, reverse=reverse)
z0, z1 = torch.split(z, [1, 1], 1)
logw = z0
return logw
class DurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.norm_2 = modules.LayerNorm(filter_channels)
self.proj = nn.Conv1d(filter_channels, 1, 1)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
def forward(self, x, x_mask, g=None):
x = torch.detach(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
x = self.drop(x)
x = self.proj(x * x_mask)
return x * x_mask
class DurationDiscriminator(nn.Module): # vits2
# TODO : not using "spk conditioning" for now according to the paper.
# Can be a better discriminator if we use it.
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
# self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
# self.norm_2 = modules.LayerNorm(filter_channels)
self.dur_proj = nn.Conv1d(1, filter_channels, 1)
self.pre_out_conv_1 = nn.Conv1d(2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
# if gin_channels != 0:
# self.cond = nn.Conv1d(gin_channels, in_channels, 1)
self.output_layer = nn.Sequential(
nn.Linear(filter_channels, 1),
nn.Sigmoid()
)
def forward_probability(self, x, x_mask, dur, g=None):
dur = self.dur_proj(dur)
x = torch.cat([x, dur], dim=1)
x = self.pre_out_conv_1(x * x_mask)
# x = torch.relu(x)
# x = self.pre_out_norm_1(x)
# x = self.drop(x)
x = self.pre_out_conv_2(x * x_mask)
# x = torch.relu(x)
# x = self.pre_out_norm_2(x)
# x = self.drop(x)
x = x * x_mask
x = x.transpose(1, 2)
output_prob = self.output_layer(x)
return output_prob
def forward(self, x, x_mask, dur_r, dur_hat, g=None):
x = torch.detach(x)
# if g is not None:
# g = torch.detach(g)
# x = x + self.cond(g)
x = self.conv_1(x * x_mask)
# x = torch.relu(x)
# x = self.norm_1(x)
# x = self.drop(x)
x = self.conv_2(x * x_mask)
# x = torch.relu(x)
# x = self.norm_2(x)
# x = self.drop(x)
output_probs = []
for dur in [dur_r, dur_hat]:
output_prob = self.forward_probability(x, x_mask, dur, g)
output_probs.append(output_prob)
return output_probs
class DurationDiscriminator2(nn.Module): # vits2 - DurationDiscriminator2
# TODO : not using "spk conditioning" for now according to the paper.
# Can be a better discriminator if we use it.
def __init__(
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.conv_1 = nn.Conv1d(
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_2 = modules.LayerNorm(filter_channels)
self.dur_proj = nn.Conv1d(1, filter_channels, 1)
self.pre_out_conv_1 = nn.Conv1d(
2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
self.pre_out_conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
# if gin_channels != 0:
# self.cond = nn.Conv1d(gin_channels, in_channels, 1)
self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
def forward_probability(self, x, x_mask, dur, g=None):
dur = self.dur_proj(dur)
x = torch.cat([x, dur], dim=1)
x = self.pre_out_conv_1(x * x_mask)
x = torch.relu(x)
x = self.pre_out_norm_1(x)
x = self.pre_out_conv_2(x * x_mask)
x = torch.relu(x)
x = self.pre_out_norm_2(x)
x = x * x_mask
x = x.transpose(1, 2)
output_prob = self.output_layer(x)
return output_prob
def forward(self, x, x_mask, dur_r, dur_hat, g=None):
x = torch.detach(x)
# if g is not None:
# g = torch.detach(g)
# x = x + self.cond(g)
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
output_probs = []
for dur in [dur_r, dur_hat]:
output_prob = self.forward_probability(x, x_mask, dur, g)
output_probs.append([output_prob])
return output_probs
class TextEncoder(nn.Module):
def __init__(self,
n_vocab,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
gin_channels=0):
super().__init__()
self.n_vocab = n_vocab
self.out_channels = out_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.gin_channels = gin_channels
self.emb = nn.Embedding(n_vocab, hidden_channels)
nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
self.encoder = attentions.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
gin_channels=self.gin_channels)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.encoder(x * x_mask, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return x, m, logs, x_mask
class ResidualCouplingTransformersLayer2(nn.Module): # vits2
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by 2"
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.half_channels = channels // 2
self.mean_only = mean_only
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
self.pre_transformer = attentions.Encoder(
hidden_channels,
hidden_channels,
n_heads=2,
n_layers=1,
kernel_size=kernel_size,
p_dropout=p_dropout,
# window_size=None,
)
self.enc = modules.WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=p_dropout,
gin_channels=gin_channels,
)
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h = h + self.pre_transformer(h * x_mask, x_mask) # vits2 residual connection
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
class ResidualCouplingTransformersLayer(nn.Module): # vits2
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
):
assert channels % 2 == 0, "channels should be divisible by 2"
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.half_channels = channels // 2
self.mean_only = mean_only
# vits2
self.pre_transformer = attentions.Encoder(
self.half_channels,
self.half_channels,
n_heads=2,
n_layers=2,
kernel_size=3,
p_dropout=0.1,
window_size=None
)
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
self.enc = modules.WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=p_dropout,
gin_channels=gin_channels,
)
# vits2
self.post_transformer = attentions.Encoder(
self.hidden_channels,
self.hidden_channels,
n_heads=2,
n_layers=2,
kernel_size=3,
p_dropout=0.1,
window_size=None
)
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
x0_ = self.pre_transformer(x0 * x_mask, x_mask) # vits2
x0_ = x0_ + x0 # vits2 residual connection
h = self.pre(x0_) * x_mask # changed from x0 to x0_ to retain x0 for the flow
h = self.enc(h, x_mask, g=g)
# vits2 - (experimental;uncomment the following 2 line to use)
# h_ = self.post_transformer(h, x_mask)
# h = h + h_ #vits2 residual connection
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
def remove_weight_norm(self): # !
self.enc.remove_weight_norm()
class FFTransformerCouplingLayer(nn.Module): # vits2
def __init__(self,
channels,
hidden_channels,
kernel_size,
n_layers,
n_heads,
p_dropout=0,
filter_channels=768,
mean_only=False,
gin_channels=0
):
assert channels % 2 == 0, "channels should be divisible by 2"
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.half_channels = channels // 2
self.mean_only = mean_only
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
self.enc = attentions.FFT(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
isflow=True,
gin_channels=gin_channels
)
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h = self.pre(x0) * x_mask
h_ = self.enc(h, x_mask, g=g)
h = h_ + h
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
class MonoTransformerFlowLayer(nn.Module): # vits2
def __init__(
self,
channels,
hidden_channels,
mean_only=False,
residual_connection=False,
# according to VITS-2 paper fig 1B set residual_connection=True
):
assert channels % 2 == 0, "channels should be divisible by 2"
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.half_channels = channels // 2
self.mean_only = mean_only
self.residual_connection = residual_connection
# vits2
self.pre_transformer = attentions.Encoder(
self.half_channels,
self.half_channels,
n_heads=2,
n_layers=2,
kernel_size=3,
p_dropout=0.1,
window_size=None
)
self.post = nn.Conv1d(self.half_channels, self.half_channels * (2 - mean_only), 1)
self.post.weight.data.zero_()
self.post.bias.data.zero_()
def forward(self, x, x_mask, g=None, reverse=False):
if self.residual_connection:
if not reverse:
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
x0_ = x0 * x_mask
x0_ = self.pre_transformer(x0, x_mask) # vits2
stats = self.post(x0_) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
x1 = m + x1 * torch.exp(logs) * x_mask
x_ = torch.cat([x0, x1], 1)
x = x + x_
logdet = torch.sum(torch.log(torch.exp(logs) + 1), [1, 2])
logdet = logdet + torch.log(torch.tensor(2)) * (x0.shape[1] * x0.shape[2])
return x, logdet
else:
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
x0 = x0 / 2
x0_ = x0 * x_mask
x0_ = self.pre_transformer(x0, x_mask) # vits2
stats = self.post(x0_) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
x1_ = ((x1 - m) / (1 + torch.exp(-logs))) * x_mask
x = torch.cat([x0, x1_], 1)
return x
else:
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
x0_ = self.pre_transformer(x0 * x_mask, x_mask) # vits2
h = x0_ + x0 # vits2
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels] * 2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:
x1 = m + x1 * torch.exp(logs) * x_mask
x = torch.cat([x0, x1], 1)
logdet = torch.sum(logs, [1, 2])
return x, logdet
else:
x1 = (x1 - m) * torch.exp(-logs) * x_mask
x = torch.cat([x0, x1], 1)
return x
class ResidualCouplingTransformersBlock(nn.Module): # vits2
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
use_transformer_flows=False,
transformer_flow_type="pre_conv",
):
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.flows = nn.ModuleList()
# TODO : clean up this mess
if use_transformer_flows:
if transformer_flow_type == "pre_conv":
for i in range(n_flows):
self.flows.append(
ResidualCouplingTransformersLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True
)
)
self.flows.append(modules.Flip())
elif transformer_flow_type == "pre_conv2":
for i in range(n_flows):
self.flows.append(
ResidualCouplingTransformersLayer2(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(modules.Flip())
elif transformer_flow_type == "fft":
for i in range(n_flows):
self.flows.append(
FFTransformerCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True
)
)
self.flows.append(modules.Flip())
elif transformer_flow_type == "mono_layer_inter_residual":
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True
)
)
self.flows.append(modules.Flip())
self.flows.append(
MonoTransformerFlowLayer(
channels, hidden_channels, mean_only=True
)
)
elif transformer_flow_type == "mono_layer_post_residual":
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(modules.Flip())
self.flows.append(
MonoTransformerFlowLayer(
channels, hidden_channels, mean_only=True,
residual_connection=True
)
)
else:
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
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
def remove_weight_norm(self): # !
for i, l in enumerate(self.flows):
if i % 2 == 0:
l.remove_weight_norm()
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
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.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
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
def remove_weight_norm(self): # !
for i, l in enumerate(self.flows):
if i % 2 == 0:
l.remove_weight_norm()
class PosteriorEncoder(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 = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, 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, x_mask
class Generator(torch.nn.Module):
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
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))
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)
def forward(self, x, g=None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
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 = 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()
class iSTFT_Generator(torch.nn.Module):
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size,
gin_channels=0, is_onnx=False):
super(iSTFT_Generator, self).__init__()
# self.h = h
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
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 = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
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))
self.post_n_fft = self.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=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft)
'''
# - for onnx
if is_onnx == True:
self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft)
else:
self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft)
def forward(self, x, g=None):
x = self.conv_pre(x)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
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 = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.conv_post(x)
spec = torch.exp(x[:, :self.post_n_fft // 2 + 1, :])
phase = math.pi * torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
out = self.stft.inverse(spec, phase).to(x.device)
return out, None
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 Multiband_iSTFT_Generator(torch.nn.Module): # !
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands,
gin_channels=0, is_onnx=False):
super(Multiband_iSTFT_Generator, self).__init__()
# self.h = h
self.subbands = subbands
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 = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
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))
self.post_n_fft = gen_istft_n_fft
self.ups.apply(init_weights)
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
self.reshape_pixelshuffle = []
self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands * (self.post_n_fft + 2), 7, 1, padding=3))
self.subband_conv_post.apply(init_weights)
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
#- for onnx
if is_onnx == True:
self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
else:
self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
def forward(self, x, g=None):
'''
stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft).to(x.device) # !
'''
stft = self.stft.to(x.device)
pqmf = PQMF(x.device)
x = self.conv_pre(x) # [B, ch, length]
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
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 = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.subband_conv_post(x)
x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1] // self.subbands, x.shape[-1]))
spec = torch.exp(x[:, :, :self.post_n_fft // 2 + 1, :])
phase = math.pi * torch.sin(x[:, :, self.post_n_fft // 2 + 1:, :])
y_mb_hat = stft.inverse(
torch.reshape(spec, (spec.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])),
torch.reshape(phase, (phase.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
y_mb_hat = y_mb_hat.squeeze(-2)
y_g_hat = pqmf.synthesis(y_mb_hat)
return y_g_hat, y_mb_hat
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 Multistream_iSTFT_Generator(torch.nn.Module):
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands,
gin_channels=0, is_onnx=False):
super(Multistream_iSTFT_Generator, self).__init__()
# self.h = h
self.subbands = subbands
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 = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
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))
self.post_n_fft = gen_istft_n_fft
self.ups.apply(init_weights)
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
self.reshape_pixelshuffle = []
self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands * (self.post_n_fft + 2), 7, 1, padding=3))
self.subband_conv_post.apply(init_weights)
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
updown_filter = torch.zeros((self.subbands, self.subbands, self.subbands)).float()
for k in range(self.subbands):
updown_filter[k, k, 0] = 1.0
self.register_buffer("updown_filter", updown_filter)
#self.multistream_conv_post = weight_norm(Conv1d(4, 1, kernel_size=63, bias=False, padding=get_padding(63, 1)))
self.multistream_conv_post = weight_norm(Conv1d(self.subbands, 1, kernel_size=63, bias=False, padding=get_padding(63, 1))) # from MB-iSTFT-VITS-44100-Ja
self.multistream_conv_post.apply(init_weights)
#- for onnx
if is_onnx == True:
self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
else:
self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
def forward(self, x, g=None):
'''
stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
win_length=self.gen_istft_n_fft).to(x.device) # !
'''
stft = self.stft.to(x.device)
# pqmf = PQMF(x.device)
x = self.conv_pre(x) # [B, ch, length]
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
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 = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.subband_conv_post(x)
x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1] // self.subbands, x.shape[-1]))
spec = torch.exp(x[:, :, :self.post_n_fft // 2 + 1, :])
phase = math.pi * torch.sin(x[:, :, self.post_n_fft // 2 + 1:, :])
y_mb_hat = stft.inverse(
torch.reshape(spec, (spec.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])),
torch.reshape(phase, (phase.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
y_mb_hat = y_mb_hat.squeeze(-2)
#y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.cuda(x.device) * self.subbands, stride=self.subbands)
y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.to(x.device) * self.subbands, stride=self.subbands)
y_g_hat = self.multistream_conv_post(y_mb_hat)
return y_g_hat, y_mb_hat
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 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 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, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, 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, 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]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
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,
n_vocab,
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,
gen_istft_n_fft,
gen_istft_hop_size,
n_speakers=0,
gin_channels=0,
use_sdp=True,
ms_istft_vits=False,
mb_istft_vits=False,
subbands=False,
istft_vits=False,
is_onnx=False,
**kwargs):
super().__init__()
self.n_vocab = n_vocab
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.n_speakers = n_speakers
self.gin_channels = gin_channels
self.ms_istft_vits = ms_istft_vits
self.mb_istft_vits = mb_istft_vits
self.istft_vits = istft_vits
self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", False)
self.use_transformer_flows = kwargs.get("use_transformer_flows", False)
self.transformer_flow_type = kwargs.get("transformer_flow_type", "mono_layer_post_residual")
if self.use_transformer_flows:
assert self.transformer_flow_type in AVAILABLE_FLOW_TYPES, f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}"
self.use_sdp = use_sdp
# self.use_duration_discriminator = kwargs.get("use_duration_discriminator", False)
self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
self.current_mas_noise_scale = self.mas_noise_scale_initial
if self.use_spk_conditioned_encoder and gin_channels > 0:
self.enc_gin_channels = gin_channels
else:
self.enc_gin_channels = 0
self.enc_p = TextEncoder(n_vocab,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
gin_channels=self.enc_gin_channels)
if mb_istft_vits == True:
print('Multi-band iSTFT VITS2')
self.dec = Multiband_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates, upsample_initial_channel, upsample_kernel_sizes,
gen_istft_n_fft, gen_istft_hop_size, subbands,
gin_channels=gin_channels, is_onnx=is_onnx)
elif ms_istft_vits == True:
print('Multi-stream iSTFT VITS2')
self.dec = Multistream_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates, upsample_initial_channel, upsample_kernel_sizes,
gen_istft_n_fft, gen_istft_hop_size, subbands,
gin_channels=gin_channels, is_onnx=is_onnx)
elif istft_vits == True:
print('iSTFT-VITS2')
self.dec = iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes,
upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft,
gen_istft_hop_size, gin_channels=gin_channels, is_onnx=is_onnx)
else:
print('No iSTFT arguments found in json file')
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) # vits 2
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
gin_channels=gin_channels)
# self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
self.flow = ResidualCouplingTransformersBlock(
inter_channels,
hidden_channels,
5,
1,
4,
gin_channels=gin_channels,
use_transformer_flows=self.use_transformer_flows,
transformer_flow_type=self.transformer_flow_type
)
if use_sdp:
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
else:
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
self.emb_g = nn.Embedding(n_speakers, gin_channels)
def forward(self, x, x_lengths, y, y_lengths, sid=None):
# x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) # vits2?
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
with torch.no_grad():
# negative cross-entropy
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2),
s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
if self.use_noise_scaled_mas:
epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale
neg_cent = neg_cent + epsilon
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
w = attn.sum(2)
if self.use_sdp:
l_length = self.dp(x, x_mask, w, g=g)
l_length = l_length / torch.sum(x_mask)
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=1.)
logw_ = torch.log(w + 1e-6) * x_mask
else:
logw_ = torch.log(w + 1e-6) * x_mask
logw = self.dp(x, x_mask, g=g)
l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging
# expand prior
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
o, o_mb = self.dec(z_slice, g=g)
return o, o_mb, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_)
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g)
if self.use_sdp:
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
else:
logw = self.dp(x, x_mask, g=g)
w = torch.exp(logw) * x_mask * length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(w_ceil, attn_mask)
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
2) # [b, t', t], [b, t, d] -> [b, d, t']
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=g, reverse=True)
o, o_mb = self.dec((z * y_mask)[:, :, :max_len], g=g)
return o, o_mb, attn, y_mask, (z, z_p, m_p, logs_p)
#'''
## currently vits-2 is not capable of voice conversion
# comment - choihkk : Assuming the use of the ResidualCouplingTransformersLayer2 module, it seems that voice conversion is possible
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
g_src = self.emb_g(sid_src).unsqueeze(-1)
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
z_p = self.flow(z, y_mask, g=g_src)
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
o_hat, o_hat_mb = self.dec(z_hat * y_mask, g=g_tgt)
return o_hat, o_hat_mb, y_mask, (z, z_p, z_hat)
#'''