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import math
import torch
from torch import nn
from torch.nn import functional as F
# from TTS.tts.layers.generic.normalization import LayerNorm2
class LayerNorm2(nn.Module):
"""Layer norm for the 2nd dimension of the input using torch primitive.
Args:
channels (int): number of channels (2nd dimension) of the input.
eps (float): to prevent 0 division
Shapes:
- input: (B, C, T)
- output: (B, C, T)
"""
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
x = x.transpose(1, -1)
x = torch.nn.functional.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
# from TTS.tts.layers.vits.transforms import piecewise_rational_quadratic_transform
from python.xvapitch.util import piecewise_rational_quadratic_transform
class DilatedDepthSeparableConv(nn.Module):
def __init__(self, channels, kernel_size, num_layers, dropout_p=0.0) -> torch.tensor:
"""Dilated Depth-wise Separable Convolution module.
::
x |-> DDSConv(x) -> LayerNorm(x) -> GeLU(x) -> Conv1x1(x) -> LayerNorm(x) -> GeLU(x) -> + -> o
|-------------------------------------------------------------------------------------^
Args:
channels ([type]): [description]
kernel_size ([type]): [description]
num_layers ([type]): [description]
dropout_p (float, optional): [description]. Defaults to 0.0.
Returns:
torch.tensor: Network output masked by the input sequence mask.
"""
super().__init__()
self.num_layers = num_layers
self.convs_sep = nn.ModuleList()
self.convs_1x1 = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(num_layers):
dilation = kernel_size ** i
padding = (kernel_size * dilation - dilation) // 2
self.convs_sep.append(
nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding)
)
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
self.norms_1.append(LayerNorm2(channels))
self.norms_2.append(LayerNorm2(channels))
self.dropout = nn.Dropout(dropout_p)
def forward(self, x, x_mask, g=None):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
"""
if g is not None:
x = x + g
for i in range(self.num_layers):
y = self.convs_sep[i](x * x_mask)
y = self.norms_1[i](y)
y = F.gelu(y)
y = self.convs_1x1[i](y)
y = self.norms_2[i](y)
y = F.gelu(y)
y = self.dropout(y)
x = x + y
return x * x_mask
class ElementwiseAffine(nn.Module):
"""Element-wise affine transform like no-population stats BatchNorm alternative.
Args:
channels (int): Number of input tensor channels.
"""
def __init__(self, channels):
super().__init__()
self.translation = nn.Parameter(torch.zeros(channels, 1))
self.log_scale = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs): # pylint: disable=unused-argument
if not reverse:
y = (x * torch.exp(self.log_scale) + self.translation) * x_mask
logdet = torch.sum(self.log_scale * x_mask, [1, 2])
return y, logdet
x = (x - self.translation) * torch.exp(-self.log_scale) * x_mask
return x
class ConvFlow(nn.Module):
"""Dilated depth separable convolutional based spline flow.
Args:
in_channels (int): Number of input tensor channels.
hidden_channels (int): Number of in network channels.
kernel_size (int): Convolutional kernel size.
num_layers (int): Number of convolutional layers.
num_bins (int, optional): Number of spline bins. Defaults to 10.
tail_bound (float, optional): Tail bound for PRQT. Defaults to 5.0.
"""
def __init__(
self,
in_channels: int,
hidden_channels: int,
kernel_size: int,
num_layers: int,
num_bins=10,
tail_bound=5.0,
):
super().__init__()
self.num_bins = num_bins
self.tail_bound = tail_bound
self.hidden_channels = hidden_channels
self.half_channels = in_channels // 2
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
self.convs = DilatedDepthSeparableConv(hidden_channels, kernel_size, num_layers, dropout_p=0.0)
self.proj = nn.Conv1d(hidden_channels, self.half_channels * (num_bins * 3 - 1), 1)
self.proj.weight.data.zero_()
self.proj.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)
h = self.convs(h, x_mask, g=g)
h = self.proj(h) * x_mask
b, c, t = x0.shape
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.hidden_channels)
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(self.hidden_channels)
unnormalized_derivatives = h[..., 2 * self.num_bins :]
x1, logabsdet = piecewise_rational_quadratic_transform(
x1,
unnormalized_widths,
unnormalized_heights,
unnormalized_derivatives,
inverse=reverse,
tails="linear",
tail_bound=self.tail_bound,
)
x = torch.cat([x0, x1], 1) * x_mask
logdet = torch.sum(logabsdet * x_mask, [1, 2])
if not reverse:
return x, logdet
return x
class StochasticDurationPredictor(nn.Module):
"""Stochastic duration predictor with Spline Flows.
It applies Variational Dequantization and Variationsl Data Augmentation.
Paper:
SDP: https://arxiv.org/pdf/2106.06103.pdf
Spline Flow: https://arxiv.org/abs/1906.04032
::
## Inference
x -> TextCondEncoder() -> Flow() -> dr_hat
noise ----------------------^
## Training
|---------------------|
x -> TextCondEncoder() -> + -> PosteriorEncoder() -> split() -> z_u, z_v -> (d - z_u) -> concat() -> Flow() -> noise
d -> DurCondEncoder() -> ^ |
|------------------------------------------------------------------------------|
Args:
in_channels (int): Number of input tensor channels.
hidden_channels (int): Number of hidden channels.
kernel_size (int): Kernel size of convolutional layers.
dropout_p (float): Dropout rate.
num_flows (int, optional): Number of flow blocks. Defaults to 4.
cond_channels (int, optional): Number of channels of conditioning tensor. Defaults to 0.
"""
def __init__(
self,
in_channels: int,
hidden_channels: int,
kernel_size: int,
dropout_p: float,
num_flows=4,
cond_channels=0,
language_emb_dim=0,
):
super().__init__()
# add language embedding dim in the input
if language_emb_dim:
in_channels += language_emb_dim
# condition encoder text
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.convs = DilatedDepthSeparableConv(hidden_channels, kernel_size, num_layers=3, dropout_p=dropout_p)
self.proj = nn.Conv1d(hidden_channels, hidden_channels, 1)
# posterior encoder
self.flows = nn.ModuleList()
self.flows.append(ElementwiseAffine(2))
self.flows += [ConvFlow(2, hidden_channels, kernel_size, num_layers=3) for _ in range(num_flows)]
# condition encoder duration
self.post_pre = nn.Conv1d(1, hidden_channels, 1)
self.post_convs = DilatedDepthSeparableConv(hidden_channels, kernel_size, num_layers=3, dropout_p=dropout_p)
self.post_proj = nn.Conv1d(hidden_channels, hidden_channels, 1)
# flow layers
self.post_flows = nn.ModuleList()
self.post_flows.append(ElementwiseAffine(2))
self.post_flows += [ConvFlow(2, hidden_channels, kernel_size, num_layers=3) for _ in range(num_flows)]
if cond_channels != 0 and cond_channels is not None:
self.cond = nn.Conv1d(cond_channels, hidden_channels, 1)
if language_emb_dim != 0 and language_emb_dim is not None:
self.cond_lang = nn.Conv1d(language_emb_dim, hidden_channels, 1)
def forward(self, x, x_mask, dr=None, g=None, lang_emb=None, reverse=False, noise_scale=1.0):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
- dr: :math:`[B, 1, T]`
- g: :math:`[B, C]`
"""
# condition encoder text
x = self.pre(x)
if g is not None:
cond = self.cond(g)
x = x + cond
if lang_emb is not None:
lang_cond = self.cond_lang(lang_emb)
x = x + lang_cond
x = self.convs(x, x_mask)
x = self.proj(x) * x_mask
if not reverse:
flows = self.flows
assert dr is not None
# condition encoder duration
h = self.post_pre(dr)
h = self.post_convs(h, x_mask)
h = self.post_proj(h) * x_mask
noise = torch.randn(dr.size(0), 2, dr.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
z_q = noise
# posterior encoder
logdet_tot_q = 0.0
for idx, flow in enumerate(self.post_flows):
z_q, logdet_q = flow(z_q, x_mask, g=(x + h))
logdet_tot_q = logdet_tot_q + logdet_q
if idx > 0:
z_q = torch.flip(z_q, [1])
z_u, z_v = torch.split(z_q, [1, 1], 1)
u = torch.sigmoid(z_u) * x_mask
z0 = (dr - u) * x_mask
# posterior encoder - neg log likelihood
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
nll_posterior_encoder = (
torch.sum(-0.5 * (math.log(2 * math.pi) + (noise ** 2)) * x_mask, [1, 2]) - logdet_tot_q
)
z0 = torch.log(torch.clamp_min(z0, 1e-5)) * x_mask
logdet_tot = torch.sum(-z0, [1, 2])
z = torch.cat([z0, z_v], 1)
# flow layers
for idx, flow in enumerate(flows):
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
logdet_tot = logdet_tot + logdet
if idx > 0:
z = torch.flip(z, [1])
# flow layers - neg log likelihood
nll_flow_layers = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
return nll_flow_layers + nll_posterior_encoder
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 = torch.flip(z, [1])
z = flow(z, x_mask, g=x, reverse=reverse)
z0, _ = torch.split(z, [1, 1], 1)
logw = z0
return logw
class StochasticPredictor(nn.Module):
def __init__(
self,
in_channels: int,
hidden_channels: int,
kernel_size: int,
dropout_p: float,
num_flows=4,
cond_channels=0,
# language_emb_dim=0,
):
super().__init__()
# add language embedding dim in the input
# if language_emb_dim:
# in_channels += language_emb_dim
# condition encoder text
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.convs = DilatedDepthSeparableConv(hidden_channels, kernel_size, num_layers=3, dropout_p=dropout_p)
self.proj = nn.Conv1d(hidden_channels, hidden_channels, 1)
# posterior encoder
self.flows = nn.ModuleList()
self.flows.append(ElementwiseAffine(2))
self.flows += [ConvFlow(2, hidden_channels, kernel_size, num_layers=3) for _ in range(num_flows)]
# condition encoder duration
self.post_pre = nn.Conv1d(1, hidden_channels, 1)
self.post_convs = DilatedDepthSeparableConv(hidden_channels, kernel_size, num_layers=3, dropout_p=dropout_p)
self.post_proj = nn.Conv1d(hidden_channels, hidden_channels, 1)
# flow layers
self.post_flows = nn.ModuleList()
self.post_flows.append(ElementwiseAffine(2))
self.post_flows += [ConvFlow(2, hidden_channels, kernel_size, num_layers=3) for _ in range(num_flows)]
if cond_channels != 0 and cond_channels is not None:
self.cond = nn.Conv1d(cond_channels, hidden_channels, 1)
# if language_emb_dim != 0 and language_emb_dim is not None:
# self.cond_lang = nn.Conv1d(language_emb_dim, hidden_channels, 1)
# def forward(self, x, x_mask, dr=None, g=None, lang_emb=None, reverse=False, noise_scale=1.0):
def forward(self, x, x_mask, dr=None, g=None, reverse=False, noise_scale=1.0):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_mask: :math:`[B, 1, T]`
- dr: :math:`[B, 1, T]`
- g: :math:`[B, C]`
"""
# condition encoder text
x = self.pre(x)
if g is not None:
x = x + self.cond(g)
# if lang_emb is not None:
# x = x + self.cond_lang(lang_emb)
x = self.convs(x, x_mask)
x = self.proj(x) * x_mask
if not reverse:
flows = self.flows
assert dr is not None
# condition encoder duration
h = self.post_pre(dr)
h = self.post_convs(h, x_mask)
h = self.post_proj(h) * x_mask
noise = torch.randn(dr.size(0), 2, dr.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
z_q = noise
# posterior encoder
logdet_tot_q = 0.0
for idx, flow in enumerate(self.post_flows):
z_q, logdet_q = flow(z_q, x_mask, g=(x + h))
logdet_tot_q = logdet_tot_q + logdet_q
if idx > 0:
z_q = torch.flip(z_q, [1])
z_u, z_v = torch.split(z_q, [1, 1], 1)
u = torch.sigmoid(z_u) * x_mask
z0 = (dr - u) * x_mask
# posterior encoder - neg log likelihood
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
nll_posterior_encoder = (
torch.sum(-0.5 * (math.log(2 * math.pi) + (noise ** 2)) * x_mask, [1, 2]) - logdet_tot_q
)
z0 = torch.log(torch.clamp_min(z0, 1e-5)) * x_mask
logdet_tot = torch.sum(-z0, [1, 2])
z = torch.cat([z0, z_v], 1)
# flow layers
for idx, flow in enumerate(flows):
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
logdet_tot = logdet_tot + logdet
if idx > 0:
z = torch.flip(z, [1])
# flow layers - neg log likelihood
nll_flow_layers = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
return nll_flow_layers + nll_posterior_encoder
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 = torch.flip(z, [1])
z = flow(z, x_mask, g=x, reverse=reverse)
z0, _ = torch.split(z, [1, 1], 1)
logw = z0
return logw
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