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optimize(infer): move modules into rvc

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源文雨
2024-06-08 00:14:03 +09:00
parent 44725ddd2c
commit eb24434260
8 changed files with 468 additions and 618 deletions
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65
infer/lib/infer_pack/models.py
View File

@@ -1,5 +1,4 @@
import math
import logging
from typing import Optional, Tuple, List
import torch
@@ -7,8 +6,9 @@ from torch import nn
from torch.nn import Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
from infer.lib.infer_pack import modules
from rvc import residuals
from rvc.norms import WN
from rvc.utils import (
get_padding,
call_weight_data_normal_if_Conv,
@@ -22,6 +22,26 @@ has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
class ResidualCouplingBlock(nn.Module):
class Flip(nn.Module):
"""
torch.jit.script() Compiled functions
can't take variable number of arguments or
use keyword-only arguments with defaults
"""
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x, torch.zeros([1], device=x.device)
def __init__(
self,
channels,
@@ -44,7 +64,7 @@ class ResidualCouplingBlock(nn.Module):
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
residuals.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
@@ -54,7 +74,7 @@ class ResidualCouplingBlock(nn.Module):
mean_only=True,
)
)
self.flows.append(modules.Flip())
self.flows.append(self.Flip())
def forward(
self,
@@ -108,7 +128,7 @@ class PosteriorEncoder(nn.Module):
self.gin_channels = gin_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = modules.WN(
self.enc = WN(
hidden_channels,
kernel_size,
dilation_rate,
@@ -167,7 +187,7 @@ class Generator(torch.nn.Module):
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
resblock = residuals.ResBlock1 if resblock == "1" else residuals.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
@@ -215,7 +235,7 @@ class Generator(torch.nn.Module):
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = F.leaky_relu(x, residuals.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
@@ -382,33 +402,21 @@ class SourceModuleHnNSF(torch.nn.Module):
sine_amp=0.1,
add_noise_std=0.003,
voiced_threshod=0,
is_half=True,
):
super(SourceModuleHnNSF, self).__init__()
self.sine_amp = sine_amp
self.noise_std = add_noise_std
self.is_half = is_half
# to produce sine waveforms
self.l_sin_gen = SineGen(
sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
)
# to merge source harmonics into a single excitation
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
self.l_tanh = torch.nn.Tanh()
# self.ddtype:int = -1
def forward(self, x: torch.Tensor, upp: int = 1):
# if self.ddtype ==-1:
# self.ddtype = self.l_linear.weight.dtype
sine_wavs, uv, _ = self.l_sin_gen(x, upp)
# print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
# if self.is_half:
# sine_wavs = sine_wavs.half()
# sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(x)))
# print(sine_wavs.dtype,self.ddtype)
# if sine_wavs.dtype != self.l_linear.weight.dtype:
sine_wavs, _, _ = self.l_sin_gen(x, upp)
sine_wavs = sine_wavs.to(dtype=self.l_linear.weight.dtype)
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
return sine_merge, None, None # noise, uv
@@ -426,7 +434,6 @@ class GeneratorNSF(torch.nn.Module):
upsample_kernel_sizes,
gin_channels,
sr,
is_half=False,
):
super(GeneratorNSF, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
@@ -434,13 +441,13 @@ class GeneratorNSF(torch.nn.Module):
self.f0_upsamp = torch.nn.Upsample(scale_factor=math.prod(upsample_rates))
self.m_source = SourceModuleHnNSF(
sampling_rate=sr, harmonic_num=0, is_half=is_half
sampling_rate=sr, harmonic_num=0
)
self.noise_convs = nn.ModuleList()
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
resblock = residuals.ResBlock1 if resblock == "1" else residuals.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
@@ -486,7 +493,7 @@ class GeneratorNSF(torch.nn.Module):
self.upp = math.prod(upsample_rates)
self.lrelu_slope = modules.LRELU_SLOPE
self.lrelu_slope = residuals.LRELU_SLOPE
def forward(
self,
@@ -584,7 +591,6 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
spk_embed_dim: int,
gin_channels: int,
sr: str | int,
**kwargs
):
super(SynthesizerTrnMs256NSFsid, self).__init__()
if isinstance(sr, str):
@@ -631,7 +637,6 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
upsample_kernel_sizes,
gin_channels=gin_channels,
sr=sr,
is_half=kwargs["is_half"],
)
self.enc_q = PosteriorEncoder(
spec_channels,
@@ -764,7 +769,6 @@ class SynthesizerTrnMs768NSFsid(SynthesizerTrnMs256NSFsid):
spk_embed_dim,
gin_channels,
sr,
**kwargs
):
super(SynthesizerTrnMs768NSFsid, self).__init__(
spec_channels,
@@ -785,7 +789,6 @@ class SynthesizerTrnMs768NSFsid(SynthesizerTrnMs256NSFsid):
spk_embed_dim,
gin_channels,
sr,
**kwargs
)
del self.enc_p
self.enc_p = TextEncoder(
@@ -812,7 +815,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
n_layers,
kernel_size,
p_dropout,
resblock,
resblock: str,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
@@ -1095,7 +1098,7 @@ class DiscriminatorS(torch.nn.Module):
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = F.leaky_relu(x, residuals.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
@@ -1179,7 +1182,7 @@ class DiscriminatorP(torch.nn.Module):
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = F.leaky_relu(x, residuals.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)

1
infer/lib/infer_pack/models_onnx.py
View File

@@ -79,7 +79,6 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
upsample_kernel_sizes,
gin_channels=gin_channels,
sr=sr,
is_half=kwargs["is_half"],
)
self.enc_q = PosteriorEncoder(
spec_channels,

548
infer/lib/infer_pack/modules.py
View File

@@ -1,548 +0,0 @@
import math
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import Conv1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from rvc.utils import (
get_padding,
call_weight_data_normal_if_Conv,
activate_add_tanh_sigmoid_multiply,
)
from rvc.transforms import piecewise_rational_quadratic_transform
from rvc.norms import LayerNorm
LRELU_SLOPE = 0.1
class DDSConv(nn.Module):
"""
Dialted and Depth-Separable Convolution
"""
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
super(DDSConv, self).__init__()
self.channels = channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.p_dropout = float(p_dropout)
self.drop = nn.Dropout(float(p_dropout))
self.convs_sep = nn.ModuleList()
self.convs_1x1 = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(n_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(LayerNorm(channels))
self.norms_2.append(LayerNorm(channels))
def forward(self, x, x_mask, g: Optional[torch.Tensor] = None):
if g is not None:
x = x + g
for i in range(self.n_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.drop(y)
x = x + y
return x * x_mask
class WN(torch.nn.Module):
def __init__(
self,
hidden_channels: int,
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 = float(p_dropout)
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.drop = nn.Dropout(float(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)
# last one is not necessary
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: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None
):
output = torch.zeros_like(x)
if g is not None:
g = self.cond_layer(g)
for i, (in_layer, res_skip_layer) in enumerate(
zip(self.in_layers, self.res_skip_layers)
):
x_in: torch.Tensor = in_layer(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 = activate_add_tanh_sigmoid_multiply(x_in, g_l, self.hidden_channels)
acts = self.drop(acts)
res_skip_acts = res_skip_layer(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)
def __prepare_scriptable__(self):
if self.gin_channels != 0:
for hook in self.cond_layer._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.cond_layer)
for l in self.in_layers:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
for l in self.res_skip_layers:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, 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(call_weight_data_normal_if_Conv)
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(call_weight_data_normal_if_Conv)
self.lrelu_slope = LRELU_SLOPE
def forward(self, x: torch.Tensor, x_mask: Optional[torch.Tensor] = None):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c2(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
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)
def __prepare_scriptable__(self):
for l in self.convs1:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
for l in self.convs2:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
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(call_weight_data_normal_if_Conv)
self.lrelu_slope = LRELU_SLOPE
def forward(self, x, x_mask: Optional[torch.Tensor] = None):
for c in self.convs:
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
def __prepare_scriptable__(self):
for l in self.convs:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class Log(nn.Module):
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
class Flip(nn.Module):
# torch.jit.script() Compiled functions \
# can't take variable number of arguments or \
# use keyword-only arguments with defaults
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x, torch.zeros([1], device=x.device)
class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super(ElementwiseAffine, self).__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels, 1))
self.logs = nn.Parameter(torch.zeros(channels, 1))
def forward(self, x, x_mask, reverse=False, **kwargs):
if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1, 2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
class ResidualCouplingLayer(nn.Module):
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(ResidualCouplingLayer, self).__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.enc = WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=float(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: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = 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)
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, torch.zeros([1])
def remove_weight_norm(self):
self.enc.remove_weight_norm()
def __prepare_scriptable__(self):
for hook in self.enc._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.enc)
return self
class ConvFlow(nn.Module):
def __init__(
self,
in_channels,
filter_channels,
kernel_size,
n_layers,
num_bins=10,
tail_bound=5.0,
):
super(ConvFlow, self).__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.num_bins = num_bins
self.tail_bound = tail_bound
self.half_channels = in_channels // 2
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
self.proj = nn.Conv1d(
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
)
self.proj.weight.data.zero_()
self.proj.bias.data.zero_()
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse=False,
):
x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
h: torch.Tensor = 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.filter_channels)
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
self.filter_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
else:
return x

4
infer/lib/jit/synthesizer.py
View File

@@ -15,12 +15,12 @@ def get_synthesizer_ckpt(cpt, device=torch.device("cpu")):
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=False)
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"])
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=False)
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"])
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q

76
rvc/attentions.py
View File

@@ -9,15 +9,15 @@ from torch.nn import functional as F
class MultiHeadAttention(nn.Module):
def __init__(
self,
channels,
out_channels,
n_heads,
p_dropout=0.0,
window_size=None,
heads_share=True,
block_length=None,
proximal_bias=False,
proximal_init=False,
channels: int,
out_channels: int,
n_heads: int,
p_dropout: float = 0.0,
window_size: int | None = None,
heads_share: bool = True,
block_length: int | None = None,
proximal_bias: bool = False,
proximal_init: bool = False,
):
super(MultiHeadAttention, self).__init__()
assert channels % n_heads == 0
@@ -60,19 +60,30 @@ class MultiHeadAttention(nn.Module):
self.conv_k.weight.copy_(self.conv_q.weight)
self.conv_k.bias.copy_(self.conv_q.bias)
def __call__(
self,
x: torch.Tensor,
c: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
return super().__call__(x, c, attn_mask=attn_mask)
def forward(
self, x: torch.Tensor, c: torch.Tensor, attn_mask: Optional[torch.Tensor] = None
):
self,
x: torch.Tensor,
c: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
q = self.conv_q(x)
k = self.conv_k(c)
v = self.conv_v(c)
x, _ = self.attention(q, k, v, mask=attn_mask)
x, _ = self._attention(q, k, v, mask=attn_mask)
x = self.conv_o(x)
return x
def attention(
def _attention(
self,
query: torch.Tensor,
key: torch.Tensor,
@@ -149,7 +160,7 @@ class MultiHeadAttention(nn.Module):
return ret
def _get_relative_embeddings(self, relative_embeddings, length: int):
max_relative_position = 2 * self.window_size + 1
# max_relative_position = 2 * self.window_size + 1
# Pad first before slice to avoid using cond ops.
pad_length: int = max(length - (self.window_size + 1), 0)
slice_start_position = max((self.window_size + 1) - length, 0)
@@ -217,13 +228,13 @@ class MultiHeadAttention(nn.Module):
class FFN(nn.Module):
def __init__(
self,
in_channels,
out_channels,
filter_channels,
kernel_size,
p_dropout=0.0,
activation: str = None,
causal=False,
in_channels: int,
out_channels: int,
filter_channels: int,
kernel_size: int,
p_dropout: float = 0.0,
activation: str | None = None,
causal: bool = False,
):
super(FFN, self).__init__()
self.in_channels = in_channels
@@ -234,33 +245,30 @@ class FFN(nn.Module):
self.activation = activation
self.causal = causal
self.is_activation = True if activation == "gelu" else False
# if causal:
# self.padding = self._causal_padding
# else:
# self.padding = self._same_padding
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
self.drop = nn.Dropout(p_dropout)
def padding(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
if self.causal:
padding = self._causal_padding(x * x_mask)
else:
padding = self._same_padding(x * x_mask)
return padding
def __call__(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
return super().__call__(x, x_mask)
def forward(self, x: torch.Tensor, x_mask: torch.Tensor):
x = self.conv_1(self.padding(x, x_mask))
def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
x = self.conv_1(self._padding(x, x_mask))
if self.is_activation:
x = x * torch.sigmoid(1.702 * x)
else:
x = torch.relu(x)
x = self.drop(x)
x = self.conv_2(self.padding(x, x_mask))
x = self.conv_2(self._padding(x, x_mask))
return x * x_mask
def _padding(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
if self.causal:
return self._causal_padding(x * x_mask)
return self._same_padding(x * x_mask)
def _causal_padding(self, x):
if self.kernel_size == 1:
return x

128
rvc/norms.py
View File

@@ -1,7 +1,10 @@
from typing import Optional
import torch
from torch import nn
from torch.nn import functional as F
from .utils import activate_add_tanh_sigmoid_multiply
class LayerNorm(nn.Module):
def __init__(self, channels: int, eps: float = 1e-5):
@@ -16,3 +19,128 @@ class LayerNorm(nn.Module):
x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
class WN(torch.nn.Module):
def __init__(
self,
hidden_channels: int,
kernel_size: int,
dilation_rate: int,
n_layers: int,
gin_channels: int = 0,
p_dropout: int = 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 = float(p_dropout)
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self.drop = nn.Dropout(float(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)
# last one is not necessary
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 __call__(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
) -> torch.Tensor:
return super().__call__(x, x_mask, g=g)
def forward(
self,
x: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
) -> torch.Tensor:
output = torch.zeros_like(x)
if g is not None:
g = self.cond_layer(g)
for i, (in_layer, res_skip_layer) in enumerate(
zip(self.in_layers, self.res_skip_layers)
):
x_in: torch.Tensor = in_layer(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 = activate_add_tanh_sigmoid_multiply(x_in, g_l, self.hidden_channels)
acts: torch.Tensor = self.drop(acts)
res_skip_acts: torch.Tensor = res_skip_layer(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)
def __prepare_scriptable__(self):
if self.gin_channels != 0:
for hook in self.cond_layer._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.cond_layer)
for l in self.in_layers:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
for l in self.res_skip_layers:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self

4
rvc/onnx/infer.py
View File

@@ -38,9 +38,9 @@ class ContentVec(Model):
super().__init__(vec_path, device)
def __call__(self, wav: np.ndarray[typing.Any, np.dtype]):
return self.__forward(wav)
return self.forward(wav)
def __forward(self, wav: np.ndarray[typing.Any, np.dtype]):
def forward(self, wav: np.ndarray[typing.Any, np.dtype]):
if wav.ndim == 2: # double channels
wav = wav.mean(-1)
assert wav.ndim == 1, wav.ndim

260
rvc/residuals.py Normal file
View File

@@ -0,0 +1,260 @@
from typing import Optional
import torch
from torch import nn
from torch.nn import Conv1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, weight_norm
from .norms import WN
from .utils import (
get_padding,
call_weight_data_normal_if_Conv,
)
LRELU_SLOPE = 0.1
class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, 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(call_weight_data_normal_if_Conv)
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(call_weight_data_normal_if_Conv)
self.lrelu_slope = LRELU_SLOPE
def forward(self, x: torch.Tensor, x_mask: Optional[torch.Tensor] = None):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c2(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
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)
def __prepare_scriptable__(self):
for l in self.convs1:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
for l in self.convs2:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
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(call_weight_data_normal_if_Conv)
self.lrelu_slope = LRELU_SLOPE
def forward(self, x, x_mask: Optional[torch.Tensor] = None):
for c in self.convs:
xt = F.leaky_relu(x, self.lrelu_slope)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
def __prepare_scriptable__(self):
for l in self.convs:
for hook in l._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(l)
return self
class ResidualCouplingLayer(nn.Module):
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(ResidualCouplingLayer, self).__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.enc = WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=float(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: torch.Tensor,
x_mask: torch.Tensor,
g: Optional[torch.Tensor] = None,
reverse: bool = 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)
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, torch.zeros([1])
def remove_weight_norm(self):
self.enc.remove_weight_norm()
def __prepare_scriptable__(self):
for hook in self.enc._forward_pre_hooks.values():
if (
hook.__module__ == "torch.nn.utils.weight_norm"
and hook.__class__.__name__ == "WeightNorm"
):
torch.nn.utils.remove_weight_norm(self.enc)
return self