406 lines
14 KiB
Python
406 lines
14 KiB
Python
from dataclasses import dataclass
|
|
from enum import Enum
|
|
from typing import Callable, Optional
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torch.nn.utils.parametrize as parametrize
|
|
|
|
MAX_WAV_VALUE = 32768.0
|
|
|
|
|
|
class KernelPredictor(torch.nn.Module):
|
|
"""Kernel predictor for the location-variable convolutions"""
|
|
|
|
def __init__(
|
|
self,
|
|
cond_channels,
|
|
conv_in_channels,
|
|
conv_out_channels,
|
|
conv_layers,
|
|
conv_kernel_size=3,
|
|
kpnet_hidden_channels=64,
|
|
kpnet_conv_size=3,
|
|
kpnet_dropout=0.0,
|
|
kpnet_nonlinear_activation="LeakyReLU",
|
|
kpnet_nonlinear_activation_params={"negative_slope": 0.1},
|
|
):
|
|
"""
|
|
Args:
|
|
cond_channels (int): number of channel for the conditioning sequence,
|
|
conv_in_channels (int): number of channel for the input sequence,
|
|
conv_out_channels (int): number of channel for the output sequence,
|
|
conv_layers (int): number of layers
|
|
"""
|
|
super().__init__()
|
|
|
|
self.conv_in_channels = conv_in_channels
|
|
self.conv_out_channels = conv_out_channels
|
|
self.conv_kernel_size = conv_kernel_size
|
|
self.conv_layers = conv_layers
|
|
|
|
kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers # l_w
|
|
kpnet_bias_channels = conv_out_channels * conv_layers # l_b
|
|
|
|
self.input_conv = nn.Sequential(
|
|
nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)
|
|
),
|
|
getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
|
|
)
|
|
|
|
self.residual_convs = nn.ModuleList()
|
|
padding = (kpnet_conv_size - 1) // 2
|
|
for _ in range(3):
|
|
self.residual_convs.append(
|
|
nn.Sequential(
|
|
nn.Dropout(kpnet_dropout),
|
|
nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(
|
|
kpnet_hidden_channels,
|
|
kpnet_hidden_channels,
|
|
kpnet_conv_size,
|
|
padding=padding,
|
|
bias=True,
|
|
)
|
|
),
|
|
getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
|
|
nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(
|
|
kpnet_hidden_channels,
|
|
kpnet_hidden_channels,
|
|
kpnet_conv_size,
|
|
padding=padding,
|
|
bias=True,
|
|
)
|
|
),
|
|
getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
|
|
)
|
|
)
|
|
self.kernel_conv = nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(
|
|
kpnet_hidden_channels,
|
|
kpnet_kernel_channels,
|
|
kpnet_conv_size,
|
|
padding=padding,
|
|
bias=True,
|
|
)
|
|
)
|
|
self.bias_conv = nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(
|
|
kpnet_hidden_channels,
|
|
kpnet_bias_channels,
|
|
kpnet_conv_size,
|
|
padding=padding,
|
|
bias=True,
|
|
)
|
|
)
|
|
|
|
def forward(self, c):
|
|
"""
|
|
Args:
|
|
c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
|
|
"""
|
|
batch, _, cond_length = c.shape
|
|
c = self.input_conv(c)
|
|
for residual_conv in self.residual_convs:
|
|
residual_conv.to(c.device)
|
|
c = c + residual_conv(c)
|
|
k = self.kernel_conv(c)
|
|
b = self.bias_conv(c)
|
|
kernels = k.contiguous().view(
|
|
batch,
|
|
self.conv_layers,
|
|
self.conv_in_channels,
|
|
self.conv_out_channels,
|
|
self.conv_kernel_size,
|
|
cond_length,
|
|
)
|
|
bias = b.contiguous().view(
|
|
batch,
|
|
self.conv_layers,
|
|
self.conv_out_channels,
|
|
cond_length,
|
|
)
|
|
|
|
return kernels, bias
|
|
|
|
def remove_weight_norm(self):
|
|
parametrize.remove_parametrizations(self.input_conv[0], "weight")
|
|
parametrize.remove_parametrizations(self.kernel_conv, "weight")
|
|
parametrize.remove_parametrizations(self.bias_conv)
|
|
for block in self.residual_convs:
|
|
parametrize.remove_parametrizations(block[1], "weight")
|
|
parametrize.remove_parametrizations(block[3], "weight")
|
|
|
|
|
|
class LVCBlock(torch.nn.Module):
|
|
"""the location-variable convolutions"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
cond_channels,
|
|
stride,
|
|
dilations=[1, 3, 9, 27],
|
|
lReLU_slope=0.2,
|
|
conv_kernel_size=3,
|
|
cond_hop_length=256,
|
|
kpnet_hidden_channels=64,
|
|
kpnet_conv_size=3,
|
|
kpnet_dropout=0.0,
|
|
):
|
|
super().__init__()
|
|
|
|
self.cond_hop_length = cond_hop_length
|
|
self.conv_layers = len(dilations)
|
|
self.conv_kernel_size = conv_kernel_size
|
|
|
|
self.kernel_predictor = KernelPredictor(
|
|
cond_channels=cond_channels,
|
|
conv_in_channels=in_channels,
|
|
conv_out_channels=2 * in_channels,
|
|
conv_layers=len(dilations),
|
|
conv_kernel_size=conv_kernel_size,
|
|
kpnet_hidden_channels=kpnet_hidden_channels,
|
|
kpnet_conv_size=kpnet_conv_size,
|
|
kpnet_dropout=kpnet_dropout,
|
|
kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope},
|
|
)
|
|
|
|
self.convt_pre = nn.Sequential(
|
|
nn.LeakyReLU(lReLU_slope),
|
|
nn.utils.parametrizations.weight_norm(
|
|
nn.ConvTranspose1d(
|
|
in_channels,
|
|
in_channels,
|
|
2 * stride,
|
|
stride=stride,
|
|
padding=stride // 2 + stride % 2,
|
|
output_padding=stride % 2,
|
|
)
|
|
),
|
|
)
|
|
|
|
self.conv_blocks = nn.ModuleList()
|
|
for dilation in dilations:
|
|
self.conv_blocks.append(
|
|
nn.Sequential(
|
|
nn.LeakyReLU(lReLU_slope),
|
|
nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(
|
|
in_channels,
|
|
in_channels,
|
|
conv_kernel_size,
|
|
padding=dilation * (conv_kernel_size - 1) // 2,
|
|
dilation=dilation,
|
|
)
|
|
),
|
|
nn.LeakyReLU(lReLU_slope),
|
|
)
|
|
)
|
|
|
|
def forward(self, x, c):
|
|
"""forward propagation of the location-variable convolutions.
|
|
Args:
|
|
x (Tensor): the input sequence (batch, in_channels, in_length)
|
|
c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
|
|
|
|
Returns:
|
|
Tensor: the output sequence (batch, in_channels, in_length)
|
|
"""
|
|
_, in_channels, _ = x.shape # (B, c_g, L')
|
|
|
|
x = self.convt_pre(x) # (B, c_g, stride * L')
|
|
kernels, bias = self.kernel_predictor(c)
|
|
|
|
for i, conv in enumerate(self.conv_blocks):
|
|
output = conv(x) # (B, c_g, stride * L')
|
|
|
|
k = kernels[:, i, :, :, :, :] # (B, 2 * c_g, c_g, kernel_size, cond_length)
|
|
b = bias[:, i, :, :] # (B, 2 * c_g, cond_length)
|
|
|
|
output = self.location_variable_convolution(
|
|
output, k, b, hop_size=self.cond_hop_length
|
|
) # (B, 2 * c_g, stride * L'): LVC
|
|
x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh(
|
|
output[:, in_channels:, :]
|
|
) # (B, c_g, stride * L'): GAU
|
|
|
|
return x
|
|
|
|
def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256):
|
|
"""perform location-variable convolution operation on the input sequence (x) using the local convolution kernl.
|
|
Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100.
|
|
Args:
|
|
x (Tensor): the input sequence (batch, in_channels, in_length).
|
|
kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
|
|
bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
|
|
dilation (int): the dilation of convolution.
|
|
hop_size (int): the hop_size of the conditioning sequence.
|
|
Returns:
|
|
(Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
|
|
"""
|
|
batch, _, in_length = x.shape
|
|
batch, _, out_channels, kernel_size, kernel_length = kernel.shape
|
|
assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched"
|
|
|
|
padding = dilation * int((kernel_size - 1) / 2)
|
|
x = F.pad(x, (padding, padding), "constant", 0) # (batch, in_channels, in_length + 2*padding)
|
|
x = x.unfold(2, hop_size + 2 * padding, hop_size) # (batch, in_channels, kernel_length, hop_size + 2*padding)
|
|
|
|
if hop_size < dilation:
|
|
x = F.pad(x, (0, dilation), "constant", 0)
|
|
x = x.unfold(
|
|
3, dilation, dilation
|
|
) # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
|
|
x = x[:, :, :, :, :hop_size]
|
|
x = x.transpose(3, 4) # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
|
|
x = x.unfold(4, kernel_size, 1) # (batch, in_channels, kernel_length, dilation, _, kernel_size)
|
|
|
|
o = torch.einsum("bildsk,biokl->bolsd", x, kernel)
|
|
o = o.to(memory_format=torch.channels_last_3d)
|
|
bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d)
|
|
o = o + bias
|
|
o = o.contiguous().view(batch, out_channels, -1)
|
|
|
|
return o
|
|
|
|
def remove_weight_norm(self):
|
|
self.kernel_predictor.remove_weight_norm()
|
|
parametrize.remove_parametrizations(self.convt_pre[1], "weight")
|
|
for block in self.conv_blocks:
|
|
parametrize.remove_parametrizations(block[1], "weight")
|
|
|
|
|
|
class UnivNetGenerator(nn.Module):
|
|
"""
|
|
UnivNet Generator
|
|
|
|
Originally from https://github.com/mindslab-ai/univnet/blob/master/model/generator.py.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
noise_dim=64,
|
|
channel_size=32,
|
|
dilations=[1, 3, 9, 27],
|
|
strides=[8, 8, 4],
|
|
lReLU_slope=0.2,
|
|
kpnet_conv_size=3,
|
|
# Below are MEL configurations options that this generator requires.
|
|
hop_length=256,
|
|
n_mel_channels=100,
|
|
):
|
|
super(UnivNetGenerator, self).__init__()
|
|
self.mel_channel = n_mel_channels
|
|
self.noise_dim = noise_dim
|
|
self.hop_length = hop_length
|
|
channel_size = channel_size
|
|
kpnet_conv_size = kpnet_conv_size
|
|
|
|
self.res_stack = nn.ModuleList()
|
|
hop_length = 1
|
|
for stride in strides:
|
|
hop_length = stride * hop_length
|
|
self.res_stack.append(
|
|
LVCBlock(
|
|
channel_size,
|
|
n_mel_channels,
|
|
stride=stride,
|
|
dilations=dilations,
|
|
lReLU_slope=lReLU_slope,
|
|
cond_hop_length=hop_length,
|
|
kpnet_conv_size=kpnet_conv_size,
|
|
)
|
|
)
|
|
|
|
self.conv_pre = nn.utils.parametrizations.weight_norm(
|
|
nn.Conv1d(noise_dim, channel_size, 7, padding=3, padding_mode="reflect")
|
|
)
|
|
|
|
self.conv_post = nn.Sequential(
|
|
nn.LeakyReLU(lReLU_slope),
|
|
nn.utils.parametrizations.weight_norm(nn.Conv1d(channel_size, 1, 7, padding=3, padding_mode="reflect")),
|
|
nn.Tanh(),
|
|
)
|
|
|
|
def forward(self, c, z):
|
|
"""
|
|
Args:
|
|
c (Tensor): the conditioning sequence of mel-spectrogram (batch, mel_channels, in_length)
|
|
z (Tensor): the noise sequence (batch, noise_dim, in_length)
|
|
|
|
"""
|
|
z = self.conv_pre(z) # (B, c_g, L)
|
|
|
|
for res_block in self.res_stack:
|
|
res_block.to(z.device)
|
|
z = res_block(z, c) # (B, c_g, L * s_0 * ... * s_i)
|
|
|
|
z = self.conv_post(z) # (B, 1, L * 256)
|
|
|
|
return z
|
|
|
|
def eval(self, inference=False):
|
|
super(UnivNetGenerator, self).eval()
|
|
# don't remove weight norm while validation in training loop
|
|
if inference:
|
|
self.remove_weight_norm()
|
|
|
|
def remove_weight_norm(self):
|
|
parametrize.remove_parametrizations(self.conv_pre, "weight")
|
|
|
|
for layer in self.conv_post:
|
|
if len(layer.state_dict()) != 0:
|
|
parametrize.remove_parametrizations(layer, "weight")
|
|
|
|
for res_block in self.res_stack:
|
|
res_block.remove_weight_norm()
|
|
|
|
def inference(self, c, z=None):
|
|
# pad input mel with zeros to cut artifact
|
|
# see https://github.com/seungwonpark/melgan/issues/8
|
|
zero = torch.full((c.shape[0], self.mel_channel, 10), -11.5129).to(c.device)
|
|
mel = torch.cat((c, zero), dim=2)
|
|
|
|
if z is None:
|
|
z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device)
|
|
|
|
audio = self.forward(mel, z)
|
|
audio = audio[:, :, : -(self.hop_length * 10)]
|
|
audio = audio.clamp(min=-1, max=1)
|
|
return audio
|
|
|
|
|
|
@dataclass
|
|
class VocType:
|
|
constructor: Callable[[], nn.Module]
|
|
model_path: str
|
|
subkey: Optional[str] = None
|
|
|
|
def optionally_index(self, model_dict):
|
|
if self.subkey is not None:
|
|
return model_dict[self.subkey]
|
|
return model_dict
|
|
|
|
|
|
class VocConf(Enum):
|
|
Univnet = VocType(UnivNetGenerator, "vocoder.pth", "model_g")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
model = UnivNetGenerator()
|
|
|
|
c = torch.randn(3, 100, 10)
|
|
z = torch.randn(3, 64, 10)
|
|
print(c.shape)
|
|
|
|
y = model(c, z)
|
|
print(y.shape)
|
|
assert y.shape == torch.Size([3, 1, 2560])
|
|
|
|
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
|
print(pytorch_total_params)
|