mirror of
https://github.com/index-tts/index-tts.git
synced 2025-11-28 10:20:24 +08:00
451 lines
18 KiB
Python
451 lines
18 KiB
Python
# Copyright (c) 2022 NVIDIA CORPORATION.
|
|
# Licensed under the MIT license.
|
|
|
|
# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
|
|
# LICENSE is in incl_licenses directory.
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.nn import Conv1d, Conv2d, ConvTranspose1d
|
|
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
|
|
|
|
import indextts.BigVGAN.activations as activations
|
|
|
|
from indextts.BigVGAN.ECAPA_TDNN import ECAPA_TDNN
|
|
from indextts.BigVGAN.utils import get_padding, init_weights
|
|
|
|
LRELU_SLOPE = 0.1
|
|
|
|
|
|
class AMPBlock1(torch.nn.Module):
|
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
|
|
super(AMPBlock1, self).__init__()
|
|
self.h = h
|
|
|
|
self.convs1 = nn.ModuleList([
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
|
padding=get_padding(kernel_size, dilation[0]))),
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
|
padding=get_padding(kernel_size, dilation[1]))),
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
|
padding=get_padding(kernel_size, dilation[2])))
|
|
])
|
|
self.convs1.apply(init_weights)
|
|
|
|
self.convs2 = nn.ModuleList([
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
|
padding=get_padding(kernel_size, 1))),
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
|
padding=get_padding(kernel_size, 1))),
|
|
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
|
padding=get_padding(kernel_size, 1)))
|
|
])
|
|
self.convs2.apply(init_weights)
|
|
|
|
self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
|
|
if self.h.get("use_cuda_kernel", False):
|
|
from indextts.BigVGAN.alias_free_activation.cuda.activation1d import Activation1d
|
|
else:
|
|
from indextts.BigVGAN.alias_free_torch import Activation1d
|
|
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
else:
|
|
raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
|
|
|
|
def forward(self, x):
|
|
acts1, acts2 = self.activations[::2], self.activations[1::2]
|
|
for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
|
|
xt = a1(x)
|
|
xt = c1(xt)
|
|
xt = a2(xt)
|
|
xt = c2(xt)
|
|
x = xt + x
|
|
|
|
return x
|
|
|
|
def remove_weight_norm(self):
|
|
for l in self.convs1:
|
|
remove_weight_norm(l)
|
|
for l in self.convs2:
|
|
remove_weight_norm(l)
|
|
|
|
|
|
class AMPBlock2(torch.nn.Module):
|
|
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
|
|
super(AMPBlock2, self).__init__()
|
|
self.h = h
|
|
|
|
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(init_weights)
|
|
|
|
self.num_layers = len(self.convs) # total number of conv layers
|
|
if self.h.get("use_cuda_kernel", False):
|
|
from indextts.BigVGAN.alias_free_activation.cuda.activation1d import Activation1d
|
|
else:
|
|
from indextts.BigVGAN.alias_free_torch import Activation1d
|
|
|
|
if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
|
|
self.activations = nn.ModuleList([
|
|
Activation1d(
|
|
activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
|
|
for _ in range(self.num_layers)
|
|
])
|
|
else:
|
|
raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
|
|
|
|
def forward(self, x):
|
|
for c, a in zip(self.convs, self.activations):
|
|
xt = a(x)
|
|
xt = c(xt)
|
|
x = xt + x
|
|
|
|
return x
|
|
|
|
def remove_weight_norm(self):
|
|
for l in self.convs:
|
|
remove_weight_norm(l)
|
|
|
|
|
|
class BigVGAN(torch.nn.Module):
|
|
# this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
|
|
def __init__(self, h, use_cuda_kernel=False):
|
|
"""
|
|
Args:
|
|
h (dict)
|
|
use_cuda_kernel (bool): whether to use custom cuda kernel for anti-aliased activation
|
|
"""
|
|
super(BigVGAN, self).__init__()
|
|
self.h = h
|
|
self.h["use_cuda_kernel"] = use_cuda_kernel
|
|
|
|
self.num_kernels = len(h.resblock_kernel_sizes)
|
|
self.num_upsamples = len(h.upsample_rates)
|
|
|
|
self.feat_upsample = h.feat_upsample
|
|
self.cond_in_each_up_layer = h.cond_d_vector_in_each_upsampling_layer
|
|
|
|
# pre conv
|
|
self.conv_pre = weight_norm(Conv1d(h.gpt_dim, h.upsample_initial_channel, 7, 1, padding=3))
|
|
|
|
# define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
|
|
resblock = AMPBlock1 if h.resblock == "1" else AMPBlock2
|
|
|
|
# transposed conv-based upsamplers. does not apply anti-aliasing
|
|
self.ups = nn.ModuleList()
|
|
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
|
|
self.ups.append(nn.ModuleList([
|
|
weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
|
|
h.upsample_initial_channel // (2 ** (i + 1)),
|
|
k, u, padding=(k - u) // 2))
|
|
]))
|
|
|
|
# residual blocks using anti-aliased multi-periodicity composition modules (AMP)
|
|
self.resblocks = nn.ModuleList()
|
|
for i in range(len(self.ups)):
|
|
ch = h.upsample_initial_channel // (2 ** (i + 1))
|
|
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
|
|
self.resblocks.append(resblock(self.h, ch, k, d, activation=h.activation))
|
|
if use_cuda_kernel:
|
|
from indextts.BigVGAN.alias_free_activation.cuda.activation1d import Activation1d
|
|
else:
|
|
from indextts.BigVGAN.alias_free_torch import Activation1d
|
|
|
|
# post conv
|
|
if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
|
|
activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
|
|
self.activation_post = Activation1d(activation=activation_post)
|
|
elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
|
|
activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
|
|
self.activation_post = Activation1d(activation=activation_post)
|
|
else:
|
|
raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
|
|
|
|
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
|
|
|
# weight initialization
|
|
for i in range(len(self.ups)):
|
|
self.ups[i].apply(init_weights)
|
|
self.conv_post.apply(init_weights)
|
|
|
|
self.speaker_encoder = ECAPA_TDNN(h.num_mels, lin_neurons=h.speaker_embedding_dim)
|
|
self.cond_layer = nn.Conv1d(h.speaker_embedding_dim, h.upsample_initial_channel, 1)
|
|
if self.cond_in_each_up_layer:
|
|
self.conds = nn.ModuleList()
|
|
for i in range(len(self.ups)):
|
|
ch = h.upsample_initial_channel // (2 ** (i + 1))
|
|
self.conds.append(nn.Conv1d(h.speaker_embedding_dim, ch, 1))
|
|
|
|
# self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
|
|
|
def forward(self, x, mel_ref, lens=None):
|
|
speaker_embedding = self.speaker_encoder(mel_ref, lens)
|
|
n_batch = x.size(0)
|
|
contrastive_loss = None
|
|
if n_batch * 2 == speaker_embedding.size(0):
|
|
spe_emb_chunk1, spe_emb_chunk2 = speaker_embedding[:n_batch, :, :], speaker_embedding[n_batch:, :, :]
|
|
contrastive_loss = self.cal_clip_loss(spe_emb_chunk1.squeeze(1), spe_emb_chunk2.squeeze(1), self.logit_scale.exp())
|
|
|
|
speaker_embedding = speaker_embedding[:n_batch, :, :]
|
|
speaker_embedding = speaker_embedding.transpose(1, 2)
|
|
|
|
# upsample feat
|
|
if self.feat_upsample:
|
|
x = torch.nn.functional.interpolate(
|
|
x.transpose(1, 2),
|
|
scale_factor=[4],
|
|
mode="linear",
|
|
).squeeze(1)
|
|
else:
|
|
x = x.transpose(1, 2)
|
|
|
|
### bigVGAN ###
|
|
# pre conv
|
|
x = self.conv_pre(x)
|
|
|
|
x = x + self.cond_layer(speaker_embedding)
|
|
|
|
for i in range(self.num_upsamples):
|
|
# upsampling
|
|
for i_up in range(len(self.ups[i])):
|
|
x = self.ups[i][i_up](x)
|
|
|
|
if self.cond_in_each_up_layer:
|
|
x = x + self.conds[i](speaker_embedding)
|
|
|
|
# AMP blocks
|
|
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
|
|
|
|
# post conv
|
|
x = self.activation_post(x)
|
|
x = self.conv_post(x)
|
|
x = torch.tanh(x)
|
|
|
|
return x, contrastive_loss
|
|
|
|
def remove_weight_norm(self):
|
|
print('Removing weight norm...')
|
|
for l in self.ups:
|
|
for l_i in l:
|
|
remove_weight_norm(l_i)
|
|
for l in self.resblocks:
|
|
l.remove_weight_norm()
|
|
remove_weight_norm(self.conv_pre)
|
|
remove_weight_norm(self.conv_post)
|
|
|
|
def cal_clip_loss(self, image_features, text_features, logit_scale):
|
|
device = image_features.device
|
|
logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale)
|
|
labels = torch.arange(logits_per_image.shape[0], device=device, dtype=torch.long)
|
|
total_loss = (
|
|
F.cross_entropy(logits_per_image, labels) +
|
|
F.cross_entropy(logits_per_text, labels)
|
|
) / 2
|
|
return total_loss
|
|
|
|
def get_logits(self, image_features, text_features, logit_scale):
|
|
logits_per_image = logit_scale * image_features @ text_features.T
|
|
logits_per_text = logit_scale * text_features @ image_features.T
|
|
return logits_per_image, logits_per_text
|
|
|
|
|
|
class DiscriminatorP(torch.nn.Module):
|
|
def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
|
super(DiscriminatorP, self).__init__()
|
|
self.period = period
|
|
self.d_mult = h.discriminator_channel_mult
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
|
self.convs = nn.ModuleList([
|
|
norm_f(Conv2d(1, int(32 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
norm_f(Conv2d(int(32 * self.d_mult), int(128 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
norm_f(Conv2d(int(128 * self.d_mult), int(512 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
norm_f(Conv2d(int(512 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
norm_f(Conv2d(int(1024 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), 1, padding=(2, 0))),
|
|
])
|
|
self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 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, 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, h):
|
|
super(MultiPeriodDiscriminator, self).__init__()
|
|
self.mpd_reshapes = h.mpd_reshapes
|
|
print("mpd_reshapes: {}".format(self.mpd_reshapes))
|
|
discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
|
|
self.discriminators = nn.ModuleList(discriminators)
|
|
|
|
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)
|
|
fmap_rs.append(fmap_r)
|
|
y_d_gs.append(y_d_g)
|
|
fmap_gs.append(fmap_g)
|
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
|
|
|
|
|
class DiscriminatorR(nn.Module):
|
|
def __init__(self, cfg, resolution):
|
|
super().__init__()
|
|
|
|
self.resolution = resolution
|
|
assert len(self.resolution) == 3, \
|
|
"MRD layer requires list with len=3, got {}".format(self.resolution)
|
|
self.lrelu_slope = LRELU_SLOPE
|
|
|
|
norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
|
|
if hasattr(cfg, "mrd_use_spectral_norm"):
|
|
print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm))
|
|
norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
|
|
self.d_mult = cfg.discriminator_channel_mult
|
|
if hasattr(cfg, "mrd_channel_mult"):
|
|
print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult))
|
|
self.d_mult = cfg.mrd_channel_mult
|
|
|
|
self.convs = nn.ModuleList([
|
|
norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))),
|
|
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
|
|
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
|
|
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
|
|
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 3), padding=(1, 1))),
|
|
])
|
|
self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
|
|
|
|
def forward(self, x):
|
|
fmap = []
|
|
|
|
x = self.spectrogram(x)
|
|
x = x.unsqueeze(1)
|
|
for l in self.convs:
|
|
x = l(x)
|
|
x = F.leaky_relu(x, self.lrelu_slope)
|
|
fmap.append(x)
|
|
x = self.conv_post(x)
|
|
fmap.append(x)
|
|
x = torch.flatten(x, 1, -1)
|
|
|
|
return x, fmap
|
|
|
|
def spectrogram(self, x):
|
|
n_fft, hop_length, win_length = self.resolution
|
|
x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
|
|
x = x.squeeze(1)
|
|
x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True)
|
|
x = torch.view_as_real(x) # [B, F, TT, 2]
|
|
mag = torch.norm(x, p=2, dim=-1) # [B, F, TT]
|
|
|
|
return mag
|
|
|
|
|
|
class MultiResolutionDiscriminator(nn.Module):
|
|
def __init__(self, cfg, debug=False):
|
|
super().__init__()
|
|
self.resolutions = cfg.resolutions
|
|
assert len(self.resolutions) == 3, \
|
|
"MRD requires list of list with len=3, each element having a list with len=3. got {}".\
|
|
format(self.resolutions)
|
|
self.discriminators = nn.ModuleList(
|
|
[DiscriminatorR(cfg, resolution) for resolution in self.resolutions]
|
|
)
|
|
|
|
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(x=y)
|
|
y_d_g, fmap_g = d(x=y_hat)
|
|
y_d_rs.append(y_d_r)
|
|
fmap_rs.append(fmap_r)
|
|
y_d_gs.append(y_d_g)
|
|
fmap_gs.append(fmap_g)
|
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
|
|
|
|
|
def feature_loss(fmap_r, fmap_g):
|
|
loss = 0
|
|
for dr, dg in zip(fmap_r, fmap_g):
|
|
for rl, gl in zip(dr, dg):
|
|
loss += torch.mean(torch.abs(rl - gl))
|
|
|
|
return loss * 2
|
|
|
|
|
|
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
|
loss = 0
|
|
r_losses = []
|
|
g_losses = []
|
|
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
|
r_loss = torch.mean((1 - dr)**2)
|
|
g_loss = torch.mean(dg**2)
|
|
loss += (r_loss + g_loss)
|
|
r_losses.append(r_loss.item())
|
|
g_losses.append(g_loss.item())
|
|
|
|
return loss, r_losses, g_losses
|
|
|
|
|
|
def generator_loss(disc_outputs):
|
|
loss = 0
|
|
gen_losses = []
|
|
for dg in disc_outputs:
|
|
l = torch.mean((1 - dg)**2)
|
|
gen_losses.append(l)
|
|
loss += l
|
|
|
|
return loss, gen_losses
|