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import torch | |
from torch import nn | |
from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
from torch.nn import functional as F | |
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
from sovits import attentions | |
from sovits import commons | |
from sovits import modules | |
from sovits.commons import init_weights, get_padding | |
from sovits.vdecoder.hifigan.hifigan import HifiGanGenerator | |
# import monotonic_align | |
class TextEncoder(nn.Module): | |
def __init__(self, | |
n_vocab, | |
out_channels, | |
hidden_channels, | |
filter_channels, | |
n_heads, | |
n_layers, | |
kernel_size, | |
p_dropout): | |
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.emb = nn.Embedding(n_vocab, hidden_channels) | |
# nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) | |
self.emb_pitch = nn.Embedding(256, hidden_channels) | |
nn.init.normal_(self.emb_pitch.weight, 0.0, hidden_channels ** -0.5) | |
self.encoder = attentions.Encoder( | |
hidden_channels, | |
filter_channels, | |
n_heads, | |
n_layers, | |
kernel_size, | |
p_dropout) | |
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
def forward(self, x, x_lengths, pitch): | |
# x = x.transpose(1,2) | |
# x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] | |
# print(x.shape) | |
x = x + self.emb_pitch(pitch) | |
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) | |
stats = self.proj(x) * x_mask | |
m, logs = torch.split(stats, self.out_channels, dim=1) | |
return x, m, logs, x_mask | |
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 | |
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 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, | |
n_speakers=0, | |
gin_channels=0, | |
use_sdp=True, | |
**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.use_sdp = use_sdp | |
self.enc_p = TextEncoder(n_vocab, | |
inter_channels, | |
hidden_channels, | |
filter_channels, | |
n_heads, | |
n_layers, | |
kernel_size, | |
p_dropout) | |
# self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, | |
# upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) | |
# | |
# from DiffSinger: modules.hifigan.hifigan.HifiGanGenerator | |
# hps = { | |
# "resblock_kernel_sizes": [3, 7, 11], | |
# "upsample_rates": [8, 8, 2, 2], | |
# "upsample_initial_channel": 128, | |
# "use_pitch_embed": True, | |
# "audio_sample_rate": kwargs["sampling_rate"], | |
# "resblock": "1", | |
# "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]] | |
# } | |
# from sovits json config hps | |
hps = { | |
"resblock_kernel_sizes": resblock_kernel_sizes, | |
"inter_channels": inter_channels, | |
"upsample_rates": upsample_rates, | |
"upsample_kernel_sizes": upsample_kernel_sizes, | |
"upsample_initial_channel": upsample_initial_channel, | |
"use_pitch_embed": True, | |
"audio_sample_rate": kwargs["sampling_rate"], | |
"resblock": "1", | |
"resblock_dilation_sizes": resblock_dilation_sizes | |
} | |
self.dec = HifiGanGenerator(h=hps) | |
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) | |
if n_speakers > 1: | |
self.emb_g = nn.Embedding(n_speakers, gin_channels) | |
def forward(self, x, x_lengths, y, y_lengths, pitch, sid=None): | |
assert 0 <= y.shape[2] - x.shape[1] * 2 <= 1, (y.shape[2], x.shape[1] * 2, sid) | |
if y.shape[2] != x.shape[1] * 2: | |
y_lengths[y_lengths == y.shape[2]] -= 1 | |
y = y[:, :, :x.shape[1] * 2] | |
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, pitch) | |
if self.n_speakers > 0: | |
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
else: | |
g = None | |
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) | |
z_p = self.flow(z, y_mask, g=g) | |
m_p = torch.repeat_interleave(m_p, repeats=2, dim=2) | |
logs_p = torch.repeat_interleave(logs_p, repeats=2, dim=2) | |
# print(x.shape, y.shape, z.shape, pitch.shape) | |
z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, torch.repeat_interleave(pitch, | |
repeats=2, | |
dim=1), | |
y_lengths, self.segment_size) | |
o = self.dec(z_slice, f0=pitch_slice) | |
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) | |
def infer(self, x, x_lengths, pitch, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): | |
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, pitch) | |
if self.n_speakers > 0: | |
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
else: | |
g = None | |
m_p = torch.repeat_interleave(m_p, repeats=2, dim=2) | |
logs_p = torch.repeat_interleave(logs_p, repeats=2, dim=2) | |
x_mask = torch.repeat_interleave(x_mask, repeats=2, dim=2) | |
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
z = self.flow(z_p, x_mask, g=g, reverse=True) | |
# o = self.dec((z * x_mask)[:, :, :max_len], g=g) | |
# print(x.shape, pitch.shape, sid) | |
# print() | |
o = self.dec((z * x_mask)[:, :, :max_len], f0=torch.repeat_interleave(pitch, repeats=2, dim=1)) | |
return o, x_mask, (z, z_p, m_p, logs_p) | |
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 = self.dec(z_hat * y_mask, g=g_tgt) | |
return o_hat, y_mask, (z, z_p, z_hat) | |