Spaces:
Running
Running
File size: 7,240 Bytes
10f957b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
import torch
from torch import nn
from torch.nn import functional as F
from vits import attentions
from vits import commons
from vits import modules
from vits.utils import f0_to_coarse
from vits_decoder.generator import Generator
class TextEncoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout):
super().__init__()
self.out_channels = out_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, kernel_size=5, padding=2)
self.pit = nn.Embedding(256, hidden_channels)
self.enc = 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, f0):
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.pre(x) * x_mask
x = x + self.pit(f0).transpose(1, 2)
x = self.enc(x * x_mask, x_mask)
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 ResidualCouplingBlock(nn.Module):
def __init__(
self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
):
super().__init__()
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:
total_logdet = 0
for flow in self.flows:
x, log_det = flow(x, x_mask, g=g, reverse=reverse)
total_logdet += log_det
return x, total_logdet
else:
total_logdet = 0
for flow in reversed(self.flows):
x, log_det = flow(x, x_mask, g=g, reverse=reverse)
total_logdet += log_det
return x, total_logdet
def remove_weight_norm(self):
for i in range(self.n_flows):
self.flows[i * 2].remove_weight_norm()
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.out_channels = out_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
def remove_weight_norm(self):
self.enc.remove_weight_norm()
class SynthesizerTrn(nn.Module):
def __init__(
self,
spec_channels,
segment_size,
hp
):
super().__init__()
self.segment_size = segment_size
self.emb_g = nn.Linear(hp.vits.spk_dim, hp.vits.gin_channels)
self.enc_p = TextEncoder(
hp.vits.ppg_dim,
hp.vits.inter_channels,
hp.vits.hidden_channels,
hp.vits.filter_channels,
2,
6,
3,
0.1,
)
self.enc_q = PosteriorEncoder(
spec_channels,
hp.vits.inter_channels,
hp.vits.hidden_channels,
5,
1,
16,
gin_channels=hp.vits.gin_channels,
)
self.flow = ResidualCouplingBlock(
hp.vits.inter_channels,
hp.vits.hidden_channels,
5,
1,
4,
gin_channels=hp.vits.spk_dim
)
self.dec = Generator(hp=hp)
def forward(self, ppg, pit, spec, spk, ppg_l, spec_l):
g = self.emb_g(F.normalize(spk)).unsqueeze(-1)
z_p, m_p, logs_p, ppg_mask = self.enc_p(
ppg, ppg_l, f0=f0_to_coarse(pit))
z_q, m_q, logs_q, spec_mask = self.enc_q(spec, spec_l, g=g)
z_slice, pit_slice, ids_slice = commons.rand_slice_segments_with_pitch(
z_q, pit, spec_l, self.segment_size)
audio = self.dec(spk, z_slice, pit_slice)
# SNAC to flow
z_f, logdet_f = self.flow(z_q, spec_mask, g=spk)
z_r, logdet_r = self.flow(z_p, spec_mask, g=spk, reverse=True)
return audio, ids_slice, spec_mask, (z_f, z_r, z_p, m_p, logs_p, z_q, m_q, logs_q, logdet_f, logdet_r)
def infer(self, ppg, pit, spk, ppg_l):
z_p, m_p, logs_p, ppg_mask = self.enc_p(
ppg, ppg_l, f0=f0_to_coarse(pit))
z, _ = self.flow(z_p, ppg_mask, g=spk, reverse=True)
o = self.dec(spk, z * ppg_mask, f0=pit)
return o
class SynthesizerInfer(nn.Module):
def __init__(
self,
spec_channels,
segment_size,
hp
):
super().__init__()
self.segment_size = segment_size
self.enc_p = TextEncoder(
hp.vits.ppg_dim,
hp.vits.inter_channels,
hp.vits.hidden_channels,
hp.vits.filter_channels,
2,
6,
3,
0.1,
)
self.flow = ResidualCouplingBlock(
hp.vits.inter_channels,
hp.vits.hidden_channels,
5,
1,
4,
gin_channels=hp.vits.spk_dim
)
self.dec = Generator(hp=hp)
def remove_weight_norm(self):
self.flow.remove_weight_norm()
self.dec.remove_weight_norm()
def pitch2source(self, f0):
return self.dec.pitch2source(f0)
def source2wav(self, source):
return self.dec.source2wav(source)
def inference(self, ppg, pit, spk, ppg_l, source):
z_p, m_p, logs_p, ppg_mask = self.enc_p(
ppg, ppg_l, f0=f0_to_coarse(pit))
z, _ = self.flow(z_p, ppg_mask, g=spk, reverse=True)
o = self.dec.inference(spk, z * ppg_mask, source)
return o
|