Spaces:
Sleeping
Sleeping
File size: 8,178 Bytes
d5d7329 |
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 |
import warnings
from logging import getLogger
from typing import Any, Literal, Sequence
import torch
from torch import nn
import so_vits_svc_fork.f0
from so_vits_svc_fork.f0 import f0_to_coarse
from so_vits_svc_fork.modules import commons as commons
from so_vits_svc_fork.modules.decoders.f0 import F0Decoder
from so_vits_svc_fork.modules.decoders.hifigan import NSFHifiGANGenerator
from so_vits_svc_fork.modules.decoders.mb_istft import (
Multiband_iSTFT_Generator,
Multistream_iSTFT_Generator,
iSTFT_Generator,
)
from so_vits_svc_fork.modules.encoders import Encoder, TextEncoder
from so_vits_svc_fork.modules.flows import ResidualCouplingBlock
LOG = getLogger(__name__)
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(
self,
spec_channels: int,
segment_size: int,
inter_channels: int,
hidden_channels: int,
filter_channels: int,
n_heads: int,
n_layers: int,
kernel_size: int,
p_dropout: int,
resblock: str,
resblock_kernel_sizes: Sequence[int],
resblock_dilation_sizes: Sequence[Sequence[int]],
upsample_rates: Sequence[int],
upsample_initial_channel: int,
upsample_kernel_sizes: Sequence[int],
gin_channels: int,
ssl_dim: int,
n_speakers: int,
sampling_rate: int = 44100,
type_: Literal["hifi-gan", "istft", "ms-istft", "mb-istft"] = "hifi-gan",
gen_istft_n_fft: int = 16,
gen_istft_hop_size: int = 4,
subbands: int = 4,
**kwargs: Any,
):
super().__init__()
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.gin_channels = gin_channels
self.ssl_dim = ssl_dim
self.n_speakers = n_speakers
self.sampling_rate = sampling_rate
self.type_ = type_
self.gen_istft_n_fft = gen_istft_n_fft
self.gen_istft_hop_size = gen_istft_hop_size
self.subbands = subbands
if kwargs:
warnings.warn(f"Unused arguments: {kwargs}")
self.emb_g = nn.Embedding(n_speakers, gin_channels)
if ssl_dim is None:
self.pre = nn.LazyConv1d(hidden_channels, kernel_size=5, padding=2)
else:
self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
self.enc_p = TextEncoder(
inter_channels,
hidden_channels,
filter_channels=filter_channels,
n_heads=n_heads,
n_layers=n_layers,
kernel_size=kernel_size,
p_dropout=p_dropout,
)
LOG.info(f"Decoder type: {type_}")
if type_ == "hifi-gan":
hps = {
"sampling_rate": sampling_rate,
"inter_channels": inter_channels,
"resblock": resblock,
"resblock_kernel_sizes": resblock_kernel_sizes,
"resblock_dilation_sizes": resblock_dilation_sizes,
"upsample_rates": upsample_rates,
"upsample_initial_channel": upsample_initial_channel,
"upsample_kernel_sizes": upsample_kernel_sizes,
"gin_channels": gin_channels,
}
self.dec = NSFHifiGANGenerator(h=hps)
self.mb = False
else:
hps = {
"initial_channel": inter_channels,
"resblock": resblock,
"resblock_kernel_sizes": resblock_kernel_sizes,
"resblock_dilation_sizes": resblock_dilation_sizes,
"upsample_rates": upsample_rates,
"upsample_initial_channel": upsample_initial_channel,
"upsample_kernel_sizes": upsample_kernel_sizes,
"gin_channels": gin_channels,
"gen_istft_n_fft": gen_istft_n_fft,
"gen_istft_hop_size": gen_istft_hop_size,
"subbands": subbands,
}
# gen_istft_n_fft, gen_istft_hop_size, subbands
if type_ == "istft":
del hps["subbands"]
self.dec = iSTFT_Generator(**hps)
elif type_ == "ms-istft":
self.dec = Multistream_iSTFT_Generator(**hps)
elif type_ == "mb-istft":
self.dec = Multiband_iSTFT_Generator(**hps)
else:
raise ValueError(f"Unknown type: {type_}")
self.mb = True
self.enc_q = Encoder(
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
)
self.f0_decoder = F0Decoder(
1,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=gin_channels,
)
self.emb_uv = nn.Embedding(2, hidden_channels)
def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
g = self.emb_g(g).transpose(1, 2)
# ssl prenet
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(
c.dtype
)
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
# f0 predict
lf0 = 2595.0 * torch.log10(1.0 + f0.unsqueeze(1) / 700.0) / 500
norm_lf0 = so_vits_svc_fork.f0.normalize_f0(lf0, x_mask, uv)
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
# encoder
z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
# flow
z_p = self.flow(z, spec_mask, g=g)
z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(
z, f0, spec_lengths, self.segment_size
)
# MB-iSTFT-VITS
if self.mb:
o, o_mb = self.dec(z_slice, g=g)
# HiFi-GAN
else:
o = self.dec(z_slice, g=g, f0=pitch_slice)
o_mb = None
return (
o,
o_mb,
ids_slice,
spec_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
pred_lf0,
norm_lf0,
lf0,
)
def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
g = self.emb_g(g).transpose(1, 2)
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(
c.dtype
)
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
if predict_f0:
lf0 = 2595.0 * torch.log10(1.0 + f0.unsqueeze(1) / 700.0) / 500
norm_lf0 = so_vits_svc_fork.f0.normalize_f0(
lf0, x_mask, uv, random_scale=False
)
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
z_p, m_p, logs_p, c_mask = self.enc_p(
x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale
)
z = self.flow(z_p, c_mask, g=g, reverse=True)
# MB-iSTFT-VITS
if self.mb:
o, o_mb = self.dec(z * c_mask, g=g)
else:
o = self.dec(z * c_mask, g=g, f0=f0)
return o
|