File size: 9,001 Bytes
c968fc3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.1-Stable/models.py
import copy
import torch
from torch import nn
from torch.nn import functional as F
from utils.util import *
from modules.transformer.attentions import Encoder
from models.tts.vits.vits import ResidualCouplingBlock, PosteriorEncoder
from models.vocoders.gan.generator.bigvgan import BigVGAN
from models.vocoders.gan.generator.hifigan import HiFiGAN
from models.vocoders.gan.generator.nsfhifigan import NSFHiFiGAN
from models.vocoders.gan.generator.melgan import MelGAN
from models.vocoders.gan.generator.apnet import APNet
from modules.encoder.condition_encoder import ConditionEncoder
def slice_pitch_segments(x, ids_str, segment_size=4):
ret = torch.zeros_like(x[:, :segment_size])
for i in range(x.size(0)):
idx_str = ids_str[i]
idx_end = idx_str + segment_size
ret[i] = x[i, idx_str:idx_end]
return ret
def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
b, d, t = x.size()
if x_lengths is None:
x_lengths = t
ids_str_max = x_lengths - segment_size + 1
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
ret = slice_segments(x, ids_str, segment_size)
ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
return ret, ret_pitch, ids_str
class ContentEncoder(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None,
):
super().__init__()
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.n_layers = n_layers
self.gin_channels = gin_channels
self.f0_emb = nn.Embedding(256, hidden_channels)
self.enc_ = Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
# condition_encoder ver.
def forward(self, x, x_mask, noice_scale=1):
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) * noice_scale) * x_mask
return z, m, logs, x_mask
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(self, spec_channels, segment_size, cfg):
super().__init__()
self.spec_channels = spec_channels
self.segment_size = segment_size
self.cfg = cfg
self.inter_channels = cfg.model.vits.inter_channels
self.hidden_channels = cfg.model.vits.hidden_channels
self.filter_channels = cfg.model.vits.filter_channels
self.n_heads = cfg.model.vits.n_heads
self.n_layers = cfg.model.vits.n_layers
self.kernel_size = cfg.model.vits.kernel_size
self.p_dropout = cfg.model.vits.p_dropout
self.n_flow_layer = cfg.model.vits.n_flow_layer
self.gin_channels = cfg.model.vits.gin_channels
self.n_speakers = cfg.model.vits.n_speakers
# f0
self.n_bins = cfg.preprocess.pitch_bin
self.f0_min = cfg.preprocess.f0_min
self.f0_max = cfg.preprocess.f0_max
# TODO: sort out the config
self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min
self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max
self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder)
self.emb_g = nn.Embedding(self.n_speakers, self.gin_channels)
self.enc_p = ContentEncoder(
self.inter_channels,
self.hidden_channels,
filter_channels=self.filter_channels,
n_heads=self.n_heads,
n_layers=self.n_layers,
kernel_size=self.kernel_size,
p_dropout=self.p_dropout,
)
assert cfg.model.generator in [
"bigvgan",
"hifigan",
"melgan",
"nsfhifigan",
"apnet",
]
self.dec_name = cfg.model.generator
temp_cfg = copy.deepcopy(cfg)
temp_cfg.preprocess.n_mel = self.inter_channels
if cfg.model.generator == "bigvgan":
temp_cfg.model.bigvgan = cfg.model.generator_config.bigvgan
self.dec = BigVGAN(temp_cfg)
elif cfg.model.generator == "hifigan":
temp_cfg.model.hifigan = cfg.model.generator_config.hifigan
self.dec = HiFiGAN(temp_cfg)
elif cfg.model.generator == "melgan":
temp_cfg.model.melgan = cfg.model.generator_config.melgan
self.dec = MelGAN(temp_cfg)
elif cfg.model.generator == "nsfhifigan":
temp_cfg.model.nsfhifigan = cfg.model.generator_config.nsfhifigan
self.dec = NSFHiFiGAN(temp_cfg) # TODO: nsf need f0
elif cfg.model.generator == "apnet":
temp_cfg.model.apnet = cfg.model.generator_config.apnet
self.dec = APNet(temp_cfg)
self.enc_q = PosteriorEncoder(
self.spec_channels,
self.inter_channels,
self.hidden_channels,
5,
1,
16,
gin_channels=self.gin_channels,
)
self.flow = ResidualCouplingBlock(
self.inter_channels,
self.hidden_channels,
5,
1,
self.n_flow_layer,
gin_channels=self.gin_channels,
)
def forward(self, data):
"""VitsSVC forward function.
Args:
data (dict): condition data & audio data, including:
B: batch size, T: target length
{
"spk_id": [B, singer_table_size]
"target_len": [B]
"mask": [B, T, 1]
"mel": [B, T, n_mel]
"linear": [B, T, n_fft // 2 + 1]
"frame_pitch": [B, T]
"frame_uv": [B, T]
"audio": [B, audio_len]
"audio_len": [B]
"contentvec_feat": [B, T, contentvec_dim]
"whisper_feat": [B, T, whisper_dim]
...
}
"""
# TODO: elegantly handle the dimensions
spec = data["linear"].transpose(1, 2)
g = data["spk_id"]
g = self.emb_g(g).transpose(1, 2)
c_lengths = data["target_len"]
spec_lengths = data["target_len"]
f0 = data["frame_pitch"]
# condition_encoder ver.
x = self.condition_encoder(data).transpose(1, 2)
x_mask = torch.unsqueeze(sequence_mask(c_lengths, f0.size(1)), 1).to(x.dtype)
# prior encoder
z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask)
# posterior encoder
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 = rand_slice_segments_with_pitch(
z, f0, spec_lengths, self.segment_size
)
if self.dec_name == "nsfhifigan":
o = self.dec(z_slice, f0=f0.float())
elif self.dec_name == "apnet":
_, _, _, _, o = self.dec(z_slice)
else:
o = self.dec(z_slice)
outputs = {
"y_hat": o,
"ids_slice": ids_slice,
"x_mask": x_mask,
"z_mask": data["mask"].transpose(1, 2),
"z": z,
"z_p": z_p,
"m_p": m_p,
"logs_p": logs_p,
"m_q": m_q,
"logs_q": logs_q,
}
return outputs
@torch.no_grad()
def infer(self, data, noise_scale=0.35, seed=52468):
# c, f0, uv, g
f0 = data["frame_pitch"]
g = data["spk_id"]
if f0.device == torch.device("cuda"):
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
c_lengths = (torch.ones(f0.size(0)) * f0.size(-1)).to(f0.device)
if g.dim() == 1:
g = g.unsqueeze(0)
g = self.emb_g(g).transpose(1, 2)
# condition_encoder ver.
x = self.condition_encoder(data).transpose(1, 2)
x_mask = torch.unsqueeze(sequence_mask(c_lengths, f0.size(1)), 1).to(x.dtype)
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, noice_scale=noise_scale)
z = self.flow(z_p, c_mask, g=g, reverse=True)
if self.dec_name == "nsfhifigan":
o = self.dec(z * c_mask, f0=f0.float())
elif self.dec_name == "apnet":
_, _, _, _, o = self.dec(z * c_mask)
else:
o = self.dec(z * c_mask)
return o, f0
|