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4f175c5 | 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 | import torch
from typing import Optional
from i18n import _i18n
from .generators.hifigan_mrf import HiFiGANMRFGenerator
from .generators.hifigan_nsf import HiFiGANNSFGenerator
from .generators.hifigan import HiFiGANGenerator
from .generators.refinegan import RefineGANGenerator
from .commons import slice_segments, rand_slice_segments
from .residuals import ResidualCouplingBlock
from .encoders import TextEncoder, PosteriorEncoder
class Synthesizer(torch.nn.Module):
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: float,
resblock: str,
resblock_kernel_sizes: list,
resblock_dilation_sizes: list,
upsample_rates: list,
upsample_initial_channel: int,
upsample_kernel_sizes: list,
spk_embed_dim: int,
gin_channels: int,
sr: int,
use_f0: bool,
text_enc_hidden_dim: int = 768,
vocoder: str = "HiFi-GAN",
randomized: bool = True,
checkpointing: bool = False,
**kwargs,
):
super().__init__()
self.segment_size = segment_size
self.use_f0 = use_f0
self.randomized = randomized
self.enc_p = TextEncoder(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
text_enc_hidden_dim,
f0=use_f0,
)
print(_i18n("using_vocoder")+": "+vocoder)
if use_f0:
if vocoder == "MRF HiFi-GAN":
self.dec = HiFiGANMRFGenerator(
in_channel=inter_channels,
upsample_initial_channel=upsample_initial_channel,
upsample_rates=upsample_rates,
upsample_kernel_sizes=upsample_kernel_sizes,
resblock_kernel_sizes=resblock_kernel_sizes,
resblock_dilations=resblock_dilation_sizes,
gin_channels=gin_channels,
sample_rate=sr,
harmonic_num=8,
checkpointing=checkpointing,
)
elif vocoder == "RefineGAN":
self.dec = RefineGANGenerator(
sample_rate=sr,
downsample_rates=upsample_rates[::-1],
upsample_rates=upsample_rates,
start_channels=16,
num_mels=inter_channels,
checkpointing=checkpointing,
)
else:
self.dec = HiFiGANNSFGenerator(
inter_channels,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
sr=sr,
checkpointing=checkpointing,
)
else:
if vocoder == "MRF HiFi-GAN":
print("MRF HiFi-GAN does not support training without pitch guidance.")
self.dec = None
elif vocoder == "RefineGAN":
print("RefineGAN does not support training without pitch guidance.")
self.dec = None
else:
self.dec = HiFiGANGenerator(
inter_channels,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
)
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,
3,
gin_channels=gin_channels,
)
self.emb_g = torch.nn.Embedding(spk_embed_dim, gin_channels)
def _remove_weight_norm_from(self, module):
for hook in module._forward_pre_hooks.values():
if getattr(hook, "__class__", None).__name__ == "WeightNorm":
torch.nn.utils.remove_weight_norm(module)
def remove_weight_norm(self):
for module in [self.dec, self.flow, self.enc_q]:
self._remove_weight_norm_from(module)
def __prepare_scriptable__(self):
self.remove_weight_norm()
return self
def forward(
self,
phone: torch.Tensor,
phone_lengths: torch.Tensor,
pitch: Optional[torch.Tensor] = None,
pitchf: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
y_lengths: Optional[torch.Tensor] = None,
ds: Optional[torch.Tensor] = None,
):
g = self.emb_g(ds).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
if y is not 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)
if self.randomized:
z_slice, ids_slice = rand_slice_segments(
z, y_lengths, self.segment_size
)
if self.use_f0:
pitchf = slice_segments(pitchf, ids_slice, self.segment_size, 2)
o = self.dec(z_slice, pitchf, g=g)
else:
o = self.dec(z_slice, g=g)
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
else:
if self.use_f0:
o = self.dec(z, pitchf, g=g)
else:
o = self.dec(z, g=g)
return o, None, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
else:
return None, None, x_mask, None, (None, None, m_p, logs_p, None, None)
@torch.jit.export
def infer(
self,
phone: torch.Tensor,
phone_lengths: torch.Tensor,
pitch: Optional[torch.Tensor] = None,
nsff0: Optional[torch.Tensor] = None,
sid: torch.Tensor = None,
rate: Optional[torch.Tensor] = None,
):
g = self.emb_g(sid).unsqueeze(-1)
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
if rate is not None:
head = int(z_p.shape[2] * (1.0 - rate.item()))
z_p, x_mask = z_p[:, :, head:], x_mask[:, :, head:]
if self.use_f0 and nsff0 is not None:
nsff0 = nsff0[:, head:]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = (
self.dec(z * x_mask, nsff0, g=g)
if self.use_f0
else self.dec(z * x_mask, g=g)
)
return o, x_mask, (z, z_p, m_p, logs_p)
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