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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
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