RVC/Retrieval-based-Voice-Conversion-WebUI
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Onnx导出拓展以及WebUI支持 (#140)

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Ναρουσέ·μ·γιουμεμί·Χινακάννα
2023-04-24 19:55:05 +08:00
committed by GitHub
parent fb1d4b1882
commit 9bac0ffaa7
11 changed files with 1101 additions and 140 deletions
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95
infer_pack/models_onnx.py
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@@ -527,7 +527,7 @@ sr2sr = {
}
class SynthesizerTrnMs256NSFsid(nn.Module):
class SynthesizerTrnMs256NSFsidO(nn.Module):
def __init__(
self,
spec_channels,
@@ -612,104 +612,15 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=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) * rnd) * x_mask
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
return o
class SynthesizerTrnMs256NSFsid_sim(nn.Module):
"""
Synthesizer for Training
"""
def __init__(
self,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
spk_embed_dim,
# hop_length,
gin_channels=0,
use_sdp=True,
**kwargs
):
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.hop_length = hop_length#
self.spk_embed_dim = spk_embed_dim
self.enc_p = TextEncoder256Sim(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
)
self.dec = GeneratorNSF(
inter_channels,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
is_half=kwargs["is_half"],
)
self.flow = ResidualCouplingBlock(
inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
)
self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
def remove_weight_norm(self):
self.dec.remove_weight_norm()
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def forward(
self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
): # y是spec不需要了现在
g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
x, x_mask = self.enc_p(phone, pitch, phone_lengths)
x = self.flow(x, x_mask, g=g, reverse=True)
o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
return o
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()