File size: 2,622 Bytes
c3b58fa |
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 |
from typing import List, Optional, Union
import torch
from rvc.layers.synthesizers import SynthesizerTrnMsNSFsid as SynthesizerBase
class SynthesizerTrnMsNSFsid(SynthesizerBase):
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: List[int],
resblock_dilation_sizes: List[List[int]],
upsample_rates: List[int],
upsample_initial_channel: int,
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr: Optional[Union[str, int]],
encoder_dim: int,
):
super().__init__(
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,
gin_channels,
sr,
encoder_dim,
True,
)
self.speaker_map = None
def remove_weight_norm(self):
self.dec.remove_weight_norm()
self.flow.remove_weight_norm()
self.enc_q.remove_weight_norm()
def construct_spkmixmap(self):
self.speaker_map = torch.zeros((self.n_speaker, 1, 1, self.gin_channels))
for i in range(self.n_speaker):
self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
self.speaker_map = self.speaker_map.unsqueeze(0)
def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
if self.speaker_map is not None: # [N, S] * [S, B, 1, H]
g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
g = g * self.speaker_map # [N, S, B, 1, H]
g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
else:
g = g.unsqueeze(0)
g = self.emb_g(g).transpose(1, 2)
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 = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
return o
|