470 lines
17 KiB
Python
470 lines
17 KiB
Python
import copy
|
|
import math
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
|
|
import modules.attentions as attentions
|
|
import modules.commons as commons
|
|
import modules.modules as modules
|
|
|
|
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
|
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
|
|
|
import utils
|
|
from modules.commons import init_weights, get_padding
|
|
from utils import f0_to_coarse
|
|
|
|
class ResidualCouplingBlock(nn.Module):
|
|
def __init__(self,
|
|
channels,
|
|
hidden_channels,
|
|
kernel_size,
|
|
dilation_rate,
|
|
n_layers,
|
|
n_flows=4,
|
|
gin_channels=0):
|
|
super().__init__()
|
|
self.channels = channels
|
|
self.hidden_channels = hidden_channels
|
|
self.kernel_size = kernel_size
|
|
self.dilation_rate = dilation_rate
|
|
self.n_layers = n_layers
|
|
self.n_flows = n_flows
|
|
self.gin_channels = gin_channels
|
|
|
|
self.flows = nn.ModuleList()
|
|
for i in range(n_flows):
|
|
self.flows.append(
|
|
modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
|
|
gin_channels=gin_channels, mean_only=True))
|
|
self.flows.append(modules.Flip())
|
|
|
|
def forward(self, x, x_mask, g=None, reverse=False):
|
|
if not reverse:
|
|
for flow in self.flows:
|
|
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
|
else:
|
|
for flow in reversed(self.flows):
|
|
x = flow(x, x_mask, g=g, reverse=reverse)
|
|
return x
|
|
|
|
|
|
class Encoder(nn.Module):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
hidden_channels,
|
|
kernel_size,
|
|
dilation_rate,
|
|
n_layers,
|
|
gin_channels=0):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.hidden_channels = hidden_channels
|
|
self.kernel_size = kernel_size
|
|
self.dilation_rate = dilation_rate
|
|
self.n_layers = n_layers
|
|
self.gin_channels = gin_channels
|
|
|
|
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
|
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
|
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
|
|
|
def forward(self, x, x_lengths, g=None):
|
|
# print(x.shape,x_lengths.shape)
|
|
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
|
x = self.pre(x) * x_mask
|
|
x = self.enc(x, x_mask, g=g)
|
|
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)) * x_mask
|
|
return z, m, logs, x_mask
|
|
|
|
|
|
class TextEncoder(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.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
|
self.f0_emb = nn.Embedding(256, hidden_channels)
|
|
|
|
self.enc_ = attentions.Encoder(
|
|
hidden_channels,
|
|
filter_channels,
|
|
n_heads,
|
|
n_layers,
|
|
kernel_size,
|
|
p_dropout)
|
|
|
|
def forward(self, x, x_mask, f0=None, noice_scale=1):
|
|
x = x + self.f0_emb(f0).transpose(1, 2)
|
|
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 DiscriminatorP(torch.nn.Module):
|
|
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
|
super(DiscriminatorP, self).__init__()
|
|
self.period = period
|
|
self.use_spectral_norm = use_spectral_norm
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
|
self.convs = nn.ModuleList([
|
|
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
|
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
|
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
|
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
|
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
|
])
|
|
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
|
|
|
def forward(self, x):
|
|
fmap = []
|
|
|
|
# 1d to 2d
|
|
b, c, t = x.shape
|
|
if t % self.period != 0: # pad first
|
|
n_pad = self.period - (t % self.period)
|
|
x = F.pad(x, (0, n_pad), "reflect")
|
|
t = t + n_pad
|
|
x = x.view(b, c, t // self.period, self.period)
|
|
|
|
for l in self.convs:
|
|
x = l(x)
|
|
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
|
fmap.append(x)
|
|
x = self.conv_post(x)
|
|
fmap.append(x)
|
|
x = torch.flatten(x, 1, -1)
|
|
|
|
return x, fmap
|
|
|
|
|
|
class DiscriminatorS(torch.nn.Module):
|
|
def __init__(self, use_spectral_norm=False):
|
|
super(DiscriminatorS, self).__init__()
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
|
self.convs = nn.ModuleList([
|
|
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
|
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
|
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
|
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
|
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
|
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
|
])
|
|
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
|
|
|
def forward(self, x):
|
|
fmap = []
|
|
|
|
for l in self.convs:
|
|
x = l(x)
|
|
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
|
fmap.append(x)
|
|
x = self.conv_post(x)
|
|
fmap.append(x)
|
|
x = torch.flatten(x, 1, -1)
|
|
|
|
return x, fmap
|
|
|
|
|
|
class MultiPeriodDiscriminator(torch.nn.Module):
|
|
def __init__(self, use_spectral_norm=False):
|
|
super(MultiPeriodDiscriminator, self).__init__()
|
|
periods = [2, 3, 5, 7, 11]
|
|
|
|
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
|
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
|
self.discriminators = nn.ModuleList(discs)
|
|
|
|
def forward(self, y, y_hat):
|
|
y_d_rs = []
|
|
y_d_gs = []
|
|
fmap_rs = []
|
|
fmap_gs = []
|
|
for i, d in enumerate(self.discriminators):
|
|
y_d_r, fmap_r = d(y)
|
|
y_d_g, fmap_g = d(y_hat)
|
|
y_d_rs.append(y_d_r)
|
|
y_d_gs.append(y_d_g)
|
|
fmap_rs.append(fmap_r)
|
|
fmap_gs.append(fmap_g)
|
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
|
|
|
|
|
class SpeakerEncoder(torch.nn.Module):
|
|
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
|
|
super(SpeakerEncoder, self).__init__()
|
|
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
|
|
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
|
|
self.relu = nn.ReLU()
|
|
|
|
def forward(self, mels):
|
|
self.lstm.flatten_parameters()
|
|
_, (hidden, _) = self.lstm(mels)
|
|
embeds_raw = self.relu(self.linear(hidden[-1]))
|
|
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
|
|
|
|
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
|
|
mel_slices = []
|
|
for i in range(0, total_frames - partial_frames, partial_hop):
|
|
mel_range = torch.arange(i, i + partial_frames)
|
|
mel_slices.append(mel_range)
|
|
|
|
return mel_slices
|
|
|
|
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
|
|
mel_len = mel.size(1)
|
|
last_mel = mel[:, -partial_frames:]
|
|
|
|
if mel_len > partial_frames:
|
|
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
|
|
mels = list(mel[:, s] for s in mel_slices)
|
|
mels.append(last_mel)
|
|
mels = torch.stack(tuple(mels), 0).squeeze(1)
|
|
|
|
with torch.no_grad():
|
|
partial_embeds = self(mels)
|
|
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
|
|
# embed = embed / torch.linalg.norm(embed, 2)
|
|
else:
|
|
with torch.no_grad():
|
|
embed = self(last_mel)
|
|
|
|
return embed
|
|
|
|
class F0Decoder(nn.Module):
|
|
def __init__(self,
|
|
out_channels,
|
|
hidden_channels,
|
|
filter_channels,
|
|
n_heads,
|
|
n_layers,
|
|
kernel_size,
|
|
p_dropout,
|
|
spk_channels=0):
|
|
super().__init__()
|
|
self.out_channels = out_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.spk_channels = spk_channels
|
|
|
|
self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
|
|
self.decoder = attentions.FFT(
|
|
hidden_channels,
|
|
filter_channels,
|
|
n_heads,
|
|
n_layers,
|
|
kernel_size,
|
|
p_dropout)
|
|
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
|
self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
|
|
self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
|
|
|
|
def forward(self, x, norm_f0, x_mask, spk_emb=None):
|
|
x = torch.detach(x)
|
|
if (spk_emb is not None):
|
|
x = x + self.cond(spk_emb)
|
|
x += self.f0_prenet(norm_f0)
|
|
x = self.prenet(x) * x_mask
|
|
x = self.decoder(x * x_mask, x_mask)
|
|
x = self.proj(x) * x_mask
|
|
return x
|
|
|
|
|
|
class SynthesizerTrn(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,
|
|
gin_channels,
|
|
ssl_dim,
|
|
n_speakers,
|
|
sampling_rate=44100,
|
|
vol_embedding=False,
|
|
vocoder_name = "nsf-hifigan",
|
|
**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.ssl_dim = ssl_dim
|
|
self.vol_embedding = vol_embedding
|
|
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
|
if vol_embedding:
|
|
self.emb_vol = nn.Linear(1, hidden_channels)
|
|
|
|
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
|
|
)
|
|
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,
|
|
}
|
|
|
|
|
|
if vocoder_name == "nsf-hifigan":
|
|
from vdecoder.hifigan.models import Generator
|
|
self.dec = Generator(h=hps)
|
|
elif vocoder_name == "nsf-snake-hifigan":
|
|
from vdecoder.hifiganwithsnake.models import Generator
|
|
self.dec = Generator(h=hps)
|
|
else:
|
|
print("[?] Unkown vocoder: use default(nsf-hifigan)")
|
|
from vdecoder.hifigan.models import Generator
|
|
self.dec = Generator(h=hps)
|
|
|
|
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)
|
|
self.character_mix = False
|
|
|
|
def EnableCharacterMix(self, n_speakers_map, device):
|
|
self.speaker_map = torch.zeros((n_speakers_map, 1, 1, self.gin_channels)).to(device)
|
|
for i in range(n_speakers_map):
|
|
self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]).to(device))
|
|
self.speaker_map = self.speaker_map.unsqueeze(0).to(device)
|
|
self.character_mix = True
|
|
|
|
def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None, vol = None):
|
|
g = self.emb_g(g).transpose(1,2)
|
|
|
|
# vol proj
|
|
vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol!=None and self.vol_embedding else 0
|
|
|
|
# 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) + vol
|
|
|
|
# f0 predict
|
|
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
|
norm_lf0 = utils.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)
|
|
|
|
# nsf decoder
|
|
o = self.dec(z_slice, g=g, f0=pitch_slice)
|
|
|
|
return o, 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, seed=52468, predict_f0=False, vol = None):
|
|
|
|
if c.device == torch.device("cuda"):
|
|
torch.cuda.manual_seed_all(seed)
|
|
else:
|
|
torch.manual_seed(seed)
|
|
|
|
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
|
|
|
if self.character_mix and len(g) > 1: # [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:
|
|
if g.dim() == 1:
|
|
g = g.unsqueeze(0)
|
|
g = self.emb_g(g).transpose(1, 2)
|
|
|
|
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
|
|
# vol proj
|
|
vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol!=None and self.vol_embedding else 0
|
|
|
|
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2) + vol
|
|
|
|
if predict_f0:
|
|
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
|
norm_lf0 = utils.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)
|
|
o = self.dec(z * c_mask, g=g, f0=f0)
|
|
return o,f0
|
|
|