893 lines
33 KiB
Python
893 lines
33 KiB
Python
import math,pdb,os
|
||
from time import time as ttime
|
||
import torch
|
||
from torch import nn
|
||
from torch.nn import functional as F
|
||
from infer_pack import modules
|
||
from infer_pack import attentions
|
||
from infer_pack import commons
|
||
from infer_pack.commons import init_weights, get_padding
|
||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||
from infer_pack.commons import init_weights
|
||
import numpy as np
|
||
from infer_pack import commons
|
||
class TextEncoder256(nn.Module):
|
||
def __init__(
|
||
self, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, f0=True ):
|
||
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.emb_phone = nn.Linear(256, hidden_channels)
|
||
self.lrelu=nn.LeakyReLU(0.1,inplace=True)
|
||
if(f0==True):
|
||
self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
|
||
self.encoder = attentions.Encoder(
|
||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||
)
|
||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||
|
||
def forward(self, phone, pitch, lengths):
|
||
if(pitch==None):
|
||
x = self.emb_phone(phone)
|
||
else:
|
||
x = self.emb_phone(phone) + self.emb_pitch(pitch)
|
||
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
|
||
x=self.lrelu(x)
|
||
x = torch.transpose(x, 1, -1) # [b, h, t]
|
||
x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
|
||
x.dtype
|
||
)
|
||
x = self.encoder(x * x_mask, x_mask)
|
||
stats = self.proj(x) * x_mask
|
||
|
||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||
return m, logs, x_mask
|
||
class TextEncoder256Sim(nn.Module):
|
||
def __init__( self, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, f0=True):
|
||
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.emb_phone = nn.Linear(256, hidden_channels)
|
||
self.lrelu=nn.LeakyReLU(0.1,inplace=True)
|
||
if(f0==True):
|
||
self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
|
||
self.encoder = attentions.Encoder(
|
||
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
|
||
)
|
||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||
|
||
def forward(self, phone, pitch, lengths):
|
||
if(pitch==None):
|
||
x = self.emb_phone(phone)
|
||
else:
|
||
x = self.emb_phone(phone) + self.emb_pitch(pitch)
|
||
x = x * math.sqrt(self.hidden_channels) # [b, t, h]
|
||
x=self.lrelu(x)
|
||
x = torch.transpose(x, 1, -1) # [b, h, t]
|
||
x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(x.dtype)
|
||
x = self.encoder(x * x_mask, x_mask)
|
||
x = self.proj(x) * x_mask
|
||
return x,x_mask
|
||
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
|
||
|
||
def remove_weight_norm(self):
|
||
for i in range(self.n_flows):
|
||
self.flows[i * 2].remove_weight_norm()
|
||
class PosteriorEncoder(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):
|
||
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
|
||
|
||
def remove_weight_norm(self):
|
||
self.enc.remove_weight_norm()
|
||
class Generator(torch.nn.Module):
|
||
def __init__(
|
||
self,
|
||
initial_channel,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
gin_channels=0,
|
||
):
|
||
super(Generator, self).__init__()
|
||
self.num_kernels = len(resblock_kernel_sizes)
|
||
self.num_upsamples = len(upsample_rates)
|
||
self.conv_pre = Conv1d(
|
||
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
||
)
|
||
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
|
||
|
||
self.ups = nn.ModuleList()
|
||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||
self.ups.append(
|
||
weight_norm(
|
||
ConvTranspose1d(
|
||
upsample_initial_channel // (2**i),
|
||
upsample_initial_channel // (2 ** (i + 1)),
|
||
k,
|
||
u,
|
||
padding=(k - u) // 2,
|
||
)
|
||
)
|
||
)
|
||
|
||
self.resblocks = nn.ModuleList()
|
||
for i in range(len(self.ups)):
|
||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||
for j, (k, d) in enumerate(
|
||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||
):
|
||
self.resblocks.append(resblock(ch, k, d))
|
||
|
||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||
self.ups.apply(init_weights)
|
||
|
||
if gin_channels != 0:
|
||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||
|
||
def forward(self, x, g=None):
|
||
x = self.conv_pre(x)
|
||
if g is not None:
|
||
x = x + self.cond(g)
|
||
|
||
for i in range(self.num_upsamples):
|
||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||
x = self.ups[i](x)
|
||
xs = None
|
||
for j in range(self.num_kernels):
|
||
if xs is None:
|
||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||
else:
|
||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||
x = xs / self.num_kernels
|
||
x = F.leaky_relu(x)
|
||
x = self.conv_post(x)
|
||
x = torch.tanh(x)
|
||
|
||
return x
|
||
|
||
def remove_weight_norm(self):
|
||
for l in self.ups:
|
||
remove_weight_norm(l)
|
||
for l in self.resblocks:
|
||
l.remove_weight_norm()
|
||
class SineGen(torch.nn.Module):
|
||
""" Definition of sine generator
|
||
SineGen(samp_rate, harmonic_num = 0,
|
||
sine_amp = 0.1, noise_std = 0.003,
|
||
voiced_threshold = 0,
|
||
flag_for_pulse=False)
|
||
samp_rate: sampling rate in Hz
|
||
harmonic_num: number of harmonic overtones (default 0)
|
||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||
noise_std: std of Gaussian noise (default 0.003)
|
||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||
segment is always sin(np.pi) or cos(0)
|
||
"""
|
||
|
||
def __init__(self, samp_rate, harmonic_num=0,
|
||
sine_amp=0.1, noise_std=0.003,
|
||
voiced_threshold=0,
|
||
flag_for_pulse=False):
|
||
super(SineGen, self).__init__()
|
||
self.sine_amp = sine_amp
|
||
self.noise_std = noise_std
|
||
self.harmonic_num = harmonic_num
|
||
self.dim = self.harmonic_num + 1
|
||
self.sampling_rate = samp_rate
|
||
self.voiced_threshold = voiced_threshold
|
||
|
||
def _f02uv(self, f0):
|
||
# generate uv signal
|
||
uv = torch.ones_like(f0)
|
||
uv = uv * (f0 > self.voiced_threshold)
|
||
return uv
|
||
|
||
def forward(self, f0,upp):
|
||
""" sine_tensor, uv = forward(f0)
|
||
input F0: tensor(batchsize=1, length, dim=1)
|
||
f0 for unvoiced steps should be 0
|
||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||
output uv: tensor(batchsize=1, length, 1)
|
||
"""
|
||
with torch.no_grad():
|
||
f0 = f0[:, None].transpose(1, 2)
|
||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,device=f0.device)
|
||
# fundamental component
|
||
f0_buf[:, :, 0] = f0[:, :, 0]
|
||
for idx in np.arange(self.harmonic_num):f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)# idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
|
||
rad_values = (f0_buf / self.sampling_rate) % 1###%1意味着n_har的乘积无法后处理优化
|
||
rand_ini = torch.rand(f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device)
|
||
rand_ini[:, 0] = 0
|
||
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
|
||
tmp_over_one = torch.cumsum(rad_values, 1)# % 1 #####%1意味着后面的cumsum无法再优化
|
||
tmp_over_one*=upp
|
||
tmp_over_one=F.interpolate(tmp_over_one.transpose(2, 1), scale_factor=upp, mode='linear', align_corners=True).transpose(2, 1)
|
||
rad_values=F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)#######
|
||
tmp_over_one%=1
|
||
tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
|
||
cumsum_shift = torch.zeros_like(rad_values)
|
||
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||
sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi)
|
||
sine_waves = sine_waves * self.sine_amp
|
||
uv = self._f02uv(f0)
|
||
uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
|
||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||
noise = noise_amp * torch.randn_like(sine_waves)
|
||
sine_waves = sine_waves * uv + noise
|
||
return sine_waves, uv, noise
|
||
class SourceModuleHnNSF(torch.nn.Module):
|
||
""" SourceModule for hn-nsf
|
||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||
add_noise_std=0.003, voiced_threshod=0)
|
||
sampling_rate: sampling_rate in Hz
|
||
harmonic_num: number of harmonic above F0 (default: 0)
|
||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||
note that amplitude of noise in unvoiced is decided
|
||
by sine_amp
|
||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||
F0_sampled (batchsize, length, 1)
|
||
Sine_source (batchsize, length, 1)
|
||
noise_source (batchsize, length 1)
|
||
uv (batchsize, length, 1)
|
||
"""
|
||
|
||
def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||
add_noise_std=0.003, voiced_threshod=0,is_half=True):
|
||
super(SourceModuleHnNSF, self).__init__()
|
||
|
||
self.sine_amp = sine_amp
|
||
self.noise_std = add_noise_std
|
||
self.is_half=is_half
|
||
# to produce sine waveforms
|
||
self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
|
||
sine_amp, add_noise_std, voiced_threshod)
|
||
|
||
# to merge source harmonics into a single excitation
|
||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||
self.l_tanh = torch.nn.Tanh()
|
||
|
||
def forward(self, x,upp=None):
|
||
sine_wavs, uv, _ = self.l_sin_gen(x,upp)
|
||
if(self.is_half==True):sine_wavs=sine_wavs.half()
|
||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||
return sine_merge,None,None# noise, uv
|
||
class GeneratorNSF(torch.nn.Module):
|
||
def __init__(
|
||
self,
|
||
initial_channel,
|
||
resblock,
|
||
resblock_kernel_sizes,
|
||
resblock_dilation_sizes,
|
||
upsample_rates,
|
||
upsample_initial_channel,
|
||
upsample_kernel_sizes,
|
||
gin_channels,
|
||
sr,
|
||
is_half=False
|
||
):
|
||
super(GeneratorNSF, self).__init__()
|
||
self.num_kernels = len(resblock_kernel_sizes)
|
||
self.num_upsamples = len(upsample_rates)
|
||
|
||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
|
||
self.m_source = SourceModuleHnNSF(
|
||
sampling_rate=sr,
|
||
harmonic_num=0,
|
||
is_half=is_half
|
||
)
|
||
self.noise_convs = nn.ModuleList()
|
||
self.conv_pre = Conv1d(
|
||
initial_channel, upsample_initial_channel, 7, 1, padding=3
|
||
)
|
||
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
|
||
|
||
self.ups = nn.ModuleList()
|
||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||
c_cur = upsample_initial_channel // (2 ** (i + 1))
|
||
self.ups.append(
|
||
weight_norm(
|
||
ConvTranspose1d(
|
||
upsample_initial_channel // (2**i),
|
||
upsample_initial_channel // (2 ** (i + 1)),
|
||
k,
|
||
u,
|
||
padding=(k - u) // 2,
|
||
)
|
||
)
|
||
)
|
||
if i + 1 < len(upsample_rates):
|
||
stride_f0 = np.prod(upsample_rates[i + 1:])
|
||
self.noise_convs.append(Conv1d(
|
||
1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
|
||
else:
|
||
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
|
||
|
||
self.resblocks = nn.ModuleList()
|
||
for i in range(len(self.ups)):
|
||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||
for j, (k, d) in enumerate(
|
||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||
):
|
||
self.resblocks.append(resblock(ch, k, d))
|
||
|
||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||
self.ups.apply(init_weights)
|
||
|
||
if gin_channels != 0:
|
||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||
|
||
self.upp=np.prod(upsample_rates)
|
||
|
||
def forward(self, x, f0,g=None):
|
||
har_source, noi_source, uv = self.m_source(f0,self.upp)
|
||
har_source = har_source.transpose(1, 2)
|
||
x = self.conv_pre(x)
|
||
if g is not None:
|
||
x = x + self.cond(g)
|
||
|
||
for i in range(self.num_upsamples):
|
||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||
x = self.ups[i](x)
|
||
x_source = self.noise_convs[i](har_source)
|
||
x = x + x_source
|
||
xs = None
|
||
for j in range(self.num_kernels):
|
||
if xs is None:
|
||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||
else:
|
||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||
x = xs / self.num_kernels
|
||
x = F.leaky_relu(x)
|
||
x = self.conv_post(x)
|
||
x = torch.tanh(x)
|
||
return x
|
||
|
||
def remove_weight_norm(self):
|
||
for l in self.ups:
|
||
remove_weight_norm(l)
|
||
for l in self.resblocks:
|
||
l.remove_weight_norm()
|
||
sr2sr={
|
||
"32k":32000,
|
||
"40k":40000,
|
||
"48k":48000,
|
||
}
|
||
class SynthesizerTrnMs256NSFsid(nn.Module):
|
||
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,
|
||
gin_channels,
|
||
sr,
|
||
**kwargs
|
||
):
|
||
|
||
super().__init__()
|
||
if(type(sr)==type("strr")):
|
||
sr=sr2sr[sr]
|
||
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 = TextEncoder256(
|
||
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, sr=sr, is_half=kwargs["is_half"]
|
||
)
|
||
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 = 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, y, y_lengths,ds):#这里ds是id，[bs,1]
|
||
# print(1,pitch.shape)#[bs,t]
|
||
g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
|
||
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
|
||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
||
z_p = self.flow(z, y_mask, g=g)
|
||
z_slice, ids_slice = commons.rand_slice_segments(
|
||
z, y_lengths, self.segment_size
|
||
)
|
||
# print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
|
||
pitchf = commons.slice_segments2(
|
||
pitchf, ids_slice, self.segment_size
|
||
)
|
||
# print(-2,pitchf.shape,z_slice.shape)
|
||
o = self.dec(z_slice,pitchf, g=g)
|
||
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
||
|
||
def infer(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) * 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, x_mask, (z, z_p, m_p, logs_p)
|
||
class SynthesizerTrnMs256NSFsid_nono(nn.Module):
|
||
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,
|
||
gin_channels,
|
||
sr=None,
|
||
**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 = TextEncoder256(
|
||
inter_channels,
|
||
hidden_channels,
|
||
filter_channels,
|
||
n_heads,
|
||
n_layers,
|
||
kernel_size,
|
||
p_dropout,f0=False
|
||
)
|
||
self.dec = Generator(
|
||
inter_channels,
|
||
resblock,
|
||
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 = 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, y, y_lengths,ds):#这里ds是id，[bs,1]
|
||
g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
|
||
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
|
||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
||
z_p = self.flow(z, y_mask, g=g)
|
||
z_slice, ids_slice = commons.rand_slice_segments(
|
||
z, y_lengths, self.segment_size
|
||
)
|
||
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)
|
||
|
||
def infer(self, phone, phone_lengths,sid,max_len=None):
|
||
g = self.emb_g(sid).unsqueeze(-1)
|
||
m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
|
||
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],g=g)
|
||
return o, x_mask, (z, z_p, m_p, logs_p)
|
||
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, y_lengths,ds): # y是spec不需要了现在
|
||
g = self.emb_g(ds).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)
|
||
z_slice, ids_slice = commons.rand_slice_segments(
|
||
x, y_lengths, self.segment_size
|
||
)
|
||
|
||
pitchf = commons.slice_segments2(
|
||
pitchf, ids_slice, self.segment_size
|
||
)
|
||
o = self.dec(z_slice, pitchf, g=g)
|
||
return o, ids_slice
|
||
def infer(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(-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, o
|
||
|
||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||
def __init__(self, use_spectral_norm=False):
|
||
super(MultiPeriodDiscriminator, self).__init__()
|
||
periods = [2, 3, 5, 7, 11,17]
|
||
# periods = [3, 5, 7, 11, 17, 23, 37]
|
||
|
||
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)
|
||
# for j in range(len(fmap_r)):
|
||
# print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
|
||
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 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 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
|
||
|