822 lines
27 KiB
Python
822 lines
27 KiB
Python
import math
|
|
import logging
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
import numpy as np
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
|
|
from torch.nn import functional as F
|
|
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
|
|
|
|
from infer.lib.infer_pack import attentions, commons, modules
|
|
from infer.lib.infer_pack.commons import get_padding, init_weights
|
|
|
|
|
|
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 TextEncoder768(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(768, 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 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:
|
|
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 SynthesizerTrnMsNSFsidM(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,
|
|
version,
|
|
**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
|
|
if version == "v1":
|
|
self.enc_p = TextEncoder256(
|
|
inter_channels,
|
|
hidden_channels,
|
|
filter_channels,
|
|
n_heads,
|
|
n_layers,
|
|
kernel_size,
|
|
p_dropout,
|
|
)
|
|
else:
|
|
self.enc_p = TextEncoder768(
|
|
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)
|
|
self.speaker_map = None
|
|
logger.debug(
|
|
f"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 construct_spkmixmap(self, n_speaker):
|
|
self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
|
|
for i in range(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
|
|
|
|
|
|
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 MultiPeriodDiscriminatorV2(torch.nn.Module):
|
|
def __init__(self, use_spectral_norm=False):
|
|
super(MultiPeriodDiscriminatorV2, self).__init__()
|
|
# periods = [2, 3, 5, 7, 11, 17]
|
|
periods = [2, 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
|