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import math | |
import torch | |
from torch import nn | |
from torch.nn import functional as F | |
from lib.infer.infer_pack import modules | |
from lib.infer.infer_pack import attentions | |
from lib.infer.infer_pack.commons import get_padding | |
from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
from lib.infer.infer_pack.commons import init_weights | |
import numpy as np | |
from lib.infer.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 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.float() | |
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 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 SynthesizerTrnMs768NSFsid(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 = 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) | |
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 SynthesizerTrnMs768NSFsid_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 = TextEncoder768( | |
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 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 | |