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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 | |
from modules.commons import init_weights, get_padding | |
from vdecoder.hifigan.models import Generator | |
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, | |
in_channels, | |
out_channels, | |
hidden_channels, | |
kernel_size, | |
dilation_rate, | |
n_layers, | |
gin_channels=0, | |
filter_channels=None, | |
n_heads=None, | |
p_dropout=None): | |
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.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_lengths, f0=None): | |
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
x = self.pre(x) * x_mask | |
x = x + self.f0_emb(f0.long()).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)) * 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 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, | |
**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.emb_g = nn.Embedding(n_speakers, gin_channels) | |
self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout) | |
hps = { | |
"sampling_rate": 48000, | |
"inter_channels": 192, | |
"resblock": "1", | |
"resblock_kernel_sizes": [3, 7, 11], | |
"resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], | |
"upsample_rates": [10, 8, 2, 2], | |
"upsample_initial_channel": 512, | |
"upsample_kernel_sizes": [16, 16, 4, 4], | |
"gin_channels": 256, | |
} | |
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) | |
def forward(self, c, c_lengths, f0, g=None): | |
g = self.emb_g(g.unsqueeze(0)).transpose(1,2) | |
z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0)) | |
z = self.flow(z_p, c_mask, g=g, reverse=True) | |
o = self.dec(z * c_mask, g=g, f0=f0.float()) | |
return o | |