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import copy
import math
from typing import List
import torch
from torch import nn
from torch.nn import functional as F
from module import commons
from module import modules
from module import attentions
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from module.commons import init_weights, get_padding
from module.mrte_model import MRTE
from module.quantize import ResidualVectorQuantizer
from text import symbols
from torch.cuda.amp import autocast
class StochasticDurationPredictor(nn.Module):
def __init__(
self,
in_channels,
filter_channels,
kernel_size,
p_dropout,
n_flows=4,
gin_channels=0,
):
super().__init__()
filter_channels = in_channels # it needs to be removed from future version.
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.n_flows = n_flows
self.gin_channels = gin_channels
self.log_flow = modules.Log()
self.flows = nn.ModuleList()
self.flows.append(modules.ElementwiseAffine(2))
for i in range(n_flows):
self.flows.append(
modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
)
self.flows.append(modules.Flip())
self.post_pre = nn.Conv1d(1, filter_channels, 1)
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.post_convs = modules.DDSConv(
filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
)
self.post_flows = nn.ModuleList()
self.post_flows.append(modules.ElementwiseAffine(2))
for i in range(4):
self.post_flows.append(
modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
)
self.post_flows.append(modules.Flip())
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.convs = modules.DDSConv(
filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
x = torch.detach(x)
x = self.pre(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.convs(x, x_mask)
x = self.proj(x) * x_mask
if not reverse:
flows = self.flows
assert w is not None
logdet_tot_q = 0
h_w = self.post_pre(w)
h_w = self.post_convs(h_w, x_mask)
h_w = self.post_proj(h_w) * x_mask
e_q = (
torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
* x_mask
)
z_q = e_q
for flow in self.post_flows:
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
logdet_tot_q += logdet_q
z_u, z1 = torch.split(z_q, [1, 1], 1)
u = torch.sigmoid(z_u) * x_mask
z0 = (w - u) * x_mask
logdet_tot_q += torch.sum(
(F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
)
logq = (
torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
- logdet_tot_q
)
logdet_tot = 0
z0, logdet = self.log_flow(z0, x_mask)
logdet_tot += logdet
z = torch.cat([z0, z1], 1)
for flow in flows:
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
logdet_tot = logdet_tot + logdet
nll = (
torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
- logdet_tot
)
return nll + logq # [b]
else:
flows = list(reversed(self.flows))
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
z = (
torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
* noise_scale
)
for flow in flows:
z = flow(z, x_mask, g=x, reverse=reverse)
z0, z1 = torch.split(z, [1, 1], 1)
logw = z0
return logw
class DurationPredictor(nn.Module):
def __init__(
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_2 = modules.LayerNorm(filter_channels)
self.proj = nn.Conv1d(filter_channels, 1, 1)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
def forward(self, x, x_mask, g=None):
x = torch.detach(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
x = self.drop(x)
x = self.proj(x * x_mask)
return x * x_mask
class TextEncoder(nn.Module):
def __init__(
self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
latent_channels=192,
):
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.latent_channels = latent_channels
self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
self.encoder_ssl = attentions.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers // 2,
kernel_size,
p_dropout,
)
self.encoder_text = attentions.Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
self.mrte = MRTE()
self.encoder2 = attentions.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers // 2,
kernel_size,
p_dropout,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, y, y_lengths, text, text_lengths, ge, test=None):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
y.dtype
)
y = self.ssl_proj(y * y_mask) * y_mask
y = self.encoder_ssl(y * y_mask, y_mask)
text_mask = torch.unsqueeze(
commons.sequence_mask(text_lengths, text.size(1)), 1
).to(y.dtype)
if test == 1:
text[:, :] = 0
text = self.text_embedding(text).transpose(1, 2)
text = self.encoder_text(text * text_mask, text_mask)
y = self.mrte(y, y_mask, text, text_mask, ge)
y = self.encoder2(y * y_mask, y_mask)
stats = self.proj(y) * y_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return y, m, logs, y_mask
def extract_latent(self, x):
x = self.ssl_proj(x)
quantized, codes, commit_loss, quantized_list = self.quantizer(x)
return codes.transpose(0, 1)
def decode_latent(self, codes, y_mask, refer, refer_mask, ge):
quantized = self.quantizer.decode(codes)
y = self.vq_proj(quantized) * y_mask
y = self.encoder_ssl(y * y_mask, y_mask)
y = self.mrte(y, y_mask, refer, refer_mask, ge)
y = self.encoder2(y * y_mask, y_mask)
stats = self.proj(y) * y_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return y, m, logs, y_mask, quantized
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 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):
if g != None:
g = g.detach()
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 WNEncoder(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, 1)
self.norm = modules.LayerNorm(out_channels)
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)
out = self.proj(x) * x_mask
out = self.norm(out)
return out
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):
print("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
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 ReferenceEncoder(nn.Module):
"""
inputs --- [N, Ty/r, n_mels*r] mels
outputs --- [N, ref_enc_gru_size]
"""
def __init__(self, spec_channels, gin_channels=0):
super().__init__()
self.spec_channels = spec_channels
ref_enc_filters = [32, 32, 64, 64, 128, 128]
K = len(ref_enc_filters)
filters = [1] + ref_enc_filters
convs = [
weight_norm(
nn.Conv2d(
in_channels=filters[i],
out_channels=filters[i + 1],
kernel_size=(3, 3),
stride=(2, 2),
padding=(1, 1),
)
)
for i in range(K)
]
self.convs = nn.ModuleList(convs)
# self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
self.gru = nn.GRU(
input_size=ref_enc_filters[-1] * out_channels,
hidden_size=256 // 2,
batch_first=True,
)
self.proj = nn.Linear(128, gin_channels)
def forward(self, inputs):
N = inputs.size(0)
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
for conv in self.convs:
out = conv(out)
# out = wn(out)
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
T = out.size(1)
N = out.size(0)
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
self.gru.flatten_parameters()
memory, out = self.gru(out) # out --- [1, N, 128]
return self.proj(out.squeeze(0)).unsqueeze(-1)
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
for i in range(n_convs):
L = (L - kernel_size + 2 * pad) // stride + 1
return L
class Quantizer_module(torch.nn.Module):
def __init__(self, n_e, e_dim):
super(Quantizer_module, self).__init__()
self.embedding = nn.Embedding(n_e, e_dim)
self.embedding.weight.data.uniform_(-1.0 / n_e, 1.0 / n_e)
def forward(self, x):
d = (
torch.sum(x**2, 1, keepdim=True)
+ torch.sum(self.embedding.weight**2, 1)
- 2 * torch.matmul(x, self.embedding.weight.T)
)
min_indicies = torch.argmin(d, 1)
z_q = self.embedding(min_indicies)
return z_q, min_indicies
class Quantizer(torch.nn.Module):
def __init__(self, embed_dim=512, n_code_groups=4, n_codes=160):
super(Quantizer, self).__init__()
assert embed_dim % n_code_groups == 0
self.quantizer_modules = nn.ModuleList(
[
Quantizer_module(n_codes, embed_dim // n_code_groups)
for _ in range(n_code_groups)
]
)
self.n_code_groups = n_code_groups
self.embed_dim = embed_dim
def forward(self, xin):
# B, C, T
B, C, T = xin.shape
xin = xin.transpose(1, 2)
x = xin.reshape(-1, self.embed_dim)
x = torch.split(x, self.embed_dim // self.n_code_groups, dim=-1)
min_indicies = []
z_q = []
for _x, m in zip(x, self.quantizer_modules):
_z_q, _min_indicies = m(_x)
z_q.append(_z_q)
min_indicies.append(_min_indicies) # B * T,
z_q = torch.cat(z_q, -1).reshape(xin.shape)
loss = 0.25 * torch.mean((z_q.detach() - xin) ** 2) + torch.mean(
(z_q - xin.detach()) ** 2
)
z_q = xin + (z_q - xin).detach()
z_q = z_q.transpose(1, 2)
codes = torch.stack(min_indicies, -1).reshape(B, T, self.n_code_groups)
return z_q, loss, codes.transpose(1, 2)
def embed(self, x):
# idx: N, 4, T
x = x.transpose(1, 2)
x = torch.split(x, 1, 2)
ret = []
for q, embed in zip(x, self.quantizer_modules):
q = embed.embedding(q.squeeze(-1))
ret.append(q)
ret = torch.cat(ret, -1)
return ret.transpose(1, 2) # N, C, T
class CodePredictor(nn.Module):
def __init__(
self,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
n_q=8,
dims=1024,
ssl_dim=768,
):
super().__init__()
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.vq_proj = nn.Conv1d(ssl_dim, hidden_channels, 1)
self.ref_enc = modules.MelStyleEncoder(
ssl_dim, style_vector_dim=hidden_channels
)
self.encoder = attentions.Encoder(
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.out_proj = nn.Conv1d(hidden_channels, (n_q - 1) * dims, 1)
self.n_q = n_q
self.dims = dims
def forward(self, x, x_mask, refer, codes, infer=False):
x = x.detach()
x = self.vq_proj(x * x_mask) * x_mask
g = self.ref_enc(refer, x_mask)
x = x + g
x = self.encoder(x * x_mask, x_mask)
x = self.out_proj(x * x_mask) * x_mask
logits = x.reshape(x.shape[0], self.n_q - 1, self.dims, x.shape[-1]).transpose(
2, 3
)
target = codes[1:].transpose(0, 1)
if not infer:
logits = logits.reshape(-1, self.dims)
target = target.reshape(-1)
loss = torch.nn.functional.cross_entropy(logits, target)
return loss
else:
_, top10_preds = torch.topk(logits, 10, dim=-1)
correct_top10 = torch.any(top10_preds == target.unsqueeze(-1), dim=-1)
top3_acc = 100 * torch.mean(correct_top10.float()).detach().cpu().item()
print("Top-10 Accuracy:", top3_acc, "%")
pred_codes = torch.argmax(logits, dim=-1)
acc = 100 * torch.mean((pred_codes == target).float()).detach().cpu().item()
print("Top-1 Accuracy:", acc, "%")
return pred_codes.transpose(0, 1)
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,
n_speakers=0,
gin_channels=0,
use_sdp=True,
semantic_frame_rate=None,
freeze_quantizer=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.n_speakers = n_speakers
self.gin_channels = gin_channels
self.use_sdp = use_sdp
self.enc_p = TextEncoder(
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
)
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, 4, gin_channels=gin_channels
)
self.ref_enc = modules.MelStyleEncoder(
spec_channels, style_vector_dim=gin_channels
)
ssl_dim = 768
assert semantic_frame_rate in ["25hz", "50hz"]
self.semantic_frame_rate = semantic_frame_rate
if semantic_frame_rate == "25hz":
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 2, stride=2)
else:
self.ssl_proj = nn.Conv1d(ssl_dim, ssl_dim, 1, stride=1)
self.quantizer = ResidualVectorQuantizer(dimension=ssl_dim, n_q=1, bins=1024)
if freeze_quantizer:
self.ssl_proj.requires_grad_(False)
self.quantizer.requires_grad_(False)
# self.enc_p.text_embedding.requires_grad_(False)
# self.enc_p.encoder_text.requires_grad_(False)
# self.enc_p.mrte.requires_grad_(False)
def forward(self, ssl, y, y_lengths, text, text_lengths):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
y.dtype
)
ge = self.ref_enc(y * y_mask, y_mask)
with autocast(enabled=False):
ssl = self.ssl_proj(ssl)
quantized, codes, commit_loss, quantized_list = self.quantizer(
ssl, layers=[0]
)
if self.semantic_frame_rate == "25hz":
quantized = F.interpolate(
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
)
x, m_p, logs_p, y_mask = self.enc_p(
quantized, y_lengths, text, text_lengths, ge
)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge)
z_p = self.flow(z, y_mask, g=ge)
z_slice, ids_slice = commons.rand_slice_segments(
z, y_lengths, self.segment_size
)
o = self.dec(z_slice, g=ge)
return (
o,
commit_loss,
ids_slice,
y_mask,
y_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
quantized,
)
def infer(self, ssl, y, y_lengths, text, text_lengths, test=None, noise_scale=0.5):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(2)), 1).to(
y.dtype
)
ge = self.ref_enc(y * y_mask, y_mask)
ssl = self.ssl_proj(ssl)
quantized, codes, commit_loss, _ = self.quantizer(ssl, layers=[0])
if self.semantic_frame_rate == "25hz":
quantized = F.interpolate(
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
)
x, m_p, logs_p, y_mask = self.enc_p(
quantized, y_lengths, text, text_lengths, ge, test=test
)
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=ge, reverse=True)
o = self.dec((z * y_mask)[:, :, :], g=ge)
return o, y_mask, (z, z_p, m_p, logs_p)
@torch.no_grad()
def decode(self, codes, text, refer, noise_scale=0.5):
ge = None
if refer is not None:
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask)
y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
quantized = self.quantizer.decode(codes)
if self.semantic_frame_rate == "25hz":
quantized = F.interpolate(
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
)
x, m_p, logs_p, y_mask = self.enc_p(
quantized, y_lengths, text, text_lengths, ge
)
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=ge, reverse=True)
o = self.dec((z * y_mask)[:, :, :], g=ge)
return o
@torch.no_grad()
def batched_decode(self, codes, y_lengths, text, text_lengths, refer, noise_scale=0.5):
ge = None
if refer is not None:
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
refer_mask = torch.unsqueeze(
commons.sequence_mask(refer_lengths, refer.size(2)), 1
).to(refer.dtype)
ge = self.ref_enc(refer * refer_mask, refer_mask)
# y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, codes.size(2)), 1).to(
# codes.dtype
# )
y_lengths = (y_lengths * 2).long().to(codes.device)
text_lengths = text_lengths.long().to(text.device)
# y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
# text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
# 假设padding之后再decode没有问题, 影响未知,但听起来好像没问题?
quantized = self.quantizer.decode(codes)
if self.semantic_frame_rate == "25hz":
quantized = F.interpolate(
quantized, size=int(quantized.shape[-1] * 2), mode="nearest"
)
x, m_p, logs_p, y_mask = self.enc_p(
quantized, y_lengths, text, text_lengths, ge
)
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=ge, reverse=True)
z_masked = (z * y_mask)[:, :, :]
# 串行。把padding部分去掉再decode
o_list:List[torch.Tensor] = []
for i in range(z_masked.shape[0]):
z_slice = z_masked[i, :, :y_lengths[i]].unsqueeze(0)
o = self.dec(z_slice, g=ge)[0, 0, :].detach()
o_list.append(o)
# 并行(会有问题)。先decode,再把padding的部分去掉
# o = self.dec(z_masked, g=ge)
# upsample_rate = int(math.prod(self.upsample_rates))
# o_lengths = y_lengths*upsample_rate
# o_list = [o[i, 0, :idx].detach() for i, idx in enumerate(o_lengths)]
return o_list
def extract_latent(self, x):
ssl = self.ssl_proj(x)
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
return codes.transpose(0, 1)