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more code refactor

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This commit is contained in:
Blaise
2024-01-16 17:14:18 +01:00
parent 0d92575115
commit 0d3d47f3c3
44 changed files with 4516 additions and 2623 deletions
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617
GPT_SoVITS/module/models.py
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@@ -12,12 +12,21 @@ 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 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):
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
@@ -31,21 +40,29 @@ class StochasticDurationPredictor(nn.Module):
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.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_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.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)
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)
@@ -66,7 +83,10 @@ class StochasticDurationPredictor(nn.Module):
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
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))
@@ -74,8 +94,13 @@ class StochasticDurationPredictor(nn.Module):
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_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)
@@ -84,12 +109,18 @@ class StochasticDurationPredictor(nn.Module):
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
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
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)
@@ -98,7 +129,9 @@ class StochasticDurationPredictor(nn.Module):
class DurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
def __init__(
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
@@ -108,9 +141,13 @@ class DurationPredictor(nn.Module):
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.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.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)
@@ -135,15 +172,17 @@ class DurationPredictor(nn.Module):
class TextEncoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
latent_channels=192):
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
@@ -160,17 +199,14 @@ class TextEncoder(nn.Module):
hidden_channels,
filter_channels,
n_heads,
n_layers//2,
n_layers // 2,
kernel_size,
p_dropout)
p_dropout,
)
self.encoder_text = attentions.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout)
hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
self.mrte = MRTE()
@@ -179,21 +215,25 @@ class TextEncoder(nn.Module):
hidden_channels,
filter_channels,
n_heads,
n_layers//2,
n_layers // 2,
kernel_size,
p_dropout)
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_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_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)
@@ -208,9 +248,9 @@ class TextEncoder(nn.Module):
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):
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
@@ -224,15 +264,18 @@ class TextEncoder(nn.Module):
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):
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
@@ -245,8 +288,16 @@ class ResidualCouplingBlock(nn.Module):
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))
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):
@@ -260,14 +311,16 @@ class ResidualCouplingBlock(nn.Module):
class PosteriorEncoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
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
@@ -278,13 +331,21 @@ class PosteriorEncoder(nn.Module):
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.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):
if g != None:
g = g.detach()
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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
@@ -294,14 +355,16 @@ class PosteriorEncoder(nn.Module):
class WNEncoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
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
@@ -312,11 +375,20 @@ class WNEncoder(nn.Module):
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.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_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
@@ -325,24 +397,45 @@ class WNEncoder(nn.Module):
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):
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.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.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)):
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)
@@ -373,7 +466,7 @@ class Generator(torch.nn.Module):
return x
def remove_weight_norm(self):
print('Removing weight norm...')
print("Removing weight norm...")
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
@@ -386,13 +479,55 @@ class DiscriminatorP(torch.nn.Module):
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.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):
@@ -421,14 +556,16 @@ 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.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):
@@ -451,7 +588,9 @@ class MultiPeriodDiscriminator(torch.nn.Module):
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]
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):
@@ -469,31 +608,40 @@ class MultiPeriodDiscriminator(torch.nn.Module):
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)]
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.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):
@@ -527,23 +675,31 @@ class Quantizer_module(torch.nn.Module):
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)
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.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
B, C, T = xin.shape
xin = xin.transpose(1, 2)
x = xin.reshape(-1, self.embed_dim)
@@ -553,38 +709,41 @@ class Quantizer(torch.nn.Module):
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,
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)
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)
# 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
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
):
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
@@ -594,19 +753,18 @@ class CodePredictor(nn.Module):
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.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)
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.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
@@ -614,7 +772,9 @@ class CodePredictor(nn.Module):
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)
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)
@@ -626,44 +786,44 @@ class CodePredictor(nn.Module):
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, "%")
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, "%")
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):
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
@@ -685,34 +845,50 @@ class SynthesizerTrn(nn.Module):
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)
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)
self.ref_enc = modules.MelStyleEncoder(
spec_channels, style_vector_dim=gin_channels
)
ssl_dim = 768
assert semantic_frame_rate in ['25hz', "50hz"]
assert semantic_frame_rate in ["25hz", "50hz"]
self.semantic_frame_rate = semantic_frame_rate
if semantic_frame_rate == '25hz':
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
)
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)
@@ -721,56 +897,85 @@ class SynthesizerTrn(nn.Module):
# 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)
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])
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")
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)
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)
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
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)
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)
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")
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)
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)
return o, y_mask, (z, z_p, m_p, logs_p)
@torch.no_grad()
def decode(self, codes,text, refer, noise_scale=0.5):
def decode(self, codes, text, refer, noise_scale=0.5):
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)
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)
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")
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)
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)
@@ -779,6 +984,6 @@ class SynthesizerTrn(nn.Module):
return o
def extract_latent(self, x):
ssl = self.ssl_proj(x)
ssl = self.ssl_proj(x)
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
return codes.transpose(0,1)
return codes.transpose(0, 1)