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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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281
GPT_SoVITS/module/models_onnx.py
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@@ -1,4 +1,3 @@
import copy
import math
from typing import Optional
import torch
@@ -11,14 +10,14 @@ from module import attentions_onnx as attentions
from f5_tts.model import DiT
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from module.commons import init_weights, get_padding
from module.quantize import ResidualVectorQuantizer
# from text import symbols
from text import symbols as symbols_v1
from text import symbols2 as symbols_v2
from torch.cuda.amp import autocast
class StochasticDurationPredictor(nn.Module):
@@ -44,29 +43,21 @@ 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)
@@ -87,10 +78,7 @@ 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))
@@ -98,13 +86,8 @@ 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)
@@ -113,18 +96,12 @@ 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)
@@ -133,9 +110,7 @@ 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
@@ -145,13 +120,9 @@ 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)
@@ -234,7 +205,7 @@ class TextEncoder(nn.Module):
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, y, text, ge, speed=1):
y_mask = torch.ones_like(y[:1,:1,:])
y_mask = torch.ones_like(y[:1, :1, :])
y = self.ssl_proj(y * y_mask) * y_mask
y = self.encoder_ssl(y * y_mask, y_mask)
@@ -246,8 +217,8 @@ class TextEncoder(nn.Module):
y = self.mrte(y, y_mask, text, text_mask, ge)
y = self.encoder2(y * y_mask, y_mask)
if(speed!=1):
y = F.interpolate(y, size=int(y.shape[-1] / speed)+1, mode="linear")
if speed != 1:
y = F.interpolate(y, size=int(y.shape[-1] / speed) + 1, mode="linear")
y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
stats = self.proj(y) * y_mask
@@ -333,9 +304,7 @@ class PosteriorEncoder(nn.Module):
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_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
@@ -345,14 +314,9 @@ class PosteriorEncoder(nn.Module):
class Encoder(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
@@ -367,7 +331,7 @@ class Encoder(nn.Module):
self.proj = nn.Conv1d(hidden_channels, out_channels, 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 = self.pre(x) * x_mask
@@ -375,6 +339,7 @@ class Encoder(nn.Module):
stats = self.proj(x) * x_mask
return stats, x_mask
class WNEncoder(nn.Module):
def __init__(
self,
@@ -407,9 +372,7 @@ class WNEncoder(nn.Module):
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
@@ -432,9 +395,7 @@ class Generator(torch.nn.Module):
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
)
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()
@@ -454,9 +415,7 @@ class Generator(torch.nn.Module):
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)
@@ -465,7 +424,7 @@ class Generator(torch.nn.Module):
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
def forward(self, x, g:Optional[torch.Tensor]=None):
def forward(self, x, g: Optional[torch.Tensor] = None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
@@ -609,9 +568,7 @@ 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):
@@ -711,10 +668,7 @@ class Quantizer(torch.nn.Module):
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)
]
[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
@@ -732,9 +686,7 @@ class Quantizer(torch.nn.Module):
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
)
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)
@@ -774,13 +726,9 @@ 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
)
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
@@ -793,9 +741,7 @@ 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)
@@ -844,7 +790,7 @@ class SynthesizerTrn(nn.Module):
semantic_frame_rate=None,
freeze_quantizer=None,
version="v2",
**kwargs
**kwargs,
):
super().__init__()
self.spec_channels = spec_channels
@@ -896,9 +842,7 @@ class SynthesizerTrn(nn.Module):
# 16,
# gin_channels=gin_channels,
# )
self.flow = ResidualCouplingBlock(
inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
)
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
# self.version=os.environ.get("version","v1")
if self.version == "v1":
@@ -923,9 +867,9 @@ class SynthesizerTrn(nn.Module):
# self.enc_p.encoder_text.requires_grad_(False)
# self.enc_p.mrte.requires_grad_(False)
def forward(self, codes, text, refer,noise_scale=0.5, speed=1):
refer_mask = torch.ones_like(refer[:1,:1,:])
if (self.version == "v1"):
def forward(self, codes, text, refer, noise_scale=0.5, speed=1):
refer_mask = torch.ones_like(refer[:1, :1, :])
if self.version == "v1":
ge = self.ref_enc(refer * refer_mask, refer_mask)
else:
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
@@ -935,10 +879,8 @@ class SynthesizerTrn(nn.Module):
dquantized = torch.cat([quantized, quantized]).permute(1, 2, 0)
quantized = dquantized.contiguous().view(1, self.ssl_dim, -1)
x, m_p, logs_p, y_mask = self.enc_p(
quantized, text, ge, speed
)
x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge, speed)
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)
@@ -951,11 +893,9 @@ class SynthesizerTrn(nn.Module):
quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
return codes.transpose(0, 1)
class CFM(torch.nn.Module):
def __init__(
self,
in_channels,dit
):
def __init__(self, in_channels, dit):
super().__init__()
# self.sigma_min = 1e-6
@@ -965,27 +905,34 @@ class CFM(torch.nn.Module):
# self.criterion = torch.nn.MSELoss()
def forward(self, mu:torch.Tensor, x_lens:torch.LongTensor, prompt:torch.Tensor, n_timesteps:torch.LongTensor, temperature:float=1.0):
def forward(
self,
mu: torch.Tensor,
x_lens: torch.LongTensor,
prompt: torch.Tensor,
n_timesteps: torch.LongTensor,
temperature: float = 1.0,
):
"""Forward diffusion"""
B, T = mu.size(0), mu.size(1)
x = torch.randn([B, self.in_channels, T], device=mu.device,dtype=mu.dtype)
x = torch.randn([B, self.in_channels, T], device=mu.device, dtype=mu.dtype)
ntimesteps = int(n_timesteps)
prompt_len = prompt.size(-1)
prompt_x = torch.zeros_like(x,dtype=mu.dtype)
prompt_x = torch.zeros_like(x, dtype=mu.dtype)
prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
x[..., :prompt_len] = 0.0
mu=mu.transpose(2,1)
t = torch.tensor(0.0,dtype=x.dtype,device=x.device)
d = torch.tensor(1.0/ntimesteps,dtype=x.dtype,device=x.device)
d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
mu = mu.transpose(2, 1)
t = torch.tensor(0.0, dtype=x.dtype, device=x.device)
d = torch.tensor(1.0 / ntimesteps, dtype=x.dtype, device=x.device)
d_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * d
for j in range(ntimesteps):
t_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * t
t_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * t
# d_tensor = torch.ones(x.shape[0], device=x.device,dtype=mu.dtype) * d
# v_pred = model(x, t_tensor, d_tensor, **extra_args)
v_pred = self.estimator(x, prompt_x, x_lens, t_tensor,d_tensor, mu).transpose(2, 1)
v_pred = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu).transpose(2, 1)
# if inference_cfg_rate>1e-5:
# neg = self.estimator(x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=True, drop_text=True).transpose(2, 1)
# v_pred=v_pred+(v_pred-neg)*inference_cfg_rate
@@ -997,47 +944,51 @@ class CFM(torch.nn.Module):
def set_no_grad(net_g):
for name, param in net_g.named_parameters():
param.requires_grad=False
param.requires_grad = False
@torch.jit.script_if_tracing
def compile_codes_length(codes):
y_lengths1 = torch.LongTensor([codes.size(2)]).to(codes.device)
return y_lengths1 * 2.5 * 1.5
@torch.jit.script_if_tracing
def compile_ref_length(refer):
refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
return refer_lengths
class SynthesizerTrnV3(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,
version="v3",
**kwargs):
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,
version="v3",
**kwargs,
):
super().__init__()
self.spec_channels = spec_channels
self.inter_channels = inter_channels
@@ -1058,41 +1009,38 @@ class SynthesizerTrnV3(nn.Module):
self.gin_channels = gin_channels
self.version = version
self.model_dim=512
self.model_dim = 512
self.use_sdp = use_sdp
self.enc_p = TextEncoder(inter_channels,hidden_channels,filter_channels,n_heads,n_layers,kernel_size,p_dropout)
self.enc_p = TextEncoder(
inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
)
# self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)###Rollback
self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels)###Rollback
self.ref_enc = modules.MelStyleEncoder(704, style_vector_dim=gin_channels) ###Rollback
# 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)
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)
freeze_quantizer
inter_channels2=512
self.bridge=nn.Sequential(
nn.Conv1d(inter_channels, inter_channels2, 1, stride=1),
nn.LeakyReLU()
)
self.wns1=Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8,gin_channels=gin_channels)
self.linear_mel=nn.Conv1d(inter_channels2,100,1,stride=1)
self.cfm = CFM(100,DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),)#text_dim is condition feature dim
if freeze_quantizer==True:
inter_channels2 = 512
self.bridge = nn.Sequential(nn.Conv1d(inter_channels, inter_channels2, 1, stride=1), nn.LeakyReLU())
self.wns1 = Encoder(inter_channels2, inter_channels2, inter_channels2, 5, 1, 8, gin_channels=gin_channels)
self.linear_mel = nn.Conv1d(inter_channels2, 100, 1, stride=1)
self.cfm = CFM(
100,
DiT(**dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=inter_channels2, conv_layers=4)),
) # text_dim is condition feature dim
if freeze_quantizer == True:
set_no_grad(self.ssl_proj)
set_no_grad(self.quantizer)
set_no_grad(self.enc_p)
@@ -1100,24 +1048,23 @@ class SynthesizerTrnV3(nn.Module):
def create_ge(self, refer):
refer_lengths = compile_ref_length(refer)
refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, refer.size(2)), 1).to(refer.dtype)
ge = self.ref_enc(refer[:,:704] * refer_mask, refer_mask)
ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
return ge
def forward(self, codes, text,ge,speed=1):
def forward(self, codes, text, ge, speed=1):
y_lengths1 = compile_codes_length(codes)
y_lengths1=compile_codes_length(codes)
quantized = self.quantizer.decode(codes)
if self.semantic_frame_rate == '25hz':
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")##BCT
x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge,speed)
fea=self.bridge(x)
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")##BCT
if self.semantic_frame_rate == "25hz":
quantized = F.interpolate(quantized, scale_factor=2, mode="nearest") ##BCT
x, m_p, logs_p, y_mask = self.enc_p(quantized, text, ge, speed)
fea = self.bridge(x)
fea = F.interpolate(fea, scale_factor=1.875, mode="nearest") ##BCT
####more wn paramter to learn mel
fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
return fea
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)