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from text import symbols
from transformer.embedding import SinePositionalEmbedding
from transformer.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from module.models import TextEncoder, PosteriorEncoder, FramePriorNet, Generator, ProsodyEncoder, ResidualCouplingBlock
from module import commons
from module import modules
class DiT(nn.Module):
def __init__(self, io_channels, cond_channels):
super().__init__()
self.in_proj = nn.Linear(io_channels+cond_channels, 512)
norm_first = False
self.position = SinePositionalEmbedding(
512, dropout=0.1, scale=False, alpha=True)
self.encoder = TransformerEncoder(
TransformerEncoderLayer(
d_model=512,
nhead=16,
dim_feedforward=2048,
dropout=0.1,
batch_first=True,
norm_first=False, ),
num_layers=12,
norm=LayerNorm(512) if norm_first else None, )
self.proj = nn.Linear(512, io_channels)
self.diffusion_embedding = SinusoidalPosEmb(512)
def forward(self, x, t, cond):
diffusion_step = self.diffusion_embedding(t).unsqueeze(1)
x = torch.cat([x, cond], dim=1).transpose(1, 2)
x = self.in_proj(x)
x = torch.cat([diffusion_step, x], dim=1)
x = self.position(x)
x, _ = self.encoder(
(x, None), src_key_padding_mask=None)
x = x[:, 1:, :]
x = self.proj(x).transpose(1, 2)
return x
from .edm import EDM2NetworkWrapper, edm_sampler
class NARPredictor(nn.Module):
def __init__(self, latent_dim=16, gin_channels=0):
super().__init__()
self.latent_dim = latent_dim
self.bert_proj = nn.Linear(1024, 192)
# self.x_proj = nn.Linear(192, 192)
self.g_emb = nn.Linear(256, 192)
self.text_emb = nn.Embedding(len(symbols), 192)
cond_channels = 192
net = DiT(latent_dim, cond_channels)
self.edm_net = EDM2NetworkWrapper(net, use_fp16=False)
def forward(self, x, x_mask, bert, spk_emb, diffusion_input):
g = self.g_emb(spk_emb).unsqueeze(1)
bert = self.bert_proj(bert.transpose(1, 2))
x = self.text_emb(x)
cond = (x + bert + g).transpose(1, 2)
cfg_dropout_prob = 0.1
null_embed = torch.zeros_like(cond, device=cond.device)
dropout_mask = torch.bernoulli(torch.full((cond.shape[0], 1, 1), cfg_dropout_prob, device=cond.device)).to(torch.bool)
cond = torch.where(dropout_mask, null_embed, cond)
P_std = 1.0
P_mean = -0.4
sigma_data = 0.4
rnd_normal = torch.randn([diffusion_input.shape[0], 1, 1], device=diffusion_input.device)
sigma = (rnd_normal * P_std + P_mean).exp()
weight = (sigma ** 2 + sigma_data ** 2) / (sigma * sigma_data) ** 2
noise = torch.randn_like(diffusion_input) * sigma
denoised, logvar = self.edm_net(diffusion_input + noise, sigma, cond=cond, return_logvar=True)
loss = (weight / logvar.exp()) * ((denoised - diffusion_input) ** 2) + logvar
loss = loss.mean()
return loss
@torch.inference_mode()
@torch.no_grad()
def infer(self, x, x_mask, bert, spk_emb, step=32):
g = self.g_emb(spk_emb).unsqueeze(1)
bert = self.bert_proj(bert.transpose(1, 2))
x = self.text_emb(x)
cond = (x + bert + g).transpose(1, 2)
noise = torch.randn([cond.shape[0], self.latent_dim, cond.shape[2]]).to(cond.device)
fakes = edm_sampler(self.edm_net, noise, cond, num_steps=step, guidance=1.7)
return fakes
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,
freeze_quantizer=False,
**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
gin_channels = hidden_channels
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)
print('n_param of enc_p', sum(p.numel() for p in self.enc_p.parameters() if p.requires_grad) / 1e6, 'M')
self.frame_prior_net = FramePriorNet(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, 3, gin_channels=gin_channels
)
self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
vq_dim = 4
self.prosody_encoder = ProsodyEncoder(768, hidden_channels, 5, 1, 6, gin_channels, vq_dim)
self.prosody_predictor = NARPredictor(vq_dim, gin_channels=gin_channels)
def forward(self, x, x_lengths, y, y_lengths, ssl, duration, bert, spk_emb, prosody_predict_only=False):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
text_long = x.clone()
g = self.ref_enc(y * y_mask, y_mask)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(duration.unsqueeze(1), attn_mask)
v_mask = (x > 0).float()
z_q, quantize_loss, indices, dur_loss, pred_dur = self.prosody_encoder(ssl, x_mask, attn, duration, v_mask)
prosody_predict_loss = self.prosody_predictor(x, x_mask, bert, spk_emb, indices)
return prosody_predict_loss, indices
def reconstruct(self, x, x_lengths, y, y_lengths, ssl, duration, bert, spk_emb, noise_scale=.4):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
g = self.ref_enc(y * y_mask, y_mask)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)
# attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
# attn = commons.generate_path(duration.unsqueeze(1), attn_mask)
# v_mask = (x > 0).float()
# _, _, pred_prosody_codes, _, _ = self.prosody_encoder(ssl, x_mask, attn, duration,v_mask)
pred_prosody_codes = self.prosody_predictor.infer(x, x_mask, bert, spk_emb)
x = self.enc_p(x, x_mask, bert)
z_q, duration = self.prosody_encoder.decode(pred_prosody_codes, x_mask)
total_duration = torch.sum(duration, dim=1)
y_lengths = total_duration.long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y_lengths.max()), 1).float()
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(duration.unsqueeze(1), attn_mask)
x = x + z_q
frame_x = torch.matmul(attn.squeeze(1), x.transpose(1, 2)).transpose(1, 2)
m_p, logs_p = self.frame_prior_net(frame_x, y_mask)
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=g, reverse=True)
o = self.dec((z * y_mask), g=g)
return o, attn, y_mask, (z, z_p, m_p, logs_p)
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