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from module.aligner import AlignmentEncoder
from module.maskgit import MLMLoss
from module.wavenet import SinusoidalPosEmb, WaveNet
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, mono_align_and_get_loss
from module import commons
from module import modules
import math
from einops import rearrange
import random
from tqdm import tqdm
from .edm import EDM2NetworkWrapper, edm_sampler
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=6,
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 audio_diffusion_pytorch import DiffusionModel, UNetV0, VDiffusion, VSampler
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)
cond_channels = 192
net = DiT(latent_dim, cond_channels)
self.model = DiffusionModel(
net=net, # The model type used for diffusion (U-Net V0 in this case)
in_channels=2, # U-Net: number of input/output (audio) channels
channels=[8, 32], # U-Net: channels at each layer
factors=[1, 4,], # U-Net: downsampling and upsampling factors at each layer
items=[1, 2], # U-Net: number of repeating items at each layer
attentions=[0, 0, ], # U-Net: attention enabled/disabled at each layer
attention_heads=8, # U-Net: number of attention heads per attention item
attention_features=64, # U-Net: number of attention features per attention item
diffusion_t=VDiffusion, # The diffusion method used
sampler_t=VSampler, # The diffusion sampler used
)
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.x_proj((x * x_mask).transpose(1, 2))
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()
loss = self.model(diffusion_input, cond=cond)
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.x_proj((x * x_mask).transpose(1, 2))
cond = (x + bert + g).transpose(1, 2)
noise = torch.randn([cond.shape[0], self.latent_dim, cond.shape[2]]).to(cond.device)
fakes = self.model.sample(noise, num_steps=step,cond=cond, show_progress=True)
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.aligner = AlignmentEncoder(
n_mel_channels=hidden_channels,
n_att_channels=256,
n_text_channels=128,
temperature=0.0005,
multi_speaker=True,
spk_channel=gin_channels
)
self.prosody_predictor = NARPredictor(vq_dim, gin_channels=gin_channels)
def forward(self, x, x_lengths, y, y_lengths, ssl, attn_priors, bert, spk_emb, ):
x_long = x.clone()
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)
v_mask = (x > 0).float()
x, x_align = self.enc_p(x, x_mask)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
attn_out, attn, duration = mono_align_and_get_loss(self.aligner, x_align, z.detach(), x_mask, y_mask, attn_priors, x_lengths, y_lengths, g)
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, bert, spk_emb, noise_scale=.4):
x_long = x.clone()
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)
x, x_align = self.enc_p(x, x_mask)
pred_prosody_codes = self.prosody_predictor.infer(x, x_mask, bert, spk_emb)
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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