| import math |
| from omegaconf import OmegaConf |
| from tqdm import tqdm |
| from module.aligner import AlignmentEncoder |
| 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 random |
|
|
|
|
| import random |
| from tqdm import tqdm |
|
|
| class SinusoidalPosEmb(nn.Module): |
| def __init__(self, dim): |
| super().__init__() |
| self.dim = dim |
|
|
| def forward(self, x): |
| device = x.device |
| half_dim = self.dim // 2 |
| emb = math.log(10000) / (half_dim - 1) |
| emb = torch.exp(torch.arange(half_dim, device=device) * -emb) |
| emb = x[:, None] * emb[None, :] |
| emb = torch.cat((emb.sin(), emb.cos()), dim=-1) |
| return emb |
|
|
| 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, mask=None): |
| if mask is None: |
| mask = torch.ones(x.shape[0], x.shape[-1], device=x.device).long() |
| mask_bool = ~mask.bool() |
| 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) |
| mask_bool = torch.cat([torch.zeros([mask_bool.shape[0], 1], device=mask_bool.device).bool(), mask_bool], dim=1) |
| x = self.position(x) |
| x, _ = self.encoder( |
| (x, None), src_key_padding_mask=mask_bool) |
| x = x[:, 1:, :] |
| x = self.proj(x).transpose(1, 2) |
| return x |
|
|
| class ReFlow(nn.Module): |
| def __init__(self, |
| io_channels, |
| cond_channels, |
| ): |
| super().__init__() |
| self.model = DiT(io_channels, cond_channels) |
| self.io_channels = io_channels |
| self.rng = torch.quasirandom.SobolEngine(1, scramble=True) |
|
|
| def forward(self, reals, cond, mask): |
| |
| |
| |
| t = self.rng.draw(reals.shape[0])[:, 0].to(reals.device) |
|
|
| alphas, sigmas = 1 - t, t |
| alphas = alphas[:, None, None] |
| sigmas = sigmas[:, None, None] |
|
|
| noise = torch.randn_like(reals) |
| noised_inputs = reals * alphas + noise * sigmas |
|
|
| targets = noise - reals |
|
|
| output = self.model(noised_inputs, t, cond, mask) |
| |
| loss = F.mse_loss(output, targets, reduction='none') |
| loss = loss * mask.unsqueeze(1) |
|
|
| return loss.mean() |
|
|
| def sample(self, cond, steps, sigma_max=1): |
| x = torch.randn(cond.shape[0], self.io_channels, cond.shape[2]).to(cond.device) |
| t = torch.linspace(sigma_max, 0, steps + 1) |
|
|
| for t_curr, t_prev in tqdm(zip(t[:-1], t[1:])): |
| t_curr_tensor = t_curr * torch.ones( |
| (x.shape[0],), dtype=x.dtype, device=x.device |
| ) |
| dt = t_prev - t_curr |
| x = x + dt * self.model(x, t_curr_tensor, cond) |
| return x |
|
|
| class NARPredictor(nn.Module): |
| def __init__(self, latent_dim=16): |
| super().__init__() |
|
|
| self.latent_dim = latent_dim |
| self.bert_proj = nn.Linear(1024, 192) |
| self.x_emb = nn.Linear(192, 192) |
| self.g_emb = nn.Linear(256, 192) |
|
|
|
|
| |
| self.reflow = ReFlow(latent_dim, 192) |
|
|
|
|
| def forward(self, x, bert, spk_emb, diffusion_input, mask): |
| g = self.g_emb(spk_emb).unsqueeze(1) |
| bert = self.bert_proj(bert.transpose(1, 2)) |
| x = self.x_emb(x.transpose(1,2)) |
|
|
| cond = (x + bert + g).transpose(1, 2) |
|
|
| loss = self.reflow(diffusion_input, cond, mask) |
|
|
| return loss |
|
|
| @torch.inference_mode() |
| @torch.no_grad() |
| def infer(self, x, bert, spk_emb, step, guidance): |
|
|
| g = self.g_emb(spk_emb).unsqueeze(1) |
| bert = self.bert_proj(bert.transpose(1, 2)) |
| x = self.x_emb(x.transpose(1,2)) |
| cond = (x + bert + g).transpose(1, 2) |
|
|
| fakes = self.reflow.sample(cond, step) |
|
|
|
|
| return fakes |
|
|
