import math import torch from einops import rearrange from grad.base import BaseModule from grad.solver import NoiseScheduleVP, MaxLikelihood, GradRaw class Mish(BaseModule): def forward(self, x): return x * torch.tanh(torch.nn.functional.softplus(x)) class Upsample(BaseModule): def __init__(self, dim): super(Upsample, self).__init__() self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1) def forward(self, x): return self.conv(x) class Downsample(BaseModule): def __init__(self, dim): super(Downsample, self).__init__() self.conv = torch.nn.Conv2d(dim, dim, 3, 2, 1) def forward(self, x): return self.conv(x) class Rezero(BaseModule): def __init__(self, fn): super(Rezero, self).__init__() self.fn = fn self.g = torch.nn.Parameter(torch.zeros(1)) def forward(self, x): return self.fn(x) * self.g class Block(BaseModule): def __init__(self, dim, dim_out, groups=8): super(Block, self).__init__() self.block = torch.nn.Sequential(torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm( groups, dim_out), Mish()) def forward(self, x, mask): output = self.block(x * mask) return output * mask class ResnetBlock(BaseModule): def __init__(self, dim, dim_out, time_emb_dim, groups=8): super(ResnetBlock, self).__init__() self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out)) self.block1 = Block(dim, dim_out, groups=groups) self.block2 = Block(dim_out, dim_out, groups=groups) if dim != dim_out: self.res_conv = torch.nn.Conv2d(dim, dim_out, 1) else: self.res_conv = torch.nn.Identity() def forward(self, x, mask, time_emb): h = self.block1(x, mask) h += self.mlp(time_emb).unsqueeze(-1).unsqueeze(-1) h = self.block2(h, mask) output = h + self.res_conv(x * mask) return output class LinearAttention(BaseModule): def __init__(self, dim, heads=4, dim_head=32): super(LinearAttention, self).__init__() self.heads = heads hidden_dim = dim_head * heads self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False) self.to_out = torch.nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): b, c, h, w = x.shape qkv = self.to_qkv(x) q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) k = k.softmax(dim=-1) context = torch.einsum('bhdn,bhen->bhde', k, v) out = torch.einsum('bhde,bhdn->bhen', context, q) out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) return self.to_out(out) class Residual(BaseModule): def __init__(self, fn): super(Residual, self).__init__() self.fn = fn def forward(self, x, *args, **kwargs): output = self.fn(x, *args, **kwargs) + x return output class SinusoidalPosEmb(BaseModule): def __init__(self, dim): super(SinusoidalPosEmb, self).__init__() self.dim = dim def forward(self, x, scale=1000): device = x.device half_dim = self.dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) emb = torch.cat((emb.sin(), emb.cos()), dim=-1) return emb class GradLogPEstimator2d(BaseModule): def __init__(self, dim, dim_mults=(1, 2, 4), emb_dim=64, n_mels=100, groups=8, pe_scale=1000): super(GradLogPEstimator2d, self).__init__() self.dim = dim self.dim_mults = dim_mults self.emb_dim = emb_dim self.groups = groups self.pe_scale = pe_scale self.spk_mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, emb_dim * 4), Mish(), torch.nn.Linear(emb_dim * 4, n_mels)) self.time_pos_emb = SinusoidalPosEmb(dim) self.mlp = torch.nn.Sequential(torch.nn.Linear(dim, dim * 4), Mish(), torch.nn.Linear(dim * 4, dim)) dims = [2 + 1, *map(lambda m: dim * m, dim_mults)] in_out = list(zip(dims[:-1], dims[1:])) self.downs = torch.nn.ModuleList([]) self.ups = torch.nn.ModuleList([]) num_resolutions = len(in_out) for ind, (dim_in, dim_out) in enumerate(in_out): # 2 downs is_last = ind >= (num_resolutions - 1) self.downs.append(torch.nn.ModuleList([ ResnetBlock(dim_in, dim_out, time_emb_dim=dim), ResnetBlock(dim_out, dim_out, time_emb_dim=dim), Residual(Rezero(LinearAttention(dim_out))), Downsample(dim_out) if not is_last else torch.nn.Identity()])) mid_dim = dims[-1] self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim) self.mid_attn = Residual(Rezero(LinearAttention(mid_dim))) self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim) for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])): # 2 ups self.ups.append(torch.nn.ModuleList([ ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim), ResnetBlock(dim_in, dim_in, time_emb_dim=dim), Residual(Rezero(LinearAttention(dim_in))), Upsample(dim_in)])) self.final_block = Block(dim, dim) self.final_conv = torch.nn.Conv2d(dim, 1, 1) def forward(self, spk, x, mask, mu, t): s = self.spk_mlp(spk) t = self.time_pos_emb(t, scale=self.pe_scale) t = self.mlp(t) s = s.unsqueeze(-1).repeat(1, 1, x.shape[-1]) x = torch.stack([mu, x, s], 1) mask = mask.unsqueeze(1) hiddens = [] masks = [mask] for resnet1, resnet2, attn, downsample in self.downs: mask_down = masks[-1] x = resnet1(x, mask_down, t) x = resnet2(x, mask_down, t) x = attn(x) hiddens.append(x) x = downsample(x * mask_down) masks.append(mask_down[:, :, :, ::2]) masks = masks[:-1] mask_mid = masks[-1] x = self.mid_block1(x, mask_mid, t) x = self.mid_attn(x) x = self.mid_block2(x, mask_mid, t) for resnet1, resnet2, attn, upsample in self.ups: mask_up = masks.pop() x = torch.cat((x, hiddens.pop()), dim=1) x = resnet1(x, mask_up, t) x = resnet2(x, mask_up, t) x = attn(x) x = upsample(x * mask_up) x = self.final_block(x, mask) output = self.final_conv(x * mask) return (output * mask).squeeze(1) def get_noise(t, beta_init, beta_term, cumulative=False): if cumulative: noise = beta_init*t + 0.5*(beta_term - beta_init)*(t**2) else: noise = beta_init + (beta_term - beta_init)*t return noise class Diffusion(BaseModule): def __init__(self, n_mels, dim, emb_dim=64, beta_min=0.05, beta_max=20, pe_scale=1000): super(Diffusion, self).__init__() self.n_mels = n_mels self.beta_min = beta_min self.beta_max = beta_max # self.solver = NoiseScheduleVP() self.solver = MaxLikelihood() # self.solver = GradRaw() self.estimator = GradLogPEstimator2d(dim, n_mels=n_mels, emb_dim=emb_dim, pe_scale=pe_scale) def forward_diffusion(self, mel, mask, mu, t): time = t.unsqueeze(-1).unsqueeze(-1) cum_noise = get_noise(time, self.beta_min, self.beta_max, cumulative=True) mean = mel*torch.exp(-0.5*cum_noise) + mu*(1.0 - torch.exp(-0.5*cum_noise)) variance = 1.0 - torch.exp(-cum_noise) z = torch.randn(mel.shape, dtype=mel.dtype, device=mel.device, requires_grad=False) xt = mean + z * torch.sqrt(variance) return xt * mask, z * mask def forward(self, spk, z, mask, mu, n_timesteps, stoc=False): return self.solver.reverse_diffusion(self.estimator, spk, z, mask, mu, n_timesteps, stoc) def loss_t(self, spk, mel, mask, mu, t): xt, z = self.forward_diffusion(mel, mask, mu, t) time = t.unsqueeze(-1).unsqueeze(-1) cum_noise = get_noise(time, self.beta_min, self.beta_max, cumulative=True) noise_estimation = self.estimator(spk, xt, mask, mu, t) noise_estimation *= torch.sqrt(1.0 - torch.exp(-cum_noise)) loss = torch.sum((noise_estimation + z)**2) / (torch.sum(mask)*self.n_mels) return loss, xt def compute_loss(self, spk, mel, mask, mu, offset=1e-5): t = torch.rand(mel.shape[0], dtype=mel.dtype, device=mel.device, requires_grad=False) t = torch.clamp(t, offset, 1.0 - offset) return self.loss_t(spk, mel, mask, mu, t)