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unet_grad_tts.py
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239 lines (189 loc) · 7.68 KB
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import math
import torch
try:
from einops import rearrange
except:
print("Einops is not installed")
pass
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
class Mish(torch.nn.Module):
def forward(self, x):
return x * torch.tanh(torch.nn.functional.softplus(x))
class Upsample(torch.nn.Module):
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(torch.nn.Module):
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(torch.nn.Module):
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(torch.nn.Module):
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(torch.nn.Module):
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(torch.nn.Module):
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(torch.nn.Module):
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(torch.nn.Module):
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 UNetGradTTSModel(ModelMixin, ConfigMixin):
def __init__(self, dim, dim_mults=(1, 2, 4), groups=8, n_spks=None, spk_emb_dim=64, n_feats=80, pe_scale=1000):
super(UNetGradTTSModel, self).__init__()
self.register_to_config(
dim=dim,
dim_mults=dim_mults,
groups=groups,
n_spks=n_spks,
spk_emb_dim=spk_emb_dim,
n_feats=n_feats,
pe_scale=pe_scale,
)
self.dim = dim
self.dim_mults = dim_mults
self.groups = groups
self.n_spks = n_spks if not isinstance(n_spks, type(None)) else 1
self.spk_emb_dim = spk_emb_dim
self.pe_scale = pe_scale
if n_spks > 1:
self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
self.spk_mlp = torch.nn.Sequential(
torch.nn.Linear(spk_emb_dim, spk_emb_dim * 4), Mish(), torch.nn.Linear(spk_emb_dim * 4, n_feats)
)
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 if n_spks > 1 else 0), *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):
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:])):
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, x, mask, mu, t, spk=None):
if self.n_spks > 1:
# Get speaker embedding
spk = self.spk_emb(spk)
if not isinstance(spk, type(None)):
s = self.spk_mlp(spk)
t = self.time_pos_emb(t, scale=self.pe_scale)
t = self.mlp(t)
if self.n_spks < 2:
x = torch.stack([mu, x], 1)
else:
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)