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6187707 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | """FreyaDiT: a non-autoregressive flow-matching DiT for Turkish TTS.
Latent frames self-attend with rotary position embeddings and cross-attend to
character-level text features refined by a small ConvNeXt stack. Trained with
an optimal-transport rectified-flow objective on frozen VoxCPM2 VAE latents.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
# spread of 2.3 Hz, against 14.9 Hz for the previous default of 0.
LEYLA_SEED = 9
def rope_freqs(dim, length, theta=10000.0, device=None):
"""Build rotary embedding angles of shape [length, dim]."""
inv = 1.0 / (theta ** (torch.arange(0, dim, 2, device=device).float() / dim))
t = torch.arange(length, device=device).float()
freqs = torch.outer(t, inv)
return torch.cat([freqs, freqs], dim=-1)
def apply_rope(x, cos, sin):
"""Apply rotary position embedding to a [B, H, N, D] tensor."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
rotated = torch.cat([-x2, x1], dim=-1)
return x * cos + rotated * sin
def additive_mask(mask, dtype):
"""Turn a bool key-padding mask [B, N] into an additive float mask [B, 1, 1, N].
SDPA's memory-efficient backend wants an additive mask rather than bool.
"""
if mask is None:
return None
out = torch.zeros(mask.shape[0], 1, 1, mask.shape[1], device=mask.device, dtype=dtype)
return out.masked_fill(~mask[:, None, None, :], float("-inf"))
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def timestep_embed(t, dim, max_period=10000):
"""Sinusoidal embedding of a scalar diffusion time t in [0, 1]."""
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(half, device=t.device) / half)
angles = t[:, None] * freqs[None]
return torch.cat([angles.cos(), angles.sin()], dim=-1)
class SelfAttn(nn.Module):
"""Multi-head self-attention over latent frames, with RoPE."""
def __init__(self, d, heads):
super().__init__()
self.h = heads
self.qkv = nn.Linear(d, 3 * d, bias=False)
self.o = nn.Linear(d, d, bias=False)
def forward(self, x, cos, sin, mask=None):
q, k, v = [rearrange(t, "b n (h d) -> b h n d", h=self.h) for t in self.qkv(x).chunk(3, -1)]
q = apply_rope(q, cos, sin)
k = apply_rope(k, cos, sin)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=additive_mask(mask, q.dtype))
return self.o(rearrange(out, "b h n d -> b n (h d)"))
class CrossAttn(nn.Module):
"""Frames attend to character features. No positional encoding on the text side;
alignment is learned through attention."""
def __init__(self, d, heads):
super().__init__()
self.h = heads
self.q = nn.Linear(d, d, bias=False)
self.kv = nn.Linear(d, 2 * d, bias=False)
self.o = nn.Linear(d, d, bias=False)
def forward(self, x, ctx, ctx_mask=None):
q = rearrange(self.q(x), "b n (h d) -> b h n d", h=self.h)
k, v = [rearrange(t, "b n (h d) -> b h n d", h=self.h) for t in self.kv(ctx).chunk(2, -1)]
out = F.scaled_dot_product_attention(q, k, v, attn_mask=additive_mask(ctx_mask, q.dtype))
return self.o(rearrange(out, "b h n d -> b n (h d)"))
class SwiGLU(nn.Module):
def __init__(self, d, ff):
super().__init__()
self.w1 = nn.Linear(d, ff, bias=False)
self.w2 = nn.Linear(d, ff, bias=False)
self.w3 = nn.Linear(ff, d, bias=False)
def forward(self, x):
return self.w3(F.silu(self.w1(x)) * self.w2(x))
class Block(nn.Module):
"""DiT block: self-attention, cross-attention, SwiGLU FFN.
Each of the three sub-layers is gated by adaLN-zero, so a single timestep
conditioning vector produces nine modulation signals (shift/scale/gate x3).
Gates start at zero so the block is initialized to identity.
"""
def __init__(self, d, heads, ff):
super().__init__()
self.n1 = nn.LayerNorm(d, elementwise_affine=False, eps=1e-6)
self.sa = SelfAttn(d, heads)
self.nx = nn.LayerNorm(d, elementwise_affine=False, eps=1e-6)
self.xa = CrossAttn(d, heads)
self.n2 = nn.LayerNorm(d, elementwise_affine=False, eps=1e-6)
self.ff = SwiGLU(d, ff)
self.ada = nn.Sequential(nn.SiLU(), nn.Linear(d, 9 * d))
nn.init.zeros_(self.ada[-1].weight)
nn.init.zeros_(self.ada[-1].bias)
def forward(self, x, c, ctx, cos, sin, fmask=None, cmask=None):
s1, b1, g1, sx, bx, gx, s2, b2, g2 = self.ada(c).chunk(9, -1)
x = x + g1.unsqueeze(1) * self.sa(modulate(self.n1(x), b1, s1), cos, sin, fmask)
x = x + gx.unsqueeze(1) * self.xa(modulate(self.nx(x), bx, sx), ctx, cmask)
x = x + g2.unsqueeze(1) * self.ff(modulate(self.n2(x), b2, s2))
return x
class ConvNeXt1d(nn.Module):
"""1-D ConvNeXt block used to refine character embeddings before cross-attention."""
def __init__(self, d, mult=2):
super().__init__()
self.dw = nn.Conv1d(d, d, 7, padding=3, groups=d)
self.n = nn.LayerNorm(d)
self.p1 = nn.Linear(d, d * mult)
self.p2 = nn.Linear(d * mult, d)
def forward(self, x):
residual = x
x = self.dw(x.transpose(1, 2)).transpose(1, 2)
x = self.n(x)
return residual + self.p2(F.gelu(self.p1(x)))
class FreyaDiT(nn.Module):
"""FreyaTTS acoustic model.
Predicts the rectified-flow velocity field over 64-dim VoxCPM2 latent
frames, conditioned on character ids. Also carries a small duration head
that regresses log frame count from mean-pooled text features.
"""
def __init__(self, vocab, feat=64, d=768, depth=22, heads=12, ff=2048, text_conv=4, fill_id=0):
super().__init__()
self.feat = feat
self.d = d
self.heads = heads
self.char_emb = nn.Embedding(vocab, d)
self.text_conv = nn.ModuleList([ConvNeXt1d(d) for _ in range(text_conv)])
self.x_proj = nn.Linear(feat, d)
self.t_mlp = nn.Sequential(nn.Linear(d, d), nn.SiLU(), nn.Linear(d, d))
self.blocks = nn.ModuleList([Block(d, heads, ff) for _ in range(depth)])
self.nf = nn.LayerNorm(d, elementwise_affine=False, eps=1e-6)
self.ada_f = nn.Sequential(nn.SiLU(), nn.Linear(d, 2 * d))
nn.init.zeros_(self.ada_f[-1].weight)
nn.init.zeros_(self.ada_f[-1].bias)
self.out = nn.Linear(d, feat)
nn.init.zeros_(self.out.weight)
nn.init.zeros_(self.out.bias)
self.dur = nn.Sequential(nn.Linear(d, d), nn.SiLU(), nn.Linear(d, 1))
def text_encode(self, text_ids):
"""Embed character ids and refine them with the ConvNeXt stack."""
x = self.char_emb(text_ids)
for conv in self.text_conv:
x = conv(x)
return x
def forward(self, x_t, t, ctx, fmask=None, cmask=None):
"""Predict the velocity field at noisy latents x_t and time t."""
B, T, _ = x_t.shape
head_dim = self.d // self.heads
base = rope_freqs(head_dim, T, device=x_t.device)
cos = torch.cos(base)[None, None]
sin = torch.sin(base)[None, None]
h = self.x_proj(x_t)
c = self.t_mlp(timestep_embed(t, self.d))
for blk in self.blocks:
h = blk(h, c, ctx, cos, sin, fmask, cmask)
s, g = self.ada_f(c).chunk(2, -1)
return self.out(modulate(self.nf(h), s, g))
def cfm_loss(self, x1, text_ids, fmask=None, cmask=None):
"""Conditional flow-matching loss on clean latents x1 (masked mean if fmask given)."""
B = x1.shape[0]
x0 = torch.randn_like(x1)
t = torch.rand(B, device=x1.device)
xt = (1 - t[:, None, None]) * x0 + t[:, None, None] * x1
ctx = self.text_encode(text_ids)
v = self(xt, t, ctx, fmask, cmask)
target = x1 - x0
if fmask is None:
return ((v - target) ** 2).mean()
m = fmask[..., None].float()
return (((v - target) ** 2) * m).sum() / (m.sum() * self.feat + 1e-6)
def dur_loss(self, text_ids, logT, cmask=None):
"""MSE loss of the duration head against log frame counts."""
te = self.text_encode(text_ids)
if cmask is not None:
pooled = (te * cmask[..., None].float()).sum(1) / (cmask.sum(1, keepdim=True) + 1e-6)
else:
pooled = te.mean(1)
pred = self.dur(pooled).squeeze(-1)
return ((pred - logT) ** 2).mean(), pred
@torch.no_grad()
def sample(self, text_ids, T, steps=32, cmask=None, seed=LEYLA_SEED):
"""Integrate the ODE from noise to latents with a fixed-step Euler solver."""
B = text_ids.shape[0]
ctx = self.text_encode(text_ids)
device = text_ids.device
if seed is None:
x = torch.randn(B, T, self.feat, device=device)
else:
gen = torch.Generator(device=device).manual_seed(int(seed))
x = torch.randn(B, T, self.feat, device=device, generator=gen)
for i in range(steps):
t = torch.full((B,), i / steps, device=x.device)
x = x + self(x, t, ctx, None, cmask) / steps
return x
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