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9781faf | 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 | # Adapted from Stability AI's stable-audio-3 (MIT License). See LICENSE.
from __future__ import annotations
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors.torch import load_file
WEIGHTS = Path(__file__).parent / "samel" / "model.safetensors"
SAMPLE_RATE = 44100
LATENT_DIM = 256
DIM = 1536
DEPTH = 12
HEADS = 24
HEAD_DIM = 64
ROPE_DIM = 32
FF_HIDDEN = 3 * DIM
SINUSOIDAL_FROM = 5
PATCH = 256
STRIDE = 16
GROUP = STRIDE + 1
WINDOW = GROUP
CHUNK = 128
OVERLAP = 32
SEQ = CHUNK * GROUP
SAMPLES_PER_FRAME = PATCH * STRIDE
MASK_NOISE = 0.1
LATENT_NOISE = 1e-3
class DynamicTanh(nn.Module):
def __init__(self, dim: int):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1))
self.gamma = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.gamma * F.tanh(self.alpha * x) + self.beta
def apply_rope(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
rot, keep = t[..., :ROPE_DIM], t[..., ROPE_DIM:]
x1, x2 = rot.chunk(2, dim=-1)
rotated = torch.cat([-x2, x1], dim=-1)
return torch.cat([rot * freqs.cos() + rotated * freqs.sin(), keep], dim=-1)
class Attention(nn.Module):
"""Differential attention: two attention maps per head, subtracted."""
def __init__(self):
super().__init__()
self.to_qkv = nn.Linear(DIM, 5 * DIM, bias=False)
self.to_out = nn.Linear(DIM, DIM, bias=False)
self.q_norm = DynamicTanh(HEAD_DIM)
self.k_norm = DynamicTanh(HEAD_DIM)
def forward(self, x: torch.Tensor, freqs: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
B, N, _ = x.shape
q, k, v, q_diff, k_diff = (
self.to_qkv(x).view(B, N, 5, HEADS, HEAD_DIM).permute(2, 0, 3, 1, 4))
q, q_diff = apply_rope(self.q_norm(q).float(), freqs), apply_rope(
self.q_norm(q_diff).float(), freqs)
k, k_diff = apply_rope(self.k_norm(k).float(), freqs), apply_rope(
self.k_norm(k_diff).float(), freqs)
out = F.scaled_dot_product_attention(q.to(v.dtype), k.to(v.dtype), v, attn_mask=mask)
out = out - F.scaled_dot_product_attention(
q_diff.to(v.dtype), k_diff.to(v.dtype), v, attn_mask=mask)
return self.to_out(out.transpose(1, 2).reshape(B, N, DIM))
class Sin(nn.Module):
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.sin(torch.pi * x)
class GatedProjection(nn.Module):
def __init__(self, activation: nn.Module):
super().__init__()
self.proj = nn.Linear(DIM, 2 * FF_HIDDEN)
self.act = activation
def forward(self, x: torch.Tensor) -> torch.Tensor:
value, gate = self.proj(x).chunk(2, dim=-1)
return value * self.act(gate)
class FeedForward(nn.Module):
def __init__(self, activation: nn.Module):
super().__init__()
self.ff = nn.Sequential(
GatedProjection(activation), nn.Identity(), nn.Linear(FF_HIDDEN, DIM))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.ff(x)
class Block(nn.Module):
def __init__(self, sinusoidal: bool):
super().__init__()
self.pre_norm = DynamicTanh(DIM)
self.self_attn = Attention()
self.ff_norm = DynamicTanh(DIM)
self.ff = FeedForward(Sin() if sinusoidal else nn.SiLU())
def forward(self, x: torch.Tensor, freqs: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
x = x + self.self_attn(self.pre_norm(x), freqs, mask)
return x + self.ff(self.ff_norm(x))
class Resampler(nn.Module):
def __init__(self):
super().__init__()
self.new_tokens = nn.Parameter(torch.zeros(1, 1, DIM))
self.blocks = nn.ModuleList(Block(i >= SINUSOIDAL_FROM) for i in range(DEPTH))
self.mapping = nn.Conv1d(DIM, 2 * PATCH, 1)
def forward(self, x: torch.Tensor, freqs: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
B, T, _ = x.shape
slots = self.new_tokens.expand(B * T, STRIDE, DIM)
slots = slots + torch.randn_like(slots) * MASK_NOISE
x = torch.cat([x.reshape(B * T, 1, DIM), slots], dim=1).reshape(B, T * GROUP, DIM)
for block in self.blocks:
x = block(x, freqs, mask)
x = x.reshape(B * T, GROUP, DIM)[:, 1:]
return self.mapping(x.reshape(B, T * STRIDE, DIM).transpose(1, 2))
class SameLDecoder(nn.Module):
def __init__(self):
super().__init__()
self.proj_in = nn.Linear(LATENT_DIM, DIM)
self.resampler = Resampler()
self.register_buffer("running_std", torch.ones(1))
position = torch.arange(SEQ)
self.register_buffer(
"mask", (position[None, :] - position[:, None]).abs() <= WINDOW, persistent=False)
self.register_buffer(
"inv_freq", 1.0 / (10000.0 ** (torch.arange(0, ROPE_DIM, 2) / ROPE_DIM)),
persistent=False)
def _rope_freqs(self) -> torch.Tensor:
freqs = torch.outer(torch.arange(SEQ, device=self.inv_freq.device).float(),
self.inv_freq.float())
return torch.cat([freqs, freqs], dim=-1)
def _decode_chunk(self, latents: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
x = latents * self.running_std
x = x + torch.randn_like(x) * self.running_std * LATENT_NOISE
x = self.resampler(self.proj_in(x.transpose(1, 2)), freqs, self.mask)
B, _, L = x.shape
return x.view(B, 2, PATCH, L).permute(0, 1, 3, 2).reshape(B, 2, L * PATCH)
@torch.no_grad()
def decode(self, latents: torch.Tensor) -> torch.Tensor:
"""(B, 256, T) latents, T >= CHUNK, to (B, 2, T * 4096) audio."""
frames = latents.shape[-1]
starts = list(range(0, frames - CHUNK + 1, CHUNK - OVERLAP))
if starts[-1] != frames - CHUNK:
starts.append(frames - CHUNK)
freqs = self._rope_freqs()
edge = OVERLAP // 2 * SAMPLES_PER_FRAME
audio = latents.new_zeros(latents.shape[0], 2, frames * SAMPLES_PER_FRAME)
for i, start in enumerate(starts):
chunk = self._decode_chunk(latents[..., start:start + CHUNK], freqs)
left = 0 if i == 0 else edge
right = chunk.shape[-1] if i == len(starts) - 1 else chunk.shape[-1] - edge
at = start * SAMPLES_PER_FRAME
audio[..., at + left:at + right] = chunk[..., left:right]
return audio
def load_decoder(device: str = "cuda",
dtype: torch.dtype = torch.float16) -> SameLDecoder:
"""Load the bundled SAME-L weights, keeping only what decoding needs."""
raw = load_file(WEIGHTS)
state = {"running_std": raw["bottleneck.running_std"]}
g, v = raw["decoder.layers.3.mapping.weight_g"], raw["decoder.layers.3.mapping.weight_v"]
state["resampler.mapping.weight"] = g * v / v.norm(dim=(1, 2), keepdim=True)
for key, tensor in raw.items():
if key.startswith("decoder.layers.1."):
state[key.replace("decoder.layers.1.", "proj_in.")] = tensor
elif key.startswith("decoder.layers.3.") and not key.endswith(
("rope.inv_freq", "mapping.weight_g", "mapping.weight_v")):
state[key.replace("decoder.layers.3.", "resampler.")
.replace("transformers.", "blocks.")] = tensor
decoder = SameLDecoder()
decoder.load_state_dict(state)
return decoder.to(device=device, dtype=dtype).eval().requires_grad_(False)
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