File size: 10,027 Bytes
ee6ccc9 6c93afe ee6ccc9 6c93afe ee6ccc9 6c93afe ee6ccc9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 | """Pure PyTorch implementation of the XEUS E-Branchformer acoustic foundation model.
Converted to SafeTensors format (bfloat16) with zero external dependencies (no ESPnet installation required).
Original paper: XEUS - A Cross-lingual Encoder for Universal Speech (CMU WAVLab).
Weights repository: https://huggingface.co/giangndm/xeus
"""
import warnings
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
class TransposeLayerNorm(nn.LayerNorm):
"""LayerNorm operating on channel dimension of (B, C, T) tensors."""
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
transposed = input_tensor.transpose(-2, -1)
normalized = F.layer_norm(
transposed, self.normalized_shape, self.weight, self.bias, self.eps
)
return normalized.transpose(-2, -1)
class ConvLayerBlock(nn.Module):
"""Convolutional downsampling block used in CNNFrontend."""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
bias: bool = True,
) -> None:
super().__init__()
self.kernel_size = kernel_size
self.stride = stride
self.conv = nn.Conv1d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias
)
self.layer_norm = TransposeLayerNorm(out_channels)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return F.gelu(self.layer_norm(self.conv(x)))
class CNNFrontend(nn.Module):
"""Wav2Vec-style 7-layer convolutional feature extractor for 16kHz audio."""
def __init__(self) -> None:
super().__init__()
shapes = [
(512, 10, 5),
(512, 3, 2),
(512, 3, 2),
(512, 3, 2),
(512, 3, 2),
(512, 2, 2),
(512, 2, 2),
]
in_channels = 1
blocks = []
for out_channels, kernel_size, stride in shapes:
blocks.append(
ConvLayerBlock(
in_channels, out_channels, kernel_size=kernel_size, stride=stride
)
)
in_channels = out_channels
self.layers = nn.Sequential(*blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.ndim == 2:
x = F.layer_norm(x, (x.shape[-1],))
x = x.unsqueeze(1)
first_weight = self.layers[0].conv.weight
if x.dtype != first_weight.dtype:
x = x.to(dtype=first_weight.dtype)
x = self.layers(x)
return x.transpose(1, 2)
class Preencoder(nn.Module):
"""Linear projection from frontend 512 dim to encoder 1024 dim."""
def __init__(self, in_dim: int = 512, out_dim: int = 1024) -> None:
super().__init__()
self.linear_out = nn.Linear(in_dim, out_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.linear_out(x)
class ConvPositionalEmbedding(nn.Module):
"""Depthwise 1D convolutional relative positional embedding."""
def __init__(self, hidden_dim: int = 1024, kernel_size: int = 31) -> None:
super().__init__()
conv = nn.Conv1d(
hidden_dim,
hidden_dim,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
groups=16,
)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
conv = nn.utils.weight_norm(conv, name="weight", dim=2)
self.convs = nn.ModuleList([conv])
def forward(self, x: torch.Tensor) -> torch.Tensor:
residual = x
x = x.transpose(1, 2)
for conv in self.convs:
x = conv(x)
x = F.gelu(x)
x = x.transpose(1, 2)
return residual + x
class CSGU(nn.Module):
"""Convolutional Spatial Gating Unit."""
def __init__(self, size: int = 3072, kernel_size: int = 31) -> None:
super().__init__()
self.norm = nn.LayerNorm(size // 2)
self.conv = nn.Conv1d(
size // 2,
size // 2,
kernel_size=kernel_size,
padding=(kernel_size - 1) // 2,
groups=size // 2,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
u, v = x.chunk(2, dim=-1)
v = self.norm(v)
v = v.transpose(1, 2)
v = self.conv(v)
v = v.transpose(1, 2)
return u * v
class ConvolutionalGatingMLP(nn.Module):
"""Branch containing CSGU gated linear unit and depthwise conv."""
def __init__(
self, size: int = 1024, linear_units: int = 3072, kernel_size: int = 31
) -> None:
super().__init__()
self.channel_proj1 = nn.Linear(size, linear_units)
self.csgu = CSGU(linear_units, kernel_size)
self.channel_proj2 = nn.Linear(linear_units // 2, size)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.channel_proj1(x)
x = F.gelu(x)
x = self.csgu(x)
return self.channel_proj2(x)
class MultiHeadedAttention(nn.Module):
"""Multi-Head Self-Attention with projection matrices."""
def __init__(self, n_head: int = 8, n_feat: int = 1024) -> None:
super().__init__()
self.d_k = n_feat // n_head
self.h = n_head
self.linear_q = nn.Linear(n_feat, n_feat)
self.linear_k = nn.Linear(n_feat, n_feat)
self.linear_v = nn.Linear(n_feat, n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b, t, d = x.shape
q = self.linear_q(x).view(b, t, self.h, self.d_k).transpose(1, 2)
k = self.linear_k(x).view(b, t, self.h, self.d_k).transpose(1, 2)
v = self.linear_v(x).view(b, t, self.h, self.d_k).transpose(1, 2)
out = F.scaled_dot_product_attention(q, k, v)
out = out.transpose(1, 2).contiguous().view(b, t, d)
return self.linear_out(out)
class EBranchformerEncoderLayer(nn.Module):
"""Single E-Branchformer layer combining Attn, CGMLP and FFN branches."""
def __init__(
self,
size: int = 1024,
linear_units: int = 4096,
cgmlp_units: int = 3072,
heads: int = 8,
kernel: int = 31,
) -> None:
super().__init__()
self.norm_mha = nn.LayerNorm(size)
self.norm_mlp = nn.LayerNorm(size)
self.mha = MultiHeadedAttention(heads, size)
self.cgmlp = ConvolutionalGatingMLP(size, cgmlp_units, kernel)
self.norm_merge = nn.LayerNorm(size)
self.merge_proj = nn.Linear(size + size, size)
self.norm_ffn = nn.LayerNorm(size)
self.ffn = nn.Sequential(
nn.Linear(size, linear_units),
nn.GELU(),
nn.Linear(linear_units, size),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Branch 1: MHA
branch1 = self.mha(self.norm_mha(x))
# Branch 2: CGMLP
branch2 = self.cgmlp(self.norm_mlp(x))
# Merge branches
merged = torch.cat([branch1, branch2], dim=-1)
merged = self.merge_proj(merged)
x = x + self.norm_merge(merged)
# FFN sublayer
x = x + self.ffn(self.norm_ffn(x))
return x
class EBranchformerEncoder(nn.Module):
"""Stack of E-Branchformer blocks with relative positional embeddings."""
def __init__(
self,
num_blocks: int = 19,
size: int = 1024,
linear_units: int = 4096,
heads: int = 8,
kernel: int = 31,
) -> None:
super().__init__()
self.embed = nn.Sequential(ConvPositionalEmbedding(size, kernel))
self.encoders = nn.ModuleList([
EBranchformerEncoderLayer(size, linear_units, 3072, heads, kernel)
for _ in range(num_blocks)
])
self.after_norm = nn.LayerNorm(size)
self.gradient_checkpointing = False
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.embed(x)
for layer in self.encoders:
if self.gradient_checkpointing and self.training:
x = torch.utils.checkpoint.checkpoint(layer, x, use_reentrant=False)
else:
x = layer(x)
return self.after_norm(x)
class XeusTrunk(nn.Module):
"""Complete XEUS acoustic foundation trunk (Frontend + Preencoder + EBranchformer)."""
DEFAULT_REPO_ID = "giangndm/xeus"
DEFAULT_FILENAME = "model.safetensors"
def __init__(
self,
num_blocks: int = 19,
hidden_dim: int = 1024,
linear_units: int = 4096,
heads: int = 8,
kernel: int = 31,
) -> None:
super().__init__()
self.frontend = CNNFrontend()
self.preencoder = Preencoder(512, hidden_dim)
self.encoder = EBranchformerEncoder(
num_blocks, hidden_dim, linear_units, heads, kernel
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.frontend(x)
x = self.preencoder(x)
return self.encoder(x)
@classmethod
def from_pretrained(
cls,
repo_id: str = DEFAULT_REPO_ID,
revision: Optional[str] = None,
filename: str = DEFAULT_FILENAME,
dtype: Optional[torch.dtype] = torch.bfloat16,
) -> "XeusTrunk":
model = cls()
if dtype is not None:
model = model.to(dtype=dtype)
ckpt_path = hf_hub_download(repo_id=repo_id, revision=revision, filename=filename)
if filename.endswith(".safetensors"):
state_dict = load_file(ckpt_path)
else:
state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
filtered_state = {
k: v
for k, v in state_dict.items()
if k.startswith(("frontend.", "preencoder.", "encoder."))
}
model.load_state_dict(filtered_state, strict=True)
return model
|