File size: 9,106 Bytes
7afafba afbebe3 7afafba afbebe3 |
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 |
import math, torch
import torch.nn as nn
from transformers import Wav2Vec2Model
from huggingface_hub import PyTorchModelHubMixin
class SEModule(nn.Module):
def __init__(self, channels, bottleneck=128):
super(SEModule, self).__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Conv1d(channels, bottleneck, kernel_size=1, padding=0),
nn.ReLU(),
# nn.BatchNorm1d(bottleneck), # I remove this layer
nn.Conv1d(bottleneck, channels, kernel_size=1, padding=0),
nn.Sigmoid(),
)
def forward(self, input):
x = self.se(input)
return input * x
class Bottle2neck(nn.Module):
def __init__(self, inplanes, planes, kernel_size=None, dilation=None, scale=8):
super(Bottle2neck, self).__init__()
width = int(math.floor(planes / scale))
self.conv1 = nn.Conv1d(inplanes, width * scale, kernel_size=1)
self.bn1 = nn.BatchNorm1d(width * scale)
self.nums = scale - 1
convs = []
bns = []
num_pad = math.floor(kernel_size / 2) * dilation
for i in range(self.nums):
convs.append(nn.Conv1d(width, width, kernel_size=kernel_size, dilation=dilation, padding=num_pad))
bns.append(nn.BatchNorm1d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.conv3 = nn.Conv1d(width * scale, planes, kernel_size=1)
self.bn3 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU()
self.width = width
self.se = SEModule(planes)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out)
spx = torch.split(out, self.width, 1)
for i in range(self.nums):
if i == 0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(sp)
sp = self.bns[i](sp)
if i == 0:
out = sp
else:
out = torch.cat((out, sp), 1)
out = torch.cat((out, spx[self.nums]), 1)
out = self.conv3(out)
out = self.relu(out)
out = self.bn3(out)
out = self.se(out)
out += residual
return out
class ECAPA_TDNN(nn.Module):
def __init__(self, C):
super(ECAPA_TDNN, self).__init__()
self.conv1 = nn.Conv1d(128, C, kernel_size=5, stride=1, padding=2)
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm1d(C)
self.layer1 = Bottle2neck(C, C, kernel_size=3, dilation=2, scale=8)
self.layer2 = Bottle2neck(C, C, kernel_size=3, dilation=3, scale=8)
self.layer3 = Bottle2neck(C, C, kernel_size=3, dilation=4, scale=8)
self.layer4 = Bottle2neck(C, C, kernel_size=3, dilation=5, scale=8)
# I fixed the shape of the output from MFA layer, that is close to the setting from ECAPA paper.
self.layer5 = nn.Conv1d(4 * C, 1536, kernel_size=1)
self.attention = nn.Sequential(
nn.Conv1d(4608, 256, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(256),
nn.Tanh(), # I add this layer
nn.Conv1d(256, 1536, kernel_size=1),
nn.Softmax(dim=2),
)
self.bn5 = nn.BatchNorm1d(3072)
self.fc6 = nn.Linear(3072, 2)
def forward(self, x):
x = x.transpose(1, 2)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x1 = self.layer1(x)
x2 = self.layer2(x + x1)
x3 = self.layer3(x + x1 + x2)
x4 = self.layer4(x + x1 + x2 + x3)
x = self.layer5(torch.cat((x1, x2, x3, x4), dim=1))
x = self.relu(x)
t = x.size()[-1]
global_x = torch.cat((x, torch.mean(x, dim=2, keepdim=True).repeat(1, 1, t), torch.sqrt(torch.var(x, dim=2, keepdim=True).clamp(min=1e-4)).repeat(1, 1, t)), dim=1)
w = self.attention(global_x)
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu ** 2).clamp(min=1e-4))
x = torch.cat((mu, sg), 1)
x = self.bn5(x)
x = self.fc6(x)
return x
class Wav2Vec2Encoder(nn.Module):
"""SSL encoder based on Hugging Face's Wav2Vec2 model."""
def __init__(self,
model_name_or_path: str = "facebook/wav2vec2-base-960h",
output_attentions: bool = False,
output_hidden_states: bool = False,
normalize_waveform: bool = False):
"""Initialize the Wav2Vec2 encoder.
Args:
model_name_or_path: HuggingFace model name or path to local model.
output_attentions: Whether to output attentions.
output_hidden_states: Whether to output hidden states.
normalize_waveform: Whether to normalize the waveform input.
"""
super().__init__()
self.model_name_or_path = model_name_or_path
self.output_attentions = output_attentions
self.output_hidden_states = output_hidden_states
self.normalize_waveform = normalize_waveform
# Load Wav2Vec2 model
self.model = Wav2Vec2Model.from_pretrained(
model_name_or_path,
gradient_checkpointing=False)
self.model.config.apply_spec_augment = False
self.model.masked_spec_embed = None
def forward(self, x):
"""Forward pass through the Wav2Vec2 encoder.
Args:
x: Input tensor of shape (batch_size, sequence_length, channels)
Returns:
Extracted features of shape (batch_size, sequence_length, 1024)
"""
# Handle shape: convert (batch_size, sequence_length, channels) to (batch_size, sequence_length)
if x.ndim == 3:
x = x.squeeze(-1) # Remove channel dimension if present
# Normalize input if specified
if self.normalize_waveform:
x = x / (torch.max(torch.abs(x), dim=1, keepdim=True)[0] + 1e-8)
# Wav2Vec2 forward pass
outputs = self.model(
x,
output_attentions=self.output_attentions,
output_hidden_states=self.output_hidden_states,
return_dict=True
)
# Extract last hidden state
last_hidden_state = outputs.last_hidden_state
return last_hidden_state
class MLPBridge(nn.Module):
def __init__(self, input_dim: int, output_dim: int, hidden_dim: int = None,
dropout: float = 0.1, activation: str = nn.ReLU, n_layers: int = 1):
"""Initialize the MLP bridge.
Args:
input_dim: The input dimension from the SSL encoder.
output_dim: The output dimension for the model.
hidden_dim: Hidden dimension size. If None, use the average of input and output dims.
dropout: Dropout probability to apply between layers.
activation: Activation function to use
n_layers: Number of MLP layers (repeats of Linear+Activation+Dropout blocks).
"""
super().__init__()
if hidden_dim is None:
hidden_dim = (input_dim + output_dim) // 2
self.input_dim = input_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.n_layers = n_layers
assert hasattr(activation, 'forward') and callable(getattr(activation, 'forward', None)), "Activation class must have a callable forward() method."
act_fn = activation
layers = []
for i in range(n_layers):
in_dim = input_dim if i == 0 else hidden_dim
out_dim = hidden_dim
layers.append(nn.Linear(in_dim, out_dim))
layers.append(act_fn)
layers.append(nn.Dropout(dropout) if dropout > 0 else nn.Identity())
# Final output layer
layers.append(nn.Linear(hidden_dim, output_dim))
layers.append(nn.Dropout(dropout) if dropout > 0 else nn.Identity())
self.mlp = nn.Sequential(*layers)
def forward(self, x):
"""Forward pass through the bridge.
Args:
x: The input tensor from the SSL encoder.
Returns:
The transformed tensor.
"""
return self.mlp(x)
class Spectra0Model(nn.Module, PyTorchModelHubMixin):
def __init__(self, **kwargs):
super().__init__()
self.ssl_encoder = Wav2Vec2Encoder("facebook/wav2vec2-xls-r-300m")
self.bridge = MLPBridge(1024, 128, hidden_dim=128, activation=nn.SELU())
self.ecapa_tdnn = ECAPA_TDNN(128)
def forward(self, x):
x = self.ssl_encoder(x)
x = self.bridge(x)
x = self.ecapa_tdnn(x)
return x
@torch.inference_mode()
def classify(self, x, threshold: float = -1.0625009):
x = self.forward(x)[:, 1]
x = (x > threshold).float()
return x.item()
# Backward-compatible alias used in examples: `from model import spectra_0`
# (class alias, not an instance)
spectra_0 = Spectra0Model
|