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cfb5e7f | 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 | """Multi-modal sentiment model combining audio and text."""
import logging
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from .base_model import BaseModel
logger = logging.getLogger(__name__)
class MultiModalSentimentModel(BaseModel):
"""Multi-modal model combining text and audio features."""
def __init__(
self,
text_dim: int = 768,
audio_dim: int = 8,
hidden_dim: int = 256,
num_classes: int = 4,
dropout: float = 0.2,
):
"""
Args:
text_dim: Text embedding dimension
audio_dim: Audio feature dimension
hidden_dim: Hidden layer dimension
num_classes: Number of sentiment classes
dropout: Dropout rate
"""
super().__init__()
self.text_dim = text_dim
self.audio_dim = audio_dim
# Text encoder
self.text_encoder = nn.Sequential(
nn.Linear(text_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
)
# Audio encoder
self.audio_encoder = nn.Sequential(
nn.Linear(audio_dim, hidden_dim // 2),
nn.ReLU(),
nn.Dropout(dropout),
)
# Fusion layer
self.fusion = nn.Sequential(
nn.Linear(hidden_dim + hidden_dim // 2, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
)
# Classification head
self.classifier = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim // 2, num_classes),
)
def forward(
self,
text_features: torch.Tensor,
audio_features: torch.Tensor,
) -> torch.Tensor:
"""
Forward pass.
Args:
text_features: Text embeddings (batch, text_dim)
audio_features: Audio features (batch, audio_dim)
Returns:
Logits (batch, num_classes)
"""
# Encode each modality
text_encoded = self.text_encoder(text_features)
audio_encoded = self.audio_encoder(audio_features)
# Concatenate and fuse
fused = torch.cat([text_encoded, audio_encoded], dim=-1)
fused = self.fusion(fused)
# Classify
logits = self.classifier(fused)
return logits
def predict(
self,
text_features: torch.Tensor,
audio_features: Optional[torch.Tensor] = None,
) -> Dict[str, Any]:
"""Make predictions."""
self.eval()
if audio_features is None:
# Text-only mode
audio_features = torch.zeros(
text_features.size(0), self.audio_dim
).to(text_features.device)
with torch.no_grad():
logits = self.forward(text_features, audio_features)
probs = torch.softmax(logits, dim=-1)
sentiment_labels = ["negative", "neutral", "positive", "sarcastic"]
predictions = []
for i, probs_i in enumerate(probs):
pred_idx = probs_i.argmax().item()
predictions.append({
"sentiment": sentiment_labels[pred_idx],
"confidence": probs_i[pred_idx].item(),
"probabilities": {
label: probs_i[j].item()
for j, label in enumerate(sentiment_labels)
},
})
return {"predictions": predictions}
class CrossModalAttention(nn.Module):
"""Cross-modal attention for audio-text fusion."""
def __init__(
self,
query_dim: int,
key_dim: int,
hidden_dim: int,
num_heads: int = 4,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = hidden_dim // num_heads
self.query = nn.Linear(query_dim, hidden_dim)
self.key = nn.Linear(key_dim, hidden_dim)
self.value = nn.Linear(key_dim, hidden_dim)
self.output = nn.Linear(hidden_dim, hidden_dim)
self.scale = self.head_dim ** -0.5
def forward(
self,
query: torch.Tensor,
key_value: torch.Tensor,
) -> torch.Tensor:
"""Cross-attention forward pass."""
batch_size = query.size(0)
# Linear projections
Q = self.query(query).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
K = self.key(key_value).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
V = self.value(key_value).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
# Attention scores
scores = torch.matmul(Q, K.transpose(-2, -1)) * self.scale
attention = torch.softmax(scores, dim=-1)
# Apply attention to values
context = torch.matmul(attention, V)
context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.num_heads * self.head_dim)
return self.output(context)
if __name__ == "__main__":
print("Testing MultiModalSentimentModel...")
model = MultiModalSentimentModel(
text_dim=768,
audio_dim=8,
hidden_dim=256,
num_classes=4,
)
# Mock inputs
text_features = torch.randn(2, 768)
audio_features = torch.randn(2, 8)
logits = model(text_features, audio_features)
print(f"Output shape: {logits.shape}")
print(f"Total parameters: {model.get_num_parameters():,}")
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