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705fa30 | 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 | # src/models/lpe_module.py
"""
Linguistic Position Encoding (LPE) Module
Core novel contribution of ChildFluency-Net.
Encodes 6 loci-of-stuttering features into a 32-dim vector.
These features are pre-computed by build_features.py using
WhisperX transcription + spaCy linguistic parsing.
Clinical basis (Bloodstein 1960, Bernstein-Ratner 1997):
Stuttering is 3-5x more likely on:
- Sentence-initial positions
- Content words (nouns, verbs) vs function words
- Longer, rarer words
- Near clause boundaries (cognitive planning spikes)
- In longer, more complex utterances (higher MLU)
Input features [6 scalars, all in 0-1 range]:
0: sentence position (0=initial=high risk, 1=final)
1: word class (1=content, 0=function)
2: syllable count (normalized, max=6 syllables)
3: clause boundary prox (1=near boundary)
4: word rarity (1=rare=high risk, 0=common)
5: MLU (sentence length / 20)
"""
import torch
import torch.nn as nn
class LPEModule(nn.Module):
def __init__(
self,
input_dim : int = 6,
output_dim : int = 32
):
super().__init__()
# Small MLP: 6 → 16 → 32
# Kept intentionally small — 6 features don't need
# a large network, just a learned non-linear transformation
self.encoder = nn.Sequential(
nn.Linear(input_dim, 16),
nn.ReLU(),
nn.Linear(16, output_dim)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x : [batch, 6] pre-computed LPE feature vectors
Returns:
: [batch, 32] LPE embeddings
"""
return self.encoder(x) |