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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 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 | # src/models/childfluency.py
"""
ChildFluency-Net — Full Model
Combines WavLM acoustic encoder + LPE linguistic encoder
into a unified architecture with dual SLD/OD classification heads.
Architecture:
[WavLM → 256-dim] + [LPE → 32-dim]
↓ concat [288-dim]
Shared MLP [64-dim]
↓
[SLD Head] [OD Head]
↓
Mean pool across windows → SLD rate % (clinical output)
Clinical output:
SLD rate > 3% → refer for specialist assessment
"""
import torch
import torch.nn as nn
from typing import Dict
from wavlm_encoder import WavLMEncoder
from lpe_module import LPEModule
class ChildFluencyNet(nn.Module):
def __init__(
self,
wavlm_name : str = "microsoft/wavlm-large",
acoustic_dim : int = 256,
lpe_dim : int = 32,
hidden_dim : int = 128,
dropout : float = 0.3
):
super().__init__()
# Branch A — acoustic
self.acoustic_encoder = WavLMEncoder(
model_name = wavlm_name,
output_dim = acoustic_dim
)
# Branch B — linguistic position
self.lpe_module = LPEModule(
input_dim = 6,
output_dim = lpe_dim
)
fusion_dim = acoustic_dim + lpe_dim # 256 + 32 = 288
# Shared MLP after fusion
self.shared_mlp = nn.Sequential(
nn.Linear(fusion_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, hidden_dim // 2) # → 64-dim
)
# Head 1 — SLD (Stuttering-Like Disfluencies)
# Detects: blocks, prolongations, sound/syllable repetitions
# These are PATHOLOGICAL — what clinicians diagnose
self.sld_head = nn.Linear(hidden_dim // 2, 1)
# Head 2 — OD (Other Disfluencies)
# Detects: word repetitions, interjections (um, uh)
# These are NORMAL — present in all speakers
self.od_head = nn.Linear(hidden_dim // 2, 1)
def forward(
self,
audio : torch.Tensor, # [batch, 64000]
lpe_features : torch.Tensor # [batch, 6]
) -> Dict[str, torch.Tensor]:
"""
Args:
audio : raw waveform, 4 seconds at 16kHz
lpe_features : 6 linguistic position features per window
Returns dict with:
sld_logit : [batch] raw score for SLD detection
od_logit : [batch] raw score for OD detection
embedding : [batch, 64] shared representation
"""
# Branch A
acoustic_emb = self.acoustic_encoder(audio) # [batch, 256]
# Branch B
lpe_emb = self.lpe_module(lpe_features) # [batch, 32]
# Fuse
fused = torch.cat([acoustic_emb, lpe_emb], dim=-1) # [batch, 288]
shared = self.shared_mlp(fused) # [batch, 64]
return {
"sld_logit" : self.sld_head(shared).squeeze(-1), # [batch]
"od_logit" : self.od_head(shared).squeeze(-1), # [batch]
"embedding" : shared # [batch, 64]
}
@torch.no_grad()
def predict_recording(self, window_outputs: list) -> Dict:
"""
Aggregates window-level predictions into recording-level diagnosis.
Called during inference on a full recording.
Args:
window_outputs : list of dicts from forward(), one per window
Returns:
sld_rate : float SLD rate % (clinical diagnostic metric)
has_stutter : bool True if SLD rate > 3%
sld_timeline: list per-window SLD probability (for visualization)
"""
sld_probs = torch.sigmoid(
torch.stack([o["sld_logit"] for o in window_outputs])
)
od_probs = torch.sigmoid(
torch.stack([o["od_logit"] for o in window_outputs])
)
sld_rate = (sld_probs > 0.5).float().mean().item() * 100.0
return {
"sld_rate" : round(sld_rate, 2),
"has_stutter" : sld_rate > 3.0,
"mean_sld_prob": round(sld_probs.mean().item(), 4),
"mean_od_prob" : round(od_probs.mean().item(), 4),
"sld_timeline" : sld_probs.cpu().tolist()
}
def count_params(self):
trainable = sum(p.numel() for p in self.parameters() if p.requires_grad)
total = sum(p.numel() for p in self.parameters())
print(f"ChildFluencyNet")
print(f" Trainable : {trainable:,}")
print(f" Total : {total:,}")
print(f" Frozen : {total - trainable:,}") |