hahascore-cascade

HaHaScore Cascade v8 — Sentence-Level Humor Strength Predictor

A multimodal sentence-level humor strength predictor (0–100 continuous score) that fuses text + audio through a Cascade Gate architecture, with Reddit pretraining for humor-style priors.

🎯 Quick Start

# Option 1: Use ONNX model (fast, no PyTorch)
import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("v8_cascade_int8.onnx")
text = np.random.randn(1, 20, 768).astype(np.float32)    # text features
audio = np.random.randn(1, 20, 791).astype(np.float32)  # audio features
cross = np.zeros((1, 20, 16), dtype=np.float32)         # optional cross-modal

scores, conf, gate = session.run(None, {
    'text': text, 'audio': audio, 'cross': cross
})
print(f"Avg score: {scores.mean():.3f}, Max: {scores.max():.3f}")

# Option 2: PyTorch
import torch
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(repo_id="Hayasuki/hahascore-cascade",
                             filename="v8_finetuned.pt")
ckpt = torch.load(ckpt_path, map_location='cpu')

📊 Performance

🏗️ Architecture

┌─────────────────────────────────────────┐
│  Reddit DistilBERT (66M params)         │  ← Continuous funniness pretraining
│  Pretrained on 30K Reddit upvotes       │
└─────────────┬───────────────────────────┘
              │ 768-dim
              ▼
┌─────────────────────────────────────────┐
│  Cascade Gate Fusion v8                 │
│  • text_proj: 768→128                   │
│  • audio_proj: 791→128 (WavLM/Prosody)  │
│  • Multi-scale Attention                │
│  • Cross-Modal Attention (4 heads)      │
│  • Dynamic Gating (text-confidence)     │
│  • BiGRU (2 layers, hidden=128)         │
│  • Sigmoid Score Head                   │
└─────────────┬───────────────────────────┘
              │ (20, 1) per segment
              ▼
       Humor Strength Score (0-1)

📦 Training Pipeline

Stage 1: Data Acquisition (Google Drive, no local overflow)

• 1M Reddit jokes (CC-BY-4.0) — continuous funniness via upvotes
• ColBERT 200K (CC-BY-2.0) — text humor detection pretraining
• 639 standup files with per-segment pseudo-labels

Stage 2: Reddit Pretrain (30K samples)

• DistilBERT (66M params)
• 1 epoch, batch=8, LR=2e-5, max_len=128
• Streaming dataset (never materialized locally)
• Final loss: 0.0177 (started at 0.0673, 74% reduction)

Stage 3: v8 Fine-tune (639 standup files)

• Cascade Gate initialized with Reddit DistilBERT
• 5 epochs, batch=4, LR=1e-4
• Text encoder frozen, audio + gate trained
• Val AUC: 0.802

Stage 4: ONNX Export

• FP32: 0.3 MB, ~3.3ms CPU
• INT8: 2.9 MB, ~3.3ms CPU (recommended)

🔬 Inputs/Outputs

Inputs

• text: float32, shape (batch, 20, 768) — pre-extracted text features
• audio: float32, shape (batch, 20, 791) — WavLM + prosody features
• cross: float32, shape (batch, 20, 16) — optional cross-modal features

Outputs

• scores: float32, shape (batch, 20, 1) — humor strength per segment [0-1]
• text_confidence: float32, shape (batch, 20, 1)
• gate_weight: float32, shape (batch, 20, 1)

📁 Files in this Repository

📚 Citation

@misc{hahascore-cascade-v8,
  author = {Das-rebel},
  title = {HaHaScore Cascade v8: Multimodal Humor Strength Predictor with Reddit Pretraining},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Das-rebel/HaHaScore}},
}

📜 License

MIT License. Reddit data: CC-BY-4.0 (credit r/Jokes).