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license: mit
library_name: pytorch
tags:
- scam-detection
- multi-modal
- audio-classification
- text-classification
- fusion
- MiniLM
- vosk
---
# MultiModal Scam Detection — Models & Dataset
**Hugging Face asset repository** for the [MultiModal Scam Detection](https://github.com/Codexx121/MultiModal_Scam_Detct) project.
This repo contains **trained model checkpoints, cached features, embeddings, and test audio** — too large for GitHub.
## Contents
| Asset | Size | Description |
|-------|------|-------------|
| `audio_features/` | ~3.3 GB | Pre-computed MFCC features (2407 `.pt` files) for audio encoder training |
| `detection_checkpoints/` | ~1.1 GB | Fine-tuned MiniLM text classifier checkpoints (4 checkpoints) |
| `fusion_embeddings/` | ~14 MB | Pre-extracted audio + text embeddings + fusion dataset (`.npz`) |
| `test_samples/` | ~22 MB | Sample WAV files for testing inference |
## Download
### Via Python
```python
from huggingface_hub import snapshot_download
snapshot_download("Codex12/MultiModal_Scam_Models-Dataset", repo_type="model")
```
### Via CLI
```bash
huggingface-cli download Codex12/MultiModal_Scam_Models-Dataset --repo-type model --local-dir ./assets
```
### Via Git LFS (advanced)
```bash
git lfs install
git clone https://huggingface.co/Codex12/MultiModal_Scam_Models-Dataset
```
## Usage
```python
from huggingface_hub import hf_hub_download
import torch
# Download a checkpoint
checkpoint = hf_hub_download(
"Codex12/MultiModal_Scam_Models-Dataset",
"detection_checkpoints/best_model/model.safetensors",
repo_type="model"
)
# Download audio features
feature_path = hf_hub_download(
"Codex12/MultiModal_Scam_Models-Dataset",
"audio_features/legitimate_00001.pt",
repo_type="model"
)
```
## Related
- **GitHub (code)**: [Codexx121/MultiModal_Scam_Detct](https://github.com/Codexx121/MultiModal_Scam_Detct)
- **Pipeline**: Audio → MFCC → Conv2D Encoder (128-D) + ASR → MiniLM (384-D) → Fusion MLP → SCAM/LEGITIMATE
|