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

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"
)

GitHub (code): Codexx121/MultiModal_Scam_Detct
Pipeline: Audio → MFCC → Conv2D Encoder (128-D) + ASR → MiniLM (384-D) → Fusion MLP → SCAM/LEGITIMATE

Codex12/
MultiModal_Scam_Models-Dataset

MultiModal Scam Detection — Models & Dataset

Contents

Download

Via Python

Via CLI

Via Git LFS (advanced)

Usage

Related