JesseHuang922/VoxSentinel_Synthetic_Detection_Dataset

--- license: cc-by-nc-4.0 task_categories: - audio-classification tags: - deepfake-detection - wav2vec2 - speech-security language: - en - ja size_categories: - 10K<n<100K --- # Wav2Vec2-Deepfake-Sentinel-Base-Dataset ## 🛡️ Project Overview This dataset is a core component of the **Wav2Vec2-Deepfake-Sentinel** project. It is specifically designed to train and evaluate robust audio deepfake detection models. By consolidating diverse spoofing techniques and high-quality authentic speech, this collection aims to provide a comprehensive defense mechanism against modern AI-generated voice fraud. ## 📊 Dataset Structure The dataset is provided in four major compressed volumes to ensure integrity and ease of transfer: | Filename | Description | Source/Notes | | :--- | :--- | :--- | | `LA.zip` | Logical Access (LA) subset | From ASVspoof 2019, covering various TTS and VC attacks. | | `Fake_or_Real.zip` | Cross-domain Deepfake data | Mix of authentic human speech and high-fidelity deepfakes. | | `In_The_Wild.zip` | Real-world spoofing scenarios | Audio collected from diverse, noisy, and unconstrained environments. | | `WaveFake.zip` | Multi-generator spoofed speech | **Enhanced Version** (See Data Quality section). | ## 🛠️ Data Quality & Refinement (Crucial Updates) To ensure the highest acoustic fidelity and avoid artifacts present in secondary processed versions, we have performed **Source-Level Data Replacement** within the `WaveFake` directory: 1. **JLSpeech Integration**: The original JLSpeech components in WaveFake have been replaced with the **official, high-quality JLSpeech source data** to ensure consistent sample rates and bit depths. 2. **JSUT (Japanese Speech) Refinement**: Replaced the Japanese subsets with the official **JSUT (Japanese Speech Corpus of Saruwatari Lab)** data, providing a more reliable baseline for multilingual deepfake detection. 3. **Integrity Check**: All audio files have been normalized to a consistent format suitable for Wav2Vec2 fine-tuning (e.g., 16kHz, Mono). ## 🚀 Usage for FinQuest 2026 This dataset is a private asset for the **2026 FinQuest Competition**. ### How to load ```python from datasets import load_dataset # Note: Access is restricted to authorized collaborators. dataset = load_dataset("JesseHuang922/Wav2Vec2-Deepfake-Sentinel-Base-Dataset")