eliya / forensics_0.3B_base_deepfake_classifier

forensics_0.3B_base_deepfake_classifier

The default speech deepfake detector of the Forensics family. WavLM-large + AASIST graph-attention, fully fine-tuned end-to-end (no frozen shortcuts) across a wide multi-source mix of TTS spoofs, voice conversion, codec artifacts, and the standard anti-spoofing benchmark suite. A combined cross-entropy + OC-Softmax + supervised-contrastive objective gives it a decision boundary that holds up well outside its own training distribution — not just on the data it saw.

Feed it 5 seconds of audio, get back a calibrated real/fake probability. Sub-1% EER on held-out data, and under 2% across most of a 14-benchmark external sweep (ASVspoof, ADD, In-the-Wild, LibriSeVoc, SONAR, and more).

• Backbone: microsoft/wavlm-large (~300M params, fully unfrozen)
• Pooling: AASIST graph-attention

Part of the Forensics family

Full family: huggingface.co/collections/eliya/forensics-speech-deepfake-detection-family

Training

Trained using an agentic training loop — see eliyasegev/autotrain .

Fine-tuned with a combined loss for robustness beyond any single objective:

• Cross-entropy (label-smoothed)
• OC-Softmax — pulls bonafide speech into a compact embedding sphere and pushes every spoof type outside it
• Supervised contrastive, real-anchor-only

Trained across a wide net of public, free-to-use research sources: SpeechFake, MD-CommonVoice, DFADD, CodecFake, ASVspoof2019-LA, EnvSDD. 5-second crops, AdamW, cosine LR schedule, and a heavy augmentation stack — codec transcoding (mp3/aac/opus/vorbis/µ-law/A-law/GSM), MUSAN noise, RIR, RawBoost, SpecAugment, FreqMask, splice/mix, and cross-class splice — so the model sees more distortion during training than it will ever encounter in the wild.

Results

Consistently sub-2% EER across almost every external benchmark, with strong results even on the harder ADD/ASVspoof2024 tracks.

Files in this repo

Setup

pip install -r requirements.txt   # torch, torchaudio, transformers, safetensors
hf download eliya/forensics_0.3B_base_deepfake_classifier --local-dir .

Run

python inference.py <audio.wav>

(Optionally override the checkpoint: python inference.py <audio.wav> <checkpoint.pt> .)

Audio is auto-converted to mono / 16 kHz and trimmed/padded to 5 s.

Output

fake_probability: <0..1>      # threshold is domain-dependent — adjust to your use case; ~0.1-0.2 is usually the best range
bonafide_score:   <0..1>      # raw P(real)
verdict: REAL | FAKE

Example

$ python inference.py real_human.wav
fake_probability: 0.0503
bonafide_score:   0.9497
verdict: REAL

$ python inference.py tts_fake.wav
fake_probability: 0.8641
bonafide_score:   0.1359
verdict: FAKE

Higher fake_probability = more likely a deepfake. Score is 1 − sigmoid(logit) , since the classifier is trained with label 1 = real, 0 = fake .

References

• WavLM: Chen et al., 2022, "WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing" , arXiv:2110.13900
• AASIST: Jung et al., 2021 (ICASSP 2022), "AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks" , arXiv:2110.01200
• OC-Softmax: Zhang et al., 2020 (IEEE SPL 2021), "One-Class Learning Towards Synthetic Voice Spoofing Detection" , arXiv:2010.13995
• Supervised Contrastive Learning: Khosla et al., NeurIPS 2020, arXiv:2004.11362
• RawBoost augmentation: Tak et al., 2021 (ICASSP 2022), arXiv:2111.04433
• FreqMask augmentation: Xie et al., 2024, arXiv:2408.06922
• SpeechFake dataset: ACL 2025, "SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods" , arXiv:2507.21463

License

CC-BY-NC-4.0 — free for personal and research use. For commercial use, contact [email protected] .