forensics_0.3B_v2_deepfake_age_gender_classifier

Deepfake detection + speaker age & gender - hardened against the newest open-source TTS systems

🔒 Gated — access requires approval. Latest release — Q3 2026.

The newest, most advanced model in the Forensics family. Purpose-built and tested against the latest open-source TTS/voice-synthesis engines — qwen3, voxcpm, and omnivoice — plus AI-generated music as an additional adversarial signal, this model achieves state-of-the-art results against the latest open-source TTS models: 99%+ accuracy on the held-out test set of each. This is the sharpest edge of the Forensics family against next-generation synthetic voice.

On top of real/fake detection, it estimates the speaker's age and gender in the same forward pass — one model, three outputs.

Access to this repo requires manual approval. Submit a request from the repo page and it'll be reviewed directly.

• Backbone: microsoft/wavlm-large (~300M params, fully unfrozen)
• Pooling: AASIST graph-attention
• Extra heads: age (regression) + gender (female / male / child)

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.

Multi-task objective: cross-entropy + OC-Softmax + supervised contrastive for deepfake detection, plus a regression loss for age and a classification loss for gender — all trained jointly.

Trained on 5M+ speech samples spanning a mix of proprietary and publicly available data — some of our own self-created data has been open-sourced as well. Built for robustness against a wide range of TTS generation methods — including some of the most advanced synthesis techniques available today, plus the newest TTS engines (qwen3, voxcpm, omnivoice). Also trained on environmental sound and music, extending fake-detection beyond speech to those domains as well. Every row additionally carries age/gender labels. AdamW, cosine LR schedule, and the full Forensics augmentation stack (codec transcoding, noise augmentation, RIR, RawBoost, SpecAugment, FreqMask, splice/mix, cross-class splice).

Results

Deepfake-detection EER across the standard external benchmark suite:

On the three newest-TTS test sets (qwen3, voxcpm, omnivoice) — the threats this model was specifically hardened against — accuracy exceeds 99%. Age/gender evaluation plots are included alongside the model weights.

Files in this repo

Setup

pip install -r requirements.txt   # torch, torchaudio, transformers, safetensors
hf download eliya/forensics_0.3B_v2_deepfake_age_gender_classifier --local-dir .

(This is a gated repo — you'll need an approved access request before the download works.)

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
age_years: <n>
gender: female | male | child  (conf ..)

Example

$ python inference.py real_human.wav
fake_probability: 0.1022
bonafide_score:   0.8978
verdict: REAL

$ python inference.py designed_tts_voice.wav
fake_probability: 0.8646
bonafide_score:   0.1354
verdict: FAKE
age_years: 34
gender: male  (conf 0.98)

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
• Age/gender head design (age regression scaled to 0-100 years, 3-class female/male/child gender): Burkhardt et al., 2023, "Speech-based Age and Gender Prediction with Transformers", arXiv:2306.16962
• CCC loss (age regression): Lin, 1989, "A Concordance Correlation Coefficient to Evaluate Reproducibility", Biometrics
• 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.

Citation

If you use this in research, evaluation, or training, please acknowledge it:

@misc{segev2026forensicsv2,
  author       = {Segev, Eliya},
  title        = {Forensics 0.3B v2: Speech Deepfake Detection + Age/Gender Classifier},
  year         = {2026}
}