Forensics - Speech Deepfake Detection Family
Collection
WavLM/XLS-R + AASIST speech deepfake detectors. Free for personal and research use (CC-BY-NC-4.0) - contact eliya@vocos.io for commercial licensing. • 4 items • Updated • 1
Acknowledge the license to access this model
Requests are reviewed manually.
This model is released under CC-BY-NC-4.0 — free for personal and research use. Commercial use is prohibited under this license; for a commercial license, contact eliya@vocos.io.
Log in or Sign Up to review the conditions and access this model content.
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
| Model | Use it for |
|---|---|
forensics_0.3B_base_deepfake_classifier |
general-purpose default |
forensics_0.3B_xlsr_wild_deepfake_classifier |
uncontrolled / real-world audio |
forensics_0.3B_v2_deepfake_age_gender_classifier 🆕 (this model, gated) |
speaker age/gender, hardened against the newest TTS threats — our latest release |
forensics_0.3B_wavlm_oc_softmax_deepfake_classifier |
tighter bonafide boundary, ensembling |
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.
Built on the same broad detection pipeline as the rest of the family (SpeechFake, DFADD, CodecFake, MD-CommonVoice, ASVspoof2019, EnvSDD, AI-generated music), every row additionally carrying age/gender labels, plus three purpose-built synthetic-voice datasets targeting the newest TTS engines: qwen3, voxcpm, and omnivoice. AdamW, cosine LR schedule, and the full Forensics augmentation stack (codec transcoding, MUSAN noise, RIR, RawBoost, SpecAugment, FreqMask, splice/mix, cross-class splice).
Results
Deepfake-detection EER across the standard external benchmark suite:
| Eval set | EER % |
|---|---|
| Val (held-out) | 1.11 |
| MLAAD (v7) | 0.71 |
| CodecFake | 0.81 |
| DFADD | 0.00 |
| MD-CommonVoice | 0.35 |
| In-the-Wild | 2.20 |
| ASVspoof2019-LA | 0.85 |
| ASVspoof2021-LA | 3.49 |
| ASVspoof2024 | 14.09 |
| ADD2022-Track1 | 16.86 |
| ADD2022-Track3 | 2.88 |
| ADD2023-Round1 | 5.53 |
| ADD2023-Round2 | 11.60 |
| LibriSeVoc | 0.08 |
| SONAR | 1.14 |
| Avg (all sets) | 4.11 |
| Avg (external only) | 4.33 |
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
| file | purpose |
|---|---|
checkpoint_epoch_9.safetensors |
model weights, safe format |
checkpoint_epoch_9.pt |
model weights, legacy pickle |
config.json |
minimal architecture metadata (also used by the Hub to track downloads) |
inference.py |
run script — prefers the .safetensors file automatically |
model.py |
architecture |
requirements.txt |
deps |
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. For commercial use, contact eliya@vocos.io.
- Downloads last month
- -
Model tree for eliya/forensics_0.3B_v2_deepfake_age_gender_classifier
Base model
microsoft/wavlm-largeCollection including eliya/forensics_0.3B_v2_deepfake_age_gender_classifier
Papers for eliya/forensics_0.3B_v2_deepfake_age_gender_classifier
SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods
Paper • 2507.21463 • Published
Temporal Variability and Multi-Viewed Self-Supervised Representations to Tackle the ASVspoof5 Deepfake Challenge
Paper • 2408.06922 • Published
Speech-based Age and Gender Prediction with Transformers
Paper • 2306.16962 • Published
RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing
Paper • 2111.04433 • Published
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Paper • 2110.13900 • Published • 3