Remove model card for now

README.md

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----
-license: mit
-library_name: sourcetrace
-pipeline_tag: audio-classification
-tags:
-  - audio
-  - audio-deepfake-detection
-  - source-tracing
-  - open-set-recognition
-  - speech
-  - mlaad
-  - stopa
-base_model:
-  - microsoft/wavlm-large
-  - facebook/encodec_24khz
-datasets:
-  - mueller91/MLAAD
-language:
-  - en
----
-
-# Codec-Residual Open-Set Audio Deepfake Source Tracing
-
-Fitted model checkpoint for **open-set audio deepfake source tracing** — attributing a
-synthetic utterance to the **generator** (acoustic model × vocoder) that produced it, and
-**rejecting** generators unseen in training. Evaluated on **MLAAD v5** (source tracing) and
-**STOPA** (speaker/attack tracing).
-
-- **Code / reproduction:** https://github.com/pujariaditya/ood_source_tracing
-- **Checkpoint:** `mlaad_v5.pt` — the trained factorized gated head (frozen WavLM-Large front-end
-  is loaded separately from `microsoft/wavlm-large`; not stored here).
-
-## Results (this checkpoint, vs published SOTA)
-
-| Benchmark | Metric | This model | SOTA |
-|-----------|--------|-----------|------|
-| MLAAD v5 | FPR95 | **0.75%** | 3.36% |
-| MLAAD v5 | OOD-EER | 2.81% | — |
-| STOPA | unknown-attack EER | **9.38%** | 16.43% |
-
-SOTA: MLAAD v5 FPR95 = PANDA / Neamtu-2026 (arXiv 2606.10758); STOPA unknown-attack EER =
-Firc/Chhibber (arXiv 2509.24674). Numbers are at the paper's protocol scale.
-
-## Method
-
-Frozen **WavLM-Large** (layers 4-7, mean|std pooling) + a classical **phase/modulation
-signature** channel + a novel **neural codec-reconstruction-residual** channel (EnCodec-24kHz
-at 3 bandwidths). These feed a **factorized gated embedding head** (acoustic-model / vocoder /
-codec sub-spaces with AM-driven FiLM gates). Open-set scoring fuses a per-class **conformal
-margin** with a **relative-Mahalanobis** density (with language-covariance inflation). See the
-GitHub repo's `docs/METHOD.md` for details.
-
-## Usage
-
-```bash
-git clone https://github.com/pujariaditya/ood_source_tracing
-cd ood_source_tracing
-pip install -e .
-
-# fetch this checkpoint
-hf download RootAccess4Life/ood-source-tracing mlaad_v5.pt --local-dir checkpoints
-
-# evaluate (reproduces v5-FPR 0.75)
-python scripts/evaluate.py --task mlaad_v5 --checkpoint checkpoints/mlaad_v5.pt
-```
-
-Programmatic load:
-
-```python
-from sourcetrace.method import Method
-model = Method.load("mlaad_v5.pt")
-scores = model.score_openset(features)   # higher = more likely a known generator
-```
-
-The checkpoint is a `torch.save` dict produced by `Method.save` (trained head weights + the
-conformal calibration, relative-Mahalanobis density, PCA-whiten transfer, and codec
-normalization statistics). It can also be regenerated from scratch: `python scripts/train.py
---task mlaad_v5 --seed 0`.
-
-## Base models & data
-
-- Front-end: [`microsoft/wavlm-large`](https://huggingface.co/microsoft/wavlm-large)
-- Codec channel: [`facebook/encodec_24khz`](https://huggingface.co/facebook/encodec_24khz)
-- Training data: [MLAAD](https://huggingface.co/datasets/mueller91/MLAAD) (v5, PANDA
-  family-level split) and [STOPA](https://doi.org/10.5281/zenodo.15606628).
-
-## License
-
-MIT (see the GitHub repository). Base-model and dataset licenses are held by their respective
-providers.