Codecfake-W2V2-AASIST-Codec — implementation notes

Codec-trained W2V2-AASIST of the Codecfake paper (Xie et al., arXiv 2405.04880), plan model #8, built 2026-11-01. Everything except the checkpoint is identical to ../Codecfake-W2V2-AASIST-CoTrain/ (same _net.py copy; see that dir's notes for the port, XLS-R truncation bit-identity proof, allow-list unpickler, score definition, TF32-off). Helper scripts (run_sets.sh, trt_export_both.sh, trt_jspaw.sh, parity.py) live in the CoTrain dir and cover both models.

Code: xieyuankun/Codecfake @ 72c32e2840eef95fbeade349425fa9c5391fa11e, no LICENSE → linked, not re-hosted.
Checkpoint: pretrained_model/codec_w2v2aasist/anti-spoofing_feat_model.pt (sha256 5a606d1f…c25a3), trained on Codecfake train only; fetched at load time from the pinned raw GitHub URL → head_codec_w2v2aasist.safetensors. Strict load 245 keys; pickled temperatures/pool ratios identical to the port.
Params 76,554,634. Score = logits[:,0] − logits[:,1] (class 0 = bona fide). trained_on: none.

Reproduction vs authors' `result/ITW_result.txt` (no 19LA file shipped for this model)

Full InTheWild (31 779): EER ours 9.274% vs authors 9.609%; Pearson (logit) 0.9989, Spearman 0.9990, median |Δ logit| 0.044 — the same 16-bit-FLAC packaging residual measured for the CoTrain sibling (float source wavs: median |Δ| 0.0035).

Throughput / TRT (2026-11-01, shared GPUs)

PyTorch: same arch as CoTrain (288 / 104 utt/s on 4070 Ti SUPER / 5060 Ti). TRT sidecar trt_codecfake-w2v2-aasist-codec.json:

card	precision	best batch	utt/s	build Spearman	J-SPAW_LA EER PT → TRT	Spearman vs PT
RTX 4070 Ti SUPER	FP32 (FP16 0.9997 < 0.9999)	32	395	1.0000	0.1252 → 0.1252	0.999999
RTX 5060 Ti	FP16 (0.99995)	16	784	0.99995	0.1252 → 0.1252	0.99996
Both accepted.

Codecfake-W2V2-AASIST-Codec — implementation notes

Reproduction vs authors' result/ITW_result.txt (no 19LA file shipped for this model)

Throughput / TRT (2026-11-01, shared GPUs)

Reproduction vs authors' `result/ITW_result.txt` (no 19LA file shipped for this model)