| # DPDA-GA-TCM — implementation notes |
|
|
| Plan: `speech-spoof-bench/docs/plans/2026-10-29-mamba-batch-arena-submission.md` (model #2). |
|
|
| ## Source |
| - Code: github.com/ductuantruong/dpda_ga @ `aab89614ee90252e10e5df35ec6aaa5ee2e179a7` (MIT, `LICENSE.source`): |
| `nets/conformer.py` + `nets/conformer_tcm.py` → `_net.py` (edits listed in its header). |
| - Weights: HF `tuantd/dpda_ga` @ `531554e0a1c28d6a6ccf22a355281e3d3acefeb5`, |
| `pcgrad/conformer_tcm/best/best_{0..4}.pth` (downloaded by `benchmarks/_ckpt_dl/dl.py`). |
| - Hyper-parameters = `main.py` defaults: emb 144, heads 4, kernel 31, 4 encoders; checkpoint |
| shapes confirm (LL 144×1024, ht_proj 144×36, depthwise conv k=31, encoder_blocks 0..3). |
| - XLS-R 300M architecture built from `xlsr2_300m.pt` cfg (symlink to `../W2V2-AASIST/`) via |
| `merge_with_parent`; base weights not loaded (the checkpoint overwrites all 588 tensors). |
|
|
| ## Checkpoint averaging (`make_avg.py` → `dpda_ga_tcm_avg4.pth`) |
| - `best_4.pth` is truncated on HF: 547,680,256 bytes vs 1,285,107,746 for the others, no ZIP |
| central directory; `torch.load` → "failed finding central directory". Confirmed locally. |
| - `main.py` eval averages `n_average_model` (default 5) n-best checkpoints, but |
| `sd = model.state_dict()` aliases the live parameters, so the following |
| `model.load_state_dict(best_1)` overwrites `sd` in place. Net effect: |
| `(2*b1 + b2 + … + b_{n-1}) / n`; best_0 never contributes. Maintainer decision (2026-10-30): |
| reproduce main.py exactly with the 4 intact files → `(2*best_1 + best_2 + best_3) / 4`, |
| float32, main.py's summation order; verified bit-identical to literally running main.py's |
| loop on a module holding the tensors. Int buffers (num_batches_tracked) get the truncated |
| float quotient, as main.py's copy into a long buffer does (unused at eval). |
| - The paper's numbers come from the 5-file version `(2*b1+b2+b3+b4)/5`, so small deviations |
| from the paper are expected. |
|
|
| ## Wrapper decisions |
| - Input: first 66,800 samples; shorter clips repeat-tiled (`utils.pad`). No resampling/normalisation. |
| - Score = `logits[:, 1]` (label 1 = bonafide in `utils.read_metadata`; `produce_evaluation_file`). |
| - `.to(device)` then `.eval()`; `extract_feat`'s `self.model.train()` removed. |
| - cuDNN TF32 disabled in `load()`: with it on, batched vs single scores differed by up to 2e-4 |
| (XLS-R conv front-end); off → 1.7e-6. 4070 Ti SUPER speed unchanged (121.7 utt/s @24 both ways); |
| 5060 Ti PyTorch drops 47.5 → 22.2 utt/s (bs 4), but that card runs TensorRT. |
| - Params: 321,213,220 (incl. the frozen 10000×144 positional table, 1.44 M). |
|
|
| ## Batch sweep (sweep.py, 66,800-sample random input, 2026-10-30) |
| | card | TF32 off best | TF32 on best | |
| |---|---|---| |
| | RTX 4070 Ti SUPER (idx 2) | 121.7 utt/s @24 | 121.7 @24 | |
| | RTX 5060 Ti (idx 3) | 22.2 @4 | 47.5 @4 | |
| Raw: `logs_sweep_g{2,3}_tf{0,1}.txt`. |
|
|
| ## Sanity (full J-SPAW_LA, PyTorch, RTX 4070 Ti SUPER) |
| - EER 12.90% (2397 trials, 0 skipped), ~110 utt/s. Comparable XLS-R models: DPDA-GA-AASIST 13.65, |
| XLSR-SLS 8.70, W2V2-AASIST 26.17. Not flipped. |
| |
| ## TensorRT (`trt_dpda_ga_tcm.py`, sidecar `trt_dpda-ga-tcm.json`, TRT 10.16) |
| Adapted from XLSR-MamBo's script (config block only). The class-token loop rewrite (`_net.py` |
| edit 4) keeps the batch axis dynamic in the traced ONNX. |
| | card | precision | best batch | utt/s (sweep) | build parity Spearman (64 ITW) | J-SPAW EER | J-SPAW Spearman vs PT / max Δ | |
| |---|---|---|---|---|---|---| |
| | RTX 4070 Ti SUPER (idx 2) | FP32 (FP16 Spearman 0.99963 < 0.9999) | 8 | 173.3 | 1.0000 | 12.8992 (=PT) | 0.999998 / 0.031 | |
| | RTX 5060 Ti (idx 3) | FP16 | 16 | 362.2 | 0.99991 | 12.8992 (=PT) | 0.999917 / 0.287 | |
| Both cards pass (|ΔEER| = 0 ≤ 0.25, Spearman ≥ 0.999). J-SPAW_LA is submitted from PyTorch scores |
| (`results_pt/`); TRT runs in `results_trt/` (idx 2) and `results_trt3/` (idx 3). `cmp_scores.py` |
| does the comparison. The unused 4070 FP16 engine was deleted. |
| |
