Datasets:
Add Whisper-MFCC-MesoNet scores for DECRO
#2
by korallll - opened
submissions/whisper-mfcc-mesonet.yaml
ADDED
|
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
schema_version: 4
|
| 2 |
+
system:
|
| 3 |
+
name: Whisper-MFCC-MesoNet
|
| 4 |
+
slug: whisper-mfcc-mesonet
|
| 5 |
+
description: '(Whisper + MFCC) MesoNet anti-spoofing countermeasure: a Whisper tiny.en audio encoder front-end concatenated
|
| 6 |
+
(2 channels) with an MFCC+Δ+ΔΔ front-end, feeding a MesoInception4 classifier. Whisper encoder fine-tuned end-to-end.
|
| 7 |
+
FP32. Upstream eval pipeline reproduced: sox silence-trim (silence 1 0.2 1% -1 0.2 1%) then a 30 s (480000-sample) repeat-pad
|
| 8 |
+
window. This is the best MesoNet configuration from the paper.'
|
| 9 |
+
code: https://github.com/piotrkawa/deepfake-whisper-features
|
| 10 |
+
checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/WhisperMFCCMesoNet
|
| 11 |
+
params_millions: 7.660881
|
| 12 |
+
paper:
|
| 13 |
+
arxiv_id: '2306.01428'
|
| 14 |
+
url: https://arxiv.org/abs/2306.01428
|
| 15 |
+
bibtex: "@inproceedings{kawa23b_interspeech,\n title = {Improved DeepFake Detection Using Whisper Features},\n author\
|
| 16 |
+
\ = {Piotr Kawa and Marcin Plata and Micha{\\l} Czuba and Piotr Szyma{\\'n}ski and Piotr Syga},\n year = {2023},\n\
|
| 17 |
+
\ booktitle = {Proc. INTERSPEECH 2023},\n pages = {4009--4013},\n doi = {10.21437/Interspeech.2023-1537},\n\
|
| 18 |
+
}\n"
|
| 19 |
+
dataset:
|
| 20 |
+
id: SpeechAntiSpoofingBenchmarks/DECRO
|
| 21 |
+
revision: bb6df2524eadaab6aa6a2366a41a2a5fe1e4104d
|
| 22 |
+
split: test
|
| 23 |
+
scores:
|
| 24 |
+
eer_percent: 30.18722995679309
|
| 25 |
+
n_trials: 37314
|
| 26 |
+
n_skipped: 0
|
| 27 |
+
artifact:
|
| 28 |
+
scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/WhisperMFCCMesoNet/resolve/225097846ce9b5cd97ffe9dc476c57d2f19af718/.eval_results/SpeechAntiSpoofingBenchmarks/DECRO/scores.txt
|
| 29 |
+
scores_sha256: bff70db0d9d5e478a87742d7488ff34fd16b2c9a69f6e67ce0923eb0519cc1c1
|
| 30 |
+
bench_version: speech-spoof-bench==0.3.4
|
| 31 |
+
reproduction:
|
| 32 |
+
reproduced_by: SpeechAntiSpoofingBenchmarks
|
| 33 |
+
reproduced_at: '2026-06-10'
|
| 34 |
+
reproduced_bench_version: speech-spoof-bench==0.3.4
|
| 35 |
+
match: scoring
|
| 36 |
+
submitter:
|
| 37 |
+
hf_username: korallll
|
| 38 |
+
contact: k.n.borodin@mtuci.ru
|
| 39 |
+
submitted_at: '2026-06-10'
|
| 40 |
+
notes: Fine-tuned Whisper+MFCC MesoNet (best MesoNet config from the paper). Reproduces the paper's In-the-Wild EER (26.72%)
|
| 41 |
+
to within 0.01 pp with the upstream sox silence-trim + 30 s repeat-pad preprocessing.
|