metadata
library_name: pytorch
tags:
- audio
- spoofing-detection
- anti-spoofing
- wav2vec2
- aasist
license: apache-2.0
pipeline_tag: audio-classification
model-index:
- name: spectra_aasist
results:
- task:
type: Speech Antispoofing
dataset:
name: ASVspoof19_LA
type: ASVspoof19_LA
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 0.159
- task:
type: Speech Antispoofing
dataset:
name: ASVspoof21_LA
type: ASVspoof21_LA
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 5.164
- task:
type: Speech Antispoofing
dataset:
name: ASVspoof21_DF
type: ASVspoof21_DF
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 2.568
- task:
type: Speech Antispoofing
dataset:
name: ASVspoof5
type: ASVspoof5
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 14.056
- task:
type: Speech Antispoofing
dataset:
name: ADD2022
type: ADD2022
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 15.205
- task:
type: Speech Antispoofing
dataset:
name: In-the-Wild
type: In-the-Wild
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 1.461
- task:
type: Speech Antispoofing
dataset:
name: AD2R1
type: AD2R1
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 0.939
- task:
type: Speech Antispoofing
dataset:
name: AD2R2
type: AD2R2
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 1.802
- task:
type: Speech Antispoofing
dataset:
name: AD3R1
type: AD3R1
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 6.502
- task:
type: Speech Antispoofing
dataset:
name: AD3R2
type: AD3R2
metrics:
- name: Equal Error Rate
type: Equal Error Rate
value: 14.481
Model Card: Spectra-AASIST (anti-spoofing / bonafide vs spoof)
Spectra-AASIST is a model for speech spoofing detection (binary classification: bonafide vs spoof) from raw audio waveforms. Architecture: SSL encoder (Wav2Vec2) → MLP projection → AASIST 2-class classifier.
- Input: waveform (float32), shape
(batch, num_samples)(typically 16 kHz). - Output: logits of shape
(batch, 2), where index 0 = spoof, index 1 = bonafide.
On first run, the model will automatically download the SSL encoder facebook/wav2vec2-xls-r-300m via transformers.
Evaluation Results
| Model | ASVspoof19 LA | ASVspoof21 LA | ASVspoof21 DF | ASVspoof5 | ADD2022 | In-the-Wild | AD2R1 | AD2R2 | AD3R1 | AD2R2 |
|---|---|---|---|---|---|---|---|---|---|---|
| Res2TCNGuard | 7.487 | 19.130 | 19.883 | 37.620 | 49.538 | 49.246 | 34.683 | 35.343 | 48.051 | 39.558 |
| AASIST3 | 27.585 | 37.407 | 33.099 | 41.001 | 47.192 | 39.626 | 36.581 | 37.351 | 41.333 | 44.278 |
| XSLS | 0.231 | 7.714 | 4.220 | 17.688 | 33.951 | 7.453 | 14.386 | 15.743 | 19.368 | 21.095 |
| TCM-ADD | 0.152 | 6.655 | 3.444 | 19.505 | 35.252 | 7.767 | 16.951 | 17.688 | 21.913 | 18.627 |
| DF Arena 1B | 43.793 | 40.137 | 42.994 | 35.333 | 42.139 | 17.598 | 12.442 | 13.292 | 33.381 | 43.42 |
| Spectra-0 | 0.181 | 6.475 | 5.410 | 14.426 | 14.716 | 1.026 | 1.578 | 2.372 | 6.535 | 15.154 |
| Spectra-AASIST | 0.159 | 5.164 | 2.568 | 14.056 | 15.205 | 1.461 | 0.939 | 1.802 | 6.427 | 12.968 |
| Spectra-AASIST3 | 0.723 | 4.506 | 1.998 | 13.82 | 15.187 | 0.961 | 0.727 | 1.806 | 6.502 | 14.481 |
Quickstart
Clone from Hugging Face
This repository is hosted on Hugging Face Hub: https://huggingface.co/lab260/spectra_aasist.
git lfs install
git clone https://huggingface.co/lab260/spectra_aasist
cd spectra_aasist
Install dependencies
pip install -U torch torchaudio transformers huggingface_hub safetensors soundfile
Single-file inference (example preprocessing)
import random
import torch
import torchaudio
import soundfile as sf
from model import spectra_aasist
def pad_random(x: torch.Tensor, max_len: int = 64600) -> torch.Tensor:
# x: (num_samples,) or (1, num_samples)
if x.ndim > 1:
x = x.squeeze()
x_len = x.shape[0]
if x_len >= max_len:
start = random.randint(0, x_len - max_len)
return x[start:start + max_len]
num_repeats = int(max_len / x_len) + 1
return x.repeat(num_repeats)[:max_len]
def load_audio_mono(path: str) -> torch.Tensor:
audio, sr = sf.read(path, dtype="float32")
audio = torch.from_numpy(audio)
if audio.ndim > 1:
# (num_samples, channels) -> mono
audio = audio.mean(dim=1)
if sr != 16000:
audio = torchaudio.functional.resample(audio, sr, 16000)
return audio
device = "cuda" if torch.cuda.is_available() else "cpu"
model = spectra_aasist.from_pretrained(pretrained_model_name_or_path=".").eval().to(device)
audio = load_audio_mono("path/to/audio.wav")
audio = torchaudio.functional.preemphasis(audio.unsqueeze(0)) # (1, T)
audio = pad_random(audio.squeeze(0), 64600).unsqueeze(0) # (1, 64600)
with torch.inference_mode():
logits = model(audio.to(device)) # (1, 2)
score_spoof = logits[0, 0].item()
score_bonafide = logits[0, 1].item()
print({"score_bonafide": score_bonafide, "score_spoof": score_spoof})
Threshold-based classification (and how to tune it)
In model.py, the SpectraAASIST class provides classify() with a default threshold chosen as an “optimal” value for the original setting:
- Default threshold:
-1.140625(it thresholdslogit_bonafide = logits[:, 1]) - Note: this threshold may not be optimal on a different dataset/domain. It’s recommended to tune the threshold on your dataset using EER (Equal Error Rate) or a target FAR/FRR.
Example:
with torch.inference_mode():
pred = model.classify(audio.to(device), threshold=-1.140625) # 1=bonafide, 0=spoof
Tuning the threshold via EER (typical workflow)
Run the model on a labeled set and collect scores for both classes.
Compute EER and the threshold
Limitations and notes
- This is a pre-release model.
- Significantly stronger models are planned for Q3–Q4 2026 — stay tuned.
License
MIT (see the license field in the model repo header).
Contacts
TG channel: https://t.me/korallll_ai email: k.n.borodin@mtuci.ru website: https://lab260.ru/