Datasets:
language:
- ru
tags:
- audio
- speech
- anti-spoofing
- audio-deepfake-detection
- tts
task_categories:
- audio-classification
pretty_name: RuASD
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
RuASD: Russian Anti-Spoofing Dataset
RuASD is a public Russian-language speech anti-spoofing dataset designed for developing and benchmarking audio deepfake detection systems. It combines spoofed utterances generated by 37 Russian-capable speech synthesis systems with bona fide recordings curated from multiple heterogeneous Russian speech corpora. In addition to clean audio, the dataset supports robustness-oriented evaluation through reproducible perturbations such as reverberation, additive noise, and codec-based channel degradation.
Models: ESpeech, F5-TTS, VITS, Piper, TeraTTS, MMS TTS, VITS2, GPT-SoVITS, CoquiTTS, XTSS, Fastpitch, RussianFastSpeech, Bark, GradTTS, FishTTS, Pyttsx3, RHVoice, Silero, Fairseq Transformer, SpeechT5, Vosk-TTS, EdgeTTS, VK Cloud, SaluteSpeech, ElevenLabs
Overview
- Purpose: Benchmark and develop Russian-language anti-spoofing and audio deepfake detection systems, with a focus on robustness to realistic channel and post-processing distortions.
- Content: Bona fide speech from multiple open Russian speech corpora and synthetic speech generated by 37 Russian-capable TTS and voice-cloning systems.
- Structure:
- Audio:
.wavfiles - Metadata: JSON with the fields
sample_id,label,group,subset,augmentation,filename,audio_relpath,source_audio,metadata_source,source_type,mos_pred,noi_pred,dis_pred,col_pred,loud_pred,cer,duration,speakers,model,transcribe,true_lines,transcription,ground_truth, andops.
- Audio:
| Field | Description |
|---|---|
sample_id |
Sample ID |
label |
real or fake |
group |
Sample group - raw or augmented |
subset |
source subset name, e.g. OpenSTT, GOLOS, or ElevenLabs |
augmentation |
Applied augmentation |
filename |
Audio filename |
audio_relpath |
Relative path to audio |
source_audio |
Original audio for augmented sample |
metadata_source |
Metadata source |
source_type |
Source type - tts, real_speech or augmented_audio |
mos_pred |
Predicted MOS |
noi_pred |
Predicted noisiness |
dis_pred |
Predicted discontinuity |
col_pred |
Predicted coloration |
loud_pred |
Predicted loudness |
cer |
Character error rate |
duration |
Duration in seconds |
speakers |
Speaker info |
model |
specific checkpoint or voice used for generation, e.g. ESpeech-TTS-1_RL-V1, xtts-ru-ipa, or ru-RU-DmitryNeural |
transcribe |
Automatic transcription |
true_lines |
Source text |
transcription |
Automatic transcription |
ground_truth |
Reference text |
ops |
Processing operations |
Statistics
- Number of TTS systems: 37
- Total spoof hours: 691.68
- Total bona-fide hours: 234.07
Table 4. Antispoofing models on clean data
| Model | Acc | Pr | Rec | F1 | RAUC | EER | t-DCF |
|---|---|---|---|---|---|---|---|
| AASIST3 | 0.769±0.0006 | 0.683±0.001 | 0.769±0.0006 | 0.724±0.001 | 0.841±0.0006 | 0.231±0.0006 | 0.702±0.002 |
| Arena-1B | 0.812±0.001 | 0.736±0.001 | 0.812±0.001 | 0.772±0.001 | 0.887±0.0005 | 0.188±0.001 | 0.385±0.001 |
| Arena-500M | 0.801±0.001 | 0.722±0.001 | 0.801±0.001 | 0.760±0.001 | 0.864±0.0005 | 0.199±0.001 | 0.655±0.002 |
| Nes2Net | 0.689±0.0007 | 0.589±0.001 | 0.689±0.0007 | 0.634±0.0008 | 0.779±0.0007 | 0.311±0.0007 | 0.696±0.001 |
| Res2TCNGaurd | 0.627±0.001 | 0.520±0.001 | 0.627±0.001 | 0.569±0.001 | 0.691±0.001 | 0.373±0.001 | 0.918±0.001 |
| ResCapsGuard | 0.677±0.001 | 0.575±0.001 | 0.677±0.001 | 0.622±0.001 | 0.718±0.001 | 0.323±0.001 | 0.896±0.001 |
| SLS with XLS-R | 0.779±0.001 | 0.700±0.001 | 0.779±0.001 | 0.737±0.001 | 0.859±0.001 | 0.221±0.001 | 0.650±0.001 |
| Wav2Vec 2.0 | 0.772±0.0006 | 0.687±0.001 | 0.772±0.0006 | 0.727±0.001 | 0.850±0.0006 | 0.228±0.0006 | 0.558±0.002 |
| TCM-ADD | 0.857±0.001 | 0.797±0.001 | 0.859±0.001 | 0.827±0.001 | 0.914±0.0004 | 0.143±0.001 | 0.424±0.001 |
| Spectra-0 | 0.962 | 0.942 | 0.962 | 0.952 | 0.985 | 0.038 | 0.124 |
Download
Using Datasets
from datasets import load_dataset
ds = load_dataset("MTUCI/RuASD")
print(ds)
Using Datasets with streaming mode
from datasets import load_dataset
ds = load_dataset("MTUCI/RuASD", streaming=True)
small_ds = ds.take(1000)
print(small_ds)
Contact
- Email: k.n.borodin@mtuci.ru
- Telegram channel: https://t.me/korallll_ai
Citation
@unpublished{ruasd2026,
author = {},
title = {},
year = {}
}