lab260/RuASD
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… See the full description on the dataset page: https://huggingface.co/datasets/lab260/RuASD.
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.
Statistics
- Number of TTS systems: 37
- Total spoof hours: 691.68
- Total bona-fide hours: 234.07
Table 4. Antispoofing models on clean data
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~~ (deprecated — use kborodin.research@gmail.com)
- Telegram channel: https://t.me/korallll_ai
Citation
@unpublished{ruasd2026,
author = {},
title = {},
year = {}
}