MTUCI/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: .wav files
  • 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, and ops.

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 = {}
}

TTS and VC models

Model	Link
Espeech Podcaster	https://hf.co/ESpeech/ESpeech-TTS-1_podcaster
Espeech RL-V1	https://hf.co/ESpeech/ESpeech-TTS-1_RL-V1
Espeech RL-V2	https://hf.co/ESpeech/ESpeech-TTS-1_RL-V1
Espeech SFT-95k	https://hf.co/ESpeech/ESpeech-TTS-1_SFT-95K
Espeech SFT-256k	https://hf.co/ESpeech/ESpeech-TTS-1_SFT-256K
F5-TTS checkpoint	https://hf.co/Misha24-10/F5-TTS_RUSSIAN
F5-TTS checkpoint	https://hf.co/hotstone228/F5-TTS-Russian
VITS checkpoint	https://hf.co/joefox/tts_vits_ru_hf
PiperTTS	https://github.com/rhasspy/piper
TeraTTS-natasha	https://hf.co/TeraTTS/natasha-g2p-vits
TeraTTS-girl_nice	https://hf.co/TeraTTS/girl_nice-g2p-vits
TeraTTS-glados	https://hf.co/TeraTTS/glados-g2p-vits
TeraTTS-glados2	https://hf.co/TeraTTS/glados2-g2p-vits
MMS	https://hf.co/facebook/mms-tts-rus
VITS checkpoint	https://hf.co/utrobinmv/tts_ru_free_hf_vits_low_multispeaker
VITS checkpoint	https://hf.co/utrobinmv/tts_ru_free_hf_vits_high_multispeaker
VITS2 checkpoint	https://hf.co/frappuccino/vits2_ru_natasha
GPT-SoVITS checkpoint	https://hf.co/alphacep/vosk-tts-ru-gpt-sovits
CoquiTTS	https://hf.co/coqui/XTTS-v2
XTTS checkpoint	https://hf.co/NeuroDonu/RU-XTTS-DonuModel
XTTS checkpoint	https://hf.co/omogr/xtts-ru-ipa
Fastpitch IPA	https://hf.co/bene-ges/tts_ru_ipa_fastpitch_ruslan
Fastpitch BERT g2p	https://hf.co/bene-ges/ru_g2p_ipa_bert_large
RussianFastPitch	https://github.com/safonovanastya/RussianFastPitch
Bark	https://hf.co/suno/bark-small
GradTTS	https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS
FishTTS	https://hf.co/fishaudio/fish-speech-1.5
Pyttsx3	https://github.com/nateshmbhat/pyttsx3
RHVoice	https://github.com/RHVoice/RHVoice
Silero	https://github.com/snakers4/silero-models
Fairseq Transformer	https://hf.co/facebook/tts_transformer-ru-cv7_css10
SpeechT5	https://hf.co/voxxer/speecht5_finetuned_commonvoice_ru_translit
Vosk-TTS	https://github.com/alphacep/vosk-tts
EdgeTTS	https://github.com/rany2/edge-tts
VK Cloud	https://cloud.vk.com/
SaluteSpeech	https://developers.sber.ru/portal/products/smartspeech
ElevenLabs	https://elevenlabs.io/