---
language:
- ru
tags:
- audio
- speech
- anti-spoofing
- audio-deepfake-detection
- tts
task_categories:
- audio-classification
pretty_name: RuASD
size_categories:
- 100K<n<1M
---

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`.
# 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       | <u>0.812±0.001</u> | <u>0.736±0.001</u> | <u>0.812±0.001</u> | <u>0.772±0.001</u> | <u>0.887±0.0005</u> | <u>0.188±0.001</u> | **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**    | <u>0.914±0.0004</u> | **0.143±0.001**    | <u>0.424±0.001</u> |

**Table 5. Antispoofing models on augmented data (EER).** **Aug.** denotes the applied degradation: **R** — RIR reverberation, **N** — MUSAN additive noise, and suffixes (_alaw_, _amr_, _g722_, _mp3_, _mlw_, _op16_, _op8_, _spx8_) indicate encode-decode transcoding with the corresponding codec. Combined labels such as **RNmp3** apply **R** + **N** followed by codec transcoding.

| Aug.                             | AAS3  | AR1B         | AR5M         | N2NT  | R2NT         | RCPS         | XSLS  | W2AS         | TCM          |
| -------------------------------- | ----- | ------------ | ------------ | ----- | ------------ | ------------ | ----- | ------------ | ------------ |
| **Codec only**                   |       |              |              |       |              |              |       |              |              |
| alaw                             | 0.468 | 0.331        | **0.237**    | 0.435 | 0.331        | 0.332        | 0.485 | <u>0.242</u> | 0.270        |
| amr                              | 0.373 | **0.133**    | <u>0.147</u> | 0.378 | 0.271        | 0.272        | 0.478 | 0.212        | 0.372        |
| g722                             | 0.279 | 0.264        | <u>0.245</u> | 0.353 | 0.323        | 0.323        | 0.473 | 0.275        | **0.190**    |
| mp3                              | 0.239 | 0.191        | **0.171**    | 0.340 | 0.322        | 0.318        | 0.475 | 0.270        | <u>0.187</u> |
| mlw                              | 0.463 | 0.330        | <u>0.238</u> | 0.449 | 0.333        | 0.325        | 0.478 | **0.234**    | 0.261        |
| op16                             | 0.264 | <u>0.216</u> | **0.176**    | 0.383 | 0.308        | 0.303        | 0.481 | 0.278        | 0.297        |
| op8                              | 0.341 | <u>0.208</u> | **0.205**    | 0.418 | 0.293        | 0.297        | 0.481 | 0.297        | 0.404        |
| spx8                             | 0.372 | **0.141**    | <u>0.147</u> | 0.370 | 0.305        | 0.302        | 0.475 | 0.273        | 0.420        |
| **Noise: N and N+Codec**         |       |              |              |       |              |              |       |              |              |
| N                                | 0.458 | 0.446        | 0.360        | 0.440 | 0.330        | <u>0.310</u> | 0.481 | **0.292**    | **0.292**    |
| Nalaw                            | 0.503 | 0.430        | 0.321        | 0.483 | 0.320        | <u>0.318</u> | 0.498 | **0.291**    | 0.380        |
| Namr                             | 0.441 | <u>0.264</u> | **0.235**    | 0.448 | 0.286        | 0.269        | 0.481 | 0.270        | 0.476        |
| Ng722                            | 0.448 | 0.428        | 0.357        | 0.434 | 0.323        | <u>0.305</u> | 0.479 | **0.296**    | **0.296**    |
| Nmp3                             | 0.448 | 0.372        | **0.291**    | 0.437 | 0.327        | 0.312        | 0.476 | <u>0.296</u> | <u>0.296</u> |
| Nmlw                             | 0.502 | 0.402        | <u>0.304</u> | 0.480 | 0.319        | 0.316        | 0.497 | **0.282**    | 0.373        |
| Nop16                            | 0.420 | 0.384        | <u>0.305</u> | 0.447 | 0.321        | **0.289**    | 0.481 | 0.319        | 0.414        |
| Nop8                             | 0.437 | 0.336        | 0.319        | 0.473 | <u>0.309</u> | **0.301**    | 0.475 | 0.348        | 0.512        |
| Nspx8                            | 0.430 | <u>0.234</u> | **0.217**    | 0.428 | 0.288        | 0.273        | 0.479 | 0.312        | 0.490        |
| **Reverberation: R and R+Codec** |       |              |              |       |              |              |       |              |              |
| R                                | 0.351 | 0.482        | **0.319**    | 0.499 | 0.332        | 0.336        | 0.483 | <u>0.331</u> | 0.456        |
| Ralaw                            | 0.472 | 0.488        | 0.373        | 0.564 | **0.313**    | <u>0.342</u> | 0.457 | 0.347        | 0.420        |
| Ramr                             | 0.444 | 0.404        | **0.288**    | 0.515 | <u>0.334</u> | 0.346        | 0.479 | 0.339        | 0.396        |
| Rg722                            | 0.397 | 0.491        | **0.305**    | 0.500 | 0.337        | 0.348        | 0.489 | <u>0.334</u> | 0.476        |
| Rmp3                             | 0.394 | 0.444        | **0.243**    | 0.515 | <u>0.326</u> | 0.336        | 0.491 | 0.336        | 0.454        |
| Rmlw                             | 0.471 | 0.488        | 0.365        | 0.565 | **0.318**    | 0.346        | 0.468 | <u>0.341</u> | 0.406        |
| Rop16                            | 0.388 | 0.444        | **0.295**    | 0.489 | <u>0.328</u> | 0.329        | 0.491 | 0.366        | 0.494        |
| Rop8                             | 0.410 | 0.421        | **0.308**    | 0.509 | <u>0.321</u> | 0.337        | 0.499 | 0.380        | 0.405        |
| Rspx8                            | 0.454 | 0.400        | **0.292**    | 0.504 | <u>0.316</u> | 0.341        | 0.490 | 0.361        | 0.413        |
| **Combined: RN and RN+Codec**    |       |              |              |       |              |              |       |              |              |
| RN                               | 0.503 | 0.486        | 0.408        | 0.527 | **0.324**    | 0.379        | 0.504 | <u>0.365</u> | 0.511        |
| RNalaw                           | 0.493 | 0.493        | 0.401        | 0.551 | **0.316**    | 0.381        | 0.528 | <u>0.372</u> | 0.422        |
| RNamr                            | 0.473 | 0.478        | <u>0.349</u> | 0.515 | **0.319**    | 0.377        | 0.512 | 0.352        | 0.385        |
| RNg722                           | 0.477 | 0.498        | 0.401        | 0.529 | **0.322**    | 0.384        | 0.504 | <u>0.366</u> | 0.473        |
| RNmp3                            | 0.469 | 0.487        | <u>0.350</u> | 0.530 | **0.332**    | 0.371        | 0.505 | 0.366        | 0.510        |
| RNmlw                            | 0.503 | 0.492        | 0.397        | 0.544 | **0.310**    | <u>0.377</u> | 0.510 | 0.379        | 0.409        |
| RNop16                           | 0.477 | 0.498        | 0.403        | 0.519 | **0.329**    | <u>0.385</u> | 0.506 | 0.400        | 0.427        |
| RNop8                            | 0.493 | 0.496        | 0.405        | 0.510 | **0.324**    | 0.384        | 0.488 | 0.390        | <u>0.379</u> |
| RNspx8                           | 0.477 | 0.449        | <u>0.333</u> | 0.507 | **0.318**    | 0.387        | 0.497 | 0.379        | 0.391        |

# Download

## Using Datasets

```python
from datasets import load_dataset

ds = load_dataset("MTUCI/RuASD")
print(ds)
```

## Git clone

```bash
git lfs install
git clone https://huggingface.co/datasets/MTUCI/RuASD
```

# Contact

- **Email:** [k.n.borodin@mtuci.ru](mailto:k.n.borodin@mtuci.ru)
- **Telegram channel:** [https://t.me/korallll_ai](https://t.me/korallll_ai)