lab260/RuASD

---
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:** `.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](https://huggingface.co/MTUCI/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](https://huggingface.co/Speech-Arena-2025/DF_Arena_1B_V_1) | 0.812±0.001 | 0.736±0.001 | 0.812±0.001 | 0.772±0.001 | 0.887±0.0005 | 0.188±0.001 | <u>0.385±0.001</u> |
| [Arena-500M](https://huggingface.co/Speech-Arena-2025/DF_Arena_500M_V_1) | 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](https://github.com/Liu-Tianchi/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](https://github.com/mtuciru/Res2TCNGuard) | 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](https://github.com/mtuciru/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](https://github.com/QiShanZhang/SLSforASVspoof-2021-DF) | 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](https://github.com/TakHemlata/SSL_Anti-spoofing) | 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](https://github.com/ductuantruong/tcm_add) | <u>0.857±0.001</u> | <u>0.797±0.001</u> | <u>0.859±0.001</u> | <u>0.827±0.001</u> | <u>0.914±0.0004</u> | <u>0.143±0.001</u> | 0.424±0.001 |
| [Spectra-0](https://huggingface.co/MTUCI/spectra_0) | **0.962** | **0.942** | **0.962** | **0.952** | **0.985** | **0.038** | **0.124** |

# Download

## Using Datasets

```python
from datasets import load_dataset

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

## Using Datasets with streaming mode

```python
from datasets import load_dataset

ds = load_dataset("MTUCI/RuASD", streaming=True)
small_ds = ds.take(1000)

print(small_ds)
```

# Contact

- **Email:** [[e-mail]](mailto:[e-mail])
- **Telegram channel:** [https://t.me/korallll_ai](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/ |