---
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:** [k.n.borodin@mtuci.ru](mailto:k.n.borodin@mtuci.ru)
- **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/                                             |