Datasets:
File size: 12,684 Bytes
3b5d4e7 92fbc55 3b5d4e7 002de4f 3b5d4e7 002de4f 3b5d4e7 002de4f 3b5d4e7 002de4f 3b5d4e7 002de4f 92fbc55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | ---
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/ | |