lab260/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… See the full description on the dataset page: https://huggingface.co/datasets/lab260/RuASD.
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1---2language:3- ru4tags:5- audio6- speech7- anti-spoofing8- audio-deepfake-detection9- tts10task_categories:11- audio-classification12pretty_name: RuASD13size_categories:14- 100K<n<1M15license: cc-by-nc-sa-4.016---17RuASD: Russian Anti-Spoofing Dataset18 19**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.20 21**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, ElevenLabs22 23# Overview24 25- **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.26- **Content:** Bona fide speech from multiple open Russian speech corpora and synthetic speech generated by 37 Russian-capable TTS and voice-cloning systems.27- **Structure:** 28 - **Audio:** `.wav` files29 - **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`.30 31| Field | Description |32| ----------------- | -------------------------------------------------------------------------------------------------------------------- |33| `sample_id` | Sample ID |34| `label` | `real` or `fake` |35| `group` | Sample group - `raw` or `augmented` |36| `subset` | source subset name, e.g. `OpenSTT`, `GOLOS`, or `ElevenLabs` |37| `augmentation` | Applied augmentation |38| `filename` | Audio filename |39| `audio_relpath` | Relative path to audio |40| `source_audio` | Original audio for augmented sample |41| `metadata_source` | Metadata source |42| `source_type` | Source type - `tts`, `real_speech` or `augmented_audio` |43| `mos_pred` | Predicted MOS |44| `noi_pred` | Predicted noisiness |45| `dis_pred` | Predicted discontinuity |46| `col_pred` | Predicted coloration |47| `loud_pred` | Predicted loudness |48| `cer` | Character error rate |49| `duration` | Duration in seconds |50| `speakers` | Speaker info |51| `model` | specific checkpoint or voice used for generation, e.g. `ESpeech-TTS-1_RL-V1`, `xtts-ru-ipa`, or `ru-RU-DmitryNeural` |52| `transcribe` | Automatic transcription |53| `true_lines` | Source text |54| `transcription` | Automatic transcription |55| `ground_truth` | Reference text |56| `ops` | Processing operations |57 58# Statistics59 60- **Number of TTS systems:** 3761- **Total spoof hours:** 691.6862- **Total bona-fide hours:** 234.0763 64Table 4. Antispoofing models on clean data 65 66| Model | Acc | Pr | Rec | F1 | RAUC | EER | t-DCF |67| ------------------------------------------------------------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------- | ------------------ | ------------------ |68| [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 |69| [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> |70| [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 |71| [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 |72| [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 |73| [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 |74| [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 |75| [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 |76| [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 |77| [Spectra-0](https://huggingface.co/MTUCI/spectra_0) | **0.962** | **0.942** | **0.962** | **0.952** | **0.985** | **0.038** | **0.124** |78 79 80# Download81 82## Using Datasets83 84```python85from datasets import load_dataset86 87ds = load_dataset("MTUCI/RuASD")88print(ds)89```90 91## Using Datasets with streaming mode92 93```python94from datasets import load_dataset95 96ds = load_dataset("MTUCI/RuASD", streaming=True)97small_ds = ds.take(1000)98 99print(small_ds)100```101 102# Contact103 104- **Email:** ~~[k.n.borodin@mtuci.ru](mailto:k.n.borodin@mtuci.ru)~~ (deprecated — use [kborodin.research@gmail.com](mailto:kborodin.research@gmail.com))105- **Telegram channel:** [https://t.me/korallll_ai](https://t.me/korallll_ai)106 107# Citation108 109```110@unpublished{ruasd2026,111 author = {},112 title = {},113 year = {}114}115```116 117 118# TTS and VC models119 120| Model | Link |121| --------------------- | -------------------------------------------------------------------------- |122| Espeech Podcaster | https://hf.co/ESpeech/ESpeech-TTS-1_podcaster |123| Espeech RL-V1 | https://hf.co/ESpeech/ESpeech-TTS-1_RL-V1 |124| Espeech RL-V2 | https://hf.co/ESpeech/ESpeech-TTS-1_RL-V1 |125| Espeech SFT-95k | https://hf.co/ESpeech/ESpeech-TTS-1_SFT-95K |126| Espeech SFT-256k | https://hf.co/ESpeech/ESpeech-TTS-1_SFT-256K |127| F5-TTS checkpoint | https://hf.co/Misha24-10/F5-TTS_RUSSIAN |128| F5-TTS checkpoint | https://hf.co/hotstone228/F5-TTS-Russian |129| VITS checkpoint | https://hf.co/joefox/tts_vits_ru_hf |130| PiperTTS | https://github.com/rhasspy/piper |131| TeraTTS-natasha | https://hf.co/TeraTTS/natasha-g2p-vits |132| TeraTTS-girl_nice | https://hf.co/TeraTTS/girl_nice-g2p-vits |133| TeraTTS-glados | https://hf.co/TeraTTS/glados-g2p-vits |134| TeraTTS-glados2 | https://hf.co/TeraTTS/glados2-g2p-vits |135| MMS | https://hf.co/facebook/mms-tts-rus |136| VITS checkpoint | https://hf.co/utrobinmv/tts_ru_free_hf_vits_low_multispeaker |137| VITS checkpoint | https://hf.co/utrobinmv/tts_ru_free_hf_vits_high_multispeaker |138| VITS2 checkpoint | https://hf.co/frappuccino/vits2_ru_natasha |139| GPT-SoVITS checkpoint | https://hf.co/alphacep/vosk-tts-ru-gpt-sovits |140| CoquiTTS | https://hf.co/coqui/XTTS-v2 |141| XTTS checkpoint | https://hf.co/NeuroDonu/RU-XTTS-DonuModel |142| XTTS checkpoint | https://hf.co/omogr/xtts-ru-ipa |143| Fastpitch IPA | https://hf.co/bene-ges/tts_ru_ipa_fastpitch_ruslan |144| Fastpitch BERT g2p | https://hf.co/bene-ges/ru_g2p_ipa_bert_large |145| RussianFastPitch | https://github.com/safonovanastya/RussianFastPitch |146| Bark | https://hf.co/suno/bark-small |147| GradTTS | https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS |148| FishTTS | https://hf.co/fishaudio/fish-speech-1.5 |149| Pyttsx3 | https://github.com/nateshmbhat/pyttsx3 |150| RHVoice | https://github.com/RHVoice/RHVoice |151| Silero | https://github.com/snakers4/silero-models |152| Fairseq Transformer | https://hf.co/facebook/tts_transformer-ru-cv7_css10 |153| SpeechT5 | https://hf.co/voxxer/speecht5_finetuned_commonvoice_ru_translit |154| Vosk-TTS | https://github.com/alphacep/vosk-tts |155| EdgeTTS | https://github.com/rany2/edge-tts |156| VK Cloud | https://cloud.vk.com/ |157| SaluteSpeech | https://developers.sber.ru/portal/products/smartspeech |158| ElevenLabs | https://elevenlabs.io/ |