Datasets:

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: [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/ \|

	---
	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/ \|