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Datasets:
MTUCI
/
RuASD

Tasks:
Audio Classification
Modalities:
Audio
Text
Formats:
webdataset
Languages:
Russian
Size:
100K - 1M
Tags:
audio
speech
anti-spoofing
audio-deepfake-detection
tts
Libraries:
Datasets
WebDataset
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+ ---
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+ language:
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+ - ru
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+ tags:
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+ - audio
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+ - speech
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+ - anti-spoofing
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+ - audio-deepfake-detection
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+ - tts
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+ task_categories:
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+ - audio-classification
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+ pretty_name: RuASD
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+ size_categories:
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+ - 100K<n<1M
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+ ---
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+
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+ RuASD: Russian Anti-Spoofing Dataset
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+
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+ **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.
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+
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+ **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
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+
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+ # Overview
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+
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+ - **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.
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+ - **Content:** Bona fide speech from multiple open Russian speech corpora and synthetic speech generated by 37 Russian-capable TTS and voice-cloning systems.
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+ - **Structure:**
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+ - **Audio:** `.wav` files
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+ - **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`.
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+ # Statistics
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+
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+ - **Number of TTS systems:** 37
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+ - **Total spoof hours:** 691.68
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+ - **Total bona-fide hours:** 234.07
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+
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+ Table 4. Antispoofing models on clean data.
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+
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+ | Model | Acc | Pr | Rec | F1 | RAUC | EER | t-DCF |
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+ | -------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------- | ------------------ | ------------------ |
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+ | 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 |
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+ | Arena-1B | <u>0.812±0.001</u> | <u>0.736±0.001</u> | <u>0.812±0.001</u> | <u>0.772±0.001</u> | <u>0.887±0.0005</u> | <u>0.188±0.001</u> | **0.385±0.001** |
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+ | Arena-500M | 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 |
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+ | 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 |
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+ | Res2TCNGaurd | 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 |
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+ | 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 |
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+ | SLS with XLS-R | 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 |
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+ | Wav2Vec 2.0 | 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 |
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+ | TCM-ADD | **0.857±0.001** | **0.797±0.001** | **0.859±0.001** | **0.827±0.001** | <u>0.914±0.0004</u> | **0.143±0.001** | <u>0.424±0.001</u> |
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+
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+ **Table 5. Antispoofing models on augmented data (EER).** **Aug.** denotes the applied degradation: **R** — RIR reverberation, **N** — MUSAN additive noise, and suffixes (_alaw_, _amr_, _g722_, _mp3_, _mlw_, _op16_, _op8_, _spx8_) indicate encode-decode transcoding with the corresponding codec. Combined labels such as **RNmp3** apply **R** + **N** followed by codec transcoding.
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+
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+ | Aug. | AAS3 | AR1B | AR5M | N2NT | R2NT | RCPS | XSLS | W2AS | TCM |
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+ | -------------------------------- | ----- | ------------ | ------------ | ----- | ------------ | ------------ | ----- | ------------ | ------------ |
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+ | **Codec only** | | | | | | | | | |
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+ | alaw | 0.468 | 0.331 | **0.237** | 0.435 | 0.331 | 0.332 | 0.485 | <u>0.242</u> | 0.270 |
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+ | amr | 0.373 | **0.133** | <u>0.147</u> | 0.378 | 0.271 | 0.272 | 0.478 | 0.212 | 0.372 |
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+ | g722 | 0.279 | 0.264 | <u>0.245</u> | 0.353 | 0.323 | 0.323 | 0.473 | 0.275 | **0.190** |
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+ | mp3 | 0.239 | 0.191 | **0.171** | 0.340 | 0.322 | 0.318 | 0.475 | 0.270 | <u>0.187</u> |
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+ | mlw | 0.463 | 0.330 | <u>0.238</u> | 0.449 | 0.333 | 0.325 | 0.478 | **0.234** | 0.261 |
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+ | op16 | 0.264 | <u>0.216</u> | **0.176** | 0.383 | 0.308 | 0.303 | 0.481 | 0.278 | 0.297 |
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+ | op8 | 0.341 | <u>0.208</u> | **0.205** | 0.418 | 0.293 | 0.297 | 0.481 | 0.297 | 0.404 |
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+ | spx8 | 0.372 | **0.141** | <u>0.147</u> | 0.370 | 0.305 | 0.302 | 0.475 | 0.273 | 0.420 |
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+ | **Noise: N and N+Codec** | | | | | | | | | |
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+ | N | 0.458 | 0.446 | 0.360 | 0.440 | 0.330 | <u>0.310</u> | 0.481 | **0.292** | **0.292** |
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+ | Nalaw | 0.503 | 0.430 | 0.321 | 0.483 | 0.320 | <u>0.318</u> | 0.498 | **0.291** | 0.380 |
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+ | Namr | 0.441 | <u>0.264</u> | **0.235** | 0.448 | 0.286 | 0.269 | 0.481 | 0.270 | 0.476 |
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+ | Ng722 | 0.448 | 0.428 | 0.357 | 0.434 | 0.323 | <u>0.305</u> | 0.479 | **0.296** | **0.296** |
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+ | Nmp3 | 0.448 | 0.372 | **0.291** | 0.437 | 0.327 | 0.312 | 0.476 | <u>0.296</u> | <u>0.296</u> |
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+ | Nmlw | 0.502 | 0.402 | <u>0.304</u> | 0.480 | 0.319 | 0.316 | 0.497 | **0.282** | 0.373 |
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+ | Nop16 | 0.420 | 0.384 | <u>0.305</u> | 0.447 | 0.321 | **0.289** | 0.481 | 0.319 | 0.414 |
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+ | Nop8 | 0.437 | 0.336 | 0.319 | 0.473 | <u>0.309</u> | **0.301** | 0.475 | 0.348 | 0.512 |
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+ | Nspx8 | 0.430 | <u>0.234</u> | **0.217** | 0.428 | 0.288 | 0.273 | 0.479 | 0.312 | 0.490 |
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+ | **Reverberation: R and R+Codec** | | | | | | | | | |
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+ | R | 0.351 | 0.482 | **0.319** | 0.499 | 0.332 | 0.336 | 0.483 | <u>0.331</u> | 0.456 |
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+ | Ralaw | 0.472 | 0.488 | 0.373 | 0.564 | **0.313** | <u>0.342</u> | 0.457 | 0.347 | 0.420 |
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+ | Ramr | 0.444 | 0.404 | **0.288** | 0.515 | <u>0.334</u> | 0.346 | 0.479 | 0.339 | 0.396 |
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+ | Rg722 | 0.397 | 0.491 | **0.305** | 0.500 | 0.337 | 0.348 | 0.489 | <u>0.334</u> | 0.476 |
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+ | Rmp3 | 0.394 | 0.444 | **0.243** | 0.515 | <u>0.326</u> | 0.336 | 0.491 | 0.336 | 0.454 |
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+ | Rmlw | 0.471 | 0.488 | 0.365 | 0.565 | **0.318** | 0.346 | 0.468 | <u>0.341</u> | 0.406 |
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+ | Rop16 | 0.388 | 0.444 | **0.295** | 0.489 | <u>0.328</u> | 0.329 | 0.491 | 0.366 | 0.494 |
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+ | Rop8 | 0.410 | 0.421 | **0.308** | 0.509 | <u>0.321</u> | 0.337 | 0.499 | 0.380 | 0.405 |
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+ | Rspx8 | 0.454 | 0.400 | **0.292** | 0.504 | <u>0.316</u> | 0.341 | 0.490 | 0.361 | 0.413 |
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+ | **Combined: RN and RN+Codec** | | | | | | | | | |
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+ | RN | 0.503 | 0.486 | 0.408 | 0.527 | **0.324** | 0.379 | 0.504 | <u>0.365</u> | 0.511 |
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+ | RNalaw | 0.493 | 0.493 | 0.401 | 0.551 | **0.316** | 0.381 | 0.528 | <u>0.372</u> | 0.422 |
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+ | RNamr | 0.473 | 0.478 | <u>0.349</u> | 0.515 | **0.319** | 0.377 | 0.512 | 0.352 | 0.385 |
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+ | RNg722 | 0.477 | 0.498 | 0.401 | 0.529 | **0.322** | 0.384 | 0.504 | <u>0.366</u> | 0.473 |
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+ | RNmp3 | 0.469 | 0.487 | <u>0.350</u> | 0.530 | **0.332** | 0.371 | 0.505 | 0.366 | 0.510 |
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+ | RNmlw | 0.503 | 0.492 | 0.397 | 0.544 | **0.310** | <u>0.377</u> | 0.510 | 0.379 | 0.409 |
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+ | RNop16 | 0.477 | 0.498 | 0.403 | 0.519 | **0.329** | <u>0.385</u> | 0.506 | 0.400 | 0.427 |
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+ | RNop8 | 0.493 | 0.496 | 0.405 | 0.510 | **0.324** | 0.384 | 0.488 | 0.390 | <u>0.379</u> |
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+ | RNspx8 | 0.477 | 0.449 | <u>0.333</u> | 0.507 | **0.318** | 0.387 | 0.497 | 0.379 | 0.391 |
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+
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+ # Download
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+
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+ ## Using Datasets
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("MTUCI/RuASD")
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+ print(ds)
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+ ```
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+
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+ ## Git clone
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+
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+ ```bash
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+ git lfs install
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+ git clone https://huggingface.co/datasets/MTUCI/RuASD
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+ ```
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+
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+ # Contact
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+
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+ - **Email:** [k.n.borodin@mtuci.ru](mailto:k.n.borodin@mtuci.ru)
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+ - **Telegram channel:** [https://t.me/korallll_ai](https://t.me/korallll_ai)