Datasets:
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README.md
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size_categories:
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- 100K<n<1M
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---
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RuASD: Russian Anti-Spoofing Dataset
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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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- **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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- **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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Table 4. Antispoofing models on clean data
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| Model
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-
| -------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------- | ------------------ | ------------------ |
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| AASIST3
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| Arena-1B
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| Arena-500M
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| Nes2Net
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| Res2TCNGaurd
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| ResCapsGuard
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| SLS with XLS-R
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| Wav2Vec 2.0
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| TCM-ADD
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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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# Download
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print(ds)
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```
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##
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```
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# Contact
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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)
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size_categories:
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- 100K<n<1M
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---
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RuASD: Russian Anti-Spoofing Dataset
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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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- **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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| Field | Description |
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| ----------------- | -------------------------------------------------------------------------------------------------------------------- |
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| `sample_id` | Sample ID |
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| `label` | `real` or `fake` |
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| `group` | Sample group - `raw` or `augmented` |
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| `subset` | source subset name, e.g. `OpenSTT`, `GOLOS`, or `ElevenLabs` |
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| `augmentation` | Applied augmentation |
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| `filename` | Audio filename |
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| `audio_relpath` | Relative path to audio |
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| `source_audio` | Original audio for augmented sample |
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| `metadata_source` | Metadata source |
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| `source_type` | Source type - `tts`, `real_speech` or `augmented_audio` |
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| `mos_pred` | Predicted MOS |
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| `noi_pred` | Predicted noisiness |
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| `dis_pred` | Predicted discontinuity |
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| `col_pred` | Predicted coloration |
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| `loud_pred` | Predicted loudness |
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| `cer` | Character error rate |
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| `duration` | Duration in seconds |
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| `speakers` | Speaker info |
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| `model` | specific checkpoint or voice used for generation, e.g. `ESpeech-TTS-1_RL-V1`, `xtts-ru-ipa`, or `ru-RU-DmitryNeural` |
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| `transcribe` | Automatic transcription |
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| `true_lines` | Source text |
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| `transcription` | Automatic transcription |
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| `ground_truth` | Reference text |
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| `ops` | Processing operations |
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# Statistics
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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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Table 4. Antispoofing models on clean data
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| Model | Acc | Pr | Rec | F1 | RAUC | EER | t-DCF |
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| ------------------------------------------------------------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------- | ------------------ | ------------------ |
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| [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 |
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| [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> |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [Spectra-0](https://huggingface.co/MTUCI/spectra_0) | **0.962** | **0.942** | **0.962** | **0.952** | **0.985** | **0.038** | **0.124** |
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# Download
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print(ds)
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```
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## Using Datasets with streaming mode
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```python
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from datasets import load_dataset
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ds = load_dataset("MTUCI/RuASD", streaming=True)
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small_ds = ds.take(1000)
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print(small_ds)
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```
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# Contact
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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)
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# Citation
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```
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@unpublished{ruasd2026,
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author = {},
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title = {},
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year = {}
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}
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```
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# TTS and VC models
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| Model | Link |
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| --------------------- | -------------------------------------------------------------------------- |
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| Espeech Podcaster | https://hf.co/ESpeech/ESpeech-TTS-1_podcaster |
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| Espeech RL-V1 | https://hf.co/ESpeech/ESpeech-TTS-1_RL-V1 |
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| Espeech RL-V2 | https://hf.co/ESpeech/ESpeech-TTS-1_RL-V1 |
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| Espeech SFT-95k | https://hf.co/ESpeech/ESpeech-TTS-1_SFT-95K |
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| Espeech SFT-256k | https://hf.co/ESpeech/ESpeech-TTS-1_SFT-256K |
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| F5-TTS checkpoint | https://hf.co/Misha24-10/F5-TTS_RUSSIAN |
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| F5-TTS checkpoint | https://hf.co/hotstone228/F5-TTS-Russian |
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| VITS checkpoint | https://hf.co/joefox/tts_vits_ru_hf |
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| PiperTTS | https://github.com/rhasspy/piper |
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| TeraTTS-natasha | https://hf.co/TeraTTS/natasha-g2p-vits |
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| TeraTTS-girl_nice | https://hf.co/TeraTTS/girl_nice-g2p-vits |
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| TeraTTS-glados | https://hf.co/TeraTTS/glados-g2p-vits |
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| TeraTTS-glados2 | https://hf.co/TeraTTS/glados2-g2p-vits |
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| MMS | https://hf.co/facebook/mms-tts-rus |
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| VITS checkpoint | https://hf.co/utrobinmv/tts_ru_free_hf_vits_low_multispeaker |
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| 136 |
+
| VITS checkpoint | https://hf.co/utrobinmv/tts_ru_free_hf_vits_high_multispeaker |
|
| 137 |
+
| VITS2 checkpoint | https://hf.co/frappuccino/vits2_ru_natasha |
|
| 138 |
+
| GPT-SoVITS checkpoint | https://hf.co/alphacep/vosk-tts-ru-gpt-sovits |
|
| 139 |
+
| CoquiTTS | https://hf.co/coqui/XTTS-v2 |
|
| 140 |
+
| XTTS checkpoint | https://hf.co/NeuroDonu/RU-XTTS-DonuModel |
|
| 141 |
+
| XTTS checkpoint | https://hf.co/omogr/xtts-ru-ipa |
|
| 142 |
+
| Fastpitch IPA | https://hf.co/bene-ges/tts_ru_ipa_fastpitch_ruslan |
|
| 143 |
+
| Fastpitch BERT g2p | https://hf.co/bene-ges/ru_g2p_ipa_bert_large |
|
| 144 |
+
| RussianFastPitch | https://github.com/safonovanastya/RussianFastPitch |
|
| 145 |
+
| Bark | https://hf.co/suno/bark-small |
|
| 146 |
+
| GradTTS | https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS |
|
| 147 |
+
| FishTTS | https://hf.co/fishaudio/fish-speech-1.5 |
|
| 148 |
+
| Pyttsx3 | https://github.com/nateshmbhat/pyttsx3 |
|
| 149 |
+
| RHVoice | https://github.com/RHVoice/RHVoice |
|
| 150 |
+
| Silero | https://github.com/snakers4/silero-models |
|
| 151 |
+
| Fairseq Transformer | https://hf.co/facebook/tts_transformer-ru-cv7_css10 |
|
| 152 |
+
| SpeechT5 | https://hf.co/voxxer/speecht5_finetuned_commonvoice_ru_translit |
|
| 153 |
+
| Vosk-TTS | https://github.com/alphacep/vosk-tts |
|
| 154 |
+
| EdgeTTS | https://github.com/rany2/edge-tts |
|
| 155 |
+
| VK Cloud | https://cloud.vk.com/ |
|
| 156 |
+
| SaluteSpeech | https://developers.sber.ru/portal/products/smartspeech |
|
| 157 |
+
| ElevenLabs | https://elevenlabs.io/ |
|