Datasets:

MTUCI
/

RuASD

	---
	language:
	- ru
	tags:
	- audio
	- speech
	- anti-spoofing
	- audio-deepfake-detection
	- tts
	task_categories:
	- audio-classification
	pretty_name: RuASD
	size_categories:
	- 100K<n<1M
	---

	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`.
	# 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 \| 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 \| <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 \|
	\| 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 \|
	\| 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 \| 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 \| 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 \| 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 \| 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 \| 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> \|

	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.

	\| Aug. \| AAS3 \| AR1B \| AR5M \| N2NT \| R2NT \| RCPS \| XSLS \| W2AS \| TCM \|
	\| -------------------------------- \| ----- \| ------------ \| ------------ \| ----- \| ------------ \| ------------ \| ----- \| ------------ \| ------------ \|
	\| Codec only \| \| \| \| \| \| \| \| \| \|
	\| alaw \| 0.468 \| 0.331 \| 0.237 \| 0.435 \| 0.331 \| 0.332 \| 0.485 \| <u>0.242</u> \| 0.270 \|
	\| amr \| 0.373 \| 0.133 \| <u>0.147</u> \| 0.378 \| 0.271 \| 0.272 \| 0.478 \| 0.212 \| 0.372 \|
	\| g722 \| 0.279 \| 0.264 \| <u>0.245</u> \| 0.353 \| 0.323 \| 0.323 \| 0.473 \| 0.275 \| 0.190 \|
	\| mp3 \| 0.239 \| 0.191 \| 0.171 \| 0.340 \| 0.322 \| 0.318 \| 0.475 \| 0.270 \| <u>0.187</u> \|
	\| mlw \| 0.463 \| 0.330 \| <u>0.238</u> \| 0.449 \| 0.333 \| 0.325 \| 0.478 \| 0.234 \| 0.261 \|
	\| op16 \| 0.264 \| <u>0.216</u> \| 0.176 \| 0.383 \| 0.308 \| 0.303 \| 0.481 \| 0.278 \| 0.297 \|
	\| op8 \| 0.341 \| <u>0.208</u> \| 0.205 \| 0.418 \| 0.293 \| 0.297 \| 0.481 \| 0.297 \| 0.404 \|
	\| spx8 \| 0.372 \| 0.141 \| <u>0.147</u> \| 0.370 \| 0.305 \| 0.302 \| 0.475 \| 0.273 \| 0.420 \|
	\| Noise: N and N+Codec \| \| \| \| \| \| \| \| \| \|
	\| N \| 0.458 \| 0.446 \| 0.360 \| 0.440 \| 0.330 \| <u>0.310</u> \| 0.481 \| 0.292 \| 0.292 \|
	\| Nalaw \| 0.503 \| 0.430 \| 0.321 \| 0.483 \| 0.320 \| <u>0.318</u> \| 0.498 \| 0.291 \| 0.380 \|
	\| Namr \| 0.441 \| <u>0.264</u> \| 0.235 \| 0.448 \| 0.286 \| 0.269 \| 0.481 \| 0.270 \| 0.476 \|
	\| Ng722 \| 0.448 \| 0.428 \| 0.357 \| 0.434 \| 0.323 \| <u>0.305</u> \| 0.479 \| 0.296 \| 0.296 \|
	\| Nmp3 \| 0.448 \| 0.372 \| 0.291 \| 0.437 \| 0.327 \| 0.312 \| 0.476 \| <u>0.296</u> \| <u>0.296</u> \|
	\| Nmlw \| 0.502 \| 0.402 \| <u>0.304</u> \| 0.480 \| 0.319 \| 0.316 \| 0.497 \| 0.282 \| 0.373 \|
	\| Nop16 \| 0.420 \| 0.384 \| <u>0.305</u> \| 0.447 \| 0.321 \| 0.289 \| 0.481 \| 0.319 \| 0.414 \|
	\| Nop8 \| 0.437 \| 0.336 \| 0.319 \| 0.473 \| <u>0.309</u> \| 0.301 \| 0.475 \| 0.348 \| 0.512 \|
	\| Nspx8 \| 0.430 \| <u>0.234</u> \| 0.217 \| 0.428 \| 0.288 \| 0.273 \| 0.479 \| 0.312 \| 0.490 \|
	\| Reverberation: R and R+Codec \| \| \| \| \| \| \| \| \| \|
	\| R \| 0.351 \| 0.482 \| 0.319 \| 0.499 \| 0.332 \| 0.336 \| 0.483 \| <u>0.331</u> \| 0.456 \|
	\| Ralaw \| 0.472 \| 0.488 \| 0.373 \| 0.564 \| 0.313 \| <u>0.342</u> \| 0.457 \| 0.347 \| 0.420 \|
	\| Ramr \| 0.444 \| 0.404 \| 0.288 \| 0.515 \| <u>0.334</u> \| 0.346 \| 0.479 \| 0.339 \| 0.396 \|
	\| Rg722 \| 0.397 \| 0.491 \| 0.305 \| 0.500 \| 0.337 \| 0.348 \| 0.489 \| <u>0.334</u> \| 0.476 \|
	\| Rmp3 \| 0.394 \| 0.444 \| 0.243 \| 0.515 \| <u>0.326</u> \| 0.336 \| 0.491 \| 0.336 \| 0.454 \|
	\| Rmlw \| 0.471 \| 0.488 \| 0.365 \| 0.565 \| 0.318 \| 0.346 \| 0.468 \| <u>0.341</u> \| 0.406 \|
	\| Rop16 \| 0.388 \| 0.444 \| 0.295 \| 0.489 \| <u>0.328</u> \| 0.329 \| 0.491 \| 0.366 \| 0.494 \|
	\| Rop8 \| 0.410 \| 0.421 \| 0.308 \| 0.509 \| <u>0.321</u> \| 0.337 \| 0.499 \| 0.380 \| 0.405 \|
	\| Rspx8 \| 0.454 \| 0.400 \| 0.292 \| 0.504 \| <u>0.316</u> \| 0.341 \| 0.490 \| 0.361 \| 0.413 \|
	\| Combined: RN and RN+Codec \| \| \| \| \| \| \| \| \| \|
	\| RN \| 0.503 \| 0.486 \| 0.408 \| 0.527 \| 0.324 \| 0.379 \| 0.504 \| <u>0.365</u> \| 0.511 \|
	\| RNalaw \| 0.493 \| 0.493 \| 0.401 \| 0.551 \| 0.316 \| 0.381 \| 0.528 \| <u>0.372</u> \| 0.422 \|
	\| RNamr \| 0.473 \| 0.478 \| <u>0.349</u> \| 0.515 \| 0.319 \| 0.377 \| 0.512 \| 0.352 \| 0.385 \|
	\| RNg722 \| 0.477 \| 0.498 \| 0.401 \| 0.529 \| 0.322 \| 0.384 \| 0.504 \| <u>0.366</u> \| 0.473 \|
	\| RNmp3 \| 0.469 \| 0.487 \| <u>0.350</u> \| 0.530 \| 0.332 \| 0.371 \| 0.505 \| 0.366 \| 0.510 \|
	\| RNmlw \| 0.503 \| 0.492 \| 0.397 \| 0.544 \| 0.310 \| <u>0.377</u> \| 0.510 \| 0.379 \| 0.409 \|
	\| RNop16 \| 0.477 \| 0.498 \| 0.403 \| 0.519 \| 0.329 \| <u>0.385</u> \| 0.506 \| 0.400 \| 0.427 \|
	\| RNop8 \| 0.493 \| 0.496 \| 0.405 \| 0.510 \| 0.324 \| 0.384 \| 0.488 \| 0.390 \| <u>0.379</u> \|
	\| RNspx8 \| 0.477 \| 0.449 \| <u>0.333</u> \| 0.507 \| 0.318 \| 0.387 \| 0.497 \| 0.379 \| 0.391 \|

	# Download

	## Using Datasets

	```python
	from datasets import load_dataset

	ds = load_dataset("MTUCI/RuASD")
	print(ds)
	```

	## Git clone

	```bash
	git lfs install
	git clone https://huggingface.co/datasets/MTUCI/RuASD
	```

	# 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)

	---
	language:
	- ru
	tags:
	- audio
	- speech
	- anti-spoofing
	- audio-deepfake-detection
	- tts
	task_categories:
	- audio-classification
	pretty_name: RuASD
	size_categories:
	- 100K<n<1M
	---

	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`.
	# 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 \| 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 \| <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 \|
	\| 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 \|
	\| 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 \| 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 \| 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 \| 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 \| 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 \| 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> \|

	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.

	\| Aug. \| AAS3 \| AR1B \| AR5M \| N2NT \| R2NT \| RCPS \| XSLS \| W2AS \| TCM \|
	\| -------------------------------- \| ----- \| ------------ \| ------------ \| ----- \| ------------ \| ------------ \| ----- \| ------------ \| ------------ \|
	\| Codec only \| \| \| \| \| \| \| \| \| \|
	\| alaw \| 0.468 \| 0.331 \| 0.237 \| 0.435 \| 0.331 \| 0.332 \| 0.485 \| <u>0.242</u> \| 0.270 \|
	\| amr \| 0.373 \| 0.133 \| <u>0.147</u> \| 0.378 \| 0.271 \| 0.272 \| 0.478 \| 0.212 \| 0.372 \|
	\| g722 \| 0.279 \| 0.264 \| <u>0.245</u> \| 0.353 \| 0.323 \| 0.323 \| 0.473 \| 0.275 \| 0.190 \|
	\| mp3 \| 0.239 \| 0.191 \| 0.171 \| 0.340 \| 0.322 \| 0.318 \| 0.475 \| 0.270 \| <u>0.187</u> \|
	\| mlw \| 0.463 \| 0.330 \| <u>0.238</u> \| 0.449 \| 0.333 \| 0.325 \| 0.478 \| 0.234 \| 0.261 \|
	\| op16 \| 0.264 \| <u>0.216</u> \| 0.176 \| 0.383 \| 0.308 \| 0.303 \| 0.481 \| 0.278 \| 0.297 \|
	\| op8 \| 0.341 \| <u>0.208</u> \| 0.205 \| 0.418 \| 0.293 \| 0.297 \| 0.481 \| 0.297 \| 0.404 \|
	\| spx8 \| 0.372 \| 0.141 \| <u>0.147</u> \| 0.370 \| 0.305 \| 0.302 \| 0.475 \| 0.273 \| 0.420 \|
	\| Noise: N and N+Codec \| \| \| \| \| \| \| \| \| \|
	\| N \| 0.458 \| 0.446 \| 0.360 \| 0.440 \| 0.330 \| <u>0.310</u> \| 0.481 \| 0.292 \| 0.292 \|
	\| Nalaw \| 0.503 \| 0.430 \| 0.321 \| 0.483 \| 0.320 \| <u>0.318</u> \| 0.498 \| 0.291 \| 0.380 \|
	\| Namr \| 0.441 \| <u>0.264</u> \| 0.235 \| 0.448 \| 0.286 \| 0.269 \| 0.481 \| 0.270 \| 0.476 \|
	\| Ng722 \| 0.448 \| 0.428 \| 0.357 \| 0.434 \| 0.323 \| <u>0.305</u> \| 0.479 \| 0.296 \| 0.296 \|
	\| Nmp3 \| 0.448 \| 0.372 \| 0.291 \| 0.437 \| 0.327 \| 0.312 \| 0.476 \| <u>0.296</u> \| <u>0.296</u> \|
	\| Nmlw \| 0.502 \| 0.402 \| <u>0.304</u> \| 0.480 \| 0.319 \| 0.316 \| 0.497 \| 0.282 \| 0.373 \|
	\| Nop16 \| 0.420 \| 0.384 \| <u>0.305</u> \| 0.447 \| 0.321 \| 0.289 \| 0.481 \| 0.319 \| 0.414 \|
	\| Nop8 \| 0.437 \| 0.336 \| 0.319 \| 0.473 \| <u>0.309</u> \| 0.301 \| 0.475 \| 0.348 \| 0.512 \|
	\| Nspx8 \| 0.430 \| <u>0.234</u> \| 0.217 \| 0.428 \| 0.288 \| 0.273 \| 0.479 \| 0.312 \| 0.490 \|
	\| Reverberation: R and R+Codec \| \| \| \| \| \| \| \| \| \|
	\| R \| 0.351 \| 0.482 \| 0.319 \| 0.499 \| 0.332 \| 0.336 \| 0.483 \| <u>0.331</u> \| 0.456 \|
	\| Ralaw \| 0.472 \| 0.488 \| 0.373 \| 0.564 \| 0.313 \| <u>0.342</u> \| 0.457 \| 0.347 \| 0.420 \|
	\| Ramr \| 0.444 \| 0.404 \| 0.288 \| 0.515 \| <u>0.334</u> \| 0.346 \| 0.479 \| 0.339 \| 0.396 \|
	\| Rg722 \| 0.397 \| 0.491 \| 0.305 \| 0.500 \| 0.337 \| 0.348 \| 0.489 \| <u>0.334</u> \| 0.476 \|
	\| Rmp3 \| 0.394 \| 0.444 \| 0.243 \| 0.515 \| <u>0.326</u> \| 0.336 \| 0.491 \| 0.336 \| 0.454 \|
	\| Rmlw \| 0.471 \| 0.488 \| 0.365 \| 0.565 \| 0.318 \| 0.346 \| 0.468 \| <u>0.341</u> \| 0.406 \|
	\| Rop16 \| 0.388 \| 0.444 \| 0.295 \| 0.489 \| <u>0.328</u> \| 0.329 \| 0.491 \| 0.366 \| 0.494 \|
	\| Rop8 \| 0.410 \| 0.421 \| 0.308 \| 0.509 \| <u>0.321</u> \| 0.337 \| 0.499 \| 0.380 \| 0.405 \|
	\| Rspx8 \| 0.454 \| 0.400 \| 0.292 \| 0.504 \| <u>0.316</u> \| 0.341 \| 0.490 \| 0.361 \| 0.413 \|
	\| Combined: RN and RN+Codec \| \| \| \| \| \| \| \| \| \|
	\| RN \| 0.503 \| 0.486 \| 0.408 \| 0.527 \| 0.324 \| 0.379 \| 0.504 \| <u>0.365</u> \| 0.511 \|
	\| RNalaw \| 0.493 \| 0.493 \| 0.401 \| 0.551 \| 0.316 \| 0.381 \| 0.528 \| <u>0.372</u> \| 0.422 \|
	\| RNamr \| 0.473 \| 0.478 \| <u>0.349</u> \| 0.515 \| 0.319 \| 0.377 \| 0.512 \| 0.352 \| 0.385 \|
	\| RNg722 \| 0.477 \| 0.498 \| 0.401 \| 0.529 \| 0.322 \| 0.384 \| 0.504 \| <u>0.366</u> \| 0.473 \|
	\| RNmp3 \| 0.469 \| 0.487 \| <u>0.350</u> \| 0.530 \| 0.332 \| 0.371 \| 0.505 \| 0.366 \| 0.510 \|
	\| RNmlw \| 0.503 \| 0.492 \| 0.397 \| 0.544 \| 0.310 \| <u>0.377</u> \| 0.510 \| 0.379 \| 0.409 \|
	\| RNop16 \| 0.477 \| 0.498 \| 0.403 \| 0.519 \| 0.329 \| <u>0.385</u> \| 0.506 \| 0.400 \| 0.427 \|
	\| RNop8 \| 0.493 \| 0.496 \| 0.405 \| 0.510 \| 0.324 \| 0.384 \| 0.488 \| 0.390 \| <u>0.379</u> \|
	\| RNspx8 \| 0.477 \| 0.449 \| <u>0.333</u> \| 0.507 \| 0.318 \| 0.387 \| 0.497 \| 0.379 \| 0.391 \|

	# Download

	## Using Datasets

	```python
	from datasets import load_dataset

	ds = load_dataset("MTUCI/RuASD")
	print(ds)
	```

	## Git clone

	```bash
	git lfs install
	git clone https://huggingface.co/datasets/MTUCI/RuASD
	```

	# 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)