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MusicDetection

> [!IMPORTANT] > Official model for our INTERSPEECH 2026 paper > "A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models" (arXiv:2507.13563). > Part of the...

KindOtherBase modelmicrosoft/wavlm-base-plusVersionMusic detectionBgrade

Model source

Kind
Other
File size
362 MB
Base model
microsoft/wavlm-base-plus
Version
vMusic detection
Updated
Oct 25, 2026
Source
Hugging Face

Source description

Music Detection with WavLM

arXiv Conference Code

[!IMPORTANT] Official model for our INTERSPEECH 2026 paper "A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models" (arXiv:2507.13563). Part of the Balalaika Russian speech data-processing pipeline — code: https://github.com/lab260ru/balalaika. If you use this resource, please cite it.

Detects if audio contains music.
EER: 2.5–3% | Based on microsoft/wavlm-base-plus the best threshold value 0.2442

Quick Start

git clone https://huggingface.co/MTUCI/MusicDetection
cd MusicDetection
pip install -r requirements.txt

Usage

from model import WavLMForMusicDetection
from safetensors import safe_open

model = WavLMForMusicDetection(batch_size=32, device='cuda')
with safe_open('music_detection.safetensors', framework="pt") as f:
    model.load_state_dict({k: f.get_tensor(k) for k in f.keys()})

probs = model.predict_proba(['audio1.mp3', 'audio2.wav'])  # → tensor([0.88, 0.11])

## Contact

- Email: kborodin.research@gmail.com
- Telegram: [@korallll_ai](https://t.me/korallll_ai)

## Citation

If you use this resource, please cite our INTERSPEECH 2026 paper:

```bibtex
@inproceedings{borodin2026balalaika,
  title     = {A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models},
  author    = {Borodin, Kirill and Vasiliev, Nikita and Kudryavtsev, Vasiliy and Maslov, Maxim and Gorodnichev, Mikhail and Rogov, Oleg and Mkrtchian, Grach},
  booktitle = {Proc. INTERSPEECH 2026},
  year      = {2026},
  note      = {arXiv:2507.13563},
  url       = {https://arxiv.org/abs/2507.13563}
}

Sources

1 source
Hugging FacePrimaryVersion 31af173d00d2af604ff0899c8a0165de63530a2e
huggingface.co/lab260/MusicDetection/tree/31af173d00d2af604ff0899c8a0165de63530a2e

Verified Oct 25

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Model artifacts

2 artifacts

music_detection.onnx

onnx · 362 MB · SHA-256 1492d2435946…6105 · Hugging Face

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music_detection.safetensors

safetensors · 362 MB · SHA-256 a4faad884099…1ee4 · Hugging Face

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Source excerpts

2 excerpts

--- license: cc-by-nc-4.0 language: - ru base_model: - microsoft/wavlm-base-plus pipeline_tag: audio-classification --- # Music Detection with WavLM [![arXiv](https://img.shields.io/badge/arXiv-2507.13563-b31b1b.svg)](https://arxiv.org/abs/2507.13563) [![Conference](https://img.shields.io/badge/INTERSPEECH-2026-1f6feb.svg)](https://arxiv.org/abs/2507.13563) [![Code](https://img.shields.io/badge/code-lab260ru%2Fbalalaika-181717.svg?logo=github)](https://github.com/lab260ru/balalaika) > [!IMPORTANT] > **Official model for our INTERSPEECH 2026 paper** > *"A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models"* ([arXiv:2507.13563](https://arxiv.org/abs/2507.13563)). > Part of the **Balalaika** Russian speech data-processing pipeline — code: [https://github.com/lab260ru/balalaika](https://github.com/lab260ru/balalaika). > If you use this resource, please [cite it](#citation). Detects if audio contains music. **EER: 2.5–3%** | Based on `microsoft/wavlm-base-plus` *the best threshold value* `0.2442` --- ## Quick Start ``` git clone https://huggingface.co/MTUCI/MusicDetection cd MusicDetection pip install -r requirements.txt ``` ## Usage ```python from model import WavLMForMusicDetection from safetensors import safe_open model = WavLMForMusicDetection(batch_size=32, device='cuda') with safe_open('music_detection.safetensors', framework="pt") as f: model.load_state_dict({k: f.get_tensor(k) for k in f.keys()}) probs = model.predict_proba(['audio1.mp3', 'audio2.wav']) # → tensor([0.88, 0.11]) ## Contact - Email: kborodin.research@gmail.com - Telegram: [@korallll_ai](https://t.me/korallll_ai) ## Citation If you use this resource, please cite our INTERSPEECH 2026 paper: ```bibtex @inproceedings{borodin2026balalaika, title = {A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models}, author = {Borodin, Kirill and Vasiliev, Nikita and Kudryavtsev, Vasiliy and Maslov, Maxim and Gorodnichev, Mikhail and Rogov, Oleg and Mkrtchian, Grach}, booktitle = {Proc. INTERSPEECH 2026}, year = {2026}, note = {arXiv:2507.13563}, url = {https://arxiv.org/abs/2507.13563} } ```

Source context: 0 downloads · 6 likes · Pipeline audio-classification · Repo lab260/MusicDetection