MusicDetection

--- license: cc-by-nc-4.0 language: - ru base_model: - microsoft/wavlm-base-plus pipeline_tag: audio-classification --- # Music Detection with WavLM [](https://arxiv.org/abs/2507.13563) [](https://arxiv.org/abs/2507.13563) [](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} } ```