--- license: cc-by-nc-4.0 language: - en - zh pipeline_tag: audio-classification tags: - music --- # MuQ & MuQ-MuLan

This is the official repository for the paper *"**MuQ**: Self-Supervised **Mu**sic Representation Learning with Mel Residual Vector **Q**uantization"*. For more detailed information, we strongly recommend referring to https://github.com/tencent-ailab/MuQ and the [paper]((https://arxiv.org/abs/2501.01108)). In this repo, the following models are released: - **MuQ**(see [this link](https://huggingface.co/OpenMuQ/MuQ-large-msd-iter)): A large music foundation model pre-trained via Self-Supervised Learning (SSL), achieving SOTA in various MIR tasks. - **MuQ-MuLan**(see [this link](https://huggingface.co/OpenMuQ/MuQ-MuLan-large)): A music-text joint embedding model trained via contrastive learning, supporting both English and Chinese texts. ## Usage To begin with, please use pip to install the official `muq` lib, and ensure that your `python>=3.8`: ```bash pip3 install muq ``` To extract music audio features using **MuQ**: ```python import torch, librosa from muq import MuQ device = 'cuda' wav, sr = librosa.load("path/to/music_audio.wav", sr = 24000) wavs = torch.tensor(wav).unsqueeze(0).to(device) # This will automatically fetch the checkpoint from huggingface muq = MuQ.from_pretrained("OpenMuQ/MuQ-large-msd-iter") muq = muq.to(device).eval() with torch.no_grad(): output = muq(wavs, output_hidden_states=True) print('Total number of layers: ', len(output.hidden_states)) print('Feature shape: ', output.last_hidden_state.shape) ``` Using **MuQ-MuLan** to extract the music and text embeddings and calculate the similarity: ```python import torch, librosa from muq import MuQMuLan # This will automatically fetch checkpoints from huggingface device = 'cuda' mulan = MuQMuLan.from_pretrained("OpenMuQ/MuQ-MuLan-large") mulan = mulan.to(device).eval() # Extract music embeddings wav, sr = librosa.load("path/to/music_audio.wav", sr = 24000) wavs = torch.tensor(wav).unsqueeze(0).to(device) with torch.no_grad(): audio_embeds = mulan(wavs = wavs) # Extract text embeddings (texts can be in English or Chinese) texts = ["classical genres, hopeful mood, piano.", "一首适合海边风景的小提琴曲，节奏欢快"] with torch.no_grad(): text_embeds = mulan(texts = texts) # Calculate dot product similarity sim = mulan.calc_similarity(audio_embeds, text_embeds) print(sim) ``` ## Model Checkpoints | Model Name | Parameters | Data | HuggingFace🤗 | | ----------- | --- | --- | ----------- | | MuQ | ~300M | MSD dataset | [OpenMuQ/MuQ-large-msd-iter](https://huggingface.co/OpenMuQ/MuQ-large-msd-iter) | | MuQ-MuLan | ~700M | music-text pairs | [OpenMuQ/MuQ-MuLan-large](https://huggingface.co/OpenMuQ/MuQ-MuLan-large) | **Note**: Please note that the open-sourced MuQ was trained on the Million Song Dataset. Due to differences in dataset size, the open-sourced model may not achieve the same level of performance as reported in the paper. ## License The code is released under the MIT license. The model weights (MuQ-large-msd-iter, MuQ-MuLan-large) are released under the CC-BY-NC 4.0 license. ## Citation ``` @article{zhu2025muq, title={MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization}, author={Haina Zhu and Yizhi Zhou and Hangting Chen and Jianwei Yu and Ziyang Ma and Rongzhi Gu and Yi Luo and Wei Tan and Xie Chen}, journal={arXiv preprint arXiv:2501.01108}, year={2025} } ```

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