MuQ Eval A1

--- language: en library_name: pytorch license: mit pipeline_tag: audio-classification tags: - audio - music - quality-assessment - MOS-prediction - music-generation --- # MusicQualityModel — A1_frozen_mlp Multi-head neural evaluator for music generation quality, built on frozen [MuQ](https://huggingface.co/OpenMuQ/MuQ-large-msd-iter) representations with learned attention pooling and per-dimension MLP prediction heads. ## Model Details - **Encoder:** `OpenMuQ/MuQ-large-msd-iter` (tuning mode: `frozen`) - **Pooling:** Attention-weighted mean pooling - **Heads:** MI, TA - **Loss:** `mse` - **Input:** Audio waveform at 24000 Hz, max 10.0s ## Performance Evaluated with 5-fold cross-validation on MusicEval (2,748 clips, 31 TTM systems). ## Usage ```python import torch import torchaudio from omegaconf import OmegaConf from huggingface_hub import hf_hub_download # Download files config_path = hf_hub_download("zhudi2825/MuQ-Eval-A1", "config.yaml") model_path = hf_hub_download("zhudi2825/MuQ-Eval-A1", "best_model.pt") # Load config and build model cfg = OmegaConf.load(config_path) from src.model import MusicQualityModel model = MusicQualityModel(cfg) ckpt = torch.load(model_path, map_location="cpu", weights_only=False) model.load_state_dict(ckpt["model_state"]) model.eval() # Run inference waveform, sr = torchaudio.load("audio.wav") if sr != 24000: waveform = torchaudio.transforms.Resample(sr, 24000)(waveform) waveform = waveform.mean(0) # mono waveform = waveform[:240000].unsqueeze(0) # [1, samples] with torch.no_grad(): preds = model(waveform) scores = model._last_expected_scores for name, score in scores.items(): print(f"{name}: {score.item():.2f}") ``` ## Training - **Dataset:** MusicEval (BAAI/MusicEval) — 5-fold stratified CV by TTM model - **Epochs:** 30 - **Batch size:** 16 - **Optimizer:** AdamW (lr=0.001, wd=0.01) - **Scheduler:** cosine with 500 warmup steps - **Precision:** bf16 ## Citation If you use this model, please cite: ```bibtex @article{zhu2026muqeval, title={Frozen Music Representations Suffice for Per-Sample Quality Prediction of Generated Music}, author={Zhu, Di and Li, Zixuan}, journal={arXiv preprint arXiv:2603.22677}, year={2026} } ```

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