| --- |
| license: cc-by-nc-4.0 |
| library_name: pytorch |
| tags: |
| - music |
| - music-structure-analysis |
| - segmentation |
| - source-separation |
| datasets: |
| - TuneyCompany/tuney-small |
| --- |
| |
| # PHRASER (Tuney-Small) |
|
|
| Weights for [PHRASER: Loop-Precise Stem-Aware Music Structure Segmentation](https://github.com/library-x/phraser_public), |
| trained on the Tuney-Small train split (426 tracks). This is the model behind the Tuney-Small |
| rows of Tables 1 and 2 in the paper. |
|
|
| - File: `phraser_tuney_small.ckpt` (PyTorch dict with `state_dict`, `config`, `epoch`) |
| - Task network: 31.07M parameters, `PhraserModel(dim_embed=128, num_heads=16)`, on frozen |
| MuQ-large features of four SCNet stems. The `dim_embed` stored in `config` is a stale |
| default; the network is built with 128. |
| - Selected by validation loss (epoch 10 of 80). |
| - SHA-256: see `SHA256SUMS`. |
|
|
| ## Usage |
|
|
| from phraser.modules.phraser import PhraserModel |
| import torch |
| model = PhraserModel(dim_embed=128, num_heads=16) |
| sd = torch.load("phraser_tuney_small.ckpt", map_location="cpu", weights_only=False)["state_dict"] |
| model.load_state_dict({k.replace("module.", ""): v for k, v in sd.items()}, strict=False) |
| |
| The end-to-end evaluation (separation, MuQ, decoding with onset snapping, scoring) is |
| `python -m phraser.tuney_small.eval_ts phraser phraser_tuney_small.ckpt`. See the repository README. |
|
|
| ## Limitations |
|
|
| The model was trained on 426 instrumental, grid-built tracks. It has no vocal supervision, and the |
| vocal output channel is untrained and must not be used. Accuracy on live or tempo-drifting music is lower. |
|
|
| ## License |
|
|
| [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial use only. The model |
| depends on MuQ weights, which are also licensed for non-commercial use only. |
|
|
