Add comprehensive model card for OMAR-RQ

This PR adds a comprehensive model card for the OMAR-RQ model, linking it to the paper [OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction](https://huggingface.co/papers/2507.03482).

Key additions include:
- A link to the official GitHub repository: https://github.com/MTG/OMAR-RQ
- Relevant metadata: `pipeline_tag: audio-classification`, `library_name: omar_rq`, `license: cc-by-nc-sa-4.0`, and additional `tags` for better discoverability.
- The paper's abstract and detailed sample usage for inference.
- A table of available OMAR-RQ models with links to their respective Hugging Face Hub pages.
- Clear licensing information and a BibTeX citation.

This will significantly improve the discoverability and usability of the model on the Hugging Face Hub.

README.md

@@ -0,0 +1,77 @@
+---
+pipeline_tag: audio-classification
+library_name: omar_rq
+license: cc-by-nc-sa-4.0
+tags:
+  - audio-feature-extraction
+  - music
+---
+
+# OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
+
+This repository contains the model weights for **OMAR-RQ**, as presented in the paper [OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction](https://huggingface.co/papers/2507.03482).
+
+## Abstract
+Developing open-source foundation models is essential for advancing research in music audio understanding and ensuring access to powerful, multipurpose representations for music information retrieval. We present OMAR-RQ, a model trained with self-supervision via masked token classification methodologies using a large-scale dataset with over 330,000 hours of music audio. We experiment with different input features and quantization options, and achieve state-of-the-art performance in music tagging, pitch estimation, chord recognition, beat tracking, segmentation, and difficulty estimation among open self-supervised models. We open-source our training and evaluation pipelines and model weights, available at this https URL .
+
+## Code
+The training, validation, and inference code, along with further details, is available at the official GitHub repository: [https://github.com/MTG/OMAR-RQ](https://github.com/MTG/OMAR-RQ).
+
+## Inference
+You can load an OMAR-RQ model directly by specifying its Hugging Face model ID. First, install the library:
+
+```bash
+pip install omar-rq
+```
+
+Then, use the following Python code for embedding extraction:
+
+```python
+import torch
+from omar_rq import get_model
+
+# Embedding extraction example
+x = torch.randn(1, 16000 * 4).cpu() # Example audio input (batch_size, samples)
+
+model_id = "mtg-upf/omar-rq-multifeature-25hz-fsq" # This repository's model ID
+model = get_model(model_id=model_id, device="cpu")
+
+embeddings = model.extract_embeddings(x, layers=[6])
+
+timestamps = torch.arange(embeddings.shape[2]) / model.eps
+
+print(f"Extracted embeddings shape: {embeddings.shape}")
+print(f"Number of timestamps: {len(timestamps)}")
+```
+
+For more details on `get_model` and `extract_embeddings` usage, please refer to the [GitHub repository](https://github.com/MTG/OMAR-RQ).
+
+## Available Models
+OMAR-RQ models are offered in different configurations, each with its own strengths and weaknesses.
+Models based on mel spectrogram (**base** and **multicodebook**) tend to perform better on semantic tasks such as auto-tagging, structure recognition, and difficulty estimation.
+On the other hand, **multifeature-24hz-fsq** offers the best performance in tonal and temporal tasks such as pitch and chord estimation, and beat tracking.
+
+| Model | Hugging Face ID | Input | Rate | Tagging | Difficulty | Pitch | Chord | Beat | Structure |
+|---|---|---|---|---|---|---|---|---|---|
+| | | Hz | _mAP_ | _MSE_ | _acc._ | _acc._ | _F1_ | _acc._ |
+| **base** | [mtg-upf/omar-rq-base](https://huggingface.co/mtg-upf/omar-rq-base) | mel | 15.63 | .482 | **1.65** | .892 | .657 | .783 | **.647** |
+| **multicodebook** | [mtg-upf/omar-rq-multicodebook](https://huggingface.co/mtg-upf/omar-rq-multicodebook) | mel | 15.63 | **.488** | 1.66 | .897 | .675 | .775 | .639 |
+| **multifeature** | [mtg-upf/omar-rq-multifeature](https://huggingface.co/mtg-upf/omar-rq-multifeature) | audio | 18.75 | .467 | 1.76 | .938 | .734 | .833 | .623 |
+| **multifeature-25hz** | [mtg-upf/omar-rq-multifeature-25hz](https://huggingface.co/mtg-upf/omar-rq-multifeature-25hz) | audio | 25 | .463 | 1.79 | .932 | .728 | .848 | .628 |
+| **multifeature-25hz-fsq**| [mtg-upf/omar-rq-multifeature-25hz-fsq](https://huggingface.co/mtg-upf/omar-rq-multifeature-25hz-fsq) | audio | 25 | .463 | 1.71 | **.940**| **.749**| **.855** | .628 |
+
+## License
+The code in the [OMAR-RQ GitHub repository](https://github.com/MTG/OMAR-RQ) is available under the [AGPL-3.0 license](https://www.gnu.org/licenses/agpl-3.0.en.html).
+The model weights on this Hugging Face Hub are released under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/) for non-commercial applications.
+
+## Citation
+If you find this work helpful or inspiring, please feel free to cite it using the following BibTeX entry:
+
+```bibtex
+@article{fust,
+  title={OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction},
+  author={Fust, Albert and Pons, Jordi and Bogdanov, Dmitry and Oñoro-Rubio, Daniel and Gómez, Emilia},
+  journal={arXiv preprint arXiv:2507.03482},
+  year={2025}
+}
+```