Update README.md

README.md

@@ -1,3 +1,53 @@
----
-license: mit
----
+---
+license: mit
+---
+
+
+## SparQLe – Speech Queries to Text via Instruction‑Tuned LLM ⚡
+
+**What it does:**
+SparQLe (Speech Routing to Query LLMs) enables direct speech-to-text understanding by aligning self‑supervised speech representations (e.g., HuBERT-like features) with instruction‑tuned Large Language Models (LLMs). This is achieved using a lightweight *modality adapter*, bridging the modalities without retraining the whole LLM. ([Moonlight][1])
+
+**Key strengths:**
+
+* **Preserves semantic content** of spoken input in the produced text ([arXiv][2])
+* **Efficiently leverages frozen SSL models**, avoiding heavy ASR backbones like Whisper ([arXiv][3])
+* **Modular design** with a query‑former (Q‑former) adapter and LLM backend ([GitHub][4])
+
+**Architecture:**
+
+1. **Speech encoder** (SSL) transforms raw input into latent features.
+2. **Modality adapter / Q‑former** aligns these with the LLM’s text embedding space.
+3. **Instruction‑tuned LLM** processes the adapted input to generate semantic text.
+
+
+## Citation
+
+If you use SparQLe in your research, please cite:
+
+```bibtex
+@misc{djanibekov2025sparqlespeechqueriestext,
+      title={SparQLe: Speech Queries to Text Translation Through LLMs},
+      author={Amirbek Djanibekov and Hanan Aldarmaki},
+      year={2025},
+      eprint={2502.09284},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.09284},
+}
+```
+
+📄 Read the full paper on arXiv: [https://arxiv.org/abs/2502.09284](https://arxiv.org/abs/2502.09284)
+
+---
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+---
+
+## Acknowledgments
+
+- This work builds upon [fairseq](https://github.com/facebookresearch/fairseq) 💙
+- The Qformer architecture is inspired by [BLIP-2](https://github.com/salesforce/BLIP-2) ✨