Update README.md

README.md

@@ -1,3 +1,42 @@
----
-license: mit
----
+---
+license: mit
+---
+# Introduction  
+MCL-MLAAD is the first multilingual benchmark for speech deepfake source tracing. It spans mono- and cross-lingual protocols, includes DSP and SSL baselines, studies language-specific fine-tuning for cross-lingual generalization, and tests robustness to unseen languages/speakers. See arXiv:2508.04143.
+
+## Download the Dataset  
+
+Install the `datasets` package:  
+```bash
+pip install datasets
+```
+Log in with your Hugging Face account:
+```bash
+huggingface-cli login
+```
+Load the dataset in Python:
+```bash
+from datasets import load_dataset
+
+# Download from HF and cache
+ds = load_dataset("xxuan-speech/MCL-MLAAD")
+
+# Optionally: Save the dataset to your own directory
+ds.save_to_disk("mcl-mlaad_local")
+```
+
+### Code: 
+A reference implementation Code is available at:  https://github.com/xuanxixi/Multilingual-Source-Tracing
+
+### Bibtex: 
+```bash
+@misc{xuan2025multilingualsourcetracingspeech,
+      title={Multilingual Source Tracing of Speech Deepfakes: A First Benchmark}, 
+      author={Xi Xuan and Yang Xiao and Rohan Kumar Das and Tomi Kinnunen},
+      year={2025},
+      eprint={2508.04143},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2508.04143}
+}
+```