Update README.md

README.md

@@ -10,6 +10,68 @@ language:
 - en
 ---
 
+### 1. Introduction
+Transformers and their variants have achieved great
+success in speech processing. However, their multi-head selfattention mechanism is computationally expensive. Therefore, one
+novel selective state space model, Mamba, has been proposed
+as an alternative. Building on its success in automatic speech
+recognition, we apply Mamba for spoofing attack detection.
+Mamba is well-suited for this task as it can capture the artifacts
+in spoofed speech signals by handling long-length sequences.
+However, Mamba’s performance may suffer when it is trained
+with limited labeled data. To mitigate this, we propose combining
+a new structure of Mamba based on a dual-column architecture
+with self-supervised learning, using the pre-trained wav2vec
+2.0 model. The experiments show that our proposed approach
+achieves competitive results and faster inference on the ASVspoof
+2021 LA and DF datasets, and on the more challenging In-theWild dataset, it emerges as the strongest candidate for spoofing
+attack detection.
+
+### 2. Setup Environment
+You need to create the running environment by [Anaconda](https://www.anaconda.com/).
+First, create and activate the environment:
+
+```bash
+conda create -n XLSR_Mamba python=3.10
+conda activate XLSR_Mamba
+```
+
+Then install the requirements:
+
+```bash
+pip install -r requirements.txt
+```
+
+Install fairseq:
+
+```bash
+git clone https://github.com/facebookresearch/fairseq.git fairseq_dir
+cd fairseq_dir
+git checkout a54021305d6b3c
+pip install --editable ./
+```
+### 3. Pretrained Model
+The pretrained model XLSR can be found at this [link](https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr2_300m.pt).
+
+### 4. Results
+
+
+Dataset | **EER (%)** | **min t-DCF** |
+--------|--------------------|--------------------|
+ASVspoof2021 LA  | **0.93**  | **0.208**          |
+ASVspoof2021 DF  | **1.88**  | **-**              | 
+In-The-Wild      | **6.71**  | **-**              |
+
+### 5. Citation
+If you find our repository valuable for your work, please consider giving a star to this repo and citing our paper:
+```
+@article{xiao2024xlsr,
+  title={{XLSR-Mamba}: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection},
+  author={Xiao, Yang and Das, Rohan Kumar},
+  journal={arXiv preprint arXiv:2411.10027},
+  year={2024}
+}
+```
 This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+- Library: [https://github.com/swagshaw/XLSR-Mamba]
+- Paper: [https://arxiv.org/pdf/2411.10027]