Update README.md

README.md

@@ -2,9 +2,192 @@
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+license: cc-by-nc-4.0
+datasets:
+- mueller91/MLAAD
+- jungjee/asvspoof5
+- Bisher/ASVspoof_2019_LA
+language:
+- en
+pipeline_tag: audio-classification
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection
+
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-blue)](https://huggingface.co/MTUCI/AASIST3)
+[![License](https://img.shields.io/badge/License-CC%20BY--NC--ND%204.0-red.svg)](https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
+This repository contains the original implementation of **AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge**.
+
+## Paper
+
+**AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge**
+
+*This is the original implementation of the paper. The model weights provided here are NOT the same weights used in the paper results.*
+
+## Overview
+
+AASIST3 is an enhanced version of the AASIST (Anti-spoofing with Adaptive Softmax and Instance-wise Temperature) architecture that incorporates **Kolmogorov-Arnold Networks (KAN)** for improved speech deepfake detection. The model leverages:
+
+- **Self-Supervised Learning (SSL) Features**: Uses Wav2Vec2 encoder for robust audio representation
+- **KAN Linear Layers**: Kolmogorov-Arnold Networks for enhanced feature transformation
+- **Graph Attention Networks (GAT)**: For spatial and temporal feature modeling
+- **Multi-branch Inference**: Multiple inference branches for robust decision making
+
+## Architecture
+
+The AASIST3 model consists of several key components:
+
+1. **Wav2Vec2 Encoder**: Extracts SSL features from raw audio
+2. **KAN Bridge**: Transforms SSL features using Kolmogorov-Arnold Networks
+3. **Residual Encoder**: Processes features through multiple residual blocks
+4. **Graph Attention Networks**: 
+   - GAT-S: Spatial attention mechanism
+   - GAT-T: Temporal attention mechanism
+5. **Multi-branch Inference**: Four parallel inference branches with master tokens
+6. **KAN Output Layer**: Final classification using KAN linear layers
+
+### Key Innovations
+
+- **KAN Integration**: Replaces traditional linear layers with KAN linear layers for better feature approximation
+- **Enhanced Regularization**: Additional dropout and regularization techniques
+- **Multi-dataset Training**: Trained on multiple ASVspoof datasets for robustness
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+git clone https://github.com/your-username/AASIST3.git
+cd AASIST3
+pip install -r requirements.txt
+```
+
+### Loading the Model
+
+```python
+from model import aasist3
+
+# Load the model from Hugging Face Hub
+model = aasist3.from_pretrained("MTUCI/AASIST3")
+model.eval()
+```
+
+### Basic Usage
+
+```python
+import torch
+import torchaudio
+
+# Load and preprocess audio
+audio, sr = torchaudio.load("audio_file.wav")
+# Ensure audio is 16kHz and mono
+if sr != 16000:
+    audio = torchaudio.transforms.Resample(sr, 16000)(audio)
+if audio.shape[0] > 1:
+    audio = torch.mean(audio, dim=0, keepdim=True)
+
+# Prepare input (model expects ~4 seconds of audio at 16kHz)
+# Pad or truncate to 64600 samples
+if audio.shape[1] < 64600:
+    audio = torch.nn.functional.pad(audio, (0, 64600 - audio.shape[1]))
+else:
+    audio = audio[:, :64600]
+
+# Run inference
+with torch.no_grad():
+    output = model(audio)
+    probabilities = torch.softmax(output, dim=1)
+    prediction = torch.argmax(probabilities, dim=1)
+    
+    # prediction: 0 = bonafide, 1 = spoof
+    print(f"Prediction: {'Bonafide' if prediction.item() == 0 else 'Spoof'}")
+    print(f"Confidence: {probabilities.max().item():.3f}")
+```
+
+## Training Details
+
+### Datasets Used
+
+The model was trained on a combination of multiple datasets:
+
+- **ASVspoof 2019 LA** (Logical Access)
+- **ASVspoof 2024 (ASVspoof5)** 
+- **MLAAD** (Multi-Language Audio Anti-Spoofing Dataset)
+- **M-AILABS** (Multi-Language Audio Dataset)
+
+### Training Configuration
+
+- **Epochs**: 20
+- **Batch Size**: 12 (training), 24 (validation)
+- **Learning Rate**: 1e-4
+- **Optimizer**: AdamW
+- **Loss Function**: CrossEntropyLoss
+- **Gradient Accumulation Steps**: 2
+
+### Hardware
+
+- **GPUs**: 2xA100 40GB
+- **Framework**: PyTorch with Accelerate for distributed training
+
+##  Advanced Usage
+
+### Custom Training
+
+```bash
+# Train the model
+bash train.sh
+```
+
+### Validation
+
+```bash
+# Run validation on test sets
+bash validate.sh
+```
+
+### Model Configuration
+
+The model can be configured through the `configs/train.yaml` file:
+
+```yaml
+# Key parameters
+num_epochs: 20
+train_batch_size: 12
+val_batch_size: 24
+learning_rate: 1e-4
+gradient_accumulation_steps: 2
+```
+
+
+## 🤝 Citation
+
+If you use this implementation in your research, please cite the original paper:
+
+```bibtex
+@inproceedings{borodin24_asvspoof,
+  title     = {AASIST3: KAN-enhanced AASIST speech deepfake detection using SSL features and additional regularization for the ASVspoof 2024 Challenge},
+  author    = {Kirill Borodin and Vasiliy Kudryavtsev and Dmitrii Korzh and Alexey Efimenko and Grach Mkrtchian and Mikhail Gorodnichev and Oleg Y. Rogov},
+  year      = {2024},
+  booktitle = {The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024)},
+  pages     = {48--55},
+  doi       = {10.21437/ASVspoof.2024-8},
+}
+```
+
+##  License
+
+This project is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) - see the [LICENSE](LICENSE) file for details.
+
+This license allows you to:
+- **Share**: Copy and redistribute the material in any medium or format
+- **Attribution**: You must give appropriate credit, provide a link to the license, and indicate if changes were made
+
+But does NOT allow:
+- **Commercial use**: You may not use the material for commercial purposes
+- **Derivatives**: You may not distribute modified versions of the material
+
+For more information, visit: https://creativecommons.org/licenses/by-nc-nd/4.0/
+
+
+**Disclaimer**: This is a research implementation. The model weights provided are for demonstration purposes and may not match the exact performance reported in the paper.