Update README.md

README.md

@@ -10,9 +10,9 @@ base_model:
 - facebook/wav2vec2-large-960h-lv60-self
 ---
 
-# 🔍 **SSL-AntiDeepfake**
+# 🔍 **AntiDeepfake**
 
-The **SSL-AntiDeepfake** project provides a series of powerful self-supervised learning (SSL) models crafted to **detect deepfake speech** with state-of-the-art accuracy.
+The **AntiDeepfake** project provides a series of powerful self-supervised learning (SSL) models crafted to **detect deepfake speech** with state-of-the-art accuracy.
 
 
 # 🤖 Available Models
@@ -32,8 +32,7 @@ All models are released on Hugging Face 🤗 with two variants:
 | Hubert-Extra-Large-AntiDeepfake | [Default](https://huggingface.co/nii-yamagishilab/hubert-xlarge-anti-deepfake), [NDA](https://huggingface.co/nii-yamagishilab/hubert-xlarge-anti-deepfake-nda)
 
 # 🛠️ Training Code & Repository
-Explore training scripts, config files, and evaluation utilities in our GitHub repository:🔗 [SSL-AntiDeepfake GitHub Repository](https://github.com/nii-yamagishilab/Ultra-SSL-AntiDeepfake)
-
+Explore training scripts, config files, and evaluation utilities in our GitHub repository:🔗 [AntiDeepfake GitHub Repository](https://github.com/nii-yamagishilab/AntiDeepfake)
 
 # 🚀 **Model Spotlight: Wav2Vec2-Large-AntiDeepfake**
 
@@ -56,18 +55,27 @@ Explore training scripts, config files, and evaluation utilities in our GitHub r
 
 # 🚀 **Inference with PyTorch**
 
-⚠️ **Important:**  
-To run inference with this model, you need to install a specific version of `fairseq` and make manual code modifications. For detailed instructions, please refer to the installation guide in our [GitHub repository](https://github.com/nii-yamagishilab/Ultra-SSL-AntiDeepfake).
-
-✅ **Easier Alternatives:**
-We recommend these models for plug-and-play inference: 
-- [MMS-300M-AntiDeepfake](https://huggingface.co/nii-yamagishilab/mms-300m-anti-deepfake)  
-- [MMS-1B-AntiDeepfake](https://huggingface.co/nii-yamagishilab/mms-1b-anti-deepfake)
-
-📦 Dependencies after installing fairseq:
+📦 Dependencies:
+```
+### New conda environments ###
+conda create --name antideepfake python==3.9.0
+conda activate antideepfake
+conda install pip==24.0
+
+### Install Fariseq ###
+# fairseq 0.10.2 on pip does not work
+git clone https://github.com/pytorch/fairseq
+cd fairseq
+# checkout this specific commit. Latest commit does not work
+git checkout 862efab86f649c04ea31545ce28d13c59560113d
+pip install --editable .
+
+### Install other packages ###
+pip install huggingface-hub==0.31.1 safetensors==0.5.3 soundfile==0.13.1 numpy==1.21.2
+```
+Additionally, you need to update line 315 in `/where/you/clone/fairseq/fairseq/checkpoint_utils.py` to:
 ```
-pip install huggingface-hub safetensors soundfile
-(pip install huggingface-hub==0.31.1 safetensors==0.5.3 soundfile==0.13.1)
+state = torch.load(f, map_location=torch.device("cpu"), weights_only=False) 
 ```
 
 🚀 Inference:
@@ -178,7 +186,7 @@ for file_name, prob in results:
     print(f"{file_name}: real prob = {prob[1]:.3f}, fake prob = {prob[0]:.3f}")
 ```
 # 📊 **Performance Metrics**
-Results shown below can be reproduced using scripts provided in our [GitHub repository](https://github.com/nii-yamagishilab/Ultra-SSL-AntiDeepfake).
+Results shown below can be reproduced using scripts provided in our [GitHub repository](https://github.com/nii-yamagishilab/AntiDeepfake).
 
 | Test Database        | ROC AUC | Accuracy | Precision | Recall | F1-score | FPR   | FNR   | EER (%) @ Threshold |
 |----------------------|---------|----------|-----------|--------|----------|-------|-------|----------------------|
@@ -227,7 +235,7 @@ Below is a breakdown of the training set used for fine-tuning.
 | Train Set            | Over 100 languages| 56370.00       | 18280.00    |
 
 # **Attribution**
-All SSL-AntiDeepfake models were developed by [Yamagishi Lab](https://yamagishilab.jp/) at the National Institute of Informatics (NII), Japan.
+All AntiDeepfake models were developed by [Yamagishi Lab](https://yamagishilab.jp/) at the National Institute of Informatics (NII), Japan.
 
 All model weights are the intellectual property of NII and are made available for research and educational purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.