Update README.md

README.md

@@ -1,9 +1,200 @@
 ---
-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+license: cc-by-nc-sa-4.0
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+- roc_auc
+base_model:
+- facebook/wav2vec2-large-960h-lv60-self
 ---
 
-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]
+# 🔍 **SSL-AntiDeepfake**
+
+The **SSL-AntiDeepfake** models are a series of self-supervised learning (SSL) models fine-tuned on multiple datasets of real and deepfake speech. 
+They are trained to classify speech as either "Real" 🟢 or "Fake" 🔴.
+| Repository | Link |
+|------------|------|
+| SSL-AntiDeepfake | [GitHub Repository](https://github.com/nii-yamagishilab/Ultra-SSL-AntiDeepfake) |
+
+
+# 🚀 **Model: Wav2Vec2-Large-AntiDeepfake**
+
+# 🌟 **Key Features**
+- **Architecture**: Wav2Vec 2.0 - [`facebook/wav2vec2-large-960h-lv60-self `](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self/tree/main) 🔗.
+- **Input**: 16kHz sampled speech with arbitrary length 🎙️.
+- **Output**: Binary classification score (<Fake score 🟢 , Real score🔴>).
+- **Training Dataset**: Totally 18k hours of fake speech and 56k hours of real speech.
+
+# 🏗️ **Architecture**
+- **Front-end Feature Extractor**: Wav2Vec2-Large.
+- **Back-end Classifier**: A fully connected layer.
+
+# ⚙️ **Training Details**
+- **Optimizer**: AdamW with a learning rate of `1e-7`.
+- **Batch Size**: Dynamic Batching, maximum length per batch is set to 100 seconds.
+- **Data Augmentation**: RawBoost series: (1)+(2) 
+- **Loss Function**: Cross-Entropy Loss.
+- **Evaluation Metrics**: Equal Error Rate (EER), ROC AUC, Accuracy, Precision, Recall, F1 Score.
+
+# 🚀 **Inference with PyTorch**
+📦 Dependencies:
+```
+pip install pip==23.1.1
+pip install huggingface-hub fairseq safetensors soundfile
+(pip install huggingface-hub==0.31.1 fairseq==0.12.2 safetensors==0.5.3 soundfile==0.13.1)
+```
+
+🚀 Inference:
+```python
+import os
+import urllib.request
+import torch
+import torchaudio
+import fairseq
+from huggingface_hub import PyTorchModelHubMixin
+
+
+# === Download Fairseq checkpoint if not present ===
+# The downloaded checkpoint is used for building front-end architecture,
+# its weights will be replaced by the model.safetensors file in this repo.
+ssl_path = "w2v_large_lv_fsh_swbd_cv.pt"
+ssl_url = "https://dl.fbaipublicfiles.com/fairseq/wav2vec/w2v_large_lv_fsh_swbd_cv.pt"
+
+if not os.path.exists(ssl_path):
+    print(f"Downloading checkpoint to {ssl_path}...")
+    urllib.request.urlretrieve(ssl_url, ssl_path)
+    print("Download complete.")
+else:
+    print(f"{ssl_path} already exists. Skipping download.")
+
+# === Wrapper for the SSL model ===
+class SSLModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        # The downloaded .pt file is used here
+        model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([ssl_path])
+        self.model = model[0]
+
+    def extract_feat(self, input_data):
+        # If input has shape (B, T, 1), squeeze the last dim
+        if input_data.ndim == 3:
+            input_data = input_data[:, :, 0]
+
+        # Extract features
+        with torch.no_grad():
+            features = self.model(input_data, mask=False, features_only=True)['x']
+        return features
+
+# === Function for reading and pre-processing waveforms ===
+def load_wav_and_preprocess(wav_path, target_sr=16000):
+    # Load audio file
+    wav, sr = torchaudio.load(wav_path)
+    # Convert to mono if stereo
+    wav = wav.mean(dim=0)
+    # Resample to target sampling rate
+    wav = torchaudio.functional.resample(wav, sr, new_freq=target_sr)
+    # Normalize waveform
+    with torch.no_grad():
+        wav = torch.nn.functional.layer_norm(wav, wav.shape)
+    # Add batch dimension and return
+    return wav.unsqueeze(0)
+
+# === The actual deepfake detection model using SSL frontend + FC backend ===
+class DeepfakeDetector(torch.nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.ssl_orig_output_dim = 1024
+        self.num_classes = 2
+
+        # Frontend: SSL model
+        self.m_ssl = SSLModel()
+
+        # Backend: Pooling + Classification
+        self.adap_pool1d = torch.nn.AdaptiveAvgPool1d(output_size=1)
+        self.proj_fc = torch.nn.Linear(
+            in_features=self.ssl_orig_output_dim,
+            out_features=self.num_classes,
+        )
+
+    def forward(self, wav):
+        emb = self.m_ssl.extract_feat(wav)  # [B, T, D]
+        emb = emb.transpose(1, 2)           # [B, D, T]
+        pooled_emb = self.adap_pool1d(emb)  # [B, D, 1]
+        pooled_emb = pooled_emb.squeeze(-1) # [B, D]
+        logits = self.proj_fc(pooled_emb)   # [B, 2]
+        return logits
+
+# === Load AntiDeepfake model from Hugging Face===
+model = DeepfakeDetector.from_pretrained("nii-yamagishilab/wav2vec-large-anti-deepfake")
+model.eval()
+
+# === Inference on a folder of audio files ===
+folder_path = "path/to/folder/contains/wavs/"
+supported_formats = (".mp3", ".wav", ".flac", ".m4a")
+
+results = []
+for root, _, files in os.walk(folder_path):
+    for file in files:
+        if file.lower().endswith(supported_formats):
+            input_path = os.path.join(root, file)
+            with torch.no_grad():
+                wav = load_wav_and_preprocess(input_path)
+                logits = model(wav)
+                probs = torch.nn.functional.softmax(logits, dim=1)
+                results.append((file, probs.cpu().numpy()[0]))
+
+# Sort results alphabetically by filename
+results.sort(key=lambda x: x[0])
+
+# Print formatted results
+print("\n=== Deepfake Detection Results ===")
+for file_name, prob in results:
+    print(f"{file_name}: real prob = {prob[1]:.3f}, fake prob = {prob[0]:.3f}")
+```
+# 📊 **Performance Metrics**
+| Test Database        | ROC AUC | Accuracy | Precision | Recall | F1-score | FPR   | FNR   | EER (%) @ Threshold |
+|----------------------|---------|----------|-----------|--------|----------|-------|-------|----------------------|
+| ADD2023              | 0.950   | 0.912    | 0.939     | 0.942  | 0.940    | 0.175 | 0.058 | 13.25 @ 0.8520       |
+| DeepVoice            | 0.991   | 0.762    | 0.340     | 0.994  | 0.507    | 0.270 | 0.006 | 4.53 @ 0.9974        |
+| FakeOrReal           | 1.000   | 0.992    | 0.994     | 0.989  | 0.991    | 0.005 | 0.011 | 0.63 @ 0.3727        |
+| FakeOrReal-norm      | 0.999   | 0.986    | 0.975     | 0.997  | 0.986    | 0.025 | 0.003 | 0.97 @ 0.7975        |
+| In-the-Wild          | 0.997   | 0.976    | 0.991     | 0.970  | 0.980    | 0.015 | 0.030 | 1.91 @ 0.3240        |
+| WildSVDD-TEST_A      | 0.739   | 0.663    | 0.616     | 0.895  | 0.729    | 0.577 | 0.105 | 34.85 @ 0.9942       |
+| WildSVDD-TEST_B      | 0.654   | 0.696    | 0.740     | 0.836  | 0.785    | 0.583 | 0.164 | 41.67 @ 0.9842       |
+
+
+# **Training Set**
+| 📚 Database             | 🌍 Language         | ✅ Genuine (hrs) | ❌ Fake (hrs) |
+|----------------------|------------------|----------------|-------------|
+| AISHELL3             | zh               | 85.62          | 0           |
+| ASVspoof2019-LA      | en               | 11.85          | 97.80       |
+| ASVspoof2021-LA      | en               | 16.40          | 116.10      |
+| ASVspoof2021-DF      | en               | 20.73          | 487.00      |
+| ASVspoof5            | en               | 413.49         | 1808.48     |
+| CFAD                 | zh               | 171.25         | 224.55      |
+| CNCeleb2             | zh               | 1084.34        | 0           |
+| Codecfake            | en, zh           | 129.66         | 808.32      |
+| CodecFake            | en               | 0              | 660.92      |
+| CVoiceFake           | en, fr, de, it, zh| 315.14         | 1561.16     |
+| DECRO                | en, zh           | 35.18          | 102.44      |
+| DFADD                | en               | 41.62          | 66.01       |
+| Diffuse or Confuse   | en               | 0              | 231.66      |
+| DiffSSD              | en               | 0              | 139.73      |
+| DSD                  | en, ja, ko       | 100.98         | 60.23       |
+| FLEURS               | 102 languages    | 1388.97        | 0           |
+| FLEURS-R             | 102 languages    | 0              | 1238.83     |
+| HABLA                | es               | 35.56          | 87.83       |
+| LibriTTS             | en               | 585.83         | 0           |
+| LibriTTS-R           | en               | 0              | 583.15      |
+| LibriTTS-Vocoded     | en               | 0              | 2345.14     |
+| LJSpeech             | en               | 23.92          | 0           |
+| MLADD                | 38 languages     | 0              | 377.96      |
+| MLS                  | 8 languages      | 50558.11       | 0           |
+| SpoofCeleb           | Multilingual     | 173.00         | 1916.20     |
+| VoiceMOS             | en               | 0              | 448.44      |
+| VoxCeleb2            | Multilingual     | 1179.62        | 0           |
+| VoxCeleb2-Vocoded    | Multilingual     | 0              | 4721.46     |
+| WaveFake             | en, ja           | 0              | 198.65      |
+| Train Set            | Over 100 languages| 56370.00       | 18280.00    |