Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language: en
+pipeline_tag: audio-classification
+library_name: transformers
+tags:
+- deepfake
+- audio
+- wav2vec2
+- pytorch
+---
+
+# 🔊 Deepfake Audio Detection Model
+
+## 📌 Overview
+This model detects whether an audio file is **REAL or FAKE (AI-generated voice)**.
+
+It is based on **Wav2Vec2 architecture** and uses transformer-based audio embeddings.
+
+---
+
+## 🎯 Task
+Binary Classification:
+- 0 → REAL AUDIO
+- 1 → FAKE AUDIO
+
+---
+
+## 📥 Input
+- Audio file (.wav)
+- Sampling rate: 16kHz
+
+---
+
+## 📤 Output
+- Fake probability (0 to 1)
+
+---
+
+## ⚙️ Model Files
+- pytorch_model.bin
+- config.json
+- preprocessor_config.json
+- tokenizer files
+
+---
+
+## 🚀 Usage
+
+```python
+from transformers import AutoProcessor, AutoModel
+import librosa
+import torch
+
+processor = AutoProcessor.from_pretrained("Simma7/audio_model")
+model = AutoModel.from_pretrained("Simma7/audio_model")
+
+audio, sr = librosa.load("test.wav", sr=16000)
+
+inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+embedding = outputs.last_hidden_state.mean(dim=1)
+prob = torch.sigmoid(embedding.mean()).item()
+
+print(prob)