Simma7/audio_model

🔊 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.

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🎯 Task

Binary Classification:

• 0 → REAL AUDIO
• 1 → FAKE AUDIO

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📥 Input

• Audio file (.wav)
• Sampling rate: 16kHz

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📤 Output

• Fake probability (0 to 1)

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⚙️ Model Files

• pytorch_model.bin
• config.json
• preprocessor_config.json
• tokenizer files

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🚀 Usage

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)