The model is built on top of facebook/wav2vec2-base, a self-supervised speech representation model, and adapted for binary deepfake audio detection.

It learns subtle acoustic artifacts and synthetic speech patterns that differentiate genuine human recordings from AI-generated or manipulated audio samples.

This model is intended for:

Deepfake voice detection
Audio authenticity verification
Research in anti-spoofing systems
Security pipelines for voice-based applications

🏗️ Training Details

Parameter	Value
Base Model	facebook/wav2vec2-base
Framework	Hugging Face Transformers
Training Hardware	GPU (CUDA)
Task Type	Audio Classification
Classes	bonafide / spoof
Audio Sample Rate	16 kHz
Input Duration	Fixed audio segments
Optimization	AdamW
Loss Function	Cross Entropy

🎯 Label Classes

🟢 Bonafide → Real / authentic speech
🔴 Spoof → Deepfake / synthetic audio

📊 Evaluation Metrics

Metric	Score
Accuracy	92.8%
Precision	89.7%
Recall	88.0%
F1 Score	88.4%

✅ The model demonstrates strong detection performance across real and spoofed samples.

📂 Dataset Description

The model was trained on a balanced subset of the ASVspoof 2021 PA dataset for binary anti-spoofing classification.

The dataset includes:

Genuine speech recordings
Spoofed / manipulated audio samples
Replay and synthetic attack scenarios

Training was performed on balanced class samples to improve robustness across both labels.

💻 Example Usage

python

import torch
import torchaudio
import numpy as np
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification

model_id = "Vansh180/deepfake-audio-wav2vec2"

feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = AutoModelForAudioClassification.from_pretrained(model_id)

model.eval()

def predict_audio(audio_path):
    wav, sr = torchaudio.load(audio_path)

    if wav.shape[0] > 1:
        wav = wav.mean(dim=0, keepdim=True)

    inputs = feature_extractor(
        wav.squeeze().numpy(),
        sampling_rate=16000,
        return_tensors="pt"
    )

    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1)

    prediction = torch.argmax(probs, dim=1).item()
    confidence = probs[0][prediction].item()

    return {
        "prediction": model.config.id2label[prediction],
        "confidence": confidence
    }

print(predict_audio("sample.wav"))