deepfake-audio-wav2vec2-ONNX

deepfake-audio-wav2vec2 (ONNX)

This is an ONNX version of Vansh180/deepfake-audio-wav2vec2. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for audio-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AudioClassificationPipeline

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🧾 Model Card — Deepfake-Audio-Wav2Vec2

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🧠 Model Overview

Deepfake-Audio-Wav2Vec2 is a fine-tuned audio classification model trained to detect real vs spoofed (deepfake) speech audio.

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

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🏗️ Training Details

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🎯 Label Classes

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

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📊 Evaluation Metrics

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

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📂 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.

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💻 Example Usage

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"))