Shanmugapriya6/voice-fake-detector-v1

Voice Fake Detector v1

This model detects whether an audio clip is real human speech or AI-generated (deepfake). It is based on a fine-tuned Wav2Vec2 architecture and designed for robust audio deepfake detection.

Overview

The model performs binary classification:

• —Real (human voice)
• —Fake (AI-generated voice)

It is suitable for deepfake detection, voice verification, and media authenticity applications.

Model Details

• —Task: Audio Classification
• —Architecture: Wav2Vec2 (Transformer-based)
• —Base Model: facebook/wav2vec2-xls-r-300m
• —Framework: PyTorch (Transformers)
• —Input: .wav audio
• —Output: Label with confidence score

Usage

from transformers import pipeline

classifier = pipeline("audio-classification", model="Shanmugapriya6/voice-fake-detector-v1")

result = classifier("audio.wav")
print(result)

Example

Input: audio.wav Output: Fake (confidence: 0.91)

Training Data

The model was trained on a combination of real and synthetic speech data.

Real Speech Datasets

• —Svarah dataset (AI4Bharat)
• —Kathbath dataset (AI4Bharat) – Tamil subset accessed via AIKosh platform
• —Indian Languages Audio Dataset (Kaggle): https://www.kaggle.com/datasets/hmsolanki/indian-languages-audio-dataset

Synthetic (Fake) Audio

• —AI-generated speech samples created using text-to-speech (TTS) systems

Preprocessing

• —Audio resampling
• —Silence trimming
• —Normalization
• —Temporal chunking

Performance

Evaluation results on ASVspoof2019 subset:

• —Accuracy: 0.9286
• —Precision: 0.9999
• —Recall: 0.9205
• —F1 Score: 0.9363
• —Equal Error Rate (EER): 0.0401

Note: Performance may vary depending on dataset and audio conditions.

Limitations

• —Performance may degrade on noisy or low-quality audio
• —Not evaluated across all languages and accents
• —May not detect highly advanced deepfake techniques

Out-of-Scope Use

• —Not intended for legal or forensic decisions
• —Not recommended for high-risk authentication systems

License

MIT