Deepfake-audio-detection Open-source Voice Detection Model - Accurately Identify Fake Audio, with an Evaluation Accuracy Close to 99%

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Deepfake Audio Detection

Developed by mo-thecreator

A fine-tuned speech processing model based on wav2vec2-base-finetuned, achieving 98.82% accuracy on the evaluation set

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #High Accuracy #Fine-tuning Optimization

Downloads 801

Release Time : 5/18/2024

Model Overview

This model is a fine-tuned version of wav2vec2-base-finetuned, primarily used for speech-related tasks with high accuracy performance

Model Features

High Accuracy

Achieves 98.82% accuracy on the evaluation set

Based on wav2vec2 Architecture

Utilizes the mature wav2vec2 architecture for fine-tuning

Efficient Training

Optimizes the training process with techniques like gradient accumulation

Model Capabilities

Speech Recognition

Speech Feature Extraction

Use Cases

Speech Processing

Speech-to-Text

Convert speech signals into text content

High-accuracy conversion results

Voice Command Recognition

Recognize voice commands and execute corresponding actions

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.1448	1.0	1900	0.9601	0.1447
0.0673	2.0	3800	0.9824	0.0817
0.0178	3.0	5700	0.9796	0.1054
0.0002	4.0	7600	0.9824	0.1074
0.0108	5.0	9500	0.9882	0.0829

Property	Details
Model Type	wav2vec2 - base - finetuned - finetuned
Training Data	None dataset
License	Apache 2.0

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Deepfake Audio Detection

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-finetuned-finetuned

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Contributors

📄 License