wav2vec2-large-ft-fake-detection Open-Source Audio Classification Model

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Wav2vec2 Large Ft Fake Detection

Developed by alexandreacff

Audio classification model fine-tuned based on facebook/wav2vec2-large for detecting forged audio

Audio Classification

Transformers

Open Source License:Apache-2.0 #Audio Forgery Detection #High-precision Classification #Speech Analysis

Downloads 1,545

Release Time : 5/9/2024

Model Overview

This model is a fine-tuned version on the alexandreacff/kaggle-fake-detection dataset, primarily used for audio authenticity detection tasks

Model Features

High Accuracy

Achieves 71.03% accuracy on the evaluation set

Fine-tuned based on wav2vec2-large

Utilizes the powerful wav2vec2-large architecture for feature extraction

Lightweight Training

Requires only 10 training epochs to achieve good results

Model Capabilities

Audio Classification

Fake Audio Detection

Audio Feature Extraction

Use Cases

Security Detection

Audio Authenticity Verification

Detect whether voice recordings have been tampered with or synthesized

71.03% accuracy

Content Moderation

Synthetic Voice Recognition

Automatically identify AI-generated voice content on platforms

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6274	0.9851	33	0.6254	0.6206
0.4961	2.0	67	0.9477	0.6159
0.3391	2.9851	100	0.9273	0.6411
0.2857	4.0	134	0.6611	0.6617
0.3186	4.9851	167	0.7654	0.6215
0.2483	6.0	201	0.9395	0.6224
0.239	6.9851	234	0.8367	0.6542
0.2049	8.0	268	0.7709	0.6860
0.224	8.9851	301	0.6694	0.7103
0.2279	9.8507	330	0.7867	0.6822

Property	Details
Model Type	wav2vec2-large-ft-fake-detection
Training Data	alexandreacff/kaggle-fake-detection dataset
Metrics	accuracy

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Wav2vec2 Large Ft Fake Detection

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-large-ft-fake-detection

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

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