WavLM-base-Deepfake_V2 Open-source Speech Processing Model - Accuracy of the Evaluation Set Reaches 99.62%, Extremely Practical

Home

Wavlm Base Deepfake V2

Developed by DavidCombei

A speech processing model fine-tuned based on microsoft/wavLM-base, achieving 99.62% accuracy on the evaluation set

Audio Classification

Transformers

#High-precision speech processing #Fine-tuned wavLM #Low-loss model

Downloads 113

Release Time : 7/6/2024

Model Overview

This model is a speech processing model fine-tuned based on the wavLM-base architecture, primarily used for unknown speech-related tasks with high accuracy characteristics

Model Features

High accuracy

Achieves 99.62% accuracy on the evaluation set

Fine-tuning optimization

Targeted fine-tuning based on the wavLM-base model

Efficient training

Optimizes training process using techniques like gradient accumulation

Model Capabilities

Speech feature extraction

High-precision classification

Use Cases

Speech processing

Speech classification

Classify and recognize speech data

99.62% accuracy

Property	Details
Model Name	wavLM-base-UTCN
Base Model	microsoft/wavLM-base
Metrics	Accuracy
Tags	generated_from_trainer

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0603	1.0	435	0.0307	0.9928
0.009	2.0	870	0.0201	0.9962
0.0109	3.0	1305	0.0176	0.9972
0.001	4.0	1740	0.0246	0.9950
0.0013	5.0	2175	0.0234	0.9962

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Wavlm Base Deepfake V2

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wavLM-base-UTCN

🚀 Quick Start

📚 Documentation

Model Information

Training Procedure

Training hyperparameters

Training results

Framework versions