wav2vec_checkpoints Open-source Speech Processing Model - Accuracy on Evaluation Set Reaches 99.48% after Fine-tuning

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Wav2vec Checkpoints

Developed by Zeyadd-Mostaffa

A fine-tuned speech processing model based on facebook/wav2vec2-base, achieving 99.48% accuracy on the evaluation set

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #High Accuracy #Fine-tuned Model

Downloads 19

Release Time : 2/8/2025

Model Overview

This model is a fine-tuned version of facebook/wav2vec2-base, primarily used for speech recognition tasks, demonstrating excellent performance on the evaluation set.

Model Features

High Accuracy

Achieved 99.48% accuracy on the evaluation set, demonstrating outstanding performance.

Efficient Training

Utilized mixed-precision training and gradient accumulation techniques to improve training efficiency.

Optimized Hyperparameters

Employed carefully tuned learning rates, batch sizes, and other hyperparameters to ensure model performance.

Model Capabilities

Speech Recognition

Audio Feature Extraction

Use Cases

Speech-to-Text

Meeting Minutes

Convert meeting recordings into text transcripts

Highly accurate text output

Voice Assistant

Used as the speech recognition module for voice assistants

Fast and accurate voice command recognition

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1389	1.0	278	0.0938	0.9727
0.0209	2.0	556	0.0347	0.9892
0.0203	3.0	834	0.0310	0.9922
0.0303	4.0	1112	0.0325	0.9925
0.0137	4.9829	1385	0.0267	0.9948

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Wav2vec Checkpoints

Model Overview

Model Features

Model Capabilities

Use Cases

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