Wav2vec2-base-finetuned-ks Open-source Speech Recognition Model - Excellent Performance in Keyword Recognition Task

Wav2vec2 Base Finetuned Ks

Developed by teoha

This model is a speech recognition model fine-tuned on the SUPERB dataset based on facebook/wav2vec2-base, demonstrating excellent performance in keyword spotting tasks.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #High Accuracy #wav2vec2 Fine-tuning

Downloads 14

Release Time : 2/12/2023

Model Overview

This is a speech recognition model based on the wav2vec2 architecture, specifically fine-tuned for keyword spotting tasks. It achieved an accuracy of 98.35% on the evaluation set.

Model Features

High Accuracy

Achieves 98.35% accuracy in keyword spotting tasks

Based on wav2vec2 Architecture

Utilizes the advanced wav2vec2 speech representation learning architecture

Efficient Fine-tuning

Fine-tuned on the SUPERB dataset, optimizing performance for keyword spotting

Model Capabilities

Speech Recognition

Keyword Detection

Use Cases

Smart Home

Voice-controlled Devices

Used to recognize user voice commands for controlling smart home devices

High accuracy in recognizing keyword commands

Voice Assistants

Wake Word Detection

Detects wake words for voice assistants

Quickly and accurately recognizes preset wake words

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5924	1.0	399	0.5081	0.9626
0.2989	2.0	798	0.1785	0.9762
0.1947	3.0	1197	0.1100	0.9806
0.1766	4.0	1596	0.0873	0.9835
0.1211	5.0	1995	0.0840	0.9835

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Wav2vec2 Base Finetuned Ks

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 wav2vec2-base-finetuned-ks

🚀 Quick Start

📚 Documentation

Training and Evaluation

Training hyperparameters

Training results

Framework versions

📄 License