Open-source speech command classification model of speechcommand-demo: Free deployment for high-accuracy speech recognition

Speechcommand Demo

Developed by SHENMU007

A fine-tuned voice command classification model based on facebook/wav2vec2-base, trained on the superb dataset with an accuracy of 98.09%

Audio Classification

Transformers

Open Source License:Apache-2.0 #Voice command recognition #High-accuracy audio classification #wav2vec2 fine-tuning

Downloads 18

Release Time : 5/23/2023

Model Overview

This model is an optimized deep learning model for voice command classification tasks, capable of accurately recognizing and classifying voice commands

Model Features

High accuracy

Achieves 98.09% accuracy on the evaluation set

Based on wav2vec2 architecture

Uses facebook/wav2vec2-base as the base model for fine-tuning

Efficient training

Optimizes training efficiency using mixed-precision training and gradient accumulation techniques

Model Capabilities

Voice command classification

Audio feature extraction

Voice command recognition

Use Cases

Smart home control

Voice-controlled devices

Recognizes user voice commands to control smart home devices

High-accuracy recognition of common control commands

Voice assistants

Basic command recognition

Provides basic command recognition capability for voice assistants

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6433	1.0	399	0.4979	0.9112
0.2406	2.0	798	0.1455	0.9750
0.1563	3.0	1197	0.1032	0.9785
0.1144	4.0	1597	0.0919	0.9806
0.1254	5.0	1995	0.0873	0.9809

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Speechcommand Demo

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 speechcommand-demo

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📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

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