Open-source speech recognition model wav2vec2-base-timit-demo-colab7: Achieve English speech-to-text conversion with free deployment

Wav2vec2 Base Timit Demo Colab7

Developed by hassnain

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, primarily used for English speech-to-text tasks.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech recognition #Low word error rate #TIMIT dataset

Downloads 16

Release Time : 5/1/2022

Model Overview

This model is a fine-tuned version of wav2vec2-base, optimized for English speech recognition tasks, capable of converting English speech into text.

Model Features

Efficient speech recognition

Based on the wav2vec2 architecture, providing efficient English speech recognition capabilities

Fine-tuning optimization

Fine-tuned on the TIMIT dataset, improving recognition accuracy in specific scenarios

Lightweight

Based on the wav2vec2-base architecture, relatively lightweight and easy to deploy

Model Capabilities

English speech recognition

Speech-to-text

Use Cases

Speech transcription

English meeting minutes

Automatically convert English meeting recordings into text transcripts

Word Error Rate (WER) 0.6478

Voice command recognition

Recognize English voice commands and convert them into executable commands

Training Loss	Epoch	Step	Validation Loss	Wer
4.8409	7.04	500	3.1487	1.0
2.6259	14.08	1000	1.5598	0.8730
1.083	21.13	1500	1.0600	0.7347
0.6061	28.17	2000	1.0697	0.7006
0.4022	35.21	2500	1.0617	0.6913
0.2884	42.25	3000	1.1962	0.6768
0.225	49.3	3500	1.1753	0.6567
0.1852	56.34	4000	1.1687	0.6478

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Wav2vec2 Base Timit Demo Colab7

Model Overview

Model Features

Model Capabilities

Use Cases

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🔧 Technical Details

Training hyperparameters

Training results

Framework versions

📄 License