wav2vec2-base-timit-demo-colab3 Open Source Speech Recognition Model - Precise Speech Recognition with Low Error Rate

Wav2vec2 Base Timit Demo Colab3

Developed by hassnain

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.6704 on the TIMIT dataset.

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Release Time : 5/1/2022

Model Overview

This is a fine-tuned model for speech recognition tasks, based on the wav2vec2 architecture, suitable for English speech-to-text applications.

Based on wav2vec2 Architecture

Uses Facebook's wav2vec2-base as the base model, featuring excellent speech feature extraction capabilities.

Low Word Error Rate

Achieved a word error rate of 0.6704 on the evaluation set, demonstrating strong performance.

Efficient Training

Utilizes mixed-precision training and a linear learning rate scheduler for high training efficiency.

English Speech Recognition

Speech-to-Text

Speech Transcription

Automatic Meeting Transcription

Automatically converts English meeting recordings into text transcripts

Word error rate 0.6704

Voice Note Conversion

Converts English voice notes into editable text

Training Loss	Epoch	Step	Validation Loss	Wer
5.0006	13.89	500	3.0706	1.0
1.8796	27.78	1000	1.1154	0.7414
0.548	41.67	1500	1.0826	0.7034
0.2747	55.56	2000	1.1016	0.6704

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