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Wav2vec2 Base 960h Timit Demo Colab
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Wav2vec2 Base 960h Timit Demo Colab

Developed by obokkkk
A speech recognition model fine-tuned based on facebook/wav2vec2-base-960h, achieving a 21.6% word error rate on the TIMIT dataset
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Fine-tuned Model#Low Word Error Rate
Downloads 20
Release Time : 4/22/2022

Model Overview

This is an automatic speech recognition (ASR) model for English speech recognition, fine-tuned based on the wav2vec2 architecture, suitable for speech-to-text tasks

Model Features

High Accuracy Speech Recognition
Achieves a 21.6% word error rate on the TIMIT evaluation set
Based on wav2vec2 Architecture
Utilizes powerful speech representation capabilities from self-supervised pre-training
Lightweight Model
The base version is relatively lightweight, suitable for deployment in various environments

Model Capabilities

English Speech Recognition
Speech-to-Text
Audio Content Transcription

Use Cases

Speech Transcription
Automated Meeting Minutes
Automatically convert English meeting recordings into text transcripts
Can achieve approximately 80% accuracy
Voice Command Recognition
Recognize user voice commands and convert them into executable commands
Education
Pronunciation Assessment
Analyze the pronunciation accuracy of English learners
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