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Wav2vec2 Base Timit Demo Google Colab

Developed by mikeluck
This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, achieving a word error rate (WER) of 0.3384 on the evaluation set.
Downloads 38
Release Time : 6/15/2022

Model Overview

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

Model Features

Low Word Error Rate
Achieved a word error rate (WER) of 0.3384 on the TIMIT dataset, demonstrating good performance.
Based on wav2vec2 Architecture
Uses facebook's wav2vec2-base as the base model, featuring powerful speech feature extraction capabilities.
Lightweight Model
The base version is relatively lightweight, suitable for deployment in resource-constrained environments.

Model Capabilities

English Speech Recognition
Speech-to-Text
Audio Content Transcription

Use Cases

Speech Transcription
Automatic Meeting Transcription
Automatically converts English meeting recordings into text transcripts.
Accuracy approximately 66.16% (1-WER)
Voice Note Conversion
Converts personal voice notes into searchable text.
Assistive Technology
Real-time Caption Generation
Generates real-time captions for English videos or live streams.
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