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Wav2vec2 Base Timit Demo Colab30
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Wav2vec2 Base Timit Demo Colab30

Developed by hassnain
A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, achieving a Word Error Rate (WER) of 0.6534 after 30 training epochs
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Low Word Error Rate#TIMIT Dataset
Downloads 17
Release Time : 5/1/2022

Model Overview

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

Model Features

Efficient Fine-tuning
Fine-tuned based on the pre-trained wav2vec2-base model, achieving good performance with only a small amount of training data
Low Word Error Rate
Achieves a Word Error Rate (WER) of 0.6534 on the evaluation set, demonstrating good performance
Lightweight
Based on the base version of the wav2vec2 architecture, suitable for deployment in resource-constrained environments

Model Capabilities

English Speech Recognition
Speech-to-Text
Audio Content Transcription

Use Cases

Speech Transcription
Meeting Minutes
Automatically convert English meeting recordings into text transcripts
Word Error Rate approximately 65.34%
Voice Notes
Convert English voice notes into searchable text
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