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Wav2vec2 Base Timit Demo Colab6
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Wav2vec2 Base Timit Demo Colab6

Developed by hassnain
This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained on the TIMIT dataset with a word error rate (WER) of 0.5282.
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Low Word Error Rate#TIMIT Dataset
Downloads 19
Release Time : 5/1/2022

Model Overview

A fine-tuned model for English speech recognition, based on the wav2vec2 architecture, suitable for speech-to-text tasks.

Model Features

Low Word Error Rate
Achieves a word error rate (WER) of 0.5282 on the evaluation set, demonstrating excellent performance.
Based on wav2vec2 Architecture
Uses facebook's wav2vec2-base as the base model, featuring powerful speech feature extraction capabilities.
Efficient Training
Utilizes mixed-precision training and linear learning rate scheduling for high training efficiency.

Model Capabilities

English Speech Recognition
Speech-to-Text

Use Cases

Speech Transcription
Meeting Transcription
Automatically converts English meeting recordings into text transcripts
Accuracy approximately 47.18% (WER=0.5282)
Voice Command Recognition
Recognizes English voice commands and converts them into executable commands
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