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Wav2vec2 Base Timit Demo Colab51

Developed by hassnain
This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.748 on the TIMIT dataset.
Downloads 16
Release Time : 5/1/2022

Model Overview

A pre-trained model for English speech recognition, fine-tuned based on the wav2vec2 architecture, suitable for Automatic Speech Recognition (ASR) tasks.

Model Features

Efficient Fine-tuning
Fine-tuned based on the powerful wav2vec2-base model, achieving good results even with limited data.
Low Word Error Rate
Achieved a word error rate (WER) of 0.748 on the evaluation set, demonstrating good performance.
End-to-End Training
Adopts an end-to-end training approach, directly mapping audio input to text output.

Model Capabilities

English Speech Recognition
Audio to Text Conversion
Automatic Speech Transcription

Use Cases

Speech Transcription
Automated Meeting Minutes
Automatically convert meeting recordings into text transcripts
Approximately 75.2% accuracy
Voice Command Recognition
Recognize simple voice commands
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