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Wav2vec2 Base Timit Demo Colab
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Wav2vec2 Base Timit Demo Colab

Developed by nawta
A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, featuring a low Word Error Rate (WER).
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#Low Word Error Rate#TIMIT Dataset
Downloads 96
Release Time : 6/27/2022

Model Overview

This is a pre-trained model for English speech recognition, demonstrating excellent performance after fine-tuning on the TIMIT dataset.

Model Features

Low Word Error Rate
Achieved a Word Error Rate (WER) of 0.0168 on the TIMIT dataset, demonstrating outstanding performance.
Based on wav2vec2 Architecture
Utilizes the facebook wav2vec2-base architecture, which excels in speech feature extraction.
Fine-tuning Optimization
Significant performance improvement achieved through 30 epochs of meticulous fine-tuning.

Model Capabilities

English Speech Recognition
Audio to Text Conversion
Speech Content Analysis

Use Cases

Speech Transcription
Meeting Minutes
Automatically convert English meeting recordings into text transcripts
Accuracy as high as 98.32% (WER=0.0168)
Voice Notes
Convert spoken notes into searchable text
Voice Assistant
Voice Command Recognition
Recognize and execute English voice commands
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