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Wav2vec2 Base Timit Demo Colab12
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Wav2vec2 Base Timit Demo Colab12
Developed by sameearif88
A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, achieving a Word Error Rate (WER) of 0.3546
Downloads 16
Release Time : 5/1/2022
Model Overview
This pre-trained model is designed for English speech recognition, achieving good recognition accuracy through fine-tuning on the TIMIT dataset
Model Features
Low Word Error Rate
Achieves an excellent Word Error Rate (WER) of 0.3546 on the evaluation set
Based on wav2vec2 Architecture
Uses Facebook's open-source wav2vec2-base model as the foundational architecture
Fine-tuning Optimization
Significantly improves the original model's recognition performance through 30 epochs of meticulous tuning
Model Capabilities
English Speech Recognition
Audio to Text Conversion
Speech Content Analysis
Use Cases
Speech Transcription
Automatic Meeting Minutes Generation
Automatically converts meeting recordings into text transcripts
Approximately 65% accuracy (estimated based on WER 0.3546)
Voice Assistants
Voice Command Recognition
Recognizes user voice commands and converts them into executable instructions
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