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Wav2vec2 Base Timit Demo Colab
A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, with a Word Error Rate (WER) of 0.3382
Downloads 24
Release Time : 3/20/2022
Model Overview
This is a model for English speech recognition, fine-tuned on the TIMIT dataset based on the wav2vec2 architecture.
Model Features
Low Word Error Rate
Achieves a Word Error Rate (WER) of 0.3382 on the TIMIT evaluation set
Based on wav2vec2 Architecture
Uses facebook's wav2vec2-base as the base model
Lightweight
Inference is based on the base version, requiring relatively low computational resources
Model Capabilities
English Speech Recognition
Audio-to-Text Conversion
Use Cases
Speech Transcription
English Speech Transcription
Converts English speech content into text
Word Error Rate 0.3382
Education
Pronunciation Assessment
Can be used in pronunciation assessment systems for English learners
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