Wav2vec2 Base Timit Google Colab
A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, achieving a word error rate (WER) of 0.3355 on the evaluation set.
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Release Time : 5/23/2022
Model Overview
This model is a fine-tuned version of wav2vec2-base, primarily designed for English speech recognition tasks.
Model Features
Low Word Error Rate
Achieved a word error rate (WER) of 0.3355 on the evaluation set, demonstrating strong performance.
Based on wav2vec2 Architecture
Utilizes facebook/wav2vec2-base as the base model, featuring robust speech feature extraction capabilities.
Fine-tuning Optimization
Optimized for specific tasks through 30 epochs of fine-tuning training.
Model Capabilities
English Speech Recognition
Audio to Text Conversion
Use Cases
Speech Transcription
Meeting Minutes
Automatically convert English meeting recordings into text transcripts
Approximately 66.45% accuracy (WER=0.3355)
Voice Notes
Convert English voice notes into searchable text
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