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Wav2vec2 Base Timit Demo Colab 1
This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, with a word error rate (WER) of 0.2574 on the evaluation set.
Downloads 18
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
Low word error rate
Achieves a word error rate (WER) of 0.2574 on the evaluation set, demonstrating good performance.
Based on wav2vec2 architecture
Uses facebook's wav2vec2-base model as the base architecture, with powerful speech feature extraction capabilities.
Fine-tuning optimization
Fine-tuned on the TIMIT dataset, optimized for specific speech recognition tasks.
Model Capabilities
English speech recognition
Speech-to-text
Continuous speech recognition
Use Cases
Speech transcription
Automatic meeting minutes transcription
Automatically convert English meeting recordings into text transcripts
Word error rate approximately 25.74%
Voice note conversion
Convert English voice notes into editable text
Voice assistant
English voice command recognition
Used to recognize and understand English voice commands
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