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Wav2vec2 Base Timit Demo Colab12
W
Wav2vec2 Base Timit Demo Colab12
Developed by sameearif88
基於facebook/wav2vec2-base模型在TIMIT數據集上微調的語音識別模型，詞錯誤率(WER)為0.3546
Downloads 16
Release Time : 5/1/2022
Model Overview
該模型是用於英語語音識別的預訓練模型，通過微調在TIMIT數據集上實現了較好的識別準確率
Model Features
低詞錯誤率
在評估集上達到0.3546的詞錯誤率(WER)，表現優異
基於wav2vec2架構
採用Facebook開源的wav2vec2-base模型作為基礎架構
微調優化
通過30輪次的精細調優，顯著提升了原模型的識別性能
Model Capabilities
英語語音識別
音頻轉文本
語音內容分析
Use Cases
語音轉錄
會議記錄自動生成
將會議錄音自動轉換為文字記錄
準確率約65%（基於WER 0.3546推算）
語音助手
語音指令識別
識別用戶語音指令並轉換為可執行命令
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