使用 🤗 Transformers 微調 XLSR-Wav2Vec2 以進行低資源語音辨識 (ASR)
Original: Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers
Automatic speech recognition (ASR) has achieved remarkable success for resource-rich languages such as English and standard Mandarin, but…
本教學詳細介紹如何利用 Hugging Face Transformers 庫，針對缺乏訓練數據的低資源語言微調 Meta 的 XLSR-Wav2Vec2 語音模型。內容涵蓋從 Common Voice 數據集下載、音訊預處理、建立專屬 Tokenizer，到使用 CTC 損失函數進行模型訓練的完整工作流。對於想在特定方言或少數語言上實現高精度語音辨識（ASR）的開發者與研究人員，這是極具價值的實戰指南。
Automatic speech recognition (ASR) has achieved remarkable success for resource-rich languages such as English and standard Mandarin, but building high-performance ASR systems for the thousands of "low-resource languages" in the world — languages that lack annotated data — has long been a major challenge. Meta's XLSR-Wav2Vec2 (Cross-Lingual Speech Representations via self-supervised learning) changed this landscape.
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