Fine-Tune Wav2Vec2-BERT for low-resource ASR with Hugging Face Transformers

AI Impact Summary

This entry documents fine-tuning Wav2Vec2-BERT (facebook/w2v-bert-2.0) with CTC for low-resource ASR, demonstrated on Mongolian Common Voice 16.0. It highlights a fast, single-pass alternative to autoregressive models like Whisper, achieving competitive WER with much less data and compute. The workflow relies on Hugging Face Transformers and datasets, plus jiwer for evaluation and accelerate for training speedups. It requires Hub authentication to access Common Voice and model checkpoints, signaling a practical path for multilingual ASR pilots but with ecosystem dependencies to manage.