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

AI Impact Summary

The note outlines fine-tuning Wav2Vec2-BERT, a 580M-parameter ASR model pre-trained on 4.5M hours across 143 languages, using CTC within the Hugging Face Transformers ecosystem. It provides a practical workflow (datasets, tokenizers, feature extractors) and a migration path to deploy a faster, more resource-efficient alternative to autoregressive models like Whisper for low-resource languages. This enables teams to train and deploy Mongolian ASR with modest hardware (e.g., 16GB GPUs) and faster iteration cycles, influencing how multilingual ASR pipelines are designed and scaled.