AI BriefingImplementing Low-Resource ASR with Wav2Vec2-BERT
Key point
This covers how to use Meta's Wav2Vec2-BERT to improve automatic speech recognition (ASR) performance for languages with limited data.
Details
Wav2Vec2-BERT, released by MetaAI, is a general-purpose audio model with 580 million parameters, pretrained on 4.5 million hours of unlabeled audio data across more than 143 languages.
The existing Whisper model performs well on English, but has significant limitations for low-resource languages such as Mongolian, where performance is very low, and its autoregressive nature results in slow inference speed.
In contrast, Wav2Vec2-BERT predicts speech using a single pass, making it much faster than Whisper, and it can achieve competitive performance even with small amounts of data.
This guide provides a complete pipeline for fine-tuning the model on a Mongolian dataset (Common Voice 16.0) using the CTC (Connectionist Temporal Classification) algorithm, along with expert tips for efficient training.
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