Fine-Tune MMS Adapter Models for low-resource ASR

AI Impact Summary

The MMS Adapter Models offer a significant opportunity for low-resource ASR, particularly by leveraging a fine-tuning approach with adapter layers. This method dramatically reduces computational requirements and enables flexible, language-specific adjustments, as demonstrated by training on datasets like Common Voice. The use of adapter layers, with weights of approximately 2.5M, allows for efficient adaptation of the MMS-1B base model across a diverse range of languages, representing a key advancement over full model fine-tuning.