AI BriefingGuide to Fine-tuning Multilingual ASR Based on XLS-R
Key point
This explains a fine-tuning method that uses Hugging Face's XLS-R model to improve speech recognition (ASR) performance for low-resource languages.
Details
XLS-R is a successor model to Wav2Vec2 released by Facebook AI, which underwent self-supervised learning using about 500,000 hours of audio data across 128 languages.
The model size ranges from 300M to 2B parameters, and it uses a method of randomly masking audio feature vectors to learn contextualized speech representations.
This guide provides a step-by-step walkthrough of fine-tuning the XLS-R model for speech recognition (ASR) tasks based on the CTC (Connectionist Temporal Classification) algorithm, using the Common Voice dataset.
It covers how to implement effective multilingual speech recognition even in low-resource environments using Hugging Face libraries such as transformers and torchaudio.
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