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Research updated

Token merging boosts Whisper efficiency across 16 languages with minimal accuracy loss

Researchers have conducted a systematic study on applying token merging to the Whisper multilingual speech recognition model family. The primary goal was to address the high computational costs associated with deploying large-scale ASR systems, particularly for low-resource languages. By dynamically combining redundant features during inference, the technique shortens sequence length without…

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Key points

  • Token merging reduces Whisper inference costs across three model sizes and sixteen languages.
  • Accuracy loss is minimal, even when combined with DoRA fine-tuning for low-resource languages.
  • The method requires no retraining, offering a practical solution for efficient multilingual deployment.

The evaluation covered three different Whisper model sizes and sixteen diverse languages. The study also investigated the interaction between token merging and parameter-efficient fine-tuning using DoRA. Results indicate that this approach significantly improves computational efficiency while maintaining transcription accuracy, even in challenging low-resource scenarios. Notably, the benefits persist even after the models have been fine-tuned, suggesting the method is robust across different deployment pipelines.

This work highlights token merging as a practical, drop-in optimization for production environments. It offers a pathway to reduce inference latency and hardware requirements for multilingual speech applications, making advanced AI capabilities more accessible and cost-effective for developers working with diverse linguistic data.

Read the original at arXiv cs.CL · by Dylan Luke Holyoak primary source Open source ↗
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