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id: "20aa5326c0e1536f4c029c819e4defd6534cd1b853c1f7f15e7dacea9b5cfc0b"
canonical_url: "https://aidr.today/20aa5326?lang=en"
title: "Whispering Wisely: Smart Subset Selection for ASR Fine-Tuning"
lang: "en"
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available_langs: ["en","vi"]
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published_at: "2026-10-27T04:00:00.000Z"
category: "Data"
topics: ["openai","llm","data-engineering"]
source_urls: ["https://arxiv.org/abs/2610.32997"]
summary: "Researchers propose an embedding-based subset selection method for fine-tuning ASR models like OpenAI's Whisper, using a greedy k-center algorithm on Whisper's pretrained encoder embeddings to select diverse training subsets. The approach is evaluated on Hebrew and Danish, showing k-center selection often outperforms random sampling, especially at smaller subset sizes, and captures broader lexical diversity."
---

# Whispering Wisely: Smart Subset Selection for ASR Fine\-Tuning

> [Open the canonical story](<https://aidr.today/20aa5326?lang=en>)

**Published:** 2026-10-27T04:00:00.000Z
**Category:** Data
**Topics:** openai, llm, data\-engineering

## Summary

Researchers propose an embedding\-based subset selection method for fine\-tuning ASR models like OpenAI's Whisper, using a greedy k\-center algorithm on Whisper's pretrained encoder embeddings to select diverse training subsets\. The approach is evaluated on Hebrew and Danish, showing k\-center selection often outperforms random sampling, especially at smaller subset sizes, and captures broader lexical diversity\.

## Sources

- [Story source](<https://arxiv.org/abs/2610.32997>)