ggml-org/whisper.cpp

tests : add WER benchmarks

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#2,454 建立於 2024年10月5日

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 (26 留言) (0 反應) (0 負責人)C++ (49,693 star) (5,535 fork)batch import
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描述

It would be nice to start measuring the word error rate (WER) of whisper.cpp across some representative dataset:

  • short audio
  • long audio
  • english
  • non-english
  • etc.

This will help us catch regressions in the future. I'm not familiar with what is typically used for TTS WER benchmarks, so looking for help from the community.

貢獻者指南

技術棧
ccpp
領域
testingmachine learningperformance
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
3-5 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
active
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
mostly clear
前置要求
Basic understanding of whisper.cppFamiliarity with word error rate (WER)Experience with audio datasets
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
40
研究方向
The issue requests adding WER benchmarks to whisper.cpp. Start by researching standard ASR benchmark datasets (e.g., LibriSpeech, Common Voice) that include short/long audio and English/non English samples. Examine the existing test infrastructure in the repository (likely in tests/ directory or Makefile) to understand how to integrate new benchmarks. Review the comments on the issue for community suggestions on dataset selection and evaluation methodology. Coordinate with maintainers to agree on a concrete set of datasets and metrics before implementing.