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This article introduces Mi-Go, a novel testing framework aimed at evaluatingthe performance and adaptability of general-purpose speech recognition machinelearning models across diverse real-world scenarios. The framework leveragesYouTube as a rich and continuously updated data source, accounting for multiplelanguages, accents, dialects, speaking styles, and audio quality levels. Todemonstrate the effectiveness of the framework, the Whisper model, developed byOpenAI, was employed as a test object. The tests involve using a total of 124YouTube videos to test all Whisper model versions. The results underscore theutility of YouTube as a valuable testing platform for speech recognitionmodels, ensuring their robustness, accuracy, and adaptability to diverselanguages and acoustic conditions. Additionally, by contrasting themachine-generated transcriptions against human-made subtitles, the Mi-Goframework can help pinpoint potential misuse of YouTube subtitles, like SearchEngine Optimization.

Mi-Go: Test Framework which uses YouTube as Data Source for Evaluating Speech Recognition Models like OpenAI's Whisper