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      "statement": "# Radford and colleagues: Whisper robust speech recognition\n\nTrained a multilingual, multitask speech-recognition model on 680,000 hours of weakly supervised audio and reported improved zero-shot robustness across diverse test sets.\n\n## Scientific scope\n\nWhisper's September 2022 release preceded the December 2022 arXiv posting. Reported robustness does not imply the best specialized model on every speech benchmark.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAlec Radford; Jong Wook Kim; Tao Xu; Greg Brockman; Christine McLeavey; Ilya Sutskever\n\nComplete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.\n\n\n\n## Original sources\n\n- [Radford and colleagues: Whisper robust speech recognition — original paper](https://arxiv.org/abs/2212.04356v1)\n- [Radford and colleagues: Whisper robust speech recognition — company research page](https://openai.com/index/whisper/)\n",
      "summary": "Trained a multilingual, multitask speech-recognition model on 680,000 hours of weakly supervised audio and reported improved zero-shot robustness across diverse test sets.",
      "title": "Radford and colleagues: Whisper robust speech recognition"
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