Whisper (2022) › Legacy
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Whisper showed what large, weakly supervised speech datasets could do with a conventional encoder-decoder transformer. Its cross-dataset zero-shot evaluation shifted attention away from a single in-domain score and toward performance under distribution change.
The task-token interface was equally useful. Transcription, English translation, language detection, and timestamp prediction became variants of one decoding problem. That design made the released checkpoints easier to integrate than a collection of unrelated speech pipelines.
The release also exposed the tradeoff behind web-scale supervision. Greater coverage improved transfer, but uneven data and noisy labels remained visible in language-level error rates and hallucinations. Whisper's lesson is not that scale removes the need for curation or evaluation. It is that scale changes where those problems appear.