Why We Stopped Treating Speech-to-Text as "Just Another AI API"

Short summary

Argues that the real challenge in speech-to-text products is pipeline engineering, not model selection. Covers audio preprocessing, async job handling for long recordings, and workflow features like search, summarization, and speaker diarization as key differentiators. Ends with a plug for the author's transcription app transvio.ai.

• •Audio preprocessing often yields bigger accuracy gains than switching STT models
• •Production transcription requires async processing, job queues, and retry mechanisms
• •Workflow features like search, export, and summarization matter more than benchmark accuracy

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