Real-time vs. batch transcription: What's the difference?

Real-time and batch speech-to-text transcription differ primarily in how they process audio, trading speed against accuracy, cost, and implementation complexity. Real-time, or streaming, models transcribe small audio segments with latency typically measured in milliseconds, making them suitable for live voice agents, captions, and meeting notes, but their limited conversational context can lead to errors. Batch models process completed recordings as a whole, using context from before and after ambiguous speech to improve word recognition, punctuation, formatting, and speaker labeling, which makes them better suited to call-center quality assurance, podcast archives, interviews, and bulk transcription despite slower turnaround times. Real-time systems generally cost more and require persistent connections and load balancing, while batch systems use simpler asynchronous workflows. Hybrid architectures can provide immediate live transcripts and later reprocess recordings with batch models for a more accurate archival version. ElevenAPI supports both approaches across more than 90 languages, offering Scribe v2 for batch transcription and Scribe v2 Realtime for low-latency applications.

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