Real-time vs. Batch Speech Transcription: How to Optimize for Each Scenario with Scribe v2

When selecting AI speech recognition (STT) models, Real-time and Batch approaches show clear differences in processing methods and performance characteristics. Real-time processing divides audio into small chunks of less than 250ms to generate text in milliseconds, but errors may occur due to limited context. In contrast, batch processing handles the entire file after recording is complete, utilizing bidirectional context to provide higher accuracy and refined formatting.

Performance and Infrastructure Comparison

The two approaches have contrasting characteristics in terms of latency, accuracy, cost, and infrastructure requirements.

• Latency: Real-time offers low latency of 100–300ms, while batch processing takes from several seconds to over 10 minutes depending on file length.
• Accuracy: Real-time provides moderate accuracy, whereas batch achieves high accuracy through full-context analysis.
• Cost and Infrastructure: Real-time incurs higher per-minute costs due to complex infrastructure and operational overhead, while batch is cost-efficient through asynchronous processing.

Scenario-Based Selection with Scribe v2

ElevenAPI's Scribe v2 model reflects these characteristics to offer features optimized for specific use cases.

• Live Voice Agent: Scribe v2 Realtime supports natural conversation flow and fast turn-taking with approximately 150ms partial transcription speed and built-in Voice Activity Detection (VAD).
• Call-center QA and Archiving: Scribe v2 (batch) aids in accurate quality assurance and reduced editing time through speaker diarization, keyterm prompting, and entity detection.

Hybrid Architecture

ElevenAPI, which supports both real-time and batch modes on a single platform, enables hybrid workflows. By using real-time transcription during live conversations and asynchronously reprocessing with the batch model for subsequent QA or archiving, a balance between speed and accuracy can be achieved. Scribe v2 supports over 90 languages and maintains high accuracy even in low-quality audio environments.