Real-time vs Batch Transcription

Transcription services generally operate in one of two modes: real-time or batch. The distinction is about when the audio gets processed, and each mode serves different use cases with different technical trade-offs.

Real-time transcription

Real-time transcription, also called live or streaming transcription, converts speech to text as it happens. The audio is sent to the ASR system in small chunks, and results appear on screen within seconds of the words being spoken. This mode powers live captions during video calls, real-time subtitles for broadcasts, and accessibility tools for the hearing impaired.

The technical challenge with real-time processing is that the system must make decisions with incomplete context. It hears a fragment of audio and must produce text immediately, without the luxury of listening to the full sentence first. This can reduce accuracy, particularly for languages with complex tonal patterns or heavy code-switching, where later context might clarify earlier ambiguity.

Batch transcription

Batch transcription processes pre-recorded audio files from start to finish. The user uploads a file, and the system returns a complete transcript after processing. Because the system has access to the entire recording, it can use bidirectional context, looking both forward and backward, to resolve ambiguities. This typically produces more accurate results than real-time mode.

Batch processing is ideal for interviews, podcast episodes, recorded meetings, archival audio, and any scenario where the content already exists as a file and immediate output is not critical.

Which mode to choose

The decision comes down to whether immediacy or accuracy is the higher priority. Live events and accessibility applications demand real-time processing. Post-production workflows, journalism, and research benefit from the higher accuracy of batch mode.

AuTrans supports both modes, allowing users to choose based on their specific needs. For most African language transcription tasks, where maximising accuracy across tonal and multilingual audio is paramount, batch transcription tends to deliver the strongest results.