Real-time vs Batch Transcription — Which One You Need

Why batch is more accurate

Batch transcription has access to the entire audio file before producing output. The model can use future audio to disambiguate the current word — "I scream" vs "ice cream" gets resolved by the next sentence's context. Beam search can run with full lookahead. Real-time can't — it has to commit to text within 1-3 seconds of hearing it, before the rest of the sentence arrives. That's why batch ASR systems consistently score 2-4 percentage points lower WER than real-time on the same audio.

When to use each

Batch (recorded files): podcasts, lectures, interviews, meetings (after the fact), voice memos, archived audio. Anywhere you have the file and want maximum accuracy. This is what Transcript.you does. Real-time (streaming): live captions during a meeting/Zoom call, voice typing on your phone, voice assistants (Siri/Alexa), live broadcast captioning, dictation software. Latency matters more than the last 2% of accuracy.

Architectures behind each

Batch typically uses encoder-decoder transformers like Whisper — the encoder sees the whole audio at once, the decoder generates text auto-regressively. Real-time uses streaming-friendly architectures: RNN-Transducer (RNN-T), CTC-based models with VAD chunking, or attention models with limited lookahead windows. Real-time systems are also smaller and faster — you can't afford a 600M-parameter model that takes 30 seconds per chunk.

In practice

Two scenarios. (1) You recorded a 60-minute podcast. Drop the MP3 into Transcript.you (batch). 2 minutes later, transcript ready, ~96% accuracy. (2) You're in a Zoom call and want live captions. Use Zoom's built-in real-time captions or an Otter-style live bot. Lower accuracy (~92%), but available during the call. After the call ends, you can re-process the recording in batch mode for a more accurate transcript.

Related terms

• ASR
• Whisper
• VAD
• WER

Last updated: September 20, 2026