I’m trying to build live captions with low latency and fallback batch transcription. What architecture and API should I use?

For live captions with low latency plus fallback batch transcription, the best approach is usually a two-path transcription architecture:

Recommended architecture

1) Real-time streaming path for captions

Use a streaming speech-to-text API that accepts audio chunks and returns partial/interim transcripts quickly.

Flow

1. Capture mic/audio input in small frames, typically 20–100 ms.
2. Send chunks continuously to the streaming transcription service over WebSocket/gRPC/HTTP2 depending on the provider.
3. Display interim results immediately for captions.
4. Finalize each segment when the API marks it stable/final.
5. Handle reconnects and speaker pauses gracefully.

Why this path

• Lowest latency
• Supports live captions
• Can produce partial text before the speaker finishes

2) Batch path for accuracy and recovery

Simultaneously, or as a fallback, record the full audio and send it to a batch transcription endpoint after the session or if streaming fails.

Use this for

• Full accuracy pass after the live event
• Filling gaps if the stream disconnects
• Post-processing with better formatting, punctuation, diarization, etc.

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Suggested API choice

If you want the simplest unified solution

Use one provider that supports both streaming and batch transcription.

Look for APIs with:

• Low-latency streaming
• Interim and final transcripts
• Word timestamps
• Speaker diarization
• Batch upload for long-form audio
• Automatic punctuation and casing
• Optional custom vocabulary

OpenAI-specific option

If you want an OpenAI-based implementation, the typical pattern is:

• Streaming/live captions: use a real-time speech-to-text / realtime audio streaming API if available in your stack.
• Fallback batch transcription: use a batch transcription endpoint for the recorded audio.

If you’re building the system yourself, the cleanest design is:

• WebSocket stream from client to backend
• Backend forwards audio to the real-time STT service
• Store raw audio in object storage
• On failure or after completion, send the file to batch transcription for reconciliation

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Production architecture

Client

• Captures audio using Web Audio / native audio APIs
• Resamples to the required format, often 16 kHz mono PCM
• Sends frames to backend over WebSocket/WebRTC

Backend

• Maintains a streaming session with transcription API
• Aggregates partial and final hypotheses
• Publishes caption updates to UI
• Persists raw audio to storage for batch fallback

Storage and fallback

• Save audio segments with timestamps
• If streaming gaps happen, re-transcribe missing segments in batch
• Merge batch result with live transcript

Caption merging strategy

• Use streaming output for immediate display
• Use batch transcript later to:
  • correct errors
  • fix punctuation
  • fill missing spans
  • reconcile timestamps

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Latency tips

To keep captions responsive:

• Use small audio frames: 20–50 ms is common
• Send audio continuously; avoid buffering too long
• Prefer 16 kHz mono unless the provider wants otherwise
• Use interim results
• Minimize network hops
• Keep a persistent connection open
• Detect silence and endpointing efficiently

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Reliability tips

• Buffer locally in case of network loss
• Store sequence numbers with audio chunks
• Reconnect and resume if supported
• Use batch transcription as a safety net
• Add transcript reconciliation after the event

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Practical recommendation

If you need a concrete implementation pattern:

1. Streaming API for live captions
2. Record full audio
3. Batch transcription after the session
4. Merge results and correct the live transcript

That gives you:

• Low latency during the event
• Better accuracy afterward
• Robust recovery if streaming fails

If you want, I can also give you:

1. a reference architecture diagram,
2. a sample WebSocket-based implementation, or
3. a provider comparison for streaming STT APIs.