I'm building live captions for webinars and need low-latency speech-to-text

For live webinar captions, you’ll usually want streaming ASR with:

• partial/interim results
• very low end-to-end latency
• good punctuation/casing
• speaker diarization if you need “Speaker 1/2”
• stable word timestamps for caption syncing

Best-fit options

Cloud (easiest, lowest ops)

• Deepgram Streaming: very low latency, strong real-time captions
• Google Speech-to-Text V2 streaming: solid accuracy, mature streaming
• AWS Transcribe Streaming: reliable, easy if you’re already on AWS
• Azure Speech to Text: good enterprise option, strong ecosystem

Open-source / self-hosted

• Whisper variants with streaming wrappers: good accuracy, but true low-latency streaming is harder
• Vosk / Kaldi: lower latency, lighter weight, but accuracy usually behind modern cloud models
• NVIDIA Riva: strong if you have GPU infrastructure

What to optimize for

• Latency target: aim for ~300–800 ms for “feels live”
• Chunk size: 100–500 ms audio frames is common
• Endpointing: tune silence detection so captions don’t lag
• Partial hypothesis handling: update captions smoothly without flicker
• Network quality: audio transport often matters more than model speed

Recommended architecture

1. Capture mic/system audio
2. Encode/send PCM or Opus chunks over WebSocket/gRPC
3. Use streaming ASR with interim transcripts
4. Apply punctuation + stabilization logic
5. Render captions with a small buffer to reduce corrections
6. Persist final transcript separately

If you want the fastest path

• Use Deepgram or Google streaming
• Send mono 16 kHz audio
• Enable interim results
• Keep a 200–500 ms buffering window
• Use VAD to detect pauses and finalize text faster

If you want, I can help you choose a stack based on:

• your cloud/provider preference,
• expected attendee count,
• need for diarization,
• and whether you need real-time captions in-browser or via your backend.