How do I build live captions from an audio stream?

To build live captions from an audio stream, you usually need a pipeline with these stages:

1. Capture audio
2. Preprocess it
3. Run speech-to-text continuously
4. Detect sentence/phrase boundaries
5. Display and update captions in real time

1) Audio input

Get audio from:

• microphone
• WebRTC stream
• RTP/RTSP
• uploaded/live media player stream

Common requirements:

• mono audio
• 16 kHz sample rate
• 16-bit PCM
• small chunks, e.g. 20–100 ms

2) Preprocess the audio

Before transcription:

• resample to the model’s expected format
• normalize volume if needed
• optionally do noise suppression and voice activity detection (VAD)

VAD helps you avoid sending silence and makes captions more responsive.

3) Speech-to-text engine

You need streaming ASR (automatic speech recognition). Options:

Cloud APIs

• Google Speech-to-Text
• Azure Speech
• AWS Transcribe
• Deepgram
• AssemblyAI

Pros: easier, scalable, often best latency
Cons: cost, privacy, vendor dependency

Self-hosted

• Whisper-based systems with streaming wrappers
• Vosk
• NVIDIA Riva
• faster-whisper + chunking logic

Pros: privacy, control
Cons: more engineering, hardware needs

4) Streaming strategy

Most ASR engines don’t truly transcribe infinite audio in one call unless they support streaming directly. Typical approaches:

A. True streaming ASR

Send audio continuously over WebSocket/gRPC/SDK and receive partial + final results.

Best for:

• low latency
• accurate live captions
• production systems

B. Chunked transcription

Split audio into overlapping windows, e.g.:

• 1–3 second chunks
• overlap 200–500 ms
• stitch results together

This is easier to implement but needs logic to:

• remove duplicates from overlap
• stabilize partial text
• handle corrections

5) Partial and final captions

For good UX, show:

• partial captions: tentative text while the speaker is still talking
• final captions: committed text once the ASR engine decides the segment is done

A common pattern:

• display partial text in lighter style
• replace it when final text arrives

6) Caption formatting

For readable captions:

• break lines at natural pauses
• limit to 1–2 lines
• keep each caption on screen for a short time
• preserve punctuation if the ASR provides it
• optionally add speaker labels if diarization is available

7) Latency target

A useful live caption system aims for:

• under 1 second for partial text
• 1–3 seconds for stable final captions

Latency comes from:

• audio buffering
• network overhead
• model inference time
• post-processing

8) Example architecture

Client

• captures microphone/stream audio
• sends PCM frames to backend via WebSocket

Backend

• runs VAD
• forwards speech frames to ASR
• receives partial/final transcript events
• sends caption updates back to client

Frontend

• renders live subtitles
• updates partial text in place
• stores finalized caption segments

9) Simple implementation outline

If you’re building this yourself:

1. Capture microphone audio in small frames
2. Convert to 16 kHz mono PCM
3. Send frames through a WebSocket to your server
4. On the server:
   • buffer audio
   • run VAD
   • feed speech segments to a streaming ASR engine
5. Return transcript events:
   • {type: "partial", text: "..."}
   • {type: "final", text: "..."}
6. Frontend displays updates in real time

10) Important engineering details

• Clocking/synchronization: keep audio timestamps aligned
• Backpressure: don’t let buffers grow too large
• Reconnect handling: recover from stream interruptions
• Noise robustness: use VAD and noise suppression when possible
• Edge cases: overlapping speakers, accents, jargon, music, silence

11) If you want the fastest path

Use a cloud streaming ASR API first. It will save a lot of time and let you validate:

• latency
• accuracy
• caption UX
• cost

Then you can decide whether to self-host later.

12) Example tech stack

A practical stack might look like:

• Frontend: React + WebSocket
• Backend: Python (FastAPI) or Node.js
• Audio processing: WebRTC/WebAudio, ffmpeg, or PyAV
• ASR: Deepgram / Google / Azure / Whisper-streaming / Vosk
• Caption rendering: custom subtitle component

If you want, I can also give you:

1. a reference architecture diagram,
2. a Python example, or
3. a browser + WebSocket live caption demo.