Real-time audio for AI: capture before processing

Inference can fall behind while the conversation continues

A live conversation does not wait for the transcription service to recover capacity. If capture and inference depend on the same path, a model failure can compromise audio recording. The product must distinguish what was received, recorded and processed.

Latency also has several parts: transport, preparation, queueing, recognition and presentation. A single average can hide the wait the person experiences. Define which result must be immediate and which can arrive later.

Record segments with identity and durable state

I implemented an audio receiver in Go with WebRTC, which transports real-time media, and Opus, which compresses audio. The flow accounts for transport clocks and writes segments with identity, an integrity digest and a recovery journal.

The journal uses atomic writes and storage limits. After a restart, the service can rebuild pending work from recorded state. This architecture supports failure handling, but it does not justify a promise that no audio will be lost.

Resume transcription without confusing unavailability with loss

The later stage keeps transcription pending when media exists and waits between attempts. A resolved or removed segment has a different outcome from an infrastructure error. An inaccessible file during a failure does not prove it was lost.

The interface must report delay and partial state. Permissions, retention and recording access must exist on the server, outside the public directory. Recording and analysis depend on use authorised by the company and the people involved.

Integrate speech with operational criteria

Consulting can connect capture, storage, transcription and existing telephony. The scope can include latency measurement, concurrency limits and interruption exercises. The proposal defines expected quality, recovery and data handling.