Streaming transcription sends audio to a speech recognizer continuously and returns text in increments before the audio session ends. The client transmits small audio chunks over an open connection, and the service responds with provisional and final results. This pattern supports live captions, voice applications, and any workflow that must react to speech without waiting for a complete file.
How streaming transcription works
A streaming transcription client opens a persistent connection and sends encoded audio chunks in chronological order. Many web APIs use WebSocket streaming, while other implementations use bidirectional remote procedure calls or another transport that can carry data in both directions.
The WebSocket Protocol defined by RFC 6455 defines a persistent two-way channel, allowing a client to send successive audio messages while receiving recognition results on the same connection. WebSocket frame fragmentation is a transport detail, while the transcription service defines application-level audio boundaries, formats, authentication, result schemas, and session controls.
As audio accumulates, the recognizer emits interim results that may be revised. It later marks individual segments as final results according to service-specific decoding rules. A separate endpoint or end-of-stream signal can indicate that an utterance or session is complete.
Streaming transcription state and recovery
Streaming transcription is stateful across a session. The client must track which text is provisional, append only finalized segments to a durable transcript, and avoid treating every interim update as a new sentence.
Production clients also plan for connection loss, rate limits, and maximum session duration. Recovery may require reconnecting, resending buffered audio, and reconciling duplicate results. Whether that can be done safely depends on the provider's session identifiers and replay behavior, so reconnection should be tested rather than assumed.
Audio chunk size affects responsiveness and overhead. Very large chunks delay recognition, while very small chunks create more network and processing work. The best setting depends on codec, network conditions, and the application's latency target.
When streaming transcription is the right mode
Streaming transcription is appropriate when text must drive an experience before the audio ends. Examples include real-time transcription for meetings, live captioning, call analytics during a conversation, and voice systems that need an utterance before choosing a response.
Batch transcription is usually simpler for completed recordings because it avoids persistent connection management and can process the full file as one job. Choosing streaming for stored media adds operational complexity without creating a live user benefit.
Frequently asked questions
What is the difference between streaming and batch transcription? Streaming transcription sends audio and receives text incrementally during the session. Batch transcription submits completed media for asynchronous processing and returns the transcript once the job completes.
Why are streaming transcription results marked interim and final? Streaming transcription produces interim text before the recognizer has heard all relevant context. Final markers tell the client that a segment is stable enough to store or pass to downstream logic.
Does streaming transcription require WebSockets? Streaming transcription does not require WebSockets. WebSockets are common because they support two-way messaging over one connection, but bidirectional streaming protocols and specialized media transports can provide the same continuous exchange.
Can streaming transcription recover after a dropped connection? Streaming transcription can recover only if the client and service support a compatible replay or reconnection method. A resilient client buffers recent audio, detects duplicates, and tests how session limits and reconnects affect the final transcript.