Streaming transcription, also known as real-time transcription, operates on a completely different principle. Instead of waiting for a complete file, the client opens a persistent connection to the ASR server (typically using a WebSocket) and sends audio data in small, continuous chunks, often as short as 100 milliseconds. The server processes these chunks immediately and sends back partial transcripts as they are generated.

Technical Characteristics

• Focus on Latency: The entire architecture is optimized for speed. The goal is to return a transcript with sub-second latency, so the text appears on the screen almost simultaneously with the spoken words.
• Dynamic and Provisional Results: A key feature of streaming models is their ability to revise their own output. As more audio context becomes available, the model may update a previously transcribed word.
• Higher Computational Cost: Streaming systems must be "always on" and ready to handle unpredictable loads. This requires dedicated computational resources that are provisioned to handle peak capacity.

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The decision between streaming and batch is a trade-off across multiple dimensions. There is no single "better" architecture; there is only the architecture that is better suited to a specific business problem.

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For many large enterprises, the choice is not a binary one. A hybrid architecture that combines both streaming and batch processing often provides the most comprehensive solution. MAny production systems use streaming for immediate insights and batch for the final archival record.

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Consider a financial services contact center. A streaming architecture can be used to transcribe the agent-customer conversation in real time. This transcript can be used to:

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1. Trigger Real-Time Alerts: If the customer says, "I want to close my account," the system can immediately flag the call for a retention specialist.
2. Provide Agent Guidance: The transcript can be fed into a knowledge base to surface relevant articles and next-best-action recommendations to the agent.

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