Welcome back! In the last lesson, you learned how to use system prompts to control the format of your audio transcriptions, making it easy to generate meeting notes or action item lists from the same recording. Now, we'll build on that foundation by exploring streaming transcription. This lesson will help you understand how streaming works and when you might want to use it in real-world applications.

In this lesson, you will:

• Understand what streaming transcription is and how it works.
• Learn how to simulate streaming transcription with the Whisper API.
• See how to implement streaming transcription in Python.
• Know when to choose streaming transcription for your applications.

By the end, you'll know how to implement streaming transcription in your own projects and when it's the right choice.

Streaming transcription breaks the audio into smaller pieces ("chunks") and sends them to the API as they become available. The API returns partial results as soon as they're ready, so you can display the transcript in real time, even as the audio is still being processed.

Think of it like watching a live sports game versus watching highlights later. With streaming, you get the action as it happens, chunk by chunk. This creates a much more interactive and responsive user experience.

Streaming transcription is ideal when:

• You have long audio files - Instead of waiting 30 seconds for a 10-minute recording, users see results immediately
• You're building interactive applications - Live captioning, voice assistants, or real-time meeting transcription
• User experience matters - When you want your app to feel fast and responsive
• You're processing live audio - Webinars, phone calls, or live events where audio is still being generated
• You want to show progress - Users can see that something is happening rather than staring at a loading screen

You might stick with regular batch processing when you're processing short files in the background, doing bulk processing, or when the user doesn't need to see results immediately.

Now, let's see how the streaming transcription function works. Note: OpenAI's Whisper API doesn't currently support real streaming, so we'll simulate the streaming behavior to demonstrate the concept:

The key difference here is the stream parameter. When set to True, the function simulates streaming by:

1. Getting the complete transcription from the Whisper API
2. Breaking it into chunks by splitting the text into words and grouping them
3. Yielding chunks progressively with artificial delays to simulate real-time processing
4. Returning a generator that yields each chunk as it becomes "available"

We also have a convenience function that makes it easy to choose between batch and streaming modes:

Let's see how to use the streaming transcription in practice:

When you run this function, you'll see the transcript appear on your screen in real time, chunk by chunk. Each piece of text appears progressively, creating a live transcription experience.

The end="" and flush=True parameters ensure that each chunk appears immediately without line breaks, creating a smooth, continuous stream of text that builds up the complete transcript.

Streaming transcription transforms the user experience by:

• Providing immediate feedback: Users know the system is working and see results right away
• Reducing perceived wait time: Even if the total processing time is the same, users feel like it's faster because they see progress
• Enabling real-time applications: You can build live captioning, voice assistants, or interactive transcription tools
• Improving engagement: Users stay engaged when they see continuous progress rather than waiting for a final result

This approach is especially powerful for long audio files where users would otherwise wait minutes for results, and for live applications where audio is still being generated.

While we're simulating streaming here, the principles remain the same for real streaming APIs:

• Chunked processing: Audio is processed in smaller pieces
• Progressive results: Users see results as they become available
• Generator patterns: Using Python generators to yield results incrementally
• User experience: Providing immediate feedback and progress indication

When working with APIs that do support real streaming, you'll use similar patterns but receive actual chunks as they're processed rather than simulating them.

Next, you'll get hands-on practice implementing streaming transcription. This will help you understand how the chunks arrive and how to build responsive transcription applications. Let's get started!