Use live captions when people need synchronized text while audio is happening. Use post-session transcription when you have a finished recording to review, correct, search, or publish. In Python, that choice depends first on whether audio is still arriving from a microphone, call, or stream—or is already stored as a file.
What is the difference between live captions and a post-session transcript?
Live captions display text in step with audio so people can follow an event as it happens. A post-session transcript is produced from completed audio or video and is intended for later reading, editing, indexing, or reuse. A transcript generated after an event cannot provide access to someone who needed the information during it.
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For a Python application, the distinction is about the input workflow, not merely how quickly text is returned. A transcription service may stream its response while processing a finished file; that does not make it live captioning. For audio that is still arriving, use a realtime transcription workflow. The Realtime transcription guide describes the ongoing-audio path, while the speech-to-text guide covers recorded-file transcription.
Which workflow should you choose?
| Need | Choose | Why |
|---|---|---|
| Text must appear during a live meeting, event, or stream | Live transcription and captioning | The input is still arriving, and the audience needs access in real time. |
| The recording is complete and will be checked or reused | File transcription | It supports a review-and-correction stage before the text is shared or published. |
| A live event will also be posted afterward | Both, with an editing step | Live text serves the event; the published recording may need a corrected transcript or captions. |
Choose live input for immediate access
Use a realtime workflow when your application must process speech from an active microphone, call, or other stream. The application needs to handle incremental text as it arrives and final text when an audio segment is committed. The Realtime transcription guide documents transcript deltas during speech and final text after an audio turn is committed.
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Plan for the caption display and its review separately from recognition. Captions need to remain synchronized with the audio and include relevant non-speech sounds, not just spoken words. Speaker changes may also matter to understanding.
Choose file transcription for completed media
Use a file workflow when recording finishes before transcription begins. This is usually the better fit when you need time to check names, terminology, speaker attribution, or formatting before making a durable record. The Python transcription API reference documents output formats including text, SRT, VTT, JSON, and other structured options, with availability dependent on the model and documented conditions.
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Do not assume every format offers the same timing detail or model support. The API reference limits timestamp granularities to documented format conditions and notes that streaming is unsupported for whisper-1. Check the current API reference for the model and format you intend to use.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallWhat does “streaming” mean in Python transcription?
The word can describe two distinct things:
- Streaming a response for a completed file: the audio is already recorded, and text is delivered incrementally as the service processes it.
- Transcribing live audio: new microphone or stream audio continues to arrive while recognition is happening.
Only the second is live transcription for an event. If your users need to read along while someone is speaking, the input itself must follow a realtime path; incremental delivery of a file’s response is not a substitute.
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What accessibility output do you actually need?
Captions include more than dialogue
W3C defines captions as text conveying the speech and non-speech audio information needed to understand content, synchronized with the audio. That can include meaningful sound cues and speaker identification. Same-language captions are distinct from subtitles that translate spoken audio, although terminology varies by region. See W3C’s explanation of captions and subtitles.
A raw recognition stream is therefore not automatically a complete caption deliverable. Your implementation and review process must account for timing, speaker changes, and relevant sounds.
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Transcripts may need visual description
A basic transcript records speech and non-speech audio. For video, that may leave out information conveyed only visually. A descriptive transcript can also include relevant visual information, such as meaningful on-screen text, actions, charts, or scene changes. W3C explains the distinction in its guidance on transcripts for audio and video.
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Requirements depend on the media type, whether content is live or prerecorded, and the applicable standard or policy. W3C’s media accessibility planning resource lists captions for synchronized video with audio at WCAG Level A for prerecorded content and Level AA for live content; audio-only media and transcripts follow different paths. WCAG Success Criterion 1.2.4 describes the live-caption requirement for synchronized media in its Understanding Captions (Live) guidance. Identify your media and conformance target before making a compliance claim.
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How should a Python project handle an event that is later published?
- Provide live text during the event. Use a realtime audio input path if attendees need captions while the event is underway.
- Keep the recording. Treat the live output as immediate access, not necessarily as the final record.
- Transcribe or review the completed recording. Check wording, speakers, timing, and relevant audio information.
- Prepare the publication format. Create synchronized captions for video or an appropriately scoped transcript for reading and reuse.
W3C notes that live captions may need minor editing for accuracy when a recording is posted afterward. The live transcript and the publication-ready version serve different moments and may require different checks; see W3C’s caption guidance.
What should you compare before implementing?
- Input timing: Is audio live, or is it a completed recording?
- Latency: Must text appear during speech, or is later delivery acceptable?
- Output: Do you need plain text, structured data, SRT or VTT subtitles, timestamps, or speaker labels? Confirm support for the selected model in the API reference.
- Accessibility scope: Is dialogue enough, or must the output also convey speakers, relevant non-speech audio, and—in a video transcript—visual information?
- Correction: Who will review recognition errors and prepare the version people will rely on?
- Privacy and deployment: Confirm the provider’s current terms, retention settings, and technical configuration against your organization’s requirements. The API workflow documentation alone does not establish a universal answer.
- Audio capture: A microphone is relevant when your Python application captures local speech; it is unnecessary when audio comes from another source.
There is no universally best workflow based on the documentation alone. It does not establish comparative accuracy, latency, cost, or privacy behavior for every project, so choose based on your input, audience, output, and operational constraints.
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