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Use live captions when people need synchronized text during a meeting, stream, or other event. Use post-session transcription when the audio is already recorded and you need text to review, correct, search, or publish later. In Python, the deciding factor is whether audio is still arriving or is a completed file—not whether the transcription service happens to return its output in pieces.
What is the difference between live captions and a post-session transcript?
Live captions provide text in time with an event’s audio so people can follow it as it happens. A post-session transcript is produced from completed audio or video for later reading and reuse. A later transcript cannot provide the same real-time access as captions during the event.
For accessibility, captions are not simply spoken words on screen. W3C defines them as text conveying the speech and relevant non-speech audio information needed to understand content, synchronized with the audio. Terminology varies by region; W3C distinguishes same-language captions from translated subtitles. W3C: Captions and subtitles
A transcript can also need more than dialogue. A basic transcript records speech and non-speech audio; a descriptive transcript can include relevant visual information, such as meaningful on-screen text, actions, charts, or scene changes. W3C: Transcripts
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Which Python transcription workflow should you choose?
| Question | Live transcription | Post-session transcription |
|---|---|---|
| When is the audio available? | It arrives from a microphone, call, or other live media stream. | The recording is complete before transcription begins. |
| When does the reader need the text? | During the event, with text synchronized to the audio. | Afterward, for reading, review, search, correction, or publication. |
| What does the application handle? | Incremental updates as speech arrives, followed by finalized text. | A completed audio file and the requested transcription response format. |
| What accessibility work remains? | Check timing, speaker changes, and relevant non-speech audio; plan to review text if the recording will later be posted. | Check whether the output needs caption timing, speaker labels, non-speech audio, or visual descriptions. |
Choose live transcription when text is needed during the event
Use a realtime workflow when the input is still arriving and attendees need access as the event proceeds. The application must handle partial transcript updates rather than treating every update as final. OpenAI’s Realtime transcription guide describes transcript deltas as speech arrives and final text when an audio turn is committed. For this live-input use case, see the Realtime transcription guide.
A Python application capturing speech locally may use a microphone as its input device; a microphone is not needed if audio comes from another source, such as a call or stream. The input device captures audio—it does not itself provide transcription or captions.
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Choose file transcription when the recording is complete
Use a file-based workflow when the event has ended and the recording is ready. This suits jobs where the output will be checked, indexed, corrected, or published afterward. The Python API reference for audio transcription documents output choices including text, SRT, VTT, JSON, and other structured formats, with availability depending on the model. Timestamp granularities also have documented format conditions, so check the reference for the model and format you intend to use.
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Does “streaming transcription” mean live captioning?
Not necessarily. A transcription service may stream its response while processing a completed recording. That is different from sending audio that is still arriving from a microphone, call, or media stream. The Realtime transcription guide directs ongoing audio input to a realtime workflow; response streaming for a completed-file request does not turn that request into live captioning.
Output support can also vary by model. The audio transcription API reference says streaming is unsupported for whisper-1. Verify current model, format, timestamp, and streaming constraints before implementation because API capabilities can change.
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What accessibility requirements apply?
Requirements depend on the media and whether it is live or prerecorded; avoid saying captions are always required without naming that context and the applicable standard or policy. W3C’s planning resource lists captions for prerecorded synchronized video with audio at WCAG Level A and captions for live synchronized video with audio at Level AA. Audio-only content and transcript requirements follow different paths in the same resource. W3C: Planning audio and video media accessibility
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If you publish a recording of a live-captioned event, retain a correction and review step. W3C notes that minor editing for accuracy will probably be needed when live captions are reused with the recording. W3C: Captions and subtitles
What should you check before building it in Python?
- Input timing: Decide whether audio is a live stream or a completed recording.
- Latency: Establish whether delayed text is acceptable or whether people need it during speech.
- Output: Specify plain text, structured data, subtitle files, timestamps, or speaker labels before selecting an API path.
- Accessibility scope: Determine whether the deliverable must include non-speech audio, speaker changes, and—for video—relevant visual information.
- Correction: Decide who will review errors and whether edits are required before publication.
- Privacy and deployment: Check whether audio may leave a device or organization and verify the selected provider’s current retention, residency, and configuration terms. The documentation cited here does not establish a universal answer for these requirements.
There is no universally best choice for every Python project. The right path follows from when the audio exists, when people need the text, and what the final accessible output must contain.
Quick Recap
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