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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTo transcribe a long recording reliably, preserve the original audio, check the selected API route’s limits, split oversized files at natural speech boundaries, and keep each transcript tied to its source offsets and request state. Assemble completed chunks in order, then check names, numbers, and uncertain passages against the audio. The key is to treat transcription as a recoverable pipeline—not one large request whose output you hope to repair later.
Choose the right workflow for the audio
First decide whether the audio is a completed recording or still arriving. For a finished recording, use file transcription. If audio is being captured live from a microphone, call, or media stream, use the Realtime transcription workflow instead. File transcription can also emit incremental events while it processes a completed recording; those progress events do not make it the live-audio workflow. See OpenAI’s speech-to-text guide.
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Check file size and route-specific limits
The speech-to-text guide documents a 25 MB maximum for the Transcriptions API and recommends compressing or splitting larger recordings into chunks of 25 MB or less. Treat that as the guide’s documented limit, not a guarantee that file size is the only constraint for every model. The Audio API FAQ notes that newer transcription routes may have model-specific duration or token validation. Verify the current limit for the model you select and leave headroom rather than aiming exactly at a boundary. See the Audio API FAQ.
The API reference lists FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV, and WebM among accepted formats, while the guide lists MP3, MP4, MPEG, MPGA, M4A, WAV, and WebM. These lists are not identical, and support can vary by route. Validate the chosen model and endpoint before converting or submitting files; the Create transcription API reference documents current parameters and model-specific behavior.
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- Plug & Play, No Drivers Required - The microphone is compatible with all operating systems - both Windows and macOS. You just need to plug the microphone to start recording. If there is no response after inserting the mic, please go to the microphone setting of your computer and select the mic as the INPUT device.
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Decide how much structure the result needs
Plain text is suitable when you need readable words and little else. Choose a structured response when a downstream system needs timing, subtitles, or speaker labels. The available response formats and timestamp options depend on the model. For models that support it, verbose_json can provide word- or segment-level timestamps; the API reference says word timestamps add latency. SRT and VTT are subtitle-oriented formats, while diarized JSON includes speaker annotations. Choose model and output format together, rather than assuming every model supports every format.
Validate and preserve the source recording
Keep an unchanged copy of the original audio. At intake, record the file type, size, duration, sample rate, channel count, and whether speech is continuous or separated by long silences. Reject unsupported inputs clearly, or convert them to a format supported by the selected route. Keep a durable reference to the original so you can audit or reprocess a transcript without relying on a converted chunk as your only source.
Also capture the intended language when known. The API reference says that supplying an ISO-639-1 language can improve accuracy and latency; it does not quantify a guaranteed improvement. Use language metadata only where the selected model supports it.
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Split long recordings without breaking context
If a recording exceeds the applicable request constraints, split it into chunks below the verified limit. Prefer sentence endings, speaker-turn boundaries, or natural silence to a fixed-duration cut through active speech. OpenAI’s guide puts the central risk plainly: “Avoid splitting in the middle of a sentence, which can remove context and reduce accuracy.”
Choose who manages chunk boundaries
The API’s optional chunking_strategy can be set to auto, in which case the server normalizes loudness and uses voice activity detection (VAD) to choose boundaries. The reference also describes manual server_vad parameters. If no strategy is set, the input is treated as one block. For gpt-4o-transcribe-diarize, chunking is required for inputs longer than 30 seconds. Check the current API reference for the selected model’s exact behavior.
Server-managed chunking is convenient when its automatic boundaries fit the job. Application-managed splitting gives you explicit source offsets and control over sentence-aware boundaries, which can make assembly and recovery easier. Neither choice removes the need to preserve timing and ordering information.
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Keep an ordered chunk manifest
For application-managed chunks, maintain a manifest that records each chunk’s sequence, start and end offsets in the original recording, input file or object key, selected model, context or prompt, and request status. Store results separately by chunk. These are reliability practices for your application, not metadata the API promises to generate for you.
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Transcribe chunks with controlled context
For ordinary speech-to-text, use a standard transcription route that supports the output you need. Where supported, provide recording-specific vocabulary—such as names, acronyms, and technical terms—and useful context from the preceding chunk. Avoid sending an ever-growing transcript with each request: carry only context that helps interpret the current segment.
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Prompt support varies by model. In particular, the diarization model does not support prompts, so a pipeline using gpt-4o-transcribe-diarize cannot depend on prompt-based vocabulary correction. The speech-to-text guide and API reference describe these model-specific differences.
Use diarization only when speaker attribution matters
If the deliverable needs speaker labels, request diarized_json from a supported diarization route. The API reference allows up to four known-speaker names and reference clips between 2 and 10 seconds. Diarization adds specialized constraints, including the chunking requirement for inputs longer than 30 seconds and the lack of prompt support. If speaker attribution is not part of the product requirement, ordinary transcription avoids those extra requirements.
Make failures recoverable
Persist every chunk’s result and status independently. If a later request fails, retry that chunk rather than restarting successful work. Use bounded backoff for transient errors and bookkeeping that prevents a retry from creating duplicate transcript segments. Record the model and request parameters alongside each result so you can identify how it was produced if you later change models or settings.
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These controls belong in your application: they make partial completion manageable, but they are not guarantees that the API will retry or assemble failed work automatically.
Assemble transcripts against the original timeline
Order completed results by their source offsets, not by the order in which requests finish. When a chunk contains local timestamps, translate them to the full recording’s timeline using that chunk’s start offset. Preserve offsets in the canonical transcript, even if the reader-facing version is plain text.
If chunks overlap, remove repeated words only when the match is clear. Keep ambiguous material for review rather than silently deleting a real repetition. For timestamped output, retain word- or segment-level timing only when the chosen model and response format provide it.
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- Check names, acronyms, technical terms, and numbers against the audio.
- Listen to speech near chunk boundaries, where lost context or duplicate text is most likely.
- Review unclear or consequential passages rather than presenting an unchecked model output as verified human ground truth.
- Use log probabilities only when the selected model and response configuration expose them; do not assume every transcript includes confidence signals.
Choose an approach by the job
| Decision | Use this approach | Important constraint |
|---|---|---|
| Completed recording or arriving audio | File transcription for a finished recording; Realtime transcription for audio still arriving | File streaming can provide incremental events for a completed recording, but is distinct from Realtime transcription |
| Within limits or oversized | Submit one request when the file fits the selected route; compress or split it when it does not | The guide documents a 25 MB maximum, while newer routes may apply model-specific duration or token limits |
| Automatic or controlled boundaries | Use automatic VAD chunking for server-selected boundaries; manage chunks in your application when explicit offsets and boundary control matter | Chunking behavior is model-dependent; diarization requires chunking above 30 seconds |
| Readable text or structured output | Use plain text for simple reading; select timestamped, subtitle, or diarized output for downstream needs | Response formats and timestamp granularities are not supported by every model |
| Speaker attribution or no attribution requirement | Use diarized JSON when speaker labels are needed; use ordinary transcription otherwise | Diarization has specialized constraints and does not support prompts |
Deliver a canonical transcript and useful views
Keep one canonical representation that preserves transcript text and source timing, then render the formats each consumer needs, such as plain text, SRT, VTT, or speaker-labeled output where supported. Store the model, response format, and relevant request parameters with each artifact. This makes it possible to trace a rendered subtitle or transcript segment back to the recording and the request that produced it.
Quick Recap
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