OpenAI: GPT-4o Mini Transcribe

GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It accepts audio with optional text guidance and produces recognized text. It is a distinct model from GPT-4o Transcribe, which uses GPT-4o, and from the newer GPT Transcribe profile with its separate keyword and multilingual-hint fields. The useful artifact here is a transcript of the recording’s original speech, prepared for search, review or a subsequent content-processing step.

Preparing the recording

OpenAI’s file-transcription guide accepts audio uploads up to 25 MB. Its current endpoint reference lists FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV and WebM, with sufficient filename and content-type metadata to identify the audio format. Larger recordings can be compressed or split into pieces of 25 MB or less. Preserve sentence boundaries when splitting, and keep each piece associated with its source recording and position so the text can later be assembled in the right order.

For voice notes, retain one identifiable transcript per recording and check any names or numbers before using the text to create tasks. For a spoken tutorial, compare the recognized wording with the original instructions before editing it for publication. A useful preparation brief identifies the spoken language and the terms that could otherwise be misheard. Transcribing the words is the first step; an interpretation, summary or translation can use the checked transcript afterward.

Prompt guidance and a single language hint

Existing GPT-4o Mini Transcribe integrations support a free-form prompt. OpenAI describes prompts for names, acronyms, recording-specific vocabulary, punctuation, capitalization, filler words and the preferred writing system. The endpoint reference says the prompt should match the audio language and can carry context from a preceding audio segment. Include relevant spellings or a short description of the subject rather than asking a transcription prompt to behave like an unrestricted chat instruction.

This model’s established language hint uses the singular language field. The producer reference documents an ISO 639-1 input-language code, such as en. The separate keywords and plural languages fields are documented for GPT Transcribe; they are not part of this Mini profile. For a series of recordings, retain the intended spoken-language setting with each input instead of interpreting it as a request to translate the result.

JSON result and optional token probabilities

The current endpoint reference documents JSON as the supported response format for GPT-4o Mini Transcribe. The recognized wording appears in the text field. When requested, token log probabilities can accompany the JSON response; these values describe the model’s token probabilities and can help identify material for closer review. They do not establish that a name, number or sentence matches the sound. Use the recording to decide whether a questionable word should be corrected.

The ordinary Mini transcription profile does not provide native SRT, VTT, verbose word-timestamp or speaker-labeled output through that JSON contract. OpenAI documents specialized models and formats for those capabilities. If the intended artifact is a subtitle track, keep the reviewed transcript available for a separate timing workflow rather than assuming that a text-only result already contains caption boundaries.

Streaming file output and documented modes

The producer’s speech-to-text guide explicitly says existing GPT-4o Mini Transcribe integrations support file streaming. A completed recording can produce partial transcript text followed by a final result while it is processed. Keep the final transcript as the completed artifact; the appearance of partial text does not mean the file workflow is continuously receiving live microphone audio. The model card separately lists transcription and Realtime endpoint families. Those producer interfaces do not imply that every session field from a different transcription model applies here.

Context, output allowance and useful checks

OpenAI’s current model specification lists a 16,000-token context window, a maximum output of 2,000 tokens and a knowledge cutoff of June 1, 2024. These are model-specification figures, not a universal duration in minutes for an audio recording. The file-size rule and a token allowance describe different constraints. For extended material, prepare the recording around the documented file workflow, carry relevant context between pieces and check that the final text includes the ending of the source.

A useful Mini Transcribe result preserves the wording and its relationship to the recording, with any optional probability data kept separate from editorial corrections. Review unfamiliar vocabulary, accents, background noise, language changes and overlapping speech with the original audio available. When revising text for readability, distinguish that editorial version from the recognized transcript so that later users can tell which wording came from the recording and which was changed during preparation.