Struct CreateTranscriptionRequest 

pub struct CreateTranscriptionRequest {
    pub file: AudioInput,
    pub model: String,
    pub language: Option<String>,
    pub prompt: Option<String>,
    pub response_format: Option<AudioResponseFormat>,
    pub temperature: Option<f32>,
    pub include: Option<Vec<TranscriptionInclude>>,
    pub timestamp_granularities: Option<Vec<TimestampGranularity>>,
    pub stream: Option<bool>,
    pub chunking_strategy: Option<TranscriptionChunkingStrategy>,
    pub known_speaker_names: Option<Vec<String>>,
    pub known_speaker_references: Option<Vec<String>>,
}

Fields

file: AudioInput

The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm.

model: String

ID of the model to use. The options are gpt-4o-transcribe, gpt-4o-mini-transcribe, whisper-1 (which is powered by our open source Whisper V2 model), and gpt-4o-transcribe-diarize.

language: Option<String>

The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

prompt: Option<String>

An optional text to guide the model’s style or continue a previous audio segment. The prompt should match the audio language. This field is not supported when using gpt-4o-transcribe-diarize.

response_format: Option<AudioResponseFormat>

The format of the output, in one of these options: json, text, srt, verbose_json, vtt, or diarized_json. For gpt-4o-transcribe and gpt-4o-mini-transcribe, the only supported format is json. For gpt-4o-transcribe-diarize, the supported formats are json, text, and diarized_json, with diarized_json required to receive speaker annotations.

temperature: Option<f32>

TThe sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.

include: Option<Vec<TranscriptionInclude>>

Additional information to include in the transcription response. logprobs will return the log probabilities of the tokens in the response to understand the model’s confidence in the transcription. logprobs only works with response_format set to json and only with the models gpt-4o-transcribe and gpt-4o-mini-transcribe. This field is not supported when using gpt-4o-transcribe-diarize.

timestamp_granularities: Option<Vec<TimestampGranularity>>

The timestamp granularities to populate for this transcription. response_format must be set verbose_json to use timestamp granularities. Either or both of these options are supported: word, or segment. Note: There is no additional latency for segment timestamps, but generating word timestamps incurs additional latency. This option is not available for gpt-4o-transcribe-diarize.

stream: Option<bool>

If set to true, the model response data will be streamed to the client as it is generated using server-sent events. See the Streaming section of the Speech-to-Text guide for more information. Note: Streaming is not supported for the whisper-1 model and will be ignored.

chunking_strategy: Option<TranscriptionChunkingStrategy>

Controls how the audio is cut into chunks. When set to "auto", the server first normalizes loudness and then uses voice activity detection (VAD) to choose boundaries. server_vad object can be provided to tweak VAD detection parameters manually. If unset, the audio is transcribed as a single block. Required when using gpt-4o-transcribe-diarize for inputs longer than 30 seconds.

known_speaker_names: Option<Vec<String>>

Optional list of speaker names that correspond to the audio samples provided in known_speaker_references[]. Each entry should be a short identifier (for example customer or agent). Up to 4 speakers are supported.

known_speaker_references: Option<Vec<String>>

Optional list of audio samples (as data URLs) that contain known speaker references matching known_speaker_names[]. Each sample must be between 2 and 10 seconds, and can use any of the same input audio formats supported by file.

Trait Implementations

impl AsyncTryFrom<CreateTranscriptionRequest> for Form

type Error = OpenAIError

The type returned in the event of a conversion error.

async fn try_from( request: CreateTranscriptionRequest, ) -> Result<Self, Self::Error>

Performs the conversion.

impl Clone for CreateTranscriptionRequest

fn clone(&self) -> CreateTranscriptionRequest

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateTranscriptionRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for CreateTranscriptionRequest

fn default() -> CreateTranscriptionRequest

Returns the “default value” for a type.

impl PartialEq for CreateTranscriptionRequest

fn eq(&self, other: &CreateTranscriptionRequest) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateTranscriptionRequest