import * as Core from 'openai/core'; import { APIResource } from 'openai/resource'; import * as TranscriptionsAPI from 'openai/resources/audio/transcriptions'; import { type Uploadable } from 'openai/core'; export declare class Transcriptions extends APIResource { /** * Transcribes audio into the input language. */ create(body: TranscriptionCreateParams, options?: Core.RequestOptions): Core.APIPromise; } export interface Transcription { text: string; } export interface TranscriptionCreateParams { /** * The audio file object (not file name) to transcribe, in one of these formats: * flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm. */ file: Uploadable; /** * ID of the model to use. Only `whisper-1` is currently available. */ model: (string & {}) | 'whisper-1'; /** * The language of the input audio. Supplying the input language in * [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format will * improve accuracy and latency. */ language?: string; /** * An optional text to guide the model's style or continue a previous audio * segment. The * [prompt](https://platform.openai.com/docs/guides/speech-to-text/prompting) * should match the audio language. */ prompt?: string; /** * The format of the transcript output, in one of these options: `json`, `text`, * `srt`, `verbose_json`, or `vtt`. */ response_format?: 'json' | 'text' | 'srt' | 'verbose_json' | 'vtt'; /** * The 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](https://en.wikipedia.org/wiki/Log_probability) to * automatically increase the temperature until certain thresholds are hit. */ temperature?: number; } export declare namespace Transcriptions { export import Transcription = TranscriptionsAPI.Transcription; export import TranscriptionCreateParams = TranscriptionsAPI.TranscriptionCreateParams; } //# sourceMappingURL=transcriptions.d.ts.map