FM / realtime

/opt/canhelp/node_modules/openai/src/resources/beta/realtime/

/opt/canhelp/node_modules/openai/src/resources/beta/realtime

Name	Size	Mode	Actions
index.ts	373	0644	edit dl rm
realtime.ts	82471	0644	edit dl rm
sessions.ts	31686	0644	edit dl rm
transcription-sessions.ts	12673	0644	edit dl rm

Edit: /opt/canhelp/node_modules/openai/src/resources/beta/realtime/transcription-sessions.ts (12673B)

// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

import { APIResource } from '../../../core/resource';
import { APIPromise } from '../../../core/api-promise';
import { buildHeaders } from '../../../internal/headers';
import { RequestOptions } from '../../../internal/request-options';

export class TranscriptionSessions extends APIResource {
  /**
   * Create an ephemeral API token for use in client-side applications with the
   * Realtime API specifically for realtime transcriptions. Can be configured with
   * the same session parameters as the `transcription_session.update` client event.
   *
   * It responds with a session object, plus a `client_secret` key which contains a
   * usable ephemeral API token that can be used to authenticate browser clients for
   * the Realtime API.
   *
   * @example
   * ```ts
   * const transcriptionSession =
   *   await client.beta.realtime.transcriptionSessions.create();
   * ```
   */
  create(body: TranscriptionSessionCreateParams, options?: RequestOptions): APIPromise<TranscriptionSession> {
    return this._client.post('/realtime/transcription_sessions', {
      body,
      ...options,
      headers: buildHeaders([{ 'OpenAI-Beta': 'assistants=v2' }, options?.headers]),
    });
  }
}

/**
 * A new Realtime transcription session configuration.
 *
 * When a session is created on the server via REST API, the session object also
 * contains an ephemeral key. Default TTL for keys is 10 minutes. This property is
 * not present when a session is updated via the WebSocket API.
 */
export interface TranscriptionSession {
  /**
   * Ephemeral key returned by the API. Only present when the session is created on
   * the server via REST API.
   */
  client_secret: TranscriptionSession.ClientSecret;

/**
   * The format of input audio. Options are `pcm16`, `g711_ulaw`, or `g711_alaw`.
   */
  input_audio_format?: string;

/**
   * Configuration of the transcription model.
   */
  input_audio_transcription?: TranscriptionSession.InputAudioTranscription;

/**
   * The set of modalities the model can respond with. To disable audio, set this to
   * ["text"].
   */
  modalities?: Array<'text' | 'audio'>;

export namespace TranscriptionSession {
  /**
   * Ephemeral key returned by the API. Only present when the session is created on
   * the server via REST API.
   */
  export interface ClientSecret {
    /**
     * Timestamp for when the token expires. Currently, all tokens expire after one
     * minute.
     */
    expires_at: number;

/**
     * Ephemeral key usable in client environments to authenticate connections to the
     * Realtime API. Use this in client-side environments rather than a standard API
     * token, which should only be used server-side.
     */
    value: string;
  }

/**
   * Configuration of the transcription model.
   */
  export interface InputAudioTranscription {
    /**
     * 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) (e.g. `en`)
     * format will improve accuracy and latency.
     */
    language?: string;

/**
     * The model to use for transcription. Can be `gpt-4o-transcribe`,
     * `gpt-4o-mini-transcribe`, or `whisper-1`.
     */
    model?: 'gpt-4o-transcribe' | 'gpt-4o-mini-transcribe' | 'whisper-1';

/**
     * 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;
  }

/**
   * Configuration for turn detection. Can be set to `null` to turn off. Server VAD
   * means that the model will detect the start and end of speech based on audio
   * volume and respond at the end of user speech.
   */
  export interface TurnDetection {
    /**
     * Amount of audio to include before the VAD detected speech (in milliseconds).
     * Defaults to 300ms.
     */
    prefix_padding_ms?: number;

/**
     * Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms.
     * With shorter values the model will respond more quickly, but may jump in on
     * short pauses from the user.
     */
    silence_duration_ms?: number;

/**
     * Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher
     * threshold will require louder audio to activate the model, and thus might
     * perform better in noisy environments.
     */
    threshold?: number;

/**
     * Type of turn detection, only `server_vad` is currently supported.
     */
    type?: string;
  }
}

export interface TranscriptionSessionCreateParams {
  /**
   * Configuration options for the generated client secret.
   */
  client_secret?: TranscriptionSessionCreateParams.ClientSecret;

/**
   * The set of items to include in the transcription. Current available items are:
   *
   * - `item.input_audio_transcription.logprobs`
   */
  include?: Array<string>;

/**
   * The format of input audio. Options are `pcm16`, `g711_ulaw`, or `g711_alaw`. For
   * `pcm16`, input audio must be 16-bit PCM at a 24kHz sample rate, single channel
   * (mono), and little-endian byte order.
   */
  input_audio_format?: 'pcm16' | 'g711_ulaw' | 'g711_alaw';

/**
   * The set of modalities the model can respond with. To disable audio, set this to
   * ["text"].
   */
  modalities?: Array<'text' | 'audio'>;

/**
   * Configuration for turn detection, ether Server VAD or Semantic VAD. This can be
   * set to `null` to turn off, in which case the client must manually trigger model
   * response. Server VAD means that the model will detect the start and end of
   * speech based on audio volume and respond at the end of user speech. Semantic VAD
   * is more advanced and uses a turn detection model (in conjunction with VAD) to
   * semantically estimate whether the user has finished speaking, then dynamically
   * sets a timeout based on this probability. For example, if user audio trails off
   * with "uhhm", the model will score a low probability of turn end and wait longer
   * for the user to continue speaking. This can be useful for more natural
   * conversations, but may have a higher latency.
   */
  turn_detection?: TranscriptionSessionCreateParams.TurnDetection;
}

export namespace TranscriptionSessionCreateParams {
  /**
   * Configuration options for the generated client secret.
   */
  export interface ClientSecret {
    /**
     * Configuration for the ephemeral token expiration.
     */
    expires_at?: ClientSecret.ExpiresAt;
  }

export namespace ClientSecret {
    /**
     * Configuration for the ephemeral token expiration.
     */
    export interface ExpiresAt {
      /**
       * The anchor point for the ephemeral token expiration. Only `created_at` is
       * currently supported.
       */
      anchor?: 'created_at';

/**
       * The number of seconds from the anchor point to the expiration. Select a value
       * between `10` and `7200`.
       */
      seconds?: number;
    }
  }

/**
   * Configuration for input audio noise reduction. This can be set to `null` to turn
   * off. Noise reduction filters audio added to the input audio buffer before it is
   * sent to VAD and the model. Filtering the audio can improve VAD and turn
   * detection accuracy (reducing false positives) and model performance by improving
   * perception of the input audio.
   */
  export interface InputAudioNoiseReduction {
    /**
     * Type of noise reduction. `near_field` is for close-talking microphones such as
     * headphones, `far_field` is for far-field microphones such as laptop or
     * conference room microphones.
     */
    type?: 'near_field' | 'far_field';
  }

/**
   * Configuration for input audio transcription. The client can optionally set the
   * language and prompt for transcription, these offer additional guidance to the
   * transcription service.
   */
  export interface InputAudioTranscription {
    /**
     * 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) (e.g. `en`)
     * format will improve accuracy and latency.
     */
    language?: string;

/**
     * The model to use for transcription, current options are `gpt-4o-transcribe`,
     * `gpt-4o-mini-transcribe`, and `whisper-1`.
     */
    model?: 'gpt-4o-transcribe' | 'gpt-4o-mini-transcribe' | 'whisper-1';

/**
     * An optional text to guide the model's style or continue a previous audio
     * segment. For `whisper-1`, the
     * [prompt is a list of keywords](https://platform.openai.com/docs/guides/speech-to-text#prompting).
     * For `gpt-4o-transcribe` models, the prompt is a free text string, for example
     * "expect words related to technology".
     */
    prompt?: string;
  }

/**
   * Configuration for turn detection, ether Server VAD or Semantic VAD. This can be
   * set to `null` to turn off, in which case the client must manually trigger model
   * response. Server VAD means that the model will detect the start and end of
   * speech based on audio volume and respond at the end of user speech. Semantic VAD
   * is more advanced and uses a turn detection model (in conjunction with VAD) to
   * semantically estimate whether the user has finished speaking, then dynamically
   * sets a timeout based on this probability. For example, if user audio trails off
   * with "uhhm", the model will score a low probability of turn end and wait longer
   * for the user to continue speaking. This can be useful for more natural
   * conversations, but may have a higher latency.
   */
  export interface TurnDetection {
    /**
     * Whether or not to automatically generate a response when a VAD stop event
     * occurs. Not available for transcription sessions.
     */
    create_response?: boolean;

/**
     * Used only for `semantic_vad` mode. The eagerness of the model to respond. `low`
     * will wait longer for the user to continue speaking, `high` will respond more
     * quickly. `auto` is the default and is equivalent to `medium`.
     */
    eagerness?: 'low' | 'medium' | 'high' | 'auto';

/**
     * Whether or not to automatically interrupt any ongoing response with output to
     * the default conversation (i.e. `conversation` of `auto`) when a VAD start event
     * occurs. Not available for transcription sessions.
     */
    interrupt_response?: boolean;

/**
     * Used only for `server_vad` mode. Amount of audio to include before the VAD
     * detected speech (in milliseconds). Defaults to 300ms.
     */
    prefix_padding_ms?: number;

/**
     * Used only for `server_vad` mode. Duration of silence to detect speech stop (in
     * milliseconds). Defaults to 500ms. With shorter values the model will respond
     * more quickly, but may jump in on short pauses from the user.
     */
    silence_duration_ms?: number;

/**
     * Used only for `server_vad` mode. Activation threshold for VAD (0.0 to 1.0), this
     * defaults to 0.5. A higher threshold will require louder audio to activate the
     * model, and thus might perform better in noisy environments.
     */
    threshold?: number;

/**
     * Type of turn detection.
     */
    type?: 'server_vad' | 'semantic_vad';
  }
}

export declare namespace TranscriptionSessions {
  export {
    type TranscriptionSession as TranscriptionSession,
    type TranscriptionSessionCreateParams as TranscriptionSessionCreateParams,
  };
}

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