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

from typing import List, Optional
from typing_extensions import Literal

from ...._models import BaseModel

__all__ = [
    "TranscriptionSessionUpdate",
    "Session",
    "SessionClientSecret",
    "SessionClientSecretExpiresAt",
    "SessionInputAudioNoiseReduction",
    "SessionInputAudioTranscription",
    "SessionTurnDetection",
]


class SessionClientSecretExpiresAt(BaseModel):
    anchor: Optional[Literal["created_at"]] = None
    """The anchor point for the ephemeral token expiration.

    Only `created_at` is currently supported.
    """

    seconds: Optional[int] = None
    """The number of seconds from the anchor point to the expiration.

    Select a value between `10` and `7200`.
    """


class SessionClientSecret(BaseModel):
    expires_at: Optional[SessionClientSecretExpiresAt] = None
    """Configuration for the ephemeral token expiration."""


class SessionInputAudioNoiseReduction(BaseModel):
    type: Optional[Literal["near_field", "far_field"]] = None
    """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.
    """


class SessionInputAudioTranscription(BaseModel):
    language: Optional[str] = None
    """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.
    """

    model: Optional[Literal["gpt-4o-transcribe", "gpt-4o-mini-transcribe", "whisper-1"]] = None
    """
    The model to use for transcription, current options are `gpt-4o-transcribe`,
    `gpt-4o-mini-transcribe`, and `whisper-1`.
    """

    prompt: Optional[str] = None
    """
    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".
    """


class SessionTurnDetection(BaseModel):
    create_response: Optional[bool] = None
    """Whether or not to automatically generate a response when a VAD stop event
    occurs.

    Not available for transcription sessions.
    """

    eagerness: Optional[Literal["low", "medium", "high", "auto"]] = None
    """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`.
    """

    interrupt_response: Optional[bool] = None
    """
    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.
    """

    prefix_padding_ms: Optional[int] = None
    """Used only for `server_vad` mode.

    Amount of audio to include before the VAD detected speech (in milliseconds).
    Defaults to 300ms.
    """

    silence_duration_ms: Optional[int] = None
    """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.
    """

    threshold: Optional[float] = None
    """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.
    """

    type: Optional[Literal["server_vad", "semantic_vad"]] = None
    """Type of turn detection."""


class Session(BaseModel):
    client_secret: Optional[SessionClientSecret] = None
    """Configuration options for the generated client secret."""

    include: Optional[List[str]] = None
    """The set of items to include in the transcription. Current available items are:

    - `item.input_audio_transcription.logprobs`
    """

    input_audio_format: Optional[Literal["pcm16", "g711_ulaw", "g711_alaw"]] = None
    """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_noise_reduction: Optional[SessionInputAudioNoiseReduction] = None
    """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.
    """

    input_audio_transcription: Optional[SessionInputAudioTranscription] = None
    """Configuration for input audio transcription.

    The client can optionally set the language and prompt for transcription, these
    offer additional guidance to the transcription service.
    """

    modalities: Optional[List[Literal["text", "audio"]]] = None
    """The set of modalities the model can respond with.

    To disable audio, set this to ["text"].
    """

    turn_detection: Optional[SessionTurnDetection] = None
    """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.
    """


class TranscriptionSessionUpdate(BaseModel):
    session: Session
    """Realtime transcription session object configuration."""

    type: Literal["transcription_session.update"]
    """The event type, must be `transcription_session.update`."""

    event_id: Optional[str] = None
    """Optional client-generated ID used to identify this event."""
