Module livekit.agents.inference

class AdaptiveInterruptionDetector(
    rtc.EventEmitter[
        Literal[
            "overlapping_speech",
            "error",
            "metrics_collected",
        ]
    ],
):
    def __init__(
        self,
        *,
        threshold: float = THRESHOLD,
        min_interruption_duration: float = MIN_INTERRUPTION_DURATION,
        max_audio_duration: float = MAX_AUDIO_DURATION,
        audio_prefix_duration: float = AUDIO_PREFIX_DURATION,
        detection_interval: float = DETECTION_INTERVAL,
        inference_timeout: float = REMOTE_INFERENCE_TIMEOUT,
        base_url: str | None = None,
        api_key: str | None = None,
        api_secret: str | None = None,
        http_session: aiohttp.ClientSession | None = None,
    ) -> None:
        """
        Initialize a AdaptiveInterruptionDetector instance.

        Args:
            threshold (float, optional): The threshold for the interruption detection, defaults to 0.5.
            min_interruption_duration (float, optional): The minimum duration, in seconds, of the interruption event, defaults to 50ms.
            max_audio_duration (float, optional): The maximum audio duration, including the audio prefix, in seconds, for the interruption detection, defaults to 3s.
            audio_prefix_duration (float, optional): The audio prefix duration, in seconds, for the interruption detection, defaults to 0.5s.
            detection_interval (float, optional): The interval between detections, in seconds, for the interruption detection, defaults to 0.1s.
            inference_timeout (float, optional): The timeout for the interruption detection, defaults to 1 second.
            base_url (str, optional): The base URL for the interruption detection, defaults to the shared LIVEKIT_REMOTE_EOT_URL environment variable.
            api_key (str, optional): The API key for the interruption detection, defaults to the LIVEKIT_INFERENCE_API_KEY environment variable.
            api_secret (str, optional): The API secret for the interruption detection, defaults to the LIVEKIT_INFERENCE_API_SECRET environment variable.
            http_session (aiohttp.ClientSession, optional): The HTTP session to use for the interruption detection.
        """
        super().__init__()
        if max_audio_duration > 3.0:
            raise ValueError("max_audio_duration must be less than or equal to 3.0 seconds")

        lk_base_url = (
            base_url
            if base_url
            else os.getenv("LIVEKIT_REMOTE_EOT_URL", get_default_inference_url())
        )
        lk_api_key: str = api_key if api_key else ""
        lk_api_secret: str = api_secret if api_secret else ""
        # use LiveKit credentials if using the inference service (production or staging)
        is_inference_url = lk_base_url in (DEFAULT_INFERENCE_URL, STAGING_INFERENCE_URL)
        if is_inference_url:
            lk_api_key = (
                api_key
                if api_key
                else os.getenv("LIVEKIT_INFERENCE_API_KEY", os.getenv("LIVEKIT_API_KEY", ""))
            )
            if not lk_api_key:
                raise ValueError(
                    "api_key is required, either as argument or set LIVEKIT_API_KEY environmental variable"
                )

            lk_api_secret = (
                api_secret
                if api_secret
                else os.getenv("LIVEKIT_INFERENCE_API_SECRET", os.getenv("LIVEKIT_API_SECRET", ""))
            )
            if not lk_api_secret:
                raise ValueError(
                    "api_secret is required, either as argument or set LIVEKIT_API_SECRET environmental variable"
                )

            use_proxy = True
        else:
            use_proxy = False

        self._opts = InterruptionOptions(
            sample_rate=SAMPLE_RATE,
            threshold=threshold,
            min_frames=math.ceil(min_interruption_duration * _FRAMES_PER_SECOND),
            max_audio_duration=max_audio_duration,
            audio_prefix_duration=audio_prefix_duration,
            detection_interval=detection_interval,
            inference_timeout=inference_timeout,
            base_url=lk_base_url,
            api_key=lk_api_key,
            api_secret=lk_api_secret,
            use_proxy=use_proxy,
        )
        self._label = f"{type(self).__module__}.{type(self).__name__}"
        self._sample_rate = SAMPLE_RATE
        self._session = http_session
        self._streams = weakref.WeakSet[InterruptionHttpStream | InterruptionWebSocketStream]()

        logger.info(
            "adaptive interruption detector initialized",
            extra={
                "base_url": self._opts.base_url,
                "detection_interval": self._opts.detection_interval,
                "audio_prefix_duration": self._opts.audio_prefix_duration,
                "max_audio_duration": self._opts.max_audio_duration,
                "min_frames": self._opts.min_frames,
                "threshold": self._opts.threshold,
                "inference_timeout": self._opts.inference_timeout,
                "use_proxy": self._opts.use_proxy,
            },
        )

    @property
    def model(self) -> str:
        return "adaptive interruption"

    @property
    def provider(self) -> str:
        return "livekit"

    @property
    def label(self) -> str:
        return self._label

    @property
    def sample_rate(self) -> int:
        return self._sample_rate

    def _emit_error(self, api_error: Exception, recoverable: bool) -> None:
        self.emit(
            "error",
            InterruptionDetectionError(
                label=self._label,
                error=api_error,
                recoverable=recoverable,
            ),
        )

    def _ensure_session(self) -> aiohttp.ClientSession:
        if not self._session:
            self._session = http_context.http_session()
        return self._session

    def stream(
        self, *, conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS
    ) -> InterruptionHttpStream | InterruptionWebSocketStream:
        try:
            stream: InterruptionHttpStream | InterruptionWebSocketStream
            if self._opts.use_proxy:
                stream = InterruptionWebSocketStream(model=self, conn_options=conn_options)
            else:
                stream = InterruptionHttpStream(model=self, conn_options=conn_options)
        except Exception as e:
            self._emit_error(e, recoverable=False)
            raise
        self._streams.add(stream)
        return stream

    def update_options(
        self,
        *,
        threshold: NotGivenOr[float] = NOT_GIVEN,
        min_interruption_duration: NotGivenOr[float] = NOT_GIVEN,
    ) -> None:
        if is_given(threshold):
            self._opts.threshold = threshold
        if is_given(min_interruption_duration):
            self._opts.min_frames = math.ceil(min_interruption_duration * _FRAMES_PER_SECOND)

        for stream in self._streams:
            stream.update_options(
                threshold=threshold, min_interruption_duration=min_interruption_duration
            )

class InterruptionDetectionError(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)
    type: Literal["interruption_detection_error"] = "interruption_detection_error"
    timestamp: float = Field(default_factory=time.time)
    label: str
    error: Exception = Field(..., exclude=True)
    recoverable: bool

class LLM(llm.LLM):
    def __init__(
        self,
        model: LLMModels | str,
        *,
        provider: str | None = None,
        base_url: str | None = None,
        api_key: str | None = None,
        api_secret: str | None = None,
        inference_class: InferenceClass | None = None,
        extra_kwargs: ChatCompletionOptions | dict[str, Any] | None = None,
    ) -> None:
        super().__init__()

        lk_base_url = base_url if base_url else get_default_inference_url()

        lk_api_key = (
            api_key
            if api_key
            else os.getenv("LIVEKIT_INFERENCE_API_KEY", os.getenv("LIVEKIT_API_KEY", ""))
        )
        if not lk_api_key:
            raise ValueError(
                "api_key is required, either as argument or set LIVEKIT_API_KEY environmental variable"
            )

        lk_api_secret = (
            api_secret
            if api_secret
            else os.getenv("LIVEKIT_INFERENCE_API_SECRET", os.getenv("LIVEKIT_API_SECRET", ""))
        )
        if not lk_api_secret:
            raise ValueError(
                "api_secret is required, either as argument or set LIVEKIT_API_SECRET environmental variable"
            )

        self._opts = _LLMOptions(
            model=model,
            provider=provider,
            base_url=lk_base_url,
            api_key=lk_api_key,
            api_secret=lk_api_secret,
            inference_class=inference_class,
            extra_kwargs=extra_kwargs or {},
        )
        self._client = openai.AsyncClient(
            api_key=create_access_token(self._opts.api_key, self._opts.api_secret),
            base_url=self._opts.base_url,
            http_client=httpx.AsyncClient(
                timeout=httpx.Timeout(connect=15.0, read=5.0, write=5.0, pool=5.0),
                follow_redirects=True,
                limits=httpx.Limits(
                    max_connections=50, max_keepalive_connections=50, keepalive_expiry=120
                ),
            ),
        )

    async def aclose(self) -> None:
        await self._client.close()

    @classmethod
    def from_model_string(cls, model: str) -> LLM:
        """Create a LLM instance from a model string"""
        return cls(model)

    def update_options(
        self,
        *,
        model: NotGivenOr[LLMModels | str] = NOT_GIVEN,
        extra_kwargs: NotGivenOr[ChatCompletionOptions | dict[str, Any]] = NOT_GIVEN,
    ) -> None:
        """Update LLM configuration options.

        Each option is read on the next ``chat()`` call, so a swap
        takes effect on the agent's next turn without recreating the
        LLM. ``extra_kwargs`` *replaces* the persistent kwargs dict
        rather than merging — pass ``{}`` to clear it.
        """
        if is_given(model):
            self._opts.model = model
        if is_given(extra_kwargs):
            self._opts.extra_kwargs = dict(extra_kwargs)

    @property
    def model(self) -> str:
        """Get the model name for this LLM instance."""
        return self._opts.model

    @property
    def provider(self) -> str:
        return "livekit"

    def chat(
        self,
        *,
        chat_ctx: ChatContext,
        tools: list[Tool] | None = None,
        conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS,
        parallel_tool_calls: NotGivenOr[bool] = NOT_GIVEN,
        tool_choice: NotGivenOr[ToolChoice] = NOT_GIVEN,
        response_format: NotGivenOr[
            completion_create_params.ResponseFormat | type[llm_utils.ResponseFormatT]
        ] = NOT_GIVEN,
        inference_class: NotGivenOr[InferenceClass] = NOT_GIVEN,
        extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN,
    ) -> LLMStream:
        extra = {}
        if is_given(extra_kwargs):
            extra.update(extra_kwargs)

        parallel_tool_calls = (
            parallel_tool_calls
            if is_given(parallel_tool_calls)
            else self._opts.extra_kwargs.get("parallel_tool_calls", NOT_GIVEN)
        )
        if is_given(parallel_tool_calls):
            extra["parallel_tool_calls"] = parallel_tool_calls

        extra_tool_choice = self._opts.extra_kwargs.get("tool_choice", NOT_GIVEN)
        tool_choice = tool_choice if is_given(tool_choice) else extra_tool_choice
        if is_given(tool_choice):
            oai_tool_choice: ChatCompletionToolChoiceOptionParam
            if isinstance(tool_choice, dict):
                oai_tool_choice = {
                    "type": "function",
                    "function": {"name": tool_choice["function"]["name"]},
                }
                extra["tool_choice"] = oai_tool_choice
            elif tool_choice in ("auto", "required", "none"):
                oai_tool_choice = tool_choice
                extra["tool_choice"] = oai_tool_choice

        if is_given(response_format):
            extra["response_format"] = llm_utils.to_openai_response_format(response_format)  # type: ignore

        extra.update(self._opts.extra_kwargs)

        effective_inference_class = (
            inference_class if is_given(inference_class) else self._opts.inference_class
        )

        self._client.api_key = create_access_token(self._opts.api_key, self._opts.api_secret)
        return LLMStream(
            self,
            model=self._opts.model,
            provider=self._opts.provider,
            inference_class=effective_inference_class,
            strict_tool_schema=True,
            client=self._client,
            chat_ctx=chat_ctx,
            tools=tools or [],
            conn_options=conn_options,
            extra_kwargs=extra,
        )

class LLMStream(llm.LLMStream):
    def __init__(
        self,
        llm_v: LLM | llm.LLM,
        *,
        model: LLMModels | str,
        provider: str | None = None,
        inference_class: InferenceClass | None = None,
        strict_tool_schema: bool,
        client: openai.AsyncClient,
        chat_ctx: llm.ChatContext,
        tools: list[Tool],
        conn_options: APIConnectOptions,
        extra_kwargs: dict[str, Any],
        provider_fmt: str = "openai",  # used internally for chat_ctx format
    ) -> None:
        super().__init__(llm_v, chat_ctx=chat_ctx, tools=tools, conn_options=conn_options)
        self._model = model
        self._provider = provider
        self._inference_class = inference_class
        self._provider_fmt = provider_fmt
        self._strict_tool_schema = strict_tool_schema
        self._client = client
        self._llm = llm_v
        self._extra_kwargs = drop_unsupported_params(model, extra_kwargs, tools=tools)
        self._tool_ctx = llm.ToolContext(tools)

    async def _run(self) -> None:
        # current function call that we're waiting for full completion (args are streamed)
        # (defined inside the _run method to make sure the state is reset for each run/attempt)
        self._oai_stream: openai.AsyncStream[ChatCompletionChunk] | None = None
        self._tool_call_id: str | None = None
        self._fnc_name: str | None = None
        self._fnc_raw_arguments: str | None = None
        self._tool_extra: dict[str, Any] | None = None
        self._tool_index: int | None = None
        retryable = True

        try:
            chat_ctx, _ = self._chat_ctx.to_provider_format(format=self._provider_fmt)
            tool_schemas = cast(
                list[ChatCompletionToolParam],
                self._tool_ctx.parse_function_tools("openai", strict=self._strict_tool_schema),
            )
            if lk_oai_debug:
                tool_choice = self._extra_kwargs.get("tool_choice", NOT_GIVEN)
                logger.debug(
                    "chat.completions.create",
                    extra={
                        "fnc_ctx": tool_schemas,
                        "tool_choice": tool_choice,
                        "chat_ctx": chat_ctx,
                    },
                )
            if not self._tools:
                # remove tool_choice from extra_kwargs if no tools are provided
                self._extra_kwargs.pop("tool_choice", None)

            extra_headers = self._extra_kwargs.setdefault("extra_headers", {})
            extra_headers.update(get_inference_headers())
            if self._provider:
                extra_headers[HEADER_INFERENCE_PROVIDER] = self._provider
            if self._inference_class:
                extra_headers[HEADER_INFERENCE_PRIORITY] = self._inference_class

            self._oai_stream = stream = await self._client.chat.completions.create(
                messages=cast(list[ChatCompletionMessageParam], chat_ctx),
                tools=tool_schemas or openai.omit,
                model=self._model,
                stream_options={"include_usage": True},
                stream=True,
                timeout=httpx.Timeout(self._conn_options.timeout),
                **self._extra_kwargs,
            )

            thinking = asyncio.Event()
            async with stream:
                async for chunk in stream:
                    for choice in chunk.choices:
                        chat_chunk = self._parse_choice(chunk.id, choice, thinking)
                        if chat_chunk is not None:
                            retryable = False
                            self._event_ch.send_nowait(chat_chunk)

                    if chunk.usage is not None:
                        retryable = False
                        tokens_details = chunk.usage.prompt_tokens_details
                        cached_tokens = tokens_details.cached_tokens if tokens_details else 0
                        usage_chunk = llm.ChatChunk(
                            id=chunk.id,
                            usage=llm.CompletionUsage(
                                completion_tokens=chunk.usage.completion_tokens,
                                prompt_tokens=chunk.usage.prompt_tokens,
                                prompt_cached_tokens=cached_tokens or 0,
                                total_tokens=chunk.usage.total_tokens,
                                service_tier=getattr(chunk, "service_tier", None),
                            ),
                        )
                        self._event_ch.send_nowait(usage_chunk)

        except openai.APITimeoutError:
            raise APITimeoutError(retryable=retryable) from None
        except openai.APIStatusError as e:
            raise APIStatusError(
                e.message,
                status_code=e.status_code,
                request_id=e.request_id,
                body=e.body,
                retryable=retryable,
            ) from None
        except Exception as e:
            raise APIConnectionError(retryable=retryable) from e

    def _parse_choice(
        self, id: str, choice: Choice, thinking: asyncio.Event
    ) -> llm.ChatChunk | None:
        delta = choice.delta

        # https://github.com/livekit/agents/issues/688
        # the delta can be None when using Azure OpenAI (content filtering)
        if delta is None:
            return None

        if delta.tool_calls:
            for tool in delta.tool_calls:
                if not tool.function:
                    continue

                call_chunk = None
                if self._tool_call_id and tool.id and tool.index != self._tool_index:
                    call_chunk = llm.ChatChunk(
                        id=id,
                        delta=llm.ChoiceDelta(
                            role="assistant",
                            content=delta.content,
                            tool_calls=[
                                llm.FunctionToolCall(
                                    arguments=self._fnc_raw_arguments or "",
                                    name=self._fnc_name or "",
                                    call_id=self._tool_call_id or "",
                                    extra=self._tool_extra,
                                )
                            ],
                        ),
                    )
                    self._tool_call_id = self._fnc_name = self._fnc_raw_arguments = None
                    self._tool_extra = None

                if tool.function.name:
                    self._tool_index = tool.index
                    self._tool_call_id = tool.id
                    self._fnc_name = tool.function.name
                    self._fnc_raw_arguments = tool.function.arguments or ""
                    # Extract extra from tool call (e.g., Google thought signatures)
                    self._tool_extra = getattr(tool, "extra_content", None)
                elif tool.function.arguments:
                    self._fnc_raw_arguments += tool.function.arguments  # type: ignore

                if call_chunk is not None:
                    return call_chunk

        if choice.finish_reason in ("tool_calls", "stop") and self._tool_call_id:
            finish_extra = getattr(delta, "extra_content", None)
            call_chunk = llm.ChatChunk(
                id=id,
                delta=llm.ChoiceDelta(
                    role="assistant",
                    content=delta.content,
                    extra=finish_extra,
                    tool_calls=[
                        llm.FunctionToolCall(
                            arguments=self._fnc_raw_arguments or "",
                            name=self._fnc_name or "",
                            call_id=self._tool_call_id or "",
                            extra=self._tool_extra,
                        )
                    ],
                ),
            )
            self._tool_call_id = self._fnc_name = self._fnc_raw_arguments = None
            self._tool_extra = None
            return call_chunk

        delta.content = llm_utils.strip_thinking_tokens(delta.content, thinking)

        # Extract extra from delta (e.g., Google thought signatures on text parts)
        delta_extra = getattr(delta, "extra_content", None)

        if not delta.content and not delta_extra:
            return None

        return llm.ChatChunk(
            id=id,
            delta=llm.ChoiceDelta(
                content=delta.content,
                role="assistant",
                extra=delta_extra,
            ),
        )

class OverlappingSpeechEvent(BaseModel):
    """Represents an overlapping speech event detected during agent speech."""

    model_config = ConfigDict(arbitrary_types_allowed=True)

    type: Literal["overlapping_speech"] = "overlapping_speech"

    created_at: float = Field(default_factory=time.time)
    """Timestamp (in seconds) when the event was emitted."""

    detected_at: float = Field(default_factory=time.time)
    """Timestamp (in seconds) when the overlap was detected."""

    is_interruption: bool = False
    """Whether interruption is detected."""

    total_duration: float = 0.0
    """RTT (Round Trip Time) time taken to perform the inference, in seconds."""

    prediction_duration: float = 0.0
    """Time taken to perform the inference from the model side, in seconds."""

    detection_delay: float = 0.0
    """Total time from the onset of the speech to the final prediction, in seconds."""

    overlap_started_at: float | None = None
    """Timestamp (in seconds) when the overlap speech started. Useful for emitting held transcripts."""

    speech_input: npt.NDArray[np.int16] | None = None
    """The audio input that was used for the inference."""

    probabilities: npt.NDArray[np.float32] | None = None
    """The raw probabilities for the interruption detection."""

    probability: float = 0.0
    """The conservative estimated probability of the interruption event."""

    num_requests: int = 0
    """Number of requests sent for this event."""

    @model_serializer(mode="wrap")
    def serialize_model(self, handler: SerializerFunctionWrapHandler) -> Any:
        # remove numpy arrays from the model dump
        copy = self.model_copy(deep=True)
        data = copy.speech_input, copy.probabilities
        copy.speech_input, copy.probabilities = None, None
        try:
            serialized = handler(copy)
        finally:
            copy.speech_input, copy.probabilities = data
        return serialized

    @classmethod
    def from_cache_entry(
        cls,
        *,
        entry: InterruptionCacheEntry,
        is_interruption: bool,
        started_at: float | None = None,
        ended_at: float | None = None,
    ) -> OverlappingSpeechEvent:
        """Initialize the event from a cache entry.

        Args:
            entry: The cache entry to initialize the event from.
            is_interruption: Whether the interruption is detected.
            started_at: The timestamp when the overlap speech started.
            ended_at: The timestamp when the overlap speech ended.

        Returns:
            The initialized event.
        """
        return cls(
            type="overlapping_speech",
            detected_at=ended_at or time.time(),
            is_interruption=is_interruption,
            overlap_started_at=started_at,
            speech_input=entry.speech_input,
            probabilities=entry.probabilities,
            total_duration=entry.get_total_duration(),
            detection_delay=entry.get_detection_delay(),
            prediction_duration=entry.get_prediction_duration(),
            probability=entry.get_probability(),
        )

class STT(stt.STT):
    @overload
    def __init__(
        self,
        model: CartesiaModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[CartesiaOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: DeepgramModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[DeepgramOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: DeepgramFluxModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[DeepgramFluxOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: AssemblyAIModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[AssemblyaiOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: ElevenlabsModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[ElevenlabsOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: XaiModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[XaiOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: SpeechmaticsModels,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[SpeechmaticsOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
        vad: NotGivenOr[vad.VAD | None] = NOT_GIVEN,
    ) -> None: ...

    @overload
    def __init__(
        self,
        model: str,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None: ...

    def __init__(
        self,
        model: NotGivenOr[STTModels | str] = NOT_GIVEN,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[STTEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[
            dict[str, Any]
            | CartesiaOptions
            | DeepgramOptions
            | DeepgramFluxOptions
            | AssemblyaiOptions
            | ElevenlabsOptions
            | XaiOptions
            | SpeechmaticsOptions
        ] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
        vad: NotGivenOr[vad.VAD | None] = NOT_GIVEN,
    ) -> None:
        """Livekit Cloud Inference STT

        Args:
            model (STTModels | str, optional): STT model to use, in "provider/model[:language]" format.
            language (str, optional): Language of the STT model.
            encoding (STTEncoding, optional): Encoding of the STT model.
            sample_rate (int, optional): Sample rate of the STT model.
            base_url (str, optional): LIVEKIT_URL, if not provided, read from environment variable.
            api_key (str, optional): LIVEKIT_API_KEY, if not provided, read from environment variable.
            api_secret (str, optional): LIVEKIT_API_SECRET, if not provided, read from environment variable.
            http_session (aiohttp.ClientSession, optional): HTTP session to use.
            extra_kwargs (dict, optional): Extra kwargs to pass to the STT model.
            fallback (FallbackModelType, optional): Fallback models - either a list of model names,
                a list of FallbackModel instances.
            conn_options (APIConnectOptions, optional): Connection options for request attempts.
            vad (VAD, optional): External Voice Activity Detector. When provided, each audio
                frame is forwarded to the VAD and `session.finalize` is sent to the inference
                gateway on end of speech. Only applicable to Speechmatics models.
        """
        # Infer diarization capability from provider-specific extra_kwargs
        # keys (see _DIARIZATION_EXTRA_KEYS). xAI uses "diarize" (same as
        # Deepgram); AssemblyAI uses "speaker_labels".
        diarization_enabled = _diarization_enabled(
            dict(extra_kwargs) if is_given(extra_kwargs) else None
        )

        # Parse language from model string if provided: "provider/model:language"
        if is_given(model) and isinstance(model, str):
            parsed_model, parsed_language = _parse_model_string(model)
            model = parsed_model
            if is_given(parsed_language) and not is_given(language):
                language = parsed_language

        vad = _resolve_vad_for_model(model, vad if is_given(vad) else None)

        super().__init__(
            capabilities=stt.STTCapabilities(
                streaming=True,
                interim_results=True,
                diarization=diarization_enabled,
                aligned_transcript="word",
                offline_recognize=False,
            ),
        )

        lk_base_url = base_url if is_given(base_url) else get_default_inference_url()

        lk_api_key = (
            api_key
            if is_given(api_key)
            else os.getenv("LIVEKIT_INFERENCE_API_KEY", os.getenv("LIVEKIT_API_KEY", ""))
        )
        if not lk_api_key:
            raise ValueError(
                "api_key is required, either as argument or set LIVEKIT_API_KEY environmental variable"
            )

        lk_api_secret = (
            api_secret
            if is_given(api_secret)
            else os.getenv("LIVEKIT_INFERENCE_API_SECRET", os.getenv("LIVEKIT_API_SECRET", ""))
        )
        if not lk_api_secret:
            raise ValueError(
                "api_secret is required, either as argument or set LIVEKIT_API_SECRET environmental variable"
            )
        fallback_models: NotGivenOr[list[FallbackModel]] = NOT_GIVEN
        if is_given(fallback):
            fallback_models = _normalize_fallback(fallback)

        self._opts = STTOptions(
            model=model,
            language=LanguageCode(language) if isinstance(language, str) else language,
            encoding=encoding if is_given(encoding) else DEFAULT_ENCODING,
            sample_rate=sample_rate if is_given(sample_rate) else DEFAULT_SAMPLE_RATE,
            base_url=lk_base_url,
            api_key=lk_api_key,
            api_secret=lk_api_secret,
            extra_kwargs=dict(extra_kwargs) if is_given(extra_kwargs) else {},
            fallback=fallback_models,
            conn_options=conn_options if is_given(conn_options) else DEFAULT_API_CONNECT_OPTIONS,
        )

        self._session = http_session
        self._vad = vad
        self._streams = weakref.WeakSet[SpeechStream]()

    @classmethod
    def from_model_string(cls, model: str) -> STT:
        """Create a STT instance from a model string

        Args:
            model (str): STT model to use, in "provider/model[:language]" format

        Returns:
            STT: STT instance
        """
        model_name, language = _parse_model_string(model)
        return cls(model=model_name, language=language)

    @property
    def model(self) -> str:
        return self._opts.model if is_given(self._opts.model) else "unknown"

    @property
    def provider(self) -> str:
        return "livekit"

    def _ensure_session(self) -> aiohttp.ClientSession:
        if not self._session:
            self._session = utils.http_context.http_session()
        return self._session

    async def _recognize_impl(
        self,
        buffer: utils.AudioBuffer,
        *,
        language: NotGivenOr[str] = NOT_GIVEN,
        conn_options: APIConnectOptions,
    ) -> stt.SpeechEvent:
        raise NotImplementedError(
            "LiveKit Inference STT does not support batch recognition, use stream() instead"
        )

    def stream(
        self,
        *,
        language: NotGivenOr[STTLanguages | str] = NOT_GIVEN,
        conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS,
    ) -> SpeechStream:
        """Create a streaming transcription session."""
        options = self._sanitize_options(language=language)
        stream = SpeechStream(
            stt=self,
            opts=options,
            conn_options=conn_options,
            vad_instance=self._vad,
        )
        self._streams.add(stream)
        return stream

    def update_options(
        self,
        *,
        model: NotGivenOr[STTModels | str] = NOT_GIVEN,
        language: NotGivenOr[STTLanguages | str] = NOT_GIVEN,
        extra: NotGivenOr[dict[str, Any]] = NOT_GIVEN,
    ) -> None:
        """Update STT configuration options."""
        if is_given(model):
            # Mirror __init__: strip ":language" suffix and apply if not overridden.
            if isinstance(model, str):
                parsed_model, parsed_language = _parse_model_string(model)
                model = parsed_model
                if is_given(parsed_language) and not is_given(language):
                    language = parsed_language

            self._opts.model = model
            self._vad = _resolve_vad_for_model(model, self._vad)
        if is_given(language):
            self._opts.language = LanguageCode(language)
        if is_given(extra):
            self._opts.extra_kwargs.update(extra)
            self._capabilities = replace(
                self._capabilities,
                diarization=_diarization_enabled(self._opts.extra_kwargs),
            )

        for stream in self._streams:
            stream.update_options(model=model, language=language, extra=extra)

    def _sanitize_options(
        self, *, language: NotGivenOr[STTLanguages | str] = NOT_GIVEN
    ) -> STTOptions:
        """Create a sanitized copy of options with language override if provided."""
        options = replace(self._opts)
        options.extra_kwargs = dict(options.extra_kwargs)

        if is_given(language):
            options.language = LanguageCode(language)

        return options

class TTS(tts.TTS):
    @overload
    def __init__(
        self,
        model: CartesiaModels,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[CartesiaOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        pass

    @overload
    def __init__(
        self,
        model: DeepgramModels,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[DeepgramOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        pass

    @overload
    def __init__(
        self,
        model: ElevenlabsModels,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[ElevenlabsOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        pass

    @overload
    def __init__(
        self,
        model: RimeModels,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[RimeOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        pass

    @overload
    def __init__(
        self,
        model: InworldModels,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[InworldOptions] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        pass

    @overload
    def __init__(
        self,
        model: str,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        pass

    def __init__(
        self,
        model: TTSModels | str,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        encoding: NotGivenOr[TTSEncoding] = NOT_GIVEN,
        sample_rate: NotGivenOr[int] = NOT_GIVEN,
        base_url: NotGivenOr[str] = NOT_GIVEN,
        api_key: NotGivenOr[str] = NOT_GIVEN,
        api_secret: NotGivenOr[str] = NOT_GIVEN,
        http_session: aiohttp.ClientSession | None = None,
        extra_kwargs: NotGivenOr[
            dict[str, Any]
            | CartesiaOptions
            | DeepgramOptions
            | ElevenlabsOptions
            | RimeOptions
            | InworldOptions
        ] = NOT_GIVEN,
        fallback: NotGivenOr[list[FallbackModelType] | FallbackModelType] = NOT_GIVEN,
        conn_options: NotGivenOr[APIConnectOptions] = NOT_GIVEN,
    ) -> None:
        """Livekit Cloud Inference TTS

        Args:
            model (TTSModels | str): TTS model to use, in "provider/model[:voice]" format
            voice (str, optional): Voice to use, use a default one if not provided
            language (str, optional): Language of the TTS model.
            encoding (TTSEncoding, optional): Encoding of the TTS model.
            sample_rate (int, optional): Sample rate of the TTS model.
            base_url (str, optional): LIVEKIT_URL, if not provided, read from environment variable.
            api_key (str, optional): LIVEKIT_API_KEY, if not provided, read from environment variable.
            api_secret (str, optional): LIVEKIT_API_SECRET, if not provided, read from environment variable.
            http_session (aiohttp.ClientSession, optional): HTTP session to use.
            extra_kwargs (dict, optional): Extra kwargs to pass to the TTS model.
            fallback (FallbackModelType, optional): Fallback models - either a list of model names,
                a list of FallbackModel instances.
            conn_options (APIConnectOptions, optional): Connection options for request attempts.
        """
        sample_rate = sample_rate if is_given(sample_rate) else DEFAULT_SAMPLE_RATE

        # Parse voice from model string if provided: "provider/model:voice"
        if isinstance(model, str):
            parsed_model, parsed_voice = _parse_model_string(model)
            model = parsed_model
            if parsed_voice is not None and not is_given(voice):
                voice = parsed_voice

        resolved_extra_kwargs = dict(extra_kwargs) if is_given(extra_kwargs) else {}
        super().__init__(
            capabilities=tts.TTSCapabilities(
                streaming=True,
                aligned_transcript=_has_aligned_transcript(model, resolved_extra_kwargs),
            ),
            sample_rate=sample_rate,
            num_channels=1,
        )

        lk_base_url = base_url if is_given(base_url) else get_default_inference_url()

        lk_api_key = (
            api_key
            if is_given(api_key)
            else os.getenv("LIVEKIT_INFERENCE_API_KEY", os.getenv("LIVEKIT_API_KEY", ""))
        )
        if not lk_api_key:
            raise ValueError(
                "api_key is required, either as argument or set LIVEKIT_API_KEY environmental variable"
            )

        lk_api_secret = (
            api_secret
            if is_given(api_secret)
            else os.getenv("LIVEKIT_INFERENCE_API_SECRET", os.getenv("LIVEKIT_API_SECRET", ""))
        )
        if not lk_api_secret:
            raise ValueError(
                "api_secret is required, either as argument or set LIVEKIT_API_SECRET environmental variable"
            )

        fallback_models: NotGivenOr[list[FallbackModel]] = NOT_GIVEN
        if is_given(fallback):
            fallback_models = _normalize_fallback(fallback)

        self._opts = _TTSOptions(
            model=model,
            voice=voice,
            language=LanguageCode(language) if isinstance(language, str) else language,
            encoding=encoding if is_given(encoding) else DEFAULT_ENCODING,
            sample_rate=sample_rate,
            base_url=lk_base_url,
            api_key=lk_api_key,
            api_secret=lk_api_secret,
            extra_kwargs=resolved_extra_kwargs,
            fallback=fallback_models,
            conn_options=conn_options if is_given(conn_options) else DEFAULT_API_CONNECT_OPTIONS,
        )
        self._session = http_session
        self._pool = utils.ConnectionPool[aiohttp.ClientWebSocketResponse](
            connect_cb=self._connect_ws,
            close_cb=self._close_ws,
            max_session_duration=300,
            mark_refreshed_on_get=True,
        )
        self._streams = weakref.WeakSet[SynthesizeStream]()

    @classmethod
    def from_model_string(cls, model: str) -> TTS:
        """Create a TTS instance from a model string

        Args:
            model (str): TTS model to use, in "provider/model[:voice_id]" format

        Returns:
            TTS: TTS instance
        """
        model, voice = _parse_model_string(model)
        return cls(model=model, voice=voice if voice else NOT_GIVEN)

    @property
    def model(self) -> str:
        return self._opts.model

    @property
    def provider(self) -> str:
        return "livekit"

    async def _connect_ws(self, timeout: float) -> aiohttp.ClientWebSocketResponse:
        session = self._ensure_session()
        base_url = self._opts.base_url
        if base_url.startswith(("http://", "https://")):
            base_url = base_url.replace("http", "ws", 1)

        headers = {
            **get_inference_headers(),
            "Authorization": f"Bearer {create_access_token(self._opts.api_key, self._opts.api_secret)}",
        }
        ws = None
        try:
            ws = await asyncio.wait_for(
                session.ws_connect(f"{base_url}/tts?model={self._opts.model}", headers=headers),
                timeout,
            )
        except aiohttp.ClientResponseError as e:
            raise create_api_error_from_http(e.message, status=e.status) from e
        except asyncio.TimeoutError as e:
            raise APITimeoutError("LiveKit Inference TTS connection timed out.") from e
        except aiohttp.ClientConnectorError as e:
            raise APIConnectionError("failed to connect to LiveKit Inference TTS") from e

        params: dict[str, Any] = {
            "type": "session.create",
            "sample_rate": str(self._opts.sample_rate),
            "encoding": self._opts.encoding,
            "extra": self._opts.extra_kwargs,
        }

        if self._opts.voice:
            params["voice"] = self._opts.voice
        if self._opts.model:
            params["model"] = self._opts.model
        if self._opts.language:
            params["language"] = self._opts.language
        if self._opts.fallback:
            models = [
                {
                    "model": m.get("model"),
                    "voice": m.get("voice"),
                    "extra": m.get("extra_kwargs", {}),
                }
                for m in self._opts.fallback
            ]
            params["fallback"] = {"models": models}

        if self._opts.conn_options:
            params["connection"] = {
                "timeout": self._opts.conn_options.timeout,
                "retries": self._opts.conn_options.max_retry,
            }

        try:
            await ws.send_str(json.dumps(params))
        except Exception as e:
            await ws.close()
            raise APIConnectionError(
                "failed to send session.create message to LiveKit Inference TTS"
            ) from e

        return ws

    async def _close_ws(self, ws: aiohttp.ClientWebSocketResponse) -> None:
        await ws.close()

    def _ensure_session(self) -> aiohttp.ClientSession:
        if not self._session:
            self._session = utils.http_context.http_session()

        return self._session

    def prewarm(self) -> None:
        self._pool.prewarm()

    def update_options(
        self,
        *,
        voice: NotGivenOr[str] = NOT_GIVEN,
        model: NotGivenOr[TTSModels | str] = NOT_GIVEN,
        language: NotGivenOr[str] = NOT_GIVEN,
        extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN,
    ) -> None:
        """
        Args:
            voice (str, optional): Voice.
            model (TTSModels | str, optional): TTS model to use.
            language (str, optional): Language code for the TTS model.
            extra_kwargs (dict, optional): Extra kwargs to pass to the TTS model.
        """
        if is_given(model):
            self._opts.model = model
        if is_given(voice):
            self._opts.voice = voice
        if is_given(language):
            self._opts.language = LanguageCode(language)
        if is_given(extra_kwargs):
            self._opts.extra_kwargs.update(extra_kwargs)

        self._capabilities.aligned_transcript = _has_aligned_transcript(
            self._opts.model, self._opts.extra_kwargs
        )

    def synthesize(
        self, text: str, *, conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS
    ) -> tts.ChunkedStream:
        return self._synthesize_with_stream(text, conn_options=conn_options)

    def stream(
        self, *, conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS
    ) -> SynthesizeStream:
        stream = SynthesizeStream(tts=self, conn_options=conn_options)
        self._streams.add(stream)
        return stream

    async def aclose(self) -> None:
        for stream in list(self._streams):
            await stream.aclose()

        self._streams.clear()
        await self._pool.aclose()