Commit 4756247c

@@ -288,10 +288,10 @@ class Responses(SyncAPIResource):
 
           truncation: The truncation strategy to use for the model response.
 
-              - `auto`: If the context of this response and previous ones exceeds the model's
-                context window size, the model will truncate the response to fit the context
-                window by dropping input items in the middle of the conversation.
-              - `disabled` (default): If a model response will exceed the context window size
+              - `auto`: If the input to this Response exceeds the model's context window size,
+                the model will truncate the response to fit the context window by dropping
+                items from the beginning of the conversation.
+              - `disabled` (default): If the input size will exceed the context window size
                 for a model, the request will fail with a 400 error.
 
           user: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use
@@ -527,10 +527,10 @@ class Responses(SyncAPIResource):
 
           truncation: The truncation strategy to use for the model response.
 
-              - `auto`: If the context of this response and previous ones exceeds the model's
-                context window size, the model will truncate the response to fit the context
-                window by dropping input items in the middle of the conversation.
-              - `disabled` (default): If a model response will exceed the context window size
+              - `auto`: If the input to this Response exceeds the model's context window size,
+                the model will truncate the response to fit the context window by dropping
+                items from the beginning of the conversation.
+              - `disabled` (default): If the input size will exceed the context window size
                 for a model, the request will fail with a 400 error.
 
           user: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use
@@ -766,10 +766,10 @@ class Responses(SyncAPIResource):
 
           truncation: The truncation strategy to use for the model response.
 
-              - `auto`: If the context of this response and previous ones exceeds the model's
-                context window size, the model will truncate the response to fit the context
-                window by dropping input items in the middle of the conversation.
-              - `disabled` (default): If a model response will exceed the context window size
+              - `auto`: If the input to this Response exceeds the model's context window size,
+                the model will truncate the response to fit the context window by dropping
+                items from the beginning of the conversation.
+              - `disabled` (default): If the input size will exceed the context window size
                 for a model, the request will fail with a 400 error.
 
           user: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use
@@ -1719,10 +1719,10 @@ class AsyncResponses(AsyncAPIResource):
 
           truncation: The truncation strategy to use for the model response.
 
-              - `auto`: If the context of this response and previous ones exceeds the model's
-                context window size, the model will truncate the response to fit the context
-                window by dropping input items in the middle of the conversation.
-              - `disabled` (default): If a model response will exceed the context window size
+              - `auto`: If the input to this Response exceeds the model's context window size,
+                the model will truncate the response to fit the context window by dropping
+                items from the beginning of the conversation.
+              - `disabled` (default): If the input size will exceed the context window size
                 for a model, the request will fail with a 400 error.
 
           user: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use
@@ -1958,10 +1958,10 @@ class AsyncResponses(AsyncAPIResource):
 
           truncation: The truncation strategy to use for the model response.
 
-              - `auto`: If the context of this response and previous ones exceeds the model's
-                context window size, the model will truncate the response to fit the context
-                window by dropping input items in the middle of the conversation.
-              - `disabled` (default): If a model response will exceed the context window size
+              - `auto`: If the input to this Response exceeds the model's context window size,
+                the model will truncate the response to fit the context window by dropping
+                items from the beginning of the conversation.
+              - `disabled` (default): If the input size will exceed the context window size
                 for a model, the request will fail with a 400 error.
 
           user: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use
@@ -2197,10 +2197,10 @@ class AsyncResponses(AsyncAPIResource):
 
           truncation: The truncation strategy to use for the model response.
 
-              - `auto`: If the context of this response and previous ones exceeds the model's
-                context window size, the model will truncate the response to fit the context
-                window by dropping input items in the middle of the conversation.
-              - `disabled` (default): If a model response will exceed the context window size
+              - `auto`: If the input to this Response exceeds the model's context window size,
+                the model will truncate the response to fit the context window by dropping
+                items from the beginning of the conversation.
+              - `disabled` (default): If the input size will exceed the context window size
                 for a model, the request will fail with a 400 error.
 
           user: This field is being replaced by `safety_identifier` and `prompt_cache_key`. Use

@@ -9,10 +9,16 @@ __all__ = ["InputAudioBufferTimeoutTriggered"]
 
 class InputAudioBufferTimeoutTriggered(BaseModel):
     audio_end_ms: int
-    """Millisecond offset where speech ended within the buffered audio."""
+    """
+    Millisecond offset of audio written to the input audio buffer at the time the
+    timeout was triggered.
+    """
 
     audio_start_ms: int
-    """Millisecond offset where speech started within the buffered audio."""
+    """
+    Millisecond offset of audio written to the input audio buffer that was after the
+    playback time of the last model response.
+    """
 
     event_id: str
     """The unique ID of the server event."""

@@ -49,8 +49,11 @@ class RealtimeAudioConfigInput(BaseModel):
     """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.
+    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

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+from typing import Optional
 from typing_extensions import TypedDict
 
 from .noise_reduction_type import NoiseReductionType
@@ -46,12 +47,15 @@ class RealtimeAudioConfigInputParam(TypedDict, total=False):
     transcription, these offer additional guidance to the transcription service.
     """
 
-    turn_detection: RealtimeAudioInputTurnDetectionParam
+    turn_detection: Optional[RealtimeAudioInputTurnDetectionParam]
     """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.
+    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

@@ -1,33 +1,38 @@
 # File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
 
-from typing import Optional
-from typing_extensions import Literal
+from typing import Union, Optional
+from typing_extensions import Literal, Annotated, TypeAlias
 
+from ..._utils import PropertyInfo
 from ..._models import BaseModel
 
-__all__ = ["RealtimeAudioInputTurnDetection"]
+__all__ = ["RealtimeAudioInputTurnDetection", "ServerVad", "SemanticVad"]
 
 
-class RealtimeAudioInputTurnDetection(BaseModel):
+class ServerVad(BaseModel):
+    type: Literal["server_vad"]
+    """Type of turn detection, `server_vad` to turn on simple Server VAD."""
+
     create_response: Optional[bool] = None
     """
     Whether or not to automatically generate a response when a VAD stop event
     occurs.
     """
 
-    eagerness: Optional[Literal["low", "medium", "high", "auto"]] = None
-    """Used only for `semantic_vad` mode.
+    idle_timeout_ms: Optional[int] = None
+    """Optional timeout after which a model response will be triggered automatically.
 
-    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`. `low`, `medium`, and `high` have max timeouts of 8s,
-    4s, and 2s respectively.
-    """
+    This is useful for situations in which a long pause from the user is unexpected,
+    such as a phone call. The model will effectively prompt the user to continue the
+    conversation based on the current context.
 
-    idle_timeout_ms: Optional[int] = None
-    """
-    Optional idle timeout after which turn detection will auto-timeout when no
-    additional audio is received and emits a `timeout_triggered` event.
+    The timeout value will be applied after the last model response's audio has
+    finished playing, i.e. it's set to the `response.done` time plus audio playback
+    duration.
+
+    An `input_audio_buffer.timeout_triggered` event (plus events associated with the
+    Response) will be emitted when the timeout is reached. Idle timeout is currently
+    only supported for `server_vad` mode.
     """
 
     interrupt_response: Optional[bool] = None
@@ -60,5 +65,34 @@ class RealtimeAudioInputTurnDetection(BaseModel):
     perform better in noisy environments.
     """
 
-    type: Optional[Literal["server_vad", "semantic_vad"]] = None
-    """Type of turn detection."""
+
+class SemanticVad(BaseModel):
+    type: Literal["semantic_vad"]
+    """Type of turn detection, `semantic_vad` to turn on Semantic VAD."""
+
+    create_response: Optional[bool] = None
+    """
+    Whether or not to automatically generate a response when a VAD stop event
+    occurs.
+    """
+
+    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`. `low`, `medium`, and `high` have max timeouts of 8s,
+    4s, and 2s respectively.
+    """
+
+    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.
+    """
+
+
+RealtimeAudioInputTurnDetection: TypeAlias = Annotated[
+    Union[ServerVad, SemanticVad, None], PropertyInfo(discriminator="type")
+]

@@ -2,32 +2,36 @@
 
 from __future__ import annotations
 
-from typing import Optional
-from typing_extensions import Literal, TypedDict
+from typing import Union, Optional
+from typing_extensions import Literal, Required, TypeAlias, TypedDict
 
-__all__ = ["RealtimeAudioInputTurnDetectionParam"]
+__all__ = ["RealtimeAudioInputTurnDetectionParam", "ServerVad", "SemanticVad"]
 
 
-class RealtimeAudioInputTurnDetectionParam(TypedDict, total=False):
+class ServerVad(TypedDict, total=False):
+    type: Required[Literal["server_vad"]]
+    """Type of turn detection, `server_vad` to turn on simple Server VAD."""
+
     create_response: bool
     """
     Whether or not to automatically generate a response when a VAD stop event
     occurs.
     """
 
-    eagerness: Literal["low", "medium", "high", "auto"]
-    """Used only for `semantic_vad` mode.
+    idle_timeout_ms: Optional[int]
+    """Optional timeout after which a model response will be triggered automatically.
 
-    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`. `low`, `medium`, and `high` have max timeouts of 8s,
-    4s, and 2s respectively.
-    """
+    This is useful for situations in which a long pause from the user is unexpected,
+    such as a phone call. The model will effectively prompt the user to continue the
+    conversation based on the current context.
 
-    idle_timeout_ms: Optional[int]
-    """
-    Optional idle timeout after which turn detection will auto-timeout when no
-    additional audio is received and emits a `timeout_triggered` event.
+    The timeout value will be applied after the last model response's audio has
+    finished playing, i.e. it's set to the `response.done` time plus audio playback
+    duration.
+
+    An `input_audio_buffer.timeout_triggered` event (plus events associated with the
+    Response) will be emitted when the timeout is reached. Idle timeout is currently
+    only supported for `server_vad` mode.
     """
 
     interrupt_response: bool
@@ -60,5 +64,32 @@ class RealtimeAudioInputTurnDetectionParam(TypedDict, total=False):
     perform better in noisy environments.
     """
 
-    type: Literal["server_vad", "semantic_vad"]
-    """Type of turn detection."""
+
+class SemanticVad(TypedDict, total=False):
+    type: Required[Literal["semantic_vad"]]
+    """Type of turn detection, `semantic_vad` to turn on Semantic VAD."""
+
+    create_response: bool
+    """
+    Whether or not to automatically generate a response when a VAD stop event
+    occurs.
+    """
+
+    eagerness: Literal["low", "medium", "high", "auto"]
+    """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`. `low`, `medium`, and `high` have max timeouts of 8s,
+    4s, and 2s respectively.
+    """
+
+    interrupt_response: bool
+    """
+    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.
+    """
+
+
+RealtimeAudioInputTurnDetectionParam: TypeAlias = Union[ServerVad, SemanticVad]

@@ -1,8 +1,9 @@
 # File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
 
 from typing import Dict, List, Union, Optional
-from typing_extensions import Literal, TypeAlias
+from typing_extensions import Literal, Annotated, TypeAlias
 
+from ..._utils import PropertyInfo
 from ..._models import BaseModel
 from .audio_transcription import AudioTranscription
 from .realtime_truncation import RealtimeTruncation
@@ -21,6 +22,8 @@ __all__ = [
     "AudioInput",
     "AudioInputNoiseReduction",
     "AudioInputTurnDetection",
+    "AudioInputTurnDetectionServerVad",
+    "AudioInputTurnDetectionSemanticVad",
     "AudioOutput",
     "ToolChoice",
     "Tool",
@@ -45,26 +48,30 @@ class AudioInputNoiseReduction(BaseModel):
     """
 
 
-class AudioInputTurnDetection(BaseModel):
+class AudioInputTurnDetectionServerVad(BaseModel):
+    type: Literal["server_vad"]
+    """Type of turn detection, `server_vad` to turn on simple Server VAD."""
+
     create_response: Optional[bool] = None
     """
     Whether or not to automatically generate a response when a VAD stop event
     occurs.
     """
 
-    eagerness: Optional[Literal["low", "medium", "high", "auto"]] = None
-    """Used only for `semantic_vad` mode.
+    idle_timeout_ms: Optional[int] = None
+    """Optional timeout after which a model response will be triggered automatically.
 
-    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`. `low`, `medium`, and `high` have max timeouts of 8s,
-    4s, and 2s respectively.
-    """
+    This is useful for situations in which a long pause from the user is unexpected,
+    such as a phone call. The model will effectively prompt the user to continue the
+    conversation based on the current context.
 
-    idle_timeout_ms: Optional[int] = None
-    """
-    Optional idle timeout after which turn detection will auto-timeout when no
-    additional audio is received and emits a `timeout_triggered` event.
+    The timeout value will be applied after the last model response's audio has
+    finished playing, i.e. it's set to the `response.done` time plus audio playback
+    duration.
+
+    An `input_audio_buffer.timeout_triggered` event (plus events associated with the
+    Response) will be emitted when the timeout is reached. Idle timeout is currently
+    only supported for `server_vad` mode.
     """
 
     interrupt_response: Optional[bool] = None
@@ -97,8 +104,38 @@ class AudioInputTurnDetection(BaseModel):
     perform better in noisy environments.
     """
 
-    type: Optional[Literal["server_vad", "semantic_vad"]] = None
-    """Type of turn detection."""
+
+class AudioInputTurnDetectionSemanticVad(BaseModel):
+    type: Literal["semantic_vad"]
+    """Type of turn detection, `semantic_vad` to turn on Semantic VAD."""
+
+    create_response: Optional[bool] = None
+    """
+    Whether or not to automatically generate a response when a VAD stop event
+    occurs.
+    """
+
+    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`. `low`, `medium`, and `high` have max timeouts of 8s,
+    4s, and 2s respectively.
+    """
+
+    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.
+    """
+
+
+AudioInputTurnDetection: TypeAlias = Annotated[
+    Union[AudioInputTurnDetectionServerVad, AudioInputTurnDetectionSemanticVad, None],
+    PropertyInfo(discriminator="type"),
+]
 
 
 class AudioInput(BaseModel):
@@ -130,8 +167,11 @@ class AudioInput(BaseModel):
     """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.
+    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

@@ -51,8 +51,11 @@ class RealtimeTranscriptionSessionAudioInput(BaseModel):
     """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.
+    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

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+from typing import Optional
 from typing_extensions import TypedDict
 
 from .noise_reduction_type import NoiseReductionType
@@ -48,12 +49,15 @@ class RealtimeTranscriptionSessionAudioInputParam(TypedDict, total=False):
     transcription, these offer additional guidance to the transcription service.
     """
 
-    turn_detection: RealtimeTranscriptionSessionAudioInputTurnDetectionParam
+    turn_detection: Optional[RealtimeTranscriptionSessionAudioInputTurnDetectionParam]
     """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.
+    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

@@ -1,32 +1,38 @@
 # File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
 
-from typing import Optional
-from typing_extensions import Literal
+from typing import Union, Optional
+from typing_extensions import Literal, Annotated, TypeAlias
 
+from ..._utils import PropertyInfo
 from ..._models import BaseModel
 
-__all__ = ["RealtimeTranscriptionSessionAudioInputTurnDetection"]
+__all__ = ["RealtimeTranscriptionSessionAudioInputTurnDetection", "ServerVad", "SemanticVad"]
 
 
-class RealtimeTranscriptionSessionAudioInputTurnDetection(BaseModel):
+class ServerVad(BaseModel):
+    type: Literal["server_vad"]
+    """Type of turn detection, `server_vad` to turn on simple Server VAD."""
+
     create_response: Optional[bool] = None
     """
     Whether or not to automatically generate a response when a VAD stop event
     occurs.
     """
 
-    eagerness: Optional[Literal["low", "medium", "high", "auto"]] = None
-    """Used only for `semantic_vad` mode.
+    idle_timeout_ms: Optional[int] = None
+    """Optional timeout after which a model response will be triggered automatically.
 
-    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`.
-    """
+    This is useful for situations in which a long pause from the user is unexpected,
+    such as a phone call. The model will effectively prompt the user to continue the
+    conversation based on the current context.
 
-    idle_timeout_ms: Optional[int] = None
-    """
-    Optional idle timeout after which turn detection will auto-timeout when no
-    additional audio is received.
+    The timeout value will be applied after the last model response's audio has
+    finished playing, i.e. it's set to the `response.done` time plus audio playback
+    duration.
+
+    An `input_audio_buffer.timeout_triggered` event (plus events associated with the
+    Response) will be emitted when the timeout is reached. Idle timeout is currently
+    only supported for `server_vad` mode.
     """
 
     interrupt_response: Optional[bool] = None
@@ -59,5 +65,34 @@ class RealtimeTranscriptionSessionAudioInputTurnDetection(BaseModel):
     perform better in noisy environments.
     """
 
-    type: Optional[Literal["server_vad", "semantic_vad"]] = None
-    """Type of turn detection."""
+
+class SemanticVad(BaseModel):
+    type: Literal["semantic_vad"]
+    """Type of turn detection, `semantic_vad` to turn on Semantic VAD."""
+
+    create_response: Optional[bool] = None
+    """
+    Whether or not to automatically generate a response when a VAD stop event
+    occurs.
+    """
+
+    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`. `low`, `medium`, and `high` have max timeouts of 8s,
+    4s, and 2s respectively.
+    """
+
+    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.
+    """
+
+
+RealtimeTranscriptionSessionAudioInputTurnDetection: TypeAlias = Annotated[
+    Union[ServerVad, SemanticVad, None], PropertyInfo(discriminator="type")
+]

@@ -2,31 +2,36 @@
 
 from __future__ import annotations
 
-from typing import Optional
-from typing_extensions import Literal, TypedDict
+from typing import Union, Optional
+from typing_extensions import Literal, Required, TypeAlias, TypedDict
 
-__all__ = ["RealtimeTranscriptionSessionAudioInputTurnDetectionParam"]
+__all__ = ["RealtimeTranscriptionSessionAudioInputTurnDetectionParam", "ServerVad", "SemanticVad"]
 
 
-class RealtimeTranscriptionSessionAudioInputTurnDetectionParam(TypedDict, total=False):
+class ServerVad(TypedDict, total=False):
+    type: Required[Literal["server_vad"]]
+    """Type of turn detection, `server_vad` to turn on simple Server VAD."""
+
     create_response: bool
     """
     Whether or not to automatically generate a response when a VAD stop event
     occurs.
     """
 
-    eagerness: Literal["low", "medium", "high", "auto"]
-    """Used only for `semantic_vad` mode.
+    idle_timeout_ms: Optional[int]
+    """Optional timeout after which a model response will be triggered automatically.
 
-    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`.
-    """
+    This is useful for situations in which a long pause from the user is unexpected,
+    such as a phone call. The model will effectively prompt the user to continue the
+    conversation based on the current context.
 
-    idle_timeout_ms: Optional[int]
-    """
-    Optional idle timeout after which turn detection will auto-timeout when no
-    additional audio is received.
+    The timeout value will be applied after the last model response's audio has
+    finished playing, i.e. it's set to the `response.done` time plus audio playback
+    duration.
+
+    An `input_audio_buffer.timeout_triggered` event (plus events associated with the
+    Response) will be emitted when the timeout is reached. Idle timeout is currently
+    only supported for `server_vad` mode.
     """
 
     interrupt_response: bool
@@ -59,5 +64,32 @@ class RealtimeTranscriptionSessionAudioInputTurnDetectionParam(TypedDict, total=
     perform better in noisy environments.
     """
 
-    type: Literal["server_vad", "semantic_vad"]
-    """Type of turn detection."""
+
+class SemanticVad(TypedDict, total=False):
+    type: Required[Literal["semantic_vad"]]
+    """Type of turn detection, `semantic_vad` to turn on Semantic VAD."""
+
+    create_response: bool
+    """
+    Whether or not to automatically generate a response when a VAD stop event
+    occurs.
+    """
+
+    eagerness: Literal["low", "medium", "high", "auto"]
+    """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`. `low`, `medium`, and `high` have max timeouts of 8s,
+    4s, and 2s respectively.
+    """
+
+    interrupt_response: bool
+    """
+    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.
+    """
+
+
+RealtimeTranscriptionSessionAudioInputTurnDetectionParam: TypeAlias = Union[ServerVad, SemanticVad]

@@ -252,10 +252,10 @@ class Response(BaseModel):
     truncation: Optional[Literal["auto", "disabled"]] = None
     """The truncation strategy to use for the model response.
 
-    - `auto`: If the context of this response and previous ones exceeds the model's
-      context window size, the model will truncate the response to fit the context
-      window by dropping input items in the middle of the conversation.
-    - `disabled` (default): If a model response will exceed the context window size
+    - `auto`: If the input to this Response exceeds the model's context window size,
+      the model will truncate the response to fit the context window by dropping
+      items from the beginning of the conversation.
+    - `disabled` (default): If the input size will exceed the context window size
       for a model, the request will fail with a 400 error.
     """
 

@@ -252,10 +252,10 @@ class ResponseCreateParamsBase(TypedDict, total=False):
     truncation: Optional[Literal["auto", "disabled"]]
     """The truncation strategy to use for the model response.
 
-    - `auto`: If the context of this response and previous ones exceeds the model's
-      context window size, the model will truncate the response to fit the context
-      window by dropping input items in the middle of the conversation.
-    - `disabled` (default): If a model response will exceed the context window size
+    - `auto`: If the input to this Response exceeds the model's context window size,
+      the model will truncate the response to fit the context window by dropping
+      items from the beginning of the conversation.
+    - `disabled` (default): If the input size will exceed the context window size
       for a model, the request will fail with a 400 error.
     """
 

@@ -1,4 +1,4 @@
 # File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
 
 __title__ = "openai"
-__version__ = "1.107.1"  # x-release-please-version
+__version__ = "1.107.2"  # x-release-please-version

@@ -44,14 +44,13 @@ class TestClientSecrets:
                             "prompt": "prompt",
                         },
                         "turn_detection": {
+                            "type": "server_vad",
                             "create_response": True,
-                            "eagerness": "low",
-                            "idle_timeout_ms": 0,
+                            "idle_timeout_ms": 5000,
                             "interrupt_response": True,
                             "prefix_padding_ms": 0,
                             "silence_duration_ms": 0,
                             "threshold": 0,
-                            "type": "server_vad",
                         },
                     },
                     "output": {
@@ -141,14 +140,13 @@ class TestAsyncClientSecrets:
                             "prompt": "prompt",
                         },
                         "turn_detection": {
+                            "type": "server_vad",
                             "create_response": True,
-                            "eagerness": "low",
-                            "idle_timeout_ms": 0,
+                            "idle_timeout_ms": 5000,
                             "interrupt_response": True,
                             "prefix_padding_ms": 0,
                             "silence_duration_ms": 0,
                             "threshold": 0,
-                            "type": "server_vad",
                         },
                     },
                     "output": {

@@ -6,13 +6,10 @@ import gc
 import os
 import sys
 import json
-import time
 import asyncio
 import inspect
-import subprocess
 import tracemalloc
 from typing import Any, Union, Protocol, cast
-from textwrap import dedent
 from unittest import mock
 from typing_extensions import Literal
 
@@ -23,6 +20,7 @@ from pydantic import ValidationError
 
 from openai import OpenAI, AsyncOpenAI, APIResponseValidationError
 from openai._types import Omit
+from openai._utils import asyncify
 from openai._models import BaseModel, FinalRequestOptions
 from openai._streaming import Stream, AsyncStream
 from openai._exceptions import OpenAIError, APIStatusError, APITimeoutError, APIResponseValidationError
@@ -30,8 +28,10 @@ from openai._base_client import (
     DEFAULT_TIMEOUT,
     HTTPX_DEFAULT_TIMEOUT,
     BaseClient,
+    OtherPlatform,
     DefaultHttpxClient,
     DefaultAsyncHttpxClient,
+    get_platform,
     make_request_options,
 )
 
@@ -1857,50 +1857,9 @@ class TestAsyncOpenAI:
             assert response.retries_taken == failures_before_success
             assert int(response.http_request.headers.get("x-stainless-retry-count")) == failures_before_success
 
-    def test_get_platform(self) -> None:
-        # A previous implementation of asyncify could leave threads unterminated when
-        # used with nest_asyncio.
-        #
-        # Since nest_asyncio.apply() is global and cannot be un-applied, this
-        # test is run in a separate process to avoid affecting other tests.
-        test_code = dedent("""
-        import asyncio
-        import nest_asyncio
-        import threading
-
-        from openai._utils import asyncify
-        from openai._base_client import get_platform
-
-        async def test_main() -> None:
-            result = await asyncify(get_platform)()
-            print(result)
-            for thread in threading.enumerate():
-                print(thread.name)
-
-        nest_asyncio.apply()
-        asyncio.run(test_main())
-        """)
-        with subprocess.Popen(
-            [sys.executable, "-c", test_code],
-            text=True,
-        ) as process:
-            timeout = 10  # seconds
-
-            start_time = time.monotonic()
-            while True:
-                return_code = process.poll()
-                if return_code is not None:
-                    if return_code != 0:
-                        raise AssertionError("calling get_platform using asyncify resulted in a non-zero exit code")
-
-                    # success
-                    break
-
-                if time.monotonic() - start_time > timeout:
-                    process.kill()
-                    raise AssertionError("calling get_platform using asyncify resulted in a hung process")
-
-                time.sleep(0.1)
+    async def test_get_platform(self) -> None:
+        platform = await asyncify(get_platform)()
+        assert isinstance(platform, (str, OtherPlatform))
 
     async def test_proxy_environment_variables(self, monkeypatch: pytest.MonkeyPatch) -> None:
         # Test that the proxy environment variables are set correctly

@@ -1,3 +1,3 @@
 {
-  ".": "1.107.1"
+  ".": "1.107.2"
 }
\ No newline at end of file

@@ -1,4 +1,4 @@
 configured_endpoints: 118
-openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/openai%2Fopenai-16cb18bed32bae8c5840fb39a1bf664026cc40463ad0c487dcb0df1bd3d72db0.yml
-openapi_spec_hash: 4cb51b22f98dee1a90bc7add82d1d132
+openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/openai%2Fopenai-94b1e3cb0bdc616ff0c2f267c33dadd95f133b1f64e647aab6c64afb292b2793.yml
+openapi_spec_hash: 2395319ac9befd59b6536ae7f9564a05
 config_hash: 930dac3aa861344867e4ac84f037b5df

@@ -1,5 +1,14 @@
 # Changelog
 
+## 1.107.2 (2025-09-12)
+
+Full Changelog: [v1.107.1...v1.107.2](https://github.com/openai/openai-python/compare/v1.107.1...v1.107.2)
+
+### Chores
+
+* **api:** Minor docs and type updates for realtime ([ab6a10d](https://github.com/openai/openai-python/commit/ab6a10da4ed7e6386695b6f5f29149d4870f85c9))
+* **tests:** simplify `get_platform` test ([01f03e0](https://github.com/openai/openai-python/commit/01f03e0ad1f9ab3f2ed8b7c13d652263c6d06378))
+
 ## 1.107.1 (2025-09-10)
 
 Full Changelog: [v1.107.0...v1.107.1](https://github.com/openai/openai-python/compare/v1.107.0...v1.107.1)

@@ -1,6 +1,6 @@
 [project]
 name = "openai"
-version = "1.107.1"
+version = "1.107.2"
 description = "The official Python library for the openai API"
 dynamic = ["readme"]
 license = "Apache-2.0"

@@ -70,7 +70,7 @@ filelock==3.12.4
 frozenlist==1.7.0
     # via aiohttp
     # via aiosignal
-griffe==1.14.0
+griffe==1.13.0
 h11==0.16.0
     # via httpcore
 httpcore==1.0.9
@@ -108,7 +108,6 @@ multidict==6.5.0
 mypy==1.14.1
 mypy-extensions==1.0.0
     # via mypy
-nest-asyncio==1.6.0
 nodeenv==1.8.0
     # via pyright
 nox==2023.4.22