Addressing code review comments
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@@ -116,6 +116,18 @@ class BaseTurnAnalyzer(ABC):
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"""
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pass
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def on_vad_start_secs_updated(self, vad_start_secs: float):
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"""Invoked when the VAD start trigger time is updated.
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The turn analyzer may choose to change its buffer size depending
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on this value.
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Args:
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vad_start_secs (float): The number of seconds of voice activity
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before triggering the user speaking event.
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"""
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pass
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@abstractmethod
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def clear(self):
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"""Reset the turn analyzer to its initial state."""
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@@ -35,15 +35,11 @@ class SmartTurnParams(BaseTurnParams):
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Parameters:
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stop_secs: Maximum silence duration in seconds before ending turn.
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pre_speech_ms: Milliseconds of audio to include before speech starts.
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vad_start_secs: Seconds VAD waits before confirming speech start (e.g. VAD STARTING window).
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This is added to `pre_speech_ms` at inference slicing time so Smart Turn can include
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the initial audio that occurred while VAD was still confirming speech.
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max_duration_secs: Maximum duration in seconds for audio segments.
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"""
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stop_secs: float = STOP_SECS
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pre_speech_ms: float = PRE_SPEECH_MS
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vad_start_secs: float = 0.0
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max_duration_secs: float = MAX_DURATION_SECONDS
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@@ -82,6 +78,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
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# Thread executor that will run the model. We only need one thread per
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# analyzer because one analyzer just handles one audio stream.
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self._executor = ThreadPoolExecutor(max_workers=1)
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self._vad_start_secs: float = 0.0
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@property
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def speech_triggered(self) -> bool:
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@@ -165,6 +162,9 @@ class BaseSmartTurn(BaseTurnAnalyzer):
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logger.debug(f"End of Turn result: {state}")
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return state, result
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def on_vad_start_secs_updated(self, vad_start_secs: float):
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self._vad_start_secs = vad_start_secs
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def clear(self):
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"""Reset the turn analyzer to its initial state."""
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self._clear(EndOfTurnState.COMPLETE)
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@@ -185,7 +185,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
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return state, None
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# Extract recent audio segment for prediction
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effective_pre_speech_ms = self._params.pre_speech_ms + (self._params.vad_start_secs * 1000)
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effective_pre_speech_ms = self._params.pre_speech_ms + (self._vad_start_secs * 1000)
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start_time = self._speech_start_time - (effective_pre_speech_ms / 1000)
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start_index = 0
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for i, (t, _) in enumerate(audio_buffer):
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@@ -108,20 +108,12 @@ class TurnAnalyzerUserTurnStopStrategy(BaseUserTurnStopStrategy):
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async def _handle_speech_control_params(self, frame: SpeechControlParamsFrame):
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"""Sync Smart Turn pre-speech buffering with VAD start delay.
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In the new user-turn-strategies pipeline, `VADUserStartedSpeakingFrame`
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is emitted only once VAD has *confirmed* speech (after `vad_params.start_secs`).
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Smart Turn should still include the initial audio collected during that
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confirmation window, so we record it in `SmartTurnParams.vad_start_secs` and
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add it at inference slicing time (preserving `pre_speech_ms` semantics).
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`VADUserStartedSpeakingFrame` is emitted only once VAD has confirmed speech
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(after `vad_params.start_secs`). Smart Turn should still include the initial
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audio collected during that confirmation window, so we let the analyzer know
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when this value has changed.
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"""
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if not frame.vad_params:
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return
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params = self._turn_analyzer.params
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if not isinstance(params, SmartTurnParams):
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return
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params.vad_start_secs = frame.vad_params.start_secs
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self._turn_analyzer.on_vad_start_secs_updated(frame.vad_params.start_secs)
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async def _handle_input_audio(self, frame: InputAudioRawFrame):
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"""Handle input audio to check if the turn is completed."""
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