Replace the round-trip push_interruption_task_frame_and_wait() mechanism
with broadcast_interruption(), which pushes an InterruptionFrame both
upstream and downstream directly from the calling processor.
This eliminates race conditions (transcription arriving before the
InterruptionFrame comes back), swallowed-event timeouts (frame blocked
before reaching the sink), and the complexity of _wait_for_interruption
flag / queue bypass / frame.complete() obligations.
- Add broadcast_interruption() to FrameProcessor
- Deprecate push_interruption_task_frame_and_wait() (delegates to new method)
- Remove event field and complete() from InterruptionFrame/InterruptionTaskFrame
- Remove _wait_for_interruption flag and all special-case logic
- Remove frame.complete() calls in stt_mute_filter and llm_response_universal
- Update all 17 call sites to use broadcast_interruption()
- Update tests
Add per-service latency breakdown metrics alongside existing user-to-bot
latency measurement. When enable_metrics=True, the observer now emits an
on_latency_breakdown event with TTFB, text aggregation, and user turn
duration metrics collected between VADUserStoppedSpeakingFrame and
BotStartedSpeakingFrame.
- Add LatencyBreakdown dataclass with ttfb, text_aggregation,
user_turn_secs fields
- Accumulate MetricsFrame data during user→bot cycles
- Reset accumulators on InterruptionFrame to discard stale metrics
- Measure user_turn_secs from actual user silence (VAD timestamp -
stop_secs) to turn release (UserStoppedSpeakingFrame)
- Filter zero-value TTFB entries from startup metric resets
- Add frame deduplication using bounded deque + set pattern
- Update example 29 with latency breakdown display
Add BotConnectedFrame (SystemFrame) pushed by SFU transports (Daily,
LiveKit, HeyGen, Tavus) when the bot joins the room. Replace the
on_transport_readiness_measured event with on_transport_timing_report
which includes both bot_connected_secs and client_connected_secs.
Azure TTS _handle_canceled was putting None (the normal completion
signal) into the audio queue for all cancellation reasons, so run_tts
treated errors identically to success—silently producing no audio.
Now error cancellations put an Exception marker in the queue, which
run_tts converts to an ErrorFrame.
Azure STT had no canceled event handler at all, so auth failures,
network errors, and rate-limit cancellations were invisible. Added
_on_handle_canceled which pushes an ErrorFrame upstream via push_error.
Fixespipecat-ai/pipecat#3892
When filter_incomplete_user_turns is enabled and an LLMMessagesUpdateFrame
replaces the context via set_messages(), the turn completion instructions
system message was lost. This caused the LLM to stop emitting turn
completion markers. Re-inject the instructions after set_messages() to
fix this.
The ServiceSettings refactor (PR #3714) changed self._settings from
dicts to dataclass subclasses, but tracing code still used .items(),
in containment, and subscript access, causing AttributeError on
every traced call. Use given_fields() for iteration and attribute
access for named fields.
When the user places an RTVIProcessor inside their pipeline and provides
a custom RTVIObserver subclass in observers, PipelineTask correctly
detects both and logs "skipping default ones." However it then
unconditionally prepends self._rtvi to the pipeline, causing the
processor to appear twice in the frame chain.
Track whether the RTVIProcessor was found externally (inside the user
pipeline) vs created internally. Only prepend it when created internally.
Fixes#3867
