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
Enables .model_dump() serialization for Pipecat Cloud collection.
All metrics now include start_time (Unix timestamp) for timeline
plotting alongside duration_secs.
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
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.
Replace the PipelineSink detection in StartupTimingObserver with an
on_pipeline_started() callback from PipelineTask via TaskObserver.
This fixes premature report emission when using ParallelPipeline,
which has its own inner PipelineSinks per branch.
Use start_offset_secs (offset from StartFrame) on ProcessorStartupTiming
instead of a wall-clock timestamp. Reports keep a single start_time
anchor for dashboard visualization. Remove _mono_to_wall conversion.
Switch ProcessorStartupTiming, StartupTimingReport, and
TransportTimingReport from dataclasses to Pydantic BaseModel. Add
start_time (Unix timestamp) fields and wall clock conversion for
monotonic observer timestamps.
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.
Introduce ClientConnectedFrame (SystemFrame) pushed by all transports
when a client connects. StartupTimingObserver uses this to measure
transport readiness — the time from StartFrame to first client
connection — via a new on_transport_readiness_measured event.
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
Tracks how long each processor start method takes during pipeline
startup by measuring StartFrame arrive/leave deltas. Emits a timing
report via the on_startup_timing_report event and auto-logs a summary.
Internal pipeline processors are excluded from reports by default.
The Whisper-based ONNX model expects 16 kHz audio, but the
_predict_endpoint method had five hardcoded references to 16000 without
checking the actual pipeline sample rate. When running at 8 kHz (e.g.
Twilio telephony), audio was fed to the feature extractor at the wrong
rate, causing the model to perceive speech at 2x speed with shifted
formant frequencies and produce incorrect end-of-turn predictions.
Add automatic resampling via numpy interpolation before feature
extraction and replace all hardcoded sample rate values with a
_MODEL_SAMPLE_RATE constant. Also fix the WAV debug logger to write
files with the correct sample rate header.
Fixes#3844
