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
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
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.
Allow pushing frames upstream through the pipeline by passing
FrameDirection.UPSTREAM. Downstream frames use the existing push queue,
while upstream frames are pushed directly from the pipeline sink.
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.
The update-docs workflow intermittently failed with "Input required and
not supplied: token" because pull_request events from fork PRs don't
have access to repository secrets. Switching to pull_request_target
runs the workflow in the base repo's context, ensuring secrets are
always available. This is safe since the workflow only runs on
already-merged PRs.
Replace _rtvi_external instance variable with a local prepend_rtvi flag
since it is only used during __init__ to decide whether to prepend the
RTVIProcessor to the pipeline.
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
Even when summarization_timeout is explicitly set to None, use a
DEFAULT_SUMMARIZATION_TIMEOUT (120s) fallback so the LLM call can
never hang indefinitely. Applied in both LLMService and the dedicated
LLM path in LLMContextSummarizer.
