The Azure TTS _handle_completed callback was putting the audio stream
completion signal (None) directly into _audio_queue while the last word
was still pending in _word_boundary_queue. This caused a race condition
where run_tts could exit and TTSStoppedFrame could be emitted before the
word processor task had a chance to process and emit the final word's
TTSTextFrame.
The fix routes the completion signal through _word_boundary_queue as a
None sentinel. The word processor task now recognizes this sentinel and
only signals _audio_queue after all pending words have been drained.
This guarantees the last word's TTSTextFrame is always emitted before
TTSStoppedFrame.
The cancellation/interruption path (_handle_canceled) is unchanged and
still signals _audio_queue directly, which is correct since word ordering
does not matter when speech is interrupted.
When the LLM returned zero text tokens (e.g. it was interrupted before producing
tokens or about to push tokens), push_aggregation() returned an empty string and
on_assistant_turn_stopped was never emitted. This left consumers waiting for an
event that would never arrive.
Now on_assistant_turn_stopped always fires, with an empty content string when
the LLM produced no text tokens.
Fixes#4292
Only treat messages[0] as the initial system prompt when determining the
summarization range. Previously, the code scanned the entire context for
the first system-role message, which caused failures when the only system
message was a mid-conversation injection (e.g. "The user has been quiet").
In that case summary_start exceeded summary_end, producing an empty range
and "No messages to summarize" errors.
Fixes#4286
The enable_logging and enable_ssml_parsing URL params used truthy checks,
so False was treated the same as None (both skipped). Also, Python's
str(False) produces "False" but the API expects lowercase "false".
Additionally, add enable_logging support to ElevenLabsHttpTTSService
which was missing entirely.
When the STT p99 timeout fires without a transcript, the turn stop
strategy previously did nothing — falling through to the 5-second
user_turn_stop_timeout. Now, a _timeout_expired flag tracks when the
timeout has elapsed so that a late transcript triggers the turn stop
immediately instead of waiting for the fallback.
Previously settings updates were ignored with a TODO comment. Now when
model/language changes via STTUpdateSettingsFrame the service disconnects
and reconnects with the new query parameters.
Key changes:
- Implement _update_settings to disconnect/reconnect on changes
- Check `is not State.OPEN` in run_stt to catch CLOSING state
- Send `done` command before closing for clean session shutdown
- Capture websocket reference in _disconnect_websocket to prevent a
concurrent _connect from having its new connection nulled by a stale
finally block
The strategy schedules background tasks during setup. Fast-running
tests could observe state before those tasks had a chance to run;
yielding once via asyncio.sleep(0) ensures they do.
Enable callers to get a compact version of context messages suitable
for serialization, logging, and debugging tools. For standard
messages, known binary data (base64 images, audio) is fully elided.
For LLM-specific messages, long string values are recursively
truncated. Adapter get_messages_for_logging() methods now use this.
Example files can live under subdirectories (e.g. foundational/01.py),
so the recording path needs its parent directory created before the
audio file is written.
Replaces the per-frame asyncio.Event signaling with a monotonic
timestamp updated on each audio frame. The handler sleeps until the
next deadline (last_audio_time + timeout), recomputing on each wake-up
to account for audio arriving during sleep.
This avoids waking the handler on every audio frame (~50/s at 20ms
chunks), and guarantees detection latency is bounded by timeout rather
than 2 * timeout.
Also renames audio_starvation_timeout to audio_idle_timeout and
associated identifiers for consistency with existing pipecat naming
(user_idle_timeout, etc.).
