Runner-created Daily rooms previously had no expiration when callers
posted partial `dailyRoomProperties` (e.g. `{"start_video_off": true}`).
The model-default `exp=None` and `eject_at_room_exp=False` meant Daily's
cron never cleaned them up, so rooms accumulated indefinitely.
Encode the policy in the runner: define `PIPECAT_CLOUD_ROOM_EXP_HOURS=4.0`,
inject `exp` and `eject_at_room_exp=True` into user-supplied properties via
`setdefault` (so explicit caller values still win), and pass
`room_exp_duration` to all four `configure()` call sites.
* VIVA SDK TT v3 support
* Format fix.
* Renamed the API naming, removed '3' from the name.
* Implementation of User turn start strategy using Krisp VIVA Interruption Prediction in scope of TT v3 support.
* TT demo tool
* Some improvements for demo scripts, audio recordin, etc.
* Enhance demo scripts with VAD selection and audio embedding features. Updated HTML report to include annotated audio players and improved response time metrics in summary formatting. Added README for setup and usage instructions.
* Refactor interrupt prediction demo to compare multiple interruption strategies (Krisp IP vs VAD). Updated README with usage instructions and output details. Enhanced audio processing with new helper functions for generating beeps and mixing audio.
* Refactor demo scripts to improve latency metrics by introducing total_delay property in TurnEvent. Update formatting in reports and visualizations to reflect accurate speech end times, including VAD wait times. Enhance HTML report with detailed latency information and adjust audio processing to account for VAD stop seconds.
* Add audio resampling functionality and update demo scripts for improved audio processing
- Introduced `resample_audio` function to handle audio resampling with linear interpolation.
- Updated `demo_audio_recorder.py` to utilize the new resampling feature, ensuring audio is saved at the requested sample rate.
- Modified `demo_interrupt_prediction.py` and `demo_turn_taking.py` to resample audio to 16 kHz for compatibility with Silero VAD.
- Adjusted imports in demo scripts to include the new resampling function.
- Enhanced error handling for sample rate discrepancies in audio recording.
* Enhance demo_interrupt_prediction.py with VAD type selection and improved processing logic
- Added support for selecting between "silero" and "krisp" VAD engines in the demo script.
- Introduced a new create_vad function to configure VAD analyzers based on the selected type.
- Updated audio processing logic to handle VAD type-specific resampling and state management.
- Modified the KrispVivaIPUserTurnStartStrategy to utilize a separate vad_flag for per-frame VAD input, improving interruption detection accuracy.
* Refactor audio processing scripts for improved readability and consistency
- Updated type hinting in `resample_audio` function to use `tuple` instead of `Tuple`.
- Simplified print statements in `demo_audio_recorder.py`, `demo_formatting.py`, and `demo_interrupt_prediction.py` for better readability.
- Adjusted argument formatting in `demo_audio_recorder.py` and `demo_formatting.py` for consistency.
- Cleaned up list comprehensions in `demo_formatting.py`, `demo_html_report.py`, and `demo_interrupt_prediction.py` for clarity.
- Enhanced error handling in `__init__.py` for the KrispVivaIPUserTurnStartStrategy import.
* Refactor VAD handling in KrispVivaIPUserTurnStartStrategy and update tests for clarity
- Simplified the argument formatting in the _handle_vad_started method for improved readability.
- Updated test assertions to reflect changes in VAD processing logic, ensuring that the vad_flag is correctly set to False during continuous state processing.
- Enhanced test cases to verify that the process method is called appropriately under different conditions.
* more format fixes.
* removed demo scripts.
* reverted wrongly removed file.
* Corrected the IP integration logic.
* style fix.
* Refactor audio processing and state management in KrispVivaIPUserTurnStartStrategy
- Removed the unused _vad_flag attribute to streamline state tracking.
- Updated the reset method to clear the audio buffer instead of resetting the vad_flag.
- Adjusted the process_frame method to use _speech_active for VAD input, enhancing clarity in the logic.
- Modified tests to reflect changes in state management and ensure proper functionality of the reset method and audio buffer handling.
* FIxed formatting
---------
Co-authored-by: Aram Poghosyan <apoghosyan@krisp.ai>
Pipecat 1.0.8 hard-required protobuf 6.x via the base `protobuf>=6.31.1,<7`
pin, blocking users whose dependency graph already constrains protobuf to
the 5.x line. The original bump (PR #4136) was only needed because
`nvidia-riva-client>=2.25.1` ships gencode compiled with protoc 6.31.1.
Changes:
- Widen base pin to `protobuf>=5.29.6,<7`.
- Regenerate `frames_pb2.py` with `grpcio-tools~=1.67.1` (protoc 5.x). Per
Google's cross-version runtime guarantee, 5.x gencode runs on both 5.x
and 6.x runtimes, so this single artifact serves all users.
- Loosen the dev pin `grpcio-tools` to `>=1.67.1,<2` so contributors can
install `pipecat[dev,nvidia]` without resolver conflict. Comment in
`frames.proto` documents the 1.67.x requirement for regeneration.
- Add an explicit `protobuf>=6.31.1,<7` to the `nvidia` extra. This
compensates for nvidia-riva-client's missing `protobuf` install
requirement (upstream packaging gap, see
https://github.com/nvidia-riva/python-clients/issues/172). When that
issue is resolved, the explicit protobuf entry in the `nvidia` extra
can be removed.
Verified: pipecat imports cleanly on both protobuf 5.29.6 and 6.33.6;
`tests/test_protobuf_serializer.py` passes; `import riva.client` succeeds
when `pipecat[nvidia]` is installed.
Nova Sonic sessions have an AWS-imposed ~8-minute time limit. This adds
transparent session continuation that rotates sessions in the background
before the limit is reached, preserving conversation context with no
user-perceptible interruption.
Implementation follows the AWS reference architecture:
- Monitor loop detects when session age exceeds threshold
- On assistant AUDIO contentStart: start buffering user audio, create next
session (sessionStart + promptStart + system instruction)
- Track SPECULATIVE/FINAL text counts as completion signal
- On completion signal: send conversation history + audioInputStart +
buffered audio to next session, then promote immediately
- Close old session in background (non-blocking)
- Dead session detection: recreate next session if idle >30s
Key design decisions:
- Session continuation enabled by default (fundamental for long conversations)
- Conversation history tracked in real-time via _sc_conversation_history
(independent of pipeline context aggregator which updates asynchronously)
- Completion signal check in _handle_content_end_event (after history update)
to ensure latest text is included in handoff
- Rolling audio buffer (default 3s) captures user audio during transition
- transition_threshold_seconds capped at 420s (7min) for safety margin
- Unified event methods (_send_text_event, _send_client_event, etc.) accept
optional stream/prompt_name params, eliminating duplicate SC methods
Also adds:
- SessionContinuationParams config (enabled, threshold, buffer, timeout)
LLMContext's NotGiven, LLMContextToolChoice, and LLMStandardMessage are
currently aliased to their OpenAI equivalents, so passing values
between the two sides type-checks implicitly. That works today but
obscures the fact that these are meant to be conceptually distinct —
if LLMContext ever diverges from OpenAI's types, every implicit
crossing would silently break.
Introduce two module-private cast helpers in open_ai_adapter.py:
- _openai_from_llm_context_tool_choice(tool_choice)
- _openai_from_llm_standard_message(message)
Both are typed no-ops today (implemented with typing.cast) but each
carries a docstring explaining why the cast is present, and every
boundary crossing now routes through a named function. Future readers
(and future greps) can find the crossings; a later divergence becomes
a mechanical find-and-update rather than hunting through adapter code.
No behavior change, no pyright error delta.
After widening TTSSettings.voice to str | None | _NotGiven (so other
TTS services can opt into None as a valid "no voice" state), pyright
flagged Speechmatics' URL builder receiving str | None where it
required str.
Speechmatics has no "no voice" mode (the URL path includes the voice
name), so override the inherited field in SpeechmaticsTTSSettings to
str | _NotGiven. The call site stays as a plain assert_given(...)
without an extra None check.
Three LLM services initialize certain Settings fields with the SDK's
NOT_GIVEN (openai.NOT_GIVEN or anthropic.NOT_GIVEN) so the value
flows unmodified into SDK API calls. The inherited field types from
LLMSettings only admit pipecat's _NotGiven, so pyright flagged each
constructor call as a flavor mismatch.
Widen the field types in each service-specific Settings subclass so
they accept both pipecat's _NotGiven (for delta-mode defaults) and
the corresponding SDK NotGiven (for store-mode passthrough):
- OpenAILLMSettings: frequency_penalty, presence_penalty, seed,
temperature, top_p, max_tokens, max_completion_tokens.
- OpenAIResponsesLLMSettings: temperature, top_p,
max_completion_tokens.
- AnthropicLLMSettings: temperature, top_k, top_p, thinking.
Every overridden field is genuinely read from self._settings and
passed directly to the SDK, so none of the overrides are vestigial.
Clears 21 pyright errors and restores test_service_settings_complete
parity with the pre-NOT_GIVEN-swap state.
asyncai/tts and google/vertex/llm are now clean after the missing-None
sweep (both benefited from the TTSSettings.voice / LLMSettings
cascades).
- src/pipecat/services/asyncai/tts.py
- src/pipecat/services/google/vertex/llm.py
Service-specific Settings subclasses declared fields as T | _NotGiven
(no None), but the services routinely pass None to those fields during
init to mean "don't override — use the vendor's default". The field
type just didn't reflect that a None value is valid, so pyright
flagged every None at the call sites.
Change the declarations to T | None | _NotGiven, matching the pattern
already used by ServiceSettings.model and TTSSettings.language. No
constructor-call changes; the default_factory stays NOT_GIVEN.
Fields touched across 11 files:
- services/settings.py: TTSSettings.voice (base class; covers
asyncai, cartesia, elevenlabs, fish, hume, kokoro, lmnt, mistral,
neuphonic, piper, resembleai, rime, xtts TTS services).
- services/aws/llm.py: latency.
- services/aws/tts.py: engine, pitch, rate, volume, lexicon_names.
- services/azure/tts.py: emphasis, pitch, rate, role, style,
style_degree, volume.
- services/google/gemini_live/llm.py: vad.
- services/google/llm.py: thinking.
- services/google/stt.py: language_codes.
- services/inworld/tts.py: speaking_rate, temperature.
- services/openai/tts.py: instructions, speed.
- services/speechmatics/stt.py: 13 fields (domain, operating_point,
max_delay, end_of_utterance_*, punctuation_overrides, *_partials,
split_sentences, enable_diarization, speaker_*, max_speakers,
prefer_current_speaker, extra_params).
- services/ultravox/llm.py: output_medium.
Clears 94 pyright errors (1035 -> 941).
Three files no longer have pyright errors after the is_given /
assert_given sweep — remove them from the ignore list (which serves as
a live todo of files with remaining type errors).
- src/pipecat/processors/gstreamer/pipeline_source.py
- src/pipecat/services/camb/tts.py
- src/pipecat/services/speechmatics/tts.py
Apply assert_given across service modules to narrow reads from
store-mode settings fields (self._settings.X, default_settings.X),
where _NotGiven is declared in the field type but should never appear
at runtime (enforced by validate_complete()).
Two idioms used:
- Inline wrap for single uses:
func(assert_given(self._settings.enable_prompt_caching), ...)
- Extract-and-reuse when the same value is used multiple times:
thinking = assert_given(self._settings.thinking)
if thinking:
params["thinking"] = thinking.model_dump(...)
43 service files touched. Cleared ~172 pyright errors; remaining
_NotGiven-related errors are in adjacent categories (flavor mismatch
between openai/anthropic NotGiven and pipecat _NotGiven, settings
field types that should allow None but don't) that need different
fixes.
In store-mode settings objects, _NotGiven should never appear (the
invariant enforced by validate_complete). But the declared field types
still include _NotGiven because the same class doubles as delta mode,
so every field read is typed X | None | _NotGiven and pyright flags
operations that assume X | None.
assert_given is a one-line extractor that narrows away _NotGiven and
raises loudly if the invariant is violated — preferable to scattering
is_given guards that defend against something that can't occur in
practice.
resolved_model = assert_given(self._settings.model) # str | None
Replace direct identity checks against NOT_GIVEN with is_given() at
sites where pyright's inability to narrow on non-singleton sentinels
was causing type errors.
- adapters/services/anthropic_adapter.py: narrow converted.system for
_resolve_system_instruction.
- services/openai/llm.py: narrow params.service_tier using OpenAI's
is_given.
- services/sarvam/llm.py: narrow tools / tool_choice using OpenAI's
is_given (aliased as openai_is_given alongside the existing
settings.is_given import).
- services/sarvam/tts.py: narrow settings.voice using settings.is_given.
Pyright can't narrow identity checks against module-level NotGiven
sentinels (they aren't typed as singletons), which leaves many
NotGiven-bearing unions stuck as unnarrowed types throughout the
codebase. Introduce is_given TypeGuard helpers so narrowing works via
isinstance under the hood.
Each helper is co-located with the NotGiven flavor it guards:
- services/settings.py: upgrade the existing is_given to a TypeGuard.
- processors/aggregators/llm_context.py: add an is_given for
LLMContext's NotGiven. Treat LLMContext's re-exported types
(LLMStandardMessage, LLMContextToolChoice, NOT_GIVEN, NotGiven) as
LLMContext's own — independent definitions that happen to coincide
with OpenAI's as an implementation detail.
- adapters/services/anthropic_adapter.py: add is_given for anthropic's
NotGiven.
- adapters/services/open_ai_adapter.py: add is_given for openai's
NotGiven.
TypedDict types are not subtypes of dict[...] in the type system
(per PEP 589), so TypedDict-based invocation param classes could not
satisfy the TypeVar bound. Mapping[str, Any] accepts TypedDicts while
preserving the "string-keyed mapping" constraint.
The original contributor's PR (#4328) landed as #4355. Rename the fragment
so the rendered changelog links to the merged PR, and add the leading `- `
bullet prefix that towncrier expects.
Extends the reconnect re-seeding fix to work cleanly on Gemini Live 2.5,
which has stricter seed requirements than 3.x and a documented audio-input /
history-recall limitation. Both initial connection and reconnect now share a
single code path (`_create_initial_response(for_reconnect=...)`), with four
well-documented cases.
On Gemini 2.5 reconnect, `turn_complete=True` is now forced on the seed so
the model produces a recap-style response immediately instead of briefly
acting "forgetful" on the user's next utterance — the latter being
especially jarring mid-conversation. When a 2.5 seed doesn't already end
with a user turn (e.g. the bot had finished speaking before the disconnect),
a blank user turn is appended to satisfy the server's seed-shape
requirement. Gemini 3.x needs neither workaround.
Tkinter's `Label` only stores `PhotoImage` references at the C level, so
Python GC eats them unless something on the Python side keeps a
reference. The canonical fix is to stash the reference on the widget
itself: `label.image = photo`. Tkinter widgets are plain Python objects,
so the assignment works at runtime, but the stub declares no `image`
attribute (correctly — there isn't one; we're adding it).
Narrow the suppression to `# type: ignore[attr-defined]` on the one
line. The existing comment above the assignment already documents why.
