Remove bundled Claude Code skills (changelog, cleanup, code-review,
docstring, pr-description, pr-submit) that now live in
https://github.com/pipecat-ai/skills. Add a section to the README
with installation instructions. The update-docs skill remains as
it is specific to this repository.
This makes each service-specific field individually visible to the delta/update mechanism (`apply_update`, `given_fields`) and removes the need for complex sync logic between `input_params` and top-level fields like `model`.
- Soniox: replace `input_params: SonioxInputParams` with 8 individual fields, simplify `_update_settings` by removing model sync logic, remove unused `is_given` import
- Gladia: replace `input_params: GladiaInputParams` with 11 individual fields, resolve deprecated `language` → `language_config` at init time rather than at `_prepare_settings` time
- Storage mode: for use in `self._settings`. All fields should be specified, i.e. should not be `NOT_GIVEN`.
- Delta mode: for use in `*UpdateSettingsFrame`.
In service of this, this commit:
- Adds a runtime check that all fields are specified in storage mode
- Updates all services to specify all fields in stored settings
- Updates all services to no longer check for `is_given` in stored settings (not necessary anymore)
- Updates relevant docstrings
- Renames `update` to `delta` in `*UpdateSettingsFrame`
- Updates community integrations guide
Move start_processing_metrics from run_stt (called per audio chunk,
producing noisy 0ms logs) to _receive_messages when the first final
token arrives for a new utterance. The existing stop_processing_metrics
in send_endpoint_transcript completes the pair, giving a meaningful
measurement of time from first recognition to finalized transcript.
Commit 859cd7c9 refactored STT TTFB measurement to use the base class
start_ttfb_metrics/stop_ttfb_metrics, which are gated behind
can_generate_metrics(). Soniox and AWS Transcribe never overrode this
method (default returns False), so TTFB was silently never reported.
Use wildcard `*UpdateSettingsFrame` to cover all frame types. Clarify that NOT_GIVEN only appears in update deltas, not in the service's current settings state.
Split the single "changed" entry into separate "added", "changed", and "deprecated" entries for clarity. Add a note about the subtle behavior change in the deprecated set_model/set_voice/set_language methods.
Simplify the reconnect example to show a common pattern (reconnect on any change) and improve the _warn_unhandled_updated_settings example to show selective handling of specific fields.
Update docstrings for ServiceSettings, LLMSettings, TTSSettings, and STTSettings to make clear these capture only the subset of service configuration that can be changed while the pipeline is running via UpdateSettingsFrame, not all constructor parameters.
Document the existing convention: use @dataclass for frames and
internal pipeline data, use Pydantic BaseModel for configuration,
parameters, metrics, and external API data.
Replace self-referential `pipecat-ai[local-smart-turn-v3]` extra in core
dependencies with the actual packages (`transformers`, `onnxruntime`).
Self-referential extras are not supported by Poetry and cause dependency
resolution failures. Since these are required by the default turn stop
strategy (LocalSmartTurnAnalyzerV3), they belong in core dependencies.
- Remove `local-smart-turn-v3` optional extra from pyproject.toml
- Remove try/except ModuleNotFoundError guard (now always installed)
- Remove `--extra local-smart-turn-v3` from CI workflows
When the InterruptionFrame does not complete within the timeout the
caller was stuck in an infinite loop logging warnings. Now the event
is set after the first timeout so the processor can continue.
Also adds a keyword timeout parameter so callers can customize the
wait duration.
- indicate clearly that it's not meant for public use
- make it clear the `self._settings` is the single source of truth for model information
- set the stage for an upcoming change where `AIService` subclasses won't have to ever worry about explicitly calling an `AIService` method to sync model name to metrics
Across all services, switch from accessing `self._model_name` or `self.model_name` in favor of `self._settings.model`.
