- Move `CommitStrategy` up in the file so it could be used by `ElevenLabsRealtimeSTTSettings`
- Fix a bug where `run_tts` would erroneously try to reconnect if a reconnection was already in flight (like a reconnection triggered by `_update_settings`)
42 examples covering STT (13), TTS (21), LLM (4), and realtime (4) services. Each demonstrates updating service settings 10 seconds after client connects, verifying the typed settings machinery end-to-end for every provider.
HumeTTSService now stores its params (description, speed, trailing_silence) in a proper `HumeTTSSettings` dataclass instead of a separate `_params` Pydantic model, making it work with `TTSUpdateSettingsFrame(update=...)`. The old `update_setting(key, value)` method is kept but deprecated.
Also removes the unused no-op `TTSService.update_setting` base method, which was never called by the `TTSUpdateSettingsFrame` pipeline.
The dataclass-based API (`*UpdateSettingsFrame(update=*Settings(...))`) is the preferred path since 0.0.103. The dict path still works but now emits a `DeprecationWarning`.
Change `TTSSettings.language` and `STTSettings.language` from `Any` to `Language | str | _NotGiven`. Add `language_to_service_language` base method and centralized `isinstance`-guarded conversion in `STTService._update_settings` (mirroring TTS). Update the TTS guard from `is not None` to `isinstance(…, Language)` so raw strings pass through unchanged.
Remove now-redundant per-service language conversion from `_update_settings` overrides (ElevenLabs, Azure, Fal, Whisper). Add `language_to_service_language` to Azure STT so the centralized conversion picks it up. Fix AWS and NVIDIA STT `__init__` to convert language at construction time, then simplify their runtime accessors to read `_settings.language` directly.
Note that for services that previously handled applying updates (through methods like `set_model` and `set_language`), we're keeping the update-applying logic (some or most of which is already well-tested) and expanding it to cover all relevant settings fields. Services under this bucket are:
- Deepgram STT
- Deepgram Sagemaker STT
- Elevenlabs STT
- Google STT
- Gradium STT
- OpenAI STT
- Speechmatics STT
Change the version specifier from `>=0.2.8` to
`~=0.2.8` for the `speechmatics-voice` package.
This ensures compatibility with future patch
versions while preventing potential breaking
changes from minor updates.
Use client_req_id-based multiplexing instead of disconnecting and
reconnecting the websocket on every interruption. This follows the
same pattern used by Cartesia, ElevenLabs, and other services via
AudioContextWordTTSService.
Key changes:
- Base class: InterruptibleWordTTSService -> AudioContextWordTTSService
- Add close_ws_on_eos: False to setup message to keep connection alive
- Add client_req_id to text, end_of_stream messages for demultiplexing
- Route audio via append_to_audio_context() instead of push_frame()
- Silently drop messages for cancelled/unknown contexts on interruption
- Add _handle_interruption() that resets context without reconnecting
- Remove no-op push_frame() override
Always create UserIdleController (timeout=0 means disabled), removing
all Optional guards. Add UserIdleTimeoutUpdateFrame to allow changing
the idle timeout at runtime.
Replace the continuous heartbeat-based timer (UserSpeakingFrame/BotSpeakingFrame
+ asyncio.Event loop) with a simple one-shot timer that starts when
BotStoppedSpeakingFrame is received and cancels on UserStartedSpeakingFrame or
BotStartedSpeakingFrame. This eliminates false idle triggers caused by gaps
between the user finishing speaking and the bot starting to speak (LLM/TTS
latency).
Guard the timer start with two conditions to prevent false triggers:
- User turn in progress: during interruptions, BotStoppedSpeaking arrives
while the user is still speaking mid-turn.
- Function calls in progress: FunctionCallsStarted arrives before
BotStoppedSpeaking because the bot speaks concurrently with the function
call starting, so the timer must wait for the result and subsequent bot
response.
Now that all services use typed `ServiceSettings` objects, this removes the interim scaffolding that supported both dict-based and typed settings paths in parallel. Specifically: removes old dict-based `_update_settings(settings: Mapping)` methods from base classes, removes `isinstance(self._settings, ServiceSettings)` guards, simplifies `process_frame` branching, and renames `_update_settings_from_typed` to `_update_settings` across all ~30 service implementations. Also renames the no-arg `_update_settings()` helper on realtime services to `_send_session_update()` to avoid collision, adds `from_mapping` overrides on `GoogleLLMSettings` and `AnthropicLLMSettings` for ThinkingConfig dict-to-object conversion, and replaces a broken no-arg `_update_settings()` call in Gemini Live with a TODO.
- NvidiaSTTService.set_model: convert to proper DeprecationWarning (model can't change at runtime for Riva streaming STT)
- NvidiaTTSService.set_model: same treatment for Riva TTS
- NvidiaSegmentedSTTService.set_model: remove — base class now routes through _update_settings_from_typed which re-creates the recognition config
- GeminiTTSService.set_voice: remove — move AVAILABLE_VOICES validation into _update_settings_from_typed so it fires on both legacy and new paths
Standardize all STT, TTS, and LLM service classes to declare `_settings` with the narrowed Settings type as a class-level annotation. This gives editors and type checkers the specific type when hovering or autocompleting on `self._settings` in each service and its subclasses. Inline `self._settings: Type = ...` assignments are replaced with plain `self._settings = ...`.
`filter_incomplete_user_turns` and `user_turn_completion_config` were only handled in the legacy dict-based `_update_settings` code path. This adds them to `LLMSettings` and introduces `LLMService._update_settings_from_typed` so the typed path handles them too.
