- Speechmatics: move config build after super().__init__ and settings
delta so turn_detection_mode (e.g. ADAPTIVE) takes effect
- Google STT: fix example passing bare Language enum instead of list
- Google TTS: add missing explicit defaults for all custom settings fields
- Soniox: fix accidental tuple wrapping of STT service in example
- Speechmatics examples: fix system->user role in kick-off messages
- Deepgram Flux: move tag from settings to __init__ (billing metadata)
- ElevenLabs STT: default tag_audio_events to None (use API default)
- Fal STT: simplify language default handling
- Google TTS: rename GoogleStreamTTSSettings to GoogleTTSSettings
The audio fields (sample rates, sample sizes, channel counts) on the deprecated `Params` class had no non-deprecated equivalent. This adds an `AudioConfig` class and `audio_config` init arg so users can specify audio configuration without relying on the deprecated `params` parameter.
Add `system_instruction` field to `LLMSettings` so it is runtime-updatable via settings.
For Google (GoogleLLMService, GoogleVertexLLMService), deprecate the init-time arg since it was already shipped. For Anthropic, AWS Bedrock, and OpenAI, remove the init-time arg entirely since it was never shipped.
Still need to handle realtime services (OpenAI Realtime, Grok Realtime, Gemini Live).
Widen version ranges for stable packages (anthropic, azure, deepgram,
groq, livekit, nvidia-riva-client, fastapi, ormsgpack, opentelemetry,
faster-whisper) and add upper bounds to previously uncapped packages
(hume, pyjwt, livekit-api, camb).
Replace CartesiaHttpTTSService's internal use of the Cartesia SDK with
direct aiohttp calls, accepting an optional aiohttp_session parameter.
Replace fal-client SDK calls in FalSTTService and FalImageGenService
with direct HTTP to bypass the SDK's aggressive retry/backoff logic
that caused significant latency regressions.
Enables .model_dump() serialization for Pipecat Cloud collection.
All metrics now include start_time (Unix timestamp) for timeline
plotting alongside duration_secs.
Add per-service latency breakdown metrics alongside existing user-to-bot
latency measurement. When enable_metrics=True, the observer now emits an
on_latency_breakdown event with TTFB, text aggregation, and user turn
duration metrics collected between VADUserStoppedSpeakingFrame and
BotStartedSpeakingFrame.
- Add LatencyBreakdown dataclass with ttfb, text_aggregation,
user_turn_secs fields
- Accumulate MetricsFrame data during user→bot cycles
- Reset accumulators on InterruptionFrame to discard stale metrics
- Measure user_turn_secs from actual user silence (VAD timestamp -
stop_secs) to turn release (UserStoppedSpeakingFrame)
- Filter zero-value TTFB entries from startup metric resets
- Add frame deduplication using bounded deque + set pattern
- Update example 29 with latency breakdown display
Add BotConnectedFrame (SystemFrame) pushed by SFU transports (Daily,
LiveKit, HeyGen, Tavus) when the bot joins the room. Replace the
on_transport_readiness_measured event with on_transport_timing_report
which includes both bot_connected_secs and client_connected_secs.
Introduce ClientConnectedFrame (SystemFrame) pushed by all transports
when a client connects. StartupTimingObserver uses this to measure
transport readiness — the time from StartFrame to first client
connection — via a new on_transport_readiness_measured event.
Tracks how long each processor start method takes during pipeline
startup by measuring StartFrame arrive/leave deltas. Emits a timing
report via the on_startup_timing_report event and auto-logs a summary.
Internal pipeline processors are excluded from reports by default.
