The AudioHook protocol requires every message to carry a `parameters`
object. `_create_message` conditionally included it only when parameters
were truthy, so pong responses and closed responses without
outputVariables were sent without the field.
Clients that validate message structure (including the Genesys reference
implementation) rejected these messages, which broke server sequence
tracking and prevented outputVariables from reaching the Architect flow.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Align with the OpenTelemetry GenAI semantic convention
gen_ai.system_instructions for system prompts. The old "system"
attribute name was unrelated to gen_ai.system (which is for
provider name).
Replace adapter-based extraction in traced_llm with direct reads from
_settings.system_instruction (priority) and context messages (fallback).
The old approach had three bugs: signature mismatch with Anthropic
adapter, key name inconsistency, and unnecessary overhead from full
message/tools conversion.
Also deduplicate the system instruction in spans -- it was appearing as
both "system" and "param.system_instruction".
These services were pushing audio frames directly via push_frame() in their
WebSocket receive loops, bypassing the base TTSService audio context
serialization queue. This causes incorrect frame ordering and broken
interruption handling.
Changes per service:
- Fish Audio: use append_to_audio_context(), replace _handle_interruption
with on_audio_context_interrupted()
- LMNT: use append_to_audio_context(), remove redundant push_frame override
- Neuphonic: use append_to_audio_context(), remove redundant push_frame and
process_frame overrides (base class handles pause/resume)
- Rime NonJson: use append_to_audio_context(), remove redundant push_frame
override
The LLMContext format already matches the expected Responses API
shape for input_audio, so no adapter conversion will be needed
once OpenAI enables support.
The API-provided full model name is more specific than the
user-provided model name (e.g. includes version/snapshot details).
Reorder the lookup in _get_model_name and add a comment where the
Responses service sets the field.
The override would re-add `instructions` after the adapter had
intentionally converted it to a developer message for empty contexts.
Added a regression test.
Rewrite docstrings to more clearly explain what SyncParallelPipeline
does: hold all output until every parallel branch finishes, so frames
produced in response to a single input are released together.
Adds a FrameOrder enum with ARRIVAL (default, existing behavior) and
PIPELINE (pushes frames in pipeline definition order). This lets callers
guarantee output ordering between parallel pipelines — e.g. ensuring
image frames precede audio frames — without needing a separate reordering
processor downstream.
Updates the 05-sync-speech-and-image example to use FrameOrder.PIPELINE,
removing the ImageBeforeAudioReorderer class entirely.
Add a `sync_with_audio` field to `OutputImageRawFrame` that routes image
frames through the audio queue in the output transport, ensuring images
are only displayed after all preceding audio has been sent. This enables
proper audio/image synchronization in pipelines like the calendar month
narration example.
Update the 05-sync-speech-and-image example to use an `ImageAudioSync`
processor that sets this flag on image frames.
The FrameProcessor two-queue architecture processes SystemFrames and
non-SystemFrames on separate concurrent async tasks. Both paths called
SyncParallelPipeline.process_frame(), which used the same per-pipeline
sink queues. A SystemFrame's wait_for_sync could steal frames from a
concurrent non-SystemFrame's wait_for_sync, corrupting synchronization
and stalling the pipeline.
This was triggered by the auto-embedded RTVI processor (added in
v0.0.101) which floods OutputTransportMessageUrgentFrame SystemFrames
through the pipeline during LLM responses.
Fix: SystemFrames (except EndFrame) now take a fast path — passed
through internal pipelines and pushed downstream directly without
touching the sink queues or drain logic. EndFrame retains the full
drain behavior as a lifecycle frame.
- Add WakePhraseUserTurnStartStrategy for gating interaction behind wake
phrase detection, with timeout and single_activation modes
- Add default_user_turn_start_strategies() and
default_user_turn_stop_strategies() helper functions
- Deprecate WakeCheckFilter in favor of the new strategy
- Extend ProcessFrameResult to stop strategies for short-circuit evaluation
- Fix MinWordsUserTurnStartStrategy including filtered text in output
* Fix empty user transcription causing spurious interruption in Nova Sonic
Skip _report_user_transcription_ended() when _user_text_buffer is empty,
which happens when the initial prompt is text-only. Previously, an empty
TranscriptionFrame was pushed upstream, triggering a chain reaction:
on_user_turn_stopped → UserStartedSpeakingFrame → interruption →
premature BotStoppedSpeaking → multiple response start/stop cycles.
* Improve TextFrame and assistant end of turn logic
Now, SPECULATIVE text results are used to push the LLMTextFrame,
AggregatedTextFrame, and TTSTextFrame. Additionally, the TTSTextFrames
are push at the end of the corresponding audio segment.
* Remove BotStoppedSpeakingFrame fallback from Nova Sonic
Now that assistant response end is detected directly from Nova Sonic
contentEnd events (END_TURN and INTERRUPTED), the BotStoppedSpeakingFrame
handler is no longer needed. Inline the cleanup logic in reset_conversation.
* Remove duplicate reconnection logic from Gradium STT
The _receive_messages method had its own while-True reconnect loop,
duplicating the reconnection handling already provided by
WebsocketService._receive_task_handler (exponential backoff, max
retries, error reporting). Flatten to just the inner message loop
and let the base class handle reconnection.
* Align Gradium STT VAD handling with base class patterns
Replace the process_frame override with a _handle_vad_user_stopped_speaking
override, which is the proper hook provided by STTService. Move
start_processing_metrics() into run_stt (matching Gladia's pattern).
Remove unused FrameDirection and VADUserStartedSpeakingFrame imports.
* Add transcript aggregation delay after flushed to capture trailing tokens
Gradium flushed response can arrive before all text tokens have been
delivered. Instead of finalizing immediately on flushed, start a short
timer (100ms) that allows trailing tokens to accumulate before pushing
the final TranscriptionFrame.
* Add changelog for PR #4066
* Change default encoding to pcm_16000
* Decouple encoding from sample_rate in Gradium STT
The encoding parameter now takes just the base type (pcm, wav, opus)
and the sample rate is derived from the pipeline audio_in_sample_rate,
assembled dynamically via input_format_from_encoding(). This fixes the
mismatch where SAMPLE_RATE=24000 was passed to the base class while
encoding defaulted to pcm_16000.
Set store=False in Responses API calls since we send full conversation
history as input items and don't use previous_response_id.
Add 5 run_inference tests for OpenAIResponsesLLMService using real
LLMContext and adapter (only HTTP client mocked).
