- 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).
Uses real LLMContext and adapter (only HTTP client is mocked) to test
basic inference, client exception propagation, system_instruction
override, empty context fallback, and max_tokens override.
Add OpenAIResponsesLLMService using the Responses API, with a dedicated
adapter that converts LLMContext messages to Responses API input items
(system→developer, tool_calls→function_call, tool→function_call_output,
multimodal content conversion, and tools schema flattening).
- New adapter: open_ai_responses_adapter.py
- New service: openai/responses/llm.py
- Examples: 07-interruptible and 14-function-calling variants
- 19 unit tests for adapter conversion logic
- Eval entries for both examples
List-valued settings like keyterm, keywords, search, redact, and replace
were being converted to strings before being passed to the SDK connect()
method. The SDK expects lists so its encode_query can produce repeated
query params (keyterm=a&keyterm=b).
When an LLM returns a tool call with no arguments (arguments=null in
the streaming chunks), the tool call is silently dropped because:
1. `tool_call.function.arguments` is None, so nothing is accumulated
and `arguments` stays as "" (empty string)
2. `if function_name and arguments:` treats "" as falsy, skipping the
entire tool call execution
OpenAI always sends arguments="{}" even for parameterless tools,
masking this bug. But vLLM, Ollama, and other OpenAI-compatible
providers may omit arguments entirely when the tool schema has no
required parameters, causing tool calls to be silently ignored.
Fix: check only `function_name` (not `arguments`) and default empty
arguments to "{}" so `json.loads` produces an empty dict. Apply the
same fallback for intermediate tool calls in multi-tool responses.
Raw strings like "de-DE" passed as the language parameter to TTS/STT services
were bypassing the Language enum resolution logic, causing silent failures
(e.g. ElevenLabs expects "de" not "de-DE"). Now raw strings are first converted
to Language enums so they go through the same resolve_language() path, with a
warning logged for unrecognized strings.
Reset stop strategies at turn start (not just turn stop) so that late
transcriptions arriving between turns do not leave stale _text that
causes premature stops on the next turn. Also cancel pending timeout
tasks in reset() for both SpeechTimeout and TurnAnalyzer strategies.
Expose enable_dialout as a configure() parameter (default False) so
dial-out examples can opt in without needing to build DailyRoomProperties
manually.
Narrow misleading Optional type hints on parameters that never accept
None, extract the duplicated token_exp_duration * 60 * 60 calculation,
remove unnecessary forward-reference quotes on DailyMeetingTokenProperties,
and clarify why enable_dialout is explicitly set to False.
