PR #4344 unconditionally switched to normalizedAlignment to fix garbled
words with pronunciation dictionaries (#4316). But normalizedAlignment
returns the post-normalized form of what was spoken - including
romanization of non-Latin scripts (Chinese rendered as pinyin), which
ends up in the LLM context and degrades subsequent turns.
Gate the switch on pronunciation_dictionary_locators being configured.
Adds a _select_alignment helper with preferred-with-fallback (both
fields are nullable per the API schema), used by both the WebSocket
and HTTP services. Tests cover dictionary mode, default mode, fallback
when preferred is missing or null, and HTTP field-name variants.
When tools change mid-conversation, LLMs can produce a few different
flavors of tool-call-related hallucination: calling tools that have
been removed, avoiding tools that have been re-added, or hallucinating
output (made-up answers or tool-call-shaped non-tool-calls) when tools
are unavailable.
This change introduces an opt-in ``add_tool_change_messages`` flag on
the LLM aggregators (preferred entry point: ``LLMContextAggregatorPair(
..., add_tool_change_messages=True)``) that appends a developer-role
message to the context whenever ``LLMSetToolsFrame`` changes the set
of advertised standard tools. Helps the LLM stay coherent across tool
changes by spelling out exactly what just became available or
unavailable. Both aggregators participate; whichever handles the
frame first wins, and the other (if any) sees an empty diff against
the shared context and stays silent — order-independent regardless of
whether the frame flows downstream or upstream.
Also tightens the existing missing-handler path (introduced in #4301):
- Reworded the terminal tool result to a neutral "The function
``X`` is not currently available." (overridable via
``LLMService.MISSING_FUNCTION_CALL_MESSAGE_TEMPLATE``). Previously
read "Error: function 'X' is not registered."
- Logs at the call site now distinguish developer error (tool
advertised but no handler registered → ``logger.error``) from
hallucination (tool not advertised → ``logger.warning``).
Includes a manual validation harness
(``examples/features/features-add-tool-change-messages.py``) that
exercises the new ``add_tool_change_messages`` mitigation by flipping
tool availability on a turn counter so its effect can be observed
end-to-end with the flag on vs. off.
Resolve and contain the user-supplied filename before serving it from
the runner's /files endpoint. Also raise a 404 (instead of returning
None) when the downloads folder is unset, and use the resolved
basename for Content-Disposition.
AWS Transcribe STT previously only supported credentials via explicit
parameters or environment variables. Services running with IAM roles
(EKS pod roles, IRSA, ECS task roles, EC2 instance profiles) or SSO
couldn't use Transcribe without exporting static credentials.
Changes:
- Add resolve_credentials() to utils.py providing a standard fallback
chain: explicit params → environment variables → boto3 credential
provider chain (instance profiles, IRSA, pod roles, SSO, etc.)
- Add AWSCredentials dataclass for type-safe credential passing
- Update AWSTranscribeSTTService to use resolve_credentials() instead
of manual os.getenv() calls
- The boto3 fallback is only attempted when both access key and secret
key are unresolved, avoiding replacement of explicitly provided creds
- boto3 is imported lazily inside the function to avoid hard dependency
for services that don't need the fallback chain
- Add 7 unit tests covering the credential resolution chain
The Bedrock LLM and Polly TTS services already support the full
credential chain via aioboto3.Session() and are not modified.
Related to #4197
Two issues were causing TTSSpeakFrame(append_to_context=True) greetings to
silently lose their trailing words and never fire on_assistant_turn_stopped:
- LLMAssistantPushAggregationFrame was emitted without a PTS, so the
transport routed it through the audio (sync) queue while word-level
TTSTextFrames travel through the clock queue. The aggregation could reach
the assistant aggregator before the final words, leaving them orphaned
in the buffer. Stamp the frame with `_word_last_pts + 1` when there are
word timestamps so it can't overtake them.
- The aggregator's LLMAssistantPushAggregationFrame handler called
push_aggregation() directly, bypassing _trigger_assistant_turn_stopped.
For TTS-only flows there is no LLMFullResponseStartFrame, so the turn
start timestamp was never set and on_assistant_turn_stopped never fired.
Open a turn (if needed) and trigger stopped from the handler.
Fixes#4264.
Two follow-ups now that LLMService is generic over its adapter:
- Add an explicit backward-compat test verifying that an LLMService
subclass with no generic parameter (the third-party-provider
pattern) instantiates and returns a usable adapter. The existing
MockLLMService (declared without brackets) already exercised this
implicitly, but it's worth a named assertion.
- Drop the now-redundant `params: SomeLLMInvocationParams = ...`
variable annotations on `adapter.get_llm_invocation_params()`
results. Since `get_llm_adapter()` now returns the precise adapter
type, and `BaseLLMAdapter` is generic in its invocation-params
type, the call already infers the right TypedDict.
Broaden `tool_resources` to `app_resources` for easy access not just in
tool handlers but in other places like custom `FrameProcessor`s.
Involves 3 changes:
- A rename: `tool_resources` -> `app_resources`
- A new property on `PipelineTask`: `app_resources`
- A new property on `FrameProcessor`: `pipeline_task`
Usage in tool handler:
async def get_weather(params: FunctionCallParams):
resources = cast(MyAppResources, params.app_resources)
...
Usage in custom `FrameProcessor`:
class MyProcessor(FrameProcessor):
async def process_frame(self, frame, direction):
await super().process_frame(frame, direction)
if self.pipeline_task is not None:
resources = cast(MyAppResources, self.pipeline_task.app_resources)
...
The previous `tool_resources` aliases (on `PipelineTask`,
`FunctionCallParams`, and `FrameProcessorSetup`) keep working but are
deprecated as of 1.2.0 and emit `DeprecationWarning`s.
The four krisp test files installed a process-wide mock of
importlib.metadata.version with `patch(...).start()` at module level and
never called .stop(). Once any of these files was collected, the mock
leaked across the rest of the test session, returning '0.0.0-dev' for
every version check. This corrupted unrelated tests that triggered
transformers' import-time dependency check (e.g. lazy imports of
LocalSmartTurnAnalyzerV3) — transformers saw tqdm=='0.0.0-dev' and
refused to load.
Wrap the pipecat imports in `with patch(...)` so the mock is active
during import (where pipecat's krisp version check needs it) and torn
down before any tests run.
Bound methods are created fresh on each attribute access, so
'self._missing_function_call_handler is self._missing_function_call_handler'
is always False. Using 'is' meant the placeholder branch never fired and
both warnings logged when a function was missing at queue time.
Switch to == so equality compares the underlying function and instance.
Strengthen the missing-at-queue-time test to assert the second warning
does not fire.
Address review feedback: a function may be unregistered between when
run_function_calls queues it and when _run_function_call executes it.
Restore the live lookup, falling back to the missing-function handler
when the entry is gone, so the call still terminates with a normal
tool result. Factor the missing-handler item construction into a
helper since it's now built in two places.
* VIVA SDK TT v3 support
* Format fix.
* Renamed the API naming, removed '3' from the name.
* Implementation of User turn start strategy using Krisp VIVA Interruption Prediction in scope of TT v3 support.
* TT demo tool
* Some improvements for demo scripts, audio recordin, etc.
* Enhance demo scripts with VAD selection and audio embedding features. Updated HTML report to include annotated audio players and improved response time metrics in summary formatting. Added README for setup and usage instructions.
* Refactor interrupt prediction demo to compare multiple interruption strategies (Krisp IP vs VAD). Updated README with usage instructions and output details. Enhanced audio processing with new helper functions for generating beeps and mixing audio.
* Refactor demo scripts to improve latency metrics by introducing total_delay property in TurnEvent. Update formatting in reports and visualizations to reflect accurate speech end times, including VAD wait times. Enhance HTML report with detailed latency information and adjust audio processing to account for VAD stop seconds.
* Add audio resampling functionality and update demo scripts for improved audio processing
- Introduced `resample_audio` function to handle audio resampling with linear interpolation.
- Updated `demo_audio_recorder.py` to utilize the new resampling feature, ensuring audio is saved at the requested sample rate.
- Modified `demo_interrupt_prediction.py` and `demo_turn_taking.py` to resample audio to 16 kHz for compatibility with Silero VAD.
- Adjusted imports in demo scripts to include the new resampling function.
- Enhanced error handling for sample rate discrepancies in audio recording.
* Enhance demo_interrupt_prediction.py with VAD type selection and improved processing logic
- Added support for selecting between "silero" and "krisp" VAD engines in the demo script.
- Introduced a new create_vad function to configure VAD analyzers based on the selected type.
- Updated audio processing logic to handle VAD type-specific resampling and state management.
- Modified the KrispVivaIPUserTurnStartStrategy to utilize a separate vad_flag for per-frame VAD input, improving interruption detection accuracy.
* Refactor audio processing scripts for improved readability and consistency
- Updated type hinting in `resample_audio` function to use `tuple` instead of `Tuple`.
- Simplified print statements in `demo_audio_recorder.py`, `demo_formatting.py`, and `demo_interrupt_prediction.py` for better readability.
- Adjusted argument formatting in `demo_audio_recorder.py` and `demo_formatting.py` for consistency.
- Cleaned up list comprehensions in `demo_formatting.py`, `demo_html_report.py`, and `demo_interrupt_prediction.py` for clarity.
- Enhanced error handling in `__init__.py` for the KrispVivaIPUserTurnStartStrategy import.
* Refactor VAD handling in KrispVivaIPUserTurnStartStrategy and update tests for clarity
- Simplified the argument formatting in the _handle_vad_started method for improved readability.
- Updated test assertions to reflect changes in VAD processing logic, ensuring that the vad_flag is correctly set to False during continuous state processing.
- Enhanced test cases to verify that the process method is called appropriately under different conditions.
* more format fixes.
* removed demo scripts.
* reverted wrongly removed file.
* Corrected the IP integration logic.
* style fix.
* Refactor audio processing and state management in KrispVivaIPUserTurnStartStrategy
- Removed the unused _vad_flag attribute to streamline state tracking.
- Updated the reset method to clear the audio buffer instead of resetting the vad_flag.
- Adjusted the process_frame method to use _speech_active for VAD input, enhancing clarity in the logic.
- Modified tests to reflect changes in state management and ensure proper functionality of the reset method and audio buffer handling.
* FIxed formatting
---------
Co-authored-by: Aram Poghosyan <apoghosyan@krisp.ai>
Adds XAITTSService in the existing xai/tts.py module, alongside the
existing XAIHttpTTSService. Connects to xAI's streaming endpoint at
wss://api.x.ai/v1/tts, streams text.delta chunks up and base64 audio.delta
chunks down on the same connection so audio starts flowing before the full
utterance is synthesized.
Extends InterruptibleTTSService since xAI's protocol is strictly sequential
per connection and exposes neither a cancel verb nor a context ID — the
only way to stop an in-flight utterance is to tear down the WebSocket,
which is exactly what InterruptibleTTSService does on interruption when
the bot is speaking.
Voice, language, codec, and sample_rate are passed as query-string params
at connect time; runtime setting changes reconnect the socket. Defaults to
raw PCM so emitted TTSAudioRawFrame objects need no decoding downstream.
Splits the existing example into voice-xai.py (WebSocket) and
voice-xai-http.py (batch HTTP) so each variant has its own entry point.
Promotes the xai extra to depend on pipecat-ai[websockets-base] since the
new service imports the websockets library.
SpeechTimeoutUserTurnStopStrategy previously collapsed two waits into
max(stt_timeout, user_speech_timeout), which over-waited for finalizing
STT services and could also end the turn early in a legacy code path.
Run them as independent timers instead:
- user_speech_timeout: policy floor, always runs to completion.
- stt_timeout: latency safety net, short-circuited by a finalized
transcript since STT has signaled it has nothing more to send.
The no-VAD fallback now waits only user_speech_timeout rather than
max(stt_timeout, user_speech_timeout); stt_timeout is defined relative
to VAD stop and has no meaning when no VAD event occurred. This
shortens the fallback wait for users who set stt_timeout greater than
user_speech_timeout.
Some TTS providers (e.g. Inworld) return verbatim tokens where spaces and
punctuation are already embedded in the token text. When downstream consumers
join these tokens with an extra space they produce "hello , world" instead of
"hello, world".
Add an opt-in `includes_inter_frame_spaces: bool = False` parameter to
`add_word_timestamps` / `_add_word_timestamps`. The flag is threaded through
`_WordTimestampEntry` and stamped onto every emitted `TTSTextFrame`.
Defaults to `False` — no behaviour change for existing services.
`InworldTTSService` passes `includes_inter_frame_spaces=True` and stops
pre-processing tokens in `_calculate_word_times`, returning them verbatim.
Tests added to `test_tts_frame_ordering.py` covering both HTTP and WebSocket
delivery paths: verbatim text preservation, PTS ordering, text-before-audio
ordering, and the Inworld punctuation-token scenario.
Made-with: Cursor
* VIVA SDK TT v3 support
* Format fix.
* Renamed the API naming, removed '3' from the name.
* Implementation of User turn start strategy using Krisp VIVA Interruption Prediction in scope of TT v3 support.
* Typo fix in voice-krisp-viva example to use KrispVivaFilter class
* style fix.
* test run error fixes.
* some test related changes.
* Fixed tests
* Stule fixes.
When the LLM returned zero text tokens (e.g. it was interrupted before producing
tokens or about to push tokens), push_aggregation() returned an empty string and
on_assistant_turn_stopped was never emitted. This left consumers waiting for an
event that would never arrive.
Now on_assistant_turn_stopped always fires, with an empty content string when
the LLM produced no text tokens.
Fixes#4292
Only treat messages[0] as the initial system prompt when determining the
summarization range. Previously, the code scanned the entire context for
the first system-role message, which caused failures when the only system
message was a mid-conversation injection (e.g. "The user has been quiet").
In that case summary_start exceeded summary_end, producing an empty range
and "No messages to summarize" errors.
Fixes#4286
When the STT p99 timeout fires without a transcript, the turn stop
strategy previously did nothing — falling through to the 5-second
user_turn_stop_timeout. Now, a _timeout_expired flag tracks when the
timeout has elapsed so that a late transcript triggers the turn stop
immediately instead of waiting for the fallback.
The strategy schedules background tasks during setup. Fast-running
tests could observe state before those tasks had a chance to run;
yielding once via asyncio.sleep(0) ensures they do.
Enable callers to get a compact version of context messages suitable
for serialization, logging, and debugging tools. For standard
messages, known binary data (base64 images, audio) is fully elided.
For LLM-specific messages, long string values are recursively
truncated. Adapter get_messages_for_logging() methods now use this.
Replaces the per-frame asyncio.Event signaling with a monotonic
timestamp updated on each audio frame. The handler sleeps until the
next deadline (last_audio_time + timeout), recomputing on each wake-up
to account for audio arriving during sleep.
This avoids waking the handler on every audio frame (~50/s at 20ms
chunks), and guarantees detection latency is bounded by timeout rather
than 2 * timeout.
Also renames audio_starvation_timeout to audio_idle_timeout and
associated identifiers for consistency with existing pipecat naming
(user_idle_timeout, etc.).
These are TypedDicts (plain dicts at runtime), so no behavioral change
— just more descriptive type hints for readers. Use ToolParam instead
of FunctionToolParam for the Responses adapter to reflect that custom
non-function tools are supported. Use ChatCompletionToolParam instead
of Any for the completions adapter return type. Update tests to use
typed params in expected values.
