pipecat-ai/pipecat v1.15.0 on GitHub

Added

Added a custom_configuration field to NvidiaTTSService.Settings, which forwards model-specific key/value parameters to NVIDIA TTS — Magpie's flush, chunk_len_threshold and max_chunk_threshold streaming controls, or Chatterbox's exaggeration_factor.
(PR #5580)
Added ClassifierUserTurnStopStrategy and ClassifierUserTurnStrategies, which let a classifier, such as JevClassifier, decide whether the user's turn is complete. They're an alternative to FilterIncompleteUserTurnStrategies for an LLM that doesn't follow the turn-completion markers well. At every pause with new words, the classifier judges the turn so far from the user's words and the bot's last line: complete ends the turn, and short and long keep it open for a few seconds unless the user continues. ClassifierUserTurnStrategies adds the classifier to the default stop strategies (smart turn), and the turn ends once both have decided; for the classifier alone, pass UserTurnStrategies(stop=[ClassifierUserTurnStopStrategy(...)]).
```
user_params = LLMUserAggregatorParams(
    user_turn_strategies=ClassifierUserTurnStrategies(
        classifier=JevClassifier(api_key=os.getenv("TYPESAFE_API_KEY")),
        context=context,
    ),
)
```
(PR #5870)
Added number_pronunciation_language to SmallestTTSService settings, which reads numerals, currency, times and dates in a language of its own, independent of the synthesis language.
(PR #5936)
Added text_matches to scripted eval expectations: one or more regular expressions the event's text must each match, such as a format a reply must open with. A miss fails the expectation with the new text_unmatched failure kind.
(PR #5991)
LLMWithBackend(predictive_delegation=PredictiveDelegation(classifier)) is an optimization that can speed up responses that need backend involvement. It uses a fast yes/no classifier (such as JevClassifier) to determine, after each user request, whether it needs to wait for a frontend inference or can go ahead and trigger an early backend delegation. The cost is money, not time: the classifier runs in parallel with the frontend, but on every turn, and an early delegation abandons a frontend request already under way. Only text frontends are supported, and only request strategies that have BackendRequestStrategy.delegate_needs_arguments set to False (like TranscriptBackendRequestStrategy).
(PR #6174)
Added AICTytoObserver, which scores the quality of the user's audio with the ai-coustics Tyto model after every 5 seconds of received audio. Each result fires the on_analysis_result event with the risk score and per-degradation scores. At the end of the call, on_call_summary receives the mean, p95, and max scores, the degraded fraction, and the dominant degradation dimension. Pass the transport's AICFilter as audio_filter to score the audio before enhancement.
(PR #6181)
AWSBedrockLLMService now emits the reasoning a model returns in reasoningContent (Claude with extended thinking, Nova 2 with reasoning enabled) as LLMThoughtStartFrame / LLMThoughtTextFrame / LLMThoughtEndFrame. Signed reasoning, such as Claude's, is kept in the context and passed back to the model on later turns, so Claude keeps its reasoning across tool calls.
(PR #6229)
TavusTransport and TavusVideoService now set the Tavus replica's facial emotion from the delivery the bot speaks in, when expressive conversation is enabled (PipelineParams(enable_expressive_conversation=True)). They convert each TTSTextFrame.delivery to a TavusEmotion and send conversation.set_emotion when it changes, as the frame's words are heard. TAVUS_DELIVERY_EMOTIONS maps Pipecat's core deliveries, such as warm to happiness, and a universal delivery for each other emotion, such as angry to anger; any other mood shows neutral. A delivery the expression policy offers that the replica has no emotion for is logged as a warning at startup.
(PR #6291)
Added the expression event to pipecat eval scenarios: the expression a bot's TTS performed, in the tags the LLM writes, such as <<excited>> or [[laughs]]. A tag the TTS can't perform is never reported, so a scenario can check that what the LLM wrote was performed.
(PR #6303)
Added expressive conversation (alpha), turned on with PipelineParams(enable_expressive_conversation=True): TTS services perform expression tags the LLM writes into its reply, instead of speaking them. The tag syntax and these APIs are likely to change between releases.
- <<name>> sets the delivery of the words that follow, such as <<excited>>, until the next delivery tag or the end of the response, and <<neutral>> returns to the voice's baseline. [[sound]] is a sound at one point, such as [[laugh]], [[pause 500]] is a pause in milliseconds, and ^^word^^ stresses a word. <<slow: A B 7>> speaks just those words in that delivery, on top of the delivery around them. <<slow>> and <<fast>> are deliveries every service that performs them names the same, for saying something carefully, such as a code, or in passing; Cartesia and MiniMax change speed only between sentences.
- Each TTS service performs what its expression adapter supports and leaves out the rest, with a warning. Adapters subclass BaseExpressionAdapter and live in pipecat.adapters.tts, beside the LLM adapters. Adapters ship for CartesiaTTSService and CartesiaHttpTTSService, ElevenLabsDialogueTTSService, ElevenLabsHttpTTSService on Eleven v3 and v4 models, FishAudioTTSService on S2 models, and MiniMaxHttpTTSService on speech-2.8 models. A service broadcasts what it can perform at start in a TTSMetadataFrame.
- The context records the reply as the LLM wrote it, tags included, and clients see the words without the tags. From a service that reports word timestamps, clients receive each word's delivery on bot-output, and each sound and pause where it is performed, as a bot-output segment of the expression_sound or expression_pause type. A service without word timestamps reports each sentence in the delivery it starts in, after its sounds and pauses. Expression sounds and pauses are sent to clients of RTVI protocol 2.3.0 or later, the protocol version now.
- Examples for each provider are in examples/expressive-conversation/.
  (PR #6303)
Added the LLM's side of expressive conversation (alpha): with PipelineParams(enable_expressive_conversation=True), an LLM service teaches itself the expression tags the TTS service it feeds can perform, by their universal names and as a list to keep to, in its system instruction, so a prompt doesn't need to describe them. It reads them from the TTSMetadataFrame that TTS service broadcasts.
- PipelineParams(expression_policy=...) takes an ExpressionPolicy, which every LLM service in the pipeline is taught, each one in an LLMSwitcher included. Its delivery and sounds say which tags are offered, by their universal names, or by default a core set such as <<calm>>, <<warm>> and [[laugh]], and its guidance lines say when to use them: presets such as ExpressionGuidance.MATCH_STYLE, which answers in the user's mood and energy, and ExpressionGuidance.SLOW_FOR_DETAILS, which says just a code or a number slowly, in a span such as <<slow: A B 7>>, and the developer's own, joined in order. Tags in the policy the TTS service can't perform are left out, with a warning.
- The instruction follows the active TTS service. A ServiceSwitcher switch, or a settings change such as a new model that changes what a TTS service performs, broadcasts its TTSMetadataFrame again, and the LLM's instruction is rebuilt from it.
- expression_instructions() returns the instruction for a service's capabilities and a policy, to read or to build a prompt of one's own, and expression_policy_warning() what that policy asks for that the service can't perform.
  (PR #6315)
Added PipecatToneReader to expressive conversation (alpha), a processor that reads each transcribed user turn with the Pipecat Tone model and sets the reading on its TranscriptionFrame: the user's likeliest emotions and how likely the turn is to be sarcastic, read from the words against the bot's previous reply. Place it after the STT service; the model downloads from Hugging Face at start and runs on the CPU. Install it with pipecat-ai[tone]. See examples/expressive-conversation/expressive-conversation-tone.py.
(PR #6319)
Added perception to expressive conversation (alpha): with PipelineParams(enable_expressive_conversation=True), the LLM reads how the user spoke, as reported by a perception producer such as an STT service or a processor after it.
- TranscriptionFrame, InterimTranscriptionFrame and EagerTranscriptionFrame carry the reading in a new perception field: PerceptionSignals, each read as the likeliest labels with how sure the reading is, such as a tone of frustrated 0.86, or as a value. An STT service that perceives sets it on the transcriptions it pushes, and a processor after the STT can set it on the transcriptions it passes along. A reading of an interim transcription is provisional. A reading of an eager transcription is written into the user message that an early reply, with enable_eager_end_of_turn, is generated from. The producer declares the signals it reports in a PerceptionMetadataFrame at start.
- The user aggregator writes the turn's final reading at the head of its user message, such as ([tone frustrated=0.86, neutral=0.10]) Can you tell me where my order is?, and the LLM service's system instruction explains how to read it: what the confidences mean, that a reading below 0.5 is a guess, and each signal the producer declares. ExpressionPolicy guidance can say what to do with a reading, such as "If the user sounds frustrated at 0.7 or above, apologize once".
- Apps get the turn's final reading in UserTurnStoppedMessage.perception, on the user aggregator's on_user_turn_stopped event. Processors after the user aggregator get the reading written into the context in LLMContextUserTurnFrame.perception. Clients get the reading in the perception field of the transcription's user-input message, which waits for a processor after the STT to read it.
  (PR #6319)
Added AssemblyAITTSService, streaming text-to-speech with per-word timings from AssemblyAI's Streaming TTS WebSocket API. Install it with pipecat-ai[assemblyai]. pipecat init offers it as "AssemblyAI".
- Settings.voice is sent as the API's voice_id: any voice by its ID, an AssemblyAI voice by its library slug, such as aai-jane, or a cloned voice by the slug the AssemblyAI dashboard shows. It defaults to Audrey, a voice from AssemblyAI's public voice library. Settings.language and Settings.model are optional.
- The service connects to the US host; set url to wss://tts.eu.assemblyai.com/v1/ws to process data in the EU.
- Interruptions cancel synthesis in flight without reconnecting. The service keeps an idle session open, replaces a session before its one-hour limit, and waits as long as the server asks before reconnecting.
- See examples/voice/voice-assemblyai.py.
  (PR #6322)
Added UserTurnStrategiesParams (in pipecat.turns.user_turn_strategies) to choose how user turn strategies combine. Pass it as user_turn_strategies_params to LLMUserAggregatorParams, UserTurnProcessor or UserTurnController. start_mode and stop_mode take a StrategyMode: with FIRST the first strategy to decide wins, and with ALL every strategy has to agree. start_mode defaults to FIRST and stop_mode to ALL. start_merge_mode and stop_merge_mode take a MergeMode: REPLACE, the default, uses the bot's strategies instead of the ones a service recommends, and EXTEND adds them to the service's.
```
# Filter incomplete turns on top of a service's stop strategies.
user_params = LLMUserAggregatorParams(
    user_turn_strategies=FilterIncompleteUserTurnStrategies(),
    user_turn_strategies_params=UserTurnStrategiesParams(
        stop_merge_mode=MergeMode.EXTEND,
    ),
)
```
(PR #6346)
Added BaseUserTurnStopStrategy.decision_scheduled. A stop strategy returns True while it's sure to decide soon on its own, as LLMTurnCompletionUserTurnStopStrategy is while a re-prompt is scheduled, and user_turn_stop_timeout then waits instead of ending the turn.
(PR #6346)
Added BaseUserTurnStopStrategy.waits_for_inference. A stop strategy that decides from the turn's inference instead of from the user, such as LLMTurnCompletionUserTurnStopStrategy, returns True. The turn controller runs the inference once the other stop strategies have decided, and that strategy then decides from it.
(PR #6346)
Added UserTurnCompletionFrame, which an LLM service using turn-completion markers pushes upstream once per inference. Its kind is "complete", "short", "long", or None when the response has no marker, and its context_frame_id is the id of the LLMContextFrame that started the inference. A tool call before any marker counts as "complete".
(PR #6346)
Added ExpressionGuidance.BREATHE to expressive conversation (alpha), a preset for an occasional audible breath before a long or difficult sentence, at most once a reply. PipecatToneTagger writes breaths only with it, drawing each as a light breath, a deep breath or an exhale at the shares people take them in, and naming each kind as the TTS service performs it: on ElevenLabs, [quick breath], [deep breath] and [exhales softly].
(PR #6385)
Added ExpressionMetadataFrame to expressive conversation (alpha), broadcast at start by a processor between the LLM and the TTS service that writes the expression tags itself. The LLM is then taught no tags, keeping how to read perception and the policy's own guidance lines when there's a reading to act on, and the assistant aggregator writes each reply to the context as its words alone.
(PR #6385)
Added PipecatToneTagger to expressive conversation (alpha), a processor that writes the expression tags for an LLM's plain replies with Pipecat Tone's tagger model. Placed between the LLM and the TTS service, it labels each sentence with a delivery, slow spans for details, stressed words and sounds, from the call so far, how the user sounded and the expression policy's presets, and writes them by their universal names, which the TTS service performs by its own, trying an optional vocabulary of your own names first. The model downloads from Hugging Face the first time, at an optional size ("17m" or "32m") and revision, or comes from an optional model_dir of your own. It loads while the pipeline is set up, once a process for every tagger to share, and runs on the CPU; if it can't load, the LLM writes the tags itself. Install it with pipecat-ai[tone]. See examples/expressive-conversation/expressive-conversation-tone-tagger.py.
(PR #6385)
Added an optional revision to PipecatToneReader, the model's release on Hugging Face, and an optional model_dir to load the model from a folder of your own instead.
(PR #6407)
Added an accent setting to CartesiaTTSService and CartesiaHttpTTSService, which picks the accent a multilingual voice speaks the language with.
(PR #6429)
Added moderation to OpenAIImageGenService.Settings, which sets the content-moderation level OpenAI's Images API applies to generated images ("low" for less restrictive filtering, or "auto").
(PR #6432)
Added a pronunciations setting to SpeechifyHttpTTSService, which tells Speechify how to speak specific words, by IPA phones or by a spoken alias.
(PR #6436)
Added TogetherLLMSettings.reasoning_effort, Together's reasoning-depth control ("low", "medium", "high", and "max" on some models). It reaches models that ignore the reasoning toggle, such as zai-org/GLM-5.3.
(PR #6437)
Added universal names to expressive conversation (alpha): the ExpressionDelivery and ExpressionSound string enums name each delivery and sound the TTS services with an expression adapter perform, the same on every service. A universal name in a tag, such as <<warm>> or [[laugh]], is sent to each service by its own name, such as ElevenLabs' warmly or Cartesia's laughter, while the spoken words' delivery, the sounds reported to clients and the context keep it as written. ExpressionCapabilities.service_vocabulary maps the names that differ, filled in for Cartesia, ElevenLabs, Fish Audio and MiniMax, and ExpressionCapabilities.delivery_name() and sound_name() give the name a service performs.
(PR #6439)
Added expressive conversation (alpha) adapters for five more TTS services, so their LLMs are taught the expression tags they perform. See examples/expressive-conversation/ for each.
- InworldTTSService and InworldHttpTTSService on inworld-tts-2 perform any delivery as a free-text instruction, such as [calm and slow], with ^^emphasis^^ in capitals, Inworld's non-verbal sounds and <break> pauses. On inworld-tts-2-flash, which ignores instructions, only sounds and pauses are performed.
- SonioxTTSService on tts-rt-v2 performs deliveries and sounds as Soniox's audio tags, such as [excited] and [laughs], with *emphasis* and approximate pauses. Its word timestamps report an audio tag as one word and a stressed word without its asterisks.
- MistralTTSService on Voxtral 3 models (voxtral-mini-tts-3-0) performs a delivery as one of Mistral's emotions, such as [emotion=warm], slow and fast as [pace=slow] and [pace=fast], and sounds such as [laugh]. The default model, voxtral-mini-tts-2603, performs none.
- GeminiTTSService on Gemini 3.8 models with the Gemini API performs any delivery as a style sent with each sentence, added to the prompt setting's style, with emphasis in capitals and inline sounds and pauses such as <laugh> and <short pause>.
- AzureTTSService and AzureHttpTTSService with a DragonHD or DragonHD Omni voice, such as en-US-Ava:DragonHDOmniLatestNeural, perform a delivery as one of Azure's HD styles, such as [excited], and its paralinguistic sounds, such as [laughter].
- ExpressionCapabilities.allows_free_text_delivery and allows_free_text_sounds say whether a service takes free text for each kind, as Inworld and Gemini do for delivery alone.
- ExpressionDelivery and ExpressionSound gain the universal names these services perform, such as playful, soft, encouraging, sniffle and whimper.
  (PR #6444)

Changed

Updated the aic extra to require aic-sdk~=3.3.1 (previously ~=3.1.0).
(PR #6181)
LLMWithBackend's frontend acknowledges a delegation to the backend in fewer words, such as "Let me look into that". A longer acknowledgement sounds awkward, and holds up a backend's answer that lands while the acknowledgement is still being spoken.
(PR #6267)
LLMWithBackend's frontend avoids the awkward double acknowledgement of a delegation. When it has acknowledged in the same response as its delegate tool call (for example "Stopping the retry test fix, I'll leave that alone for you."), which it sometimes does, the reply it generates when the call settles completes the thought (for example "Checking CI on main for you now!"), or adds nothing when the first part said it all.
(PR #6314)
When filtering incomplete turns, a turn the LLM marks ◐ or ○ now stays open until the user is re-prompted (after UserTurnCompletionConfig.incomplete_short_timeout or incomplete_long_timeout). Before, user_turn_stop_timeout ended the turn first and fired on_user_turn_stop_timeout.
(PR #6346)
⚠️ Several stop strategies in one UserTurnStrategies.stop list now all have to agree before a user turn ends, because stop_mode defaults to StrategyMode.ALL. For example, TurnAnalyzerUserTurnStopStrategy next to SpeechTimeoutUserTurnStopStrategy now ends the turn only once both say the user is done. Pass UserTurnStrategiesParams(stop_mode=StrategyMode.FIRST) to end the turn on the first stop strategy that decides, as before. A list with a single stop strategy behaves as before.
(PR #6346)
⚠️ WakePhraseUserTurnStartStrategy now starts turns on its own: once awake, it starts a turn when the user speaks or a final transcription arrives. Use it as the only start strategy, start=[WakePhraseUserTurnStartStrategy(...)]. In a StrategyMode.FIRST list, the start strategies after it no longer decide once it's awake. To combine it with others, such as MinWordsUserTurnStartStrategy, use start_mode=StrategyMode.ALL.
(PR #6346)
⚠️ A custom stop strategy that decides from the turn's inference, for example one that called trigger_user_turn_finalized() next to deferred() strategies, must now return True from BaseUserTurnStopStrategy.waits_for_inference. Otherwise the turn controller treats it as a strategy that listens to the user: in a StrategyMode.ALL list the inference doesn't run until user_turn_stop_timeout ends the turn, and in a StrategyMode.FIRST list the turn ends without waiting for it.
(PR #6346)
FilterIncompleteUserTurnStrategies now works with an eager end of turn and with the stop strategies a service recommends (with stop_merge_mode=MergeMode.EXTEND). With an eager end of turn, the speculative answer goes out once the transcript commits and the LLM marks the turn complete, in either order, and ◐ or ○ withdraws it.
(PR #6346)
⚠️ A user turn stop strategy now only reports that the user is done. BaseUserTurnStopStrategy.report_user_turn_stopped(), which replaces trigger_user_turn_stopped(), fires on_user_turn_stopped and no longer on_user_turn_inference_triggered, because the turn controller now runs the turn's inference itself once the stop strategies have decided. A custom stop strategy starts an inference only to speculate, with start_user_turn_speculation(). The deprecated trigger_user_turn_inference_triggered() ignores a call without a speculation, with a warning the first time.
(PR #6346)
⚠️ Turning on turn-completion markers with only the deprecated filter_incomplete_user_turns LLM setting no longer re-prompts a user who goes quiet after ◐ or ○, since LLMTurnCompletionUserTurnStopStrategy now does the re-prompting. Use user_turn_strategies=FilterIncompleteUserTurnStrategies() instead.
(PR #6346)
When the user speaks again after a turn's answer has started but before the turn ended, that answer is now cancelled: the user aggregator interrupts the bot, or broadcasts a UserTurnResponseCancelFrame when the turn doesn't interrupt the bot. UserTurnController and UserTurnProcessor have a new on_user_turn_resumed event, emitted when the user starts speaking again in an open turn.
(PR #6346)
ServiceSwitcher now passes a ServiceMetadataFrame from a service outside it to its inactive services too, so a service that reads another service's metadata has it when it becomes active. Only the active service's own metadata leaves the switcher, as before.
(PR #6365)
With enable_eager_end_of_turn, the user aggregator also broadcasts an LLMContextUserTurnFrame for the turn an early reply answers, with speculation=True, ahead of the reply. Processors after the LLM learn the turn before the held reply is released. The turn is broadcast again, as written to the context, when it ends, so a processor that keeps the conversation should skip speculative turns.
(PR #6385)
Changed the default model of InceptionLLMService from mercury-2 to mercury-3. Inception is retiring Mercury 2 and 2.5 on November 15, 2026, and they are closed to new signups. To keep using Mercury 2 until then, set settings=InceptionLLMService.Settings(model="mercury-2").
(PR #6397)
SmallestTTSService.Settings gained a date_format field, which reads all-numeric dates day-first ("EU") or month-first ("US").
(PR #6431)
SmallestTTSService.Settings' content_filter now also takes Smallest's "redact" action, which drops a matched term and speaks the rest.
(PR #6431)
The MoQ transport example now uses OpenAILLMService's default model instead of pinning gpt-4o, which OpenAI shuts down on 2027-01-14.
(PR #6433)
OpenAIResponsesLLMService and OpenAIResponsesHttpLLMService now send reasoning.effort="none" for gpt-6.1-terra when reasoning isn't configured, matching the other models whose lowest documented effort is none.
(PR #6434)
Renamed RELAY_NOTE to BACKEND_MESSAGE_RELAY_INSTRUCTION and STRATEGY_PLACEHOLDER to PAIRING_INSTRUCTION_STRATEGY_PLACEHOLDER in pipecat.pipeline.llm_with_backend.
(PR #6435)
pipecat.pipeline.llm_with_backend now exports only the classes an app builds with: LLMWithBackend, BackendConnector, PredictiveDelegation, and the request strategies. Import the prompt texts and the marks on backend messages from pipecat.pipeline.llm_with_backend.prompts, and DELEGATE_TOOL_NAME and ConnectorContext from pipecat.pipeline.llm_with_backend.connector.
(PR #6435)
Updated PipecatToneReader to download the Pipecat Tone reader's reader-rc2 release by default (previously reader-rc1), which reads sarcasm with the bot's reply before each turn as context. Pass revision="reader-rc1" to keep rc1.
(PR #6450)

Deprecated

Deprecated BaseUserTurnStartStrategy.trigger_user_turn_started() and BaseUserTurnStopStrategy.trigger_user_turn_stopped(). Use report_user_turn_started() and report_user_turn_stopped() instead: a strategy reports its decision, and the turn controller decides when the turn starts and ends. A subclass that overrides the old method keeps getting the reports until 2.0.0.
(PR #6346)
Deprecated BaseUserTurnStopStrategy.trigger_user_turn_inference_triggered() and trigger_user_turn_speculation_cancelled(). Use start_user_turn_speculation() and cancel_user_turn_speculation() instead.
(PR #6346)
Deprecated EagerEndOfTurnCancelFrame. Use UserTurnResponseCancelFrame instead. Frames pushed under the old name still cancel.
(PR #6346)
Deprecated deferred() and DeferredUserTurnStopStrategy, with no replacement. Pass the wrapped strategy itself: the turn controller now runs the turn's inference once the stop strategies have decided.
(PR #6346)
Deprecated ProcessFrameResult. Use StrategyMode instead: user turn strategies no longer need to return anything from process_frame(), and every strategy sees every frame. Until 2.0.0, a StrategyMode.FIRST list still honors ProcessFrameResult.STOP, with a deprecation warning.
(PR #6346)
Deprecated ExternalUserTurnCompletionStopStrategy. Use ExternalUserTurnStopStrategy instead, and push a ProposedUserStoppedSpeakingFrame once the user is done. For the LLM's turn-completion markers, use LLMTurnCompletionUserTurnStopStrategy.
(PR #6346)
Deprecated BaseUserTurnStopStrategy.trigger_user_turn_finalized(). Use report_user_turn_stopped() instead, which fires the same event.
(PR #6346)

Fixed

LLMWithBackend's frontend now more reliably delegates a stop or a change to work it has handed over to the backend. Before, gpt-4.1 and Claude often answered a user's "never mind" themselves, with no call, and the backend carried on with its work. Asked how the work is going before any of the backend's progress has reached it, the frontend now says there is no update yet instead of describing work it cannot see.
(PR #6174)
NvidiaLLMService now recognizes a leading <think> block that follows whitespace, and no longer passes the whitespace between </think> and the answer on to TTS.
(PR #6229)
AWSBedrockLLMService no longer sends Amazon Nova's <thinking>...</thinking> reasoning to TTS. Nova (v1) models write it into the answer text before calling a tool, so voice bots were reading it aloud. A <thinking> block at the start of the response, or of any text the model writes between tool calls, is now emitted as LLMThoughtStartFrame / LLMThoughtTextFrame / LLMThoughtEndFrame.
(PR #6229)
Fixed TTS services without word timestamps joining streamed sentences in the context with two spaces, and pushing skipped text, such as a code block, ahead of the sentence spoken before it.
(PR #6366)
Fixed SkipTagsAggregator recognizing only the first of several tag pairs, and never opening a pair whose start and end are the same, such as ("**", "**").
(PR #6367)
Fixed TTS services with word timestamps recording a closing tag written against a word with a space before it in the assistant context, as <card>4111 </card> is yours for <card>4111</card> is yours, and leaving it out when the bot was interrupted right after that word.
(PR #6368)
Fixed the Pipecat Cloud documentation link printed by pipecat init (and used in the generated README), which pointed to a page that no longer exists.
(PR #6387)
Fixed JevClassifier, ClefClassifier and OpenAIDecisionsClassifier letting a broken TLS connection through as an ssl.SSLError instead of a ClassifierError, so code that falls back when a classifier can't answer missed it.
(PR #6409)
Fixed nanoseconds_to_str() (used when printing a frame's pts) dropping a microsecond through float rounding, e.g. 2.3 s printed as 0:00:02.299999.
(PR #6411)
Fixed FilterIncompleteUserTurnStrategies leaving the bot silent when the user made a sound with no words while the LLM was answering, for example right before a tool call. The bot now responds as it does to an empty user turn that interrupted it, following LLMUserAggregatorParams.empty_user_turn: by default it asks the user to repeat what they said.
(PR #6422)
Fixed AudioVolumeTracker raising BufferError on update when another thread, such as a profiler, held the frame that measured the volume, which stopped VAD for the rest of the session.
(PR #6425)
Fixed a user turn ending on the user's earlier words with segmented STT services such as WhisperSTTService, OpenAISTTService, GroqSTTService and MoonshineSTTService. When the user spoke again before an earlier segment was transcribed, that segment's transcript was marked finalized, so the stop strategies took it as the user's last words and could end the turn before the new words were transcribed. SegmentedSTTService now marks a transcript finalized only when the user hasn't spoken again since its segment ended.
(PR #6446)
NvidiaTTSService no longer freezes the bot while it fetches its synthesis config from the server on startup. A slow NVIDIA endpoint used to block the whole event loop, so nothing else could run and the pipeline's setup timeout couldn't fire until the call returned. The call now runs in a thread.
(PR #6449)
Fixed segmented STT services (SegmentedSTTService and the services built on it, such as WhisperSTTService, MoonshineSTTService and NvidiaSegmentedSTTService) leaving their transcription task running when the pipeline's setup timed out, for example because a service after them couldn't connect. The runner warned about it as a dangling task, and a long-running server never released it.
(PR #6452)

Performance

OpenAIResponsesLLMService (WebSocket) now replies sooner after an interruption: the next request no longer waits for the interrupted response to finish. The two run on separate "lanes" (each with a stream_id), so the interrupted one finishes on the side while the next request can still send only the new messages, continuing from the previous response (previous_response_id), rather than the full context.
(PR #6174)