When a non-uninterruptible frame was being processed slowly and an
uninterruptible frame was waiting in the queue, _start_interruption
skipped task cancellation. This caused interruptions to stall until
the slow frame finished, even though it had no reason to block them.
The fix: only skip cancellation when the *current* frame is
uninterruptible. Uninterruptible frames already in the queue are
preserved regardless, because __create_process_task calls
__reset_process_queue internally, which always retains them.
Fixes: https://github.com/pipecat-ai/pipecat/issues/4412
grok-3 is being retired from the xAI API on May 15, 2026. Switch the
default to grok-4.20-non-reasoning, which xAI recommends for non-reasoning
workloads and is appropriate for real-time voice AI.
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.
Flip the default Inworld TTS model from inworld-tts-1.5-max to
inworld-tts-2 across:
- InworldHttpTTSService (HTTP)
- InworldTTSService (WebSocket)
- InworldRealtimeLLMService (cascade Realtime)
inworld-tts-1.5-max and inworld-tts-1.5-mini remain valid options;
existing users can pin the prior model explicitly via the model
setting. Docstring examples updated to reference the new default.
Polly TTS, Bedrock LLM, and AgentCore previously did
`arg or os.getenv("AWS_...")` and handed the result straight to
aioboto3. When only one of `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY`
was set, aioboto3 received a half-populated kwarg and errored instead of
falling through to the boto3 credential provider chain (instance
profiles, IRSA, ECS task roles, SSO, etc.).
Route credential resolution through the shared `resolve_credentials()`
helper introduced for AWS Transcribe so all four services follow the
same `explicit → env → boto3 chain` fallback. Add an
`AWSCredentials.to_boto_kwargs()` method to bridge the dataclass field
names (`access_key`, `secret_key`) to the aioboto3 kwargs
(`aws_access_key_id`, `aws_secret_access_key`).
No public API changes. Behaviour is identical for fully-explicit and
fully-env-var configurations; partial env vars now correctly trigger
the chain instead of erroring.
