a best effort version of the bot's output
- The `RTVIObserver` now emits `bot-output` messages based off
the new `AggregatedTextFrame`s (`bot-tts-text` and
`bot-llm-text` are still supported and generated, but
`bot-transcript` is now deprecated in lieu of this new, more
thorough, message).
- The new `RTVIBotOutputMessage` includes the fields:
- `spoken`: A boolean indicating whether the text was spoken by TTS
- `aggregated_by`: A string representing how the text was aggregated
("sentence", "word", "my custom aggregation")
- Introduced new fields to `RTVIObserver` to support the new
`bot-output` messaging:
- `bot_output_enabled`: Defaults to True. Set to false to disable
bot-output messages.
- `skip_aggregator_types`: Defaults to `None`. Set to a list of
strings that match aggregation types that should not be included
in bot-output messages. (Ex. `credit_card`)
`CartesiaTTSService`:
- Modified use of custom default text_aggregator to avoid deprecation warnings and push users
towards use of transformers or the `LLMTextProcessor`
- Added convenience methods for taking advantage of Cartesia's SSML tags: spell, emotion,
pauses, volume, and speed.
`RimeTTSService`:
- Modified use of custom default text_aggregator to avoid deprecation warnings and push users
towards use of transformers or the `LLMTextProcessor`
- Added convenience methods for taking advantage of Rime's customization options: spell,
pauses, pronunciations, and inline speed control.
Introduced `LLMTextProcessor`: A new processor meant to allow customization for how
LLMTextFrames should be aggregated and considered. It's purpose is to turn
`LLMTextFrame`s into `AggregatedTextFrame`s. By default, a TTSService will still
aggregate `LLMTextFrame`s by sentence for the service to consume. However, if you
wish to override how the llm text is aggregated, you should no longer override the
TTS's internal text_aggregator, but instead, insert this processor between your LLM
and TTS in the pipeline.
This frame introduces an `aggregated_by` field to describe the type of text included
in the frame and allows unspoken groupings of text to be pushed through the pipeline
and treated similar to TTSTextFrames.
Adding support for setting whether or not the text in the TextFrame
should be added to the LLM context (by the LLM assistant aggregator).
Defaults to `True`.
Modified the BaseTextAggregator type so that when text gets aggregated, metadata can
be associated with it. Currently, that just means a `type`, so that the aggregation
can be classified or described. Changes made to support this:
- **IMPORTANT**: Aggregators are now expected to strip leading/trailing white space
characters before returning their aggregation from `aggregation()` or `.text`. This
way all aggregators have a consistent contract allowing downstream use to know how
to stitch aggregations back together
- Introduced a new `Aggregation` dataclass to represent both the aggregated `text` and
a string identifying the `type` of aggregation (ex. "sentence", "word", "my custom
aggregation")
- **BREAKING**: `BaseTextAggregator.text` now returns an `Aggregation` (instead of `str`).
To update: `aggregated_text = myAggregator.text` -> `aggregated_text = myAggregator.text.text`
- **BREAKING**: `BaseTextAggregator.aggregate()` now returns `Optional[Aggregation]`
(instead of `Optional[str]`). To update:
```
aggregation = myAggregator.aggregate(text)
if (aggregation):
print(f"successfully aggregated text: {aggregation.text}") // instead of {aggregation}
```
- `SimpleTextAggregator`, `SkipTagsAggregator`, `PatternPairAggregator` updated to
produce/consume `Aggregation` objects.
- All uses of the above Aggregators have been updated accordingly.
Now:
- For TTS word-by-word output and `TTSSpeakFrames`: `TTSTextFrame`s' have `includes_inter_frame_spaces=False`.
- For all other TTS output: `TTSTextFrame` pass through the received text frames' `includes_inter_frame_spaces` value. So far, this value has always been `True`: LLMs send text chunks already containing all necessary spaces.
- `LLMTextFrame`s set `includes_inter_frame_spaces=False` at init time, per the aforementioned assumption.
* feat: Add ErrorFrame emission to TTS/STT services for pipeline error detection
- Add ErrorFrame emission to all major TTS/STT services during initialization and runtime failures
- Services updated: Cartesia, ElevenLabs, Deepgram, AssemblyAI, Rime, Azure
- ErrorFrame objects emitted with fatal=False for graceful degradation
- Enables on_pipeline_error event handler to detect service failures programmatically
- Add comprehensive pytest test suite to verify ErrorFrame emission
- Fixes issue where services failed gracefully but didn't emit ErrorFrame objects
This allows developers to implement real-time error monitoring and alerting
using the on_pipeline_error event handler introduced in v0.0.90.
* Update STT and TTS services to use consistent error handling pattern
- Improves error handling consistency across all services
* Add changelog entry for STT/TTS error handling improvements
* Linting issues Resolved
* Azure STT ErrorFrames added with consistent patterns
* Cartesia STT and Deepgram STT; additional fixes made
* Removed Fatal Flags across services, removed duplication
* Moving the changelog entry to the correct place.
* Refactoring some classes to use yield instead of push_error directly.
* Fixing ruff format.
---------
Co-authored-by: Filipi Fuchter <filipi87@gmail.com>
