Fix: Ensure EndFrame propagates through AIService before stop()

This fix addresses a critical bug where EndFrame (and potentially other
system frames) would trigger the stop() method in AIService but never
be pushed downstream to subsequent processors, causing pipelines to hang.

The issue occurred because AIService.process_frame() would call stop(frame)
for EndFrame without first pushing it downstream. This meant that downstream
processors never received the shutdown signal, leaving the pipeline in a
waiting state.

The fix ensures EndFrame is pushed downstream BEFORE calling stop(), following
the same pattern used by RTVIProcessor and properly-implemented processors.
This guarantees that:
1. Downstream processors receive the EndFrame for proper cleanup
2. The stop() method can then safely perform service-specific cleanup
3. The ordering prevents race conditions during shutdown

This bug affected all AI services inheriting from AIService that didn't
override process_frame() to explicitly handle EndFrame, including scenarios
with TTS services, LLM services, and other AI service implementations.

Fixes pipeline hangs during graceful shutdown when EndFrame is sent.

CHANGELOG.md

@@ -5,340 +5,22 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.0.96] - 2025-11-26 🦃 "Happy Thanksgiving!" 🦃
+## [Unreleased]
 
 ### Added
 
-- Enhanced error handling across the framework:
-
-  - Added `on_error` callback to `FrameProcessor` for centralized error
-    handling.
-
-  - Renamed `push_error(error: ErrorFrame)` to `push_error_frame(error: ErrorFrame)`
-    for clarity.
-
-  - Added new `push_error` method for simplified error reporting:
-
-    ```python
-    async def push_error(error_msg: str,
-                         exception: Optional[Exception] = None,
-                         fatal: bool = False)
-    ```
-
-  - Standardized error logging by replacing `logger.exception` calls with
-    `logger.error` throughout the codebase.
-
-- Added `cache_read_input_tokens`, `cache_creation_input_tokens` and
-  `reasoning_tokens` to OTel spans for LLM call
-
-- Added `LiveKitRESTHelper` utility class for managing LiveKit rooms via REST API.
-
-- Added `DeepgramSageMakerSTTService` which connects to a SageMaker hosted
-  Deepgram STT model. Added `07c-interruptible-deepgram-sagemaker.py`
-  foundational example.
-
-- Added `SageMakerBidiClient` to connect to SageMaker hosted BiDi compatible
-  services.
-
-- Added support for `include_timestamps` and `enable_logging` in
-  `ElevenLabsRealtimeSTTService`. When `include_timestamps` is enabled,
-  timestamp data is included in the `TranscriptionFrame`'s `result`
-  parameter.
-
-- Added optional speaking rate control to `InworldTTSService`.
-
-- Introduced a new `AggregatedTextFrame` type to support passing text along with
-  an `aggregated_by` field to describe the type of text
-  included. `TTSTextFrame`s now inherit from `AggregatedTextFrame`. With this
-  inheritance, an observer can watch for `AggregatedTextFrame`s to accumlate the
-  perceived output and determine whether or not the text was spoken based on if
-  that frame is also a `TTSTextFrame`.
-
-  With this frame, the llm token stream can be transformed into custom
-  composable chunks, allowing for aggregation outside the TTS service. This
-  makes it possible to listen for or handle those aggregations and sets the
-  stage for doing things like composing a best effort of the perceived llm
-  output in a more digestable form and to do so whether or not it is processed
-  by a TTS or if even a TTS exists.
-
-- 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.
-
-- New `bot-output` RTVI message to represent what the bot actually "says".
-
-  - 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`)
-
-  - Introduced new methods, `add_text_transformer()` and
-    `remove_text_transformer()`, to `RTVIObserver` to support providing (and
-    subsequently removing) callbacks for various types of aggregations (or all
-    aggregations with `*`) that can modify the text before being sent as a
-    `bot-output` or `tts-text` message. (Think obscuring the credit card or
-    inserting extra detail the client might want that the context doesn't need.)
-
-- In `MiniMaxHttpTTSService`:
-
-  - Added support for speech-2.6-hd and speech-2.6-turbo models
-
-  - Added languages: Afrikaans, Bulgarian, Catalan, Danish, Persian, Filipino,
-    Hebrew, Croatian, Hungarian, Malay, Norwegian, Nynorsk, Slovak, Slovenian,
-    Swedish, and Tamil
-
-  - Added new emotions: calm and fluent
-
-- Added `enable_logging` to `SimliVideoService` input parameters. It's disabled
-  by default.
-
-### Changed
-
-- Updated `FishAudioTTSService` default model to `s1`.
-
-- Updated `DeepgramTTSService` to use Deepgram's TTS websocket API. ⚠️ This is
-  a potential breaking change, which only affects you if you're self-hosting
-  `DeepgramTTSService`. The new service uses Websockets and improves TTFB
-  latency.
-
-- Updated `daily-python` to 0.22.0.
-
-- `BaseTextAggregator` changes:
-
-  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 change: `BaseTextAggregator.text` now returns an `Aggregation`
-    (instead of `str`).
-
-    Before:
-
-    ```python
-    aggregated_text = myAggregator.text
-    ```
-
-    Now:
-
-    ```python
-    aggregated_text = myAggregator.text.text
-    ```
-
-  - ⚠️ Breaking change: `BaseTextAggregator.aggregate()` now returns
-    `Optional[Aggregation]` (instead of `Optional[str]`).
-
-    Before:
-
-    ```python
-    aggregation = myAggregator.aggregate(text)
-    print(f"successfully aggregated text: {aggregation}")
-    ```
-
-    Now:
-
-    ```python
-    aggregation = myAggregator.aggregate(text)
-    if aggregation:
-      print(f"successfully aggregated text: {aggregation.text}")
-    ```
-
-  - `SimpleTextAggregator`, `SkipTagsAggregator`, `PatternPairAggregator`
-    updated to produce/consume `Aggregation` objects.
-
-  - All uses of the above Aggregators have been updated accordingly.
-
-- Augmented the `PatternPairAggregator` so that matched patterns can be treated
-  as their own aggregation, taking advantage of the new. To that end:
-
-  - Introduced a new, preferred version of `add_pattern` to support a new option
-    for treating a match as a separate aggregation returned from
-    `aggregate()`. This replaces the now deprecated `add_pattern_pair` method
-    and you provide a `MatchAction` in lieu of the `remove_match` field.
-
-    - `MatchAction` enum: `REMOVE`, `KEEP`, `AGGREGATE`, allowing customization
-      for how a match should be handled.
-
-      - `REMOVE`: The text along with its delimiters will be removed from the
-        streaming text. Sentence aggregation will continue on as if this text
-        did not exist.
-
-      - `KEEP`: The delimiters will be removed, but the content between them
-        will be kept. Sentence aggregation will continue on with the internal
-        text included.
-
-      - `AGGREGATE`: The delimiters will be removed and the content between will
-        be treated as a separate aggregation. Any text before the start of the
-        pattern will be returned early, whether or not a complete sentence was
-        found. Then the pattern will be returned. Then the aggregation will
-        continue on sentence matching after the closing delimiter is found. The
-        content between the delimiters is not aggregated by sentence. It is
-        aggregated as one single block of text.
-
-    - `PatternMatch` now extends `Aggregation` and provides richer info to
-      handlers.
-
-  - ⚠️ Breaking change: The `PatternMatch` type returned to handlers registered
-    via `on_pattern_match` has been updated to subclass from the new
-    `Aggregation` type, which means that `content` has been replaced with
-    `text` and `pattern_id` has been replaced with `type`:
-
-    ```python
-    async dev on_match_tag(match: PatternMatch):
-       pattern = match.type # instead of match.pattern_id
-       text = match.text # instead of match.content
-    ```
-
-- `TextFrame` now includes the field `append_to_context` to support setting
-  whether or not the encompassing text should be added to the LLM context (by
-  the LLM assistant aggregator). It defaults to `True`.
-
-- `TTSService` base class updates:
-
-  - `TTSService`s now accept a new `skip_aggregator_types` to avoid speaking
-    certain aggregation types (now determined/returned by the aggregator)
-
-  - Introduced the ability to do a just-in-time transform of text before it gets
-    sent to the TTS service via callbacks you can set up via a new init field,
-    `text_transforms` or a new method `add_text_transformer()`. This makes it
-    possible to do things like introduce TTS-specific tags for spelling or
-    emotion or change the pronunciation of something on the
-    fly. `remove_text_transformer` has also been added to support removing a
-    registered transform callback.
-
-  - TTS services push `AggregatedTextFrame` in addition to `TTSTextFrame`s when
-    either an aggregation occurs that should not be spoken or when the TTS
-    service supports word-by-word timestamping. In the latter case, the
-    `TTSService` preliminarily generates an `AggregatedTextFrame`, aggregated by
-    sentence to generate the full sentence content as early as possible.
-
-- Updated `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.
-
-- Updated `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.
-
-### Deprecated
-
-- The TTS constructor field, `text_aggregator` is deprecated in favor of the new
-  `LLMTextProcessor`. TTSServices still have an internal aggregator for support
-  of default behavior, but if you want to override the aggregation behavior, you
-  should use the new processor.
-
-- The RTVI `bot-transcription` event is deprecated in favor of the new
-  `bot-output` message which is the canonical representation of bot output
-  (spoken or not). The code still emits a transcription message for backwards
-  compatibility while transition occurs.
-
-- Deprecated `add_pattern_pair` in the `PatternPairAggregator` which takes a
-  `pattern_id` and `remove_match` field in favor of the new `add_pattern` method
-  which takes a `type` and an `action`
-
-- `english_normalization` input parameter for `MiniMaxHttpTTSService` is
-  deprecated, use `test_normalization` instead.
-
-### Fixed
-
-- Fixed an issue in `AWSBedrockLLMService` where the `aws_region` arg was
-  always set to `us-east-1`.
-
-- Fixed an issue with `DeepgramFluxSTTService` where it sometimes failed to reconnect.
-
-- Fixed an issue in `ElevenLabsRealtimeSTTService` where dynamic language
-  updates were not working.
-
-- Fixed an issue in `ElevenLabsRealtimeSTTService` where setting the sample
-  rate would result in transcripts failing.
-
-- Fixed `InworldTTSService` audio config payload to use camelCase keys expected
-  by the Inworld API.
-
-## [0.0.95] - 2025-11-18
-
-### Added
-
-- Added ai-coustics integrated VAD (`AICVADAnalyzer`) with `AICFilter` factory and
-  example wiring; leverages the enhancement model for robust detection with no
-  ONNX dependency or added processing complexity.
-
-- Added a watchdog to `DeepgramFluxSTTService` to prevent dangling tasks in case the
-  user was speaking and we stop receiving audio.
-
-- Introduced a minimum confidence parameter in `DeepgramFluxSTTService` to avoid
-  generating transcriptions below a defined threshold.
-
 - Added `ElevenLabsRealtimeSTTService` which implements the Realtime STT
   service from ElevenLabs.
 
-- Added word-level timestamps support to Hume TTS service
+- Added a `TTSService.includes_inter_frame_spaces` property getter, so that TTS
+  services that subclass `TTSService` can indicate whether the text in the
+  `TTSTextFrame`s they push already contain any necessary inter-frame spaces.
 
 ### Changed
 
-- ⚠️ Breaking change: `LLMContext.create_image_message()`,
-  `LLMContext.create_audio_message()`, `LLMContext.add_image_frame_message()`
-  and `LLMContext.add_audio_frames_message()` are now async methods. This fixes
-  an issue where the asyncio event loop would be blocked while encoding audio or
-  images.
-
-- `ConsumerProcessor` now queues frames from the producer internally instead of
-  pushing them directly. This allows us to subclass consumer processors and
-  manipulate frames before they are pushed.
-
-- `BaseTextFilter` only require subclasses to implement the `filter()` method.
-
-- Extracted the logic for retrying connections, and create a new `send_with_retry`
-  method inside `WebSocketService`.
-
-- Refactored `DeepgramFluxSTTService` to automatically reconnect if sending a
-  message fails.
-
 - Updated all STT and TTS services to use consistent error handling pattern with
   `push_error()` method for better pipeline error event integration.
 
-- Added support for `maybe_capture_participant_camera()` and
-  `maybe_capture_participant_screen()` for `SmallWebRTCTransport` in the runner
-  utils.
-
 - Added Hindi support for Rime TTS services.
 
 - Updated `GeminiTTSService` to use Google Cloud Text-to-Speech streaming API
@@ -358,11 +40,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-- Fixed a `SimliVideoService` connection issue.
-
-- Fixed an issue in the `Runner` where, when using `SmallWebRTCTransport`, the
-  `request_data` was not being passed to the `SmallWebRTCRunnerArguments` body.
-
 - Fixed subtle issue of assistant context messages ending up with double spaces
   between words or sentences.
 
@@ -377,6 +54,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Prevented `HeyGenVideoService` from automatically disconnecting after 5 minutes.
 
+### Added
+
+- Added ai-coustics integrated VAD (`AICVADAnalyzer`) with `AICFilter` factory and 
+  example wiring; leverages the enhancement model for robust detection with no 
+  ONNX dependency or added processing complexity.
+
 ## [0.0.94] - 2025-11-10
 
 ### Changed

env.example

@@ -44,7 +44,6 @@ DAILY_SAMPLE_ROOM_URL=https://...
 
 # Deepgram
 DEEPGRAM_API_KEY=...
-SAGEMAKER_ENDPOINT_NAME=...
 
 # DeepSeek
 DEEPSEEK_API_KEY=...

examples/foundational/07ae-interruptible-hume.py

@@ -13,29 +13,24 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame, TTSTextFrame
+from pipecat.frames.frames import LLMRunFrame
-from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    LLMContextAggregatorPair,
-)
 from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
 
-
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -93,7 +88,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             stt,
             context_aggregator.user(),  # User responses
             llm,  # LLM
-            tts,  # TTS (HumeTTSService with word timestamps)
+            tts,  # TTS
             transport.output(),  # Transport bot output
             context_aggregator.assistant(),  # Assistant spoken responses
         ]
@@ -107,14 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             audio_out_sample_rate=HUME_SAMPLE_RATE,
         ),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        observers=[
+        observers=[RTVIObserver(rtvi)],
-            RTVIObserver(rtvi),
-            DebugLogObserver(
-                frame_types={
-                    TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
-                }
-            ),
-        ],
     )
 
     @rtvi.event_handler("on_client_ready")
@@ -124,9 +112,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
-        logger.info(
-            "💡 Word timestamps are enabled! Watch the console for TTSTextFrame logs showing each word with its PTS."
-        )
         # Kick off the conversation.
         messages.append({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])

examples/foundational/07c-interruptible-deepgram-flux.py

@@ -52,10 +52,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramFluxSTTService(
+    stt = DeepgramFluxSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        params=DeepgramFluxSTTService.InputParams(min_confidence=0.3),
-    )
 
     tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
 

examples/foundational/12-describe-image-openai.py

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         # Kick off the conversation.
         image = Image.open(image_path)
-        message = await LLMContext.create_image_message(
+        message = LLMContext.create_image_message(
             image=image.tobytes(),
             format="RGB",
             size=image.size,

examples/foundational/12a-describe-image-anthropic.py

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         # Kick off the conversation.
         image = Image.open(image_path)
-        message = await LLMContext.create_image_message(
+        message = LLMContext.create_image_message(
             image=image.tobytes(),
             format="RGB",
             size=image.size,

examples/foundational/12b-describe-image-aws.py

@@ -117,7 +117,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         # Kick off the conversation.
         image = Image.open(image_path)
-        message = await LLMContext.create_image_message(
+        message = LLMContext.create_image_message(
             image=image.tobytes(),
             format="RGB",
             size=image.size,

examples/foundational/12c-describe-image-gemini-flash.py

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         # Kick off the conversation.
         image = Image.open(image_path)
-        message = await LLMContext.create_image_message(
+        message = LLMContext.create_image_message(
             image=image.tobytes(),
             format="RGB",
             size=image.size,

examples/foundational/14d-function-calling-moondream-video.py

@@ -15,21 +15,14 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import (
+from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
-    Frame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMRunFrame,
-    TextFrame,
-    UserImageRequestFrame,
-)
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frame_processor import FrameDirection
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
     create_transport,
@@ -73,27 +66,6 @@ async def fetch_user_image(params: FunctionCallParams):
     # await params.result_callback({"result": "Image is being captured."})
 
 
-class MoondreamTextFrameWrapper(FrameProcessor):
-    """Wraps Moondream-provided TextFrames with LLM response start/end frames.
-
-    This processor detects TextFrames and automatically wraps them with
-    LLMFullResponseStartFrame and LLMFullResponseEndFrame to provide proper
-    response boundaries for downstream processors.
-    """
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        # If we receive a TextFrame, wrap it with response start/end frames
-        if isinstance(frame, TextFrame):
-            await self.push_frame(LLMFullResponseStartFrame(), direction)
-            await self.push_frame(frame, direction)
-            await self.push_frame(LLMFullResponseEndFrame(), direction)
-        else:
-            # For all other frames, just pass them through
-            await self.push_frame(frame, direction)
-
-
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -158,12 +130,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # If you run into weird description, try with use_cpu=True
     moondream = MoondreamService()
 
-    # Wrap TextFrames with LLM response start/end frames, which makes Moondream
-    # output be treated like LLM responses for the purpose of context
-    # aggregation. Without this, the assistant context aggregator would ignore
-    # Moondream output (if the TTS service is disabled).
-    moondream_text_wrapper = MoondreamTextFrameWrapper()
-
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
@@ -171,7 +137,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             context_aggregator.user(),  # User responses
             ParallelPipeline(
                 [llm],  # LLM
-                [moondream, moondream_text_wrapper],
+                [moondream],
             ),
             tts,  # TTS
             transport.output(),  # Transport bot output

examples/foundational/22d-natural-conversation-gemini-audio.py

@@ -391,7 +391,7 @@ class AudioAccumulator(FrameProcessor):
             )
             self._user_speaking = False
             context = LLMContext()
-            await context.add_audio_frames_message(audio_frames=self._audio_frames)
+            context.add_audio_frames_message(audio_frames=self._audio_frames)
             await self.push_frame(LLMContextFrame(context=context))
         elif isinstance(frame, InputAudioRawFrame):
             # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest

examples/foundational/30-observer.py

@@ -150,7 +150,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             LLMLogObserver(),
             DebugLogObserver(
                 frame_types={
-                    TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
+                    TTSTextFrame: (BaseOutputTransport, FrameEndpoint.DESTINATION),
                     UserStartedSpeakingFrame: (BaseInputTransport, FrameEndpoint.SOURCE),
                     EndFrame: None,
                 }

examples/foundational/35-pattern-pair-voice-switching.py

@@ -62,11 +62,7 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.text.pattern_pair_aggregator import (
+from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
-    MatchAction,
-    PatternMatch,
-    PatternPairAggregator,
-)
 
 load_dotenv(override=True)
 
@@ -110,16 +106,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     pattern_aggregator = PatternPairAggregator()
 
     # Add pattern for voice switching
-    pattern_aggregator.add_pattern(
+    pattern_aggregator.add_pattern_pair(
-        type="voice",
+        pattern_id="voice_tag",
         start_pattern="<voice>",
         end_pattern="</voice>",
-        action=MatchAction.REMOVE,  # Remove tags from final text
+        remove_match=True,
     )
 
     # Register handler for voice switching
     async def on_voice_tag(match: PatternMatch):
-        voice_name = match.text.strip().lower()
+        voice_name = match.content.strip().lower()
         if voice_name in VOICE_IDS:
             # First flush any existing audio to finish the current context
             await tts.flush_audio()
@@ -129,7 +125,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         else:
             logger.warning(f"Unknown voice: {voice_name}")
 
-    pattern_aggregator.on_pattern_match("voice", on_voice_tag)
+    pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag)
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 

examples/foundational/39-mcp-stdio.py

@@ -155,7 +155,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         You are a helpful LLM in a WebRTC call.
         Your goal is to demonstrate your capabilities in a succinct way.
         You have access to tools to search the Rijksmuseum collection.
-        Offer, for example, to show a floral still life, use the `search_artwork` tool.
+        Offer, for example, to show the earliest Rembrandt work from the museum. Use the `search_artwork` tool.
         The tool may respond with a JSON object with an `artworks` array. Choose the art from that array.
         Once the tool has responded, tell the user the title and use the `open_image_in_browser` tool.
         Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.

examples/foundational/39a-mcp-run-sse.py

@@ -9,6 +9,7 @@ import os
 
 from dotenv import load_dotenv
 from loguru import logger
+from mcp.client.session_group import SseServerParameters
 
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
@@ -22,16 +23,16 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService
-from pipecat.services.deepgram.stt_sagemaker import DeepgramSageMakerSTTService
+from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.mcp_service import MCPClient
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
 
-
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -60,42 +61,56 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    # Initialize Deepgram SageMaker STT Service
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-    # This requires:
+
-    # - AWS credentials configured (via environment variables or AWS CLI)
+    tts = CartesiaTTSService(
-    # - A deployed SageMaker endpoint with Deepgram model
+        api_key=os.getenv("CARTESIA_API_KEY"),
-    stt = DeepgramSageMakerSTTService(
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        endpoint_name=os.getenv("SAGEMAKER_ENDPOINT_NAME"),
-        region=os.getenv("AWS_REGION"),
     )
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
+    llm = AnthropicLLMService(
-
+        api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
-    llm = AWSBedrockLLMService(
-        aws_region=os.getenv("AWS_REGION"),
-        model="us.amazon.nova-pro-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
     )
 
-    messages = [
+    try:
-        {
+        # https://docs.mcp.run/integrating/tutorials/mcp-run-sse-openai-agents/
-            "role": "system",
+        mcp = MCPClient(server_params=SseServerParameters(url=os.getenv("MCP_RUN_SSE_URL")))
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+    except Exception as e:
-        },
+        logger.error(f"error setting up mcp")
-    ]
+        logger.exception("error trace:")
 
-    context = LLMContext(messages)
+    tools = {}
+    try:
+        tools = await mcp.register_tools(llm)
+    except Exception as e:
+        logger.error(f"error registering tools")
+        logger.exception("error trace:")
+
+    system = f"""
+    You are a helpful LLM in a WebRTC call.
+    Your goal is to demonstrate your capabilities in a succinct way.
+    You have access to a number of tools provided by mcp.run. Use any and all tools to help users.
+    Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
+    Respond to what the user said in a creative and helpful way.
+    When asked for today's date, use 'https://www.datetoday.net/'.
+    Don't overexplain what you are doing.
+    Just respond with short sentences when you are carrying out tool calls.
+    """
+
+    messages = [{"role": "system", "content": system}]
+
+    context = LLMContext(messages, tools)
     context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
-            stt,  # STT
+            stt,
-            context_aggregator.user(),  # User responses
+            context_aggregator.user(),  # User spoken responses
             llm,  # LLM
             tts,  # TTS
             transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+            context_aggregator.assistant(),  # Assistant spoken responses and tool context
         ]
     )
 
@@ -110,9 +125,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
+        logger.info(f"Client connected: {client}")
         # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
@@ -132,6 +146,14 @@ async def bot(runner_args: RunnerArguments):
 
 
 if __name__ == "__main__":
+    if not os.getenv("MCP_RUN_SSE_URL"):
+        logger.error(
+            f"Please set MCP_RUN_SSE_URL environment variable for this example. See https://mcp.run"
+        )
+        import sys
+
+        sys.exit(1)
+
     from pipecat.runner.run import main
 
     main()

examples/foundational/39b-multiple-mcp.py

@@ -7,7 +7,6 @@
 
 import asyncio
 import io
-import json
 import os
 import re
 import shutil
@@ -16,7 +15,7 @@ import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 from mcp import StdioServerParameters
-from mcp.client.session_group import StreamableHttpParameters
+from mcp.client.session_group import SseServerParameters
 from PIL import Image
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
@@ -67,12 +66,10 @@ class UrlToImageProcessor(FrameProcessor):
             await self.push_frame(frame, direction)
 
     def extract_url(self, text: str):
-        data = json.loads(text)
+        pattern = r"!\[[^\]]*\]\((https?://[^)]+\.(png|jpg|jpeg|PNG|JPG|JPEG|gif))\)"
-        if "artObject" in data:
+        match = re.search(pattern, text)
-            return data["artObject"]["webImage"]["url"]
+        if match:
-        if "artworks" in data and len(data["artworks"]):
+            return match.group(1)
-            return data["artworks"][0]["webImage"]["url"]
-
         return None
 
     async def run_image_process(self, image_url: str):
@@ -135,11 +132,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         system = f"""
         You are a helpful LLM in a WebRTC call.
         Your goal is to demonstrate your capabilities in a succinct way.
-        You have access to tools to search the Rijksmuseum collection and the user's GitHub repositories and account.
+        You have access to tools to search the Rijksmuseum collection.
-        Offer, for example, to show a floral still life, use the `search_artwork` tool.
+        Offer, for example, to show the earliest Rembrandt work from the museum. Use the `search_artwork` tool.
         The tool may respond with a JSON object with an `artworks` array. Choose the art from that array.
         Once the tool has responded, tell the user the title and use the `open_image_in_browser` tool.
-        You can also offer to answer users questions about their GitHub repositories and account.
         Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
         Respond to what the user said in a creative and helpful way.
         Don't overexplain what you are doing.
@@ -149,11 +145,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         messages = [{"role": "system", "content": system}]
 
         try:
-            rijksmuseum_mcp = MCPClient(
+            mcp = MCPClient(
                 server_params=StdioServerParameters(
                     command=shutil.which("npx"),
                     # https://github.com/r-huijts/rijksmuseum-mcp
-                    args=["-y", "mcp-server-rijksmuseum"],
+                    args=["-y", "mcp-server-error setting up mcp"],
                     env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
                 )
             )
@@ -161,32 +157,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.error(f"error setting up rijksmuseum mcp")
             logger.exception("error trace:")
         try:
-            # Github MCP docs: https://github.com/github/github-mcp-server
+            # https://docs.mcp.run/integrating/tutorials/mcp-run-sse-openai-agents/
-            # Enable Github Copilot on your GitHub account. Free tier is ok. (https://github.com/settings/copilot)
+            # ie. "https://www.mcp.run/api/mcp/sse?..."
-            # Generate a personal access token. It must be a Fine-grained token, classic tokens are not supported. (https://github.com/settings/personal-access-tokens)
+            # ensure the profile has a tool or few installed
-            # Set permissions you want to use (eg. "all repositories", "profile: read/write", etc)
+            mcp_run = MCPClient(server_params=SseServerParameters(url=os.getenv("MCP_RUN_SSE_URL")))
-            github_mcp = MCPClient(
-                server_params=StreamableHttpParameters(
-                    url="https://api.githubcopilot.com/mcp/",
-                    headers={
-                        "Authorization": f"Bearer {os.getenv('GITHUB_PERSONAL_ACCESS_TOKEN')}"
-                    },
-                )
-            )
         except Exception as e:
             logger.error(f"error setting up mcp.run")
             logger.exception("error trace:")
 
-        rijksmuseum_tools = {}
+        tools = {}
-        github_tools = {}
+        run_tools = {}
         try:
-            rijksmuseum_tools = await rijksmuseum_mcp.register_tools(llm)
+            tools = await mcp.register_tools(llm)
-            github_tools = await github_mcp.register_tools(llm)
+            run_tools = await mcp_run.register_tools(llm)
         except Exception as e:
             logger.error(f"error registering tools")
             logger.exception("error trace:")
 
-        all_standard_tools = rijksmuseum_tools.standard_tools + github_tools.standard_tools
+        all_standard_tools = run_tools.standard_tools + tools.standard_tools
         all_tools = ToolsSchema(standard_tools=all_standard_tools)
 
         context = LLMContext(messages, all_tools)
@@ -238,9 +226,9 @@ async def bot(runner_args: RunnerArguments):
 
 
 if __name__ == "__main__":
-    if not os.getenv("RIJKSMUSEUM_API_KEY") or not os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN"):
+    if not os.getenv("RIJKSMUSEUM_API_KEY") or not os.getenv("MCP_RUN_SSE_URL"):
         logger.error(
-            f"Please set `RIJKSMUSEUM_API_KEY` and `GITHUB_PERSONAL_ACCESS_TOKEN` environment variables. See https://github.com/r-huijts/rijksmuseum-mcp."
+            f"Please set RIJKSMUSEUM_API_KEY and MCP_RUN_SSE_URL environment variables. See https://github.com/r-huijts/rijksmuseum-mcp and https://mcp.run"
         )
         import sys
 

pyproject.toml

@@ -49,14 +49,14 @@ aic = [ "aic-sdk~=1.1.0" ]
 anthropic = [ "anthropic~=0.49.0" ]
 assemblyai = [ "pipecat-ai[websockets-base]" ]
 asyncai = [ "pipecat-ai[websockets-base]" ]
-aws = [ "aioboto3~=15.5.0", "pipecat-ai[websockets-base]" ]
+aws = [ "aioboto3~=15.0.0", "pipecat-ai[websockets-base]" ]
-aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.2.0; python_version>='3.12'" ]
+aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.1; python_version>='3.12'" ]
 azure = [ "azure-cognitiveservices-speech~=1.42.0"]
 cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
 cerebras = []
-daily = [ "daily-python~=0.22.0" ]
-deepgram = [ "deepgram-sdk~=4.7.0", "pipecat-ai[websockets-base]" ]
 deepseek = []
+daily = [ "daily-python~=0.21.0" ]
+deepgram = [ "deepgram-sdk~=4.7.0" ]
 elevenlabs = [ "pipecat-ai[websockets-base]" ]
 fal = [ "fal-client~=0.5.9" ]
 fireworks = []
@@ -69,21 +69,19 @@ gstreamer = [ "pygobject~=3.50.0" ]
 heygen = [ "livekit>=1.0.13", "pipecat-ai[websockets-base]" ]
 hume = [ "hume>=0.11.2" ]
 inworld = []
-koala = [ "pvkoala~=2.0.3" ]
 krisp = [ "pipecat-ai-krisp~=0.4.0" ]
+koala = [ "pvkoala~=2.0.3" ]
 langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ]
-livekit = [ "livekit~=1.0.13", "livekit-api~=1.0.5", "tenacity>=8.2.3,<10.0.0", "pyjwt>=2.10.1" ]
+livekit = [ "livekit~=1.0.13", "livekit-api~=1.0.5", "tenacity>=8.2.3,<10.0.0" ]
 lmnt = [ "pipecat-ai[websockets-base]" ]
 local = [ "pyaudio~=0.2.14" ]
-local-smart-turn = [ "coremltools>=8.0", "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
-local-smart-turn-v3 = [ "transformers", "onnxruntime>=1.20.1,<2" ]
 mcp = [ "mcp[cli]>=1.11.0,<2" ]
 mem0 = [ "mem0ai~=0.1.94" ]
 mistral = []
 mlx-whisper = [ "mlx-whisper~=0.4.2" ]
 moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0" ]
-neuphonic = [ "pipecat-ai[websockets-base]" ]
 nim = []
+neuphonic = [ "pipecat-ai[websockets-base]" ]
 noisereduce = [ "noisereduce~=3.0.3" ]
 openai = [ "pipecat-ai[websockets-base]" ]
 openpipe = [ "openpipe>=4.50.0,<6" ]
@@ -91,16 +89,17 @@ openrouter = []
 perplexity = []
 playht = [ "pipecat-ai[websockets-base]" ]
 qwen = []
-remote-smart-turn = []
 rime = [ "pipecat-ai[websockets-base]" ]
 riva = [ "nvidia-riva-client~=2.21.1" ]
 runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<0.122.0", "pipecat-ai-small-webrtc-prebuilt>=1.0.0"]
-sagemaker = ["aws_sdk_sagemaker_runtime_http2; python_version>='3.12'"]
 sambanova = []
 sarvam = [ "sarvamai==0.1.21", "pipecat-ai[websockets-base]" ]
 sentry = [ "sentry-sdk>=2.28.0,<3" ]
+local-smart-turn = [ "coremltools>=8.0", "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
+local-smart-turn-v3 = [ "transformers", "onnxruntime>=1.20.1,<2" ]
+remote-smart-turn = []
 silero = [ "onnxruntime>=1.20.1,<2" ]
-simli = [ "simli-ai~=1.0.3"]
+simli = [ "simli-ai~=0.1.25"]
 soniox = [ "pipecat-ai[websockets-base]" ]
 soundfile = [ "soundfile~=0.13.1" ]
 speechmatics = [ "speechmatics-rt>=0.5.0" ]

scripts/evals/run-release-evals.py

@@ -30,8 +30,8 @@ EVAL_SIMPLE_MATH = EvalConfig(
 )
 
 EVAL_WEATHER = EvalConfig(
-    prompt="What's the weather in San Francisco (in farhenheit or celsius)?",
+    prompt="What's the weather in San Francisco?",
-    eval="The user says something specific about the current weather in San Francisco, including the degrees (in farhenheit or celsius).",
+    eval="The user says something specific about the current weather in San Francisco, including the degrees.",
 )
 
 EVAL_ONLINE_SEARCH = EvalConfig(
@@ -70,7 +70,7 @@ EVAL_VOICEMAIL = EvalConfig(
 
 EVAL_CONVERSATION = EvalConfig(
     prompt="Hello, this is Mark.",
-    eval="The user acknowledges the greeting.",
+    eval="The user replies with a greeting.",
     eval_speaks_first=True,
 )
 

src/pipecat/extensions/ivr/ivr_navigator.py

@@ -31,11 +31,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.llm_service import LLMService
-from pipecat.utils.text.pattern_pair_aggregator import (
+from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator
-    MatchAction,
-    PatternMatch,
-    PatternPairAggregator,
-)
 
 
 class IVRStatus(Enum):
@@ -118,15 +114,15 @@ class IVRProcessor(FrameProcessor):
     def _setup_xml_patterns(self):
         """Set up XML pattern detection and handlers."""
         # Register DTMF pattern
-        self._aggregator.add_pattern("dtmf", "<dtmf>", "</dtmf>", action=MatchAction.REMOVE)
+        self._aggregator.add_pattern_pair("dtmf", "<dtmf>", "</dtmf>", remove_match=True)
         self._aggregator.on_pattern_match("dtmf", self._handle_dtmf_action)
 
         # Register mode pattern
-        self._aggregator.add_pattern("mode", "<mode>", "</mode>", action=MatchAction.REMOVE)
+        self._aggregator.add_pattern_pair("mode", "<mode>", "</mode>", remove_match=True)
         self._aggregator.on_pattern_match("mode", self._handle_mode_action)
 
         # Register IVR pattern
-        self._aggregator.add_pattern("ivr", "<ivr>", "</ivr>", action=MatchAction.REMOVE)
+        self._aggregator.add_pattern_pair("ivr", "<ivr>", "</ivr>", remove_match=True)
         self._aggregator.on_pattern_match("ivr", self._handle_ivr_action)
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -152,7 +148,7 @@ class IVRProcessor(FrameProcessor):
             result = await self._aggregator.aggregate(frame.text)
             if result:
                 # Push aggregated text that doesn't contain XML patterns
-                await self.push_frame(LLMTextFrame(result.text), direction)
+                await self.push_frame(LLMTextFrame(result), direction)
 
         else:
             await self.push_frame(frame, direction)
@@ -163,7 +159,7 @@ class IVRProcessor(FrameProcessor):
         Args:
             match: The pattern match containing DTMF content.
         """
-        value = match.text
+        value = match.content
         logger.debug(f"DTMF detected: {value}")
 
         try:
@@ -184,7 +180,7 @@ class IVRProcessor(FrameProcessor):
         Args:
             match: The pattern match containing IVR status content.
         """
-        status = match.text
+        status = match.content
         logger.trace(f"IVR status detected: {status}")
 
         # Convert string to enum, with validation
@@ -215,7 +211,7 @@ class IVRProcessor(FrameProcessor):
         Args:
             match: The pattern match containing mode content.
         """
-        mode = match.text
+        mode = match.content
         logger.debug(f"Mode detected: {mode}")
         if mode == "conversation":
             await self._handle_conversation()

src/pipecat/frames/frames.py

@@ -12,7 +12,6 @@ and LLM processing.
 """
 
 from dataclasses import dataclass, field
-from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -327,21 +326,22 @@ class TextFrame(DataFrame):
 
     Parameters:
         text: The text content.
-        skip_tts: Whether this text should skip TTS processing.
     """
 
     text: str
-    skip_tts: bool = field(default=False, kw_only=True)
+    skip_tts: bool = field(init=False)
     # Whether any necessary inter-frame (leading/trailing) spaces are already
     # included in the text.
+    # NOTE: Ideally this would be available at init time with a default value,
+    # but that would impact how subclasses can be initialized (it would require
+    # mandatory fields of theirs to have defaults to preserve
+    # non-default-before-default argument order)
     includes_inter_frame_spaces: bool = field(init=False)
-    # Whether this text frame should be appended to the LLM context.
-    append_to_context: bool = field(init=False)
 
     def __post_init__(self):
         super().__post_init__()
+        self.skip_tts = False
         self.includes_inter_frame_spaces = False
-        self.append_to_context = True
 
     def __str__(self):
         pts = format_pts(self.pts)
@@ -352,38 +352,11 @@ class TextFrame(DataFrame):
 class LLMTextFrame(TextFrame):
     """Text frame generated by LLM services."""
 
-    def __post_init__(self):
+    pass
-        super().__post_init__()
-        # LLM services send text frames with all necessary spaces included
-        self.includes_inter_frame_spaces = True
-
-
-class AggregationType(str, Enum):
-    """Built-in aggregation strings."""
-
-    SENTENCE = "sentence"
-    WORD = "word"
-
-    def __str__(self):
-        return self.value
 
 
 @dataclass
-class AggregatedTextFrame(TextFrame):
+class TTSTextFrame(TextFrame):
-    """Text frame representing an aggregation of TextFrames.
-
-    This frame contains multiple TextFrames aggregated together for processing
-    or output along with a field to indicate how they are aggregated.
-
-    Parameters:
-        aggregated_by: Method used to aggregate the text frames.
-    """
-
-    aggregated_by: AggregationType | str
-
-
-@dataclass
-class TTSTextFrame(AggregatedTextFrame):
     """Text frame generated by Text-to-Speech services."""
 
     pass
@@ -831,13 +804,11 @@ class ErrorFrame(SystemFrame):
         error: Description of the error that occurred.
         fatal: Whether the error is fatal and requires bot shutdown.
         processor: The frame processor that generated the error.
-        exception: The exception that occurred.
     """
 
     error: str
     fatal: bool = False
     processor: Optional["FrameProcessor"] = None
-    exception: Optional[Exception] = None
 
     def __str__(self):
         return f"{self.name}(error: {self.error}, fatal: {self.fatal})"
@@ -1626,23 +1597,24 @@ class LLMFullResponseStartFrame(ControlFrame):
 
     Used to indicate the beginning of an LLM response. Followed by one or
     more TextFrames and a final LLMFullResponseEndFrame.
-
-    Parameters:
-        skip_tts: Whether LLM output should skip TTS processing.
     """
 
-    skip_tts: bool = field(default=False, kw_only=True)
+    skip_tts: bool = field(init=False)
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.skip_tts = False
 
 
 @dataclass
 class LLMFullResponseEndFrame(ControlFrame):
-    """Frame indicating the end of an LLM response.
+    """Frame indicating the end of an LLM response."""
 
-    Parameters:
+    skip_tts: bool = field(init=False)
-        skip_tts: Whether LLM output should skip TTS processing.
-    """
 
-    skip_tts: bool = field(default=False, kw_only=True)
+    def __post_init__(self):
+        super().__post_init__()
+        self.skip_tts = False
 
 
 @dataclass

src/pipecat/processors/aggregators/llm_context.py

@@ -14,7 +14,6 @@ translation from this universal context into whatever format it needs, using a
 service-specific adapter.
 """
 
-import asyncio
 import base64
 import io
 import wave
@@ -138,7 +137,7 @@ class LLMContext:
         return {"role": role, "content": content}
 
     @staticmethod
-    async def create_image_message(
+    def create_image_message(
         *,
         role: str = "user",
         format: str,
@@ -155,21 +154,15 @@ class LLMContext:
             image: Raw image bytes.
             text: Optional text to include with the image.
         """
-
+        buffer = io.BytesIO()
-        def encode_image():
+        Image.frombytes(format, size, image).save(buffer, format="JPEG")
-            buffer = io.BytesIO()
+        encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
-            Image.frombytes(format, size, image).save(buffer, format="JPEG")
-            encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
-            return encoded_image
-
-        encoded_image = await asyncio.to_thread(encode_image)
-
         url = f"data:image/jpeg;base64,{encoded_image}"
 
         return LLMContext.create_image_url_message(role=role, url=url, text=text)
 
     @staticmethod
-    async def create_audio_message(
+    def create_audio_message(
         *, role: str = "user", audio_frames: list[AudioRawFrame], text: str = "Audio follows"
     ) -> LLMContextMessage:
         """Create a context message containing audio.
@@ -179,26 +172,21 @@ class LLMContext:
             audio_frames: List of audio frame objects to include.
             text: Optional text to include with the audio.
         """
+        sample_rate = audio_frames[0].sample_rate
+        num_channels = audio_frames[0].num_channels
 
-        async def encode_audio():
+        content = []
-            sample_rate = audio_frames[0].sample_rate
+        content.append({"type": "text", "text": text})
-            num_channels = audio_frames[0].num_channels
+        data = b"".join(frame.audio for frame in audio_frames)
 
-            content = []
+        with io.BytesIO() as buffer:
-            content.append({"type": "text", "text": text})
+            with wave.open(buffer, "wb") as wf:
-            data = b"".join(frame.audio for frame in audio_frames)
+                wf.setsampwidth(2)
+                wf.setnchannels(num_channels)
+                wf.setframerate(sample_rate)
+                wf.writeframes(data)
 
-            with io.BytesIO() as buffer:
+        encoded_audio = base64.b64encode(buffer.getvalue()).decode("utf-8")
-                with wave.open(buffer, "wb") as wf:
-                    wf.setsampwidth(2)
-                    wf.setnchannels(num_channels)
-                    wf.setframerate(sample_rate)
-                    wf.writeframes(data)
-
-            encoded_audio = base64.b64encode(buffer.getvalue()).decode("utf-8")
-            return encoded_audio
-
-        encoded_audio = await asyncio.to_thread(encode_audio)
 
         content.append(
             {
@@ -333,7 +321,7 @@ class LLMContext:
         """
         self._tool_choice = tool_choice
 
-    async def add_image_frame_message(
+    def add_image_frame_message(
         self, *, format: str, size: tuple[int, int], image: bytes, text: Optional[str] = None
     ):
         """Add a message containing an image frame.
@@ -344,12 +332,10 @@ class LLMContext:
             image: Raw image bytes.
             text: Optional text to include with the image.
         """
-        message = await LLMContext.create_image_message(
+        message = LLMContext.create_image_message(format=format, size=size, image=image, text=text)
-            format=format, size=size, image=image, text=text
-        )
         self.add_message(message)
 
-    async def add_audio_frames_message(
+    def add_audio_frames_message(
         self, *, audio_frames: list[AudioRawFrame], text: str = "Audio follows"
     ):
         """Add a message containing audio frames.
@@ -358,7 +344,7 @@ class LLMContext:
             audio_frames: List of audio frame objects to include.
             text: Optional text to include with the audio.
         """
-        message = await LLMContext.create_audio_message(audio_frames=audio_frames, text=text)
+        message = LLMContext.create_audio_message(audio_frames=audio_frames, text=text)
         self.add_message(message)
 
     @staticmethod

src/pipecat/processors/aggregators/llm_response.py

@@ -1001,7 +1001,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         await self.push_aggregation()
 
     async def _handle_text(self, frame: TextFrame):
-        if not self._started or not frame.append_to_context:
+        if not self._started:
             return
 
         if self._params.expect_stripped_words:

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -66,7 +66,7 @@ from pipecat.processors.aggregators.llm_response import (
     LLMUserAggregatorParams,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.utils.string import TextPartForConcatenation, concatenate_aggregated_text
+from pipecat.utils.string import concatenate_aggregated_text
 from pipecat.utils.time import time_now_iso8601
 
 
@@ -90,7 +90,15 @@ class LLMContextAggregator(FrameProcessor):
         self._context = context
         self._role = role
 
-        self._aggregation: List[TextPartForConcatenation] = []
+        self._aggregation: List[str] = []
+
+        # Whether to add spaces between text parts.
+        # (Currently only used by LLMAssistantAggregator, but could be expanded
+        # to LLMUserAggregator in the future if needed; that would require
+        # additional work since LLMUserAggregator currently trims spaces from
+        # incoming frames before determining whether it "really" received any
+        # text).
+        self._add_spaces = True
 
     @property
     def messages(self) -> List[LLMContextMessage]:
@@ -183,7 +191,7 @@ class LLMContextAggregator(FrameProcessor):
         Returns:
             The concatenated aggregation string.
         """
-        return concatenate_aggregated_text(self._aggregation)
+        return concatenate_aggregated_text(self._aggregation, self._add_spaces)
 
 
 class LLMUserAggregator(LLMContextAggregator):
@@ -433,12 +441,7 @@ class LLMUserAggregator(LLMContextAggregator):
         if not text.strip():
             return
 
-        # Transcriptions never include inter-part spaces (so far).
+        self._aggregation.append(text)
-        self._aggregation.append(
-            TextPartForConcatenation(
-                text, includes_inter_part_spaces=frame.includes_inter_frame_spaces
-            )
-        )
         # We just got a final result, so let's reset interim results.
         self._seen_interim_results = False
         # Reset aggregation timer.
@@ -793,7 +796,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
 
         logger.debug(f"{self} Appending UserImageRawFrame to LLM context (size: {frame.size})")
 
-        await self._context.add_image_frame_message(
+        self._context.add_image_frame_message(
             format=frame.format,
             size=frame.size,
             image=frame.image,
@@ -811,18 +814,18 @@ class LLMAssistantAggregator(LLMContextAggregator):
         await self.push_aggregation()
 
     async def _handle_text(self, frame: TextFrame):
-        if not self._started or not frame.append_to_context:
+        if not self._started:
             return
 
         # Make sure we really have text (spaces count, too!)
         if len(frame.text) == 0:
             return
 
-        self._aggregation.append(
+        # Track whether we need to add spaces between text parts
-            TextPartForConcatenation(
+        # Assumption: we can just keep track of the latest frame's value
-                frame.text, includes_inter_part_spaces=frame.includes_inter_frame_spaces
+        self._add_spaces = not frame.includes_inter_frame_spaces
-            )
+
-        )
+        self._aggregation.append(frame.text)
 
     def _context_updated_task_finished(self, task: asyncio.Task):
         self._context_updated_tasks.discard(task)

src/pipecat/processors/aggregators/llm_text_processor.py

@@ -1,106 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""LLM text processor module for processing and aggregating raw LLM output text.
-
-This processor will convert LLMTextFrames into AggregatedTextFrames based on the
-configured text aggregator. Using the customizable aggregator, it provides
-functionality to handle or manipulate LLM text frames before they are sent to other
-components such as TTS services or context aggregators. It can be used to pre-aggregate
-and categorize, modify, or filter direct output tokens from the LLM.
-"""
-
-from typing import Optional
-
-from pipecat.frames.frames import (
-    AggregatedTextFrame,
-    EndFrame,
-    Frame,
-    InterruptionFrame,
-    LLMFullResponseEndFrame,
-    LLMTextFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
-from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator
-
-
-class LLMTextProcessor(FrameProcessor):
-    """A processor for handling or manipulating LLM text frames before they are processed further.
-
-    This processor will convert LLMTextFrames into AggregatedTextFrames based on the configured
-    text aggregator. Using the customizable aggregator, it provides functionality to handle or
-    manipulate LLM text frames before they are sent to other components such as TTS services or
-    context aggregators. It can be used to pre-aggregate and categorize, modify, or filter direct
-    output tokens from the LLM.
-    """
-
-    def __init__(self, *, text_aggregator: Optional[BaseTextAggregator] = None, **kwargs):
-        """Initialize the LLM text processor.
-
-        Args:
-            text_aggregator: An optional text aggregator to use for processing LLM text frames. By
-                default, a SimpleTextAggregator aggregating by sentence will be used.
-            **kwargs: Additional arguments passed to parent class.
-
-        TODO: Allow transformations per aggregation type or all (and deprecate the TTS filters).
-        """
-        super().__init__(**kwargs)
-        self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process an LLMTextFrames using the aggregator to generate AggregatedTextFrames.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction of frame flow in the pipeline.
-        """
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, InterruptionFrame):
-            await self._handle_interruption(frame)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, LLMTextFrame):
-            await self._handle_llm_text(frame)
-        elif isinstance(frame, LLMFullResponseEndFrame):
-            await self._handle_llm_end(frame.skip_tts)
-            await self.push_frame(frame, direction)
-        elif isinstance(frame, EndFrame):
-            await self._handle_llm_end()
-            await self.push_frame(frame, direction)
-        else:
-            await self.push_frame(frame, direction)
-
-    async def _handle_interruption(self, _):
-        """Handle interruptions by resetting the text aggregator."""
-        await self._text_aggregator.handle_interruption()
-
-    async def reset(self):
-        """Reset the internal state of the text processor and its aggregator."""
-        await self._text_aggregator.reset()
-
-    async def _handle_llm_text(self, in_frame: LLMTextFrame):
-        aggregation = await self._text_aggregator.aggregate(in_frame.text)
-        if aggregation:
-            out_frame = AggregatedTextFrame(
-                text=aggregation.text,
-                aggregated_by=aggregation.type,
-            )
-            out_frame.skip_tts = in_frame.skip_tts
-            await self.push_frame(out_frame)
-
-    async def _handle_llm_end(self, skip_tts: bool = False):
-        # Flush any remaining aggregated text at the end of the LLM response
-        aggregation = self._text_aggregator.text
-        await self._text_aggregator.reset()
-        text = aggregation.text.strip()
-        if text:
-            out_frame = AggregatedTextFrame(
-                text=text,
-                aggregated_by=aggregation.type,
-            )
-            out_frame.skip_tts = skip_tts
-            await self.push_frame(out_frame)

src/pipecat/processors/consumer_processor.py

@@ -83,4 +83,4 @@ class ConsumerProcessor(FrameProcessor):
         while True:
             frame = await self._queue.get()
             new_frame = await self._transformer(frame)
-            await self.queue_frame(new_frame, self._direction)
+            await self.push_frame(new_frame, self._direction)

src/pipecat/processors/filters/wake_check_filter.py

@@ -126,4 +126,6 @@ class WakeCheckFilter(FrameProcessor):
             else:
                 await self.push_frame(frame, direction)
         except Exception as e:
-            await self.push_error(error_msg=f"Error in wake word filter: {e}", exception=e)
+            error_msg = f"Error in wake word filter: {e}"
+            logger.exception(error_msg)
+            await self.push_error(ErrorFrame(error_msg))

src/pipecat/processors/frame_processor.py

@@ -142,7 +142,6 @@ class FrameProcessor(BaseObject):
     - on_after_process_frame: Called after a frame is processed
     - on_before_push_frame: Called before a frame is pushed
     - on_after_push_frame: Called after a frame is pushed
-    - on_error: Called when an error is raised in the frame processing.
     """
 
     def __init__(
@@ -235,7 +234,6 @@ class FrameProcessor(BaseObject):
         self._register_event_handler("on_after_process_frame", sync=True)
         self._register_event_handler("on_before_push_frame", sync=True)
         self._register_event_handler("on_after_push_frame", sync=True)
-        self._register_event_handler("on_error", sync=True)
 
     @property
     def id(self) -> int:
@@ -632,43 +630,7 @@ class FrameProcessor(BaseObject):
         elif isinstance(frame, (FrameProcessorResumeFrame, FrameProcessorResumeUrgentFrame)):
             await self.__resume(frame)
 
-    async def push_error(
+    async def push_error(self, error: ErrorFrame):
-        self,
-        error_msg: str,
-        exception: Optional[Exception] = None,
-        fatal: bool = False,
-    ):
-        """Creates and pushes an ErrorFrame upstream.
-
-        Creates and pushes an ErrorFrame upstream to notify other processors in the
-        pipeline about an error condition. The error frame will include context about
-        which processor generated the error.
-
-        Args:
-            error_msg: Descriptive message explaining the error condition.
-            exception: Optional exception object that caused the error, if available.
-                This provides additional context for debugging and error handling.
-            fatal: Whether this error should be considered fatal to the pipeline.
-                Fatal errors typically cause the entire pipeline to stop processing.
-                Defaults to False for non-fatal errors.
-
-        Example::
-
-            ```python
-            # Non-fatal error
-            await self.push_error("Failed to process audio chunk, skipping")
-
-            # Fatal error with exception context
-            try:
-                result = some_critical_operation()
-            except Exception as e:
-                await self.push_error("Critical operation failed", exception=e, fatal=True)
-            ```
-        """
-        error_frame = ErrorFrame(error=error_msg, fatal=fatal, exception=exception, processor=self)
-        await self.push_error_frame(error=error_frame)
-
-    async def push_error_frame(self, error: ErrorFrame):
         """Push an error frame upstream.
 
         Args:
@@ -676,8 +638,6 @@ class FrameProcessor(BaseObject):
         """
         if not error.processor:
             error.processor = self
-        await self._call_event_handler("on_error", error)
-        logger.error(f"{error.processor} error: {error.error}")
         await self.push_frame(error, FrameDirection.UPSTREAM)
 
     async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
@@ -799,10 +759,8 @@ class FrameProcessor(BaseObject):
                 await self.__cancel_process_task()
                 self.__create_process_task()
         except Exception as e:
-            await self.push_error(
+            logger.exception(f"Uncaught exception in {self} when handling _start_interruption: {e}")
-                error_msg=f"Uncaught exception handling _start_interruption: {e}",
+            await self.push_error(ErrorFrame(str(e)))
-                exception=e,
-            )
 
     async def __internal_push_frame(self, frame: Frame, direction: FrameDirection):
         """Internal method to push frames to adjacent processors.
@@ -839,7 +797,8 @@ class FrameProcessor(BaseObject):
                     await self._observer.on_push_frame(data)
                 await self._prev.queue_frame(frame, direction)
         except Exception as e:
-            await self.push_error(error_msg=f"Uncaught exception: {e}", exception=e)
+            logger.exception(f"Uncaught exception in {self}: {e}")
+            await self.push_error(ErrorFrame(str(e)))
 
     def _check_started(self, frame: Frame):
         """Check if the processor has been started.
@@ -915,7 +874,8 @@ class FrameProcessor(BaseObject):
 
             await self._call_event_handler("on_after_process_frame", frame)
         except Exception as e:
-            await self.push_error(error_msg=f"Error processing frame: {e}", exception=e)
+            logger.exception(f"{self}: error processing frame: {e}")
+            await self.push_error(ErrorFrame(str(e)))
 
     async def __input_frame_task_handler(self):
         """Handle frames from the input queue.

src/pipecat/processors/frameworks/langchain.py

@@ -24,7 +24,7 @@ try:
     from langchain_core.messages import AIMessageChunk
     from langchain_core.runnables import Runnable
 except ModuleNotFoundError as e:
-    logger.error("In order to use Langchain, you need to `pip install pipecat-ai[langchain]`. ")
+    logger.exception("In order to use Langchain, you need to `pip install pipecat-ai[langchain]`. ")
     raise Exception(f"Missing module: {e}")
 
 
@@ -113,6 +113,6 @@ class LangchainProcessor(FrameProcessor):
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} an unknown error occurred: {e}")
         finally:
             await self.push_frame(LLMFullResponseEndFrame())

src/pipecat/processors/frameworks/rtvi.py

@@ -24,7 +24,6 @@ from typing import (
     Literal,
     Mapping,
     Optional,
-    Tuple,
     Union,
 )
 
@@ -33,8 +32,6 @@ from pydantic import BaseModel, Field, PrivateAttr, ValidationError
 
 from pipecat.audio.utils import calculate_audio_volume
 from pipecat.frames.frames import (
-    AggregatedTextFrame,
-    AggregationType,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -707,29 +704,6 @@ class RTVITextMessageData(BaseModel):
     text: str
 
 
-class RTVIBotOutputMessageData(RTVITextMessageData):
-    """Data for bot output RTVI messages.
-
-    Extends RTVITextMessageData to include metadata about the output.
-    """
-
-    spoken: bool = False  # Indicates if the text has been spoken by TTS
-    aggregated_by: AggregationType | str
-    # Indicates what form the text is in (e.g., by word, sentence, etc.)
-
-
-class RTVIBotOutputMessage(BaseModel):
-    """Message containing bot output text.
-
-    An event meant to holistically represent what the bot is outputting,
-    along with metadata about the output and if it has been spoken.
-    """
-
-    label: RTVIMessageLiteral = RTVI_MESSAGE_LABEL
-    type: Literal["bot-output"] = "bot-output"
-    data: RTVIBotOutputMessageData
-
-
 class RTVIBotTranscriptionMessage(BaseModel):
     """Message containing bot transcription text.
 
@@ -922,7 +896,6 @@ class RTVIObserverParams:
         Parameter `errors_enabled` is deprecated. Error messages are always enabled.
 
     Parameters:
-        bot_output_enabled: Indicates if bot output messages should be sent.
         bot_llm_enabled: Indicates if the bot's LLM messages should be sent.
         bot_tts_enabled: Indicates if the bot's TTS messages should be sent.
         bot_speaking_enabled: Indicates if the bot's started/stopped speaking messages should be sent.
@@ -934,17 +907,9 @@ class RTVIObserverParams:
         metrics_enabled: Indicates if metrics messages should be sent.
         system_logs_enabled: Indicates if system logs should be sent.
         errors_enabled: [Deprecated] Indicates if errors messages should be sent.
-        skip_aggregator_types: List of aggregation types to skip sending as tts/output messages.
-          Note: if using this to avoid sending secure information, be sure to also disable
-                bot_llm_enabled to avoid leaking through LLM messages.
-        bot_output_transforms: A list of callables to transform text before just before sending it
-            to TTS. Each callable takes the aggregated text and its type, and returns the
-            transformed text. To register, provide a list of tuples of
-            (aggregation_type | '*', transform_function).
         audio_level_period_secs: How often audio levels should be sent if enabled.
     """
 
-    bot_output_enabled: bool = True
     bot_llm_enabled: bool = True
     bot_tts_enabled: bool = True
     bot_speaking_enabled: bool = True
@@ -956,15 +921,6 @@ class RTVIObserverParams:
     metrics_enabled: bool = True
     system_logs_enabled: bool = False
     errors_enabled: Optional[bool] = None
-    skip_aggregator_types: Optional[List[AggregationType | str]] = None
-    bot_output_transforms: Optional[
-        List[
-            Tuple[
-                AggregationType | str,
-                Callable[[str, AggregationType | str], Awaitable[str]],
-            ]
-        ]
-    ] = None
     audio_level_period_secs: float = 0.15
 
 
@@ -1017,45 +973,8 @@ class RTVIObserver(BaseObserver):
                     DeprecationWarning,
                 )
 
-        self._aggregation_transforms: List[
-            Tuple[AggregationType | str, Callable[[str, AggregationType | str], Awaitable[str]]]
-        ] = self._params.bot_output_transforms or []
-
-    def add_bot_output_transformer(
-        self,
-        transform_function: Callable[[str, AggregationType | str], Awaitable[str]],
-        aggregation_type: AggregationType | str = "*",
-    ):
-        """Transform text for a specific aggregation type before sending as Bot Output or TTS.
-
-        Args:
-            transform_function: The function to apply for transformation. This function should take
-                the text and aggregation type as input and return the transformed text.
-                Ex.: async def my_transform(text: str, aggregation_type: str) -> str:
-            aggregation_type: The type of aggregation to transform. This value defaults to "*" to
-                handle all text before sending to the client.
-        """
-        self._aggregation_transforms.append((aggregation_type, transform_function))
-
-    def remove_bot_output_transformer(
-        self,
-        transform_function: Callable[[str, AggregationType | str], Awaitable[str]],
-        aggregation_type: AggregationType | str = "*",
-    ):
-        """Remove a text transformer for a specific aggregation type.
-
-        Args:
-            transform_function: The function to remove.
-            aggregation_type: The type of aggregation to remove the transformer for.
-        """
-        self._aggregation_transforms = [
-            (agg_type, func)
-            for agg_type, func in self._aggregation_transforms
-            if not (agg_type == aggregation_type and func == transform_function)
-        ]
-
     async def _logger_sink(self, message):
-        """Logger sink so we can send system logs to RTVI clients."""
+        """Logger sink so we cna send system logs to RTVI clients."""
         message = RTVISystemLogMessage(data=RTVITextMessageData(text=message))
         await self.send_rtvi_message(message)
 
@@ -1129,15 +1048,12 @@ class RTVIObserver(BaseObserver):
             await self.send_rtvi_message(RTVIBotTTSStartedMessage())
         elif isinstance(frame, TTSStoppedFrame) and self._params.bot_tts_enabled:
             await self.send_rtvi_message(RTVIBotTTSStoppedMessage())
-        elif isinstance(frame, AggregatedTextFrame) and (
+        elif isinstance(frame, TTSTextFrame) and self._params.bot_tts_enabled:
-            self._params.bot_output_enabled or self._params.bot_tts_enabled
+            if isinstance(src, BaseOutputTransport):
-        ):
+                message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=frame.text))
-            if isinstance(frame, TTSTextFrame) and not isinstance(src, BaseOutputTransport):
+                await self.send_rtvi_message(message)
-                # This check is to make sure we handle the frame when it has gone
-                # through the transport and has correct timing.
-                mark_as_seen = False
             else:
-                await self._handle_aggregated_llm_text(frame)
+                mark_as_seen = False
         elif isinstance(frame, MetricsFrame) and self._params.metrics_enabled:
             await self._handle_metrics(frame)
         elif isinstance(frame, RTVIServerMessageFrame):
@@ -1168,6 +1084,15 @@ class RTVIObserver(BaseObserver):
         if mark_as_seen:
             self._frames_seen.add(frame.id)
 
+    async def _push_bot_transcription(self):
+        """Push accumulated bot transcription as a message."""
+        if len(self._bot_transcription) > 0:
+            message = RTVIBotTranscriptionMessage(
+                data=RTVITextMessageData(text=self._bot_transcription)
+            )
+            await self.send_rtvi_message(message)
+            self._bot_transcription = ""
+
     async def _handle_interruptions(self, frame: Frame):
         """Handle user speaking interruption frames."""
         message = None
@@ -1190,45 +1115,14 @@ class RTVIObserver(BaseObserver):
         if message:
             await self.send_rtvi_message(message)
 
-    async def _handle_aggregated_llm_text(self, frame: AggregatedTextFrame):
-        """Handle aggregated LLM text output frames."""
-        # Skip certain aggregator types if configured to do so.
-        if (
-            self._params.skip_aggregator_types
-            and frame.aggregated_by in self._params.skip_aggregator_types
-        ):
-            return
-
-        text = frame.text
-        type = frame.aggregated_by
-        for aggregation_type, transform in self._aggregation_transforms:
-            if aggregation_type == type or aggregation_type == "*":
-                text = await transform(text, type)
-
-        isTTS = isinstance(frame, TTSTextFrame)
-        if self._params.bot_output_enabled:
-            message = RTVIBotOutputMessage(
-                data=RTVIBotOutputMessageData(text=text, spoken=isTTS, aggregated_by=type)
-            )
-            await self.send_rtvi_message(message)
-
-        if isTTS and self._params.bot_tts_enabled:
-            tts_message = RTVIBotTTSTextMessage(data=RTVITextMessageData(text=text))
-            await self.send_rtvi_message(tts_message)
-
     async def _handle_llm_text_frame(self, frame: LLMTextFrame):
         """Handle LLM text output frames."""
         message = RTVIBotLLMTextMessage(data=RTVITextMessageData(text=frame.text))
         await self.send_rtvi_message(message)
 
-        # TODO (mrkb): Remove all this logic when we fully deprecate bot-transcription messages.
         self._bot_transcription += frame.text
-
+        if match_endofsentence(self._bot_transcription):
-        if match_endofsentence(self._bot_transcription) and len(self._bot_transcription) > 0:
+            await self._push_bot_transcription()
-            await self.send_rtvi_message(
-                RTVIBotTranscriptionMessage(data=RTVITextMessageData(text=self._bot_transcription))
-            )
-            self._bot_transcription = ""
 
     async def _handle_user_transcriptions(self, frame: Frame):
         """Handle user transcription frames."""
@@ -1354,7 +1248,7 @@ class RTVIProcessor(FrameProcessor):
         # Default to 0.3.0 which is the last version before actually having a
         # "client-version".
         self._client_version = [0, 3, 0]
-        self._llm_skip_tts: bool = False  # Keep in sync with llm_service.py's configuration.
+        self._skip_tts: bool = False  # Keep in sync with llm_service.py
 
         self._registered_actions: Dict[str, RTVIAction] = {}
         self._registered_services: Dict[str, RTVIService] = {}
@@ -1547,7 +1441,7 @@ class RTVIProcessor(FrameProcessor):
         elif isinstance(frame, RTVIActionFrame):
             await self._action_queue.put(frame)
         elif isinstance(frame, LLMConfigureOutputFrame):
-            self._llm_skip_tts = frame.skip_tts
+            self._skip_tts = frame.skip_tts
             await self.push_frame(frame, direction)
         # Other frames
         else:
@@ -1803,9 +1697,9 @@ class RTVIProcessor(FrameProcessor):
         opts = data.options if data.options is not None else RTVISendTextOptions()
         if opts.run_immediately:
             await self.interrupt_bot()
-        cur_llm_skip_tts = self._llm_skip_tts
+        cur_skip_tts = self._skip_tts
         should_skip_tts = not opts.audio_response
-        toggle_skip_tts = cur_llm_skip_tts != should_skip_tts
+        toggle_skip_tts = cur_skip_tts != should_skip_tts
         if toggle_skip_tts:
             output_frame = LLMConfigureOutputFrame(skip_tts=should_skip_tts)
             await self.push_frame(output_frame)
@@ -1815,7 +1709,7 @@ class RTVIProcessor(FrameProcessor):
         )
         await self.push_frame(text_frame)
         if toggle_skip_tts:
-            output_frame = LLMConfigureOutputFrame(skip_tts=cur_llm_skip_tts)
+            output_frame = LLMConfigureOutputFrame(skip_tts=cur_skip_tts)
             await self.push_frame(output_frame)
 
     async def _handle_update_context(self, data: RTVIAppendToContextData):

src/pipecat/processors/frameworks/strands_agents.py

@@ -23,7 +23,7 @@ try:
     from strands import Agent
     from strands.multiagent.graph import Graph
 except ModuleNotFoundError as e:
-    logger.error("In order to use Strands Agents, you need to `pip install strands-agents`.")
+    logger.exception("In order to use Strands Agents, you need to `pip install strands-agents`.")
     raise Exception(f"Missing module: {e}")
 
 
@@ -143,7 +143,7 @@ class StrandsAgentsProcessor(FrameProcessor):
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} an unknown error occurred: {e}")
         finally:
             if ttfb_tracking:
                 await self.stop_ttfb_metrics()

src/pipecat/processors/transcript_processor.py

@@ -26,7 +26,7 @@ from pipecat.frames.frames import (
     TTSTextFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.utils.string import TextPartForConcatenation, concatenate_aggregated_text
+from pipecat.utils.string import concatenate_aggregated_text
 from pipecat.utils.time import time_now_iso8601
 
 
@@ -98,9 +98,15 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
             **kwargs: Additional arguments passed to parent class.
         """
         super().__init__(**kwargs)
-        self._current_text_parts: List[TextPartForConcatenation] = []
+        self._current_text_parts: List[str] = []
         self._aggregation_start_time: Optional[str] = None
 
+        # Whether to add spaces between text parts.
+        # (The use of this could be expanded to the UserTranscriptProcessor in
+        # the future if needed; currently the UserTranscriptProcessor assumes
+        # that user transcription frames do not need aggregation).
+        self._add_spaces = True
+
     async def _emit_aggregated_text(self):
         """Aggregates and emits text fragments as a transcript message.
 
@@ -141,7 +147,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
                 Result: "Hello there how are you"
         """
         if self._current_text_parts and self._aggregation_start_time:
-            content = concatenate_aggregated_text(self._current_text_parts)
+            content = concatenate_aggregated_text(self._current_text_parts, self._add_spaces)
             if content:
                 logger.trace(f"Emitting aggregated assistant message: {content}")
                 message = TranscriptionMessage(
@@ -185,11 +191,11 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor):
             if not self._aggregation_start_time:
                 self._aggregation_start_time = time_now_iso8601()
 
-            self._current_text_parts.append(
+            # Track whether we need to add spaces between text parts
-                TextPartForConcatenation(
+            # Assumption: we can just keep track of the latest frame's value
-                    frame.text, includes_inter_part_spaces=frame.includes_inter_frame_spaces
+            self._add_spaces = not frame.includes_inter_frame_spaces
-                )
+
-            )
+            self._current_text_parts.append(frame.text)
 
             # Push frame.
             await self.push_frame(frame, direction)

src/pipecat/runner/run.py

@@ -264,10 +264,7 @@ def _setup_webrtc_routes(
         # Prepare runner arguments with the callback to run your bot
         async def webrtc_connection_callback(connection):
             bot_module = _get_bot_module()
-
+            runner_args = SmallWebRTCRunnerArguments(webrtc_connection=connection)
-            runner_args = SmallWebRTCRunnerArguments(
-                webrtc_connection=connection, body=request.request_data
-            )
             background_tasks.add_task(bot_module.bot, runner_args)
 
         # Delegate handling to SmallWebRTCRequestHandler
@@ -329,8 +326,7 @@ def _setup_webrtc_routes(
                         type=request_data["type"],
                         pc_id=request_data.get("pc_id"),
                         restart_pc=request_data.get("restart_pc"),
-                        request_data=request_data.get("request_data")
+                        request_data=request_data,
-                        or request_data.get("requestData"),
                     )
                     return await offer(webrtc_request, background_tasks)
                 elif request.method == HTTPMethod.PATCH.value:

src/pipecat/runner/utils.py

@@ -281,14 +281,6 @@ async def maybe_capture_participant_camera(
     except ImportError:
         pass
 
-    try:
-        from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
-
-        if isinstance(transport, SmallWebRTCTransport):
-            await transport.capture_participant_video(video_source="camera")
-    except ImportError:
-        pass
-
 
 async def maybe_capture_participant_screen(
     transport: BaseTransport, client: Any, framerate: int = 0
@@ -311,14 +303,6 @@ async def maybe_capture_participant_screen(
     except ImportError:
         pass
 
-    try:
-        from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
-
-        if isinstance(transport, SmallWebRTCTransport):
-            await transport.capture_participant_video(video_source="screenVideo")
-    except ImportError:
-        pass
-
 
 def _smallwebrtc_sdp_cleanup_ice_candidates(text: str, pattern: str) -> str:
     """Clean up ICE candidates in SDP text for SmallWebRTC.

src/pipecat/serializers/plivo.py

@@ -199,7 +199,7 @@ class PlivoFrameSerializer(FrameSerializer):
                         )
 
         except Exception as e:
-            logger.error(f"Failed to hang up Plivo call: {e}")
+            logger.exception(f"Failed to hang up Plivo call: {e}")
 
     async def deserialize(self, data: str | bytes) -> Frame | None:
         """Deserializes Plivo WebSocket data to Pipecat frames.

src/pipecat/serializers/telnyx.py

@@ -225,7 +225,7 @@ class TelnyxFrameSerializer(FrameSerializer):
                         )
 
         except Exception as e:
-            logger.error(f"Failed to hang up Telnyx call: {e}")
+            logger.exception(f"Failed to hang up Telnyx call: {e}")
 
     async def deserialize(self, data: str | bytes) -> Frame | None:
         """Deserializes Telnyx WebSocket data to Pipecat frames.

src/pipecat/serializers/twilio.py

@@ -236,7 +236,7 @@ class TwilioFrameSerializer(FrameSerializer):
                         )
 
         except Exception as e:
-            logger.error(f"Failed to hang up Twilio call: {e}")
+            logger.exception(f"Failed to hang up Twilio call: {e}")
 
     async def deserialize(self, data: str | bytes) -> Frame | None:
         """Deserializes Twilio WebSocket data to Pipecat frames.

src/pipecat/services/ai_service.py

@@ -152,6 +152,9 @@ class AIService(FrameProcessor):
         elif isinstance(frame, CancelFrame):
             await self.cancel(frame)
         elif isinstance(frame, EndFrame):
+            # Push EndFrame before stop(), because stop() may wait on tasks to
+            # finish and downstream processors need to receive the EndFrame.
+            await self.push_frame(frame, direction)
             await self.stop(frame)
 
     async def process_generator(self, generator: AsyncGenerator[Frame | None, None]):
@@ -166,6 +169,6 @@ class AIService(FrameProcessor):
         async for f in generator:
             if f:
                 if isinstance(f, ErrorFrame):
-                    await self.push_error_frame(f)
+                    await self.push_error(f)
                 else:
                     await self.push_frame(f)

src/pipecat/services/anthropic/llm.py

@@ -327,7 +327,7 @@ class AnthropicLLMService(LLMService):
         cache_read_input_tokens = 0
 
         try:
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseStartFrame())
             await self.start_processing_metrics()
 
             params_from_context = self._get_llm_invocation_params(context)
@@ -373,9 +373,9 @@ class AnthropicLLMService(LLMService):
 
                 if event.type == "content_block_delta":
                     if hasattr(event.delta, "text"):
-                        await self.push_frame(
+                        frame = LLMTextFrame(event.delta.text)
-                            LLMTextFrame(event.delta.text, skip_tts=self._get_skip_tts())
+                        frame.includes_inter_frame_spaces = True
-                        )
+                        await self.push_frame(frame)
                         completion_tokens_estimate += self._estimate_tokens(event.delta.text)
                     elif hasattr(event.delta, "partial_json") and tool_use_block:
                         json_accumulator += event.delta.partial_json
@@ -460,10 +460,11 @@ class AnthropicLLMService(LLMService):
         except httpx.TimeoutException:
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(f"{e}"))
         finally:
             await self.stop_processing_metrics()
-            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseEndFrame())
             comp_tokens = (
                 completion_tokens
                 if not use_completion_tokens_estimate

src/pipecat/services/assemblyai/stt.py

@@ -206,8 +206,9 @@ class AssemblyAISTTService(STTService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             self._connected = False
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             raise
 
     async def _disconnect(self):
@@ -232,7 +233,8 @@ class AssemblyAISTTService(STTService):
                     logger.warning("Timed out waiting for termination message from server")
 
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
             if self._receive_task:
                 await self.cancel_task(self._receive_task)
@@ -240,7 +242,8 @@ class AssemblyAISTTService(STTService):
             await self._websocket.close()
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
         finally:
             self._websocket = None
@@ -259,11 +262,13 @@ class AssemblyAISTTService(STTService):
                 except websockets.exceptions.ConnectionClosedOK:
                     break
                 except Exception as e:
-                    await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                    logger.error(f"{self} exception: {e}")
+                    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                     break
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     def _parse_message(self, message: Dict[str, Any]) -> BaseMessage:
         """Parse a raw message into the appropriate message type."""
@@ -292,7 +297,8 @@ class AssemblyAISTTService(STTService):
             elif isinstance(parsed_message, TerminationMessage):
                 await self._handle_termination(parsed_message)
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _handle_termination(self, message: TerminationMessage):
         """Handle termination message."""

src/pipecat/services/asyncai/tts.py

@@ -146,6 +146,15 @@ class AsyncAITTSService(InterruptibleTTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that AsyncAI TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that AsyncAI's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Async language format.
 
@@ -228,7 +237,8 @@ class AsyncAITTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -240,7 +250,8 @@ class AsyncAITTSService(InterruptibleTTSService):
                 logger.debug("Disconnecting from Async")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._websocket = None
             self._started = False
@@ -285,11 +296,12 @@ class AsyncAITTSService(InterruptibleTTSService):
                 )
                 await self.push_frame(frame)
             elif msg.get("error_code"):
+                logger.error(f"{self} error: {msg}")
                 await self.push_frame(TTSStoppedFrame())
                 await self.stop_all_metrics()
-                await self.push_error(error_msg=f"Error: {msg['message']}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {msg['message']}"))
             else:
-                await self.push_error(error_msg=f"Unknown message type: {msg}")
+                logger.error(f"{self} error, unknown message type: {msg}")
 
     async def _keepalive_task_handler(self):
         """Send periodic keepalive messages to maintain WebSocket connection."""
@@ -332,14 +344,16 @@ class AsyncAITTSService(InterruptibleTTSService):
                 await self._get_websocket().send(msg)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class AsyncAIHttpTTSService(TTSService):
@@ -419,6 +433,15 @@ class AsyncAIHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that AsyncAI TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that AsyncAI's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Async language format.
 
@@ -472,7 +495,8 @@ class AsyncAIHttpTTSService(TTSService):
             async with self._session.post(url, json=payload, headers=headers) as response:
                 if response.status != 200:
                     error_text = await response.text()
-                    await self.push_error(error_msg=f"Async API error: {error_text}")
+                    logger.error(f"Async API error: {error_text}")
+                    await self.push_error(ErrorFrame(error=f"Async API error: {error_text}"))
                     raise Exception(f"Async API returned status {response.status}: {error_text}")
 
                 audio_data = await response.read()
@@ -488,7 +512,8 @@ class AsyncAIHttpTTSService(TTSService):
             yield frame
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/aws/__init__.py

@@ -8,10 +8,8 @@ import sys
 
 from pipecat.services import DeprecatedModuleProxy
 
-from .agent_core import *
 from .llm import *
 from .nova_sonic import *
-from .sagemaker import *
 from .stt import *
 from .tts import *
 

src/pipecat/services/aws/agent_core.py

@@ -1,258 +0,0 @@
-#
-# Copyright (c) 2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""AWS AgentCore Processor Module.
-
-This module defines the AWSAgentCoreProcessor, which invokes agents hosted on
-Amazon Bedrock AgentCore Runtime and streams their responses as LLMTextFrames.
-"""
-
-import asyncio
-import json
-import os
-from typing import Callable, Optional
-
-import aioboto3
-from loguru import logger
-
-from pipecat.frames.frames import (
-    Frame,
-    LLMContextFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMTextFrame,
-)
-from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-
-
-def default_context_to_payload_transformer(
-    context: LLMContext | OpenAILLMContext,
-) -> Optional[str]:
-    """Default transformer to create AgentCore payload from LLM context.
-
-    Extracts the latest user or system message text and wraps it in {"prompt": "<text>"}.
-
-    Args:
-        context: The LLM context containing conversation messages.
-
-    Returns:
-        A JSON string payload for AgentCore, or None if no valid message found.
-    """
-    messages = context.messages
-
-    if not messages:
-        return None
-
-    last_message = messages[-1]
-    if isinstance(last_message, LLMSpecificMessage) or last_message.get("role") not in (
-        "user",
-        "system",
-    ):
-        return None
-
-    content = last_message.get("content")
-    if not content:
-        return None
-
-    if isinstance(content, str):
-        prompt = content
-    elif isinstance(content, list):
-        prompt = " ".join([part.get("text", "") for part in content])
-    else:
-        return None
-
-    return json.dumps({"prompt": prompt})
-
-
-def default_response_to_output_transformer(response_line: str) -> Optional[str]:
-    """Default transformer to extract output text from AgentCore response.
-
-    Expects responses with {"response": "<text>"} format.
-
-    Args:
-        response_line: The raw response line from AgentCore (without "data: " prefix).
-
-    Returns:
-        The extracted output text, or None if no text found.
-    """
-    response_json = json.loads(response_line)
-    return response_json.get("response")
-
-
-class AWSAgentCoreProcessor(FrameProcessor):
-    """Processor that runs an Amazon Bedrock AgentCore agent.
-
-    Input:
-        - LLMContextFrame: Supplies a context used to invoke the agent.
-
-    Output:
-        - LLMTextFrame: The agent's text response(s).
-          A single agent invocation may result in multiple text frames.
-
-    This processor transforms the input context to a payload for the AgentCore
-    agent, and transforms the agent's response(s) into output text frame(s). Both
-    mappings are configurable via transformers. Below is the default behavior.
-
-    Input transformer (context_to_payload_transformer):
-        - Grabs the latest user or system message (if it's the latest message)
-        - Extracts its text content
-        - Constructs a payload that looks like {"prompt": "<text>"}
-
-    Output transformer (response_to_output_transformer):
-        - Expects responses that look like {"response": "<text>"}
-        - Extracts the text for use in the LLMTextFrame(s)
-    """
-
-    def __init__(
-        self,
-        agentArn: str,
-        aws_access_key: Optional[str] = None,
-        aws_secret_key: Optional[str] = None,
-        aws_session_token: Optional[str] = None,
-        aws_region: Optional[str] = None,
-        context_to_payload_transformer: Optional[
-            Callable[[LLMContext | OpenAILLMContext], Optional[str]]
-        ] = None,
-        response_to_output_transformer: Optional[Callable[[str], Optional[str]]] = None,
-        **kwargs,
-    ):
-        """Initialize the AWS AgentCore processor.
-
-        Args:
-            agentArn: The Amazon Web Services Resource Name (ARN) of the agent.
-            aws_access_key: AWS access key ID. If None, uses default credentials.
-            aws_secret_key: AWS secret access key. If None, uses default credentials.
-            aws_session_token: AWS session token for temporary credentials.
-            aws_region: AWS region.
-            context_to_payload_transformer: Optional callable to transform
-                LLMContext into AgentCore payload string. If None, uses
-                default_context_to_payload_transformer.
-            response_to_output_transformer: Optional callable to extract output text
-                from AgentCore response. If None, uses
-                default_response_to_output_transformer.
-            **kwargs: Additional arguments passed to parent FrameProcessor.
-        """
-        super().__init__(**kwargs)
-
-        self._agentArn = agentArn
-        self._aws_session = aioboto3.Session()
-
-        # Store AWS session parameters for creating client in async context
-        self._aws_params = {
-            "aws_access_key_id": aws_access_key or os.getenv("AWS_ACCESS_KEY_ID"),
-            "aws_secret_access_key": aws_secret_key or os.getenv("AWS_SECRET_ACCESS_KEY"),
-            "aws_session_token": aws_session_token or os.getenv("AWS_SESSION_TOKEN"),
-            "region_name": aws_region or os.getenv("AWS_REGION", "us-east-1"),
-        }
-
-        # Set transformers with defaults
-        self._context_to_payload_transformer = (
-            context_to_payload_transformer or default_context_to_payload_transformer
-        )
-        self._response_to_output_transformer = (
-            response_to_output_transformer or default_response_to_output_transformer
-        )
-
-        # State for managing output response bookends
-        self._output_response_open = False
-        self._last_text_frame_time: Optional[float] = None
-        self._close_task: Optional[asyncio.Task] = None
-        self._output_response_timeout = 1.0  # seconds
-
-    async def _close_output_response_after_timeout(self):
-        """Close the output response after timeout if no new text frames arrive."""
-        await asyncio.sleep(self._output_response_timeout)
-        if self._output_response_open:
-            self._output_response_open = False
-            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
-
-    async def _push_text_frame(self, text: str):
-        """Push a text frame, managing output response bookends."""
-        # Cancel any pending close task
-        if self._close_task and not self._close_task.done():
-            await self.cancel_task(self._close_task)
-
-        # Open output response if needed
-        if not self._output_response_open:
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
-            self._output_response_open = True
-
-        # Push the text frame
-        await self.push_frame(LLMTextFrame(text, skip_tts=self._get_skip_tts()))
-        self._last_text_frame_time = asyncio.get_event_loop().time()
-
-        # Schedule closing the output response after timeout
-        self._close_task = self.create_task(self._close_output_response_after_timeout())
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process incoming frames and handle LLM message frames.
-
-        Args:
-            frame: The incoming frame to process.
-            direction: The direction of frame flow in the pipeline.
-        """
-        await super().process_frame(frame, direction)
-        if isinstance(frame, (LLMContextFrame, OpenAILLMContextFrame)):
-            # Create payload to invoke AgentCore agent
-            payload = self._context_to_payload_transformer(frame.context)
-
-            if not payload:
-                return
-
-            async with self._aws_session.client("bedrock-agentcore", **self._aws_params) as client:
-                # Invoke the AgentCore agent
-                response = await client.invoke_agent_runtime(
-                    agentRuntimeArn=self._agentArn, payload=payload.encode()
-                )
-
-                # Determine if this is a streamed multi-part response, which
-                # will affect our parsing
-                is_multi_part_response = "text/event-stream" in response.get("contentType", "")
-
-                # Handle each response part (there may be one, for single
-                # responses, or multiple, for streamed multi-part responses)
-                async for part in response.get("response", []):
-                    part_string = part.decode("utf-8")
-
-                    # In streamed multi-part responses, each part might have
-                    # one or more lines, each of which starts with "data: ".
-                    # Treat each line as a response.
-                    if is_multi_part_response:
-                        for line in part_string.split("\n"):
-                            # Get response text from this line
-                            if not line:
-                                continue
-                            if not line.startswith("data: "):
-                                logger.warning(f"Expected line to start with 'data: ', got: {line}")
-                                continue
-                            line = line[6:]  # omit "data: "
-
-                            # Transform response line to output text
-                            text = self._response_to_output_transformer(line)
-                            if text:
-                                await self._push_text_frame(text)
-
-                    # In single-part responses, the whole part is one response
-                    # and there's no "data: " prefix
-                    else:
-                        # Transform response part string to output text
-                        text = self._response_to_output_transformer(part_string)
-                        if text:
-                            await self._push_text_frame(text)
-
-                # Final close if output response is still open after all parts processed
-                if self._output_response_open:
-                    if self._close_task and not self._close_task.done():
-                        await self.cancel_task(self._close_task)
-                    self._output_response_open = False
-                    await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
-        else:
-            await self.push_frame(frame, direction)

src/pipecat/services/aws/llm.py

@@ -734,7 +734,7 @@ class AWSBedrockLLMService(LLMService):
         aws_access_key: Optional[str] = None,
         aws_secret_key: Optional[str] = None,
         aws_session_token: Optional[str] = None,
-        aws_region: Optional[str] = None,
+        aws_region: str = "us-east-1",
         params: Optional[InputParams] = None,
         client_config: Optional[Config] = None,
         retry_timeout_secs: Optional[float] = 5.0,
@@ -981,7 +981,7 @@ class AWSBedrockLLMService(LLMService):
         using_noop_tool = False
 
         try:
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseStartFrame())
             await self.start_processing_metrics()
 
             await self.start_ttfb_metrics()
@@ -1078,9 +1078,9 @@ class AWSBedrockLLMService(LLMService):
                     if "contentBlockDelta" in event:
                         delta = event["contentBlockDelta"]["delta"]
                         if "text" in delta:
-                            await self.push_frame(
+                            frame = LLMTextFrame(delta["text"])
-                                LLMTextFrame(delta["text"], skip_tts=self._get_skip_tts())
+                            frame.includes_inter_frame_spaces = True
-                            )
+                            await self.push_frame(frame)
                             completion_tokens_estimate += self._estimate_tokens(delta["text"])
                         elif "toolUse" in delta and "input" in delta["toolUse"]:
                             # Handle partial JSON for tool use
@@ -1138,10 +1138,10 @@ class AWSBedrockLLMService(LLMService):
         except (ReadTimeoutError, asyncio.TimeoutError):
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} exception: {e}")
         finally:
             await self.stop_processing_metrics()
-            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseEndFrame())
             comp_tokens = (
                 completion_tokens
                 if not use_completion_tokens_estimate

src/pipecat/services/aws/nova_sonic/llm.py

@@ -27,7 +27,6 @@ from pydantic import BaseModel, Field
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.adapters.services.aws_nova_sonic_adapter import AWSNovaSonicLLMAdapter, Role
 from pipecat.frames.frames import (
-    AggregationType,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -453,7 +452,7 @@ class AWSNovaSonicLLMService(LLMService):
             self._ready_to_send_context = True
             await self._finish_connecting_if_context_available()
         except Exception as e:
-            await self.push_error(error_msg=f"Initialization error: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             await self._disconnect()
 
     async def _process_completed_function_calls(self, send_new_results: bool):
@@ -577,7 +576,7 @@ class AWSNovaSonicLLMService(LLMService):
 
             logger.info("Finished disconnecting")
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     def _create_client(self) -> BedrockRuntimeClient:
         config = Config(
@@ -885,7 +884,7 @@ class AWSNovaSonicLLMService(LLMService):
                 # Errors are kind of expected while disconnecting, so just
                 # ignore them and do nothing
                 return
-            await self.push_error(error_msg=f"Error processing responses: {e}", exception=e)
+            logger.error(f"{self} error processing responses: {e}")
             if self._wants_connection:
                 await self.reset_conversation()
 
@@ -1016,7 +1015,7 @@ class AWSNovaSonicLLMService(LLMService):
         logger.debug("Assistant response started")
 
         # Report the start of the assistant response.
-        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseStartFrame())
 
         # Report that equivalent of TTS (this is a speech-to-speech model) started
         await self.push_frame(TTSStartedFrame())
@@ -1028,7 +1027,7 @@ class AWSNovaSonicLLMService(LLMService):
         logger.debug(f"Assistant response text added: {text}")
 
         # Report the text of the assistant response.
-        frame = TTSTextFrame(text, aggregated_by=AggregationType.SENTENCE)
+        frame = TTSTextFrame(text)
         frame.includes_inter_frame_spaces = True
         await self.push_frame(frame)
 
@@ -1062,16 +1061,14 @@ class AWSNovaSonicLLMService(LLMService):
                 # We also need to re-push the LLMFullResponseStartFrame since the
                 # TTSTextFrame would be ignored otherwise (the interruption frame
                 # would have cleared the assistant aggregator state).
-                await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+                await self.push_frame(LLMFullResponseStartFrame())
-                frame = TTSTextFrame(
+                frame = TTSTextFrame(self._assistant_text_buffer)
-                    self._assistant_text_buffer, aggregated_by=AggregationType.SENTENCE
-                )
                 frame.includes_inter_frame_spaces = True
                 await self.push_frame(frame)
             self._may_need_repush_assistant_text = False
 
         # Report the end of the assistant response.
-        await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseEndFrame())
 
         # Report that equivalent of TTS (this is a speech-to-speech model) stopped.
         await self.push_frame(TTSStoppedFrame())

src/pipecat/services/aws/sagemaker/bidi_client.py

@@ -1,283 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""AWS SageMaker bidirectional streaming client.
-
-This module provides a client for streaming bidirectional communication with
-SageMaker endpoints using the HTTP/2 protocol. Supports sending audio, text,
-and JSON data to SageMaker model endpoints and receiving streaming responses.
-"""
-
-import os
-from typing import Optional
-
-from loguru import logger
-
-try:
-    from aws_sdk_sagemaker_runtime_http2.client import SageMakerRuntimeHTTP2Client
-    from aws_sdk_sagemaker_runtime_http2.config import Config, HTTPAuthSchemeResolver
-    from aws_sdk_sagemaker_runtime_http2.models import (
-        InvokeEndpointWithBidirectionalStreamInput,
-        RequestPayloadPart,
-        RequestStreamEventPayloadPart,
-        ResponseStreamEvent,
-    )
-    from smithy_aws_core.auth.sigv4 import SigV4AuthScheme
-    from smithy_aws_core.identity import EnvironmentCredentialsResolver
-    from smithy_core.aio.eventstream import DuplexEventStream
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use SageMaker BiDi client, you need to `pip install pipecat-ai[sagemaker]`."
-    )
-    raise Exception(f"Missing module: {e}")
-
-
-class SageMakerBidiClient:
-    """Client for bidirectional streaming with AWS SageMaker endpoints.
-
-    Handles low-level HTTP/2 bidirectional streaming protocol for communicating
-    with SageMaker model endpoints. Provides methods for sending various data
-    types (audio, text, JSON) and receiving streaming responses.
-
-    This client uses AWS SigV4 authentication and supports credential resolution
-    from environment variables, AWS CLI configuration, and instance metadata.
-
-    Example::
-
-        client = SageMakerBidiClient(
-            endpoint_name="my-deepgram-endpoint",
-            region="us-east-2",
-            model_invocation_path="v1/listen",
-            model_query_string="model=nova-3&language=en"
-        )
-        await client.start_session()
-        await client.send_audio_chunk(audio_bytes)
-        response = await client.receive_response()
-        await client.close_session()
-    """
-
-    def __init__(
-        self,
-        endpoint_name: str,
-        region: str,
-        model_invocation_path: str = "",
-        model_query_string: str = "",
-    ):
-        """Initialize the SageMaker BiDi client.
-
-        Args:
-            endpoint_name: Name of the SageMaker endpoint to connect to.
-            region: AWS region where the endpoint is deployed.
-            model_invocation_path: API path for the model invocation (e.g., "v1/listen").
-            model_query_string: Query string parameters for the model (e.g., "model=nova-3").
-        """
-        self.endpoint_name = endpoint_name
-        self.region = region
-        self.model_invocation_path = model_invocation_path
-        self.model_query_string = model_query_string
-        self.bidi_endpoint = f"https://runtime.sagemaker.{region}.amazonaws.com:8443"
-        self._client: Optional[SageMakerRuntimeHTTP2Client] = None
-        self._stream: Optional[
-            DuplexEventStream[RequestStreamEventPayloadPart, ResponseStreamEvent, any]
-        ] = None
-        self._output_stream = None
-        self._is_active = False
-
-    def _initialize_client(self):
-        """Initialize the SageMaker Runtime HTTP2 client with AWS credentials.
-
-        Creates and configures the SageMaker Runtime HTTP2 client with SigV4
-        authentication. Attempts to resolve AWS credentials from environment
-        variables, AWS CLI configuration, or instance metadata.
-        """
-        logger.debug(f"Initializing SageMaker BiDi client for region: {self.region}")
-        logger.debug(f"Using endpoint URI: {self.bidi_endpoint}")
-
-        # Check for AWS credentials
-        has_env_creds = bool(os.getenv("AWS_ACCESS_KEY_ID") and os.getenv("AWS_SECRET_ACCESS_KEY"))
-
-        if not has_env_creds:
-            logger.warning(
-                "AWS credentials not found in environment variables. "
-                "Attempting to use EnvironmentCredentialsResolver which will check "
-                "AWS CLI configuration and instance metadata."
-            )
-
-        config = Config(
-            endpoint_uri=self.bidi_endpoint,
-            region=self.region,
-            aws_credentials_identity_resolver=EnvironmentCredentialsResolver(),
-            auth_scheme_resolver=HTTPAuthSchemeResolver(),
-            auth_schemes={"aws.auth#sigv4": SigV4AuthScheme(service="sagemaker")},
-        )
-        self._client = SageMakerRuntimeHTTP2Client(config=config)
-
-    async def start_session(self):
-        """Start a bidirectional streaming session with the SageMaker endpoint.
-
-        Initializes the client if needed, creates the bidirectional stream, and
-        establishes the connection to the SageMaker endpoint. Must be called
-        before sending or receiving data.
-
-        Returns:
-            The output stream for receiving responses.
-
-        Raises:
-            RuntimeError: If client initialization or connection fails.
-        """
-        if not self._client:
-            self._initialize_client()
-
-        logger.debug(f"Starting BiDi session with endpoint: {self.endpoint_name}")
-        logger.debug(f"Model invocation path: {self.model_invocation_path}")
-        logger.debug(f"Model query string: {self.model_query_string}")
-
-        # Create the bidirectional stream
-        stream_input = InvokeEndpointWithBidirectionalStreamInput(
-            endpoint_name=self.endpoint_name,
-            model_invocation_path=self.model_invocation_path,
-            model_query_string=self.model_query_string,
-        )
-
-        try:
-            self._stream = await self._client.invoke_endpoint_with_bidirectional_stream(
-                stream_input
-            )
-            self._is_active = True
-
-            # Get output stream
-            output = await self._stream.await_output()
-            self._output_stream = output[1]
-
-            logger.debug("BiDi session started successfully")
-            return self._output_stream
-
-        except Exception as e:
-            logger.error(f"Failed to start BiDi session: {e}")
-            self._is_active = False
-            raise RuntimeError(f"Failed to start SageMaker BiDi session: {e}")
-
-    async def send_data(self, data_bytes: bytes, data_type: Optional[str] = None):
-        """Send a chunk of data to the stream.
-
-        Generic method for sending any type of data to the SageMaker endpoint.
-        Use the convenience methods (send_audio_chunk, send_text, send_json)
-        for common data types.
-
-        Args:
-            data_bytes: Raw bytes to send.
-            data_type: Optional data type header. Common values are "BINARY" for
-                audio/binary data and "UTF8" for text/JSON data.
-
-        Raises:
-            RuntimeError: If session is not active or send fails.
-        """
-        if not self._is_active or not self._stream:
-            raise RuntimeError("BiDi session not active")
-
-        try:
-            payload = RequestPayloadPart(bytes_=data_bytes, data_type=data_type)
-            event = RequestStreamEventPayloadPart(value=payload)
-            await self._stream.input_stream.send(event)
-        except Exception as e:
-            logger.error(f"Failed to send data: {e}")
-            raise
-
-    async def send_audio_chunk(self, audio_bytes: bytes):
-        """Send a chunk of audio data to the stream.
-
-        Convenience method for sending audio data. Automatically sets the data
-        type to "BINARY".
-
-        Args:
-            audio_bytes: Raw audio bytes to send (e.g., PCM audio data).
-
-        Raises:
-            RuntimeError: If session is not active or send fails.
-        """
-        await self.send_data(audio_bytes, data_type="BINARY")
-
-    async def send_text(self, text: str):
-        """Send text data to the stream.
-
-        Convenience method for sending text data. Automatically encodes the text
-        as UTF-8 and sets the data type to "UTF8".
-
-        Args:
-            text: Text string to send.
-
-        Raises:
-            RuntimeError: If session is not active or send fails.
-        """
-        await self.send_data(text.encode("utf-8"), data_type="UTF8")
-
-    async def send_json(self, data: dict):
-        """Send JSON data to the stream.
-
-        Convenience method for sending JSON-encoded messages. Useful for control
-        messages like KeepAlive or CloseStream. Automatically serializes the
-        dictionary to JSON, encodes as UTF-8, and sets the data type to "UTF8".
-
-        Args:
-            data: Dictionary to send as JSON (e.g., {"type": "KeepAlive"}).
-
-        Raises:
-            RuntimeError: If session is not active or send fails.
-        """
-        import json
-
-        await self.send_data(json.dumps(data).encode("utf-8"), data_type="UTF8")
-
-    async def receive_response(self) -> Optional[ResponseStreamEvent]:
-        """Receive a response from the stream.
-
-        Blocks until a response is available from the SageMaker endpoint. Returns
-        None when the stream is closed.
-
-        Returns:
-            The response event containing payload data, or None if stream is closed.
-
-        Raises:
-            RuntimeError: If session is not active.
-        """
-        if not self._is_active or not self._output_stream:
-            raise RuntimeError("BiDi session not active")
-
-        try:
-            result = await self._output_stream.receive()
-            return result
-        except Exception as e:
-            logger.error(f"Failed to receive response: {e}")
-            raise
-
-    async def close_session(self):
-        """Close the bidirectional streaming session.
-
-        Gracefully closes the input stream and marks the session as inactive.
-        Safe to call multiple times.
-        """
-        if not self._is_active:
-            return
-
-        logger.debug("Closing BiDi session...")
-        self._is_active = False
-
-        try:
-            if self._stream:
-                await self._stream.input_stream.close()
-            logger.debug("BiDi session closed successfully")
-        except Exception as e:
-            logger.warning(f"Error closing BiDi session: {e}")
-
-    @property
-    def is_active(self) -> bool:
-        """Check if the session is currently active.
-
-        Returns:
-            True if session is active, False otherwise.
-        """
-        return self._is_active

src/pipecat/services/aws/stt.py

@@ -140,7 +140,8 @@ class AWSTranscribeSTTService(STTService):
                     return
                 logger.warning("WebSocket connection not established after connect")
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 retry_count += 1
                 if retry_count < max_retries:
                     await asyncio.sleep(1)  # Wait before retrying
@@ -181,7 +182,8 @@ class AWSTranscribeSTTService(STTService):
                 try:
                     await self._connect()
                 except Exception as e:
-                    yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                    logger.error(f"{self} exception: {e}")
+                    yield ErrorFrame(error=f"{self} error: {e}")
                     return
 
             # Format the audio data according to AWS event stream format
@@ -198,11 +200,13 @@ class AWSTranscribeSTTService(STTService):
                 await self._disconnect()
                 # Don't yield error here - we'll retry on next frame
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 await self._disconnect()
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
             await self._disconnect()
 
     async def _connect(self):
@@ -285,7 +289,8 @@ class AWSTranscribeSTTService(STTService):
 
                 await self._call_event_handler("on_connected")
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 await self._disconnect()
                 raise
 
@@ -305,7 +310,8 @@ class AWSTranscribeSTTService(STTService):
                 await self._ws_client.send(json.dumps(end_stream))
             await self._ws_client.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._ws_client = None
             await self._call_event_handler("on_disconnected")
@@ -523,15 +529,15 @@ class AWSTranscribeSTTService(STTService):
                                     )
                 elif headers.get(":message-type") == "exception":
                     error_msg = payload.get("Message", "Unknown error")
-                    await self.push_error(error_msg=f"AWS Transcribe error: {error_msg}")
+                    logger.error(f"{self} Exception from AWS: {error_msg}")
+                    await self.push_frame(ErrorFrame(f"AWS Transcribe error: {error_msg}"))
                 else:
                     logger.debug(f"{self} Other message type received: {headers}")
                     logger.debug(f"{self} Payload: {payload}")
             except websockets.exceptions.ConnectionClosed as e:
-                await self.push_error(
+                logger.error(f"{self} WebSocket connection closed in receive loop: {e}")
-                    error_msg=f"WebSocket connection closed in receive loop", exception=e
-                )
                 break
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 break

src/pipecat/services/aws/tts.py

@@ -209,6 +209,15 @@ class AWSPollyTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that AWS TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that AWS's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to AWS Polly language format.
 
@@ -312,6 +321,7 @@ class AWSPollyTTSService(TTSService):
 
                 yield TTSStoppedFrame()
         except (BotoCoreError, ClientError) as error:
+            logger.exception(f"{self} error generating TTS: {error}")
             error_message = f"AWS Polly TTS error: {str(error)}"
             yield ErrorFrame(error=error_message)
 

src/pipecat/services/azure/image.py

@@ -91,6 +91,7 @@ class AzureImageGenServiceREST(ImageGenService):
             while status != "succeeded":
                 attempts_left -= 1
                 if attempts_left == 0:
+                    logger.error(f"{self} error: image generation timed out")
                     yield ErrorFrame("Image generation timed out")
                     return
 
@@ -103,6 +104,7 @@ class AzureImageGenServiceREST(ImageGenService):
 
             image_url = json_response["result"]["data"][0]["url"] if json_response else None
             if not image_url:
+                logger.error(f"{self} error: image generation failed")
                 yield ErrorFrame("Image generation failed")
                 return
 

src/pipecat/services/azure/realtime/llm.py

@@ -61,5 +61,5 @@ class AzureRealtimeLLMService(OpenAIRealtimeLLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"initialization error: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None

src/pipecat/services/azure/stt.py

@@ -121,7 +121,8 @@ class AzureSTTService(STTService):
                 self._audio_stream.write(audio)
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
     async def start(self, frame: StartFrame):
         """Start the speech recognition service.
@@ -150,9 +151,8 @@ class AzureSTTService(STTService):
             self._speech_recognizer.recognized.connect(self._on_handle_recognized)
             self._speech_recognizer.start_continuous_recognition_async()
         except Exception as e:
-            await self.push_error(
+            logger.error(f"{self} exception during initialization: {e}")
-                error_msg=f"Uncaught exception during initialization: {e}", exception=e
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-            )
 
     async def stop(self, frame: EndFrame):
         """Stop the speech recognition service.

src/pipecat/services/azure/tts.py

@@ -151,6 +151,15 @@ class AzureBaseTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Azure TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Azure's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Azure language format.
 
@@ -327,6 +336,7 @@ class AzureTTSService(AzureBaseTTSService):
         try:
             if self._speech_synthesizer is None:
                 error_msg = "Speech synthesizer not initialized."
+                logger.error(error_msg)
                 yield ErrorFrame(error=error_msg)
                 return
 
@@ -354,13 +364,15 @@ class AzureTTSService(AzureBaseTTSService):
                 yield TTSStoppedFrame()
 
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 # Could add reconnection logic here if needed
                 return
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class AzureHttpTTSService(AzureBaseTTSService):
@@ -437,6 +449,5 @@ class AzureHttpTTSService(AzureBaseTTSService):
             cancellation_details = result.cancellation_details
             logger.warning(f"Speech synthesis canceled: {cancellation_details.reason}")
             if cancellation_details.reason == CancellationReason.Error:
-                yield ErrorFrame(
+                logger.error(f"{self} error: {cancellation_details.error_details}")
-                    error=f"Unknown error occurred: {cancellation_details.error_details}"
+                yield ErrorFrame(error=f"{self} error: {cancellation_details.error_details}")
-                )

src/pipecat/services/cartesia/stt.py

@@ -276,7 +276,8 @@ class CartesiaSTTService(WebsocketSTTService):
             self._websocket = await websocket_connect(ws_url, additional_headers=headers)
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _disconnect_websocket(self):
         try:
@@ -284,7 +285,8 @@ class CartesiaSTTService(WebsocketSTTService):
                 logger.debug("Disconnecting from Cartesia STT")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._websocket = None
             await self._call_event_handler("on_disconnected")
@@ -317,7 +319,8 @@ class CartesiaSTTService(WebsocketSTTService):
 
             elif data["type"] == "error":
                 error_msg = data.get("message", "Unknown error")
-                await self.push_error(error_msg=error_msg)
+                logger.error(f"Cartesia error: {error_msg}")
+                await self.push_error(ErrorFrame(error=error_msg))
 
     @traced_stt
     async def _handle_transcription(

src/pipecat/services/cartesia/tts.py

@@ -10,8 +10,7 @@ import base64
 import json
 import uuid
 import warnings
-from enum import Enum
+from typing import AsyncGenerator, List, Literal, Optional, Union
-from typing import AsyncGenerator, List, Literal, Optional
 
 from loguru import logger
 from pydantic import BaseModel, Field
@@ -126,72 +125,6 @@ def language_to_cartesia_language(language: Language) -> Optional[str]:
     return resolve_language(language, LANGUAGE_MAP, use_base_code=True)
 
 
-class CartesiaEmotion(str, Enum):
-    """Predefined Emotions supported by Cartesia."""
-
-    # Primary emotions supported by Cartesia
-    NEUTRAL = "neutral"
-    ANGRY = "angry"
-    EXCITED = "excited"
-    CONTENT = "content"
-    SAD = "sad"
-    SCARED = "scared"
-    # Additional emotions supported by Cartesia
-    HAPPY = "happy"
-    ENTHUSIASTIC = "enthusiastic"
-    ELATED = "elated"
-    EUPHORIC = "euphoric"
-    TRIUMPHANT = "triumphant"
-    AMAZED = "amazed"
-    SURPRISED = "surprised"
-    FLIRTATIOUS = "flirtatious"
-    JOKING_COMEDIC = "joking/comedic"
-    CURIOUS = "curious"
-    PEACEFUL = "peaceful"
-    SERENE = "serene"
-    CALM = "calm"
-    GRATEFUL = "grateful"
-    AFFECTIONATE = "affectionate"
-    TRUST = "trust"
-    SYMPATHETIC = "sympathetic"
-    ANTICIPATION = "anticipation"
-    MYSTERIOUS = "mysterious"
-    MAD = "mad"
-    OUTRAGED = "outraged"
-    FRUSTRATED = "frustrated"
-    AGITATED = "agitated"
-    THREATENED = "threatened"
-    DISGUSTED = "disgusted"
-    CONTEMPT = "contempt"
-    ENVIOUS = "envious"
-    SARCASTIC = "sarcastic"
-    IRONIC = "ironic"
-    DEJECTED = "dejected"
-    MELANCHOLIC = "melancholic"
-    DISAPPOINTED = "disappointed"
-    HURT = "hurt"
-    GUILTY = "guilty"
-    BORED = "bored"
-    TIRED = "tired"
-    REJECTED = "rejected"
-    NOSTALGIC = "nostalgic"
-    WISTFUL = "wistful"
-    APOLOGETIC = "apologetic"
-    HESITANT = "hesitant"
-    INSECURE = "insecure"
-    CONFUSED = "confused"
-    RESIGNED = "resigned"
-    ANXIOUS = "anxious"
-    PANICKED = "panicked"
-    ALARMED = "alarmed"
-    PROUD = "proud"
-    CONFIDENT = "confident"
-    DISTANT = "distant"
-    SKEPTICAL = "skeptical"
-    CONTEMPLATIVE = "contemplative"
-    DETERMINED = "determined"
-
-
 class CartesiaTTSService(AudioContextWordTTSService):
     """Cartesia TTS service with WebSocket streaming and word timestamps.
 
@@ -249,10 +182,6 @@ class CartesiaTTSService(AudioContextWordTTSService):
             container: Audio container format.
             params: Additional input parameters for voice customization.
             text_aggregator: Custom text aggregator for processing input text.
-
-                .. deprecated:: 0.0.95
-                    Use an LLMTextProcessor before the TTSService for custom text aggregation.
-
             aggregate_sentences: Whether to aggregate sentences within the TTSService.
             **kwargs: Additional arguments passed to the parent service.
         """
@@ -271,18 +200,10 @@ class CartesiaTTSService(AudioContextWordTTSService):
             push_text_frames=False,
             pause_frame_processing=True,
             sample_rate=sample_rate,
-            text_aggregator=text_aggregator,
+            text_aggregator=text_aggregator or SkipTagsAggregator([("<spell>", "</spell>")]),
             **kwargs,
         )
 
-        if not text_aggregator:
-            # Always skip tags added for spelled-out text
-            # Note: This is primarily to support backwards compatibility.
-            #    The preferred way of taking advantage of Cartesia SSML Tags is
-            #    to use an LLMTextProcessor and/or a text_transformer to identify
-            #    and insert these tags for the purpose of the TTS service alone.
-            self._text_aggregator = SkipTagsAggregator([("<spell>", "</spell>")])
-
         params = params or CartesiaTTSService.InputParams()
 
         self._api_key = api_key
@@ -336,27 +257,6 @@ class CartesiaTTSService(AudioContextWordTTSService):
         """
         return language_to_cartesia_language(language)
 
-    # A set of Cartesia-specific helpers for text transformations
-    def SPELL(text: str) -> str:
-        """Wrap text in Cartesia spell tag."""
-        return f"<spell>{text}</spell>"
-
-    def EMOTION_TAG(emotion: CartesiaEmotion) -> str:
-        """Convenience method to create an emotion tag."""
-        return f'<emotion value="{emotion}" />'
-
-    def PAUSE_TAG(seconds: float) -> str:
-        """Convenience method to create a pause tag."""
-        return f'<break time="{seconds}s" />'
-
-    def VOLUME_TAG(volume: float) -> str:
-        """Convenience method to create a volume tag."""
-        return f'<volume ratio="{volume}" />'
-
-    def SPEED_TAG(speed: float) -> str:
-        """Convenience method to create a speed tag."""
-        return f'<speed ratio="{speed}" />'
-
     def _is_cjk_language(self, language: str) -> bool:
         """Check if the given language is CJK (Chinese, Japanese, Korean).
 
@@ -497,7 +397,8 @@ class CartesiaTTSService(AudioContextWordTTSService):
             )
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -509,7 +410,8 @@ class CartesiaTTSService(AudioContextWordTTSService):
                 logger.debug("Disconnecting from Cartesia")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._context_id = None
             self._websocket = None
@@ -562,12 +464,13 @@ class CartesiaTTSService(AudioContextWordTTSService):
                 )
                 await self.append_to_audio_context(msg["context_id"], frame)
             elif msg["type"] == "error":
+                logger.error(f"{self} error: {msg}")
                 await self.push_frame(TTSStoppedFrame())
                 await self.stop_all_metrics()
-                await self.push_error(error_msg=f"Error: {msg}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {msg['error']}"))
                 self._context_id = None
             else:
-                await self.push_error(error_msg=f"Error, unknown message type: {msg}")
+                logger.error(f"{self} error, unknown message type: {msg}")
 
     async def _receive_messages(self):
         while True:
@@ -605,14 +508,16 @@ class CartesiaTTSService(AudioContextWordTTSService):
                 await self._get_websocket().send(msg)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class CartesiaHttpTTSService(TTSService):
@@ -803,7 +708,8 @@ class CartesiaHttpTTSService(TTSService):
             async with session.post(url, json=payload, headers=headers) as response:
                 if response.status != 200:
                     error_text = await response.text()
-                    yield ErrorFrame(error=f"Cartesia API error: {error_text}")
+                    logger.error(f"Cartesia API error: {error_text}")
+                    await self.push_error(ErrorFrame(error=f"Cartesia API error: {error_text}"))
                     raise Exception(f"Cartesia API returned status {response.status}: {error_text}")
 
                 audio_data = await response.read()
@@ -819,7 +725,8 @@ class CartesiaHttpTTSService(TTSService):
             yield frame
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/deepgram/flux/stt.py

@@ -6,9 +6,7 @@
 
 """Deepgram Flux speech-to-text service implementation."""
 
-import asyncio
 import json
-import time
 from enum import Enum
 from typing import Any, AsyncGenerator, Dict, Optional
 from urllib.parse import urlencode
@@ -96,7 +94,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
             mip_opt_out: Optional. Opts out requests from the Deepgram Model Improvement Program
                 (default False).
             tag: List of tags to label requests for identification during usage reporting.
-            min_confidence: Optional. Minimum confidence required confidence to create a TranscriptionFrame
         """
 
         eager_eot_threshold: Optional[float] = None
@@ -105,7 +102,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         keyterm: list = []
         mip_opt_out: Optional[bool] = None
         tag: list = []
-        min_confidence: Optional[float] = None  # New parameter
 
     def __init__(
         self,
@@ -150,17 +146,7 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                     params=params
                 )
         """
-        # Note: For DeepgramFluxSTTService, differently from other processes, we need to create
+        super().__init__(sample_rate=sample_rate, **kwargs)
-        # the _receive_task inside _connect_websocket, because the websocket should only be
-        # considered connected and ready to send audio once we receive from Flux the message
-        # which confirms the connection has been established.
-        # If we try to keep the logic reconnect_on_error, when receiving a message, the
-        # _receive_task_handler would try to reconnect in case of error, invoking the
-        # _connect_websocket again and leading to a case where the first _receive_task_handler
-        # was never destroyed.
-        # So we can keep it here as false, because inside the method send_with_retry, it will
-        # already try to reconnect if needed.
-        super().__init__(sample_rate=sample_rate, reconnect_on_error=False, **kwargs)
 
         self._api_key = api_key
         self._url = url
@@ -177,13 +163,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         self._register_event_handler("on_end_of_turn")
         self._register_event_handler("on_eager_end_of_turn")
         self._register_event_handler("on_update")
-        self._connection_established_event = asyncio.Event()
-        # Watchdog task to prevent dangling tasks
-        # If we stop sending audio to Flux after we have received that the User has started speaking
-        # we never receive the user stopped speaking event unless we resume sending audio to it.
-        self._last_stt_time = None
-        self._watchdog_task = None
-        self._user_is_speaking = False
 
     async def _connect(self):
         """Connect to WebSocket and start background tasks.
@@ -193,6 +172,9 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         """
         await self._connect_websocket()
 
+        if self._websocket and not self._receive_task:
+            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
+
     async def _disconnect(self):
         """Disconnect from WebSocket and clean up tasks.
 
@@ -200,32 +182,21 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         and cleans up resources to prevent memory leaks.
         """
         try:
+            # Cancel background tasks BEFORE closing websocket
+            if self._receive_task:
+                await self.cancel_task(self._receive_task, timeout=2.0)
+                self._receive_task = None
+
+            # Now close the websocket
             await self._disconnect_websocket()
+
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # Reset state only after everything is cleaned up
             self._websocket = None
 
-    async def _send_silence(self, duration_secs: float = 0.5):
-        """Send a block of silence of the specified duration (default 500 ms)."""
-        sample_width = 2  # bytes per sample for 16-bit PCM
-        num_channels = 1  # mono
-        num_samples = int(self.sample_rate * duration_secs)
-        silence = b"\x00" * (num_samples * sample_width * num_channels)
-        await self._websocket.send(silence)
-
-    async def _watchdog_task_handler(self):
-        while self._websocket and self._websocket.state is State.OPEN:
-            now = time.monotonic()
-            # More than 500 ms without sending new audio to Flux
-            if self._user_is_speaking and self._last_stt_time and now - self._last_stt_time > 0.5:
-                logger.warning("Sending silence to Flux to prevent dangling task")
-                await self._send_silence()
-                self._last_stt_time = time.monotonic()
-            # check every 100ms
-            await asyncio.sleep(0.1)
-
     async def _connect_websocket(self):
         """Establish WebSocket connection to API.
 
@@ -237,30 +208,15 @@ class DeepgramFluxSTTService(WebsocketSTTService):
             if self._websocket and self._websocket.state is State.OPEN:
                 return
 
-            self._connection_established_event.clear()
-            self._user_is_speaking = False
             self._websocket = await websocket_connect(
                 self._websocket_url,
                 additional_headers={"Authorization": f"Token {self._api_key}"},
             )
-
-            # Creating the receiver task
-            if not self._receive_task:
-                self._receive_task = self.create_task(
-                    self._receive_task_handler(self._report_error)
-                )
-
-            # Creating the watchdog task
-            if not self._watchdog_task:
-                self._watchdog_task = self.create_task(self._watchdog_task_handler())
-
-            # Now wait for the connection established event
-            logger.debug("WebSocket connected, waiting for server confirmation...")
-            await self._connection_established_event.wait()
             logger.debug("Connected to Deepgram Flux Websocket")
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -271,16 +227,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         metrics collection. Handles disconnection errors gracefully.
         """
         try:
-            # Cancel background tasks BEFORE closing websocket
-            if self._receive_task:
-                await self.cancel_task(self._receive_task, timeout=2.0)
-                self._receive_task = None
-            if self._watchdog_task:
-                await self.cancel_task(self._watchdog_task, timeout=2.0)
-                self._watchdog_task = None
-                self._last_stt_time = None
-
-            self._connection_established_event.clear()
             await self.stop_all_metrics()
 
             if self._websocket:
@@ -288,7 +234,8 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 logger.debug("Disconnecting from Deepgram Flux Websocket")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._websocket = None
             await self._call_event_handler("on_disconnected")
@@ -298,13 +245,10 @@ class DeepgramFluxSTTService(WebsocketSTTService):
 
         This signals to the server that no more audio data will be sent.
         """
-        try:
+        if self._websocket:
-            if self._websocket:
+            logger.debug("Sending CloseStream message to Deepgram Flux")
-                logger.debug("Sending CloseStream message to Deepgram Flux")
+            message = {"type": "CloseStream"}
-                message = {"type": "CloseStream"}
+            await self._websocket.send(json.dumps(message))
-                await self._websocket.send(json.dumps(message))
-        except Exception as e:
-            await self.push_error(error_msg=f"Error sending closeStream: {e}", exception=e)
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -391,13 +335,15 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 are issues sending the audio data.
         """
         if not self._websocket:
+            logger.error("Not connected to Deepgram Flux.")
+            yield ErrorFrame("Not connected to Deepgram Flux.")
             return
 
         try:
-            self._last_stt_time = time.monotonic()
+            await self._websocket.send(audio)
-            await self.send_with_retry(audio, self._report_error)
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
             return
 
         yield None
@@ -474,7 +420,8 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                     # Skip malformed messages
                     continue
                 except Exception as e:
-                    await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                    logger.error(f"{self} exception: {e}")
+                    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                     # Error will be handled inside WebsocketService->_receive_task_handler
                     raise
             else:
@@ -516,8 +463,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         transcription processing.
         """
         logger.info("Connected to Flux - ready to stream audio")
-        # Notify connection is established
-        self._connection_established_event.set()
 
     async def _handle_fatal_error(self, data: Dict[str, Any]):
         """Handle fatal error messages from Deepgram Flux.
@@ -585,7 +530,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
             transcript: maybe the first few words of the turn.
         """
         logger.debug("User started speaking")
-        self._user_is_speaking = True
         await self.push_interruption_task_frame_and_wait()
         await self.broadcast_frame(UserStartedSpeakingFrame)
         await self.start_metrics()
@@ -606,22 +550,6 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         logger.trace(f"Received event TurnResumed: {event}")
         await self._call_event_handler("on_turn_resumed")
 
-    def _calculate_average_confidence(self, transcript_data) -> Optional[float]:
-        """Calculate the average confidence from transcript data.
-
-        Return None if the data is missing or invalid.
-        """
-        # Example: Assume transcript_data has a list of words with confidence
-        words = transcript_data.get("words")
-        if not words or not isinstance(words, list):
-            return None
-        confidences = [
-            w.get("confidence") for w in words if isinstance(w.get("confidence"), (float, int))
-        ]
-        if not confidences:
-            return None
-        return sum(confidences) / len(confidences)
-
     async def _handle_end_of_turn(self, transcript: str, data: Dict[str, Any]):
         """Handle EndOfTurn events from Deepgram Flux.
 
@@ -641,26 +569,16 @@ class DeepgramFluxSTTService(WebsocketSTTService):
             data: The TurnInfo message data containing event type, transcript and some extra metadata.
         """
         logger.debug("User stopped speaking")
-        self._user_is_speaking = False
 
-        # Compute the average confidence
+        await self.push_frame(
-        average_confidence = self._calculate_average_confidence(data)
+            TranscriptionFrame(
-
+                transcript,
-        if not self._params.min_confidence or average_confidence > self._params.min_confidence:
+                self._user_id,
-            await self.push_frame(
+                time_now_iso8601(),
-                TranscriptionFrame(
+                self._language,
-                    transcript,
+                result=data,
-                    self._user_id,
-                    time_now_iso8601(),
-                    self._language,
-                    result=data,
-                )
             )
-        else:
+        )
-            logger.warning(
-                f"Transcription confidence below min_confidence threshold: {average_confidence}"
-            )
-
         await self._handle_transcription(transcript, True, self._language)
         await self.stop_processing_metrics()
         await self.push_frame(UserStoppedSpeakingFrame(), FrameDirection.DOWNSTREAM)

src/pipecat/services/deepgram/stt.py

@@ -233,7 +233,7 @@ class DeepgramSTTService(STTService):
             )
 
         if not await self._connection.start(options=self._settings, addons=self._addons):
-            await self.push_error(error_msg=f"Unable to connect to Deepgram")
+            logger.error(f"{self}: unable to connect to Deepgram")
 
     async def _disconnect(self):
         if await self._connection.is_connected():
@@ -256,7 +256,7 @@ class DeepgramSTTService(STTService):
     async def _on_error(self, *args, **kwargs):
         error: ErrorResponse = kwargs["error"]
         logger.warning(f"{self} connection error, will retry: {error}")
-        await self.push_error(error_msg=f"{error}")
+        await self.push_error(ErrorFrame(error=f"{error}"))
         await self.stop_all_metrics()
         # NOTE(aleix): we don't disconnect (i.e. call finish on the connection)
         # because this triggers more errors internally in the Deepgram SDK. So,

src/pipecat/services/deepgram/stt_sagemaker.py

@@ -1,444 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Deepgram speech-to-text service for AWS SageMaker.
-
-This module provides a Pipecat STT service that connects to Deepgram models
-deployed on AWS SageMaker endpoints. Uses HTTP/2 bidirectional streaming for
-low-latency real-time transcription with support for interim results, multiple
-languages, and various Deepgram features.
-"""
-
-import asyncio
-import json
-from typing import AsyncGenerator, Optional
-
-from loguru import logger
-
-from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
-    ErrorFrame,
-    Frame,
-    InterimTranscriptionFrame,
-    StartFrame,
-    TranscriptionFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.aws.sagemaker.bidi_client import SageMakerBidiClient
-from pipecat.services.stt_service import STTService
-from pipecat.transcriptions.language import Language
-from pipecat.utils.time import time_now_iso8601
-from pipecat.utils.tracing.service_decorators import traced_stt
-
-try:
-    from deepgram import LiveOptions
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use DeepgramSageMakerSTTService, you need to `pip install pipecat-ai[deepgram,sagemaker]`."
-    )
-    raise Exception(f"Missing module: {e}")
-
-
-class DeepgramSageMakerSTTService(STTService):
-    """Deepgram speech-to-text service for AWS SageMaker.
-
-    Provides real-time speech recognition using Deepgram models deployed on
-    AWS SageMaker endpoints. Uses HTTP/2 bidirectional streaming for low-latency
-    transcription with support for interim results, speaker diarization, and
-    multiple languages.
-
-    Requirements:
-
-    - AWS credentials configured (via environment variables, AWS CLI, or instance metadata)
-    - A deployed SageMaker endpoint with Deepgram model: https://developers.deepgram.com/docs/deploy-amazon-sagemaker
-    - Deepgram SDK for LiveOptions configuration
-
-    Example::
-
-        stt = DeepgramSageMakerSTTService(
-            endpoint_name="my-deepgram-endpoint",
-            region="us-east-2",
-            live_options=LiveOptions(
-                model="nova-3",
-                language="en",
-                interim_results=True,
-                punctuate=True,
-            ),
-        )
-    """
-
-    def __init__(
-        self,
-        *,
-        endpoint_name: str,
-        region: str,
-        sample_rate: Optional[int] = None,
-        live_options: Optional[LiveOptions] = None,
-        **kwargs,
-    ):
-        """Initialize the Deepgram SageMaker STT service.
-
-        Args:
-            endpoint_name: Name of the SageMaker endpoint with Deepgram model
-                deployed (e.g., "my-deepgram-nova-3-endpoint").
-            region: AWS region where the endpoint is deployed (e.g., "us-east-2").
-            sample_rate: Audio sample rate in Hz. If None, uses value from
-                live_options or defaults to the value from StartFrame.
-            live_options: Deepgram LiveOptions for detailed configuration. If None,
-                uses sensible defaults (nova-3 model, English, interim results enabled).
-            **kwargs: Additional arguments passed to the parent STTService.
-        """
-        sample_rate = sample_rate or (live_options.sample_rate if live_options else None)
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        self._endpoint_name = endpoint_name
-        self._region = region
-
-        # Create default options similar to DeepgramSTTService
-        default_options = LiveOptions(
-            encoding="linear16",
-            language=Language.EN,
-            model="nova-3",
-            channels=1,
-            interim_results=True,
-            punctuate=True,
-        )
-
-        # Merge with provided options
-        merged_options = default_options.to_dict()
-        if live_options:
-            default_model = default_options.model
-            merged_options.update(live_options.to_dict())
-            # Handle the "None" string bug from deepgram-sdk
-            if "model" in merged_options and merged_options["model"] == "None":
-                merged_options["model"] = default_model
-
-        # Convert Language enum to string if needed
-        if "language" in merged_options and isinstance(merged_options["language"], Language):
-            merged_options["language"] = merged_options["language"].value
-
-        self.set_model_name(merged_options["model"])
-        self._settings = merged_options
-
-        self._client: Optional[SageMakerBidiClient] = None
-        self._response_task: Optional[asyncio.Task] = None
-        self._keepalive_task: Optional[asyncio.Task] = None
-
-    def can_generate_metrics(self) -> bool:
-        """Check if this service can generate processing metrics.
-
-        Returns:
-            True, as Deepgram SageMaker service supports metrics generation.
-        """
-        return True
-
-    async def set_model(self, model: str):
-        """Set the Deepgram model and reconnect.
-
-        Disconnects from the current session, updates the model setting, and
-        establishes a new connection with the updated model.
-
-        Args:
-            model: The Deepgram model name to use (e.g., "nova-3").
-        """
-        await super().set_model(model)
-        logger.info(f"Switching STT model to: [{model}]")
-        self._settings["model"] = model
-        await self._disconnect()
-        await self._connect()
-
-    async def set_language(self, language: Language):
-        """Set the recognition language and reconnect.
-
-        Disconnects from the current session, updates the language setting, and
-        establishes a new connection with the updated language.
-
-        Args:
-            language: The language to use for speech recognition (e.g., Language.EN,
-                Language.ES).
-        """
-        logger.info(f"Switching STT language to: [{language}]")
-        self._settings["language"] = language
-        await self._disconnect()
-        await self._connect()
-
-    async def start(self, frame: StartFrame):
-        """Start the Deepgram SageMaker STT service.
-
-        Args:
-            frame: The start frame containing initialization parameters.
-        """
-        await super().start(frame)
-        self._settings["sample_rate"] = self.sample_rate
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the Deepgram SageMaker STT service.
-
-        Args:
-            frame: The end frame.
-        """
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel the Deepgram SageMaker STT service.
-
-        Args:
-            frame: The cancel frame.
-        """
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        """Send audio data to Deepgram for transcription.
-
-        Args:
-            audio: Raw audio bytes to transcribe.
-
-        Yields:
-            Frame: None (transcription results come via BiDi stream callbacks).
-        """
-        if self._client and self._client.is_active:
-            try:
-                await self._client.send_audio_chunk(audio)
-            except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
-        yield None
-
-    async def _connect(self):
-        """Connect to the SageMaker endpoint and start the BiDi session.
-
-        Builds the Deepgram query string from settings, creates the BiDi client,
-        starts the streaming session, and launches background tasks for processing
-        responses and sending KeepAlive messages.
-        """
-        logger.debug("Connecting to Deepgram on SageMaker...")
-
-        # Update sample rate in settings
-        self._settings["sample_rate"] = self.sample_rate
-
-        # Build query string from settings, converting booleans to strings
-        query_params = {}
-        for key, value in self._settings.items():
-            if value is not None:
-                # Convert boolean values to lowercase strings for Deepgram API
-                if isinstance(value, bool):
-                    query_params[key] = str(value).lower()
-                else:
-                    query_params[key] = str(value)
-
-        query_string = "&".join(f"{k}={v}" for k, v in query_params.items())
-
-        # Create BiDi client
-        self._client = SageMakerBidiClient(
-            endpoint_name=self._endpoint_name,
-            region=self._region,
-            model_invocation_path="v1/listen",
-            model_query_string=query_string,
-        )
-
-        try:
-            # Start the session
-            await self._client.start_session()
-
-            # Start processing responses in the background
-            self._response_task = self.create_task(self._process_responses())
-
-            # Start keepalive task to maintain connection
-            self._keepalive_task = self.create_task(self._send_keepalive())
-
-            logger.debug("Connected to Deepgram on SageMaker")
-            await self._call_event_handler("on_connected")
-
-        except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
-            await self._call_event_handler("on_connection_error", str(e))
-
-    async def _disconnect(self):
-        """Disconnect from the SageMaker endpoint.
-
-        Sends a CloseStream message to Deepgram, cancels background tasks
-        (KeepAlive and response processing), and closes the BiDi session.
-        Safe to call multiple times.
-        """
-        if self._client and self._client.is_active:
-            logger.debug("Disconnecting from Deepgram on SageMaker...")
-
-            # Send CloseStream message to Deepgram
-            try:
-                await self._client.send_json({"type": "CloseStream"})
-            except Exception as e:
-                logger.warning(f"Failed to send CloseStream message: {e}")
-
-            # Cancel keepalive task
-            if self._keepalive_task and not self._keepalive_task.done():
-                await self.cancel_task(self._keepalive_task)
-
-            # Cancel response processing task
-            if self._response_task and not self._response_task.done():
-                await self.cancel_task(self._response_task)
-
-            # Close the BiDi session
-            await self._client.close_session()
-
-            logger.debug("Disconnected from Deepgram on SageMaker")
-            await self._call_event_handler("on_disconnected")
-
-    async def _send_keepalive(self):
-        """Send periodic KeepAlive messages to maintain the connection.
-
-        Sends a KeepAlive JSON message to Deepgram every 5 seconds while the
-        connection is active. This prevents the connection from timing out during
-        periods of silence.
-        """
-        while self._client and self._client.is_active:
-            await asyncio.sleep(5)
-            if self._client and self._client.is_active:
-                try:
-                    await self._client.send_json({"type": "KeepAlive"})
-                except Exception as e:
-                    logger.warning(f"Failed to send KeepAlive: {e}")
-
-    async def _process_responses(self):
-        """Process streaming responses from Deepgram on SageMaker.
-
-        Continuously receives responses from the BiDi stream, decodes the payload,
-        parses JSON responses from Deepgram, and processes transcription results.
-        Runs as a background task until the connection is closed or cancelled.
-        """
-        try:
-            while self._client and self._client.is_active:
-                result = await self._client.receive_response()
-
-                if result is None:
-                    break
-
-                # Check if this is a PayloadPart with bytes
-                if hasattr(result, "value") and hasattr(result.value, "bytes_"):
-                    if result.value.bytes_:
-                        response_data = result.value.bytes_.decode("utf-8")
-
-                        try:
-                            # Parse JSON response from Deepgram
-                            parsed = json.loads(response_data)
-
-                            # Extract and process transcript if available
-                            if "channel" in parsed:
-                                await self._handle_transcript_response(parsed)
-
-                        except json.JSONDecodeError:
-                            logger.warning(f"Non-JSON response: {response_data}")
-
-        except asyncio.CancelledError:
-            logger.debug("Response processor cancelled")
-        except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
-        finally:
-            logger.debug("Response processor stopped")
-
-    async def _handle_transcript_response(self, parsed: dict):
-        """Handle a transcript response from Deepgram.
-
-        Extracts the transcript text, determines if it's final or interim, extracts
-        language information, and pushes the appropriate frame (TranscriptionFrame
-        or InterimTranscriptionFrame) downstream.
-
-        Args:
-            parsed: The parsed JSON response from Deepgram containing channel,
-                alternatives, transcript, and metadata.
-        """
-        alternatives = parsed.get("channel", {}).get("alternatives", [])
-        if not alternatives or not alternatives[0].get("transcript"):
-            return
-
-        transcript = alternatives[0]["transcript"]
-        if not transcript.strip():
-            return
-
-        # Stop TTFB metrics on first transcript
-        await self.stop_ttfb_metrics()
-
-        is_final = parsed.get("is_final", False)
-        speech_final = parsed.get("speech_final", False)
-
-        # Extract language if available
-        language = None
-        if alternatives[0].get("languages"):
-            language = alternatives[0]["languages"][0]
-            language = Language(language)
-
-        if is_final and speech_final:
-            # Final transcription
-            await self.push_frame(
-                TranscriptionFrame(
-                    transcript,
-                    self._user_id,
-                    time_now_iso8601(),
-                    language,
-                    result=parsed,
-                )
-            )
-            await self._handle_transcription(transcript, is_final, language)
-            await self.stop_processing_metrics()
-        else:
-            # Interim transcription
-            await self.push_frame(
-                InterimTranscriptionFrame(
-                    transcript,
-                    self._user_id,
-                    time_now_iso8601(),
-                    language,
-                    result=parsed,
-                )
-            )
-
-    @traced_stt
-    async def _handle_transcription(
-        self, transcript: str, is_final: bool, language: Optional[Language] = None
-    ):
-        """Handle a transcription result with tracing.
-
-        This method is decorated with @traced_stt for observability and tracing
-        integration. The actual transcription processing is handled by the parent
-        class and observers.
-
-        Args:
-            transcript: The transcribed text.
-            is_final: Whether this is a final transcription result.
-            language: The detected language of the transcription, if available.
-        """
-        pass
-
-    async def start_metrics(self):
-        """Start TTFB and processing metrics collection."""
-        await self.start_ttfb_metrics()
-        await self.start_processing_metrics()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process frames with Deepgram SageMaker-specific handling.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction of frame processing.
-        """
-        await super().process_frame(frame, direction)
-
-        # Start metrics when user starts speaking (if VAD is not provided by Deepgram)
-        if isinstance(frame, UserStartedSpeakingFrame):
-            await self.start_metrics()
-        elif isinstance(frame, UserStoppedSpeakingFrame):
-            # Send finalize message to Deepgram when user stops speaking
-            # This tells Deepgram to flush any remaining audio and return final results
-            if self._client and self._client.is_active:
-                try:
-                    await self._client.send_json({"type": "Finalize"})
-                except Exception as e:
-                    logger.warning(f"Error sending Finalize message: {e}")

src/pipecat/services/deepgram/tts.py

@@ -10,45 +10,35 @@ This module provides integration with Deepgram's text-to-speech API
 for generating speech from text using various voice models.
 """
 
-import json
 from typing import AsyncGenerator, Optional
 
 import aiohttp
 from loguru import logger
 
 from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
     ErrorFrame,
     Frame,
-    InterruptionFrame,
-    LLMFullResponseEndFrame,
-    StartFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.tts_service import TTSService
-from pipecat.services.tts_service import TTSService, WebsocketTTSService
 from pipecat.utils.tracing.service_decorators import traced_tts
 
 try:
-    from websockets.asyncio.client import connect as websocket_connect
+    from deepgram import DeepgramClient, DeepgramClientOptions, SpeakOptions
-    from websockets.protocol import State
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
-    logger.error(
+    logger.error("In order to use Deepgram, you need to `pip install pipecat-ai[deepgram]`.")
-        "In order to use DeepgramWebsocketTTSService, you need to `pip install pipecat-ai[deepgram]`."
-    )
     raise Exception(f"Missing module: {e}")
 
 
-class DeepgramTTSService(WebsocketTTSService):
+class DeepgramTTSService(TTSService):
-    """Deepgram WebSocket-based text-to-speech service.
+    """Deepgram text-to-speech service.
 
-    Provides real-time text-to-speech synthesis using Deepgram's WebSocket API.
+    Provides text-to-speech synthesis using Deepgram's streaming API.
-    Supports streaming audio generation with interruption handling via the Clear
+    Supports various voice models and audio encoding formats with
-    message for conversational AI use cases.
+    configurable sample rates and quality settings.
     """
 
     def __init__(
@@ -56,211 +46,51 @@ class DeepgramTTSService(WebsocketTTSService):
         *,
         api_key: str,
         voice: str = "aura-2-helena-en",
-        base_url: str = "wss://api.deepgram.com",
+        base_url: str = "",
         sample_rate: Optional[int] = None,
         encoding: str = "linear16",
         **kwargs,
     ):
-        """Initialize the Deepgram WebSocket TTS service.
+        """Initialize the Deepgram TTS service.
 
         Args:
             api_key: Deepgram API key for authentication.
             voice: Voice model to use for synthesis. Defaults to "aura-2-helena-en".
-            base_url: WebSocket base URL for Deepgram API. Defaults to "wss://api.deepgram.com".
+            base_url: Custom base URL for Deepgram API. Uses default if empty.
             sample_rate: Audio sample rate in Hz. If None, uses service default.
             encoding: Audio encoding format. Defaults to "linear16".
-            **kwargs: Additional arguments passed to parent InterruptibleTTSService class.
+            **kwargs: Additional arguments passed to parent TTSService class.
         """
         super().__init__(sample_rate=sample_rate, **kwargs)
 
-        self._api_key = api_key
-        self._base_url = base_url
         self._settings = {
             "encoding": encoding,
         }
         self.set_voice(voice)
 
-        self._receive_task = None
+        client_options = DeepgramClientOptions(url=base_url)
+        self._deepgram_client = DeepgramClient(api_key, config=client_options)
 
     def can_generate_metrics(self) -> bool:
         """Check if the service can generate metrics.
 
         Returns:
-            True, as Deepgram WebSocket TTS service supports metrics generation.
+            True, as Deepgram TTS service supports metrics generation.
         """
         return True
 
-    async def start(self, frame: StartFrame):
+    @property
-        """Start the Deepgram WebSocket TTS service.
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Deepgram TTSTextFrames include necessary inter-frame spaces.
 
-        Args:
+        Returns:
-            frame: The start frame containing initialization parameters.
+            True, indicating that Deepgram's text frames include necessary inter-frame spaces.
         """
-        await super().start(frame)
+        return True
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the Deepgram WebSocket TTS service.
-
-        Args:
-            frame: The end frame.
-        """
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel the Deepgram WebSocket TTS service.
-
-        Args:
-            frame: The cancel frame.
-        """
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process frames with special handling for LLM response end.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction of frame processing.
-        """
-        await super().process_frame(frame, direction)
-
-        # When the LLM finishes responding, flush any remaining text in Deepgram's buffer
-        if isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
-            await self.flush_audio()
-
-    async def _connect(self):
-        """Connect to Deepgram WebSocket and start receive task."""
-        await self._connect_websocket()
-
-        if self._websocket and not self._receive_task:
-            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
-
-    async def _disconnect(self):
-        """Disconnect from Deepgram WebSocket and clean up tasks."""
-        if self._receive_task:
-            await self.cancel_task(self._receive_task)
-            self._receive_task = None
-
-        await self._disconnect_websocket()
-
-    async def _connect_websocket(self):
-        """Connect to Deepgram WebSocket API with configured settings."""
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                return
-
-            logger.debug("Connecting to Deepgram WebSocket")
-
-            # Build WebSocket URL with query parameters
-            params = []
-            params.append(f"model={self._voice_id}")
-            params.append(f"encoding={self._settings['encoding']}")
-            params.append(f"sample_rate={self.sample_rate}")
-
-            url = f"{self._base_url}/v1/speak?{'&'.join(params)}"
-
-            headers = {"Authorization": f"Token {self._api_key}"}
-
-            self._websocket = await websocket_connect(url, additional_headers=headers)
-
-            await self._call_event_handler("on_connected")
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
-            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-            self._websocket = None
-            await self._call_event_handler("on_connection_error", f"{e}")
-
-    async def _disconnect_websocket(self):
-        """Close WebSocket connection and reset state."""
-        try:
-            await self.stop_all_metrics()
-
-            if self._websocket:
-                logger.debug("Disconnecting from Deepgram WebSocket")
-                # Send Close message to gracefully close the connection
-                await self._websocket.send(json.dumps({"type": "Close"}))
-                await self._websocket.close()
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
-            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-        finally:
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
-
-    def _get_websocket(self):
-        """Get active websocket connection or raise exception."""
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
-        """Handle interruption by sending Clear message to Deepgram.
-
-        The Clear message will clear Deepgram's internal text buffer and stop
-        sending audio, allowing for a new response to be generated.
-        """
-        await super()._handle_interruption(frame, direction)
-
-        # Send Clear message to stop current audio generation
-        if self._websocket:
-            try:
-                clear_msg = {"type": "Clear"}
-                await self._websocket.send(json.dumps(clear_msg))
-            except Exception as e:
-                logger.error(f"{self} error sending Clear message: {e}")
-
-    async def _receive_messages(self):
-        """Receive and process messages from Deepgram WebSocket."""
-        async for message in self._get_websocket():
-            if isinstance(message, bytes):
-                # Binary message contains audio data
-                await self.stop_ttfb_metrics()
-                frame = TTSAudioRawFrame(message, self.sample_rate, 1)
-                await self.push_frame(frame)
-            elif isinstance(message, str):
-                # Text message contains metadata or control messages
-                try:
-                    msg = json.loads(message)
-                    msg_type = msg.get("type")
-
-                    if msg_type == "Metadata":
-                        logger.trace(f"Received metadata: {msg}")
-                    elif msg_type == "Flushed":
-                        logger.trace(f"Received Flushed: {msg}")
-                        # Flushed indicates the end of audio generation for the current buffer
-                        # This happens after flush_audio() is called
-                        await self.push_frame(TTSStoppedFrame())
-                    elif msg_type == "Cleared":
-                        logger.trace(f"Received Cleared: {msg}")
-                        # Buffer has been cleared after interruption
-                        # TTSStoppedFrame will be sent by the interruption handler
-                    elif msg_type == "Warning":
-                        logger.warning(
-                            f"{self} warning: {msg.get('description', 'Unknown warning')}"
-                        )
-                    else:
-                        logger.debug(f"Received unknown message type: {msg}")
-                except json.JSONDecodeError:
-                    logger.error(f"Invalid JSON message: {message}")
-
-    async def flush_audio(self):
-        """Flush any pending audio synthesis by sending Flush command.
-
-        This should be called when the LLM finishes a complete response to force
-        generation of audio from Deepgram's internal text buffer.
-        """
-        if self._websocket:
-            try:
-                flush_msg = {"type": "Flush"}
-                await self._websocket.send(json.dumps(flush_msg))
-            except Exception as e:
-                logger.error(f"{self} error sending Flush message: {e}")
 
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using Deepgram's WebSocket TTS API.
+        """Generate speech from text using Deepgram's TTS API.
 
         Args:
             text: The text to synthesize into speech.
@@ -270,27 +100,33 @@ class DeepgramTTSService(WebsocketTTSService):
         """
         logger.debug(f"{self}: Generating TTS [{text}]")
 
+        options = SpeakOptions(
+            model=self._voice_id,
+            encoding=self._settings["encoding"],
+            sample_rate=self.sample_rate,
+            container="none",
+        )
+
         try:
-            # Reconnect if the websocket is closed
-            if not self._websocket or self._websocket.state is State.CLOSED:
-                await self._connect()
-
             await self.start_ttfb_metrics()
-            await self.start_tts_usage_metrics(text)
 
+            response = await self._deepgram_client.speak.asyncrest.v("1").stream_raw(
+                {"text": text}, options
+            )
+
+            await self.start_tts_usage_metrics(text)
             yield TTSStartedFrame()
 
-            # Send text message to Deepgram
+            async for data in response.aiter_bytes():
-            # Note: We don't send Flush here - that should only be sent when the
+                await self.stop_ttfb_metrics()
-            # LLM finishes a complete response via flush_audio()
+                if data:
-            speak_msg = {"type": "Speak", "text": text}
+                    yield TTSAudioRawFrame(audio=data, sample_rate=self.sample_rate, num_channels=1)
-            await self._get_websocket().send(json.dumps(speak_msg))
 
-            # The actual audio frames will be handled in _receive_messages
+            yield TTSStoppedFrame()
-            yield None
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class DeepgramHttpTTSService(TTSService):
@@ -341,6 +177,15 @@ class DeepgramHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Deepgram TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Deepgram's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         """Generate speech from text using Deepgram's TTS API.
@@ -400,4 +245,5 @@ class DeepgramHttpTTSService(TTSService):
             yield TTSStoppedFrame()
 
         except Exception as e:
+            logger.exception(f"{self} exception: {e}")
             yield ErrorFrame(f"Error getting audio: {str(e)}")

src/pipecat/services/elevenlabs/stt.py

@@ -351,7 +351,8 @@ class ElevenLabsSTTService(SegmentedSTTService):
                 )
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 def audio_format_from_sample_rate(sample_rate: int) -> str:
@@ -415,8 +416,6 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
                 Only used when commit_strategy is VAD. None uses ElevenLabs default.
             min_silence_duration_ms: Minimum silence duration for VAD (50-2000ms).
                 Only used when commit_strategy is VAD. None uses ElevenLabs default.
-            include_timestamps: Whether to include word-level timestamps in transcripts.
-            enable_logging: Whether to enable logging on ElevenLabs' side.
         """
 
         language_code: Optional[str] = None
@@ -425,8 +424,6 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
         vad_threshold: Optional[float] = None
         min_speech_duration_ms: Optional[int] = None
         min_silence_duration_ms: Optional[int] = None
-        include_timestamps: bool = False
-        enable_logging: bool = False
 
     def __init__(
         self,
@@ -462,8 +459,6 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
         self._audio_format = ""  # initialized in start()
         self._receive_task = None
 
-        self._settings = {"language": params.language_code}
-
     def can_generate_metrics(self) -> bool:
         """Check if the service can generate processing metrics.
 
@@ -482,13 +477,7 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
             Changing language requires reconnecting to the WebSocket.
         """
         logger.info(f"Switching STT language to: [{language}]")
-        new_language = (
+        self._params.language_code = language.value if isinstance(language, Language) else language
-            language_to_elevenlabs_language(language)
-            if isinstance(language, Language)
-            else language
-        )
-        self._params.language_code = new_language
-        self._settings["language"] = new_language
         # Reconnect with new settings
         await self._disconnect()
         await self._connect()
@@ -597,6 +586,7 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
                 }
                 await self._websocket.send(json.dumps(message))
             except Exception as e:
+                logger.error(f"Error sending audio: {e}")
                 yield ErrorFrame(f"ElevenLabs Realtime STT error: {str(e)}")
 
         yield None
@@ -630,16 +620,10 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
             if self._params.language_code:
                 params.append(f"language_code={self._params.language_code}")
 
-            params.append(f"audio_format={self._audio_format}")
+            params.append(f"encoding={self._audio_format}")
+            params.append(f"sample_rate={self.sample_rate}")
             params.append(f"commit_strategy={self._params.commit_strategy.value}")
 
-            # Add optional parameters
-            if self._params.include_timestamps:
-                params.append(f"include_timestamps={str(self._params.include_timestamps).lower()}")
-
-            if self._params.enable_logging:
-                params.append(f"enable_logging={str(self._params.enable_logging).lower()}")
-
             # Add VAD parameters if using VAD commit strategy and values are specified
             if self._params.commit_strategy == CommitStrategy.VAD:
                 if self._params.vad_silence_threshold_secs is not None:
@@ -661,9 +645,8 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
             await self._call_event_handler("on_connected")
             logger.debug("Connected to ElevenLabs Realtime STT")
         except Exception as e:
-            await self.push_error(
+            logger.error(f"{self}: unable to connect to ElevenLabs Realtime STT: {e}")
-                error_msg=f"Unable to connect to ElevenLabs Realtime STT: {e}", exception=e
+            await self.push_error(ErrorFrame(f"Connection error: {str(e)}"))
-            )
 
     async def _disconnect_websocket(self):
         """Disconnect from ElevenLabs Realtime STT WebSocket."""
@@ -672,7 +655,7 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
                 logger.debug("Disconnecting from ElevenLabs Realtime STT")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
         finally:
             self._websocket = None
             await self._call_event_handler("on_disconnected")
@@ -729,20 +712,15 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
         elif message_type == "committed_transcript_with_timestamps":
             await self._on_committed_transcript_with_timestamps(data)
 
-        elif message_type == "error":
+        elif message_type == "input_error":
-            error_msg = data.get("error", "Unknown error")
+            error_msg = data.get("error", "Unknown input error")
-            logger.error(f"ElevenLabs error: {error_msg}")
+            logger.error(f"ElevenLabs input error: {error_msg}")
-            await self.push_error(error_msg=f"Error: {error_msg}")
+            await self.push_error(ErrorFrame(f"Input error: {error_msg}"))
 
-        elif message_type == "auth_error":
+        elif message_type in ["auth_error", "quota_exceeded", "transcriber_error", "error"]:
-            error_msg = data.get("error", "Authentication error")
+            error_msg = data.get("error", data.get("message", "Unknown error"))
-            logger.error(f"ElevenLabs auth error: {error_msg}")
+            logger.error(f"ElevenLabs error ({message_type}): {error_msg}")
-            await self.push_error(error_msg=f"Auth error: {error_msg}")
+            await self.push_error(ErrorFrame(f"{message_type}: {error_msg}"))
-
-        elif message_type == "quota_exceeded_error":
-            error_msg = data.get("error", "Quota exceeded")
-            logger.error(f"ElevenLabs quota exceeded: {error_msg}")
-            await self.push_error(error_msg=f"Quota exceeded: {error_msg}")
 
         else:
             logger.debug(f"Unknown message type: {message_type}")
@@ -787,11 +765,6 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
         Args:
             data: Committed transcript data.
         """
-        # If timestamps are enabled, skip this message and wait for the
-        # committed_transcript_with_timestamps message which contains all the data
-        if self._params.include_timestamps:
-            return
-
         text = data.get("text", "").strip()
         if not text:
             return
@@ -819,18 +792,6 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
     async def _on_committed_transcript_with_timestamps(self, data: dict):
         """Handle committed transcript with word-level timestamps.
 
-        This message is sent when include_timestamps=true. The result data includes:
-        - text: The transcribed text
-        - language_code: Detected language (if available)
-        - words: Array of word objects with timing information:
-            - text: The word text
-            - start: Start time in seconds
-            - end: End time in seconds
-            - type: "word" or "spacing"
-            - speaker_id: Speaker identifier (if available)
-            - logprob: Log probability score (if available)
-            - characters: Array of character strings (if available)
-
         Args:
             data: Committed transcript data with timestamps.
         """
@@ -838,24 +799,9 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
         if not text:
             return
 
-        await self.stop_ttfb_metrics()
-        await self.stop_processing_metrics()
-
-        # Get language if provided
-        language = data.get("language_code")
-
         logger.debug(f"Committed transcript with timestamps: [{text}]")
+        logger.trace(f"Timestamps: {data.get('words', [])}")
 
-        await self._handle_transcription(text, True, language)
+        # This is sent after the committed_transcript, so we don't need to
-
+        # push another TranscriptionFrame, but we could use the timestamps
-        # This message is sent after committed_transcript when include_timestamps=true.
+        # for additional processing if needed in the future
-        # It contains the full transcript data including text and word-level timestamps.
-        await self.push_frame(
-            TranscriptionFrame(
-                text,
-                self._user_id,
-                time_now_iso8601(),
-                language,
-                result=data,
-            )
-        )

src/pipecat/services/elevenlabs/tts.py

@@ -424,7 +424,8 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                         json.dumps({"context_id": self._context_id, "close_context": True})
                     )
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._context_id = None
             self._started = False
 
@@ -535,8 +536,9 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             self._websocket = None
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             await self._call_event_handler("on_connection_error", f"{e}")
 
     async def _disconnect_websocket(self):
@@ -551,7 +553,8 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                 await self._websocket.close()
                 logger.debug("Disconnected from ElevenLabs")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._context_id = None
@@ -581,7 +584,8 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                     json.dumps({"context_id": self._context_id, "close_context": True})
                 )
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._context_id = None
             self._started = False
             self._partial_word = ""
@@ -736,13 +740,15 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                 else:
                     await self._send_text(text)
             except Exception as e:
+                logger.error(f"{self} exception: {e}")
                 yield TTSStoppedFrame()
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 self._started = False
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class ElevenLabsHttpTTSService(WordTTSService):
@@ -1037,6 +1043,7 @@ class ElevenLabsHttpTTSService(WordTTSService):
             ) as response:
                 if response.status != 200:
                     error_text = await response.text()
+                    logger.error(f"{self} error: {error_text}")
                     yield ErrorFrame(error=f"ElevenLabs API error: {error_text}")
                     return
 
@@ -1084,7 +1091,8 @@ class ElevenLabsHttpTTSService(WordTTSService):
                         logger.warning(f"Failed to parse JSON from stream: {e}")
                         continue
                     except Exception as e:
-                        yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                        logger.error(f"{self} exception: {e}")
+                        yield ErrorFrame(error=f"{self} error: {e}")
                         continue
 
                 # After processing all chunks, emit any remaining partial word
@@ -1108,7 +1116,8 @@ class ElevenLabsHttpTTSService(WordTTSService):
                     self._previous_text = text
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             # Let the parent class handle TTSStoppedFrame

src/pipecat/services/fal/image.py

@@ -110,6 +110,7 @@ class FalImageGenService(ImageGenService):
         image_url = response["images"][0]["url"] if response else None
 
         if not image_url:
+            logger.error(f"{self} error: image generation failed")
             yield ErrorFrame("Image generation failed")
             return
 

src/pipecat/services/fal/stt.py

@@ -290,4 +290,5 @@ class FalSTTService(SegmentedSTTService):
                     )
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/fish/tts.py

@@ -76,7 +76,7 @@ class FishAudioTTSService(InterruptibleTTSService):
         api_key: str,
         reference_id: Optional[str] = None,  # This is the voice ID
         model: Optional[str] = None,  # Deprecated
-        model_id: str = "s1",
+        model_id: str = "speech-1.5",
         output_format: FishAudioOutputFormat = "pcm",
         sample_rate: Optional[int] = None,
         params: Optional[InputParams] = None,
@@ -93,7 +93,7 @@ class FishAudioTTSService(InterruptibleTTSService):
                 The `model` parameter is deprecated and will be removed in version 0.1.0.
                 Use `reference_id` instead to specify the voice model.
 
-            model_id: Specify which Fish Audio TTS model to use (e.g. "s1")
+            model_id: Specify which Fish Audio TTS model to use (e.g. "speech-1.5")
             output_format: Audio output format. Defaults to "pcm".
             sample_rate: Audio sample rate. If None, uses default.
             params: Additional input parameters for voice customization.
@@ -159,6 +159,15 @@ class FishAudioTTSService(InterruptibleTTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Fish Audio TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Fish Audio's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     async def set_model(self, model: str):
         """Set the TTS model and reconnect.
 
@@ -228,7 +237,8 @@ class FishAudioTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -242,7 +252,8 @@ class FishAudioTTSService(InterruptibleTTSService):
                 await self._websocket.send(ormsgpack.packb(stop_message))
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._request_id = None
             self._started = False
@@ -284,7 +295,8 @@ class FishAudioTTSService(InterruptibleTTSService):
                                 continue
 
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
@@ -320,7 +332,8 @@ class FishAudioTTSService(InterruptibleTTSService):
                 flush_message = {"event": "flush"}
                 await self._get_websocket().send(ormsgpack.packb(flush_message))
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
@@ -328,4 +341,5 @@ class FishAudioTTSService(InterruptibleTTSService):
             yield None
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/gladia/stt.py

@@ -468,7 +468,8 @@ class GladiaSTTService(STTService):
                             break
 
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 self._connection_active = False
 
                 if not self._should_reconnect:
@@ -558,7 +559,8 @@ class GladiaSTTService(STTService):
         except websockets.exceptions.ConnectionClosed:
             logger.debug("Connection closed during keepalive")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _receive_task_handler(self):
         try:
@@ -621,7 +623,8 @@ class GladiaSTTService(STTService):
             # Expected when closing the connection
             pass
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _maybe_reconnect(self) -> bool:
         """Handle exponential backoff reconnection logic."""
@@ -629,9 +632,7 @@ class GladiaSTTService(STTService):
             return False
         self._reconnection_attempts += 1
         if self._reconnection_attempts > self._max_reconnection_attempts:
-            await self.push_error(
+            logger.error(f"Max reconnection attempts ({self._max_reconnection_attempts}) reached")
-                error_msg=f"Max reconnection attempts ({self._max_reconnection_attempts}) reached",
-            )
             self._should_reconnect = False
             return False
         delay = self._reconnection_delay * (2 ** (self._reconnection_attempts - 1))

src/pipecat/services/google/gemini_live/llm.py

@@ -27,7 +27,6 @@ from pydantic import BaseModel, Field
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
 from pipecat.frames.frames import (
-    AggregationType,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -1175,7 +1174,7 @@ class GeminiLiveLLMService(LLMService):
             self._connection_task = self.create_task(self._connection_task_handler(config=config))
 
         except Exception as e:
-            await self.push_error(error_msg=f"Initialization error: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"{self} Initialization error: {e}"))
 
     async def _connection_task_handler(self, config: LiveConnectConfig):
         async with self._client.aio.live.connect(model=self._model_name, config=config) as session:
@@ -1252,11 +1251,11 @@ class GeminiLiveLLMService(LLMService):
         )
 
         if self._consecutive_failures >= MAX_CONSECUTIVE_FAILURES:
-            error_msg = (
+            logger.error(
                 f"Max consecutive failures ({MAX_CONSECUTIVE_FAILURES}) reached, "
                 "treating as fatal error"
             )
-            await self.push_error(error_msg=error_msg, exception=error)
+            await self.push_error(ErrorFrame(error=f"{self} Error in receive loop: {error}"))
             return False
         else:
             logger.info(
@@ -1284,7 +1283,7 @@ class GeminiLiveLLMService(LLMService):
             self._completed_tool_calls = set()
             self._disconnecting = False
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     async def _send_user_audio(self, frame):
         """Send user audio frame to Gemini Live API."""
@@ -1448,11 +1447,13 @@ class GeminiLiveLLMService(LLMService):
                 # Update bot responding state and send service start frame
                 # (AUDIO modality case)
                 await self._set_bot_is_responding(True)
-                await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+                await self.push_frame(LLMFullResponseStartFrame())
 
             self._bot_text_buffer += text
             self._search_result_buffer += text  # Also accumulate for grounding
-            frame = LLMTextFrame(text=text, skip_tts=self._get_skip_tts())
+            frame = LLMTextFrame(text=text)
+            # Gemini Live text already includes any necessary inter-chunk spaces
+            frame.includes_inter_frame_spaces = True
             await self.push_frame(frame)
 
         # Check for grounding metadata in server content
@@ -1491,7 +1492,7 @@ class GeminiLiveLLMService(LLMService):
         if not self._bot_is_responding:
             await self._set_bot_is_responding(True)
             await self.push_frame(TTSStartedFrame())
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseStartFrame())
 
         self._bot_audio_buffer.extend(audio)
         frame = TTSAudioRawFrame(
@@ -1552,10 +1553,10 @@ class GeminiLiveLLMService(LLMService):
             if not text:
                 # AUDIO modality case
                 await self.push_frame(TTSStoppedFrame())
-                await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+                await self.push_frame(LLMFullResponseEndFrame())
             else:
                 # TEXT modality case
-                await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+                await self.push_frame(LLMFullResponseEndFrame())
 
     @traced_stt
     async def _handle_user_transcription(
@@ -1643,9 +1644,9 @@ class GeminiLiveLLMService(LLMService):
         if not self._bot_is_responding:
             await self._set_bot_is_responding(True)
             await self.push_frame(TTSStartedFrame())
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseStartFrame())
 
-        frame = TTSTextFrame(text=text, aggregated_by=AggregationType.SENTENCE)
+        frame = TTSTextFrame(text=text)
         # Gemini Live text already includes any necessary inter-chunk spaces
         frame.includes_inter_frame_spaces = True
 
@@ -1723,8 +1724,6 @@ class GeminiLiveLLMService(LLMService):
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
             total_tokens=total_tokens,
-            cache_read_input_tokens=usage.cached_content_token_count,
-            reasoning_tokens=usage.thoughts_token_count,
         )
 
         await self.start_llm_usage_metrics(tokens)
@@ -1745,7 +1744,7 @@ class GeminiLiveLLMService(LLMService):
         # state management, and that exponential backoff for retries can have
         # cost/stability implications for a service cluster, let's just treat a
         # send-side error as fatal.
-        await self.push_error(error_msg=f"Send error: {error}")
+        await self.push_error(ErrorFrame(error=f"{self} Send error: {error}", fatal=True))
 
     def create_context_aggregator(
         self,

src/pipecat/services/google/image.py

@@ -110,6 +110,7 @@ class GoogleImageGenService(ImageGenService):
             await self.stop_ttfb_metrics()
 
             if not response or not response.generated_images:
+                logger.error(f"{self} error: image generation failed")
                 yield ErrorFrame("Image generation failed")
                 return
 
@@ -127,4 +128,5 @@ class GoogleImageGenService(ImageGenService):
                 yield frame
 
         except Exception as e:
+            logger.error(f"{self} error generating image: {e}")
             yield ErrorFrame(f"Image generation error: {str(e)}")

src/pipecat/services/google/llm.py

@@ -793,7 +793,7 @@ class GoogleLLMService(LLMService):
                 return
             generation_params.setdefault("thinking_config", {})["thinking_budget"] = 0
         except Exception as e:
-            logger.error(f"Failed to unset thinking budget: {e}")
+            logger.exception(f"Failed to unset thinking budget: {e}")
 
     async def _stream_content(
         self, params_from_context: GeminiLLMInvocationParams
@@ -876,7 +876,7 @@ class GoogleLLMService(LLMService):
 
     @traced_llm
     async def _process_context(self, context: OpenAILLMContext | LLMContext):
-        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseStartFrame())
 
         prompt_tokens = 0
         completion_tokens = 0
@@ -920,9 +920,9 @@ class GoogleLLMService(LLMService):
                         for part in candidate.content.parts:
                             if not part.thought and part.text:
                                 search_result += part.text
-                                await self.push_frame(
+                                frame = LLMTextFrame(part.text)
-                                    LLMTextFrame(part.text, skip_tts=self._get_skip_tts())
+                                frame.includes_inter_frame_spaces = True
-                                )
+                                await self.push_frame(frame)
                             elif part.function_call:
                                 function_call = part.function_call
                                 id = function_call.id or str(uuid.uuid4())
@@ -985,7 +985,7 @@ class GoogleLLMService(LLMService):
         except DeadlineExceeded:
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} exception: {e}")
         finally:
             if grounding_metadata and isinstance(grounding_metadata, dict):
                 llm_search_frame = LLMSearchResponseFrame(
@@ -1004,7 +1004,7 @@ class GoogleLLMService(LLMService):
                     reasoning_tokens=reasoning_tokens,
                 )
             )
-            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseEndFrame())
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process incoming frames and handle different frame types.

src/pipecat/services/google/llm_openai.py

@@ -136,9 +136,7 @@ class GoogleLLMOpenAIBetaService(OpenAILLMService):
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(
+                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))
-                    LLMTextFrame(chunk.choices[0].delta.content, skip_tts=self._get_skip_tts())
-                )
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to

src/pipecat/services/google/stt.py

@@ -774,7 +774,8 @@ class GoogleSTTService(STTService):
                 yield cloud_speech.StreamingRecognizeRequest(audio=audio_data)
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             raise
 
     async def _stream_audio(self):
@@ -805,13 +806,15 @@ class GoogleSTTService(STTService):
                         break
 
                 except Exception as e:
-                    await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                    logger.error(f"{self} exception: {e}")
+                    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
                     await asyncio.sleep(1)  # Brief delay before reconnecting
                     self._stream_start_time = int(time.time() * 1000)
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
         """Process an audio chunk for STT transcription.
@@ -899,7 +902,8 @@ class GoogleSTTService(STTService):
             )
             raise
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             # Re-raise the exception to let it propagate (e.g. in the case of a
             # timeout, propagate to _stream_audio to reconnect)
             raise

src/pipecat/services/google/tts.py

@@ -596,6 +596,15 @@ class GoogleHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Google TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Google's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Google TTS language format.
 
@@ -737,6 +746,7 @@ class GoogleHttpTTSService(TTSService):
             yield TTSStoppedFrame()
 
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             error_message = f"TTS generation error: {str(e)}"
             yield ErrorFrame(error=error_message)
 
@@ -793,6 +803,15 @@ class GoogleBaseTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Google and Gemini TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Google's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Google TTS language format.
 
@@ -995,7 +1014,9 @@ class GoogleTTSService(GoogleBaseTTSService):
                 yield frame
 
         except Exception as e:
-            await self.push_error(error_msg=f"TTS generation error: {str(e)}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            error_message = f"TTS generation error: {str(e)}"
+            yield ErrorFrame(error=error_message)
 
 
 class GeminiTTSService(GoogleBaseTTSService):
@@ -1245,5 +1266,6 @@ class GeminiTTSService(GoogleBaseTTSService):
                 yield frame
 
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             error_message = f"Gemini TTS generation error: {str(e)}"
             yield ErrorFrame(error=error_message)

src/pipecat/services/grok/llm.py

@@ -123,8 +123,6 @@ class GrokLLMService(OpenAILLMService):
         self._prompt_tokens = 0
         self._completion_tokens = 0
         self._total_tokens = 0
-        self._cache_read_input_tokens = None
-        self._reasoning_tokens = None
         self._has_reported_prompt_tokens = False
         self._is_processing = True
 
@@ -139,8 +137,6 @@ class GrokLLMService(OpenAILLMService):
                     prompt_tokens=self._prompt_tokens,
                     completion_tokens=self._completion_tokens,
                     total_tokens=self._total_tokens,
-                    cache_read_input_tokens=self._cache_read_input_tokens,
-                    reasoning_tokens=self._reasoning_tokens,
                 )
                 await super().start_llm_usage_metrics(tokens)
 
@@ -153,7 +149,7 @@ class GrokLLMService(OpenAILLMService):
 
         Args:
             tokens: The token usage metrics for the current chunk of processing,
-                containing prompt_tokens, completion_tokens, and optional cached/reasoning tokens.
+                containing prompt_tokens and completion_tokens counts.
         """
         # Only accumulate metrics during active processing
         if not self._is_processing:
@@ -168,13 +164,6 @@ class GrokLLMService(OpenAILLMService):
         if tokens.completion_tokens > self._completion_tokens:
             self._completion_tokens = tokens.completion_tokens
 
-        # Capture cached & reasoning tokens (these typically only appear once per request)
-        if tokens.cache_read_input_tokens is not None:
-            self._cache_read_input_tokens = tokens.cache_read_input_tokens
-
-        if tokens.reasoning_tokens is not None:
-            self._reasoning_tokens = tokens.reasoning_tokens
-
     def create_context_aggregator(
         self,
         context: OpenAILLMContext,

src/pipecat/services/groq/tts.py

@@ -111,6 +111,15 @@ class GroqTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Groq TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Groq's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         """Generate speech from text using Groq's TTS API.
@@ -146,6 +155,7 @@ class GroqTTSService(TTSService):
                     bytes = w.readframes(num_frames)
                     yield TTSAudioRawFrame(bytes, frame_rate, channels)
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
         yield TTSStoppedFrame()

src/pipecat/services/heygen/client.py

@@ -179,7 +179,7 @@ class HeyGenClient:
                 await self._task_manager.cancel_task(self._event_task)
                 self._event_task = None
         except Exception as e:
-            logger.error(f"Exception during cleanup: {e}")
+            logger.exception(f"Exception during cleanup: {e}")
 
     async def start(self, frame: StartFrame, audio_chunk_size: int) -> None:
         """Start the client and establish all necessary connections.

src/pipecat/services/hume/tts.py

@@ -14,14 +14,12 @@ from pydantic import BaseModel
 from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
-    InterruptionFrame,
     StartFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.tts_service import TTSService
-from pipecat.services.tts_service import WordTTSService
 from pipecat.utils.tracing.service_decorators import traced_tts
 
 try:
@@ -31,7 +29,6 @@ try:
         PostedUtterance,
         PostedUtteranceVoiceWithId,
     )
-    from hume.tts.types import TimestampMessage
 except ModuleNotFoundError as e:  # pragma: no cover - import-time guidance
     logger.error(f"Exception: {e}")
     logger.error("In order to use Hume, you need to `pip install pipecat-ai[hume]`.")
@@ -41,7 +38,7 @@ except ModuleNotFoundError as e:  # pragma: no cover - import-time guidance
 HUME_SAMPLE_RATE = 48_000  # Hume TTS streams at 48 kHz
 
 
-class HumeTTSService(WordTTSService):
+class HumeTTSService(TTSService):
     """Hume Octave Text-to-Speech service.
 
     Streams PCM audio via Hume's HTTP output streaming (JSON chunks) endpoint
@@ -51,7 +48,6 @@ class HumeTTSService(WordTTSService):
 
     - Generates speech from text using Hume TTS.
     - Streams PCM audio.
-    - Supports word-level timestamps for precise audio-text synchronization.
     - Supports dynamic updates of voice and synthesis parameters at runtime.
     - Provides metrics for Time To First Byte (TTFB) and TTS usage.
     """
@@ -96,13 +92,7 @@ class HumeTTSService(WordTTSService):
                 f"Hume TTS streams at {HUME_SAMPLE_RATE} Hz; configured sample_rate={sample_rate}"
             )
 
-        # WordTTSService sets push_text_frames=False by default, which we want
+        super().__init__(sample_rate=sample_rate, **kwargs)
-        super().__init__(
-            sample_rate=sample_rate,
-            push_text_frames=False,
-            push_stop_frames=True,
-            **kwargs,
-        )
 
         self._client = AsyncHumeClient(api_key=api_key)
         self._params = params or HumeTTSService.InputParams()
@@ -112,10 +102,6 @@ class HumeTTSService(WordTTSService):
 
         self._audio_bytes = b""
 
-        # Track cumulative time for word timestamps across utterances
-        self._cumulative_time = 0.0
-        self._started = False
-
     def can_generate_metrics(self) -> bool:
         """Can generate metrics.
 
@@ -124,6 +110,15 @@ class HumeTTSService(WordTTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Hume TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Hume's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     async def start(self, frame: StartFrame) -> None:
         """Start the service.
 
@@ -131,27 +126,6 @@ class HumeTTSService(WordTTSService):
             frame: The start frame.
         """
         await super().start(frame)
-        self._reset_state()
-
-    def _reset_state(self):
-        """Reset internal state variables."""
-        self._cumulative_time = 0.0
-        self._started = False
-
-    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
-        """Push a frame and handle state changes.
-
-        Args:
-            frame: The frame to push.
-            direction: The direction to push the frame.
-        """
-        await super().push_frame(frame, direction)
-        if isinstance(frame, (InterruptionFrame, TTSStoppedFrame)):
-            # Reset timing on interruption or stop
-            self._reset_state()
-
-            if isinstance(frame, TTSStoppedFrame):
-                await self.add_word_timestamps([("Reset", 0)])
 
     async def update_setting(self, key: str, value: Any) -> None:
         """Runtime updates via `TTSUpdateSettingsFrame`.
@@ -168,7 +142,7 @@ class HumeTTSService(WordTTSService):
 
         if key_l == "voice_id":
             self.set_voice(str(value))
-            logger.debug(f"HumeTTSService voice_id set to: {self.voice}")
+            logger.info(f"HumeTTSService voice_id set to: {self.voice}")
         elif key_l == "description":
             self._params.description = None if value is None else str(value)
         elif key_l == "speed":
@@ -181,7 +155,7 @@ class HumeTTSService(WordTTSService):
 
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using Hume TTS with word timestamps.
+        """Generate speech from text using Hume TTS.
 
         Args:
             text: The text to be synthesized.
@@ -212,12 +186,7 @@ class HumeTTSService(WordTTSService):
 
         await self.start_ttfb_metrics()
         await self.start_tts_usage_metrics(text)
-
+        yield TTSStartedFrame()
-        # Start TTS sequence if not already started
-        if not self._started:
-            self.start_word_timestamps()
-            yield TTSStartedFrame()
-            self._started = True
 
         try:
             # Instant mode is always enabled here (not user-configurable)
@@ -228,50 +197,23 @@ class HumeTTSService(WordTTSService):
             # Use version "2" by default if no description is provided
             # Version "1" is needed when description is used
             version = "1" if self._params.description is not None else "2"
-
-            # Track the duration of this utterance based on the last timestamp
-            utterance_duration = 0.0
-
             async for chunk in self._client.tts.synthesize_json_streaming(
                 utterances=[utterance],
                 format=pcm_fmt,
                 instant_mode=True,
                 version=version,
-                include_timestamp_types=["word"],  # Request word-level timestamps
             ):
-                # Process audio chunks
                 audio_b64 = getattr(chunk, "audio", None)
-                if audio_b64:
+                if not audio_b64:
-                    await self.stop_ttfb_metrics()
+                    continue
-                    pcm_bytes = base64.b64decode(audio_b64)
-                    self._audio_bytes += pcm_bytes
 
-                    # Buffer audio until we have enough to avoid glitches
+                pcm_bytes = base64.b64decode(audio_b64)
-                    if len(self._audio_bytes) >= self.chunk_size:
+                self._audio_bytes += pcm_bytes
-                        frame = TTSAudioRawFrame(
-                            audio=self._audio_bytes,
-                            sample_rate=self.sample_rate,
-                            num_channels=1,
-                        )
-                        yield frame
-                        self._audio_bytes = b""
 
-                # Process timestamp messages
+                # Buffer audio until we have enough to avoid glitches
-                if isinstance(chunk, TimestampMessage):
+                if len(self._audio_bytes) < self.chunk_size:
-                    timestamp = chunk.timestamp
+                    continue
-                    if timestamp.type == "word":
-                        # Convert milliseconds to seconds and add cumulative offset
-                        word_start_time = self._cumulative_time + (timestamp.time.begin / 1000.0)
-                        word_end_time = self._cumulative_time + (timestamp.time.end / 1000.0)
 
-                        # Track the maximum end time for this utterance
-                        utterance_duration = max(utterance_duration, word_end_time)
-
-                        # Add word timestamp
-                        await self.add_word_timestamps([(timestamp.text, word_start_time)])
-
-            # Flush any remaining audio bytes
-            if self._audio_bytes:
                 frame = TTSAudioRawFrame(
                     audio=self._audio_bytes,
                     sample_rate=self.sample_rate,
@@ -282,13 +224,10 @@ class HumeTTSService(WordTTSService):
 
                 self._audio_bytes = b""
 
-            # Update cumulative time for next utterance
-            if utterance_duration > 0:
-                self._cumulative_time = utterance_duration
-
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # Ensure TTFB timer is stopped even on early failures
             await self.stop_ttfb_metrics()
-            # Let the parent class handle TTSStoppedFrame via push_stop_frames
+            yield TTSStoppedFrame()

src/pipecat/services/inworld/tts.py

@@ -146,8 +146,6 @@ class InworldTTSService(TTSService):
         Parameters:
             temperature: Voice temperature control for synthesis variability (e.g., 1.1).
                         Valid range: [0, 2]. Higher values increase variability.
-            speaking_rate: Speaking speed control (range: [0.5, 1.5]). Defaults to 1.0 when
-                           unset.
 
         Note:
             Language is automatically inferred from the input text by Inworld's TTS models,
@@ -155,7 +153,6 @@ class InworldTTSService(TTSService):
         """
 
         temperature: Optional[float] = None  # optional temperature control (range: [0, 2])
-        speaking_rate: Optional[float] = None  # optional speaking rate control (range: [0.5, 1.5])
 
     def __init__(
         self,
@@ -201,7 +198,6 @@ class InworldTTSService(TTSService):
                      - Other formats as supported by Inworld API
             params: Optional input parameters for additional configuration. Use this to specify:
                    - temperature: Voice temperature control for variability (range: [0, 2], e.g., 1.1, optional)
-                   - speaking_rate: Set desired speaking speed (range: [0.5, 1.5], optional)
                    Language is automatically inferred from input text.
             **kwargs: Additional arguments passed to the parent TTSService class.
 
@@ -232,18 +228,15 @@ class InworldTTSService(TTSService):
         self._settings = {
             "voiceId": voice_id,  # Voice selection from direct parameter
             "modelId": model,  # TTS model selection from direct parameter
-            "audioConfig": {  # Audio format configuration
+            "audio_config": {  # Audio format configuration
-                "audioEncoding": encoding,  # Format: LINEAR16, MP3, etc.
+                "audio_encoding": encoding,  # Format: LINEAR16, MP3, etc.
-                "sampleRateHertz": 0,  # Will be set in start() from parent service
+                "sample_rate_hertz": 0,  # Will be set in start() from parent service
             },
         }
 
         # Add optional temperature parameter if provided (valid range: [0, 2])
         if params and params.temperature is not None:
             self._settings["temperature"] = params.temperature
-        # Add optional speaking rate if provided (valid range: [0.5, 1.5])
-        if params and params.speaking_rate is not None:
-            self._settings["audioConfig"]["speakingRate"] = params.speaking_rate
 
         # Register voice and model with parent service for metrics and tracking
         self.set_voice(voice_id)  # Used for logging and metrics
@@ -257,6 +250,15 @@ class InworldTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Inworld TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Inworld's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     async def start(self, frame: StartFrame):
         """Start the Inworld TTS service.
 
@@ -264,7 +266,7 @@ class InworldTTSService(TTSService):
             frame: The start frame containing initialization parameters.
         """
         await super().start(frame)
-        self._settings["audioConfig"]["sampleRateHertz"] = self.sample_rate
+        self._settings["audio_config"]["sample_rate_hertz"] = self.sample_rate
 
     async def stop(self, frame: EndFrame):
         """Stop the Inworld TTS service.
@@ -330,7 +332,9 @@ class InworldTTSService(TTSService):
             "text": text,  # Text to synthesize
             "voiceId": self._settings["voiceId"],  # Voice selection (Ashley, Hades, etc.)
             "modelId": self._settings["modelId"],  # TTS model (inworld-tts-1)
-            "audioConfig": self._settings["audioConfig"],  # Audio format settings (LINEAR16, 48kHz)
+            "audio_config": self._settings[
+                "audio_config"
+            ],  # Audio format settings (LINEAR16, 48kHz)
         }
 
         # Add optional temperature parameter if configured (valid range: [0, 2])
@@ -397,7 +401,8 @@ class InworldTTSService(TTSService):
             # STEP 7: ERROR HANDLING
             # ================================================================================
             # Log any unexpected errors and notify the pipeline
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # ================================================================================
             # STEP 8: CLEANUP AND COMPLETION
@@ -512,7 +517,7 @@ class InworldTTSService(TTSService):
         # Extract the base64-encoded audio content from response
         if "audioContent" not in response_data:
             logger.error("No audioContent in Inworld API response")
-            yield ErrorFrame(error="No audioContent in response")
+            await self.push_error(ErrorFrame(error="No audioContent in response"))
             return
 
         # ================================================================================

src/pipecat/services/llm_service.py

@@ -9,7 +9,17 @@
 import asyncio
 import inspect
 from dataclasses import dataclass
-from typing import Any, Awaitable, Callable, Dict, Mapping, Optional, Protocol, Sequence, Type
+from typing import (
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    Mapping,
+    Optional,
+    Protocol,
+    Sequence,
+    Type,
+)
 
 from loguru import logger
 
@@ -275,13 +285,17 @@ class LLMService(AIService):
         elif isinstance(frame, LLMConfigureOutputFrame):
             self._skip_tts = frame.skip_tts
 
-    def _get_skip_tts(self) -> bool:
+    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
-        """Get the current skip_tts configuration.
+        """Pushes a frame.
 
-        Returns:
+        Args:
-            The current skip_tts setting for frames generated by this LLM.
+            frame: The frame to push.
+            direction: The direction of frame pushing.
         """
-        return self._skip_tts
+        if isinstance(frame, (LLMTextFrame, LLMFullResponseStartFrame, LLMFullResponseEndFrame)):
+            frame.skip_tts = self._skip_tts
+
+        await super().push_frame(frame, direction)
 
     async def _handle_interruptions(self, _: InterruptionFrame):
         for function_name, entry in self._functions.items():

src/pipecat/services/lmnt/tts.py

@@ -124,6 +124,15 @@ class LmntTTSService(InterruptibleTTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that LMNT TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that LMNT's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to LMNT service language format.
 
@@ -214,7 +223,8 @@ class LmntTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -230,7 +240,8 @@ class LmntTTSService(InterruptibleTTSService):
                 # await self._websocket.send(json.dumps({"eof": True}))
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting from LMNT: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._websocket = None
@@ -264,9 +275,10 @@ class LmntTTSService(InterruptibleTTSService):
                 try:
                     msg = json.loads(message)
                     if "error" in msg:
+                        logger.error(f"{self} error: {msg['error']}")
                         await self.push_frame(TTSStoppedFrame())
                         await self.stop_all_metrics()
-                        await self.push_error(error_msg=f"Error: {msg['error']}")
+                        await self.push_error(ErrorFrame(error=f"{self} error: {msg['error']}"))
                         return
                 except json.JSONDecodeError:
                     logger.error(f"Invalid JSON message: {message}")
@@ -299,11 +311,13 @@ class LmntTTSService(InterruptibleTTSService):
                 await self._get_websocket().send(json.dumps({"flush": True}))
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/mcp_service.py

@@ -176,6 +176,7 @@ class MCPClient(BaseObject):
         except Exception as e:
             error_msg = f"Error calling mcp tool {params.function_name}: {str(e)}"
             logger.error(error_msg)
+            logger.exception("Full exception details:")
             await params.result_callback(error_msg)
 
     async def _stdio_list_tools(self) -> ToolsSchema:
@@ -206,6 +207,7 @@ class MCPClient(BaseObject):
         except Exception as e:
             error_msg = f"Error calling mcp tool {params.function_name}: {str(e)}"
             logger.error(error_msg)
+            logger.exception("Full exception details:")
             await params.result_callback(error_msg)
 
     async def _streamable_http_list_tools(self) -> ToolsSchema:
@@ -244,6 +246,7 @@ class MCPClient(BaseObject):
         except Exception as e:
             error_msg = f"Error calling mcp tool {params.function_name}: {str(e)}"
             logger.error(error_msg)
+            logger.exception("Full exception details:")
             await params.result_callback(error_msg)
 
     async def _call_tool(self, session, function_name, arguments, result_callback):
@@ -299,6 +302,7 @@ class MCPClient(BaseObject):
 
             except Exception as e:
                 logger.error(f"Failed to read tool '{tool_name}': {str(e)}")
+                logger.exception("Full exception details:")
                 continue
 
         logger.debug(f"Completed reading {len(tool_schemas)} tools")

src/pipecat/services/mem0/memory.py

@@ -253,9 +253,8 @@ class Mem0MemoryService(FrameProcessor):
                     # Otherwise, pass the enhanced context frame downstream
                     await self.push_frame(frame)
             except Exception as e:
-                await self.push_error(
+                logger.error(f"Error processing with Mem0: {str(e)}")
-                    error_msg=f"Error processing with Mem0: {str(e)}", exception=e
+                await self.push_frame(ErrorFrame(f"Error processing with Mem0: {str(e)}"))
-                )
                 await self.push_frame(frame)  # Still pass the original frame through
         else:
             # For non-context frames, just pass them through

src/pipecat/services/minimax/tts.py

@@ -40,40 +40,24 @@ def language_to_minimax_language(language: Language) -> Optional[str]:
         The corresponding MiniMax language name, or None if not supported.
     """
     LANGUAGE_MAP = {
-        Language.AF: "Afrikaans",
         Language.AR: "Arabic",
-        Language.BG: "Bulgarian",
-        Language.CA: "Catalan",
         Language.CS: "Czech",
-        Language.DA: "Danish",
         Language.DE: "German",
         Language.EL: "Greek",
         Language.EN: "English",
         Language.ES: "Spanish",
-        Language.FA: "Persian",  # ⚠️ Only supported by speech-2.6-* models
         Language.FI: "Finnish",
-        Language.FIL: "Filipino",  # ⚠️ Only supported by speech-2.6-* models
         Language.FR: "French",
-        Language.HE: "Hebrew",
         Language.HI: "Hindi",
-        Language.HR: "Croatian",
-        Language.HU: "Hungarian",
         Language.ID: "Indonesian",
         Language.IT: "Italian",
         Language.JA: "Japanese",
         Language.KO: "Korean",
-        Language.MS: "Malay",
-        Language.NB: "Norwegian",
-        Language.NN: "Nynorsk",
         Language.NL: "Dutch",
         Language.PL: "Polish",
         Language.PT: "Portuguese",
         Language.RO: "Romanian",
         Language.RU: "Russian",
-        Language.SK: "Slovak",
-        Language.SL: "Slovenian",
-        Language.SV: "Swedish",
-        Language.TA: "Tamil",  # ⚠️ Only supported by speech-2.6-* models
         Language.TH: "Thai",
         Language.TR: "Turkish",
         Language.UK: "Ukrainian",
@@ -100,22 +84,13 @@ class MiniMaxHttpTTSService(TTSService):
         """Configuration parameters for MiniMax TTS.
 
         Parameters:
-            language: Language for TTS generation. Supports 40 languages.
+            language: Language for TTS generation.
-                Note: Filipino, Tamil, and Persian require speech-2.6-* models.
             speed: Speech speed (range: 0.5 to 2.0).
             volume: Speech volume (range: 0 to 10).
             pitch: Pitch adjustment (range: -12 to 12).
             emotion: Emotional tone (options: "happy", "sad", "angry", "fearful",
-                "disgusted", "surprised", "calm", "fluent").
+                "disgusted", "surprised", "neutral").
-            english_normalization: Deprecated; use `text_normalization` instead
+            english_normalization: Whether to apply English text normalization.
-
-                .. deprecated:: 0.0.96
-                    The `english_normalization` parameter is deprecated and will be removed in a future version.
-                    Use the `text_normalization` parameter instead.
-
-            text_normalization: Enable text normalization (Chinese/English).
-            latex_read: Enable LaTeX formula reading.
-            exclude_aggregated_audio: Whether to exclude aggregated audio in final chunk.
         """
 
         language: Optional[Language] = Language.EN
@@ -123,10 +98,7 @@ class MiniMaxHttpTTSService(TTSService):
         volume: Optional[float] = 1.0
         pitch: Optional[int] = 0
         emotion: Optional[str] = None
-        english_normalization: Optional[bool] = None  # Deprecated
+        english_normalization: Optional[bool] = None
-        text_normalization: Optional[bool] = None
-        latex_read: Optional[bool] = None
-        exclude_aggregated_audio: Optional[bool] = None
 
     def __init__(
         self,
@@ -148,12 +120,9 @@ class MiniMaxHttpTTSService(TTSService):
             base_url: API base URL, defaults to MiniMax's T2A endpoint.
                 Global: https://api.minimax.io/v1/t2a_v2
                 Mainland China: https://api.minimaxi.chat/v1/t2a_v2
-                Western United States: https://api-uw.minimax.io/v1/t2a_v2
             group_id: MiniMax Group ID to identify project.
-            model: TTS model name. Defaults to "speech-02-turbo". Options include:
+            model: TTS model name. Defaults to "speech-02-turbo". Options include
-                "speech-2.6-hd", "speech-2.6-turbo" (latest, supports Filipino/Tamil/Persian),
+                "speech-02-hd", "speech-02-turbo", "speech-01-hd", "speech-01-turbo".
-                "speech-02-hd", "speech-02-turbo",
-                "speech-01-hd", "speech-01-turbo".
             voice_id: Voice identifier. Defaults to "Calm_Woman".
             aiohttp_session: aiohttp.ClientSession for API communication.
             sample_rate: Output audio sample rate in Hz. If None, uses pipeline default.
@@ -207,34 +176,15 @@ class MiniMaxHttpTTSService(TTSService):
                 "disgusted",
                 "surprised",
                 "neutral",
-                "fluent",
             ]
             if params.emotion in supported_emotions:
                 self._settings["voice_setting"]["emotion"] = params.emotion
             else:
-                logger.warning(
+                logger.warning(f"Unsupported emotion: {params.emotion}. Using default.")
-                    f"Unsupported emotion: {params.emotion}. Supported emotions: {supported_emotions}"
-                )
 
-        # If `english_normalization`, add `text_normalization` and print warning
+        # Add english_normalization if provided
         if params.english_normalization is not None:
-            import warnings
+            self._settings["english_normalization"] = params.english_normalization
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "Parameter `english_normalization` is deprecated and will be removed in a future version. Use `text_normalization` instead.",
-                    DeprecationWarning,
-                )
-            self._settings["voice_setting"]["text_normalization"] = params.english_normalization
-
-        # Add text_normalization if provided (corrected parameter name)
-        if params.text_normalization is not None:
-            self._settings["voice_setting"]["text_normalization"] = params.text_normalization
-
-        # Add latex_read if provided
-        if params.latex_read is not None:
-            self._settings["voice_setting"]["latex_read"] = params.latex_read
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -244,6 +194,15 @@ class MiniMaxHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that MiniMax TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that MiniMax's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to MiniMax service language format.
 
@@ -281,7 +240,7 @@ class MiniMaxHttpTTSService(TTSService):
         """
         await super().start(frame)
         self._settings["audio_setting"]["sample_rate"] = self.sample_rate
-        logger.debug(f"MiniMax TTS initialized with sample_rate: {self.sample_rate}")
+        logger.debug(f"MiniMax TTS initialized with sample rate: {self.sample_rate}")
 
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
@@ -314,6 +273,7 @@ class MiniMaxHttpTTSService(TTSService):
             ) as response:
                 if response.status != 200:
                     error_message = f"MiniMax TTS error: HTTP {response.status}"
+                    logger.error(error_message)
                     yield ErrorFrame(error=error_message)
                     return
 
@@ -379,19 +339,16 @@ class MiniMaxHttpTTSService(TTSService):
                                             num_channels=1,
                                         )
                                 except ValueError as e:
-                                    logger.error(
+                                    logger.error(f"Error converting hex to binary: {e}")
-                                        f"Error converting hex to binary: {e}",
-                                    )
                                     continue
 
                         except json.JSONDecodeError as e:
-                            logger.error(
+                            logger.error(f"Error decoding JSON: {e}, data: {data_block[:100]}")
-                                f"Error decoding JSON: {e}, data: {data_block[:100]}",
-                            )
                             continue
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/moondream/vision.py

@@ -110,6 +110,7 @@ class MoondreamService(VisionService):
                   if analysis fails.
         """
         if not self._model:
+            logger.error(f"{self} error: Moondream model not available ({self.model_name})")
             yield ErrorFrame("Moondream model not available")
             return
 

src/pipecat/services/neuphonic/tts.py

@@ -151,6 +151,15 @@ class NeuphonicTTSService(InterruptibleTTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Neuphonic TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Neuphonic's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Neuphonic service language format.
 
@@ -285,7 +294,8 @@ class NeuphonicTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -298,7 +308,8 @@ class NeuphonicTTSService(InterruptibleTTSService):
                 logger.debug("Disconnecting from Neuphonic")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._websocket = None
@@ -363,14 +374,16 @@ class NeuphonicTTSService(InterruptibleTTSService):
                 await self._send_text(text)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class NeuphonicHttpTTSService(TTSService):
@@ -436,6 +449,15 @@ class NeuphonicHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Neuphonic TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Neuphonic's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Neuphonic service language format.
 
@@ -534,6 +556,7 @@ class NeuphonicHttpTTSService(TTSService):
                 if response.status != 200:
                     error_text = await response.text()
                     error_message = f"Neuphonic API error: HTTP {response.status} - {error_text}"
+                    logger.error(error_message)
                     yield ErrorFrame(error=error_message)
                     return
 
@@ -563,7 +586,8 @@ class NeuphonicHttpTTSService(TTSService):
                             yield TTSAudioRawFrame(audio_bytes, self.sample_rate, 1)
 
                     except Exception as e:
-                        yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                        logger.error(f"{self} exception: {e}")
+                        yield ErrorFrame(error=f"{self} error: {e}")
                         # Don't yield error frame for individual message failures
                         continue
 
@@ -571,7 +595,8 @@ class NeuphonicHttpTTSService(TTSService):
             logger.debug("TTS generation cancelled")
             raise
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/openai/base_llm.py

@@ -13,7 +13,13 @@ from typing import Any, Dict, List, Mapping, Optional
 
 import httpx
 from loguru import logger
-from openai import NOT_GIVEN, APITimeoutError, AsyncOpenAI, AsyncStream, DefaultAsyncHttpxClient
+from openai import (
+    NOT_GIVEN,
+    APITimeoutError,
+    AsyncOpenAI,
+    AsyncStream,
+    DefaultAsyncHttpxClient,
+)
 from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
 from pydantic import BaseModel, Field
 
@@ -340,17 +346,11 @@ class BaseOpenAILLMService(LLMService):
                     if chunk.usage.prompt_tokens_details
                     else None
                 )
-                reasoning_tokens = (
-                    chunk.usage.completion_tokens_details.reasoning_tokens
-                    if chunk.usage.completion_tokens_details
-                    else None
-                )
                 tokens = LLMTokenUsage(
                     prompt_tokens=chunk.usage.prompt_tokens,
                     completion_tokens=chunk.usage.completion_tokens,
                     total_tokens=chunk.usage.total_tokens,
                     cache_read_input_tokens=cached_tokens,
-                    reasoning_tokens=reasoning_tokens,
                 )
                 await self.start_llm_usage_metrics(tokens)
 
@@ -390,20 +390,16 @@ class BaseOpenAILLMService(LLMService):
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(
+                frame = LLMTextFrame(chunk.choices[0].delta.content)
-                    LLMTextFrame(chunk.choices[0].delta.content, skip_tts=self._get_skip_tts())
+                frame.includes_inter_frame_spaces = True
-                )
+                await self.push_frame(frame)
 
             # When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
             # we need to get LLMTextFrame for the transcript
             elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
                 "transcript"
             ):
-                await self.push_frame(
+                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"]))
-                    LLMTextFrame(
-                        chunk.choices[0].delta.audio["transcript"], skip_tts=self._get_skip_tts()
-                    )
-                )
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to
@@ -463,11 +459,11 @@ class BaseOpenAILLMService(LLMService):
 
         if context:
             try:
-                await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+                await self.push_frame(LLMFullResponseStartFrame())
                 await self.start_processing_metrics()
                 await self._process_context(context)
             except httpx.TimeoutException:
                 await self._call_event_handler("on_completion_timeout")
             finally:
                 await self.stop_processing_metrics()
-                await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+                await self.push_frame(LLMFullResponseEndFrame())

src/pipecat/services/openai/image.py

@@ -76,6 +76,7 @@ class OpenAIImageGenService(ImageGenService):
         image_url = image.data[0].url
 
         if not image_url:
+            logger.error(f"{self} No image provided in response: {image}")
             yield ErrorFrame("Image generation failed")
             return
 

src/pipecat/services/openai/realtime/llm.py

@@ -15,9 +15,10 @@ from typing import Optional
 from loguru import logger
 
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
+from pipecat.adapters.services.open_ai_realtime_adapter import (
+    OpenAIRealtimeLLMAdapter,
+)
 from pipecat.frames.frames import (
-    AggregationType,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -55,6 +56,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.llm_service import FunctionCallFromLLM, LLMService
+from pipecat.services.openai.llm import OpenAIContextAggregatorPair
 from pipecat.transcriptions.language import Language
 from pipecat.utils.time import time_now_iso8601
 from pipecat.utils.tracing.service_decorators import traced_openai_realtime, traced_stt
@@ -282,7 +284,7 @@ class OpenAIRealtimeLLMService(LLMService):
         await self._truncate_current_audio_response()
         await self.stop_all_metrics()
         if self._current_assistant_response:
-            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseEndFrame())
             # Only push TTSStoppedFrame if audio modality is enabled
             if self._is_modality_enabled("audio"):
                 await self.push_frame(TTSStoppedFrame())
@@ -441,7 +443,7 @@ class OpenAIRealtimeLLMService(LLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None
 
     async def _disconnect(self):
@@ -458,7 +460,7 @@ class OpenAIRealtimeLLMService(LLMService):
             self._completed_tool_calls = set()
             self._disconnecting = False
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     async def _ws_send(self, realtime_message):
         try:
@@ -471,11 +473,12 @@ class OpenAIRealtimeLLMService(LLMService):
                 # somehow *started* the websocket send attempt while we still
                 # had a connection)
                 return
+            logger.error(f"Error sending message to websocket: {e}")
             # In server-to-server contexts, a WebSocket error should be quite rare. Given how hard
             # it is to recover from a send-side error with proper state management, and that exponential
             # backoff for retries can have cost/stability implications for a service cluster, let's just
             # treat a send-side error as fatal.
-            await self.push_error(error_msg=f"Error sending client event: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"Error sending client event: {e}"))
 
     async def _update_settings(self):
         settings = self._session_properties
@@ -606,7 +609,7 @@ class OpenAIRealtimeLLMService(LLMService):
 
         if evt.item.role == "assistant":
             self._current_assistant_response = evt.item
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseStartFrame())
 
     async def _handle_evt_conversation_item_done(self, evt):
         """Handle conversation.item.done event - item is fully completed."""
@@ -653,25 +656,18 @@ class OpenAIRealtimeLLMService(LLMService):
     async def _handle_evt_response_done(self, evt):
         # todo: figure out whether there's anything we need to do for "cancelled" events
         # usage metrics
-        cached_tokens = (
-            evt.response.usage.input_token_details.cached_tokens
-            if hasattr(evt.response.usage, "input_token_details")
-            and evt.response.usage.input_token_details
-            else None
-        )
         tokens = LLMTokenUsage(
             prompt_tokens=evt.response.usage.input_tokens,
             completion_tokens=evt.response.usage.output_tokens,
             total_tokens=evt.response.usage.total_tokens,
-            cache_read_input_tokens=cached_tokens,
         )
         await self.start_llm_usage_metrics(tokens)
         await self.stop_processing_metrics()
-        await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseEndFrame())
         self._current_assistant_response = None
         # error handling
         if evt.response.status == "failed":
-            await self.push_error(error_msg=evt.response.status_details["error"]["message"])
+            await self.push_error(ErrorFrame(error=evt.response.status_details["error"]["message"]))
             return
         # response content
         for item in evt.response.output:
@@ -681,14 +677,16 @@ class OpenAIRealtimeLLMService(LLMService):
         # We receive text deltas (as opposed to audio transcript deltas) when
         # the output modality is "text"
         if evt.delta:
-            frame = LLMTextFrame(evt.delta, skip_tts=self._get_skip_tts())
+            frame = LLMTextFrame(evt.delta)
+            # OpenAI Realtime text already includes any necessary inter-chunk spaces
+            frame.includes_inter_frame_spaces = True
             await self.push_frame(frame)
 
     async def _handle_evt_audio_transcript_delta(self, evt):
         # We receive audio transcript deltas (as opposed to text deltas) when
         # the output modality is "audio" (the default)
         if evt.delta:
-            frame = TTSTextFrame(evt.delta, aggregated_by=AggregationType.SENTENCE)
+            frame = TTSTextFrame(evt.delta)
             # OpenAI Realtime text already includes any necessary inter-chunk spaces
             frame.includes_inter_frame_spaces = True
             await self.push_frame(frame)
@@ -763,7 +761,7 @@ class OpenAIRealtimeLLMService(LLMService):
 
     async def _handle_evt_error(self, evt):
         # Errors are fatal to this connection. Send an ErrorFrame.
-        await self.push_error(error_msg=f"Error: {evt}")
+        await self.push_error(ErrorFrame(error=f"Error: {evt}"))
 
     #
     # state and client events for the current conversation
@@ -813,9 +811,9 @@ class OpenAIRealtimeLLMService(LLMService):
             # We're done configuring the LLM for this session
             self._llm_needs_conversation_setup = False
 
-        logger.debug("Creating response")
+        logger.debug(f"Creating response")
 
-        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseStartFrame())
         await self.start_processing_metrics()
         await self.start_ttfb_metrics()
         await self.send_client_event(

src/pipecat/services/openai/tts.py

@@ -131,6 +131,15 @@ class OpenAITTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that OpenAI TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that OpenAI's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     async def set_model(self, model: str):
         """Set the TTS model to use.
 
@@ -206,4 +215,5 @@ class OpenAITTSService(TTSService):
                         yield frame
                 yield TTSStoppedFrame()
         except BadRequestError as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.exception(f"{self} error generating TTS: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/openai_realtime_beta/azure.py

@@ -79,5 +79,5 @@ class AzureRealtimeBetaLLMService(OpenAIRealtimeBetaLLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None

src/pipecat/services/openai_realtime_beta/openai.py

@@ -17,7 +17,6 @@ from loguru import logger
 
 from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
 from pipecat.frames.frames import (
-    AggregationType,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -265,7 +264,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         await self._truncate_current_audio_response()
         await self.stop_all_metrics()
         if self._current_assistant_response:
-            await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseEndFrame())
             # Only push TTSStoppedFrame if audio modality is enabled
             if self._is_modality_enabled("audio"):
                 await self.push_frame(TTSStoppedFrame())
@@ -425,7 +424,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None
 
     async def _disconnect(self):
@@ -441,7 +440,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
                 self._receive_task = None
             self._disconnecting = False
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     async def _ws_send(self, realtime_message):
         try:
@@ -450,11 +449,12 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         except Exception as e:
             if self._disconnecting:
                 return
+            logger.error(f"Error sending message to websocket: {e}")
             # In server-to-server contexts, a WebSocket error should be quite rare. Given how hard
             # it is to recover from a send-side error with proper state management, and that exponential
             # backoff for retries can have cost/stability implications for a service cluster, let's just
             # treat a send-side error as fatal.
-            await self.push_error(error_msg=f"Error sending client event: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"Error sending client event: {e}"))
 
     async def _update_settings(self):
         settings = self._session_properties
@@ -564,7 +564,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
             self._user_and_response_message_tuple = (evt.item, {"done": False, "output": []})
         elif evt.item.role == "assistant":
             self._current_assistant_response = evt.item
-            await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMFullResponseStartFrame())
 
     async def _handle_evt_input_audio_transcription_delta(self, evt):
         if self._send_transcription_frames:
@@ -623,7 +623,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         )
         await self.start_llm_usage_metrics(tokens)
         await self.stop_processing_metrics()
-        await self.push_frame(LLMFullResponseEndFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseEndFrame())
         self._current_assistant_response = None
         # error handling
         if evt.response.status == "failed":
@@ -647,12 +647,12 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
     async def _handle_evt_text_delta(self, evt):
         if evt.delta:
-            await self.push_frame(LLMTextFrame(evt.delta, skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMTextFrame(evt.delta))
 
     async def _handle_evt_audio_transcript_delta(self, evt):
         if evt.delta:
-            await self.push_frame(LLMTextFrame(evt.delta, skip_tts=self._get_skip_tts()))
+            await self.push_frame(LLMTextFrame(evt.delta))
-            await self.push_frame(TTSTextFrame(evt.delta, aggregated_by=AggregationType.SENTENCE))
+            await self.push_frame(TTSTextFrame(evt.delta))
 
     async def _handle_evt_speech_started(self, evt):
         await self._truncate_current_audio_response()
@@ -685,7 +685,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
     async def _handle_evt_error(self, evt):
         # Errors are fatal to this connection. Send an ErrorFrame.
-        await self.push_error(error_msg=f"Error: {evt}")
+        await self.push_error(ErrorFrame(error=f"Error: {evt}"))
 
     async def _handle_assistant_output(self, output):
         # We haven't seen intermixed audio and function_call items in the same response. But let's
@@ -747,7 +747,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
         logger.debug(f"Creating response: {self._context.get_messages_for_logging()}")
 
-        await self.push_frame(LLMFullResponseStartFrame(skip_tts=self._get_skip_tts()))
+        await self.push_frame(LLMFullResponseStartFrame())
         await self.start_processing_metrics()
         await self.start_ttfb_metrics()
         await self.send_client_event(

src/pipecat/services/piper/tts.py

@@ -66,6 +66,15 @@ class PiperTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Piper TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Piper's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         """Generate speech from text using Piper's HTTP API.
@@ -88,6 +97,9 @@ class PiperTTSService(TTSService):
             ) as response:
                 if response.status != 200:
                     error = await response.text()
+                    logger.error(
+                        f"{self} error getting audio (status: {response.status}, error: {error})"
+                    )
                     yield ErrorFrame(
                         error=f"Error getting audio (status: {response.status}, error: {error})"
                     )
@@ -106,7 +118,7 @@ class PiperTTSService(TTSService):
                     yield frame
         except Exception as e:
             logger.error(f"{self} exception: {e}")
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             logger.debug(f"{self}: Finished TTS [{text}]")
             await self.stop_ttfb_metrics()

src/pipecat/services/playht/tts.py

@@ -266,7 +266,8 @@ class PlayHTTTSService(InterruptibleTTSService):
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -279,7 +280,8 @@ class PlayHTTTSService(InterruptibleTTSService):
                 logger.debug("Disconnecting from PlayHT")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._request_id = None
             self._websocket = None
@@ -349,7 +351,8 @@ class PlayHTTTSService(InterruptibleTTSService):
                             await self.push_frame(TTSStoppedFrame())
                             self._request_id = None
                     elif "error" in msg:
-                        await self.push_error(error_msg=f"Error: {msg['error']}")
+                        logger.error(f"{self} error: {msg}")
+                        await self.push_error(ErrorFrame(error=f"{self} error: {msg['error']}"))
                 except json.JSONDecodeError:
                     logger.error(f"Invalid JSON message: {message}")
 
@@ -391,7 +394,8 @@ class PlayHTTTSService(InterruptibleTTSService):
                 await self._get_websocket().send(json.dumps(tts_command))
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
@@ -401,7 +405,8 @@ class PlayHTTTSService(InterruptibleTTSService):
             yield None
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class PlayHTHttpTTSService(TTSService):
@@ -621,7 +626,8 @@ class PlayHTHttpTTSService(TTSService):
                             yield frame
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/rime/tts.py

@@ -113,10 +113,6 @@ class RimeTTSService(AudioContextWordTTSService):
             sample_rate: Audio sample rate in Hz.
             params: Additional configuration parameters.
             text_aggregator: Custom text aggregator for processing input text.
-
-                .. deprecated:: 0.0.95
-                    Use an LLMTextProcessor before the TTSService for custom text aggregation.
-
             aggregate_sentences: Whether to aggregate sentences within the TTSService.
             **kwargs: Additional arguments passed to parent class.
         """
@@ -127,17 +123,10 @@ class RimeTTSService(AudioContextWordTTSService):
             push_stop_frames=True,
             pause_frame_processing=True,
             sample_rate=sample_rate,
+            text_aggregator=text_aggregator or SkipTagsAggregator([("spell(", ")")]),
             **kwargs,
         )
 
-        if not text_aggregator:
-            # Always skip tags added for spelled-out text
-            # Note: This is primarily to support backwards compatibility.
-            #    The preferred way of taking advantage of Rime spelling is
-            #    to use an LLMTextProcessor and/or a text_transformer to identify
-            #    and insert these tags for the purpose of the TTS service alone.
-            self._text_aggregator = SkipTagsAggregator([("spell(", ")")])
-
         params = params or RimeTTSService.InputParams()
 
         # Store service configuration
@@ -163,7 +152,6 @@ class RimeTTSService(AudioContextWordTTSService):
         self._context_id = None  # Tracks current turn
         self._receive_task = None
         self._cumulative_time = 0  # Accumulates time across messages
-        self._extra_msg_fields = {}  # Extra fields for next message
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -193,31 +181,6 @@ class RimeTTSService(AudioContextWordTTSService):
         self._model = model
         await super().set_model(model)
 
-    # A set of Rime-specific helpers for text transformations
-    def SPELL(text: str) -> str:
-        """Wrap text in Rime spell function."""
-        return f"spell({text})"
-
-    def PAUSE_TAG(seconds: float) -> str:
-        """Convenience method to create a pause tag."""
-        return f"<{seconds * 1000}>"
-
-    def PRONOUNCE(self, text: str, word: str, phoneme: str) -> str:
-        """Convenience method to support Rime's custom pronunciations feature.
-
-        https://docs.rime.ai/api-reference/custom-pronunciation
-        """
-        self._extra_msg_fields["phonemizeBetweenBrackets"] = True
-        return text.replace(word, f"{phoneme}")
-
-    def INLINE_SPEED(self, text: str, speed: float) -> str:
-        """Convenience method to support inline speeds."""
-        if not self._extra_msg_fields:
-            self._extra_msg_fields = {}
-        speed_vals = self._extra_msg_fields.get("inlineSpeedAlpha", "").split(",")
-        self._extra_msg_fields["inlineSpeedAlpha"] = ",".join(speed_vals + [str(speed)])
-        return f"[{text}]"
-
     async def _update_settings(self, settings: Mapping[str, Any]):
         """Update service settings and reconnect if voice changed."""
         prev_voice = self._voice_id
@@ -230,11 +193,7 @@ class RimeTTSService(AudioContextWordTTSService):
 
     def _build_msg(self, text: str = "") -> dict:
         """Build JSON message for Rime API."""
-        msg = {"text": text, "contextId": self._context_id}
+        return {"text": text, "contextId": self._context_id}
-        if self._extra_msg_fields:
-            msg |= self._extra_msg_fields
-            self._extra_msg_fields = {}
-        return msg
 
     def _build_clear_msg(self) -> dict:
         """Build clear operation message."""
@@ -300,7 +259,8 @@ class RimeTTSService(AudioContextWordTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -312,7 +272,8 @@ class RimeTTSService(AudioContextWordTTSService):
                 await self._websocket.send(json.dumps(self._build_eos_msg()))
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._context_id = None
             self._websocket = None
@@ -405,9 +366,10 @@ class RimeTTSService(AudioContextWordTTSService):
                         logger.debug(f"Updated cumulative time to: {self._cumulative_time}")
 
             elif msg["type"] == "error":
+                logger.error(f"{self} error: {msg}")
                 await self.push_frame(TTSStoppedFrame())
                 await self.stop_all_metrics()
-                await self.push_error(error_msg=f"Error: {msg['message']}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {msg['message']}"))
                 self._context_id = None
 
     async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
@@ -449,14 +411,16 @@ class RimeTTSService(AudioContextWordTTSService):
                 await self._get_websocket().send(json.dumps(msg))
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class RimeHttpTTSService(TTSService):
@@ -537,6 +501,15 @@ class RimeHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Rime TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Rime's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> str | None:
         """Convert pipecat language to Rime language code.
 
@@ -587,6 +560,7 @@ class RimeHttpTTSService(TTSService):
             ) as response:
                 if response.status != 200:
                     error_message = f"Rime TTS error: HTTP {response.status}"
+                    logger.error(error_message)
                     yield ErrorFrame(error=error_message)
                     return
 
@@ -604,7 +578,8 @@ class RimeHttpTTSService(TTSService):
                     yield frame
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/riva/stt.py

@@ -655,10 +655,12 @@ class RivaSegmentedSTTService(SegmentedSTTService):
                     logger.debug("No transcription results found in Riva response")
 
             except AttributeError as ae:
+                logger.error(f"Unexpected response structure from Riva: {ae}")
                 yield ErrorFrame(f"Unexpected Riva response format: {str(ae)}")
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class ParakeetSTTService(RivaSTTService):

src/pipecat/services/riva/tts.py

@@ -113,6 +113,15 @@ class RivaTTSService(TTSService):
             riva.client.proto.riva_tts_pb2.RivaSynthesisConfigRequest()
         )
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Riva TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Riva's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     async def set_model(self, model: str):
         """Attempt to set the TTS model.
 
@@ -157,6 +166,7 @@ class RivaTTSService(TTSService):
                 add_response(None)
             except Exception as e:
                 logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 add_response(None)
 
         await self.start_ttfb_metrics()
@@ -180,7 +190,7 @@ class RivaTTSService(TTSService):
                 yield frame
                 resp = await asyncio.wait_for(queue.get(), timeout=RIVA_TTS_TIMEOUT_SECS)
         except asyncio.TimeoutError:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} timeout waiting for audio response")
 
         await self.start_tts_usage_metrics(text)
         yield TTSStoppedFrame()

src/pipecat/services/sambanova/llm.py

@@ -14,7 +14,9 @@ from openai import AsyncStream
 from openai.types.chat import ChatCompletionChunk
 
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
-from pipecat.frames.frames import LLMTextFrame
+from pipecat.frames.frames import (
+    LLMTextFrame,
+)
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -174,20 +176,16 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(
+                frame = LLMTextFrame(chunk.choices[0].delta.content)
-                    LLMTextFrame(chunk.choices[0].delta.content, skip_tts=self._get_skip_tts())
+                frame.includes_inter_frame_spaces = True
-                )
+                await self.push_frame(frame)
 
             # When gpt-4o-audio / gpt-4o-mini-audio is used for llm or stt+llm
             # we need to get LLMTextFrame for the transcript
             elif hasattr(chunk.choices[0].delta, "audio") and chunk.choices[0].delta.audio.get(
                 "transcript"
             ):
-                await self.push_frame(
+                await self.push_frame(LLMTextFrame(chunk.choices[0].delta.audio["transcript"]))
-                    LLMTextFrame(
-                        chunk.choices[0].delta.audio["transcript"], skip_tts=self._get_skip_tts()
-                    )
-                )
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to

src/pipecat/services/sarvam/stt.py

@@ -275,7 +275,8 @@ class SarvamSTTService(STTService):
                 await self._socket_client.translate(**method_kwargs)
 
         except Exception as e:
-            yield ErrorFrame(error=f"Error sending audio to Sarvam: {e}", exception=e)
+            logger.error(f"Error sending audio to Sarvam: {e}")
+            await self.push_error(ErrorFrame(f"Failed to send audio: {e}"))
 
         yield None
 
@@ -331,11 +332,13 @@ class SarvamSTTService(STTService):
             logger.info("Connected to Sarvam successfully")
 
         except ApiError as e:
-            await self.push_error(error_msg=f"Sarvam API error: {e}", exception=e)
+            logger.error(f"Sarvam API error: {e}")
+            await self.push_error(ErrorFrame(f"Sarvam API error: {e}"))
         except Exception as e:
+            logger.error(f"Failed to connect to Sarvam: {e}")
             self._socket_client = None
             self._websocket_context = None
-            await self.push_error(error_msg=f"Failed to connect to Sarvam: {e}", exception=e)
+            await self.push_error(ErrorFrame(f"Failed to connect to Sarvam: {e}"))
 
     async def _disconnect(self):
         """Disconnect from Sarvam WebSocket API using SDK."""
@@ -348,9 +351,7 @@ class SarvamSTTService(STTService):
                 # Exit the async context manager
                 await self._websocket_context.__aexit__(None, None, None)
             except Exception as e:
-                await self.push_error(
+                logger.error(f"Error closing WebSocket connection: {e}")
-                    error_msg=f"Error closing WebSocket connection: {e}", exception=e
-                )
             finally:
                 logger.debug("Disconnected from Sarvam WebSocket")
                 self._socket_client = None
@@ -370,7 +371,8 @@ class SarvamSTTService(STTService):
             # Messages will be handled via the _message_handler callback
             await self._socket_client.start_listening()
         except Exception as e:
-            await self.push_error(error_msg=f"Sarvam receive task error: {e}", exception=e)
+            logger.error(f"Error in Sarvam receive task: {e}")
+            await self.push_error(ErrorFrame(f"Sarvam receive task error: {e}"))
 
     async def _handle_message(self, message):
         """Handle incoming WebSocket message from Sarvam SDK.
@@ -425,7 +427,8 @@ class SarvamSTTService(STTService):
                 await self.stop_processing_metrics()
 
         except Exception as e:
-            await self.push_error(error_msg=f"Failed to handle message: {e}", exception=e)
+            logger.error(f"Error handling Sarvam message: {e}")
+            await self.push_error(ErrorFrame(f"Failed to handle message: {e}"))
             await self.stop_all_metrics()
 
     @traced_stt

src/pipecat/services/sarvam/tts.py

@@ -195,6 +195,15 @@ class SarvamHttpTTSService(TTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Sarvam TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Sarvam's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Sarvam AI language format.
 
@@ -254,7 +263,8 @@ class SarvamHttpTTSService(TTSService):
             async with self._session.post(url, json=payload, headers=headers) as response:
                 if response.status != 200:
                     error_text = await response.text()
-                    yield ErrorFrame(error=f"Sarvam API error: {error_text}")
+                    logger.error(f"Sarvam API error: {error_text}")
+                    await self.push_error(ErrorFrame(error=f"Sarvam API error: {error_text}"))
                     return
 
                 response_data = await response.json()
@@ -263,7 +273,8 @@ class SarvamHttpTTSService(TTSService):
 
             # Decode base64 audio data
             if "audios" not in response_data or not response_data["audios"]:
-                yield ErrorFrame(error="No audio data received")
+                logger.error("No audio data received from Sarvam API")
+                await self.push_error(ErrorFrame(error="No audio data received"))
                 return
 
             # Get the first audio (there should be only one for single text input)
@@ -284,7 +295,8 @@ class SarvamHttpTTSService(TTSService):
             yield frame
 
         except Exception as e:
-            yield ErrorFrame(error=f"Error generating TTS: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()
@@ -455,6 +467,15 @@ class SarvamTTSService(InterruptibleTTSService):
         """
         return True
 
+    @property
+    def includes_inter_frame_spaces(self) -> bool:
+        """Indicates that Sarvam TTSTextFrames include necessary inter-frame spaces.
+
+        Returns:
+            True, indicating that Sarvam's text frames include necessary inter-frame spaces.
+        """
+        return True
+
     def language_to_service_language(self, language: Language) -> Optional[str]:
         """Convert a Language enum to Sarvam AI language format.
 
@@ -557,7 +578,8 @@ class SarvamTTSService(InterruptibleTTSService):
             await self._disconnect_websocket()
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # Reset state only after everything is cleaned up
             self._started = False
@@ -581,9 +603,8 @@ class SarvamTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(
+            logger.error(f"{self} exception: {e}")
-                error_msg=f"Error connecting to Sarvam TTS Websocket: {e}", exception=e
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-            )
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -599,7 +620,8 @@ class SarvamTTSService(InterruptibleTTSService):
             await self._websocket.send(json.dumps(config_message))
             logger.debug("Configuration sent successfully")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             raise
 
     async def _disconnect_websocket(self):
@@ -611,7 +633,8 @@ class SarvamTTSService(InterruptibleTTSService):
                 logger.debug("Disconnecting from Sarvam")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._websocket = None
@@ -635,7 +658,7 @@ class SarvamTTSService(InterruptibleTTSService):
                     await self.push_frame(frame)
                 elif msg.get("type") == "error":
                     error_msg = msg["data"]["message"]
-                    await self.push_error(error_msg=f"TTS Error: {error_msg}")
+                    logger.error(f"TTS Error: {error_msg}")
 
                     # If it's a timeout error, the connection might need to be reset
                     if "too long" in error_msg.lower() or "timeout" in error_msg.lower():
@@ -697,11 +720,13 @@ class SarvamTTSService(InterruptibleTTSService):
                 await self._send_text(text)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/simli/video.py

@@ -48,14 +48,12 @@ class SimliVideoService(FrameProcessor):
         """Input parameters for Simli video configuration.
 
         Parameters:
-            enable_logging: Whether to enable Simli logging.
             max_session_length: Absolute maximum session duration in seconds.
                 Avatar will disconnect after this time even if it's speaking.
             max_idle_time: Maximum duration in seconds the avatar is not speaking
                 before the avatar disconnects.
         """
 
-        enable_logging: Optional[bool] = None
         max_session_length: Optional[int] = None
         max_idle_time: Optional[int] = None
 
@@ -86,10 +84,6 @@ class SimliVideoService(FrameProcessor):
                     Please use 'api_key' and 'face_id' parameters instead.
 
             use_turn_server: Whether to use TURN server for connection. Defaults to False.
-
-                .. deprecated:: 0.0.95
-                    The 'use_turn_server' parameter is deprecated and will be removed in a future version.
-
             latency_interval: Latency interval setting for sending health checks to check
                 the latency to Simli Servers. Defaults to 0.
             simli_url: URL of the simli servers. Can be changed for custom deployments
@@ -141,22 +135,15 @@ class SimliVideoService(FrameProcessor):
 
             config = SimliConfig(**config_kwargs)
 
-        if use_turn_server:
-            warnings.warn(
-                "The 'use_turn_server' parameter is deprecated and will be removed in a future version.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
         self._initialized = False
         # Add buffer time to session limits
         config.maxIdleTime += 5
         config.maxSessionLength += 5
         self._simli_client = SimliClient(
-            config=config,
+            config,
-            latencyInterval=latency_interval,
+            use_turn_server,
+            latency_interval,
             simliURL=simli_url,
-            enable_logging=params.enable_logging or False,
         )
 
         self._pipecat_resampler: AudioResampler = None
@@ -181,7 +168,7 @@ class SimliVideoService(FrameProcessor):
             self._audio_task = self.create_task(self._consume_and_process_audio())
             self._video_task = self.create_task(self._consume_and_process_video())
         except Exception as e:
-            await self.push_error(error_msg=f"Unable to start connection: {e}", exception=e)
+            logger.error(f"{self}: unable to start connection: {e}")
 
     async def _consume_and_process_audio(self):
         """Consume audio frames from Simli and push them downstream."""
@@ -259,7 +246,7 @@ class SimliVideoService(FrameProcessor):
                         await self._simli_client.send(audioBytes)
                 return
             except Exception as e:
-                await self.push_error(error_msg=f"Error sending audio: {e}", exception=e)
+                logger.exception(f"{self} exception: {e}")
         elif isinstance(frame, TTSStoppedFrame):
             try:
                 if self._previously_interrupted and len(self._audio_buffer) > 0:
@@ -267,7 +254,7 @@ class SimliVideoService(FrameProcessor):
                     self._previously_interrupted = False
                     self._audio_buffer = bytearray()
             except Exception as e:
-                await self.push_error(error_msg=f"Error stopping TTS: {e}", exception=e)
+                logger.exception(f"{self} exception: {e}")
             return
         elif isinstance(frame, (EndFrame, CancelFrame)):
             await self._stop()

src/pipecat/services/soniox/stt.py

@@ -194,7 +194,7 @@ class SonioxSTTService(STTService):
         self._websocket = await websocket_connect(self._url)
 
         if not self._websocket:
-            await self.push_error(error_msg=f"Unable to connect to Soniox API at {self._url}")
+            logger.error(f"Unable to connect to Soniox API at {self._url}")
 
         # If vad_force_turn_endpoint is not enabled, we need to enable endpoint detection.
         # Either one or the other is required.
@@ -327,7 +327,8 @@ class SonioxSTTService(STTService):
             # Expected when closing the connection
             logger.debug("WebSocket connection closed, keepalive task stopped.")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _receive_task_handler(self):
         if not self._websocket:
@@ -403,8 +404,13 @@ class SonioxSTTService(STTService):
                 if error_code or error_message:
                     # In case of error, still send the final transcript (if any remaining in the buffer).
                     await send_endpoint_transcript()
+                    logger.error(
+                        f"{self} error: {error_code} (_receive_task_handler) - {error_message}"
+                    )
                     await self.push_error(
-                        error_msg=f"Error: {error_code} (_receive_task_handler) - {error_message}"
+                        ErrorFrame(
+                            error=f"{self} error: {error_code} (_receive_task_handler) - {error_message}"
+                        )
                     )
 
                 finished = content.get("finished")
@@ -419,4 +425,5 @@ class SonioxSTTService(STTService):
             # Expected when closing the connection.
             pass
         except Exception as e:
-            await self.push_error(error_msg=f"Error receiving message: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))

src/pipecat/services/speechmatics/stt.py

@@ -467,7 +467,8 @@ class SpeechmaticsSTTService(STTService):
                 await self._client.send_audio(audio)
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
             await self._disconnect()
 
     def update_params(
@@ -513,7 +514,8 @@ class SpeechmaticsSTTService(STTService):
                 self._client.send_message(payload), self.get_event_loop()
             )
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             raise RuntimeError(f"error sending message to STT: {e}")
 
     async def _connect(self) -> None:
@@ -579,7 +581,8 @@ class SpeechmaticsSTTService(STTService):
             logger.debug(f"{self} Connected to Speechmatics STT service")
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting to Speechmatics: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._client = None
 
     async def _disconnect(self) -> None:
@@ -593,9 +596,8 @@ class SpeechmaticsSTTService(STTService):
         except asyncio.TimeoutError:
             logger.warning(f"{self} Timeout while closing Speechmatics client connection")
         except Exception as e:
-            await self.push_error(
+            logger.error(f"{self} exception: {e}")
-                error_msg=f"Error disconnecting from Speechmatics: {e}", exception=e
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-            )
         finally:
             self._client = None
             await self._call_event_handler("on_disconnected")