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Remove deprecated text_aggregator and text_filter params from TTS

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Remove the deprecated text_aggregator parameter from TTSService,
CartesiaTTSService, and RimeTTSService, and the deprecated text_filter
parameter from TTSService. Users should use LLMTextProcessor before
the TTS service instead. Update the voice-switching example to use
LLMTextProcessor with PatternPairAggregator.
This commit is contained in:
Mark Backman
2026-03-31 19:12:33 -04:00
parent a3c7f6c2af
commit e74930b954
4 changed files with 34 additions and 80 deletions
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28
examples/features/features-pattern-pair-voice-switching.py
View File

@@ -45,7 +45,7 @@ from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.frames.frames import LLMRunFrame, TTSUpdateSettingsFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -54,6 +54,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
LLMContextAggregatorPair,
LLMUserAggregatorParams,
)
from pipecat.processors.aggregators.llm_text_processor import LLMTextProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
@@ -100,39 +101,43 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
# Create pattern pair aggregator for voice switching
pattern_aggregator = PatternPairAggregator()
llm_text_aggregator = PatternPairAggregator()
# Add pattern for voice switching
pattern_aggregator.add_pattern(
llm_text_aggregator.add_pattern(
type="voice",
start_pattern="<voice>",
end_pattern="</voice>",
action=MatchAction.REMOVE, # Remove tags from final text
action=MatchAction.AGGREGATE,
)
# Register handler for voice switching
async def on_voice_tag(match: PatternMatch):
voice_name = match.text.strip().lower()
if voice_name in VOICE_IDS:
# First flush any existing audio to finish the current context
await tts.flush_audio()
# Then set the new voice
await tts.set_voice(VOICE_IDS[voice_name])
await llm_text_processor.push_frame(
TTSUpdateSettingsFrame(
delta=CartesiaTTSService.Settings(voice=VOICE_IDS[voice_name])
)
)
logger.info(f"Switched to {voice_name} voice")
else:
logger.warning(f"Unknown voice: {voice_name}")
pattern_aggregator.on_pattern_match("voice", on_voice_tag)
llm_text_aggregator.on_pattern_match("voice", on_voice_tag)
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
# Process LLM text through the pattern aggregator before TTS
llm_text_processor = LLMTextProcessor(text_aggregator=llm_text_aggregator)
# Initialize TTS with narrator voice as default
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
settings=CartesiaTTSService.Settings(
voice=VOICE_IDS["narrator"],
),
text_aggregator=pattern_aggregator,
skip_aggregator_types=["voice"], # Skip voice tags in TTS speech
)
# System prompt for storytelling with voice switching
@@ -204,7 +209,8 @@ Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue.""
stt,
user_aggregator,
llm,
tts, # TTS with pattern aggregator
llm_text_processor,
tts,
transport.output(),
assistant_aggregator,
]

25
src/pipecat/services/cartesia/tts.py
View File

@@ -28,7 +28,6 @@ from pipecat.frames.frames import (
from pipecat.services.settings import NOT_GIVEN, TTSSettings, _NotGiven
from pipecat.services.tts_service import TextAggregationMode, TTSService, WebsocketTTSService
from pipecat.transcriptions.language import Language, resolve_language
from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator
from pipecat.utils.tracing.service_decorators import traced_tts
@@ -240,7 +239,6 @@ class CartesiaTTSService(WebsocketTTSService):
container: str = "raw",
params: Optional[InputParams] = None,
settings: Optional[Settings] = None,
text_aggregator: Optional[BaseTextAggregator] = None,
text_aggregation_mode: Optional[TextAggregationMode] = None,
aggregate_sentences: Optional[bool] = None,
**kwargs,
@@ -271,11 +269,6 @@ class CartesiaTTSService(WebsocketTTSService):
settings: Runtime-updatable settings. When provided alongside deprecated
parameters, ``settings`` values take precedence.
text_aggregator: Custom text aggregator for processing input text.
.. deprecated:: 0.0.95
Use an LLMTextProcessor before the TTSService for custom text aggregation.
text_aggregation_mode: How to aggregate incoming text before synthesis.
aggregate_sentences: Whether to aggregate sentences within the TTSService.
@@ -337,20 +330,18 @@ class CartesiaTTSService(WebsocketTTSService):
pause_frame_processing=False,
sample_rate=sample_rate,
push_start_frame=True,
text_aggregator=text_aggregator,
settings=default_settings,
**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>")], aggregation_type=self._text_aggregation_mode
)
# 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>")], aggregation_type=self._text_aggregation_mode
)
self._api_key = api_key
self._cartesia_version = cartesia_version

24
src/pipecat/services/rime/tts.py
View File

@@ -37,7 +37,6 @@ from pipecat.services.tts_service import (
WebsocketTTSService,
)
from pipecat.transcriptions.language import Language, resolve_language
from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator
from pipecat.utils.tracing.service_decorators import traced_tts
@@ -176,7 +175,6 @@ class RimeTTSService(WebsocketTTSService):
sample_rate: Optional[int] = None,
params: Optional[InputParams] = None,
settings: Optional[Settings] = None,
text_aggregator: Optional[BaseTextAggregator] = None,
text_aggregation_mode: Optional[TextAggregationMode] = None,
aggregate_sentences: Optional[bool] = None,
**kwargs,
@@ -204,11 +202,6 @@ class RimeTTSService(WebsocketTTSService):
settings: Runtime-updatable settings. When provided alongside deprecated
parameters, ``settings`` values take precedence.
text_aggregator: Custom text aggregator for processing input text.
.. deprecated:: 0.0.95
Use an LLMTextProcessor before the TTSService for custom text aggregation.
text_aggregation_mode: How to aggregate incoming text before synthesis.
aggregate_sentences: Deprecated. Use text_aggregation_mode instead.
@@ -282,15 +275,14 @@ class RimeTTSService(WebsocketTTSService):
self._audio_format = "pcm"
self._sampling_rate = 0 # updated in start()
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(", ")")], aggregation_type=self._text_aggregation_mode
)
# 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(", ")")], aggregation_type=self._text_aggregation_mode
)
# Store service configuration
self._api_key = api_key

37
src/pipecat/services/tts_service.py
View File

@@ -58,7 +58,6 @@ from pipecat.services.ai_service import AIService
from pipecat.services.settings import TTSSettings, is_given
from pipecat.services.websocket_service import WebsocketService
from pipecat.transcriptions.language import Language
from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.text.base_text_filter import BaseTextFilter
from pipecat.utils.text.simple_text_aggregator import SimpleTextAggregator
from pipecat.utils.time import seconds_to_nanoseconds
@@ -168,8 +167,6 @@ class TTSService(AIService):
append_trailing_space: bool = False,
# TTS output sample rate
sample_rate: Optional[int] = None,
# Text aggregator to aggregate incoming tokens and decide when to push to the TTS.
text_aggregator: Optional[BaseTextAggregator] = None,
# Types of text aggregations that should not be spoken.
skip_aggregator_types: Optional[List[str]] = [],
# A list of callables to transform text before just before sending it to TTS.
@@ -182,7 +179,6 @@ class TTSService(AIService):
] = None,
# Text filter executed after text has been aggregated.
text_filters: Optional[Sequence[BaseTextFilter]] = None,
text_filter: Optional[BaseTextFilter] = None,
# Audio transport destination of the generated frames.
transport_destination: Optional[str] = None,
settings: Optional[TTSSettings] = None,
@@ -215,11 +211,6 @@ class TTSService(AIService):
append_trailing_space: Whether to append a trailing space to text before sending to TTS.
This helps prevent some TTS services from vocalizing trailing punctuation (e.g., "dot").
sample_rate: Output sample rate for generated audio.
text_aggregator: Custom text aggregator for processing incoming text.
.. deprecated:: 0.0.95
Use an LLMTextProcessor before the TTSService for custom text aggregation.
skip_aggregator_types: List of aggregation types that should not be spoken.
text_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
@@ -227,11 +218,6 @@ class TTSService(AIService):
(aggregation_type | '*', transform_function).
text_filters: Sequence of text filters to apply after aggregation.
text_filter: Single text filter (deprecated, use text_filters).
.. deprecated:: 0.0.59
Use `text_filters` instead, which allows multiple filters.
transport_destination: Destination for generated audio frames.
settings: The runtime-updatable settings for the TTS service.
reuse_context_id_within_turn: Whether the service should reuse context IDs within the
@@ -300,18 +286,7 @@ class TTSService(AIService):
self._append_trailing_space: bool = append_trailing_space
self._init_sample_rate = sample_rate
self._sample_rate = 0
self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator(
aggregation_type=self._text_aggregation_mode
)
if text_aggregator:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("always")
warnings.warn(
"Parameter 'text_aggregator' is deprecated. Use an LLMTextProcessor before the TTSService for custom text aggregation.",
DeprecationWarning,
)
self._text_aggregator = SimpleTextAggregator(aggregation_type=self._text_aggregation_mode)
self._skip_aggregator_types: List[str] = skip_aggregator_types or []
self._text_transforms: List[
@@ -320,16 +295,6 @@ class TTSService(AIService):
# TODO: Deprecate _text_filters when added to LLMTextProcessor
self._text_filters: Sequence[BaseTextFilter] = text_filters or []
self._transport_destination: Optional[str] = transport_destination
if text_filter:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("always")
warnings.warn(
"Parameter 'text_filter' is deprecated, use 'text_filters' instead.",
DeprecationWarning,
)
self._text_filters = [text_filter]
self._resampler = create_stream_resampler()