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Final PR Feedback changes

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This commit is contained in:
mattie ruth backman
2025-11-14 13:51:31 -05:00
parent 71b87fd420
commit 713b488bb6
5 changed files with 56 additions and 43 deletions
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54
src/pipecat/services/tts_service.py
View File

@@ -584,36 +584,38 @@ class TTSService(AIService):
await filter.reset_interruption()
text = await filter.filter(text)
if text:
if not self._push_text_frames:
# In a typical pipeline, there is an assistant context aggregator
# that listens for TTSTextFrames to add spoken text to the context.
# If the TTS service supports word timestamps, then _push_text_frames
# is set to False and these are sent word by word as part of the
# _words_task_handler in the WordTTSService subclass. However, to
# support use cases where an observer may want the full text before
# the audio is generated, we send along the AggregatedTextFrame here,
# but we set append_to_context to False so it does not cause duplication
# in the context. This is primarily used by the RTVIObserver to
# generate a complete bot-output.
src_frame.append_to_context = False
await self.push_frame(src_frame)
# Note: Text transformations only affect the text sent to the TTS. This allows
# for explicit TTS-specific modifications (e.g., inserting TTS supported tags
# for spelling or emotion or replacing an @ with "at"). For TTS services that
# support word-level timestamps, this DOES affect the resulting context as the
# the context is built from the TTSTextFrames generated during word timestamping.
for aggregation_type, transform in self._text_transforms:
if aggregation_type == type or aggregation_type == "*":
text = await transform(text, type)
await self.process_generator(self.run_tts(text))
if not text.strip():
await self.stop_processing_metrics()
return
# To support use cases that may want to know the text before it's spoken, we
# push the AggregatedTextFrame version before transforming and sending to TTS.
# However, we do not want to add this text to the assistant context until it
# is spoken, so we set append_to_context to False.
src_frame.append_to_context = False
await self.push_frame(src_frame)
# Note: Text transformations are meant to only affect the text sent to the TTS for
# TTS-specific purposes. This allows for explicit TTS modifications (e.g., inserting
# TTS supported tags for spelling or emotion or replacing an @ with "at"). For TTS
# services that support word-level timestamps, this CAN affect the resulting context
# since the TTSTextFrames are generated from the TTS output stream
transformed_text = text
for aggregation_type, transform in self._text_transforms:
if aggregation_type == type or aggregation_type == "*":
transformed_text = await transform(transformed_text, type)
await self.process_generator(self.run_tts(transformed_text))
await self.stop_processing_metrics()
if self._push_text_frames:
# In the case where the TTS service does not support word timestamps,
# we send the full aggregated text after the audio. This way, if we are
# interrupted, the text is not added to the assistant context.
# In TTS services that support word timestamps, the TTSTextFrames
# are pushed as words are spoken. However, in the case where the TTS service
# does not support word timestamps (i.e. _push_text_frames is True), we send
# the original (non-transformed) text after the TTS generation has completed.
# This way, if we are interrupted, the text is not added to the assistant
# context and the context that IS added does not include TTS-specific tags
# or transformations.
frame = TTSTextFrame(text, aggregated_by=type)
frame.includes_inter_frame_spaces = self.includes_inter_frame_spaces
await self.push_frame(frame)

11
src/pipecat/utils/text/base_text_aggregator.py
View File

@@ -13,9 +13,20 @@ aggregated text should be sent for speech synthesis.
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from typing import Optional
class AggregationType(str, Enum):
"""Built-in aggregation strings."""
SENTENCE = "sentence"
WORD = "word"
def __str__(self):
return self.value
@dataclass
class Aggregation:
"""Data class representing aggregated text and its type.

22
src/pipecat/utils/text/pattern_pair_aggregator.py
View File

@@ -18,7 +18,7 @@ from typing import Awaitable, Callable, List, Optional, Tuple
from loguru import logger
from pipecat.utils.string import match_endofsentence
from pipecat.utils.text.base_text_aggregator import Aggregation, BaseTextAggregator
from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType, BaseTextAggregator
class MatchAction(Enum):
@@ -110,8 +110,8 @@ class PatternPairAggregator(BaseTextAggregator):
"""
pattern_start = self._match_start_of_pattern(self._text)
if pattern_start:
return Aggregation(self._text, pattern_start[1].get("type", "sentence"))
return Aggregation(self._text, "sentence")
return Aggregation(self._text, pattern_start[1].get("type", AggregationType.SENTENCE))
return Aggregation(self._text, AggregationType.SENTENCE)
def add_pattern(
self,
@@ -128,8 +128,8 @@ class PatternPairAggregator(BaseTextAggregator):
Args:
type: Identifier for this pattern pair. Should be unique and ideally descriptive.
(e.g., 'code', 'speaker', 'custom'). type can not be 'sentence' as that is
reserved for the default behavior.
(e.g., 'code', 'speaker', 'custom'). type can not be 'sentence' or 'word' as
those are reserved for the default behavior.
start_pattern: Pattern that marks the beginning of content.
end_pattern: Pattern that marks the end of content.
action: What to do when a complete pattern is matched:
@@ -143,9 +143,9 @@ class PatternPairAggregator(BaseTextAggregator):
Returns:
Self for method chaining.
"""
if type == "sentence":
if type in [AggregationType.SENTENCE, AggregationType.WORD]:
raise ValueError(
"The aggregation type 'sentence' is reserved for default behavior and can not be used for custom patterns."
f"The aggregation type '{type}' is reserved for default behavior and can not be used for custom patterns."
)
self._patterns[type] = {
"start": start_pattern,
@@ -169,8 +169,8 @@ class PatternPairAggregator(BaseTextAggregator):
Args:
pattern_id: Identifier for this pattern pair. Should be unique and ideally descriptive.
(e.g., 'code', 'speaker', 'custom'). pattern_id can not be 'sentence' as that is
reserved for the default behavior.
(e.g., 'code', 'speaker', 'custom'). pattern_id can not be 'sentence' or 'word'
as those arereserved for the default behavior.
start_pattern: Pattern that marks the beginning of content.
end_pattern: Pattern that marks the end of content.
remove_match: If True, the matched pattern will be removed from the text. (Same as MatchAction.REMOVE)
@@ -345,7 +345,7 @@ class PatternPairAggregator(BaseTextAggregator):
# Otherwise, strip the text up to the start pattern and return it
result = self._text[: pattern_start[0]]
self._text = self._text[pattern_start[0] :]
return PatternMatch(content=result, type="sentence", full_match=result)
return PatternMatch(content=result, type=AggregationType.SENTENCE, full_match=result)
# Find sentence boundary if no incomplete patterns
eos_marker = match_endofsentence(self._text)
@@ -353,7 +353,7 @@ class PatternPairAggregator(BaseTextAggregator):
# Extract text up to the sentence boundary
result = self._text[:eos_marker]
self._text = self._text[eos_marker:]
return PatternMatch(content=result, type="sentence", full_match=result)
return PatternMatch(content=result, type=AggregationType.SENTENCE, full_match=result)
# No complete sentence found yet
return None

6
src/pipecat/utils/text/simple_text_aggregator.py
View File

@@ -14,7 +14,7 @@ text processing scenarios.
from typing import Optional
from pipecat.utils.string import match_endofsentence
from pipecat.utils.text.base_text_aggregator import Aggregation, BaseTextAggregator
from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType, BaseTextAggregator
class SimpleTextAggregator(BaseTextAggregator):
@@ -39,7 +39,7 @@ class SimpleTextAggregator(BaseTextAggregator):
Returns:
The text that has been accumulated in the buffer.
"""
return Aggregation(self._text, "sentence")
return Aggregation(self._text, AggregationType.SENTENCE)
async def aggregate(self, text: str) -> Optional[Aggregation]:
"""Aggregate text and return completed sentences.
@@ -64,7 +64,7 @@ class SimpleTextAggregator(BaseTextAggregator):
result = self._text[:eos_end_marker]
self._text = self._text[eos_end_marker:]
return Aggregation(result, "sentence") if result else None
return Aggregation(result, AggregationType.SENTENCE) if result else None
async def handle_interruption(self):
"""Handle interruptions by clearing the text buffer.

6
src/pipecat/utils/text/skip_tags_aggregator.py
View File

@@ -14,7 +14,7 @@ as a unit regardless of internal punctuation.
from typing import Optional, Sequence
from pipecat.utils.string import StartEndTags, match_endofsentence, parse_start_end_tags
from pipecat.utils.text.base_text_aggregator import Aggregation, BaseTextAggregator
from pipecat.utils.text.base_text_aggregator import Aggregation, AggregationType, BaseTextAggregator
class SkipTagsAggregator(BaseTextAggregator):
@@ -49,7 +49,7 @@ class SkipTagsAggregator(BaseTextAggregator):
Returns:
The current text buffer content that hasn't been processed yet.
"""
return Aggregation(self._text, "sentence")
return Aggregation(self._text, AggregationType.SENTENCE)
async def aggregate(self, text: str) -> Optional[Aggregation]:
"""Aggregate text while respecting tag boundaries.
@@ -80,7 +80,7 @@ class SkipTagsAggregator(BaseTextAggregator):
# Extract text up to the sentence boundary
result = self._text[:eos_marker]
self._text = self._text[eos_marker:]
return Aggregation(result, "sentence")
return Aggregation(result, AggregationType.SENTENCE)
# No complete sentence found yet
return None