pipeline: add UserTranscriptionAggregator

src/dailyai/pipeline/aggregators.py

@@ -21,6 +21,79 @@ from dailyai.services.ai_services import AIService
 from typing import AsyncGenerator, Coroutine, List
 
 
+class BasicResponseAggregator(FrameProcessor):
+    """This frame processor aggregates frames between a start and an end frame
+    into complete text frame sentences.
+
+    For example, frame input/output:
+        UserStartedSpeakingFrame() -> None
+        TranscriptionFrame("Hello,") -> None
+        TranscriptionFrame(" world.") -> None
+        UserStoppedSpeakingFrame() -> TextFrame("Hello world.")
+
+    Doctest:
+    >>> async def print_frames(aggregator, frame):
+    ...     async for frame in aggregator.process_frame(frame):
+    ...         if isinstance(frame, TextFrame):
+    ...             print(frame.text)
+
+    >>> aggregator = BasicResponseAggregator(start_frame = UserStartedSpeakingFrame,
+    ...                                      end_frame=UserStoppedSpeakingFrame,
+    ...                                      accumulator_frame=TranscriptionFrame,
+    ...                                      pass_through=False)
+    >>> asyncio.run(print_frames(aggregator, UserStartedSpeakingFrame()))
+    >>> asyncio.run(print_frames(aggregator, TranscriptionFrame("Hello,", 1, 1)))
+    >>> asyncio.run(print_frames(aggregator, TranscriptionFrame("world.",  1, 2)))
+    >>> asyncio.run(print_frames(aggregator, UserStoppedSpeakingFrame()))
+    Hello, world.
+
+    """
+
+    def __init__(
+        self,
+        *,
+        start_frame,
+        end_frame,
+        accumulator_frame,
+        pass_through=True,
+    ):
+        self.aggregation = ""
+        self.aggregating = False
+        self._start_frame = start_frame
+        self._end_frame = end_frame
+        self._accumulator_frame = accumulator_frame
+        self._pass_through = pass_through
+
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+        if isinstance(frame, self._start_frame):
+            self.aggregating = True
+        elif isinstance(frame, self._end_frame):
+            self.aggregating = False
+            # Sometimes VAD triggers quickly on and off. If we don't get any transcription,
+            # it creates empty LLM message queue frames
+            if len(self.aggregation) > 0:
+                output = self.aggregation
+                self.aggregation = ""
+                yield self._end_frame()
+                yield TextFrame(output.strip())
+        elif isinstance(frame, self._accumulator_frame) and self.aggregating:
+            self.aggregation += f" {frame.text}"
+            if self._pass_through:
+                yield frame
+        else:
+            yield frame
+
+
+class UserTranscriptionAggregator(BasicResponseAggregator):
+    def __init__(self):
+        super().__init__(
+            start_frame=UserStartedSpeakingFrame,
+            end_frame=UserStoppedSpeakingFrame,
+            accumulator_frame=TranscriptionFrame,
+            pass_through=False,
+        )
+
+
 class ResponseAggregator(FrameProcessor):
 
     def __init__(