130 lines
4.1 KiB
Python
130 lines
4.1 KiB
Python
import asyncio
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import doctest
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import functools
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import unittest
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from pipecat.pipeline.aggregators import (
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GatedAggregator,
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ParallelPipeline,
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SentenceAggregator,
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StatelessTextTransformer,
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)
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from pipecat.pipeline.frames import (
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AudioFrame,
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EndFrame,
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ImageFrame,
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LLMResponseEndFrame,
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LLMResponseStartFrame,
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Frame,
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TextFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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class TestDailyFrameAggregators(unittest.IsolatedAsyncioTestCase):
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async def test_sentence_aggregator(self):
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sentence = "Hello, world. How are you? I am fine"
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expected_sentences = ["Hello, world.", " How are you?", " I am fine "]
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aggregator = SentenceAggregator()
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for word in sentence.split(" "):
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async for sentence in aggregator.process_frame(TextFrame(word + " ")):
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self.assertIsInstance(sentence, TextFrame)
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if isinstance(sentence, TextFrame):
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self.assertEqual(sentence.text, expected_sentences.pop(0))
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async for sentence in aggregator.process_frame(EndFrame()):
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if len(expected_sentences):
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self.assertIsInstance(sentence, TextFrame)
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if isinstance(sentence, TextFrame):
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self.assertEqual(sentence.text, expected_sentences.pop(0))
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else:
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self.assertIsInstance(sentence, EndFrame)
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self.assertEqual(expected_sentences, [])
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async def test_gated_accumulator(self):
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gated_aggregator = GatedAggregator(
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gate_open_fn=lambda frame: isinstance(
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frame, ImageFrame), gate_close_fn=lambda frame: isinstance(
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frame, LLMResponseStartFrame), start_open=False, )
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frames = [
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LLMResponseStartFrame(),
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TextFrame("Hello, "),
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TextFrame("world."),
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AudioFrame(b"hello"),
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ImageFrame(b"image", (0, 0)),
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AudioFrame(b"world"),
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LLMResponseEndFrame(),
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]
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expected_output_frames = [
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ImageFrame(b"image", (0, 0)),
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LLMResponseStartFrame(),
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TextFrame("Hello, "),
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TextFrame("world."),
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AudioFrame(b"hello"),
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AudioFrame(b"world"),
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LLMResponseEndFrame(),
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]
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for frame in frames:
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async for out_frame in gated_aggregator.process_frame(frame):
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self.assertEqual(out_frame, expected_output_frames.pop(0))
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self.assertEqual(expected_output_frames, [])
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async def test_parallel_pipeline(self):
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async def slow_add(sleep_time: float, name: str, x: str):
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await asyncio.sleep(sleep_time)
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return ":".join([x, name])
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pipe1_annotation = StatelessTextTransformer(
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functools.partial(slow_add, 0.1, 'pipe1'))
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pipe2_annotation = StatelessTextTransformer(
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functools.partial(slow_add, 0.2, 'pipe2'))
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sentence_aggregator = SentenceAggregator()
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add_dots = StatelessTextTransformer(lambda x: x + ".")
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source = asyncio.Queue()
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sink = asyncio.Queue()
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pipeline = Pipeline(
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[
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ParallelPipeline(
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[[pipe1_annotation], [sentence_aggregator, pipe2_annotation]]
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),
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add_dots,
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],
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source,
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sink,
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)
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frames = [
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TextFrame("Hello, "),
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TextFrame("world."),
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EndFrame()
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]
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expected_output_frames: list[Frame] = [
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TextFrame(text='Hello, :pipe1.'),
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TextFrame(text='world.:pipe1.'),
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TextFrame(text='Hello, world.:pipe2.'),
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EndFrame()
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]
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for frame in frames:
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await source.put(frame)
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await pipeline.run_pipeline()
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while not sink.empty():
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frame = await sink.get()
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self.assertEqual(frame, expected_output_frames.pop(0))
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def load_tests(loader, tests, ignore):
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""" Run doctests on the aggregators module. """
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from pipecat.pipeline import aggregators
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tests.addTests(doctest.DocTestSuite(aggregators))
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return tests
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