pipeline: renamed ParallelTask to SyncParallelPipeline
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@@ -14,21 +14,18 @@ from dataclasses import dataclass
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from pipecat.frames.frames import (
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AppFrame,
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Frame,
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ImageRawFrame,
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LLMFullResponseStartFrame,
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LLMMessagesFrame,
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TextFrame
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
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from pipecat.pipeline.task import PipelineTask
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from pipecat.pipeline.parallel_task import ParallelTask
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.processors.aggregators.gated import GatedAggregator
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from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
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from pipecat.processors.aggregators.sentence import SentenceAggregator
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from pipecat.services.cartesia import CartesiaHttpTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.fal import FalImageGenService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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@@ -88,9 +85,9 @@ async def main():
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)
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)
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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tts = CartesiaHttpTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = OpenAILLMService(
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@@ -105,24 +102,23 @@ async def main():
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key=os.getenv("FAL_KEY"),
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)
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gated_aggregator = GatedAggregator(
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gate_open_fn=lambda frame: isinstance(frame, ImageRawFrame),
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gate_close_fn=lambda frame: isinstance(frame, LLMFullResponseStartFrame),
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start_open=False
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)
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sentence_aggregator = SentenceAggregator()
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month_prepender = MonthPrepender()
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llm_full_response_aggregator = LLMFullResponseAggregator()
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# With `SyncParallelPipeline` we synchronize audio and images by pushing
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# them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2 I3 A3). To do
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# that, each pipeline runs concurrently and `SyncParallelPipeline` will
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# wait for the input frame to be processed.
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#
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# Note that `SyncParallelPipeline` requires all processors in it to be
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# synchronous (which is the default for most processors).
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pipeline = Pipeline([
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llm, # LLM
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sentence_aggregator, # Aggregates LLM output into full sentences
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ParallelTask( # Run pipelines in parallel aggregating the result
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[month_prepender, tts], # Create "Month: sentence" and output audio
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[llm_full_response_aggregator, imagegen] # Aggregate full LLM response
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SyncParallelPipeline( # Run pipelines in parallel aggregating the result
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[month_prepender, tts], # Create "Month: sentence" and output audio
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[imagegen] # Generate image
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),
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gated_aggregator, # Queues everything until an image is available
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transport.output() # Transport output
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])
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@@ -12,17 +12,17 @@ import sys
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import tkinter as tk
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from pipecat.frames.frames import AudioRawFrame, Frame, URLImageRawFrame, LLMMessagesFrame, TextFrame
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
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from pipecat.pipeline.task import PipelineTask
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from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
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from pipecat.processors.aggregators.sentence import SentenceAggregator
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.services.cartesia import CartesiaHttpTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.fal import FalImageGenService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.local.tk import TkLocalTransport
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from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport
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from loguru import logger
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@@ -60,6 +60,7 @@ async def main():
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def __init__(self):
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super().__init__()
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self.audio = bytearray()
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self.frame = None
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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@@ -84,9 +85,10 @@ async def main():
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
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tts = CartesiaHttpTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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imagegen = FalImageGenService(
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params=FalImageGenService.InputParams(
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@@ -95,7 +97,7 @@ async def main():
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aiohttp_session=session,
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key=os.getenv("FAL_KEY"))
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aggregator = LLMFullResponseAggregator()
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sentence_aggregator = SentenceAggregator()
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description = ImageDescription()
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@@ -103,12 +105,22 @@ async def main():
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image_grabber = ImageGrabber()
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# With `SyncParallelPipeline` we synchronize audio and images by
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# pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
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# I3 A3). To do that, each pipeline runs concurrently and
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# `SyncParallelPipeline` will wait for the input frame to be
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# processed.
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#
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# Note that `SyncParallelPipeline` requires all processors in it to
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# be synchronous (which is the default for most processors).
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pipeline = Pipeline([
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llm,
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aggregator,
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description,
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ParallelPipeline([tts, audio_grabber],
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[imagegen, image_grabber])
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llm, # LLM
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sentence_aggregator, # Aggregates LLM output into full sentences
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description, # Store sentence
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SyncParallelPipeline(
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[tts, audio_grabber], # Generate and store audio for the given sentence
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[imagegen, image_grabber] # Generate and storeimage for the given sentence
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)
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])
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task = PipelineTask(pipeline)
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