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Remove 05a example, which was broken and isn't currently a priority to fix

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Paul Kompfner
2026-03-19 15:52:48 -04:00
committed by Mark Backman
parent 3b5a8acfe2
commit aec8d13eec
1 changed files with 0 additions and 202 deletions
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examples/foundational/05a-local-sync-speech-and-image.py
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#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import tkinter as tk
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from pipecat.frames.frames import (
Frame,
LLMContextFrame,
OutputAudioRawFrame,
TextFrame,
TTSAudioRawFrame,
URLImageRawFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.sentence import SentenceAggregator
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
from pipecat.services.fal.image import FalImageGenService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
tk_root = tk.Tk()
tk_root.title("Calendar")
runner = PipelineRunner()
async def get_month_data(month):
messages = [
{
"role": "user",
"content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
}
]
class ImageDescription(FrameProcessor):
def __init__(self):
super().__init__()
self.text = ""
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
self.text = frame.text
await self.push_frame(frame, direction)
class AudioGrabber(FrameProcessor):
def __init__(self):
super().__init__()
self.audio = bytearray()
self.frame = None
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TTSAudioRawFrame):
self.audio.extend(frame.audio)
self.frame = OutputAudioRawFrame(
bytes(self.audio), frame.sample_rate, frame.num_channels
)
await self.push_frame(frame, direction)
class ImageGrabber(FrameProcessor):
def __init__(self):
super().__init__()
self.frame = None
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, URLImageRawFrame):
self.frame = frame
await self.push_frame(frame, direction)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
tts = CartesiaHttpTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
settings=CartesiaHttpTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
),
)
imagegen = FalImageGenService(
settings=FalImageGenService.Settings(
image_size="square_hd",
),
aiohttp_session=session,
key=os.getenv("FAL_KEY"),
)
sentence_aggregator = SentenceAggregator()
description = ImageDescription()
audio_grabber = AudioGrabber()
image_grabber = ImageGrabber()
# With `SyncParallelPipeline` we synchronize audio and images by
# pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
# I3 A3). To do that, each pipeline runs concurrently and
# `SyncParallelPipeline` will wait for the input frame to be
# processed.
#
# Note that `SyncParallelPipeline` requires the last processor in
# each of the pipelines to be synchronous. In this case, we use
# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
# requests and wait for the response.
pipeline = Pipeline(
[
llm, # LLM
sentence_aggregator, # Aggregates LLM output into full sentences
description, # Store sentence
SyncParallelPipeline(
[tts, audio_grabber], # Generate and store audio for the given sentence
[imagegen, image_grabber], # Generate and storeimage for the given sentence
),
]
)
task = PipelineTask(pipeline)
await task.queue_frame(LLMContextFrame(LLMContext(messages)))
await task.stop_when_done()
await runner.run(task)
return {
"month": month,
"text": description.text,
"image": image_grabber.frame,
"audio": audio_grabber.frame,
}
transport = TkLocalTransport(
tk_root,
TkTransportParams(
audio_out_enabled=True,
video_out_enabled=True,
video_out_width=1024,
video_out_height=1024,
),
)
pipeline = Pipeline([transport.output()])
task = PipelineTask(pipeline)
# We only specify a few months as we create tasks all at once and we
# might get rate limited otherwise.
months: list[str] = [
"January",
"February",
]
# We create one task per month. This will be executed concurrently.
month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
# Now we wait for each month task in the order they're completed. The
# benefit is we'll have as little delay as possible before the first
# month, and likely no delay between months, but the months won't
# display in order.
async def show_images(month_tasks):
for month_data_task in asyncio.as_completed(month_tasks):
data = await month_data_task
await task.queue_frames([data["image"], data["audio"]])
await runner.stop_when_done()
async def run_tk():
while not task.has_finished():
tk_root.update()
tk_root.update_idletasks()
await asyncio.sleep(0.1)
await asyncio.gather(runner.run(task), show_images(month_tasks), run_tk())
if __name__ == "__main__":
asyncio.run(main())