Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
This commit is contained in:
Mark Backman
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examples/getting-started/04-sync-speech-and-image.py
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examples/getting-started/04-sync-speech-and-image.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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from dataclasses import dataclass
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.frames.frames import (
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DataFrame,
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Frame,
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LLMContextFrame,
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LLMFullResponseStartFrame,
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OutputImageRawFrame,
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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 FrameOrder, SyncParallelPipeline
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from pipecat.pipeline.task import PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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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.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaHttpTTSService
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from pipecat.services.fal.image import FalImageGenService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.tts_service import TextAggregationMode
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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load_dotenv(override=True)
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@dataclass
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class MonthFrame(DataFrame):
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month: str
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def __str__(self):
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return f"{self.name}(month: {self.month})"
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class MarkImageForPlaybackSync(FrameProcessor):
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"""Marks output image frames to be synchronized with audio playback."""
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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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if isinstance(frame, OutputImageRawFrame):
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frame.sync_with_audio = True
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await self.push_frame(frame, direction)
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class MonthPrepender(FrameProcessor):
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def __init__(self):
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super().__init__()
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self.most_recent_month = "Placeholder, month frame not yet received"
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self.prepend_to_next_text_frame = False
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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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if isinstance(frame, MonthFrame):
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self.most_recent_month = frame.month
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elif self.prepend_to_next_text_frame and isinstance(frame, TextFrame):
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await self.push_frame(TextFrame(f"{self.most_recent_month}: {frame.text}"))
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self.prepend_to_next_text_frame = False
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elif isinstance(frame, LLMFullResponseStartFrame):
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self.prepend_to_next_text_frame = True
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await self.push_frame(frame)
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else:
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await self.push_frame(frame, direction)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_out_enabled=True,
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video_out_enabled=True,
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video_out_width=1024,
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video_out_height=1024,
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),
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"webrtc": lambda: TransportParams(
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audio_out_enabled=True,
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video_out_enabled=True,
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video_out_width=1024,
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video_out_height=1024,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""Run the Calendar Month Narration bot using WebRTC transport.
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Args:
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webrtc_connection: The WebRTC connection to use
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room_name: Optional room name for display purposes
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"""
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logger.info(f"Starting bot")
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# Create an HTTP session for API calls
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async with aiohttp.ClientSession() as session:
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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tts = CartesiaHttpTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaHttpTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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# No need to aggregate by sentences (the default), as we already know we're getting full sentences
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# (Otherwise the service will unnecessarily wait for follow-up input to confirm the sentence is complete,
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# which, sadly, actually breaks the synchronization mechanism)
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text_aggregation_mode=TextAggregationMode.TOKEN,
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)
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imagegen = FalImageGenService(
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settings=FalImageGenService.Settings(
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image_size="square_hd",
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),
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aiohttp_session=session,
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key=os.getenv("FAL_KEY"),
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)
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sentence_aggregator = SentenceAggregator()
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month_prepender = MonthPrepender()
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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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# We use `FrameOrder.PIPELINE` so that each synchronized batch of output
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# frames is pushed in the order the pipelines are listed: image first,
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# then audio. This ensures the transport receives the image before the
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# audio frames it should accompany.
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#
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# Note that `SyncParallelPipeline` requires the last processor in each
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# of the pipelines to be synchronous. In this case, we use
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# `FalImageGenService` and `CartesiaHttpTTSService` which make HTTP
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# requests and wait for the response.
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pipeline = Pipeline(
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[
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llm, # LLM
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sentence_aggregator, # Aggregates LLM output into full sentences
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SyncParallelPipeline( # Run pipelines in parallel aggregating the result
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[
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imagegen, # Generate image
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MarkImageForPlaybackSync(), # Mark image as needing sync w/audio during playback
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],
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[month_prepender, tts], # Create "Month: sentence" and output audio
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frame_order=FrameOrder.PIPELINE,
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),
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transport.output(), # Transport output
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]
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)
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frames = []
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for month in [
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"January",
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"February",
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"March",
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"April",
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"May",
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"June",
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"July",
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"August",
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"September",
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"October",
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"November",
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"December",
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]:
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messages = [
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{
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"role": "user",
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"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.",
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}
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]
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frames.append(MonthFrame(month=month))
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frames.append(LLMContextFrame(LLMContext(messages)))
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task = PipelineTask(
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pipeline,
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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# Set up transport event handlers
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Start the month narration once connected
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await task.queue_frames(frames)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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# Run the pipeline
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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