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/features/audio-recording.py
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examples/features/audio-recording.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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"""Audio Recording Example with Pipecat.
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This example demonstrates how to record audio from a conversation between a user and an AI assistant,
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saving both merged and individual audio tracks. It showcases the AudioBufferProcessor's capabilities
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to handle both combined and separate audio streams.
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The example:
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1. Sets up a basic conversation with an AI assistant
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2. Records the entire conversation
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3. Saves three separate WAV files:
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- A merged recording of both participants
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- Individual recording of user audio
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- Individual recording of assistant audio
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Requirements:
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- OpenAI API key (for GPT-4)
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- Cartesia API key (for text-to-speech)
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- Daily API key (for video/audio transport)
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Environment variables (.env file):
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OPENAI_API_KEY=your_openai_key
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CARTESIA_API_KEY=your_cartesia_key
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DAILY_API_KEY=your_daily_key
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DEEPGRAM_API_KEY=your_deepgram_key
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The recordings will be saved in a 'recordings' directory with timestamps:
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recordings/
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merged_20240315_123456.wav (Combined audio)
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user_20240315_123456.wav (User audio only)
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bot_20240315_123456.wav (Bot audio only)
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Note:
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This example requires the AudioBufferProcessor with track-specific audio support,
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which provides both 'on_audio_data' and 'on_track_audio_data' events for
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handling merged and separate audio tracks respectively.
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"""
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import datetime
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import io
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import os
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import wave
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import aiofiles
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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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.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
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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 CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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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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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
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"""Save audio data to a WAV file."""
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if len(audio) > 0:
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with io.BytesIO() as buffer:
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with wave.open(buffer, "wb") as wf:
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wf.setsampwidth(2)
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wf.setnchannels(num_channels)
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wf.setframerate(sample_rate)
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wf.writeframes(audio)
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async with aiofiles.open(filename, "wb") as file:
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await file.write(buffer.getvalue())
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logger.info(f"Audio saved to {filename}")
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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_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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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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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121",
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),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
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),
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)
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# Create audio buffer processor
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audiobuffer = AudioBufferProcessor()
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context = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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user_aggregator,
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llm,
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tts,
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transport.output(),
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audiobuffer, # Add audio buffer to pipeline
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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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 recording audio
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await audiobuffer.start_recording()
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# Start conversation - empty prompt to let LLM follow system instructions
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await task.queue_frames([LLMRunFrame()])
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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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# Handler for merged audio
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@audiobuffer.event_handler("on_audio_data")
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async def on_audio_data(buffer, audio, sample_rate, num_channels):
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"recordings/merged_{timestamp}.wav"
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os.makedirs("recordings", exist_ok=True)
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await save_audio_file(audio, filename, sample_rate, num_channels)
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# Handler for separate tracks
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@audiobuffer.event_handler("on_track_audio_data")
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async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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os.makedirs("recordings", exist_ok=True)
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# Save user audio
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user_filename = f"recordings/user_{timestamp}.wav"
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await save_audio_file(user_audio, user_filename, sample_rate, 1)
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# Save bot audio
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bot_filename = f"recordings/bot_{timestamp}.wav"
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await save_audio_file(bot_audio, bot_filename, sample_rate, 1)
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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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