openai--openai-agents-python/docs/ko/voice/quickstart.md

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빠른 시작

사전 요구 사항

Agents SDK의 기본 빠른 시작 지침을 따르고 가상 환경을 설정했는지 확인합니다. 그런 다음 SDK에서 선택적 음성 종속성을 설치합니다.

pip install 'openai-agents[voice]'

개념

알아야 할 주요 개념은 3단계 프로세스인 [VoicePipeline][agents.voice.pipeline.VoicePipeline]입니다.

1. 음성-텍스트 변환 모델을 실행하여 오디오를 텍스트로 변환합니다.
2. 일반적으로 에이전트 워크플로인 코드를 실행하여 결과를 생성합니다.
3. 텍스트-음성 변환 모델을 실행하여 결과 텍스트를 다시 오디오로 변환합니다.

graph LR
    %% Input
    A["🎤 Audio Input"]

    %% Voice Pipeline
    subgraph Voice_Pipeline [Voice Pipeline]
        direction TB
        B["Transcribe (speech-to-text)"]
        C["Your Code"]:::highlight
        D["Text-to-speech"]
        B --> C --> D
    end

    %% Output
    E["🎧 Audio Output"]

    %% Flow
    A --> Voice_Pipeline
    Voice_Pipeline --> E

    %% Custom styling
    classDef highlight fill:#ffcc66,stroke:#333,stroke-width:1px,font-weight:700;

에이전트

먼저 몇 가지 에이전트를 설정하겠습니다. 이 SDK로 에이전트를 만들어 본 적이 있다면 익숙할 것입니다. 몇 개의 에이전트와 하나의 핸드오프, 하나의 도구를 사용합니다.

import asyncio
import random

from agents import (
    Agent,
    function_tool,
)
from agents.extensions.handoff_prompt import prompt_with_handoff_instructions



@function_tool
def get_weather(city: str) -> str:
    """Get the weather for a given city."""
    print(f"[debug] get_weather called with city: {city}")
    choices = ["sunny", "cloudy", "rainy", "snowy"]
    return f"The weather in {city} is {random.choice(choices)}."


spanish_agent = Agent(
    name="Spanish",
    handoff_description="A Spanish-speaking agent.",
    instructions=prompt_with_handoff_instructions(
        "You're speaking to a human, so be polite and concise. Speak in Spanish.",
    ),
    model="gpt-5.6-sol",
)

agent = Agent(
    name="Assistant",
    instructions=prompt_with_handoff_instructions(
        "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, hand off to the Spanish agent.",
    ),
    model="gpt-5.6-sol",
    handoffs=[spanish_agent],
    tools=[get_weather],
)

음성 파이프라인

[SingleAgentVoiceWorkflow][agents.voice.workflow.SingleAgentVoiceWorkflow]를 워크플로로 사용하여 간단한 음성 파이프라인을 설정합니다.

from agents.voice import SingleAgentVoiceWorkflow, VoicePipeline
pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent))

파이프라인 실행

import numpy as np
import sounddevice as sd
from agents.voice import AudioInput

# For simplicity, we'll just create 3 seconds of silence
# In reality, you'd get microphone data
buffer = np.zeros(24000 * 3, dtype=np.int16)
audio_input = AudioInput(buffer=buffer)

result = await pipeline.run(audio_input)

# Create an audio player using `sounddevice`
player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
player.start()

# Play the audio stream as it comes in
async for event in result.stream():
    if event.type == "voice_stream_event_audio":
        player.write(event.data)

전체 구성

import asyncio
import random

import numpy as np
import sounddevice as sd

from agents import (
    Agent,
    function_tool,
    set_tracing_disabled,
)
from agents.voice import (
    AudioInput,
    SingleAgentVoiceWorkflow,
    VoicePipeline,
)
from agents.extensions.handoff_prompt import prompt_with_handoff_instructions


@function_tool
def get_weather(city: str) -> str:
    """Get the weather for a given city."""
    print(f"[debug] get_weather called with city: {city}")
    choices = ["sunny", "cloudy", "rainy", "snowy"]
    return f"The weather in {city} is {random.choice(choices)}."


spanish_agent = Agent(
    name="Spanish",
    handoff_description="A Spanish-speaking agent.",
    instructions=prompt_with_handoff_instructions(
        "You're speaking to a human, so be polite and concise. Speak in Spanish.",
    ),
    model="gpt-5.6-sol",
)

agent = Agent(
    name="Assistant",
    instructions=prompt_with_handoff_instructions(
        "You're speaking to a human, so be polite and concise. If the user speaks in Spanish, hand off to the Spanish agent.",
    ),
    model="gpt-5.6-sol",
    handoffs=[spanish_agent],
    tools=[get_weather],
)


async def main():
    pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(agent))
    buffer = np.zeros(24000 * 3, dtype=np.int16)
    audio_input = AudioInput(buffer=buffer)

    result = await pipeline.run(audio_input)

    # Create an audio player using `sounddevice`
    player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
    player.start()

    # Play the audio stream as it comes in
    async for event in result.stream():
        if event.type == "voice_stream_event_audio":
            player.write(event.data)


if __name__ == "__main__":
    asyncio.run(main())

이 예제를 실행하면 에이전트가 사용자에게 음성으로 응답합니다! 에이전트와 직접 대화할 수 있는 데모는 examples/voice/static의 코드 예제를 확인하세요.