クイックスタート

前提条件

まずは クイックスタート手順 に従って Agents SDK をセットアップし、仮想環境を作成してください。その後、SDK の音声関連のオプション依存関係をインストールします:

pip install 'openai-agents[voice]'

コンセプト

押さえておくべき主な概念は VoicePipeline です。これは次の 3 ステップから成るプロセスです。

1. speech-to-text モデルを実行して音声をテキストに変換します。
2. 通常はエージェント的ワークフローであるあなたのコードを実行し、結果を生成します。
3. text-to-speech モデルを実行して結果のテキストを再び音声に変換します。

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;

エージェント

まず、いくつかの エージェント をセットアップしましょう。この SDK でエージェントを構築したことがあれば、見覚えがあるはずです。ここでは複数の エージェント、ハンドオフ、そしてツールを用意します。

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-4o-mini",
)

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, handoff to the spanish agent.",
    ),
    model="gpt-4o-mini",
    handoffs=[spanish_agent],
    tools=[get_weather],
)

音声パイプライン

SingleAgentVoiceWorkflow をワークフローとして、シンプルな音声パイプラインを構築します。

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

パイプラインの実行

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)

まとめて実行

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-4o-mini",
)

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, handoff to the spanish agent.",
    ),
    model="gpt-4o-mini",
    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())

この例を実行すると、エージェントがあなたに話しかけます。実際にエージェントと会話できるデモは、examples/voice/static をご覧ください。