以下是本次课程的正文：

我们在前面课程中对智能体相关的架构、核心能力等进行了非常详细、完整的介绍，详细读者也大致了解了什么是智能体、智能体的核心是什么、智能体能解决哪些问题了。但是要自己去实现一个智能体还不是那么容易，因此，我们在接下来的几课会通过实战案例来帮助读者利用智能体技术做出4个实用的小工具，让读者通过代码实战更好地掌握大模型智能体技术。本课我们就讲解第一个实战案例：利用智能体实现一个个性化语音助手。

23.1 整体业务流程

23.2 工具模块

from langchain_core.tools import BaseTool # langchain的工具模块import noisereduce as nr  # 去噪import librosa  # 加载音频并获取前10simport soundfile as sfimport whisper  # 从声音中提取文字。import osimport requests from openai import OpenAI  # OpenAI模型的sdkimport refrom pydub import AudioSegment # 声音合并

1. 抽取工具

class ExtractAudioTool(BaseTool):    name: str = "extract_audio"    description: str = ("Extracts the first 10 seconds of an audio file, Input should be a JSON with keys "                        "audio_path, output_audio_file and output_text_file. The return of this tools should be a "                        "JSON with keys output_audio_file and output_text_file, the output_audio_file is the "                        "extracted audio file path, the output_text_file is the extracted audio's text file path.")

    def _run(self, inputs: dict) -> dict:        audio_path = inputs['audio_path']        output_audio_file = inputs['output_audio_file']        output_text_file = inputs['output_text_file']        whisper_model = "large"        # 1. 提取音频的前10秒        print("Extracting first 10 seconds of audio...")        audio = AudioSegment.from_file(audio_path)        first_10_seconds = audio[:10000]  # 单位为毫秒        temp_audio_file = "temp_10s.wav"        first_10_seconds.export(temp_audio_file, format="wav")

        # 2. 去掉背景声音        print("Reducing background noise...")        y, sr_rate = librosa.load(temp_audio_file, sr=None)  # 加载音频        noise_profile = y[:sr_rate]         reduced_noise_audio = nr.reduce_noise(y=y, sr=sr_rate, y_noise=noise_profile)        sf.write(output_audio_file, reduced_noise_audio, sr_rate)

        # 删除临时文件        os.remove(temp_audio_file)

        # 3. 转录音频为文字        print("Transcribing audio with Whisper...")        model = whisper.load_model(whisper_model)        result = model.transcribe(output_audio_file, language="zh")        voice_text = result["text"]

        # 保存文字到文件        with open(output_text_file, "w", encoding="utf-8") as f:            f.write(voice_text)

        print("Text successfully extracted and saved.")

        outputs = {            "output_audio_file": output_audio_file,            "output_text_file": output_text_file        }
        return outputs

2. 文件上传工具

class UploadAudioTool(BaseTool):    name: str = "upload_audio"    description: str = ("Uploads audio(output_audio_file) to cloud storage and returns "                        "the file id：voice_file_id.")

    def _run(self, output_audio_file: str) -> str:        client = OpenAI(api_key=STEP_KEY, base_url="https://api.stepfun.com/v1")

        response = client.files.create(            file=open(output_audio_file, "rb"),            purpose="storage"        )

        # 获取文件 ID 和文件名        voice_file_id = response.id        # filename = response.filename        # print(f"File ID: {file_id}, Filename: {filename}")        return voice_file_id

3. 克隆工具

class CloneVoiceTool(BaseTool):    name: str = "clone_voice"    description: str = (        "Clones the voice from an uploaded audio file id and voice text,"        "Input should be a JSON with keys voice_file_id and output_text_file."        "return the cloned voice id：cloned_voice_id.")

    def _run(self, inputs: dict) -> str:        voice_file_id = inputs['voice_file_id']        output_text_file = inputs['output_text_file']

        # 读取文本文件内容        with open(output_text_file, "r", encoding="utf-8") as file:            voice_text = file.read()

        url = "https://api.stepfun.com/v1/audio/voices"        headers = {            "Content-Type": "application/json",            "Authorization": f"Bearer {STEP_KEY}"  # 替换为实际的 API Key        }        data = {            "file_id": voice_file_id,            "model": "step-tts-mini",            "text": voice_text        }

        response = requests.post(url, headers=headers, json=data)

        # 打印响应结果        if response.status_code == 200:            print("成功：",                  response.json())  # {'id': 'voice-tone-DCV8jkPKFM', 'object': 'audio.voice', 'duplicated': True}            cloned_voice_id = response.json()['id']            return cloned_voice_id        else:            print("失败：", response.status_code, response.text)            return ""

4. 声音合成工具