PodAgent让自动生成播客更自然，三步实现内容、配音与表达的统一。
PodAgent: A Comprehensive Framework for Podcast Generation
- 用主持人-嘉宾-撰稿人多智能体协作生成深度讨论内容
- 语音匹配准确率达87.4%，通过语音池实现角色适配
- 大模型引导合成让对话更生动，适合内容创作与音视频开发
现有自动音频生成方法难以有效生成类播客音频节目。核心挑战在于深度内容生成与恰当富表现力的语音输出。本文提出PodAgent，一个完整的播客生成框架：1）设计主持人-嘉宾-撰稿人多智能体协作系统，生成信息丰富的话题讨论内容；2）构建语音池实现语音角色精准匹配；3）采用大语言模型增强的语音合成方法，生成富有表现力的对话语音。由于缺乏标准化评估标准，我们制定了全面的评估指南以有效衡量模型性能。实验结果表明，PodAgent在话题讨论对话生成上显著优于直接使用GPT-4，语音匹配准确率达87.4%，并通过大模型引导合成产生更具表现力的语音。演示页：https://podcast-agent.github.io/demo/。源码：https://github.com/yujxx/PodAgent。
原文摘要 · Abstract (English)
Existing Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. The key challenges lie in in-depth content generation, appropriate and expressive voice production. This paper proposed PodAgent, a comprehensive framework for creating audio programs. PodAgent 1) generates informative topic-discussion content by designing a Host-Guest-Writer multi-agent collaboration system, 2) builds a voice pool for suitable voice-role matching and 3) utilizes LLM-enhanced speech synthesis method to generate expressive conversational speech. Given the absence of standardized evaluation criteria for podcast-like audio generation, we developed comprehensive assessment guidelines to effectively evaluate the model's performance. Experimental results demonstrate PodAgent's effectiveness, significantly surpassing direct GPT-4 generation in topic-discussion dialogue content, achieving an 87.4% voice-matching accuracy, and producing more expressive speech through LLM-guided synthesis. Demo page: https://podcast-agent.github.io/demo/. Source code: https://github.com/yujxx/PodAgent.
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