Podcast Workflow — AI Podcast Generator

输入选题范围 / Give it a topic domain → 自动产出可发布的播客音频 / Get publish-ready podcast audio

Quick Start / 快速开始

English: Give Claude a topic (e.g., "Shanghai real estate"), and it will research hot topics, write a podcast script, run compliance checks, and synthesize speech — all in one flow.

中文： 给 Claude 一个选题范围（如"上海房产"），它会自动搜索热点、撰写播客文稿、合规审查、语音合成，一站完成。

Trigger Words / 触发词

Say any of these to activate the workflow:

• /podcast-workflow
• "做一期播客" / "Make a podcast"
• "帮我生成播客" / "Generate a podcast"
• "播客工作流" / "Podcast workflow"
• "用我的声音做播客" / "Clone my voice for podcast"
• "podcast about {topic}"
• "关于{主题}的播客"

API Configuration / API 配置

Platform:     Xiaomi MiMo API Open Platform
Endpoint:     https://token-plan-cn.xiaomimimo.com/v1
Auth Header:  api-key: {MIMO_API_KEY}
SSL Note:     Python requests need ssl.CERT_NONE
Web Search:   Activate at https://platform.xiaomimimo.com/#/console/plugin

Models Used

Two Modes / 两种模式

Complete Workflow / 完整流程

[User provides topic domain / 用户给定选题范围]
                    ↓
[Step 1: Topic Research / 热点搜索]
  → MiMo Web Search (if activated) or model knowledge
  → Output: 3-5 candidates with title, angle, heat score
                    ↓
[Step 2: User Selects / 用户选题]
  → Display candidates, user picks one
                    ↓
[Step 3: Script Writing / 文稿撰写]
  → MiMo-V2.5-Pro generates conversational script
  → Each paragraph tagged with [Emotion: xxx]
                    ↓
[Step 4: Compliance Check / 合规检查]
  → Sensitive words, misinformation, format, legal risk
  → Output: pass/fail report with specific suggestions
                    ↓
[Step 5: Human Review / 用户确认]
  → Save script to Desktop, show report
  → User approves or requests changes
                    ↓
[Step 6: TTS Synthesis / TTS语音合成]
  → Split by emotion tags → Generate per-chunk → Merge
  → Output: WAV file on Desktop

Step 1: Topic Research / 热点搜索

With Web Search Plugin / 使用 Web Search

{
  "model": "mimo-v2.5-pro",
  "messages": [
    {"role": "user", "content": "搜索{领域}的最新热点，列出5个最热门的播客选题..."}
  ],
  "tools": [{"type": "web_search", "max_keyword": 5, "force_search": true}],
  "max_completion_tokens": 2048,
  "temperature": 1.0,
  "stream": false,
  "thinking": {"type": "disabled"}
}

Without Web Search / 无 Web Search

Fall back to model knowledge. Still generates 3-5 candidates with:

• Title / 标题
• Narrative angle / 切入角度
• Heat score (High/Medium/Low) / 预估热度
• One-line rationale / 一句话理由

Step 2: Script Writing / 文稿撰写

System Prompt

You are a podcast scriptwriter who writes as if telling a friend about something interesting you just discovered.

Tone: Like chatting at a café with a good friend — sharing an interesting finding, not giving a lecture.

Rules:
1. Short sentences, occasional long ones for rhythm variation
2. Natural conversational language (你知道吗, 说白了, 其实, 关键是)
3. Emotion should be restrained but genuine — curious, slightly surprised, thoughtful, NOT excited or loud
4. Tag each paragraph with emotion: [情绪：xxx] / [Emotion: xxx]
5. Use …… for natural pauses, ！ for mild emphasis, ？ for curiosity
6. No markdown, no sound effect cues in brackets
7. No prompt words or technical markers

User Prompt Template

Write a podcast script for:

Topic: {selected title}

Requirements:
- Like sharing a discovery with a good friend — natural, restrained, genuine
- Not too excited, not too flat
- Duration: ~{N} minutes (~{word count} words)
- Each paragraph starts with emotion tag, e.g., [情绪：好奇] / [Emotion: Curious]
- Natural opening, no pleasantries
- Thoughtful ending with lingering resonance

Output the script directly, no preamble or postscript.

Emotion Tags / 情绪标注

Every paragraph MUST start with an emotion tag. This is critical for TTS style matching.

Step 3: Compliance Check / 合规审查

Review dimensions:

1. Sensitive words / 敏感词 — Political, discriminatory, violent content
2. Misinformation / 虚假信息 — False data, misleading claims
3. Format issues / 格式问题 — Prompt residue, technical markers
4. Legal risk / 法律风险 — Privacy, defamation, copyright
5. Marketing bias / 广告嫌疑 — Undisclosed commercial promotion

Output: Structured report with pass/fail per dimension and specific fix suggestions.

Step 4: User Review / 用户确认

• Save script to Desktop: 播客文稿_{主题}.txt
• Display compliance report
• Wait for user approval
• User may request: delete content, adjust tone, modify specific wording

Step 5: TTS Synthesis / TTS语音合成

Audio Format

• Output: WAV (24kHz, 16bit, mono)
• Delivery: Can convert to MP3 for smaller size

Built-in Voice Mode / 内置音色

{
  "model": "mimo-v2.5-tts",
  "messages": [
    {"role": "user", "content": "用自然、亲切、有节奏感的播客风格朗读以下内容。"},
    {"role": "assistant", "content": "{script content}"}
  ],
  "audio": {"format": "wav", "voice": "mimo_default"}
}

Available voices: mimo_default, default_zh, default_en, and more at MiMo Studio

Voice Clone Mode / 声音克隆

{
  "model": "mimo-v2.5-tts-voiceclone",
  "messages": [
    {"role": "user", "content": "{emotion-specific style instruction}"},
    {"role": "assistant", "content": "{script content}"}
  ],
  "audio": {
    "format": "wav",
    "voice": "data:audio/wav;base64,{base64-encoded voice sample}"
  }
}

Chunking Strategy / 分段策略

1. Split by [情绪：xxx] / [Emotion: xxx] tags
2. Sub-split within each section to 300-400 chars max
3. Match each chunk with its emotion's style instruction
4. Generate TTS per chunk, then merge with Python wave module

Voice Recording Requirements / 录音要求

• Quiet environment, no background noise
• Normal pace, conversational tone
• Cover various sounds and tones (for Chinese: zh/ch/sh, b/p/m/f, ü, nasal sounds)
• Duration: 30-60 seconds
• Format: WAV or M4A (M4A needs conversion)

M4A to WAV Conversion / M4A 转 WAV

afconvert -f WAVE -d LEI16@24000 -c 1 input.m4a output.wav

Audio Merge Script / 合并音频

import wave, os

chunk_dir = '/tmp/tts_chunks'
chunk_files = sorted([f for f in os.listdir(chunk_dir) if f.endswith('.wav')])

with wave.open(os.path.join(chunk_dir, chunk_files[0]), 'rb') as w:
    params = w.getparams()

with wave.open('output.wav', 'wb') as out:
    out.setparams(params)
    for cf in chunk_files:
        with wave.open(os.path.join(chunk_dir, cf), 'rb') as w:
            out.writeframes(w.readframes(w.getnframes()))

Output Files / 输出文件

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