Podcast Generation Skill

Overview

This skill generates high-quality podcast audio from text content. The workflow includes creating a structured JSON script (conversational dialogue) and executing audio generation through text-to-speech synthesis.

Core Capabilities

• Convert any text content (articles, reports, documentation) into podcast scripts
• Generate natural two-host conversational dialogue (male and female hosts)
• Synthesize speech audio using text-to-speech
• Mix audio chunks into a final podcast MP3 file
• Support both English and Chinese content

Workflow

Step 1: Understand Requirements

When a user requests podcast generation, identify:

• Source content: The text/article/report to convert into a podcast
• Language: English or Chinese (based on content)
• Output location: Where to save the generated podcast
• You don't need to check the folder under /mnt/user-data

Step 2: Create Structured Script JSON

Generate a structured JSON script file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}-script.json

The JSON structure:

{
  "locale": "en",
  "lines": [
    {"speaker": "male", "paragraph": "dialogue text"},
    {"speaker": "female", "paragraph": "dialogue text"}
  ]
}

Step 3: Execute Generation

Call the Python script:

python /mnt/skills/public/podcast-generation/scripts/generate.py \
  --script-file /mnt/user-data/workspace/script-file.json \
  --output-file /mnt/user-data/outputs/generated-podcast.mp3 \
  --transcript-file /mnt/user-data/outputs/generated-podcast-transcript.md

Parameters:

• --script-file: Absolute path to JSON script file (required)
• --output-file: Absolute path to output MP3 file (required)
• --transcript-file: Absolute path to output transcript markdown file (optional, but recommended)

[!IMPORTANT]

• Execute the script in one complete call. Do NOT split the workflow into separate steps.
• The script handles all TTS API calls and audio generation internally.
• Do NOT read the Python file, just call it with the parameters.
• Always include --transcript-file to generate a readable transcript for the user.
• The TTS provider and its concurrency are selected automatically from environment variables — you do not choose or tune them.

Script JSON Format

The script JSON file must follow this structure:

{
  "title": "The History of Artificial Intelligence",
  "locale": "en",
  "lines": [
    {"speaker": "male", "paragraph": "Hello Deer! Welcome back to another episode."},
    {"speaker": "female", "paragraph": "Hey everyone! Today we have an exciting topic to discuss."},
    {"speaker": "male", "paragraph": "That's right! We're going to talk about..."}
  ]
}

Fields:

• title: Title of the podcast episode (optional, used as heading in transcript)
• locale: Language code - "en" for English or "zh" for Chinese
• lines: Array of dialogue lines
  • speaker: Either "male" or "female"
  • paragraph: The dialogue text for this speaker

Script Writing Guidelines

When creating the script JSON, follow these guidelines:

Format Requirements

• Only two hosts: male and female, alternating naturally
• Target runtime: approximately 10 minutes of dialogue (around 40-60 lines)
• Start with the male host saying a greeting that includes "Hello Deer"

Tone & Style

• Natural, conversational dialogue - like two friends chatting
• Use casual expressions and conversational transitions
• Avoid overly formal language or academic tone
• Include reactions, follow-up questions, and natural interjections

Content Guidelines

• Frequent back-and-forth between hosts
• Keep sentences short and easy to follow when spoken
• Plain text only - no markdown formatting in the output
• Translate technical concepts into accessible language
• No mathematical formulas, code, or complex notation
• Make content engaging and accessible for audio-only listeners
• Exclude meta information like dates, author names, or document structure

Podcast Generation Example

User request: "Generate a podcast about the history of artificial intelligence"

Step 1: Create script file /mnt/user-data/workspace/ai-history-script.json:

{
  "title": "The History of Artificial Intelligence",
  "locale": "en",
  "lines": [
    {"speaker": "male", "paragraph": "Hello Deer! Welcome back to another fascinating episode. Today we're diving into something that's literally shaping our future - the history of artificial intelligence."},
    {"speaker": "female", "paragraph": "Oh, I love this topic! You know, AI feels so modern, but it actually has roots going back over seventy years."},
    {"speaker": "male", "paragraph": "Exactly! It all started back in the 1950s. The term artificial intelligence was actually coined by John McCarthy in 1956 at a famous conference at Dartmouth."},
    {"speaker": "female", "paragraph": "Wait, so they were already thinking about machines that could think back then? That's incredible!"},
    {"speaker": "male", "paragraph": "Right? The early pioneers were so optimistic. They thought we'd have human-level AI within a generation."},
    {"speaker": "female", "paragraph": "But things didn't quite work out that way, did they?"},
    {"speaker": "male", "paragraph": "No, not at all. The 1970s brought what's called the first AI winter..."}
  ]
}

Step 2: Execute generation:

python /mnt/skills/public/podcast-generation/scripts/generate.py \
  --script-file /mnt/user-data/workspace/ai-history-script.json \
  --output-file /mnt/user-data/outputs/ai-history-podcast.mp3 \
  --transcript-file /mnt/user-data/outputs/ai-history-transcript.md

This will generate:

• ai-history-podcast.mp3: The audio podcast file
• ai-history-transcript.md: A readable markdown transcript of the podcast

Specific Templates

Read the following template file only when matching the user request.

• Tech Explainer - For converting technical documentation and tutorials

Output Format

The generated podcast follows the "Hello Deer" format:

• Two hosts: one male, one female
• Natural conversational dialogue
• Starts with "Hello Deer" greeting
• Target duration: approximately 10 minutes
• Alternating speakers for engaging flow

Output Handling

The command exits with status 1 and writes diagnostics to stderr if generation fails. Only present the MP3 after a successful command; an existing audio file or a newly written transcript does not establish that audio generation succeeded.

After generation:

• Podcasts and transcripts are saved in /mnt/user-data/outputs/
• Share both the podcast MP3 and transcript MD with user using present_files tool
• Provide brief description of the generation result (topic, duration, hosts)
• Offer to regenerate if adjustments needed

Requirements

The following environment variables must be set:

• For Volcengine: VOLCENGINE_TTS_APPID and VOLCENGINE_TTS_ACCESS_TOKEN
• For MiniMax: MINIMAX_API_KEY
• VOLCENGINE_TTS_CLUSTER: Volcengine TTS cluster (optional, defaults to "volcano_tts")
• VOLCENGINE_TTS_VOICE_TYPE_MALE: Volcengine male voice type (optional, defaults to zh_male_yangguangqingnian_moon_bigtts)
• VOLCENGINE_TTS_VOICE_TYPE_FEMALE: Volcengine female voice type (optional, defaults to zh_female_sajiaonvyou_moon_bigtts)

Voice type overrides are trimmed; unset or blank values use the listed defaults.

Notes

• Always execute the full pipeline in one call - no need to test individual steps or worry about timeouts
• The script JSON should match the content language (en or zh)
• Technical content should be simplified for audio accessibility in the script
• Complex notations (formulas, code) should be translated to plain language in the script
• Long content may result in longer podcasts

Providers (Volcengine / MiniMax)

Auto-selected by environment variables:

• VOLCENGINE_TTS_APPID + VOLCENGINE_TTS_ACCESS_TOKEN set → Volcengine TTS (default).
• Only MINIMAX_API_KEY set → MiniMax TTS (/v1/t2a_v2).
• Force with PODCAST_GENERATION_PROVIDER=volcengine|minimax.

MiniMax overrides: MINIMAX_API_HOST (default https://api.minimaxi.com), MINIMAX_TTS_MODEL (default speech-2.6-hd), MINIMAX_TTS_VOICE_MALE (default male-qn-qingse), MINIMAX_TTS_VOICE_FEMALE (default female-tianmei).

Concurrency is owned by each provider internally — MiniMax runs single-threaded to reduce rate-limit failures, Volcengine uses 4 workers. There is no caller-facing concurrency knob; transient rate limits are handled by automatic retry with backoff.