mirror of
https://gitee.com/wanwujie/deer-flow
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9f5658fa0e
- Add podcast-generation skill for creating tech explainer podcasts - Include generate.py script with TTS synthesis capabilities - Add tech-explainer template for structured podcast content - Increase sandbox command timeout from 30s to 600s to support longer-running skill scripts
125 lines
4.7 KiB
Markdown
125 lines
4.7 KiB
Markdown
---
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name: podcast-generation
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description: Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
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---
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# Podcast Generation Skill
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## Overview
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This skill generates high-quality podcast audio from text content using a multi-stage pipeline. The workflow includes script generation (converting input to conversational dialogue), text-to-speech synthesis, and audio mixing to produce the final podcast.
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## Core Capabilities
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- Convert any text content (articles, reports, documentation) into podcast scripts
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- Generate natural two-host conversational dialogue (male and female hosts)
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- Synthesize speech audio using text-to-speech
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- Mix audio chunks into a final podcast MP3 file
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- Support both English and Chinese content
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## Workflow
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### Step 1: Understand Requirements
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When a user requests podcast generation, identify:
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- Source content: The text/article/report to convert into a podcast
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- Language: English or Chinese (auto-detected from content)
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- Output location: Where to save the generated podcast
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- You don't need to check the folder under `/mnt/user-data`
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### Step 2: Prepare Input Content
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The input content should be plain text or markdown. Save it to a text file in `/mnt/user-data/workspace/` with naming pattern: `{descriptive-name}-content.md`
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### Step 3: Execute Generation
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Call the Python script directly without any concerns about timeout or the need for pre-testing:
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```bash
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python /mnt/skills/public/podcast-generation/scripts/generate.py \
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--input-file /mnt/user-data/workspace/content-file.md \
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--output-file /mnt/user-data/outputs/generated-podcast.mp3 \
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--locale en
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```
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Parameters:
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- `--input-file`: Absolute path to input text/markdown file (required)
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- `--output-file`: Absolute path to output MP3 file (required)
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- `--locale`: Language locale - "en" for English or "zh" for Chinese (optional, auto-detected if not specified)
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> [!IMPORTANT]
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> - Execute the script in one complete call. Do NOT split the workflow into separate steps (e.g., testing script generation first, then TTS).
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> - The script handles all external API calls and audio generation internally with proper timeout management.
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> - Do NOT read the Python file, just call it with the parameters.
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## Podcast Generation Example
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User request: "Generate a podcast about the history of artificial intelligence"
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Step 1: Create content file `/mnt/user-data/workspace/ai-history-content.md` with the source text:
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```markdown
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# The History of Artificial Intelligence
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Artificial intelligence has a rich history spanning over seven decades...
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## Early Beginnings (1950s)
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The term "artificial intelligence" was coined by John McCarthy in 1956...
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## The First AI Winter (1970s)
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After initial enthusiasm, AI research faced significant setbacks...
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## Modern Era (2010s-Present)
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Deep learning revolutionized the field with breakthrough results...
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```
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Step 2: Execute generation:
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```bash
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python /mnt/skills/public/podcast-generation/scripts/generate.py \
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--input-file /mnt/user-data/workspace/ai-history-content.md \
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--output-file /mnt/user-data/outputs/ai-history-podcast.mp3 \
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--locale en
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```
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## Specific Templates
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Read the following template file only when matching the user request.
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- [Tech Explainer](templates/tech-explainer.md) - For converting technical documentation and tutorials
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## Output Format
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The generated podcast follows the "Hello Deer" format:
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- Two hosts: one male, one female
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- Natural conversational dialogue
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- Starts with "Hello Deer" greeting
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- Target duration: approximately 10 minutes
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- Alternating speakers for engaging flow
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## Output Handling
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After generation:
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- Podcasts are saved in `/mnt/user-data/outputs/`
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- Share generated podcast with user using `present_files` tool
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- Provide brief description of the generation result (topic, duration, hosts)
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- Offer to regenerate if adjustments needed
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## Requirements
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The following environment variables must be set:
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- `OPENAI_API_KEY` or equivalent LLM API key for script generation
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- `VOLCENGINE_TTS_APPID`: Volcengine TTS application ID
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- `VOLCENGINE_TTS_ACCESS_TOKEN`: Volcengine TTS access token
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- `VOLCENGINE_TTS_CLUSTER`: Volcengine TTS cluster (optional, defaults to "volcano_tts")
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## Notes
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- **Always execute the full pipeline in one call** - no need to test individual steps or worry about timeouts
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- Input content language is auto-detected and matched in output
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- The script generation uses LLM to create natural conversational dialogue
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- Technical content is automatically simplified for audio accessibility
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- Complex notations (formulas, code) are translated to plain language
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- Long content may result in longer podcasts
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