wanwu/deer-flow
1
0
Fork 0
mirror of https://gitee.com/wanwujie/deer-flow synced 2026-04-03 14:22:13 +08:00
Code Issues Packages Projects Releases Wiki Activity

feat: add podcast generation skill

Browse Source 
- 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
This commit is contained in:
hetaoBackend
2026-01-25 22:37:53 +08:00
committed by hetao
parent 038f5d44f4
commit 3fa16467a2
5 changed files with 542 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
.gitignore vendored
View File

@@ -7,6 +7,11 @@
ehthumbs.db
Thumbs.db
# Python cache
__pycache__/
*.pyc
*.pyo
# Virtual environments
.venv
venv/

2
backend/src/sandbox/local/local_sandbox.py
View File

@@ -142,7 +142,7 @@ class LocalSandbox(Sandbox):
shell=True,
capture_output=True,
text=True,
timeout=30,
timeout=600,
)
output = result.stdout
if result.stderr:

124
skills/public/podcast-generation/SKILL.md Normal file
View File

@@ -0,0 +1,124 @@
---
name: podcast-generation
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.
---
# Podcast Generation Skill
## Overview
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.
## 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 (auto-detected from content)
- Output location: Where to save the generated podcast
- You don't need to check the folder under `/mnt/user-data`
### Step 2: Prepare Input Content
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`
### Step 3: Execute Generation
Call the Python script directly without any concerns about timeout or the need for pre-testing:
```bash
python /mnt/skills/public/podcast-generation/scripts/generate.py \
--input-file /mnt/user-data/workspace/content-file.md \
--output-file /mnt/user-data/outputs/generated-podcast.mp3 \
--locale en
```
Parameters:
- `--input-file`: Absolute path to input text/markdown file (required)
- `--output-file`: Absolute path to output MP3 file (required)
- `--locale`: Language locale - "en" for English or "zh" for Chinese (optional, auto-detected if not specified)
> [!IMPORTANT]
> - Execute the script in one complete call. Do NOT split the workflow into separate steps (e.g., testing script generation first, then TTS).
> - The script handles all external API calls and audio generation internally with proper timeout management.
> - Do NOT read the Python file, just call it with the parameters.
## Podcast Generation Example
User request: "Generate a podcast about the history of artificial intelligence"
Step 1: Create content file `/mnt/user-data/workspace/ai-history-content.md` with the source text:
```markdown
# The History of Artificial Intelligence
Artificial intelligence has a rich history spanning over seven decades...
## Early Beginnings (1950s)
The term "artificial intelligence" was coined by John McCarthy in 1956...
## The First AI Winter (1970s)
After initial enthusiasm, AI research faced significant setbacks...
## Modern Era (2010s-Present)
Deep learning revolutionized the field with breakthrough results...
```
Step 2: Execute generation:
```bash
python /mnt/skills/public/podcast-generation/scripts/generate.py \
--input-file /mnt/user-data/workspace/ai-history-content.md \
--output-file /mnt/user-data/outputs/ai-history-podcast.mp3 \
--locale en
```
## Specific Templates
Read the following template file only when matching the user request.
- [Tech Explainer](templates/tech-explainer.md) - 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
After generation:
- Podcasts are saved in `/mnt/user-data/outputs/`
- Share generated podcast 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:
- `OPENAI_API_KEY` or equivalent LLM API key for script generation
- `VOLCENGINE_TTS_APPID`: Volcengine TTS application ID
- `VOLCENGINE_TTS_ACCESS_TOKEN`: Volcengine TTS access token
- `VOLCENGINE_TTS_CLUSTER`: Volcengine TTS cluster (optional, defaults to "volcano_tts")
## Notes
- **Always execute the full pipeline in one call** - no need to test individual steps or worry about timeouts
- Input content language is auto-detected and matched in output
- The script generation uses LLM to create natural conversational dialogue
- Technical content is automatically simplified for audio accessibility
- Complex notations (formulas, code) are translated to plain language
- Long content may result in longer podcasts

349
skills/public/podcast-generation/scripts/generate.py Normal file
View File

@@ -0,0 +1,349 @@
import argparse
import base64
import json
import logging
import os
import re
import uuid
from typing import Literal, Optional
import requests
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Types
class ScriptLine:
def __init__(self, speaker: Literal["male", "female"] = "male", paragraph: str = ""):
self.speaker = speaker
self.paragraph = paragraph
class Script:
def __init__(self, locale: Literal["en", "zh"] = "en", lines: list[ScriptLine] = None):
self.locale = locale
self.lines = lines or []
@classmethod
def from_dict(cls, data: dict) -> "Script":
script = cls(locale=data.get("locale", "en"))
for line in data.get("lines", []):
script.lines.append(
ScriptLine(
speaker=line.get("speaker", "male"),
paragraph=line.get("paragraph", ""),
)
)
return script
# Prompt template for script generation
SCRIPT_WRITER_PROMPT = """You are a skilled podcast script writer for "Hello Deer", a conversational podcast show with two hosts.
Transform the provided content into an engaging podcast script following these guidelines:
## Format Requirements
- Output as JSON with this structure: {{"locale": "en" or "zh", "lines": [{{"speaker": "male" or "female", "paragraph": "dialogue text"}}]}}
- Only two hosts: male and female, alternating naturally
- Target runtime: approximately 10 minutes of dialogue
- 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
## Language
- Match the locale of the input content
- Use "{locale}" for the output locale
Now transform this content into a podcast script:
{content}
"""
def extract_json_from_text(text: str) -> dict:
"""Extract JSON from text that might contain markdown code blocks or extra content."""
# Try to find JSON in markdown code blocks first
json_block_pattern = r"```(?:json)?\s*(\{[\s\S]*?\})\s*```"
match = re.search(json_block_pattern, text)
if match:
return json.loads(match.group(1))
# Try to find raw JSON object
json_pattern = r"\{[\s\S]*\}"
match = re.search(json_pattern, text)
if match:
return json.loads(match.group(0))
# Last resort: try parsing the whole text
return json.loads(text)
def generate_script(content: str, locale: str) -> Script:
"""Generate podcast script from content using LLM."""
logger.info("Generating podcast script...")
api_key = os.getenv("OPENAI_API_KEY")
base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
model = os.getenv("OPENAI_MODEL", "gpt-4o")
if not api_key:
raise ValueError("OPENAI_API_KEY environment variable is not set")
prompt = SCRIPT_WRITER_PROMPT.format(content=content, locale=locale)
# First try with JSON mode
try:
response = requests.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model,
"messages": [
{"role": "system", "content": "You are a podcast script writer. Always respond with valid JSON only, no markdown formatting."},
{"role": "user", "content": prompt},
],
"response_format": {"type": "json_object"},
},
)
if response.status_code != 200:
raise Exception(f"LLM API error: {response.status_code} - {response.text}")
result = response.json()
logger.info(f"API response keys: {result.keys()}")
if "error" in result:
raise Exception(f"API error: {result['error']}")
response_content = result["choices"][0]["message"]["content"]
logger.info(f"LLM response preview: {response_content[:200]}...")
script_json = json.loads(response_content)
except (json.JSONDecodeError, KeyError) as e:
# Fallback: try without JSON mode for models that don't support it
logger.warning(f"JSON mode failed ({e}), trying without response_format...")
response = requests.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model,
"messages": [
{"role": "system", "content": "You are a podcast script writer. Respond with valid JSON only, no markdown or extra text."},
{"role": "user", "content": prompt},
],
},
)
if response.status_code != 200:
raise Exception(f"LLM API error: {response.status_code} - {response.text}")
result = response.json()
response_content = result["choices"][0]["message"]["content"]
logger.debug(f"LLM response (fallback): {response_content[:500]}...")
script_json = extract_json_from_text(response_content)
# Validate structure
if "lines" not in script_json:
raise ValueError(f"Invalid script format: missing 'lines' key. Got keys: {list(script_json.keys())}")
script = Script.from_dict(script_json)
logger.info(f"Generated script with {len(script.lines)} lines")
return script
def text_to_speech(text: str, voice_type: str) -> Optional[bytes]:
"""Convert text to speech using Volcengine TTS."""
app_id = os.getenv("VOLCENGINE_TTS_APPID")
access_token = os.getenv("VOLCENGINE_TTS_ACCESS_TOKEN")
cluster = os.getenv("VOLCENGINE_TTS_CLUSTER", "volcano_tts")
if not app_id or not access_token:
raise ValueError(
"VOLCENGINE_TTS_APPID and VOLCENGINE_TTS_ACCESS_TOKEN environment variables must be set"
)
url = "https://openspeech.bytedance.com/api/v1/tts"
# Authentication: Bearer token with semicolon separator
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer;{access_token}",
}
payload = {
"app": {
"appid": app_id,
"token": "access_token", # literal string, not the actual token
"cluster": cluster,
},
"user": {"uid": "podcast-generator"},
"audio": {
"voice_type": voice_type,
"encoding": "mp3",
"speed_ratio": 1.2,
},
"request": {
"reqid": str(uuid.uuid4()), # must be unique UUID
"text": text,
"text_type": "plain",
"operation": "query",
},
}
try:
response = requests.post(url, json=payload, headers=headers)
if response.status_code != 200:
logger.error(f"TTS API error: {response.status_code} - {response.text}")
return None
result = response.json()
if result.get("code") != 3000:
logger.error(f"TTS error: {result.get('message')} (code: {result.get('code')})")
return None
audio_data = result.get("data")
if audio_data:
return base64.b64decode(audio_data)
except Exception as e:
logger.error(f"TTS error: {str(e)}")
return None
def tts_node(script: Script) -> list[bytes]:
"""Convert script lines to audio chunks using TTS."""
logger.info("Converting script to audio...")
audio_chunks = []
for i, line in enumerate(script.lines):
# Select voice based on speaker gender
if line.speaker == "male":
voice_type = "zh_male_yangguangqingnian_moon_bigtts" # Male voice
else:
voice_type = "zh_female_sajiaonvyou_moon_bigtts" # Female voice
logger.info(f"Processing line {i + 1}/{len(script.lines)} ({line.speaker})")
audio = text_to_speech(line.paragraph, voice_type)
if audio:
audio_chunks.append(audio)
else:
logger.warning(f"Failed to generate audio for line {i + 1}")
logger.info(f"Generated {len(audio_chunks)} audio chunks")
return audio_chunks
def mix_audio(audio_chunks: list[bytes]) -> bytes:
"""Combine audio chunks into a single audio file."""
logger.info("Mixing audio chunks...")
output = b"".join(audio_chunks)
logger.info("Audio mixing complete")
return output
def detect_locale(content: str) -> str:
"""Auto-detect content locale based on character analysis."""
chinese_chars = sum(1 for char in content if "\u4e00" <= char <= "\u9fff")
total_chars = len(content)
if total_chars > 0 and chinese_chars / total_chars > 0.1:
return "zh"
return "en"
def generate_podcast(
input_file: str,
output_file: str,
locale: Optional[str] = None,
) -> str:
"""Generate a podcast from input content."""
# Read input content
with open(input_file, "r", encoding="utf-8") as f:
content = f.read()
if not content.strip():
raise ValueError("Input file is empty")
# Auto-detect locale if not specified
if not locale:
locale = detect_locale(content)
logger.info(f"Auto-detected locale: {locale}")
# Step 1: Generate script
script = generate_script(content, locale)
# Step 2: Convert to audio
audio_chunks = tts_node(script)
if not audio_chunks:
raise Exception("Failed to generate any audio")
# Step 3: Mix audio
output_audio = mix_audio(audio_chunks)
# Save output
output_dir = os.path.dirname(output_file)
if output_dir:
os.makedirs(output_dir, exist_ok=True)
with open(output_file, "wb") as f:
f.write(output_audio)
return f"Successfully generated podcast to {output_file}"
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate podcast from text content")
parser.add_argument(
"--input-file",
required=True,
help="Absolute path to input text/markdown file",
)
parser.add_argument(
"--output-file",
required=True,
help="Output path for generated podcast MP3",
)
parser.add_argument(
"--locale",
choices=["en", "zh"],
default=None,
help="Language locale (auto-detected if not specified)",
)
args = parser.parse_args()
try:
result = generate_podcast(
args.input_file,
args.output_file,
args.locale,
)
print(result)
except Exception as e:
import traceback
print(f"Error generating podcast: {e}")
traceback.print_exc()

63
skills/public/podcast-generation/templates/tech-explainer.md Normal file
View File

@@ -0,0 +1,63 @@
# Tech Explainer Podcast Template
Use this template when converting technical documentation, API guides, or developer tutorials into podcasts.
## Input Preparation
When the user wants to convert technical content to a podcast, help them structure the input:
1. **Simplify Code Examples**: Replace code snippets with plain language descriptions
- Instead of showing actual code, describe what the code does
- Focus on concepts rather than syntax
2. **Remove Complex Notation**:
- Mathematical formulas should be explained in words
- API endpoints described by function rather than URL paths
- Configuration examples summarized as settings descriptions
3. **Add Context**:
- Explain why the technology matters
- Include real-world use cases
- Add analogies for complex concepts
## Example Transformation
### Original Technical Content:
```markdown
# Using the API
POST /api/v1/users
{
"name": "John",
"email": "john@example.com"
}
Response: 201 Created
```
### Podcast-Ready Content:
```markdown
# Creating Users with the API
The user creation feature allows applications to register new users in the system.
When you want to add a new user, you send their name and email address to the server.
If everything goes well, the server confirms the user was created successfully.
This is commonly used in signup flows, admin dashboards, or when importing users from other systems.
```
## Generation Command
```bash
python /mnt/skills/public/podcast-generation/scripts/generate.py \
--input-file /mnt/user-data/workspace/tech-content.md \
--output-file /mnt/user-data/outputs/tech-explainer-podcast.mp3 \
--locale en
```
## Tips for Technical Podcasts
- Keep episodes focused on one main concept
- Use analogies to explain abstract concepts
- Include practical "why this matters" context
- Avoid jargon without explanation
- Make the dialogue accessible to beginners