| name | ai-video-generator |
| description | Generate short-form videos with AI — script writing, text-to-speech narration, stock footage selection, subtitle generation, and video assembly. Use when: creating TikTok/YouTube Shorts/Reels content, automating video production, building content pipelines. |
| license | Apache-2.0 |
| compatibility | Python 3.10+, FFmpeg |
| metadata | {"author":"terminal-skills","version":"1.0.0","category":"content","tags":["video-generation","tiktok","youtube-shorts","content-creation","ai-video"],"use-cases":["Generate 50 short-form videos per day for TikTok/YouTube Shorts","Build an automated content pipeline: topic → script → voice → video","Create educational or explainer videos with AI narration"],"agents":["claude-code","openai-codex","gemini-cli","cursor"]} |
AI Video Generator — Short-Form Content Pipeline
Overview
Automate creation of short-form videos (TikTok, YouTube Shorts, Instagram Reels) using AI for every step: topic research, script writing, text-to-speech narration, stock footage matching, subtitle generation, and final assembly. Inspired by MoneyPrinterTurbo (53k+ stars).
Instructions
Step 1: Set Up the Environment
pip install anthropic openai requests moviepy pydub whisperx srt
sudo apt install ffmpeg
API keys needed: Anthropic or OpenAI (scripts), ElevenLabs or OpenAI TTS (voice), Pexels (free stock footage).
Step 2: AI Script Writing
import anthropic
def generate_script(topic, duration_seconds=45):
"""Generate a video script optimized for short-form content."""
client = anthropic.Anthropic()
prompt = f"""Write a {duration_seconds}-second video script about: {topic}
Format:
HOOK (first 3 seconds): A shocking statement or question that stops scrolling
BODY (main content): 3-5 punchy facts or points, each 1-2 sentences
CTA (last 5 seconds): Call to action — follow, like, comment
Rules:
- Conversational, no complex sentences
- Each sentence on its own line
- ~{duration_seconds * 2.5:.0f} words ({duration_seconds}s at 150wpm)
- Use power words: secret, shocking, nobody tells you, actually
- No emojis or hashtags — this is a voiceover script
"""
response = client.messages.create(
model="claude-sonnet-4-20250514", max_tokens=500,
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
Step 3: Text-to-Speech Narration
import requests, os
def generate_voice_elevenlabs(text, output_path='narration.mp3'):
"""Generate voiceover using ElevenLabs."""
url = "https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM"
headers = {"xi-api-key": os.environ["ELEVENLABS_API_KEY"], "Content-Type": "application/json"}
data = {"text": text, "model_id": "eleven_turbo_v2_5",
"voice_settings": {"stability": 0.5, "similarity_boost": 0.75}}
response = requests.post(url, json=data, headers=headers)
with open(output_path, 'wb') as f:
f.write(response.content)
return output_path
def generate_voice_openai(text, output_path='narration.mp3'):
"""Generate voiceover using OpenAI TTS (cheaper alternative)."""
from openai import OpenAI
client = OpenAI()
response = client.audio.speech.create(model="tts-1-hd", voice="onyx", input=text)
response.stream_to_file(output_path)
return output_path
Step 4: Stock Footage Selection
def search_pexels_videos(query, count=5):
"""Search Pexels for portrait-oriented stock video clips."""
url = "https://api.pexels.com/videos/search"
headers = {"Authorization": os.environ["PEXELS_API_KEY"]}
params = {"query": query, "per_page": count, "orientation": "portrait", "size": "medium"}
response = requests.get(url, headers=headers, params=params)
videos = response.json().get('videos', [])
results = []
for v in videos:
files = sorted(v['video_files'], key=lambda x: x.get('height', 0), reverse=True)
hd = next((f for f in files if f.get('height', 0) >= 720), files[0])
results.append({'id': v['id'], 'url': hd['link'], 'duration': v['duration']})
return results
Step 5: Subtitle Generation
def generate_subtitles(audio_path, output_srt='subtitles.srt'):
"""Generate word-level subtitles using WhisperX."""
import whisperx, srt
from datetime import timedelta
model = whisperx.load_model("base", device="cpu")
audio = whisperx.load_audio(audio_path)
result = model.transcribe(audio)
align_model, metadata = whisperx.load_align_model(language_code="en")
aligned = whisperx.align(result["segments"], align_model, metadata, audio)
subs = []
words = [w for seg in aligned["segments"] for w in seg.get("words", [])]
for i in range(0, len(words), 4):
group = words[i:i + 4]
if not group: continue
start = timedelta(seconds=group[0].get('start', 0))
end = timedelta(seconds=group[-1].get('end', 0))
text = ' '.join(w['word'] for w in group)
subs.append(srt.Subtitle(index=len(subs)+1, start=start, end=end, content=text))
with open(output_srt, 'w') as f:
f.write(srt.compose(subs))
return output_srt
Step 6: Video Assembly with FFmpeg
import subprocess
def assemble_video(clips, narration, subtitles, output='final.mp4'):
"""Assemble final video: concatenate clips, add narration and subtitles."""
concat_list = 'concat_list.txt'
with open(concat_list, 'w') as f:
for clip in clips:
f.write(f"file '{clip}'\n")
subprocess.run([
'ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', concat_list,
'-vf', 'scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920',
'-c:v', 'libx264', '-preset', 'fast', '-an', 'temp_video.mp4'
], check=True)
subtitle_filter = (f"subtitles={subtitles}:force_style='"
"FontName=Arial,FontSize=18,PrimaryColour=&H00FFFFFF,"
"OutlineColour=&H00000000,Outline=2,Bold=1,Alignment=2'")
subprocess.run([
'ffmpeg', '-y', '-i', 'temp_video.mp4', '-i', narration,
'-vf', subtitle_filter, '-c:v', 'libx264', '-c:a', 'aac',
'-shortest', output
], check=True)
output
Step 7: Full Pipeline
def generate_video(topic, output_dir='./output'):
"""Complete pipeline: topic -> finished video."""
import os
os.makedirs(output_dir, exist_ok=True)
script = generate_script(topic)
narration = generate_voice_elevenlabs(script, f'{output_dir}/narration.mp3')
keywords = topic.split()[:3]
videos = search_pexels_videos(' '.join(keywords), count=3)
clips = []
for i, v in enumerate(videos):
path = f'{output_dir}/clip_{i}.mp4'
requests.get(v['url'], stream=True)
clips.append(path)
subs = generate_subtitles(narration, f'{output_dir}/subs.srt')
return assemble_video(clips, narration, subs, f'{output_dir}/final.mp4')
Examples
Example 1: Generate a Batch of Tech Fact Videos
A creator produces 5 technology-themed short videos for TikTok in one run:
topics = [
"AI tools nobody talks about",
"Apps that feel illegal to use for free",
"Websites that will blow your mind",
"Free AI tools every student needs",
"Tech gadgets under $50 that changed my life"
]
for topic in topics:
output = generate_video(topic, output_dir=f'./output/{topic[:30]}')
print(f"Video ready: {output}")
Example 2: Daily Finance Shorts for YouTube
A finance channel automates daily Shorts upload with trending money topics:
import schedule
def daily_finance_video():
topic = "3 passive income ideas that actually work in 2025"
script = generate_script(topic, duration_seconds=55)
narration = generate_voice_openai(script, './daily/narration.mp3')
videos = search_pexels_videos("money finance investing", count=4)
final = assemble_video(['./daily/clip_0.mp4', './daily/clip_1.mp4'],
narration, './daily/subs.srt', './daily/final.mp4')
schedule.every().day.at("08:00").do(daily_finance_video)
Guidelines
- Respect copyright — only use royalty-free stock footage (Pexels, Pixabay) or your own content
- Disclose AI usage — YouTube and TikTok require disclosure of AI-generated content
- Review before publishing — always watch the final video; AI scripts can contain inaccuracies
- Optimize for the first 3 seconds — the hook determines whether viewers stay or scroll
- Test multiple voices — ElevenLabs offers dozens of voices; find one that fits your niche
- Monitor performance — track views, retention, and click-through to iterate on content style
References
