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Description

An open source automated video creation tool for generating short-form video content. Short Video Maker combines text-to-speech, automatic captions, background videos, and music to create engaging short videos from simple text inputs.

This project is meant to provide a free alternative to heavy GPU-power hungry video generation (and a free alternative to expensive, third-party API calls). It doesn't generate a video from scratch based on an image or an image prompt.

The repository was open-sourced by the AI Agents A-Z Youtube Channel. We encourage you to check out the channel for more AI-related content and tutorials.

The server exposes an MCP and a REST server.

While the MCP server can be used with an AI Agent (like n8n) the REST endpoints provide more flexibility for video generation.

You can find example n8n workflows created with the REST/MCP server in this repository.

TOC

Getting started

• Requirements
• How to run the server
• Web UI
• Tutorial
• Examples

Usage

• Environment variables
• REST API
• Configuration options
• MCP

Info

• Features
• How it works
• Limitations
• Concepts
• Troubleshooting
• Deploying in the cloud
• FAQ
• Dependencies
• Contributing
• License
• Acknowledgements

Tutorial with n8n

Examples

  <video src="https://github.com/user-attachments/assets/1b488e7d-1b40-439d-8767-6ab51dbc0922" width="480" height="270"></video>


  <video src="https://github.com/user-attachments/assets/bb7ce80f-e6e1-44e5-ba4e-9b13d917f55b" width="270" height="480"></video>

Features

• Generate complete short videos from text prompts
• Text-to-speech conversion
• Automatic caption generation and styling
• Background video search and selection via Pexels
• Background music with genre/mood selection
• Serve as both REST API and Model Context Protocol (MCP) server

How It Works

Shorts Creator takes simple text inputs and search terms, then:

1. Converts text to speech using Kokoro TTS
2. Generates accurate captions via Whisper
3. Finds relevant background videos from Pexels
4. Composes all elements with Remotion
5. Renders a professional-looking short video with perfectly timed captions

Limitations

• The project only capable generating videos with English voiceover (kokoro-js doesn’t support other languages at the moment)
• The background videos are sourced from Pexels

General Requirements

• internet
• free pexels api key
• ≥ 3 gb free RAM, my recommendation is 4gb RAM
• ≥ 2 vCPU
• ≥ 5gb disc space

Concepts

Scene

Each video is assembled from multiple scenes. These scenes consists of

1. Text: Narration, the text the TTS will read and create captions from.
2. Search terms: The keywords the server should use to find videos from Pexels API. If none can be found, joker terms are being used (nature, globe, space, ocean)

Getting started

Docker (recommended)

There are three docker images, for three different use cases. Generally speaking, most of the time you want to spin up the tiny one.

Tiny

• Uses the tiny.en whisper.cpp model
• Uses the q4 quantized kokoro model
• CONCURRENCY=1 to overcome OOM errors coming from Remotion with limited resources
• VIDEO_CACHE_SIZE_IN_BYTES=2097152000 (2gb) to overcome OOM errors coming from Remotion with limited resources

docker run -it --rm --name short-video-maker -p 3123:3123 -e LOG_LEVEL=debug -e PEXELS_API_KEY= gyoridavid/short-video-maker:latest-tiny

Normal

• Uses the base.en whisper.cpp model
• Uses the fp32 kokoro model
• CONCURRENCY=1 to overcome OOM errors coming from Remotion with limited resources
• VIDEO_CACHE_SIZE_IN_BYTES=2097152000 (2gb) to overcome OOM errors coming from Remotion with limited resources

docker run -it --rm --name short-video-maker -p 3123:3123 -e LOG_LEVEL=debug -e PEXELS_API_KEY= gyoridavid/short-video-maker:latest

Cuda

If you own an Nvidia GPU and you want use a larger whisper model with GPU acceleration, you can use the CUDA optimised Docker image.

• Uses the medium.en whisper.cpp model (with GPU acceleration)
• Uses fp32 kokoro model
• CONCURRENCY=1 to overcome OOM errors coming from Remotion with limited resources
• VIDEO_CACHE_SIZE_IN_BYTES=2097152000 (2gb) to overcome OOM errors coming from Remotion with limited resources

docker run -it --rm --name short-video-maker -p 3123:3123 -e LOG_LEVEL=debug -e PEXELS_API_KEY= --gpus=all gyoridavid/short-video-maker:latest-cuda

Docker compose

You might use Docker Compose to run n8n or other services, and you want to combine them. Make sure you add the shared network to the service configuration.

version: "3"

services:
  short-video-maker:
    image: gyoridavid/short-video-maker:latest-tiny
    environment:
      - LOG_LEVEL=debug
      - PEXELS_API_KEY=
    ports:
      - "3123:3123"
    volumes:
	    - ./videos:/app/data/videos # expose the generated videos

If you are using the Self-hosted AI starter kit you