AI Frameworks · SamurAIGPT

Text-To-Video-AI

Text-To-Video-AI is an open-source Python framework that generates videos from text prompts by orchestrating multiple AI services—LLMs for script generation, text-to-speech for voiceovers, and video generation APIs (Veo, Sora, Kling, etc.). It outputs short-form video (9:16 or 16:9) with auto-captioning, B-roll, and music integration.

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Source: GitHub — github.com/SamurAIGPT/Text-To-Video-AI

770

GitHub stars

298

Forks

Jupyter Notebook

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	SamurAIGPT/Text-To-Video-AI
Owner	SamurAIGPT
Primary language	Jupyter Notebook
License	MIT — OSI-approved
Stars	770
Forks	298
Open issues	1
Latest release	Unknown
Last updated	2026-06-16
Source	https://github.com/SamurAIGPT/Text-To-Video-AI

What Text-To-Video-AI is

A Jupyter Notebook–based pipeline that chains OpenAI/Groq/Gemini for scripting, EdgeTTS or ElevenLabs for audio, Whisper or Deepgram for alignment, Pexels for B-roll, and Muapi-proxied video models (Veo3, Sora, Kling, Seedance, LTX, etc.) for main video generation. Output is rendered via Remotion (React composition engine) and FFmpeg.

Quickstart

Get the Text-To-Video-AI source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/SamurAIGPT/Text-To-Video-AI.git2cd Text-To-Video-AI3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

YouTube Shorts / TikTok / Instagram Reels Automation

Generate short-form video content in bulk for social platforms. The framework handles script, voiceover, captions, B-roll, and music in one pipeline—ideal for faceless channels or content agencies automating production.

Educational & Explainer Video Production

Create explainer videos, tutorials, or educational content at scale. Whisper-timestamped captions and configurable voiceovers support multilingual and accessibility use cases.

Rapid Prototyping of AI Video Concepts

For teams evaluating different text-to-video models (Veo vs. Sora vs. Kling), this framework provides a unified interface to test model outputs without re-implementing integrations each time.

Implementation considerations

Requires Python 3.8+, FFmpeg, ImageMagick, and six separate API keys (Pexels minimum; add OpenAI/Groq/Gemini, Muapi, optionally ElevenLabs/Deepgram). Setup complexity is moderate.
All video generation is delegated to Muapi-proxied models; local inference is not supported. Cost scales with video length and model choice (fast vs. standard variants).
No built-in queuing, retry logic, or error recovery for failed API calls. Production use requires wrapping with monitoring and job management.
Output is single MP4 file. No support for multi-shot editing, client revisions, or workflow management within the framework itself.
Docker image not provided; platform-specific dependencies (FFmpeg, ImageMagick) must be manually installed. Windows setup requires separate documentation.

When to avoid it — and what to weigh

Requires Production-Grade Uptime & SLA — This is a developer tool / reference implementation, not a managed service. Relies on multiple third-party APIs (Pexels, Muapi, LLM providers, TTS). No guaranteed SLA or dedicated support.
Needs Real-Time or Interactive Video Generation — The pipeline is batch-oriented with latency measured in minutes (LLM → TTS → video model → render). Not suitable for live streaming or sub-second interactive use cases.
Strict Cost Predictability Required — Costs depend on API consumption (OpenAI/Groq/Gemini pricing, video model billing via Muapi, ElevenLabs/Deepgram if used). No usage caps or cost controls built in; can be expensive at scale without careful API key management.
Complex Video Editing or Motion Graphics — Limited to Remotion compositions and FFmpeg operations. No advanced timeline editing, keying, or 3D effects. If you need frame-level editing control or motion graphics, use dedicated video editors.

License & commercial use

MIT License. Permissive OSI-approved license allowing commercial use, modification, and distribution with no warranty and with attribution required only in source, not binary.

MIT license permits commercial use without restriction. However, commercial deployment depends entirely on third-party API terms (OpenAI, Muapi, Pexels, etc.). Each service has its own ToS; verify video-generation rights and credit requirements with Muapi and chosen video models. No indemnity from this project.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

API keys (OpenAI, Muapi, Pexels, etc.) must be stored securely in .env file; .env is not version-controlled by default but developer discipline is required. All video generation and LLM calls are remote (no local inference), so no sensitive data stored on-disk post-generation. No built-in audit logging or rate limiting. Muapi and upstream model providers' security posture unknown from this repo. Input prompts are sent to LLM and video providers; review data residency and retention policies before processing sensitive content.

Alternatives to consider

Synthesia, Descript, or Runway ML (Managed SaaS)

Production-grade UI, guaranteed uptime, included support, built-in workflows. Higher cost but eliminates API management and deployment burden.

Replicate or Modal (Serverless GPU Platforms)

Can host open-source video diffusion models locally. Gives cost control and data privacy but requires ML ops expertise and longer latency than managed APIs.

Custom in-house Orchestration (FastAPI + Celery + Video Models)

Full control, custom workflows, no dependency on Muapi. Requires substantial engineering investment and ongoing model maintenance.

Software development agency

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Evaluate Text-To-Video-AI for your workflow. Our team can assess API costs, integration complexity, and deployment strategy—or explore managed alternatives like Synthesia if you need production SLA.

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Text-To-Video-AI FAQ

Can I use this in production without the Premium API?

Yes, if you handle API key management, error recovery, and scaling yourself. The framework is open-source but provides no SLA or managed ops. For production, either self-host with monitoring or use the Premium API.

What's the typical latency to generate one video?

Unknown from documentation. Depends on LLM response time (seconds), TTS (seconds), video model (minutes), and FFmpeg render (minutes). Likely 5–15 minutes end-to-end for a 30-second video.

Do I need to pay for all six API services?

Pexels (always required for B-roll). Choose one LLM (OpenAI, Groq, or Gemini). EdgeTTS is free; ElevenLabs is paid. Whisper (free) or Deepgram (paid) for captions. Muapi required for video generation. Minimum cost is Pexels + one LLM + Muapi.

Can I host this myself without relying on external APIs?

Partially. You can run Whisper and EdgeTTS locally, fetch B-roll manually. But video generation is hardcoded to Muapi (Google Veo, Sora, Kling, etc.), which cannot run locally at production quality. LLM inference also requires external APIs or self-hosted LLM (e.g., Ollama).

Software development & web development with DEV.co

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