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AI‑Video‑Transcriber: Transcribe and Summarize Any Video with AI

January 16, 2026
Category: Practical Open Source Projects
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Open Source AI FastAPI Whisper Video Transcription

AI‑Video‑Transcriber – Transcribe & Summarize Any Video with AI

In an era where video content is everywhere – from YouTube tutorials to TikTok short‑clips – the ability to quickly turn spoken content into searchable, readable text has become indispensable. Whether you’re a content creator looking to draft captions, a researcher scouring interviews, or a developer building a new media platform, you need a reliable, open‑source solution that supports dozens of video sites and dozens of languages.

Meet AI‑Video‑Transcriber

AI‑Video‑Transcriber is a ready‑to‑deploy AI assistant that takes a video URL, downloads the media, runs a state‑of‑the‑art Whisper model for accurate speech‑to‑text, refines the transcript, and finally produces a concise, well‑structured summary in your chosen language. All of this happens in a web UI backed by FastAPI and runs effortlessly on a laptop or in a Docker container.

Key Features

  • 🔄 Supports 30+ video platforms via yt‑dlp (YouTube, TikTok, Bilibili, Facebook, Instagram, Twitter, etc.)
  • 🎤 Accurate transcription using Faster‑Whisper with selectable model sizes (tiny, base, small, medium, large)
  • ✍️ Automatic typo‑fix, sentence completion, and paragraphing
  • 🗣️ Multi‑language summaries (English, Chinese, Japanese, Korean, Spanish, French, German, Portuguese, Russian, Arabic & more)
  • 🔤 Auto‑translation with GPT‑4o when the requested summary language differs from the source language
  • 📱 Mobile‑friendly interface and real‑time progress feedback
  • ⚙️ Docker‑ready, or install with a simple shell script
  • 📦 Open‑source under the Apache‑2.0 license – free to fork, modify, and redistribute

Why This Tool Stands Out

Criteria AI‑Video‑Transcriber Competitors Notes
Open‑source ✔️ Mixed (mostly closed) No vendor lock‑in
Multi‑platform ✔️ Varies Leverages yt‑dlp’s plugin ecosystem
Speed/accuracy Fast‑Whisper models Google Speech‑to‑Text Comparable accuracy, lower cost
Language coverage 100+ via Whisper Limited Great for global teams
Summarization GPT‑4o fallback OpenAI API only Adds value with AI summarization
Deployment Docker & CLI Docker or manual Simplified environment setup

Quick Start Guide

You have three ways to get the tool up and running.

1. Automatic Shell Installation

# Clone the repo
git clone https://github.com/wendy7756/AI-Video-Transcriber.git
cd AI-Video-Transcriber

# Make the installation script executable
chmod +x install.sh

# Run it
./install.sh
The script installs Python dependencies, sets up a virtual environment, and downloads FFmpeg (if missing). It then spins up a FastAPI server on http://localhost:8000.

2. Deploy via Docker Compose

# Clone repository
git clone https://github.com/wendy7756/AI-Video-Transcriber.git
cd AI-Video-Transcriber

# Copy env template and set your key
cp .env.example .env
# Edit .env and set OPENAI_API_KEY

# Start services
docker-compose up -d
You can adjust WHISPER_MODEL_SIZE within .env to balance speed vs. memory.

3. Manual Installation

# Create a virtualenv (macOS or Linux)
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Install FFmpeg
brew install ffmpeg   # macOS
# or
sudo apt update && sudo apt install ffmpeg

# Run the server
python3 start.py

Tip: For long videos (>30 min), start the server with --prod to avoid SSE disconnects:

python3 start.py --prod

How It Works Under the Hood

flowchart TD
    A[User enters video URL] --> B[yt‑dlp downloads video]
    B --> C[ffmpeg extracts audio]
    C --> D[Fast-Whisper transcribes]
    D --> E[Text optimizer (typo/correct)
    E --> F[OpenAI GPT-4o for summarization or translation]
    F --> G[Web UI shows results & download links]
  • yt‑dlp: Handles over 200 media sites, ensuring broad coverage.
  • Faster‑Whisper: Lightweight, GPU‑friendly speech model.
  • OpenAI GPT‑4o: Adds context‑aware cleanup, paraphrasing, and summary generation.
  • FastAPI: Provides low‑latency REST endpoints for both backend and frontend.

Frequently Asked Questions

Q: Is the program free to use?

A: The tool is open‑source under Apache‑2.0. The only cost is the optional OpenAI API key for summaries and translations.

Q: My summary is in a different language—can I get a translation?

A: Yes. If the selected summary language differs from the detected transcript language, the UI automatically generates a translated transcript using GPT‑4o.

Q: The transcription is slow on my laptop—what can I do?

A: Reduce the Whisper model size (tiny or base). Alternatively, run in Docker on a machine with a GPU.

Q: I encountered a 500 error—why?

A: Most often it’s an environment issue. Ensure FFmpeg is installed, your virtualenv is active, and a valid OPENAI_API_KEY is set. Check logs with docker logs or the console output.

Q: How much memory does it need?

A: Base Docker images are ~128 MB. During transcription you'll need 0.5–2 GB depending on video length and model size. For heavy usage, give the container at least 4 GB of RAM.

Performance Tips

Action Impact
Use tiny or base Whisper model Faster, less memory
Offload models to GPU Dramatically faster transcriptions
Run in production mode (--prod) Keeps SSE connections alive for long tasks
Limit Docker memory (-m 1g) Prevents out‑of‑memory crashes
Use a fast network Faster video downloads

Contributing

We love community contributions! Whether you add a new Whisper dialect, improve the frontend UX, or optimize the Docker image, all pull requests are welcome.

  1. Fork the repo.
  2. Create a feature branch: git checkout -b feature/your-awesome-idea.
  3. Commit and push.
  4. Open a Pull Request.

Also consider opening issues for bugs, feature requests, or documentation suggestions.

Wrap‑Up

AI‑Video‑Transcriber empowers anyone to turn audio from any video into clean, summarized text—all open‑source, cross‑platform, and powered by the latest AI. No proprietary dashboards, no pay‑walls—just copy‑paste a link, choose a language, and let the AI do the heavy lifting. Grab the code, spin it up in minutes, and start transcribing.

Links

  • Repository: https://github.com/wendy7756/AI-Video-Transcriber
  • Docker Hub: https://hub.docker.com/r/ai-video-transcriber
  • Documentation: https://github.com/wendy7756/AI-Video-Transcriber#readme
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