vid2cc-AI 🎙️🎬

vid2cc-AI is a high-performance CLI tool designed to bridge the gap between raw video and accessible content. By leveraging OpenAI's Whisper models and FFmpeg's robust media handling, it automates the creation of perfectly synced .srt subtitles.

Table of contents

• 🚀 Key Features
• ⚙️ Installation
  • 1. Prerequisite: FFmpeg
  • 2. Install vid2cc-AI
• 📖 How To Use
  • 🛠️ Advanced Options
  • 📦 Batch Processing
  • 📦 Usage as a Library
• ☁️ Run on Google Colab (with UI)
• 🧪 Testing
• 🗺️ Roadmap
• 🛠️ Tech Stack
• 📄 License

🚀 Key Features

• AI-Driven Transcription: Powered by OpenAI Whisper for industry-leading accuracy.
• Hardware Acceleration: Automatic CUDA detection for GPU-accelerated processing.
• Intelligent Pre-processing: FFmpeg-based audio extraction optimized for speech recognition (16kHz Mono).
• Professional Packaging: Fully installable via pip with a dedicated command-line entry point.

⚙️ Installation

1. Prerequisite: FFmpeg

This tool requires FFmpeg to be installed on your system.

For a complete step-by-step guide on how to install FFmpeg on Windows (Winget/Choco), macOS (Homebrew), or Linux (Apt/Dnf/Pacman), please refer to the dedicated guide:

👉 FFmpeg Installation Guide

2. Install vid2cc-AI

pip install vid2cc-ai

*Install directly from the source for development:

git clone https://github.com/0xdilshan/vid2cc-AI.git
cd vid2cc-AI
pip install -e .

📖 How To Use

Once installed, the vid2cc command is available globally in your terminal.

Examples

For maximum accuracy with toggleable subs:

vid2cc example.mp4 --model large --embed

🛠️ Advanced Options

Fine-tune your output using the following flags:

📦 Batch Processing

No need to run the command for every single file. You can pass multiple videos at once:

# Process all mp4 files in the current directory
vid2cc *.mp4 --model small --embed

# Process multiple specific files
vid2cc video1.mp4 video2.mkv video3.mov --model base --embed

📦 Usage as a Library

You can integrate vid2cc-AI directly into your Python projects:

from vid2cc_ai import Transcriber, extract_audio

# Extract and Transcribe
extract_audio("video.mp4", "audio.wav")
ts = Transcriber("base")
segments = ts.transcribe("audio.wav")

for s in segments:
    print(f"[{s['start']:.2f}s] {s['text']}")

Run on Google Colab (with UI)

You can run vid2cc-ai directly in your browser using Google Colab. This version includes a friendly interface to manage your Google Drive files and transcription settings without writing code.

1. Open the Notebook: .

2. Install & Mount: Run the first cell to install vid2cc-ai and connect your Google Drive.

3. Configure UI: * Video Path: Right-click your video in the Colab file sidebar and select "Copy Path."

   • Model: Choose turbo or small for speed, large for accuracy.
   • Output: Select if you want Soft Subtitles (toggleable) or Hardcoded (burned-in).

4. Start: Click "Start Processing" and find your result in your Drive folder.

⚡ For 10x faster transcription, ensure your Colab runtime is set to GPU (Runtime > Change runtime type > T4 GPU).

🧪 Testing

# Install test dependencies
pip install pytest

# Run the test suite
pytest

🗺️ Roadmap

• Local video → SRT subtitle/ transcription
• Embed subtitles into video containers (--embed)
• Burn-in subtitles (--hardcode)
• Set custom output directory (--output-dir)
• Multilingual transcription
• Support translation to English
• [ ] Transcription from YouTube/Vimeo URLs (yt-dlp)
• Google Colab notebook support

🛠️ Tech Stack

• Inference: OpenAI Whisper
• Media Engine: FFmpeg
• Core: Python 3.9+, PyTorch
• CLI Framework: Argparse

📄 License

Distributed under the MIT License.
See LICENSE for more information.