AutoTranscript GUI 🎙️

AutoTranscript is a powerful, GPU-accelerated subtitle generator built on top of OpenAI's Whisper model. It features both a command-line interface (CLI) and a beautiful CustomTkinter-based GUI for users who prefer a graphical workflow.

Supports:

• Languages such as: English, Chinese, Japanese, Korean.
• Local audio/video files
• Subtitle translation to English
• OpenAI API (for higher quality translations)

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✨ Features

• 🖥️ Full-featured GUI with progress tracking, real-time logs, and OpenAI config
• 📜 Generate .srt subtitle files from media files
• 🌍 Supports multilingual transcription and optional translation to English
• 🧠 Uses Faster-Whisper for fast GPU-accelerated transcription
• 🔐 API key manager for OpenAI GPT models

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📸 GUI Preview

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🧩 Requirements

• Python 3.8+
• ffmpeg (must be installed)
• NVIDIA GPU with CUDA (recommended)
• PyTorch with CUDA

Requirements for Releases

• The rar file contains everything you need to get started without having to install anything.

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📦 Installation

git clone https://github.com/jjaruna/autoTranscriptGUI.git
cd autoTranscriptGUI
pip install -r requirements.txt

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🚀 Launch the GUI

python AutoTranscriptGUI.py

🔍 Whisper Model Comparison Summary

Model	VRAM (Min)	⚙️ Performance	🎯 Use Case	🌐 Translate into English
tiny	≥ 1 GB	⚡ Very Fast	Quick tests, low-resource devices	✅
base	≥ 2 GB	⚡ Fast	Simple transcriptions, short audio	✅
small	≥ 4 GB	⚖️ Balanced	Decent accuracy and speed for general use	✅
medium	≥ 8 GB	🕒 Slower	High-quality results for longer files	✅
large-v1	≥ 10 GB	🐢 Slower	Older but still strong performer	✅
large-v2	≥ 10 GB	🐢 Slower	More robust, especially with noisy inputs	✅
large-v3	≥ 12 GB	🐌 Slowest	Highest accuracy offline, latest version	✅
large-v3-turbo	≥ 8–10 GB	⚡ Fastest	High-speed, high-accuracy, great multilingual support	❌

🧠 Recommendation

After testing the large-v3-turbo model more than 10 times, I can confidently say it is the fastest and most accurate among all Whisper models included in this app.

🖥️ My system has 4GB of VRAM, and despite being under the recommended VRAM for large models, large-v3-turbo still performed exceptionally well.

⚠️ Note: Your experience may vary depending on your GPU and available VRAM. Use this recommendation as a reference, not a guarantee. If you encounter performance issues, try smaller models like medium or small.

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⚙️ OpenAI API Setup (Optional)

To enable OpenAI-powered translation:

1. Click "Add API Key" in the GUI
2. Enter your OpenAI key and model (gpt-4, gpt-3.5-turbo, etc.)
3. It will be saved to .env file automatically

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🖥️ CLI Mode (Optional)

You can still use the command-line version via autosub.py:

python autosub.py myvideo.mp4 -l ja --translate --model base

CLI Options

Option	Description
filename	File path
-l, --language	Force language (e.g. en, es, zh)
-t, --translate	Translate to English
-o, --openai	Use OpenAI API
--model	Whisper model to use
--debug	Enable debug mode
--keep	Keep intermediate WAV file

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📝 Output

• Subtitles are saved as .srt files in the same folder as your media.
• If translated, original and translated text will be preserved.

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🧪 Example GUI Workflow

1. Open GUI
2. Select video/audio file
3. Choose language and Whisper model
4. (Optional) Enable "Translate to English"
5. (Optional) Enable "Use OpenAI"
6. Click Start Transcription
7. Wait for progress bar and logs to finish

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🙏 Credits

• Built with OpenAI Whisper
• Powered by Faster-Whisper
• GUI built with CustomTkinter
• Thank you General Koi, for the great help in testing and reviewing the Japanese transcripts.

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📄 License

MIT License — free for personal and commercial use.