https://github.com/jjaruna/autotranscriptgui
🎙️ Powerful GUI tool to transcribe and translate audio/video files using Whisper and OpenAI — fast, simple, and GPU-optimized.
https://github.com/jjaruna/autotranscriptgui
fast-whisper gui openai-api subtitulos transcripcion transcription translate whisper
Last synced: about 1 month ago
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🎙️ Powerful GUI tool to transcribe and translate audio/video files using Whisper and OpenAI — fast, simple, and GPU-optimized.
- Host: GitHub
- URL: https://github.com/jjaruna/autotranscriptgui
- Owner: jjaruna
- Created: 2025-07-05T07:35:46.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-05T08:15:30.000Z (12 months ago)
- Last Synced: 2025-07-05T08:34:16.549Z (12 months ago)
- Topics: fast-whisper, gui, openai-api, subtitulos, transcripcion, transcription, translate, whisper
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.
- Translate or transcribe YouTube videos using only the link.
- Subtitle translation to English.
- OpenAI API (for higher quality translations) NOT AVAILABLE
---
## ✨ Features
- 🖥️ Full-featured **GUI with progress tracking**, real-time logs.
- 📜 Generate `.srt` subtitle files from media files
- 🌍 Supports multilingual transcription and optional **translation to English**
- 🧠 Uses [Faster-Whisper](https://github.com/guillaumekln/faster-whisper) for fast GPU-accelerated transcription
---
## YOUTUBE TUTORIAL IN SPANISH
(https://www.youtube.com/watch?v=dB6D1i1BjXc)
---
## 📸 GUI Preview
> 
---
## 🧩 Requirements
- Python
- NVIDIA GPU with CUDA (recommended)
- Visual C++ Redistributable 14
---
## Installation for Releases
- Extract the .rar file.
- Go to the app folder.
- At the top of the path bar, type cmd.
- In the console, type: pip install -r requirements.txt.
- Go back to the .bat file and run it.
---
## 📦 Installation
```bash
git clone https://github.com/jjaruna/autoTranscriptGUI.git
cd autoTranscriptGUI
pip install -r requirements.txt
```
---
## 🚀 Launch the GUI
```bash
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`.
---
## 🖥️ CLI Mode (Optional)
You can still use the command-line version via `autosub.py`:
```bash
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 |
---
## 📝 Output
- Subtitles are saved as `.srt` files in the same folder as your media.
- If translated, original and translated text will be preserved.
---
## 🧪 Example GUI Workflow
1. Open GUI
2. Select video/audio file
3. Choose language and Whisper model
4. (Optional) Enable "Translate to English"
5. Click **Start Transcription**
---
## 🙏 Credits
- Built with [OpenAI Whisper](https://github.com/openai/whisper)
- Powered by [Faster-Whisper](https://github.com/guillaumekln/faster-whisper)
- GUI built with [CustomTkinter](https://github.com/TomSchimansky/CustomTkinter)
- Thank you General Koi, for the great help in testing and reviewing the Japanese transcripts.
---
## 📄 License
MIT License — free for personal and commercial use.