🎙️ Speech-to-Text Whisper GUI Python

Convert MP4 · MP3 · WAV · M4A · FLAC · MOV · AVI · MKV to Text & Subtitles

Turn any video or audio file into text, subtitles, or data — with one click!

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📸 Screenshot

Main application interface

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🎥 Complete Video Tutorial

▶️ Click the image above to watch the full tutorial

• ✅ How to install Python & FFmpeg
• ✅ Setting up the project step by step
• ✅ Transcribing MP4/MP3 files
• ✅ Batch processing multiple files
• ✅ GPU acceleration setup (CUDA)
• ✅ Real-time demo with different file formats

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📥 What can you transcribe?

If you have...	You can get...
🎥 MP4 Video	📝 Text transcript
🎵 MP3 Audio	🎬 SRT subtitles
🎶 WAV Recording	🌐 VTT web subtitles
📀 M4A Podcast	📊 JSON data
🎼 FLAC Music	📈 TSV for Excel
🎬 MOV Video	✅ All formats at once!
🎞️ AVI Video
🗃️ MKV Video

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🚀 One-click conversions

• MP4 → TXT : Extract text from YouTube videos, lectures, movies
• MP3 → SRT : Create subtitles for your podcast
• WAV → JSON : Get word-by-word timestamps
• MOV → VTT : Web-ready subtitles
• Batch folder → All formats : Process 100+ files overnight

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✨ Features

• 🎯 Drag & Drop support
• 🚀 GPU (CUDA) acceleration
• 📁 Batch processing (entire folders)
• 🌍 8 languages support
• 📄 Multiple output formats (TXT, SRT, VTT, JSON, TSV)
• 🎨 Modern dark theme UI
• ⚡ Keyboard shortcuts

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🎯 Supported Languages

Language	Code	Language	Code
English	en	Spanish	es
French	fr	German	de
Italian	it	Japanese	ja
Chinese	zh	Russian	ru

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🚀 For Beginners – Just Download & Run

No Python or coding needed.

👉 Download speech_to_text.exe (Latest Release)

1. Download the .exe file
2. Double-click to open the app
3. Select your video/audio file
4. Click "START TRANSCRIPTION"

Watch the video above for a step-by-step walkthrough.

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🐍 For Developers – Run from Source

If you want to modify the code or contribute:

📦 Installation

Prerequisites

• Python 3.8 or higher
• FFmpeg

Step 1: Install FFmpeg

Windows: winget install ffmpeg

macOS: brew install ffmpeg

Linux (Ubuntu/Debian): sudo apt update sudo apt install ffmpeg

Step 2: Clone Repository

git clone https://github.com/techpostdev/speech-to-text cd speech-to-text

Step 3: Install Python Packages

pip install -r requirements.txt

Step 4: Run Application

python speech-to-text.py

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🛠️ For Developers – Build Your Own EXE

If you want to modify the code and create your own executable:

Step 1: Install PyInstaller

pip install pyinstaller

Step 2: Build the EXE

python -m PyInstaller --onefile --windowed --collect-all whisper speech_to_text.py

Step 3: Find Your EXE

The executable will be created in the dist/ folder as speech_to_text.exe

Step 4: Test Your EXE

Double-click the EXE file to make sure everything works.

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🚀 GPU Acceleration (Optional)

For 10x faster transcription with NVIDIA GPU:

pip uninstall torch torchaudio pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118

Verify GPU is working: python -c "import torch; print(torch.cuda.is_available())"

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🎮 How to Use

Single File:

1. Click "BROWSE FILE" or Drag & Drop
2. Select Model (Tiny/Base/Small/Medium)
3. Select Language
4. Choose Output Formats
5. Click "START TRANSCRIPTION"

Batch Processing:

1. Click "SCAN FOLDER"
2. Select folder with files
3. Check "BATCH PROCESS ALL FILES"
4. Click "START TRANSCRIPTION"

⌨️ Keyboard Shortcuts

Shortcut	Action
Ctrl + O	Browse File
Ctrl + B	Scan Folder
Ctrl + Enter	Start Transcription

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❓ FAQ

Q: "ffmpeg not found" error? A: Install FFmpeg (see installation steps above)

Q: Slow transcription? A: First run downloads models. Enable GPU for faster processing.

Q: "mel_filters.npz" error? A: Run pip install --upgrade openai-whisper

Q: Can I transcribe 2-hour movies? A: Yes, use "Base" or "Tiny" model with 8GB+ RAM.

Q: Does it work offline? A: Yes, after first model download (1-3GB).

🔧 Troubleshooting

Problem	Solution
pip not found	Reinstall Python with PATH option
torch not found	Run pip install torch
CUDA out of memory	Use smaller model (Tiny/Base)
File not supported	Convert to MP3 or MP4
Permission denied	Run as administrator

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📊 Model Comparison

Model	Speed	Accuracy	RAM	Best For
Tiny	⚡Fastest	70%	1GB	Testing
Base	⚡⚡Fast	85%	2GB	General Use
Small	⚡Medium	90%	3GB	Important meetings
Medium	🐌 Slow	95%	5GB	Professional

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🛣️ Roadmap

• Faster-Whisper support
• Speaker diarization
• DOCX export
• Auto language detection
• Real-time transcription

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

MIT License - Free for personal and commercial use

👤 Author

Tech Post

• GitHub: @techpostdev
• Project Link: https://github.com/techpostdev/speech-to-text

🙏 Credits

• OpenAI Whisper
• PyTorch
• FFmpeg

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📁 Project Structure

speech-to-text/ ├── speech_to_text.py ├── requirements.txt ├── README.md ├── LICENSE ├── .gitignore ├── assets/ │ └── Screenshot_1.png └── docs/ ├── installation.md ├── usage.md └── faq.md

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