START_HERE.md

🎤 START HERE - Whisper Transcriber Project

👋 Welcome!

You now have a complete, production-ready SRT generator using OpenAI Whisper!

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🎯 What You Have

A fully-functional transcription system that can:

✅ Upload audio/video files ✅ Download from YouTube ✅ Auto-detect 99+ languages ✅ Generate SRT, VTT, TXT, JSON ✅ Identify speakers (optional) ✅ Handle large files automatically ✅ Show real-time progress ✅ Provide public API

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📁 Project Files

hf/
├── 🚀 app.py                    # Main Gradio app (RUN THIS!)
├── 📦 requirements.txt          # Dependencies
├── 🚫 .gitignore                # Git ignore rules
│
├── 🛠️  utils/                    # Core modules (1,391 lines)
│   ├── audio_processor.py      # Audio extraction & chunking
│   ├── downloader.py           # YouTube & URL downloads
│   ├── transcription.py        # Whisper transcription
│   ├── formatters.py           # SRT/VTT/TXT/JSON output
│   └── diarization.py          # Speaker identification
│
└── 📚 Documentation/
    ├── ⚡ QUICK_START.md        # READ THIS FIRST!
    ├── 🧪 LOCAL_TESTING.md     # Test locally
    ├── 🚀 DEPLOYMENT.md        # Deploy to HF Spaces
    ├── 📋 PROJECT_SUMMARY.md   # Technical details
    └── 📖 README.md            # Full documentation

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🚀 Quick Start (Choose One)

Option A: Deploy to Hugging Face (5 minutes)

Easiest option - No local setup needed!

1. Go to huggingface.co/spaces
2. Create new Space (Gradio SDK)
3. Upload all files from this folder
4. Wait 5-10 minutes for build
5. Done! Your app is live 🎉

👉 See QUICK_START.md for detailed steps

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Option B: Run Locally (10 minutes)

Full control - Run on your computer

# 1. Install FFmpeg
choco install ffmpeg  # Windows
brew install ffmpeg   # Mac
apt install ffmpeg    # Linux

# 2. Setup Python
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

# 3. Run!
python app.py

Then open: http://127.0.0.1:7860

👉 See LOCAL_TESTING.md for detailed steps

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📖 Documentation Guide

New to the project?

1. Start with QUICK_START.md (5-min read)
2. Then README.md for full features

Want to test locally? → LOCAL_TESTING.md

Ready to deploy? → DEPLOYMENT.md

Need technical details? → PROJECT_SUMMARY.md

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🎯 First Steps After Setup

Test with a Sample

1. Find a short audio file (1-2 minutes)

   • Or use a YouTube URL

2. Run the app

   • Upload the file
   • Select "Small" model
   • Click "Generate Transcription"

3. Download results

   • Try the SRT file first
   • Open in a text editor

Example YouTube URL to test:

https://www.youtube.com/watch?v=dQw4w9WgXcQ

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⚙️ Basic Settings

Model Selection

• Tiny: Fastest (use for testing)
• Small: Recommended (good balance)
• Medium: Best quality (slower)

Language

• Auto-detect: Works great! (recommended)
• Manual: Select if you know the language

Speaker Diarization

• Off: Faster (default)
• On: Identifies different speakers (requires HF token)

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📊 What to Expect

Processing Time (10-minute audio)

Setup	Model	Time
CPU	Tiny	~1 min
CPU	Small	~3-5 min
CPU	Medium	~8-10 min
GPU	Small	~1 min

Output Files

After processing, you get 4 files:

1. 📄 filename.srt - Most common, for video players
2. 📄 filename.vtt - For web players
3. 📄 filename.txt - Plain text transcript
4. 📄 filename.json - Full data with word timestamps

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🔌 API Usage (Advanced)

Yes, this has an API! Use it in your code:

from gradio_client import Client

client = Client("YOUR_SPACE_URL")
result = client.predict(
    url_input="https://youtube.com/watch?v=...",
    model_size="small",
    language="auto",
    enable_diarization=False
)

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💡 Pro Tips

For Best Results

• Use high-quality audio (clear speech)
• Choose specific language if known
• Use Medium model for final production

For Speed

• Use Tiny model for quick tests
• Keep files under 10 minutes
• Disable speaker diarization

For YouTube

• Some videos may be restricted
• Use direct file upload as fallback
• Works with unlisted videos

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🆘 Common Issues

"ModuleNotFoundError"

→ Run: pip install -r requirements.txt

"FFmpeg not found"

→ Install FFmpeg (see QUICK_START.md)

"YouTube download failed"

→ Video may be restricted, try file upload

"Slow processing"

→ Normal on CPU, use smaller model or GPU

"Speaker diarization not working"

→ Need HF_TOKEN (see DEPLOYMENT.md)

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🎨 Features Included

Input Methods

✅ File upload (drag & drop) ✅ YouTube URLs ✅ Direct media URLs ✅ Multiple formats (MP3, MP4, WAV, etc.)

Processing

✅ Auto audio extraction from video ✅ Large file chunking (>30 min) ✅ Multi-language support (99+) ✅ Word-level timestamps ✅ Speaker identification (optional)

Output

✅ SRT subtitles ✅ VTT web format ✅ Plain text ✅ JSON with metadata ✅ Preview in browser

UI/UX

✅ Real-time progress bars ✅ Clear error messages ✅ Download buttons for all formats ✅ Model selection ✅ Language selection ✅ Clean, modern interface

Technical

✅ Public API endpoint ✅ Automatic cleanup ✅ GPU support (auto-detected) ✅ Error handling ✅ Memory efficient

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🚀 Next Steps

1. Choose your deployment option (HF Spaces or Local)
2. Read the relevant guide (QUICK_START.md or LOCAL_TESTING.md)
3. Test with a sample file
4. Share your app! (if deployed to HF Spaces)

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📞 Need Help?

Documentation:

• QUICK_START.md - Basic setup
• LOCAL_TESTING.md - Local development
• DEPLOYMENT.md - HF Spaces deployment
• README.md - Full documentation

Support:

• Check the documentation first
• Review error messages
• Open an issue on GitHub

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✅ Project Checklist

Before Deploying

• Read QUICK_START.md
• Choose deployment method
• Test locally (optional but recommended)
• Prepare sample files for testing

After Deploying

• Test basic transcription
• Try YouTube download
• Test different models
• Share with users!

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🎉 You're All Set!

Your Whisper Transcriber is ready to go!

Next step: Open QUICK_START.md and choose your deployment method.

Questions? Check the documentation files above.

Ready to transcribe? Let's go! 🎤

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Built with:

• OpenAI Whisper (speech recognition)
• Gradio (web interface)
• PyTorch (deep learning)
• Pyannote.audio (speaker diarization)
• FFmpeg (audio/video processing)
• yt-dlp (YouTube downloads)

License: MIT (free for personal and commercial use)

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Happy transcribing! 🎊