---

title: Modular Voice Transcriber emoji: 🗣️ colorFrom: blue colorTo: purple sdk: gradio app_file: app.py python_version: 3.10 pinned: false

Modular Voice Transcriber

A comprehensive, modular Gradio-based web interface for speech-to-text transcription supporting multiple STT engines including OpenAI Whisper, Wav2Vec2, HuBERT Arabic, Tawasul, Vosk, and Coqui STT models.

🌟 Features

• Comprehensive STT Support: 7+ different speech-to-text engines
• Multiple Models: OpenAI Whisper, Wav2Vec2 Arabic, HuBERT, Tawasul, Vosk, Coqui STT
• Arabic Language Focus: Specialized models for Arabic dialect recognition
• Web Interface: User-friendly Gradio interface with image gallery
• Real-time Processing: Live audio recording and transcription
• Quality Analysis: Audio quality feedback and recommendations
• Device Support: CPU/GPU automatic detection and selection
• Authentication: Support for private HuggingFace models
• Static Class Support: Optimized memory usage for certain models
• Visual Interface: Interactive image gallery with thumbnail navigation

🚀 Quick Start

Option 1: Automated Setup (Recommended)

# Essential models (Whisper + Wav2Vec2)
python setup.py --profile essential --test

# Or specific models
python setup.py --profile whisper-only --test
python setup.py --profile wav2vec2-only --test

Option 2: Manual Installation

# Base installation (Whisper + Wav2Vec2)
pip install -r requirements.txt

# Specific model installations
pip install -r requirements_whisper.txt     # OpenAI Whisper only
pip install -r requirements_wav2vec2.txt    # Wav2Vec2 only
pip install -r requirements_hubert.txt      # HuBERT Arabic only
pip install -r requirements_tawasul.txt     # Tawasul Arabic only
pip install -r requirements_vosk.txt        # Vosk offline only
pip install -r requirements_coqui.txt       # Coqui STT only

# Or install with specific extras
pip install -e .[essential]    # Whisper + Wav2Vec2
pip install -e .[all-stt]     # All models

🎯 Supported STT Models

🔧 Usage

Start the Interface

python gradio_voice_transcriber_clean.py

Using Different Models

1. Select Model: Choose from dropdown (WhisperSTT, Wav2Vec2ArabicSTT, HubertArabicSTT, TawasulSTT, VoskSTT, CoquiSTT)
2. Configure: Set model size, device, language, and authentication if needed
3. Load: Click "Load Model" (first time downloads model automatically)
4. Transcribe: Record audio or upload audio files
5. Gallery: Browse sample images using the interactive thumbnail gallery

Authentication for Private Models

Some experimental models require HuggingFace authentication:

# Option 1: Use helper script
python setup_hf_auth.py

# Option 2: Manual login
pip install huggingface-hub
huggingface-cli login

# Option 3: Use token in interface
# Get token from: https://huggingface.co/settings/tokens
# Enter in "HuggingFace Token" field

🏗️ Adding New STT Models

The system is designed to be easily extensible:

1. Create STT Class: Inherit from BaseSTT in stt/your_model.py
2. Register Model: Add to STT_MODELS in gradio_voice_transcriber_clean.py
3. Configure Options: Update get_model_options() method
4. Test: Run your model through the interface

See STT_INTEGRATION_GUIDE.md for detailed instructions.

🎨 Interface Features

Image Gallery

The enhanced interface (gradio_voice_transcript_temp.py) includes:

• Interactive Gallery: Browse sample images with thumbnail navigation
• Horizontal Scrolling: Smooth image browsing experience
• Thumbnail Selection: Click thumbnails to view full images
• Gallery Controls: Navigation and zoom functionality

Static Class Models

Some models (like TawasulSTT) use static class implementation for:

• Memory Efficiency: Reduced memory footprint
• Faster Loading: Optimized model initialization
• Shared Resources: Better resource management

📁 Model Storage Locations

Different STT models store their files in various locations:

📁 Project Structure

STT-trails/
├── gradio_voice_transcriber.py          # Main comprehensive interface  
├── gradio_voice_transcript_temp.py      # Enhanced interface with image gallery
├── stt/                                  # STT implementations
│   ├── stt_base.py                      # Base class for all STT models
│   ├── whisper_stt.py                   # OpenAI Whisper implementation
│   ├── wav2vec2_arabic_stt.py           # Wav2Vec2 Arabic model
│   ├── hubert_arabic_stt.py             # HuBERT Arabic dialect model
│   ├── tawasul_stt.py                   # Tawasul Arabic model (static class)
│   ├── vosk_stt.py                      # Vosk offline STT
│   ├── coqui_stt.py                     # Coqui open-source STT
│   └── example_custom_stt.py            # Template for new models
├── setup.py                             # Installation helper
├── setup_hf_auth.py                     # HuggingFace authentication helper
├── test_*.py                            # Model testing scripts
├── requirements*.txt                     # Dependencies for each model
├── recordings/                          # Audio recordings directory
├── INSTALL.md                           # Detailed installation guide
├── STT_INTEGRATION_GUIDE.md             # Developer integration guide
└── pyproject.toml                       # Project configuration

🧪 Testing

# Test specific models
python test_whisper_local.py         # Test Whisper models
python test_wav2vec2_arabic.py       # Test Wav2Vec2 Arabic
python test_hubert_arabic.py         # Test HuBERT Arabic
python test_tawasul.py               # Test Tawasul Arabic
python test_vosk.py                  # Test Vosk offline STT
python test_coqui.py                 # Test Coqui STT

# Test installation
python setup.py --profile essential --test

# Run the interface
python gradio_voice_transcriber.py           # Main interface
python gradio_voice_transcript_temp.py       # Interface with image gallery

🔍 Troubleshooting

Model Loading Issues

• HuggingFace Authentication: Use setup_hf_auth.py or manual token
• Memory Issues: Use smaller models or CPU-only mode
• Internet Required: First model download needs internet connection

Audio Issues

• No Audio Detected: Check microphone permissions and volume
• Poor Quality: Use audio quality analysis feature
• Wrong Language: Select appropriate model for your language

Performance Tips

• Use GPU: Automatic if CUDA PyTorch is installed
• Chunk Long Audio: Handled automatically for 20+ second clips
• Choose Right Model: Balance size vs. accuracy for your use case

📄 License

This project is open source. See individual model licenses:

• OpenAI Whisper: MIT License
• Wav2Vec2: MIT License
• HuggingFace Transformers: Apache 2.0

🤝 Contributing

1. Fork the repository
2. Create your feature branch
3. Add your STT model following the integration guide
4. Submit a pull request

📚 Resources

• OpenAI Whisper
• Wav2Vec2 Paper
• HuggingFace Models
• Gradio Documentation

---

Made with ❤️ for the speech recognition community