VoxScribe 🎙️

VoxScribe: A platform to test Opensource Speech-to-Text models

VoxScribe is a lightweight, unified platform for testing and comparing multiple open-source speech-to-text (STT) models through a single interface. Born from real-world enterprise challenges where proprietary STT solutions become prohibitively expensive at scale, VoxScribe democratizes access to cutting-edge open-source alternatives.

The Problem We Solve

Startups transcribing speech at scale face a common dilemma: cost vs. control. A contact center processing 100,000 hours of calls monthly can easily spend $150,000+ on transcription alone. While open-source STT models like Whisper, Voxtral, Parakeet, and Canary-Qwen now rival proprietary solutions in accuracy, evaluating them has been a nightmare:

• Dependency Hell 🔥: Conflicting library versions between models (transformers version conflicts between Voxtral and NeMo models)
• Inconsistent APIs 🔄: Each model requires different integration approaches
• Complex Setup ⚙️: Hours or days managing CUDA drivers, Python environments, and debugging
• Limited Comparison 📊: No unified way to test multiple models against your specific use cases

What VoxScribe Offers

✅ Unified Interface: Test 5+ open-source STT models through a single FastAPI backend and clean web UI
✅ Dependency Management: Handles version conflicts and library incompatibilities automatically
✅ Side-by-Side Comparison: Upload audio and compare transcriptions across multiple models
✅ Model Caching: Intelligent caching for faster subsequent runs
✅ Clean API: RESTful endpoints for easy integration into existing workflows
✅ Cost Control: Self-hosted solution puts you in control of transcription costs

Supported Models

• OpenAI Whisper - Industry standard baseline [6-models]
• Mistral Voxtral - Latest transformer-based approach [2-models]
• NVIDIA Parakeet - Enterprise-grade accuracy [1-model]
• Canary-Qwen-2.5B - Multilingual capabilities [1-model]
• IBM-Granite-3.3 - Easy to add new models [2-models]

Architecture

├── backend.py          # FastAPI backend with STT logic
├── public/             # Frontend static files
│   ├── index.html      # Main HTML interface
│   ├── styles.css      # CSS styling with dark/light theme
│   └── app.js          # JavaScript frontend logic
├── run.py              # Startup script
└── requirements.txt    # Python dependencies

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Features

Backend (FastAPI)

• RESTful API for all STT operations
• Unified model management for Whisper, Voxtral, Parakeet, Canary
• Automatic dependency handling with version conflict resolution
• File upload and processing with background tasks
• Model comparison endpoint for side-by-side evaluation
• Dependency installation endpoints with subprocess management

Frontend (HTML/CSS/JS)

• Modern responsive design with dark/light theme toggle
• Drag & drop file upload with audio preview
• Real-time status updates for dependencies and models
• Single model transcription with engine/model selection
• Multi-model comparison with checkbox selection
• Progress tracking and result visualization
• Download options for CSV and text formats

Quick Start

Prerequisites

• AWS EC2 g6.xlarge instance with Amazon Linux 2023 6.1 or Deep Learning OSS Nvidia Driver AMI GPU PyTorch 2.8 (Amazon Linux 2023) [ Recommended_]
• NVIDIA GPU drivers installed

Installation Steps

1. Install NVIDIA GRID drivers if using Amazon Linux 2023 6.1 else skip this step

   # Follow AWS documentation for GRID driver installation
   # https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-nvidia-driver.html#nvidia-GRID-driver
   

2. Verify CUDA installation

   nvidia-smi
   

3. Install system dependencies

   sudo dnf update -y
   sudo dnf install git -y
   

4. Install Miniconda

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   bash Miniconda3-latest-Linux-x86_64.sh
   

   • Accept the license agreement (type yes)
   • Confirm installation location (default is fine)
   • Do you wish to update your shell profile to automatically initialize conda (type yes when prompted)

5. Restart your shell or source bashrc

   source ~/.bashrc
   

6. Create and activate conda environment

   conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
   conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
   conda create -n voxscribe python=3.12 -y
   conda activate voxscribe
   

7. Install ffmpeg in Conda env

   conda install ffmpeg -y
   

8. Clone the repository

   git clone https://github.com/Fraser27/VoxScribe.git
   cd VoxScribe
   

9. Install Python dependencies

   pip install -r requirements.txt
   

10. Start the application

python run.py

11. Open your browser

    http://localhost:8000
    

API Endpoints

System Status

• GET /api/status - Get system and dependency status
• GET /api/models - Get available models and cache status

Transcription

• POST /api/transcribe - Single model transcription
• POST /api/compare - Multi-model comparison

Dependencies

• POST /api/install-dependency - Install missing dependencies

Model Support

Dependency Management

The system automatically handles version conflicts between:

• Voxtral: Requires transformers 4.56.0+
• NeMo models: Require transformers 4.51.3

Installation buttons are provided in the UI for missing dependencies.

File Support

Supported audio formats: WAV, MP3, FLAC, M4A, OGG

Development

Backend Development

# Run with auto-reload
uvicorn backend:app --reload --host 0.0.0.0 --port 8000

Frontend Development

Static files are served from the public/ directory. Changes to HTML, CSS, or JS files are reflected immediately.

Adding New Models

1. Update MODEL_REGISTRY in backend.py
2. Add loading logic in load_model() function
3. Add transcription logic in transcribe_audio() function

Benefits over Streamlit

1. No ScriptRunContext warnings - Clean separation eliminates context issues
2. Better performance - FastAPI is faster and more efficient
3. Modern UI - Custom HTML/CSS/JS with better UX
4. API-first design - Can be integrated with other applications
5. Easier deployment - Standard web application deployment
6. Better error handling - Proper HTTP status codes and error responses
7. Scalability - Can handle multiple concurrent requests

Deployment

Local Development

python run.py

Production

uvicorn backend:app --host 0.0.0.0 --port 8000 --workers 4

Docker (Optional)

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "backend:app", "--host", "0.0.0.0", "--port", "8000"]

Troubleshooting

Common Issues

1. Missing dependencies: Use the install buttons in the UI
2. Model download failures: Check internet connection and disk space
3. Audio processing errors: Ensure ffmpeg is installed
4. CUDA issues: Check PyTorch CUDA installation

Logs

Server logs are displayed in the terminal where you run python run.py.

Contributing

1. Backend changes: Modify backend.py
2. Frontend changes: Modify files in public/
3. New features: Add API endpoints and corresponding UI elements
4. Testing: Use the built-in FastAPI docs at /docs