WhisperLiveKit: Real-time Local Speech-to-Text

August 30, 2025

Practical Open Source Projects

Open Source Python Real-time AI Speech-to-Text Whisper

WhisperLiveKit: Revolutionizing Real-Time Speech Processing Locally

In the rapidly evolving landscape of AI-powered tools, WhisperLiveKit emerges as a standout open-source project, offering cutting-edge real-time, local speech-to-text, translation, and speaker diarization capabilities. Developed by QuentinFuxa, this project addresses the core limitations of processing audio in small, real-time chunks, which often leads to dropped words and poor transcription accuracy with standard models.

Instead, WhisperLiveKit harnesses advanced research like SimulStreaming (for ultra-low latency transcription with AlignAtt policy) and WhisperStreaming (for low latency transcription with LocalAgreement policy). It also integrates Streaming Sortformer and Diart for sophisticated real-time speaker diarization, alongside Silero VAD for efficient voice activity detection. This combination ensures intelligent buffering and incremental processing, delivering superior results.

Key Features and Benefits:

Real-time Performance: Achieve low-latency transcription directly in your browser.
Fully Local Processing: Maintain data privacy and control with on-device processing.
Speaker Diarization: Accurately identify and distinguish between multiple speakers.
State-of-the-Art Models: Built upon leading research for maximum accuracy and efficiency.
Server & Web UI: Comes with a ready-to-use backend server and a simple, functional frontend.
Flexibility: Supports various Whisper models (e.g., base, medium, large-v3), multiple languages, and optional backends like faster-whisper.

Getting Started with WhisperLiveKit:

Installation is straightforward using pip:

pip install whisperlivekit

Ensure you have FFmpeg installed on your system. The project provides clear instructions for installation on Ubuntu/Debian, macOS, and Windows.

To start the transcription server with the base model for English:

whisperlivekit-server --model base --language en

Then, simply open http://localhost:8000 in your browser to begin speaking and see your words transcribed in real-time.

A significant advantage of WhisperLiveKit is its extensive customization. Users can easily switch between models, enable/disable diarization, select different backends, and configure various parameters for optimal performance. The project also provides a Python API for seamless integration into custom applications.

Deployment Options:

WhisperLiveKit supports various deployment methods:

Docker: Easily deploy with GPU or CPU support using provided Dockerfiles.
Production Servers: Guidance on using ASGI servers like Uvicorn and Gunicorn for scalable deployments.
Nginx Configuration: Recommended setup for production environments to manage traffic and HTTPS.

Use Cases:

WhisperLiveKit is versatile and can be applied in numerous scenarios:

Meeting Transcription: Automatically capture meeting minutes and action items.
Accessibility Tools: Help hearing-impaired individuals follow conversations in real-time.
Content Creation: Transcribe podcasts, videos, and audio for subtitles and searchable content.
Customer Service: Analyze support calls with speaker identification for quality assurance and training.

With its robust features, ease of use, and commitment to local, open-source processing, WhisperLiveKit is an invaluable tool for developers and organizations looking to leverage the power of advanced speech recognition.

Original Article: View Original