Introduction
What is Whisper Web?
The Whisper Web Transcription Server is a Python-based real-time speech-to-text transcription system powered by OpenAI’s Whisper models. It leverages state-of-the-art models like Distil-Whisper to transcribe audio input in real-time.
Key Features
Real-time Transcription: Transcribes spoken words into text almost instantaneously, making it ideal for use cases like live captioning or interactive voice-driven applications.
Customizable Model Configurations: The system can be fine-tuned with various model configurations to accommodate different use cases, such as adjusting the sampling rate, block size, or selecting from different Whisper model sizes.
API Integration: Exposes a RESTful API for easy integration with other services. You can send and retrieve transcriptions via HTTP requests, allowing for real-time updates in web or mobile apps.
Multi-threaded Asynchronous Processing: Leverages asynchronous programming (via asyncio) for optimal performance, allowing the transcription engine to handle high volumes of audio input while processing transcription results concurrently.
Memory-Efficient and Scalable: Designed to work efficiently even with resource-intensive models, offering scalable transcription performance with lower resource consumption.
Architectural Overview
The Whisper Web system follows an event-driven, microservice-oriented architecture that enables real-time audio processing and transcription. The system is designed with loose coupling between components, allowing for scalability and maintainability.
Functional Architecture Overview
Functional architecture overview showing the main components and their interactions
System Architecture
High-level view of the main components and their interactions
Execution Flow Chart
Detailed execution flow of the transcription process
Package Overview
Package structure overview showing the main components and their relationships