Detailed Introduction
RealtimeSTT is a speech-to-text library designed for realtime applications, delivering low-latency transcription with high quality. It supports local and GPU-accelerated inference, multiple voice activity detection (VAD) strategies and wake-word activation, making it suitable for voice assistants, live captioning and interactive systems. The project is community-driven and focuses on usability and realtime performance.
Main Features
- Low-latency realtime transcription with options for small realtime models and larger final models.
- Multiple VAD approaches (WebRTCVAD, SileroVAD) for improved detection in noisy environments.
- Optional wake-word support (Porcupine / OpenWakeWord) with callback and event hooks.
- Command-line tools and a Python SDK for easy integration into existing applications.
Use Cases
RealtimeSTT fits voice assistants, live meeting captions, realtime voice input, live-stream subtitles, and any interactive systems requiring immediate text feedback. It can run locally to preserve privacy or on GPU-equipped servers for higher-accuracy realtime transcription.
Technical Features
The project combines modern models (e.g., Faster_Whisper) with multi-stage VAD pipelines, supports CUDA acceleration, streaming batch processing, and callback-based APIs. Configuration allows tuning realtime batch sizes, post-speech silence thresholds, and beam search parameters to balance latency and accuracy.