whisper_android and RuntimeSpeechRecognizer
These are ecosystem siblings—both are independent implementations of OpenAI's Whisper model optimized for different platforms (Android via TensorFlow Lite and Unreal Engine via whisper.cpp), rather than tools designed to work together or replace each other.
About whisper_android
vilassn/whisper_android
Offline Speech Recognition with OpenAI Whisper and TensorFlow Lite for Android
Provides dual implementation paths via TensorFlow Lite Java and Native APIs, allowing developers to choose between ease of integration and optimized performance. Includes a Python conversion pipeline to transform OpenAI Whisper models into TFLite format, plus support for live streaming transcription through buffer-based audio input alongside file-based batch processing. The architecture handles multilingual models with configurable vocabulary filters and manages audio preprocessing at 16kHz mono format for inference compatibility.
About RuntimeSpeechRecognizer
gtreshchev/RuntimeSpeechRecognizer
Cross-platform, real-time, offline speech recognition plugin for Unreal Engine. Based on Whisper OpenAI technology, whisper.cpp.
Leverages whisper.cpp for local inference without external API calls, enabling private audio processing entirely on-device. Provides both C++ and Blueprint APIs for Unreal Engine integration, with support for multiple audio input sources and real-time streaming transcription. Includes automatic model downloading and caching, allowing developers to embed various Whisper model sizes optimized for accuracy versus performance trade-offs.
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